smangrul/hug_stack · Datasets at Hugging Face

# How to propose a Flax/JAX + Transformers project Great that you've opened this document! While we at 🤗 are proposing a couple of projects, we strongly believe that the community can come up with much more **creative**, **fun**, and **impactful** projects on their own. This being said, we are really looking forward to seeing your project proposal! ## What a project should be about The proposed project should fall into the machine learning fields of **Natural Language Processing (NLP)** and/or **Computer Vision (CV)** (possibly also **Speech Recognition (ASR)** depending on whether Speech Recognition models are available in Flax in due time) and aim at solving a specific task. Possible tasks can belong to: * text classification * text generation * image recognition * image processing * image captioning * audio classification * and other tasks you can think of! The clearer a task is defined, the better your project proposal is. *E.g.* "Using a T5 model to learn grammar correction in French" or "Adapting a pre-trained CLIP model for zero-shot image classification in Spanish" are **well-defined and clear** project proposals, while something like "Train a language model" or "Image classification" are **too vague**. There is no limit to your creativity as long as the project is feasible and ethical. The more creative & specific your project proposal, the more interesting it will be, and the more likely will you find motivated team members to work on your project! To get an idea of how to formulate your project proposals, you can browse through existing project proposals on the [forum](https://discuss.huggingface.co/c/flax-jax-projects/22). ## How to submit a project proposal First, you should make sure that you are [logged in](https://huggingface.co/login?sso=bm9uY2U9OTRlNjZjZmZhYjMwMmJmMWMyYjc5MmFiMTMyMzY5ODYmcmV0dXJuX3Nzb191cmw9aHR0cHMlM0ElMkYlMkZkaXNjdXNzLmh1Z2dpbmdmYWNlLmNvJTJGc2Vzc2lvbiUyRnNzb19sb2dpbg%3D%3D&sig=429ad8924bcb33c40f9823027ea749abb55d393f4f58924f36a2dba3ab0a48da) with your Hugging Face account on the forum. Second, make sure that your project idea doesn't already exist by checking [existing projects](https://discuss.huggingface.co/c/flax-jax-projects/22). If your project already exists - great! This means that you can comment and improve the existing idea and join the project to form a team! If your project idea already exists for a different language, feel free to submit the same project idea, just in a different language. Third, having ensured that your project doesn't exist, click on the *"New Topic"* button on the [Flax/JAX Projects Forum category](https://discuss.huggingface.co/c/flax-jax-projects/22) to create a new project proposal. Fourth, make sure that your project proposal includes the following information: 1. *A clear description of the project* 2. *In which language should the project be conducted?* English, German, Chinese, ...? It can also be a multi-lingual project 3. *Which model should be used?* If you want to adapt an existing model, you can add the link to one of the 4000 available checkpoints in JAX [here](https://huggingface.co/models?filter=jax) If you want to train a model from scratch, you can simply state the model architecture to be used, *e.g.* BERT, CLIP, etc. You can also base your project on a model that is not part of transformers. For an overview of libraries based on JAX, you can take a look at [awesome-jax](https://github.com/n2cholas/awesome-jax#awesome-jax-). **Note** that for a project that is not based on Transformers it will be more difficult for the 🤗 team to help you. Also have a look at the section [Quickstart Flax & Jax in Transformers](https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects#quickstart-flax-and-jax-in-transformers) to see what model architectures are currently supported in 🤗 Transformers. 4. *What data should be used?* It is important to state at least what kind of data you would like to use. Ideally, you can already point to publicly available data or a dataset in the 🤗 Datasets library. 5. *Are similar training scripts available in Flax/JAX?* It would be important to find similar training scripts that already exist in Flax/JAX. *E.g.* if you are working on a Seq-to-Seq task, you can make use of the [`run_summarization_flax.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/summarization/run_summarization_flax.py) script which is very similar to any seq2seq training. Also have a look at the section [Quickstart Flax & Jax in Transformers](https://github.com/huggingface/transformers/tree/main/examples/research_projects/jax-projects#quickstart-flax-and-jax-in-transformers) to see what training scripts are currently supported in 🤗 Transformers. 6. *(Optionally) What are possible challenges?* List possible difficulties with your project. *E.g.* If you know that training convergence usually takes a lot of time, it is worth stating this here! 7. *(Optionally) What is the desired project outcome?* - How would you like to demo your project? One could *e.g.* create a Streamlit application. 8. *(Optionally) Links to read upon* - Can you provide any links that would help the reader to better understand your project idea? Feel free to copy-paste the following format for your project proposal and fill out the respective sections: ``` # <FILL ME: Name of project> <FILL ME: A clear description of the project> ## 2. Language The model will be trained in <FILL ME: which language?>. ## 3. Model <FILL ME: 3. Which model should be used?> ## 4. Datasets <FILL ME: 4. Which data should be used?> Possible links to publicly available datasets include: - <FILL ME: Link 1 to dataset> - <FILL ME: Link 2 to dataset> - <FILL ME: Link 3 to dataset> ## 5. Training scripts <FILL ME: 5. Are there publicly available training scripts that can be used/tweaked for the project?> We can make use of <FILL ME: link to training script> to train the model.> ## 6. (Optional) Challenges <(Optionally) FILL ME: 6. What are possible challenges?> ## 7. (Optional) Desired project outcome <(Optionally) FILL ME: 7. What is the desired project outcome? A demo?> ## 8. (Optional) Reads The following links can be useful to better understand the project and what has previously been done. - <FILL ME: Link 1 to read> - <FILL ME: Link 2 to read> - <FILL ME: Link 3 to read> ``` To see how a proposed project looks like, please have a look at submitted project proposals [here](https://discuss.huggingface.co/c/flax-jax-projects/22). ## Will my project proposal be selected? Having submitted a project proposal, you can now promote your idea in the Slack channel `#flax-jax-community-week` to try to convince other participants to join your project! Once other people have joined your project, one of the organizers (`@Suzana, @valhalla, @osanseviero, @patrickvonplaten`) will officially create a team for your project and add your project to [this google sheet](https://docs.google.com/spreadsheets/d/1GpHebL7qrwJOc9olTpIPgjf8vOS0jNb6zR_B8x_Jtik/edit?usp=sharing).

transformers/examples/research_projects/jax-projects/HOW_TO_PROPOSE_PROJECT.md/0

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# Model parallel language model training example The following example showcases how to train/fine-tune GPTNeo model with model parallelism using the JAX/Flax backend and the [`pjit`](https://jax.readthedocs.io/en/latest/jax.experimental.pjit.html) transformation. > Note: The example is experimental and might have bugs. Also currently it only supports single V3-8. The `partition.py` file defines the `PyTree` of `ParitionSpec` for the GPTNeo model which describes how the model will be sharded. The actual sharding is auto-matically handled by `pjit`. The weights are sharded across all local devices. To adapt the script for other models, we need to also change the `ParitionSpec` accordingly. TODO: Add more explantion. Before training, let's prepare our model first. To be able to shard the model, the sharded dimension needs to be a multiple of devices it'll be sharded on. But GPTNeo's vocab size is 50257, so we need to resize the embeddings accordingly. ```python from transformers import FlaxGPTNeoForCausalLM, GPTNeoConfig model = FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B") emb = jnp.zeros((50264, model.config.hidden_size)) # update the first 50257 weights using pre-trained weights emb = emb.at[:50257, :].set(model.params["transformer"]["wte"]["embedding"]) params = model.params params["transformer"]["wte"]["embedding"] = emb # initialize a random model with the right vocab_size config = GPTNeoConfig.from_pretrained("EleutherAI/gpt-neo-1.3B", vocab_size=50264) model = FlaxGPTNeoForCausalLM(config) # assign the pre-trained weights and save the model. model.params = params model.save_pretrained("gpt-neo-1.3B") ``` ### Train Model ```bash python run_clm_mp.py \ --model_name_or_path gpt-neo-1.3B \ --tokenizer_name gpt2 \ --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \ --do_train --do_eval \ --block_size 1024 \ --num_train_epochs 5 \ --learning_rate 4e-6 \ --per_device_train_batch_size 3 --per_device_eval_batch_size 3 \ --overwrite_output_dir --output_dir ~/tmp/flax-clm \ --cache_dir ~/datasets_cache/wikitext --dtype bfloat16 \ --logging_steps 96 --eval_steps 96 ```

transformers/examples/research_projects/jax-projects/model_parallel/README.md/0

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<jupyter_start><jupyter_code># %pip install-r requirements.txt from IPython.display import clear_output, Image, display import PIL.Image import io import json import torch import numpy as np from processing_image import Preprocess from visualizing_image import SingleImageViz from modeling_frcnn import GeneralizedRCNN from utils import Config import utils from transformers import LxmertForQuestionAnswering, LxmertTokenizer import wget import pickle import os # URL = "https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/images/input.jpg", URL = "https://vqa.cloudcv.org/media/test2014/COCO_test2014_000000262567.jpg" OBJ_URL = "https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/objects_vocab.txt" ATTR_URL = "https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/attributes_vocab.txt" GQA_URL = "https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_label2ans.json" VQA_URL = "https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_label2ans.json" # for visualizing output def showarray(a, fmt="jpeg"): a = np.uint8(np.clip(a, 0, 255)) f = io.BytesIO() PIL.Image.fromarray(a).save(f, fmt) display(Image(data=f.getvalue())) # load object, attribute, and answer labels objids = utils.get_data(OBJ_URL) attrids = utils.get_data(ATTR_URL) gqa_answers = utils.get_data(GQA_URL) vqa_answers = utils.get_data(VQA_URL) # load models and model components frcnn_cfg = Config.from_pretrained("unc-nlp/frcnn-vg-finetuned") frcnn = GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned", config=frcnn_cfg) image_preprocess = Preprocess(frcnn_cfg) lxmert_tokenizer = LxmertTokenizer.from_pretrained("unc-nlp/lxmert-base-uncased") lxmert_gqa = LxmertForQuestionAnswering.from_pretrained("unc-nlp/lxmert-gqa-uncased") lxmert_vqa = LxmertForQuestionAnswering.from_pretrained("unc-nlp/lxmert-vqa-uncased") # image viz frcnn_visualizer = SingleImageViz(URL, id2obj=objids, id2attr=attrids) # run frcnn images, sizes, scales_yx = image_preprocess(URL) output_dict = frcnn( images, sizes, scales_yx=scales_yx, padding="max_detections", max_detections=frcnn_cfg.max_detections, return_tensors="pt", ) # add boxes and labels to the image frcnn_visualizer.draw_boxes( output_dict.get("boxes"), output_dict.pop("obj_ids"), output_dict.pop("obj_probs"), output_dict.pop("attr_ids"), output_dict.pop("attr_probs"), ) showarray(frcnn_visualizer._get_buffer()) test_questions_for_url1 = [ "Where is this scene?", "what is the man riding?", "What is the man wearing?", "What is the color of the horse?", ] test_questions_for_url2 = [ "Where is the cat?", "What is near the disk?", "What is the color of the table?", "What is the color of the cat?", "What is the shape of the monitor?", ] # Very important that the boxes are normalized normalized_boxes = output_dict.get("normalized_boxes") features = output_dict.get("roi_features") for test_question in test_questions_for_url2: # run lxmert test_question = [test_question] inputs = lxmert_tokenizer( test_question, padding="max_length", max_length=20, truncation=True, return_token_type_ids=True, return_attention_mask=True, add_special_tokens=True, return_tensors="pt", ) # run lxmert(s) output_gqa = lxmert_gqa( input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, visual_feats=features, visual_pos=normalized_boxes, token_type_ids=inputs.token_type_ids, output_attentions=False, ) output_vqa = lxmert_vqa( input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, visual_feats=features, visual_pos=normalized_boxes, token_type_ids=inputs.token_type_ids, output_attentions=False, ) # get prediction pred_vqa = output_vqa["question_answering_score"].argmax(-1) pred_gqa = output_gqa["question_answering_score"].argmax(-1) print("Question:", test_question) print("prediction from LXMERT GQA:", gqa_answers[pred_gqa]) print("prediction from LXMERT VQA:", vqa_answers[pred_vqa])<jupyter_output>Question: ['Where is the cat?'] prediction from LXMERT GQA: desk prediction from LXMERT VQA: desk Question: ['What is near the disk?'] prediction from LXMERT GQA: can prediction from LXMERT VQA: cat Question: ['What is the color of the table?'] prediction from LXMERT GQA: brown prediction from LXMERT VQA: brown Question: ['What is the color of the cat?'] prediction from LXMERT GQA: black prediction from LXMERT VQA: black and white Question: ['What is the shape of the monitor?'] prediction from LXMERT GQA: square prediction from LXMERT VQA: rectangle

transformers/examples/research_projects/lxmert/demo.ipynb/0

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# Copyright 2020-present, the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Once a model has been fine-pruned, the weights that are masked during the forward pass can be pruned once for all. For instance, once the a model from the :class:`~emmental.MaskedBertForSequenceClassification` is trained, it can be saved (and then loaded) as a standard :class:`~transformers.BertForSequenceClassification`. """ import argparse import os import shutil import torch from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer def main(args): pruning_method = args.pruning_method threshold = args.threshold model_name_or_path = args.model_name_or_path.rstrip("/") target_model_path = args.target_model_path print(f"Load fine-pruned model from {model_name_or_path}") model = torch.load(os.path.join(model_name_or_path, "pytorch_model.bin")) pruned_model = {} for name, tensor in model.items(): if "embeddings" in name or "LayerNorm" in name or "pooler" in name: pruned_model[name] = tensor print(f"Copied layer {name}") elif "classifier" in name or "qa_output" in name: pruned_model[name] = tensor print(f"Copied layer {name}") elif "bias" in name: pruned_model[name] = tensor print(f"Copied layer {name}") else: if pruning_method == "magnitude": mask = MagnitudeBinarizer.apply(inputs=tensor, threshold=threshold) pruned_model[name] = tensor * mask print(f"Pruned layer {name}") elif pruning_method == "topK": if "mask_scores" in name: continue prefix_ = name[:-6] scores = model[f"{prefix_}mask_scores"] mask = TopKBinarizer.apply(scores, threshold) pruned_model[name] = tensor * mask print(f"Pruned layer {name}") elif pruning_method == "sigmoied_threshold": if "mask_scores" in name: continue prefix_ = name[:-6] scores = model[f"{prefix_}mask_scores"] mask = ThresholdBinarizer.apply(scores, threshold, True) pruned_model[name] = tensor * mask print(f"Pruned layer {name}") elif pruning_method == "l0": if "mask_scores" in name: continue prefix_ = name[:-6] scores = model[f"{prefix_}mask_scores"] l, r = -0.1, 1.1 s = torch.sigmoid(scores) s_bar = s * (r - l) + l mask = s_bar.clamp(min=0.0, max=1.0) pruned_model[name] = tensor * mask print(f"Pruned layer {name}") else: raise ValueError("Unknown pruning method") if target_model_path is None: target_model_path = os.path.join( os.path.dirname(model_name_or_path), f"bertarized_{os.path.basename(model_name_or_path)}" ) if not os.path.isdir(target_model_path): shutil.copytree(model_name_or_path, target_model_path) print(f"\nCreated folder {target_model_path}") torch.save(pruned_model, os.path.join(target_model_path, "pytorch_model.bin")) print("\nPruned model saved! See you later!") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--pruning_method", choices=["l0", "magnitude", "topK", "sigmoied_threshold"], type=str, required=True, help=( "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning," " sigmoied_threshold = Soft movement pruning)" ), ) parser.add_argument( "--threshold", type=float, required=False, help=( "For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model. " "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared. " "Not needed for `l0`" ), ) parser.add_argument( "--model_name_or_path", type=str, required=True, help="Folder containing the model that was previously fine-pruned", ) parser.add_argument( "--target_model_path", default=None, type=str, required=False, help="Folder containing the model that was previously fine-pruned", ) args = parser.parse_args() main(args)

transformers/examples/research_projects/movement-pruning/bertarize.py/0

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# Performer fine-tuning Example authors: @TevenLeScao, @Patrickvonplaten Paper authors: Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller ## Requirements `datasets`, `flax` and `jax`. `wandb` integration is built-in if you want to use it. ## Examples `sanity_script.sh` will launch performer fine-tuning from the bert-base-cased checkpoint on the Simple Wikipedia dataset (a small, easy-language English Wikipedia) from `datasets`. `full_script.sh` will launch performer fine-tuning from the bert-large-cased checkpoint on the English Wikipedia dataset from `datasets`. Here are a few key arguments: - Remove the `--performer` argument to use a standard Bert model. - Add `--reinitialize` to start from a blank model rather than a Bert checkpoint. - You may change the Bert size by passing a different [checkpoint](https://huggingface.co/transformers/pretrained_models.html) to the `--model_name_or_path` argument. - Passing your user name to the `--wandb_user_name` argument will trigger weights and biases logging. - You can choose a dataset with `--dataset_name` and `--dataset_config`. Our [viewer](https://huggingface.co/datasets/viewer/) will help you find what you need.

transformers/examples/research_projects/performer/README.md/0

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import os import time import numpy as np import onnxruntime as ort os.environ["ORT_TENSORRT_INT8_ENABLE"] = "1" os.environ["ORT_TENSORRT_INT8_USE_NATIVE_CALIBRATION_TABLE"] = "0" os.environ["ORT_TENSORRT_ENGINE_CACHE_ENABLE"] = "1" sess_opt = ort.SessionOptions() sess_opt.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL print("Create inference session...") execution_provider = ["TensorrtExecutionProvider", "CUDAExecutionProvider"] sess = ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider) run_opt = ort.RunOptions() sequence = 128 batch = 1 input_ids = np.ones((batch, sequence), dtype=np.int64) attention_mask = np.ones((batch, sequence), dtype=np.int64) token_type_ids = np.ones((batch, sequence), dtype=np.int64) print("Warm up phase...") sess.run( None, { sess.get_inputs()[0].name: input_ids, sess.get_inputs()[1].name: attention_mask, sess.get_inputs()[2].name: token_type_ids, }, run_options=run_opt, ) print("Start inference...") start_time = time.time() max_iters = 2000 predict = {} for iter in range(max_iters): predict = sess.run( None, { sess.get_inputs()[0].name: input_ids, sess.get_inputs()[1].name: attention_mask, sess.get_inputs()[2].name: token_type_ids, }, run_options=run_opt, ) print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))

transformers/examples/research_projects/quantization-qdqbert/ort-infer-benchmark.py/0

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to a snake Moses' assistant Egyptian royal court let his rod turn in to a snake The Pokémon Company Nintendo world's top-selling toy brand, the top-selling trading card game over 20 seasons

transformers/examples/research_projects/rag-end2end-retriever/test_run/dummy-train-data/test.target/0

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""" Evaluation script for RAG models.""" import argparse import ast import logging import os import sys import pandas as pd import torch from tqdm import tqdm from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration from transformers import logging as transformers_logging sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip from utils_rag import exact_match_score, f1_score # noqa: E402 # isort:skip logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) transformers_logging.set_verbosity_info() def infer_model_type(model_name_or_path): if "token" in model_name_or_path: return "rag_token" if "sequence" in model_name_or_path: return "rag_sequence" if "bart" in model_name_or_path: return "bart" return None def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): return max(metric_fn(prediction, gt) for gt in ground_truths) def get_scores(args, preds_path, gold_data_path): hypos = [line.strip() for line in open(preds_path, "r").readlines()] answers = [] if args.gold_data_mode == "qa": data = pd.read_csv(gold_data_path, sep="\t", header=None) for answer_list in data[1]: ground_truths = ast.literal_eval(answer_list) answers.append(ground_truths) else: references = [line.strip() for line in open(gold_data_path, "r").readlines()] answers = [[reference] for reference in references] f1 = em = total = 0 for prediction, ground_truths in zip(hypos, answers): total += 1 em += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths) f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths) em = 100.0 * em / total f1 = 100.0 * f1 / total logger.info(f"F1: {f1:.2f}") logger.info(f"EM: {em:.2f}") def get_precision_at_k(args, preds_path, gold_data_path): k = args.k hypos = [line.strip() for line in open(preds_path, "r").readlines()] references = [line.strip() for line in open(gold_data_path, "r").readlines()] em = total = 0 for hypo, reference in zip(hypos, references): hypo_provenance = set(hypo.split("\t")[:k]) ref_provenance = set(reference.split("\t")) total += 1 em += len(hypo_provenance & ref_provenance) / k em = 100.0 * em / total logger.info(f"Precision@{k}: {em: .2f}") def evaluate_batch_retrieval(args, rag_model, questions): def strip_title(title): if title.startswith('"'): title = title[1:] if title.endswith('"'): title = title[:-1] return title retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus( questions, return_tensors="pt", padding=True, truncation=True, )["input_ids"].to(args.device) question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids) question_enc_pool_output = question_enc_outputs[0] result = rag_model.retriever( retriever_input_ids, question_enc_pool_output.cpu().detach().to(torch.float32).numpy(), prefix=rag_model.rag.generator.config.prefix, n_docs=rag_model.config.n_docs, return_tensors="pt", ) all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids) provenance_strings = [] for docs in all_docs: provenance = [strip_title(title) for title in docs["title"]] provenance_strings.append("\t".join(provenance)) return provenance_strings def evaluate_batch_e2e(args, rag_model, questions): with torch.no_grad(): inputs_dict = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus( questions, return_tensors="pt", padding=True, truncation=True ) input_ids = inputs_dict.input_ids.to(args.device) attention_mask = inputs_dict.attention_mask.to(args.device) outputs = rag_model.generate( # rag_model overwrites generate input_ids, attention_mask=attention_mask, num_beams=args.num_beams, min_length=args.min_length, max_length=args.max_length, early_stopping=False, num_return_sequences=1, bad_words_ids=[[0, 0]], # BART likes to repeat BOS tokens, dont allow it to generate more than one ) answers = rag_model.retriever.generator_tokenizer.batch_decode(outputs, skip_special_tokens=True) if args.print_predictions: for q, a in zip(questions, answers): logger.info("Q: {} - A: {}".format(q, a)) return answers def get_args(): parser = argparse.ArgumentParser() parser.add_argument( "--model_type", choices=["rag_sequence", "rag_token", "bart"], type=str, help=( "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the" " model_name_or_path" ), ) parser.add_argument( "--index_name", default=None, choices=["exact", "compressed", "legacy"], type=str, help="RAG model retriever type", ) parser.add_argument( "--index_path", default=None, type=str, help="Path to the retrieval index", ) parser.add_argument("--n_docs", default=5, type=int, help="Number of retrieved docs") parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained checkpoints or model identifier from huggingface.co/models", ) parser.add_argument( "--eval_mode", choices=["e2e", "retrieval"], default="e2e", type=str, help=( "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates" " precision@k." ), ) parser.add_argument("--k", default=1, type=int, help="k for the precision@k calculation") parser.add_argument( "--evaluation_set", default=None, type=str, required=True, help="Path to a file containing evaluation samples", ) parser.add_argument( "--gold_data_path", default=None, type=str, required=True, help="Path to a tab-separated file with gold samples", ) parser.add_argument( "--gold_data_mode", default="qa", type=str, choices=["qa", "ans"], help=( "Format of the gold data file" "qa - a single line in the following format: question [tab] answer_list" "ans - a single line of the gold file contains the expected answer string" ), ) parser.add_argument( "--predictions_path", type=str, default="predictions.txt", help="Name of the predictions file, to be stored in the checkpoints directory", ) parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument( "--eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.", ) parser.add_argument( "--recalculate", help="Recalculate predictions even if the prediction file exists", action="store_true", ) parser.add_argument( "--num_beams", default=4, type=int, help="Number of beams to be used when generating answers", ) parser.add_argument("--min_length", default=1, type=int, help="Min length of the generated answers") parser.add_argument("--max_length", default=50, type=int, help="Max length of the generated answers") parser.add_argument( "--print_predictions", action="store_true", help="If True, prints predictions while evaluating.", ) parser.add_argument( "--print_docs", action="store_true", help="If True, prints docs retried while generating.", ) args = parser.parse_args() args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") return args def main(args): model_kwargs = {} if args.model_type is None: args.model_type = infer_model_type(args.model_name_or_path) assert args.model_type is not None if args.model_type.startswith("rag"): model_class = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration model_kwargs["n_docs"] = args.n_docs if args.index_name is not None: model_kwargs["index_name"] = args.index_name if args.index_path is not None: model_kwargs["index_path"] = args.index_path else: model_class = BartForConditionalGeneration checkpoints = ( [f.path for f in os.scandir(args.model_name_or_path) if f.is_dir()] if args.eval_all_checkpoints else [args.model_name_or_path] ) logger.info("Evaluate the following checkpoints: %s", checkpoints) score_fn = get_scores if args.eval_mode == "e2e" else get_precision_at_k evaluate_batch_fn = evaluate_batch_e2e if args.eval_mode == "e2e" else evaluate_batch_retrieval for checkpoint in checkpoints: if os.path.exists(args.predictions_path) and (not args.recalculate): logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path)) score_fn(args, args.predictions_path, args.gold_data_path) continue logger.info("***** Running evaluation for {} *****".format(checkpoint)) logger.info(" Batch size = %d", args.eval_batch_size) logger.info(" Predictions will be stored under {}".format(args.predictions_path)) if args.model_type.startswith("rag"): retriever = RagRetriever.from_pretrained(checkpoint, **model_kwargs) model = model_class.from_pretrained(checkpoint, retriever=retriever, **model_kwargs) model.retriever.init_retrieval() else: model = model_class.from_pretrained(checkpoint, **model_kwargs) model.to(args.device) with open(args.evaluation_set, "r") as eval_file, open(args.predictions_path, "w") as preds_file: questions = [] for line in tqdm(eval_file): questions.append(line.strip()) if len(questions) == args.eval_batch_size: answers = evaluate_batch_fn(args, model, questions) preds_file.write("\n".join(answers) + "\n") preds_file.flush() questions = [] if len(questions) > 0: answers = evaluate_batch_fn(args, model, questions) preds_file.write("\n".join(answers)) preds_file.flush() score_fn(args, args.predictions_path, args.gold_data_path) if __name__ == "__main__": args = get_args() main(args)

transformers/examples/research_projects/rag/eval_rag.py/0

{ "file_path": "transformers/examples/research_projects/rag/eval_rag.py", "repo_id": "transformers", "token_count": 4854 }

291

# coding=utf-8 # Copyright 2022 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fine-tuning the library models for sequence classification.""" import argparse import dataclasses import json import logging import math import os import random import shutil from typing import List, Optional import datasets import numpy as np import pandas as pd import torch from datasets import load_dataset, load_metric from torch.utils.data import DataLoader from tqdm.auto import tqdm from transformers import ( AdamW, AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, default_data_collator, get_scheduler, set_seed, ) from transformers.file_utils import ExplicitEnum from transformers.trainer_utils import IntervalStrategy logger = logging.getLogger(__name__) class Split(ExplicitEnum): TRAIN = "train" EVAL = "eval" TEST = "test" INFER = "infer" @dataclasses.dataclass class FTModelArguments: """Arguments pertaining to which config/tokenizer/model we are going to fine-tune from.""" model_name_or_path: str = dataclasses.field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} ) use_fast_tokenizer: Optional[bool] = dataclasses.field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) cache_dir: Optional[str] = dataclasses.field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}, ) @dataclasses.dataclass class FTDataArguments: """Arguments pertaining to what data we are going to input our model for training and evaluation.""" train_file: str = dataclasses.field( default=None, metadata={"help": "A csv or a json file containing the training data."} ) eval_file: Optional[str] = dataclasses.field( default=None, metadata={"help": "A csv or a json file containing the validation data."} ) test_file: Optional[str] = dataclasses.field( default=None, metadata={"help": "A csv or a json file containing the test data."} ) infer_file: Optional[str] = dataclasses.field( default=None, metadata={"help": "A csv or a json file containing the data to predict on."} ) task_name: Optional[str] = dataclasses.field( default=None, metadata={"help": "The name of the task to train on."}, ) label_list: Optional[List[str]] = dataclasses.field( default=None, metadata={"help": "The list of labels for the task."} ) max_length: Optional[int] = dataclasses.field( default=128, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) pad_to_max_length: Optional[bool] = dataclasses.field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) @dataclasses.dataclass class FTTrainingArguments: """Training arguments pertaining to the training loop itself.""" output_dir: str = dataclasses.field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."} ) do_train: Optional[bool] = dataclasses.field( default=False, metadata={"help": "Whether to run training or not."}, ) do_eval: Optional[bool] = dataclasses.field( default=False, metadata={"help": "Whether to run evaluation on the validation set or not."}, ) do_predict: Optional[bool] = dataclasses.field( default=False, metadata={"help": "Whether to run inference on the inference set or not."}, ) seed: Optional[int] = dataclasses.field( default=42, metadata={"help": "Random seed that will be set at the beginning of training."}, ) per_device_train_batch_size: Optional[int] = dataclasses.field( default=8, metadata={"help": "The batch size per GPU/TPU core/CPU for training."}, ) per_device_eval_batch_size: Optional[int] = dataclasses.field( default=8, metadata={"help": "The batch size per GPU/TPU core/CPU for evaluation."}, ) weight_decay: Optional[float] = dataclasses.field( default=0.0, metadata={ "help": ( "The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in" " [`AdamW`] optimizer." ) }, ) learning_rate: Optional[float] = dataclasses.field( default=5e-5, metadata={"help": "The initial learning rate for [`AdamW`] optimizer."}, ) gradient_accumulation_steps: Optional[int] = dataclasses.field( default=1, metadata={ "help": ( "Number of updates steps to accumulate the gradients for, before performing a backward/update pass." ) }, ) max_steps: Optional[int] = dataclasses.field( default=-1, metadata={ "help": ( "If set to a positive number, the total number of training steps to perform. Overrides" " `num_train_epochs`." ) }, ) lr_scheduler_type: Optional[str] = dataclasses.field( default="linear", metadata={"help": "The scheduler type to use."} ) warmup_steps: Optional[int] = dataclasses.field( default=1, metadata={ "help": ( "Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of" " `warmup_ratio`." ) }, ) evaluation_strategy: Optional[str] = dataclasses.field( default="no", metadata={ "help": 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]' }, ) eval_steps: Optional[int] = dataclasses.field( default=1, metadata={"help": 'Number of update steps between two evaluations if `evaluation_strategy="steps"`.'}, ) eval_metric: Optional[str] = dataclasses.field( default="accuracy", metadata={"help": "The evaluation metric used for the task."} ) keep_checkpoint_max: Optional[int] = dataclasses.field( default=1, metadata={"help": "The maximum number of best checkpoint files to keep."}, ) early_stopping_patience: Optional[int] = dataclasses.field( default=10, metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}, ) early_stopping_threshold: Optional[float] = dataclasses.field( default=0.0, metadata={ "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions." }, ) def train(args, accelerator, model, tokenizer, train_dataloader, optimizer, lr_scheduler, eval_dataloader=None): """Train a model on the given training data.""" total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(" Num examples = %d", args.num_examples[Split.TRAIN.value]) logger.info(" Instantaneous batch size per device = %d", args.per_device_train_batch_size) logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", total_batch_size) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", args.max_steps) # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_steps), disable=not accelerator.is_local_main_process) checkpoints = None eval_results = None best_checkpoint = None best_eval_result = None early_stopping_patience_counter = 0 should_training_stop = False epoch = 0 completed_steps = 0 train_loss = 0.0 model.zero_grad() for _ in range(args.num_train_epochs): epoch += 1 model.train() for step, batch in enumerate(train_dataloader): outputs = model(**batch) loss = outputs.loss loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) train_loss += loss.item() if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) completed_steps += 1 # Evaluate during training if ( eval_dataloader is not None and args.evaluation_strategy == IntervalStrategy.STEPS.value and args.eval_steps > 0 and completed_steps % args.eval_steps == 0 ): accelerator.wait_for_everyone() new_checkpoint = f"checkpoint-{IntervalStrategy.STEPS.value}-{completed_steps}" new_eval_result = evaluate(args, accelerator, eval_dataloader, "eval", model, new_checkpoint)[ args.eval_metric ] logger.info( "Evaluation result at step %d: %s = %f", completed_steps, args.eval_metric, new_eval_result ) if checkpoints is None: checkpoints = np.array([new_checkpoint]) eval_results = np.array([new_eval_result]) best_checkpoint = new_checkpoint best_eval_result = new_eval_result else: if new_eval_result - best_eval_result > args.early_stopping_threshold: best_checkpoint = new_checkpoint best_eval_result = new_eval_result early_stopping_patience_counter = 0 else: if new_eval_result == best_eval_result: best_checkpoint = new_checkpoint best_eval_result = new_eval_result early_stopping_patience_counter += 1 if early_stopping_patience_counter >= args.early_stopping_patience: should_training_stop = True checkpoints = np.append(checkpoints, [new_checkpoint], axis=0) eval_results = np.append(eval_results, [new_eval_result], axis=0) sorted_ids = np.argsort(eval_results) eval_results = eval_results[sorted_ids] checkpoints = checkpoints[sorted_ids] if len(checkpoints) > args.keep_checkpoint_max: # Delete the current worst checkpoint checkpoint_to_remove, *checkpoints = checkpoints eval_results = eval_results[1:] if checkpoint_to_remove != new_checkpoint: if accelerator.is_main_process: shutil.rmtree(os.path.join(args.output_dir, checkpoint_to_remove), ignore_errors=True) accelerator.wait_for_everyone() if new_checkpoint in checkpoints: # Save model checkpoint checkpoint_output_dir = os.path.join(args.output_dir, new_checkpoint) if accelerator.is_main_process: if not os.path.exists(checkpoint_output_dir): os.makedirs(checkpoint_output_dir) accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained(checkpoint_output_dir, save_function=accelerator.save) if accelerator.is_main_process: tokenizer.save_pretrained(checkpoint_output_dir) logger.info("Saving model checkpoint to %s", checkpoint_output_dir) if completed_steps >= args.max_steps: break if should_training_stop: break # Evaluate during training if eval_dataloader is not None and args.evaluation_strategy == IntervalStrategy.EPOCH.value: accelerator.wait_for_everyone() new_checkpoint = f"checkpoint-{IntervalStrategy.EPOCH.value}-{epoch}" new_eval_result = evaluate(args, accelerator, eval_dataloader, "eval", model, new_checkpoint)[ args.eval_metric ] logger.info("Evaluation result at epoch %d: %s = %f", epoch, args.eval_metric, new_eval_result) if checkpoints is None: checkpoints = np.array([new_checkpoint]) eval_results = np.array([new_eval_result]) best_checkpoint = new_checkpoint best_eval_result = new_eval_result else: if new_eval_result - best_eval_result > args.early_stopping_threshold: best_checkpoint = new_checkpoint best_eval_result = new_eval_result early_stopping_patience_counter = 0 else: if new_eval_result == best_eval_result: best_checkpoint = new_checkpoint best_eval_result = new_eval_result early_stopping_patience_counter += 1 if early_stopping_patience_counter >= args.early_stopping_patience: should_training_stop = True checkpoints = np.append(checkpoints, [new_checkpoint], axis=0) eval_results = np.append(eval_results, [new_eval_result], axis=0) sorted_ids = np.argsort(eval_results) eval_results = eval_results[sorted_ids] checkpoints = checkpoints[sorted_ids] if len(checkpoints) > args.keep_checkpoint_max: # Delete the current worst checkpoint checkpoint_to_remove, *checkpoints = checkpoints eval_results = eval_results[1:] if checkpoint_to_remove != new_checkpoint: if accelerator.is_main_process: shutil.rmtree(os.path.join(args.output_dir, checkpoint_to_remove), ignore_errors=True) accelerator.wait_for_everyone() if new_checkpoint in checkpoints: # Save model checkpoint checkpoint_output_dir = os.path.join(args.output_dir, new_checkpoint) if accelerator.is_main_process: if not os.path.exists(checkpoint_output_dir): os.makedirs(checkpoint_output_dir) accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained(checkpoint_output_dir, save_function=accelerator.save) if accelerator.is_main_process: tokenizer.save_pretrained(checkpoint_output_dir) logger.info("Saving model checkpoint to %s", checkpoint_output_dir) if completed_steps >= args.max_steps: break if should_training_stop: break if best_checkpoint is not None: # Save the best checkpoint logger.info("Best checkpoint: %s", best_checkpoint) logger.info("Best evaluation result: %s = %f", args.eval_metric, best_eval_result) best_checkpoint_output_dir = os.path.join(args.output_dir, best_checkpoint) if accelerator.is_main_process: shutil.move(best_checkpoint_output_dir, os.path.join(args.output_dir, "best-checkpoint")) shutil.rmtree(best_checkpoint_output_dir, ignore_errors=True) accelerator.wait_for_everyone() else: # Assume that the last checkpoint is the best checkpoint and save it checkpoint_output_dir = os.path.join(args.output_dir, "best-checkpoint") if not os.path.exists(checkpoint_output_dir): os.makedirs(checkpoint_output_dir) accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained(checkpoint_output_dir, save_function=accelerator.save) if accelerator.is_main_process: tokenizer.save_pretrained(checkpoint_output_dir) logger.info("Saving model checkpoint to %s", checkpoint_output_dir) return completed_steps, train_loss / completed_steps def evaluate(args, accelerator, dataloader, eval_set, model, checkpoint, has_labels=True, write_to_file=True): """Evaluate a model checkpoint on the given evaluation data.""" num_examples = args.num_examples[eval_set] eval_metric = None completed_steps = 0 eval_loss = 0.0 all_predictions = None all_references = None all_probabilities = None if has_labels: # Get the metric function eval_metric = load_metric(args.eval_metric) eval_results = {} model.eval() for _, batch in enumerate(dataloader): with torch.no_grad(): outputs = model(**batch) eval_loss += outputs.loss.item() logits = outputs.logits predictions = logits.argmax(dim=-1) if not args.is_regression else logits.squeeze() predictions = accelerator.gather(predictions) if all_predictions is None: all_predictions = predictions.detach().cpu().numpy() else: all_predictions = np.append(all_predictions, predictions.detach().cpu().numpy(), axis=0) if not args.is_regression: probabilities = logits.softmax(dim=-1).max(dim=-1).values probabilities = accelerator.gather(probabilities) if all_probabilities is None: all_probabilities = probabilities.detach().cpu().numpy() else: all_probabilities = np.append(all_probabilities, probabilities.detach().cpu().numpy(), axis=0) if has_labels: references = batch["labels"] references = accelerator.gather(references) if all_references is None: all_references = references.detach().cpu().numpy() else: all_references = np.append(all_references, references.detach().cpu().numpy(), axis=0) eval_metric.add_batch( predictions=predictions, references=references, ) completed_steps += 1 if has_labels: eval_results.update(eval_metric.compute()) eval_results["completed_steps"] = completed_steps eval_results["avg_eval_loss"] = eval_loss / completed_steps if write_to_file: accelerator.wait_for_everyone() if accelerator.is_main_process: results_file = os.path.join(args.output_dir, f"{eval_set}_results_{checkpoint}.json") with open(results_file, "w") as f: json.dump(eval_results, f, indent=4, sort_keys=True) if write_to_file: accelerator.wait_for_everyone() if accelerator.is_main_process: output_file = os.path.join(args.output_dir, f"{eval_set}_output_{checkpoint}.csv") if not args.is_regression: assert len(all_predictions) == len(all_probabilities) df = pd.DataFrame(list(zip(all_predictions, all_probabilities)), columns=["prediction", "probability"]) else: df = pd.DataFrame(all_predictions, columns=["prediction"]) df = df.head(num_examples) df.to_csv(output_file, header=True, index=False) return eval_results def load_from_pretrained(args, pretrained_model_name_or_path): """Load the pretrained model and tokenizer.""" # In distributed training, the .from_pretrained methods guarantee that only # one local process can concurrently perform this procedure. config = AutoConfig.from_pretrained( pretrained_model_name_or_path, num_labels=args.num_labels if hasattr(args, "num_labels") else None, finetuning_task=args.task_name.lower(), cache_dir=args.cache_dir, ) tokenizer = AutoTokenizer.from_pretrained( pretrained_model_name_or_path, use_fast=args.use_fast_tokenizer, cache_dir=args.cache_dir ) model = AutoModelForSequenceClassification.from_pretrained( pretrained_model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=True, cache_dir=args.cache_dir, ) return config, tokenizer, model def finetune(accelerator, model_name_or_path, train_file, output_dir, **kwargs): """Fine-tuning a pre-trained model on a downstream task. Args: accelerator: An instance of an accelerator for distributed training (on multi-GPU, TPU) or mixed precision training. model_name_or_path: Path to pretrained model or model identifier from huggingface.co/models. train_file: A csv or a json file containing the training data. output_dir: The output directory where the model predictions and checkpoints will be written. **kwargs: Dictionary of key/value pairs with which to update the configuration object after loading. The values in kwargs of any keys which are configuration attributes will be used to override the loaded values. """ # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state) # Setup logging, we only want one process per machine to log things on the # screen. accelerator.is_local_main_process is only True for one process per # machine. logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR) model_args = FTModelArguments(model_name_or_path=model_name_or_path) data_args = FTDataArguments(train_file=train_file) training_args = FTTrainingArguments(output_dir=output_dir) args = argparse.Namespace() for arg_class in (model_args, data_args, training_args): for key, value in vars(arg_class).items(): setattr(args, key, value) for key, value in kwargs.items(): if hasattr(args, key): setattr(args, key, value) # Sanity checks data_files = {} args.data_file_extension = None # You need to provide the training data as we always run training args.do_train = True assert args.train_file is not None data_files[Split.TRAIN.value] = args.train_file if args.do_eval or args.evaluation_strategy != IntervalStrategy.NO.value: assert args.eval_file is not None data_files[Split.EVAL.value] = args.eval_file if args.do_eval and args.test_file is not None: data_files[Split.TEST.value] = args.test_file if args.do_predict: assert args.infer_file is not None data_files[Split.INFER.value] = args.infer_file for key in data_files: extension = data_files[key].split(".")[-1] assert extension in ["csv", "json"], f"`{key}_file` should be a csv or a json file." if args.data_file_extension is None: args.data_file_extension = extension else: assert extension == args.data_file_extension, f"`{key}_file` should be a {args.data_file_extension} file`." assert ( args.eval_metric in datasets.list_metrics() ), f"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}." # Handle the output directory creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # You need to provide your CSV/JSON data files. # # For CSV/JSON files, this script will use as labels the column called 'label' # and as pair of sentences the sentences in columns called 'sentence1' and # 'sentence2' if these columns exist or the first two columns not named # 'label' if at least two columns are provided. # # If the CSVs/JSONs contain only one non-label column, the script does single # sentence classification on this single column. # # In distributed training, the load_dataset function guarantees that only one # local process can download the dataset. # Loading the dataset from local csv or json files. raw_datasets = load_dataset(args.data_file_extension, data_files=data_files) # Labels is_regression = raw_datasets[Split.TRAIN.value].features["label"].dtype in ["float32", "float64"] args.is_regression = is_regression if args.is_regression: label_list = None num_labels = 1 else: label_list = args.label_list assert label_list is not None label_list.sort() # Let's sort it for determinism num_labels = len(label_list) args.num_labels = num_labels # Load pre-trained model config, tokenizer, model = load_from_pretrained(args, args.model_name_or_path) # Preprocessing the datasets non_label_column_names = [name for name in raw_datasets[Split.TRAIN.value].column_names if name != "label"] if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names: sentence1_key, sentence2_key = "sentence1", "sentence2" else: if len(non_label_column_names) >= 2: sentence1_key, sentence2_key = non_label_column_names[:2] else: sentence1_key, sentence2_key = non_label_column_names[0], None label_to_id = {v: i for i, v in enumerate(label_list)} config.label2id = label_to_id config.id2label = {id: label for label, id in config.label2id.items()} padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): # Tokenize the texts texts = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True) if "label" in examples: if label_to_id is not None: # Map labels to IDs (not necessary for GLUE tasks) result["labels"] = [label_to_id[l] for l in examples["label"]] else: # In all cases, rename the column to labels because the model will # expect that. result["labels"] = examples["label"] return result with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, remove_columns=raw_datasets[Split.TRAIN.value].column_names, desc="Running tokenizer on dataset", ) num_examples = {} splits = [s.value for s in Split] for split in splits: if split in processed_datasets: num_examples[split] = len(processed_datasets[split]) args.num_examples = num_examples train_dataset = processed_datasets[Split.TRAIN.value] eval_dataset = processed_datasets[Split.EVAL.value] if Split.EVAL.value in processed_datasets else None test_dataset = processed_datasets[Split.TEST.value] if Split.TEST.value in processed_datasets else None infer_dataset = processed_datasets[Split.INFER.value] if Split.INFER.value in processed_datasets else None # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info("Sample %d of the training set: %s.", index, train_dataset[index]) # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data # collator that will just convert everything to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by # padding to the maximum length of the samples passed). When using mixed # precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple of # 8s, which will enable the use of Tensor Cores on NVIDIA hardware with # compute capability >= 7.5 (Volta). data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)) train_dataloader = DataLoader( train_dataset, batch_size=args.per_device_train_batch_size, shuffle=True, collate_fn=data_collator, ) eval_dataloader, test_dataloader, infer_dataloader = None, None, None if eval_dataset is not None: eval_dataloader = DataLoader( eval_dataset, batch_size=args.per_device_eval_batch_size, collate_fn=data_collator ) if test_dataset is not None: test_dataloader = DataLoader( test_dataset, batch_size=args.per_device_eval_batch_size, collate_fn=data_collator ) if infer_dataset is not None: infer_dataloader = DataLoader( infer_dataset, batch_size=args.per_device_eval_batch_size, collate_fn=data_collator ) # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, test_dataloader, infer_dataloader = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, test_dataloader, infer_dataloader ) # Note -> the training dataloader needs to be prepared before we grab its # length below (cause its length will be shorter in multiprocess) # Scheduler and math around the number of training steps. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_steps == -1: args.max_steps = args.num_train_epochs * num_update_steps_per_epoch else: args.num_train_epochs = math.ceil(args.max_steps / num_update_steps_per_epoch) lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=args.max_steps, ) # Train completed_steps, avg_train_loss = train( args, accelerator, model, tokenizer, train_dataloader, optimizer, lr_scheduler, eval_dataloader ) accelerator.wait_for_everyone() logger.info("Training job completed: completed_steps = %d, avg_train_loss = %f", completed_steps, avg_train_loss) args.model_name_or_path = os.path.join(args.output_dir, "best-checkpoint") logger.info("Loading the best checkpoint: %s", args.model_name_or_path) config, tokenizer, model = load_from_pretrained(args, args.model_name_or_path) model = accelerator.prepare(model) if args.do_eval: # Evaluate if eval_dataloader is not None: logger.info("***** Running evaluation on the eval data using the best checkpoint *****") eval_results = evaluate(args, accelerator, eval_dataloader, Split.EVAL.value, model, "best-checkpoint") avg_eval_loss = eval_results["avg_eval_loss"] eval_metric = eval_results[args.eval_metric] logger.info("Evaluation job completed: avg_eval_loss = %f", avg_eval_loss) logger.info("Evaluation result for the best checkpoint: %s = %f", args.eval_metric, eval_metric) if test_dataloader is not None: logger.info("***** Running evaluation on the test data using the best checkpoint *****") eval_results = evaluate(args, accelerator, test_dataloader, Split.TEST.value, model, "best-checkpoint") avg_eval_loss = eval_results["avg_eval_loss"] eval_metric = eval_results[args.eval_metric] logger.info("Test job completed: avg_test_loss = %f", avg_eval_loss) logger.info("Test result for the best checkpoint: %s = %f", args.eval_metric, eval_metric) if args.do_predict: # Predict if infer_dataloader is not None: logger.info("***** Running inference using the best checkpoint *****") evaluate( args, accelerator, infer_dataloader, Split.INFER.value, model, "best-checkpoint", has_labels=False ) logger.info("Inference job completed.") # Release all references to the internal objects stored and call the garbage # collector. You should call this method between two trainings with different # models/optimizers. accelerator.free_memory()

transformers/examples/research_projects/self-training-text-classification/finetuning.py/0

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# the proper usage is documented in the README, you need to specify data_dir, output_dir and model_name_or_path # run ./finetune.sh --help to see all the possible options python finetune.py \ --learning_rate=3e-5 \ --fp16 \ --gpus 1 \ --do_train \ --do_predict \ --n_val 1000 \ --val_check_interval 0.1 \ "$@"

transformers/examples/research_projects/seq2seq-distillation/finetune.sh/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2022 The Microsoft and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for tapex on table-based fact verification tasks. Adapted from script: https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py """ import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import numpy as np import pandas as pd from datasets import load_dataset import transformers from transformers import ( AutoConfig, BartForSequenceClassification, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, TapexTokenizer, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.17.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") logger = logging.getLogger(__name__) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: Optional[str] = field( default="tab_fact", metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default="tab_fact", metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}, ) max_seq_length: int = field( default=1024, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) max_predict_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of prediction examples to this " "value if set." ) }, ) train_file: Optional[str] = field( default=None, metadata={"help": "A csv or a json file containing the training data."} ) validation_file: Optional[str] = field( default=None, metadata={"help": "A csv or a json file containing the validation data."} ) test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."}) def __post_init__(self): if self.dataset_name is not None: pass elif self.train_file is None or self.validation_file is None: raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.") else: train_extension = self.train_file.split(".")[-1] assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file." validation_extension = self.validation_file.split(".")[-1] assert ( validation_extension == train_extension ), "`validation_file` should have the same extension (csv or json) as `train_file`." @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default=None, metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) datasets.utils.logging.set_verbosity(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub). # # For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table. # # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this # single column. You can easily tweak this behavior (see below) # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir ) else: # Loading a dataset from your local files. # CSV/JSON training and evaluation files are needed. data_files = {"train": data_args.train_file, "validation": data_args.validation_file} # Get the test dataset: you can provide your own CSV/JSON test file (see below) # when you use `do_predict` without specifying a GLUE benchmark task. if training_args.do_predict: if data_args.test_file is not None: train_extension = data_args.train_file.split(".")[-1] test_extension = data_args.test_file.split(".")[-1] assert ( test_extension == train_extension ), "`test_file` should have the same extension (csv or json) as `train_file`." data_files["test"] = data_args.test_file else: raise ValueError("Need either a GLUE task or a test file for `do_predict`.") for key in data_files.keys(): logger.info(f"load a local file for {key}: {data_files[key]}") if data_args.train_file.endswith(".csv"): # Loading a dataset from local csv files raw_datasets = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir) else: # Loading a dataset from local json files raw_datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir) # See more about loading any type of standard or custom dataset at # https://huggingface.co/docs/datasets/loading_datasets. # Labels label_list = raw_datasets["train"].features["label"].names num_labels = len(label_list) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=num_labels, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=True if model_args.use_auth_token else None, ) # load tapex tokenizer tokenizer = TapexTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, token=True if model_args.use_auth_token else None, add_prefix_space=True, ) model = BartForSequenceClassification.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=True if model_args.use_auth_token else None, ) # Padding strategy if data_args.pad_to_max_length: padding = "max_length" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch padding = False # Some models have set the order of the labels to use, so let's make sure we do use it. model.config.label2id = {"Refused": 0, "Entailed": 1} model.config.id2label = {0: "Refused", 1: "Entailed"} if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the " f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) def preprocess_tabfact_function(examples): # Tokenize the texts def _convert_table_text_to_pandas(_table_text): """Runs the structured pandas table object for _table_text. An example _table_text can be: round#clubs remaining\nfirst round#156\n """ _table_content = [_table_row.split("#") for _table_row in _table_text.strip("\n").split("\n")] _table_pd = pd.DataFrame.from_records(_table_content[1:], columns=_table_content[0]) return _table_pd questions = examples["statement"] tables = list(map(_convert_table_text_to_pandas, examples["table_text"])) result = tokenizer(tables, questions, padding=padding, max_length=max_seq_length, truncation=True) result["label"] = examples["label"] return result with training_args.main_process_first(desc="dataset map pre-processing"): raw_datasets = raw_datasets.map( preprocess_tabfact_function, batched=True, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset", ) if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if data_args.max_train_samples is not None: train_dataset = train_dataset.select(range(data_args.max_train_samples)) if training_args.do_eval: if "validation" not in raw_datasets and "validation_matched" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = raw_datasets["validation"] if data_args.max_eval_samples is not None: eval_dataset = eval_dataset.select(range(data_args.max_eval_samples)) if training_args.do_predict or data_args.test_file is not None: if "test" not in raw_datasets and "test_matched" not in raw_datasets: raise ValueError("--do_predict requires a test dataset") predict_dataset = raw_datasets["test"] if data_args.max_predict_samples is not None: predict_dataset = predict_dataset.select(range(data_args.max_predict_samples)) # Log a few random samples from the training set: if training_args.do_train: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(p: EvalPrediction): preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions preds = np.argmax(preds, axis=1) return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()} # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: data_collator = default_data_collator elif training_args.fp16: data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) else: data_collator = None # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, compute_metrics=compute_metrics, tokenizer=tokenizer, data_collator=data_collator, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) ) metrics["train_samples"] = min(max_train_samples, len(train_dataset)) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate(eval_dataset=eval_dataset) max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) if training_args.do_predict: logger.info("*** Predict ***") # Removing the `label` columns because it contains -1 and Trainer won't like that. predict_dataset = predict_dataset.remove_columns("label") predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions predictions = np.argmax(predictions, axis=1) output_predict_file = os.path.join(training_args.output_dir, "predict_results_tabfact.txt") if trainer.is_world_process_zero(): with open(output_predict_file, "w") as writer: logger.info("***** Predict Results *****") writer.write("index\tprediction\n") for index, item in enumerate(predictions): item = label_list[item] writer.write(f"{index}\t{item}\n") kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"} if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

transformers/examples/research_projects/tapex/run_tabfact_with_tapex.py/0

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#!/usr/bin/env python3 import json import logging import os import re import sys from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Union import datasets import numpy as np import torch import torchaudio from packaging import version from torch import nn import transformers from transformers import ( HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, Wav2Vec2Processor, is_apex_available, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process if is_apex_available(): from apex import amp if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"): _is_native_amp_available = True from torch.cuda.amp import autocast logger = logging.getLogger(__name__) def list_field(default=None, metadata=None): return field(default_factory=lambda: default, metadata=metadata) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) freeze_feature_extractor: Optional[bool] = field( default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."} ) attention_dropout: Optional[float] = field( default=0.1, metadata={"help": "The dropout ratio for the attention probabilities."} ) activation_dropout: Optional[float] = field( default=0.1, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} ) hidden_dropout: Optional[float] = field( default=0.1, metadata={ "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler." }, ) feat_proj_dropout: Optional[float] = field( default=0.1, metadata={"help": "The dropout probability for all 1D convolutional layers in feature extractor."}, ) mask_time_prob: Optional[float] = field( default=0.05, metadata={ "help": ( "Propability of each feature vector along the time axis to be chosen as the start of the vector " "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature " "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``." ) }, ) layerdrop: Optional[float] = field(default=0.0, metadata={"help": "The LayerDrop probability."}) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_split_name: Optional[str] = field( default="train+validation", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_val_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of validation examples to this " "value if set." ) }, ) chars_to_ignore: List[str] = list_field( default=[",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�"], metadata={"help": "A list of characters to remove from the transcripts."}, ) @dataclass class DataCollatorCTCWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor (:class:`~transformers.Wav2Vec2Processor`) The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). max_length_labels (:obj:`int`, `optional`): Maximum length of the ``labels`` returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ processor: Wav2Vec2Processor padding: Union[bool, str] = True max_length: Optional[int] = None max_length_labels: Optional[int] = None pad_to_multiple_of: Optional[int] = None pad_to_multiple_of_labels: Optional[int] = None def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lengths and need # different padding methods input_features = [{"input_values": feature["input_values"]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.pad( input_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) labels_batch = self.processor.pad( labels=label_features, padding=self.padding, max_length=self.max_length_labels, pad_to_multiple_of=self.pad_to_multiple_of_labels, return_tensors="pt", ) # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels return batch class CTCTrainer(Trainer): def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor: """ Perform a training step on a batch of inputs. Subclass and override to inject custom behavior. Args: model (:obj:`nn.Module`): The model to train. inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`): The inputs and targets of the model. The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument :obj:`labels`. Check your model's documentation for all accepted arguments. Return: :obj:`torch.Tensor`: The tensor with training loss on this batch. """ model.train() inputs = self._prepare_inputs(inputs) if self.use_amp: with autocast(): loss = self.compute_loss(model, inputs) else: loss = self.compute_loss(model, inputs) if self.args.n_gpu > 1: if model.module.config.ctc_loss_reduction == "mean": loss = loss.mean() elif model.module.config.ctc_loss_reduction == "sum": loss = loss.sum() / (inputs["labels"] >= 0).sum() else: raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']") if self.args.gradient_accumulation_steps > 1: loss = loss / self.args.gradient_accumulation_steps if self.use_amp: self.scaler.scale(loss).backward() elif self.use_apex: with amp.scale_loss(loss, self.optimizer) as scaled_loss: scaled_loss.backward() elif self.deepspeed: self.deepspeed.backward(loss) else: loss.backward() return loss.detach() def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: train_dataset = datasets.load_dataset( "common_voice", data_args.dataset_config_name, split=data_args.train_split_name ) eval_dataset = datasets.load_dataset("common_voice", data_args.dataset_config_name, split="test") # Create and save tokenizer chars_to_ignore_regex = f'[{"".join(data_args.chars_to_ignore)}]' def remove_special_characters(batch): batch["text"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).lower() + " " return batch train_dataset = train_dataset.map(remove_special_characters, remove_columns=["sentence"]) eval_dataset = eval_dataset.map(remove_special_characters, remove_columns=["sentence"]) def extract_all_chars(batch): all_text = " ".join(batch["text"]) vocab = list(set(all_text)) return {"vocab": [vocab], "all_text": [all_text]} vocab_train = train_dataset.map( extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=train_dataset.column_names, ) vocab_test = train_dataset.map( extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=eval_dataset.column_names, ) vocab_list = list(set(vocab_train["vocab"][0]) | set(vocab_test["vocab"][0])) vocab_dict = {v: k for k, v in enumerate(vocab_list)} vocab_dict["|"] = vocab_dict[" "] del vocab_dict[" "] vocab_dict["[UNK]"] = len(vocab_dict) vocab_dict["[PAD]"] = len(vocab_dict) with open("vocab.json", "w") as vocab_file: json.dump(vocab_dict, vocab_file) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. tokenizer = Wav2Vec2CTCTokenizer( "vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|", ) feature_extractor = Wav2Vec2FeatureExtractor( feature_size=1, sampling_rate=16_000, padding_value=0.0, do_normalize=True, return_attention_mask=True ) processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) model = Wav2Vec2ForCTC.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, activation_dropout=model_args.activation_dropout, attention_dropout=model_args.attention_dropout, hidden_dropout=model_args.hidden_dropout, feat_proj_dropout=model_args.feat_proj_dropout, mask_time_prob=model_args.mask_time_prob, gradient_checkpointing=training_args.gradient_checkpointing, layerdrop=model_args.layerdrop, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer), ) if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) if data_args.max_val_samples is not None: eval_dataset = eval_dataset.select(range(data_args.max_val_samples)) resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays and tokenize the targets. def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() batch["sampling_rate"] = 16_000 batch["target_text"] = batch["text"] return batch train_dataset = train_dataset.map( speech_file_to_array_fn, remove_columns=train_dataset.column_names, num_proc=data_args.preprocessing_num_workers, ) eval_dataset = eval_dataset.map( speech_file_to_array_fn, remove_columns=eval_dataset.column_names, num_proc=data_args.preprocessing_num_workers, ) def prepare_dataset(batch): # check that all files have the correct sampling rate assert ( len(set(batch["sampling_rate"])) == 1 ), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}." processed_batch = processor( audio=batch["speech"], text=batch["target_text"], sampling_rate=batch["sampling_rate"][0] ) batch.update(processed_batch) return batch train_dataset = train_dataset.map( prepare_dataset, remove_columns=train_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) eval_dataset = eval_dataset.map( prepare_dataset, remove_columns=eval_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) # Metric wer_metric = datasets.load_metric("wer") def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id pred_str = processor.batch_decode(pred_ids) # we do not want to group tokens when computing the metrics label_str = processor.batch_decode(pred.label_ids, group_tokens=False) wer = wer_metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} if model_args.freeze_feature_extractor: model.freeze_feature_extractor() # Data collator data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) # Initialize our Trainer trainer = CTCTrainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=processor.feature_extractor, ) # Training if training_args.do_train: if last_checkpoint is not None: checkpoint = last_checkpoint elif os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None # Save the feature_extractor and the tokenizer if is_main_process(training_args.local_rank): processor.save_pretrained(training_args.output_dir) train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) ) metrics["train_samples"] = min(max_train_samples, len(train_dataset)) trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate() max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset) metrics["eval_samples"] = min(max_val_samples, len(eval_dataset)) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) return results if __name__ == "__main__": main()

transformers/examples/research_projects/wav2vec2/run_common_voice.py/0

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# TFVisionTextDualEncoder and CLIP model training examples The following example showcases how to train a CLIP-like vision-text dual encoder model using a pre-trained vision and text encoder. Such a model can be used for natural language image search and potentially zero-shot image classification. The model is inspired by [CLIP](https://openai.com/blog/clip/), introduced by Alec Radford et al. The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their captions into the same embedding space, such that the caption embeddings are located near the embeddings of the images they describe. ### Download COCO dataset (2017) This example uses COCO dataset (2017) through a custom dataset script, which requires users to manually download the COCO dataset before training. ```bash mkdir data cd data wget http://images.cocodataset.org/zips/train2017.zip wget http://images.cocodataset.org/zips/val2017.zip wget http://images.cocodataset.org/zips/test2017.zip wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip wget http://images.cocodataset.org/annotations/image_info_test2017.zip cd .. ``` Having downloaded COCO dataset manually you should be able to load with the `ydshieh/coc_dataset_script` dataset loading script: ```py import os import datasets COCO_DIR = os.path.join(os.getcwd(), "data") ds = datasets.load_dataset("ydshieh/coco_dataset_script", "2017", data_dir=COCO_DIR) ``` ### Create a model from a vision encoder model and a text encoder model We can either load a CLIP-like vision-text dual encoder model from an existing dual encoder model, or by using a pre-trained vision encoder model and a pre-trained text encoder model. If you wish to load an existing dual encoder model, please use the `--model_name_or_path` argument. If you want to use separate pre-trained vision and text models, please use the `--vision_model_name_or_path` and `--text_model_name_or_path` arguments instead. ### Train the model Finally, we can run the example script to train the model: ```bash python examples/tensorflow/contrastive-image-text/run_clip.py \ --output_dir ./clip-roberta-finetuned \ --vision_model_name_or_path openai/clip-vit-base-patch32 \ --text_model_name_or_path roberta-base \ --data_dir $PWD/data \ --dataset_name ydshieh/coco_dataset_script \ --dataset_config_name=2017 \ --image_column image_path \ --caption_column caption \ --remove_unused_columns=False \ --do_train --do_eval \ --per_device_train_batch_size="64" \ --per_device_eval_batch_size="64" \ --learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \ --overwrite_output_dir \ --push_to_hub ```

transformers/examples/tensorflow/contrastive-image-text/README.md/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model on token classification tasks (NER, POS, CHUNKS) """ import json import logging import os import random import warnings from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import tensorflow as tf from datasets import ClassLabel, load_dataset import transformers from transformers import ( CONFIG_MAPPING, AutoConfig, AutoTokenizer, DataCollatorForTokenClassification, HfArgumentParser, PushToHubCallback, TFAutoModelForTokenClassification, TFTrainingArguments, create_optimizer, set_seed, ) from transformers.utils import send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) logger.addHandler(logging.StreamHandler()) require_version("datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/token-classification/requirements.txt") # region Command-line arguments @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) use_auth_token: bool = field( default=None, metadata={ "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option " "should only be set to `True` for repositories you trust and in which you have read the code, as it will " "execute code present on the Hub on your local machine." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ task_name: Optional[str] = field(default="ner", metadata={"help": "The name of the task (ner, pos...)."}) dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field( default=None, metadata={"help": "The input training data file (a csv or JSON file)."} ) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate on (a csv or JSON file)."}, ) test_file: Optional[str] = field( default=None, metadata={"help": "An optional input test data file to predict on (a csv or JSON file)."}, ) text_column_name: Optional[str] = field( default=None, metadata={"help": "The column name of text to input in the file (a csv or JSON file)."} ) label_column_name: Optional[str] = field( default=None, metadata={"help": "The column name of label to input in the file (a csv or JSON file)."} ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_length: Optional[int] = field(default=256, metadata={"help": "Max length (in tokens) for truncation/padding"}) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to model maximum sentence length. " "If False, will pad the samples dynamically when batching to the maximum length in the batch. More " "efficient on GPU but very bad for TPU." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) max_predict_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of prediction examples to this " "value if set." ) }, ) label_all_tokens: bool = field( default=False, metadata={ "help": ( "Whether to put the label for one word on all tokens of generated by that word or just on the " "one (in which case the other tokens will have a padding index)." ) }, ) return_entity_level_metrics: bool = field( default=False, metadata={"help": "Whether to return all the entity levels during evaluation or just the overall ones."}, ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." self.task_name = self.task_name.lower() # endregion def main(): # region Argument Parsing parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() if model_args.use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.", FutureWarning, ) if model_args.token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") model_args.token = model_args.use_auth_token # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_ner", model_args, data_args, framework="tensorflow") # endregion # region Setup logging # we only want one process per machine to log things on the screen. # accelerator.is_local_main_process is only True for one process per machine. logger.setLevel(logging.INFO) datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() # If passed along, set the training seed now. if training_args.seed is not None: set_seed(training_args.seed) # endregion # region Loading datasets # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'tokens' or the first column if no column called # 'tokens' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, token=model_args.token, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] raw_datasets = load_dataset( extension, data_files=data_files, token=model_args.token, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets. if raw_datasets["train"] is not None: column_names = raw_datasets["train"].column_names features = raw_datasets["train"].features else: column_names = raw_datasets["validation"].column_names features = raw_datasets["validation"].features if data_args.text_column_name is not None: text_column_name = data_args.text_column_name elif "tokens" in column_names: text_column_name = "tokens" else: text_column_name = column_names[0] if data_args.label_column_name is not None: label_column_name = data_args.label_column_name elif f"{data_args.task_name}_tags" in column_names: label_column_name = f"{data_args.task_name}_tags" else: label_column_name = column_names[1] # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the # unique labels. def get_label_list(labels): unique_labels = set() for label in labels: unique_labels = unique_labels | set(label) label_list = list(unique_labels) label_list.sort() return label_list if isinstance(features[label_column_name].feature, ClassLabel): label_list = features[label_column_name].feature.names # No need to convert the labels since they are already ints. label_to_id = {i: i for i in range(len(label_list))} else: label_list = get_label_list(raw_datasets["train"][label_column_name]) label_to_id = {l: i for i, l in enumerate(label_list)} num_labels = len(label_list) # endregion # region Load config and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if model_args.config_name: config = AutoConfig.from_pretrained( model_args.config_name, num_labels=num_labels, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained( model_args.model_name_or_path, num_labels=num_labels, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path if not tokenizer_name_or_path: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script. " "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if config.model_type in {"gpt2", "roberta"}: tokenizer = AutoTokenizer.from_pretrained( tokenizer_name_or_path, use_fast=True, add_prefix_space=True, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) else: tokenizer = AutoTokenizer.from_pretrained( tokenizer_name_or_path, use_fast=True, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) # endregion # region Preprocessing the raw datasets # First we tokenize all the texts. padding = "max_length" if data_args.pad_to_max_length else False # Tokenize all texts and align the labels with them. def tokenize_and_align_labels(examples): tokenized_inputs = tokenizer( examples[text_column_name], max_length=data_args.max_length, padding=padding, truncation=True, # We use this argument because the texts in our dataset are lists of words (with a label for each word). is_split_into_words=True, ) labels = [] for i, label in enumerate(examples[label_column_name]): word_ids = tokenized_inputs.word_ids(batch_index=i) previous_word_idx = None label_ids = [] for word_idx in word_ids: # Special tokens have a word id that is None. We set the label to -100 so they are automatically # ignored in the loss function. if word_idx is None: label_ids.append(-100) # We set the label for the first token of each word. elif word_idx != previous_word_idx: label_ids.append(label_to_id[label[word_idx]]) # For the other tokens in a word, we set the label to either the current label or -100, depending on # the label_all_tokens flag. else: label_ids.append(label_to_id[label[word_idx]] if data_args.label_all_tokens else -100) previous_word_idx = word_idx labels.append(label_ids) tokenized_inputs["labels"] = labels return tokenized_inputs processed_raw_datasets = raw_datasets.map( tokenize_and_align_labels, batched=True, remove_columns=raw_datasets["train"].column_names, desc="Running tokenizer on dataset", ) train_dataset = processed_raw_datasets["train"] eval_dataset = processed_raw_datasets["validation"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # endregion with training_args.strategy.scope(): # region Initialize model if model_args.model_name_or_path: model = TFAutoModelForTokenClassification.from_pretrained( model_args.model_name_or_path, config=config, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) else: logger.info("Training new model from scratch") model = TFAutoModelForTokenClassification.from_config( config, token=model_args.token, trust_remote_code=model_args.trust_remote_code ) # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch # on a small vocab and want a smaller embedding size, remove this test. embeddings = model.get_input_embeddings() # Matt: This is a temporary workaround as we transition our models to exclusively using Keras embeddings. # As soon as the transition is complete, all embeddings should be keras.Embeddings layers, and # the weights will always be in embeddings.embeddings. if hasattr(embeddings, "embeddings"): embedding_size = embeddings.embeddings.shape[0] else: embedding_size = embeddings.weight.shape[0] if len(tokenizer) > embedding_size: model.resize_token_embeddings(len(tokenizer)) # endregion # region Create TF datasets # We need the DataCollatorForTokenClassification here, as we need to correctly pad labels as # well as inputs. collate_fn = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="np") num_replicas = training_args.strategy.num_replicas_in_sync total_train_batch_size = training_args.per_device_train_batch_size * num_replicas dataset_options = tf.data.Options() dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names # yourself if you use this method, whereas they are automatically inferred from the model input names when # using model.prepare_tf_dataset() # For more info see the docs: # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset tf_train_dataset = model.prepare_tf_dataset( train_dataset, collate_fn=collate_fn, batch_size=total_train_batch_size, shuffle=True, ).with_options(dataset_options) total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas tf_eval_dataset = model.prepare_tf_dataset( eval_dataset, collate_fn=collate_fn, batch_size=total_eval_batch_size, shuffle=False, ).with_options(dataset_options) # endregion # region Optimizer, loss and compilation num_train_steps = int(len(tf_train_dataset) * training_args.num_train_epochs) if training_args.warmup_steps > 0: num_warmup_steps = training_args.warmup_steps elif training_args.warmup_ratio > 0: num_warmup_steps = int(num_train_steps * training_args.warmup_ratio) else: num_warmup_steps = 0 optimizer, lr_schedule = create_optimizer( init_lr=training_args.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, adam_beta1=training_args.adam_beta1, adam_beta2=training_args.adam_beta2, adam_epsilon=training_args.adam_epsilon, weight_decay_rate=training_args.weight_decay, adam_global_clipnorm=training_args.max_grad_norm, ) # Transformers models compute the right loss for their task by default when labels are passed, and will # use this for training unless you specify your own loss function in compile(). model.compile(optimizer=optimizer, jit_compile=training_args.xla) # endregion # Metrics metric = evaluate.load("seqeval", cache_dir=model_args.cache_dir) def get_labels(y_pred, y_true): # Transform predictions and references tensos to numpy arrays # Remove ignored index (special tokens) true_predictions = [ [label_list[p] for (p, l) in zip(pred, gold_label) if l != -100] for pred, gold_label in zip(y_pred, y_true) ] true_labels = [ [label_list[l] for (p, l) in zip(pred, gold_label) if l != -100] for pred, gold_label in zip(y_pred, y_true) ] return true_predictions, true_labels def compute_metrics(): results = metric.compute() if data_args.return_entity_level_metrics: # Unpack nested dictionaries final_results = {} for key, value in results.items(): if isinstance(value, dict): for n, v in value.items(): final_results[f"{key}_{n}"] = v else: final_results[key] = value return final_results else: return { "precision": results["overall_precision"], "recall": results["overall_recall"], "f1": results["overall_f1"], "accuracy": results["overall_accuracy"], } # endregion # region Preparing push_to_hub and model card push_to_hub_model_id = training_args.push_to_hub_model_id model_name = model_args.model_name_or_path.split("/")[-1] if not push_to_hub_model_id: if data_args.dataset_name is not None: push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}" else: push_to_hub_model_id = f"{model_name}-finetuned-token-classification" model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "token-classification"} if data_args.dataset_name is not None: model_card_kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: model_card_kwargs["dataset_args"] = data_args.dataset_config_name model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: model_card_kwargs["dataset"] = data_args.dataset_name if training_args.push_to_hub: callbacks = [ PushToHubCallback( output_dir=training_args.output_dir, hub_model_id=push_to_hub_model_id, hub_token=training_args.push_to_hub_token, tokenizer=tokenizer, **model_card_kwargs, ) ] else: callbacks = [] # endregion # region Training logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {training_args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}") logger.info(f" Total train batch size = {total_train_batch_size}") # Only show the progress bar once on each machine. model.fit( tf_train_dataset, validation_data=tf_eval_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks, ) # endregion # region Predictions # If you have variable batch sizes (i.e. not using pad_to_max_length), then # this bit might fail on TF < 2.8 because TF can't concatenate outputs of varying seq # length from predict(). try: predictions = model.predict(tf_eval_dataset, batch_size=training_args.per_device_eval_batch_size)["logits"] except tf.python.framework.errors_impl.InvalidArgumentError: raise ValueError( "Concatenating predictions failed! If your version of TensorFlow is 2.8.0 or older " "then you will need to use --pad_to_max_length to generate predictions, as older " "versions of TensorFlow cannot concatenate variable-length predictions as RaggedTensor." ) if isinstance(predictions, tf.RaggedTensor): predictions = predictions.to_tensor(default_value=-100) predictions = tf.math.argmax(predictions, axis=-1).numpy() if "label" in eval_dataset: labels = eval_dataset.with_format("tf")["label"] else: labels = eval_dataset.with_format("tf")["labels"] if isinstance(labels, tf.RaggedTensor): labels = labels.to_tensor(default_value=-100) labels = labels.numpy() attention_mask = eval_dataset.with_format("tf")["attention_mask"] if isinstance(attention_mask, tf.RaggedTensor): attention_mask = attention_mask.to_tensor(default_value=-100) attention_mask = attention_mask.numpy() labels[attention_mask == 0] = -100 preds, refs = get_labels(predictions, labels) metric.add_batch( predictions=preds, references=refs, ) eval_metric = compute_metrics() logger.info("Evaluation metrics:") for key, val in eval_metric.items(): logger.info(f"{key}: {val:.4f}") if training_args.output_dir is not None: output_eval_file = os.path.join(training_args.output_dir, "all_results.json") with open(output_eval_file, "w") as writer: writer.write(json.dumps(eval_metric)) # endregion if training_args.output_dir is not None and not training_args.push_to_hub: # If we're not pushing to hub, at least save a local copy when we're done model.save_pretrained(training_args.output_dir) if __name__ == "__main__": main()

transformers/examples/tensorflow/token-classification/run_ner.py/0

{ "file_path": "transformers/examples/tensorflow/token-classification/run_ner.py", "repo_id": "transformers", "token_count": 11688 }

297

#!/usr/bin/env bash # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script evals the following fsmt models # it covers: # - facebook/wmt19-ru-en # - facebook/wmt19-en-ru # - facebook/wmt19-de-en # - facebook/wmt19-en-de # this script needs to be run from the top level of the transformers repo if [ ! -d "src/transformers" ]; then echo "Error: This script needs to be run from the top of the transformers repo" exit 1 fi # In these scripts you may have to lower BS if you get CUDA OOM (or increase it if you have a large GPU) ### a short estimate version for quick testing ### export PAIR=en-ru export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=8 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src | head -10 > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref | head -10 > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ### Normal eval ### # ru-en export PAIR=ru-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=50 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS # (target BLEU: 41.3 http://matrix.statmt.org/matrix/output/1907?run_id=6937) # en-ru export PAIR=en-ru export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=50 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS # (target BLEU: 36.4 http://matrix.statmt.org/matrix/output/1914?score_id=37605) # en-de export PAIR=en-de export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS # (target BLEU: 43.1 http://matrix.statmt.org/matrix/output/1909?run_id=6862) # de-en export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=50 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS # (target BLEU: 42.3 http://matrix.statmt.org/matrix/output/1902?run_id=6750) ### Searching hparams eval ### # en-ru export PAIR=ru-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=32 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target CUDA_VISIBLE_DEVICES="0" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1" # en-ru export PAIR=en-ru export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=16 mkdir -p $DATA_DIR mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target CUDA_VISIBLE_DEVICES="0" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:8:11:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1 early_stopping=true:false" # en-de export PAIR=en-de export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=16 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target CUDA_VISIBLE_DEVICES="1" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:8:11:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1 early_stopping=true:false" # de-en export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=16 mkdir -p $DATA_DIR mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target CUDA_VISIBLE_DEVICES="1" PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --search="num_beams=5:8:11:15 length_penalty=0.6:0.7:0.8:0.9:1.0:1.1 early_stopping=true:false"

transformers/scripts/fsmt/eval-facebook-wmt19.sh/0

{ "file_path": "transformers/scripts/fsmt/eval-facebook-wmt19.sh", "repo_id": "transformers", "token_count": 2623 }

298

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team and the librosa & torchaudio authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Audio processing functions to extract features from audio waveforms. This code is pure numpy to support all frameworks and remove unnecessary dependencies. """ import warnings from typing import Optional, Union import numpy as np def hertz_to_mel(freq: Union[float, np.ndarray], mel_scale: str = "htk") -> Union[float, np.ndarray]: """ Convert frequency from hertz to mels. Args: freq (`float` or `np.ndarray`): The frequency, or multiple frequencies, in hertz (Hz). mel_scale (`str`, *optional*, defaults to `"htk"`): The mel frequency scale to use, `"htk"`, `"kaldi"` or `"slaney"`. Returns: `float` or `np.ndarray`: The frequencies on the mel scale. """ if mel_scale not in ["slaney", "htk", "kaldi"]: raise ValueError('mel_scale should be one of "htk", "slaney" or "kaldi".') if mel_scale == "htk": return 2595.0 * np.log10(1.0 + (freq / 700.0)) elif mel_scale == "kaldi": return 1127.0 * np.log(1.0 + (freq / 700.0)) min_log_hertz = 1000.0 min_log_mel = 15.0 logstep = 27.0 / np.log(6.4) mels = 3.0 * freq / 200.0 if isinstance(freq, np.ndarray): log_region = freq >= min_log_hertz mels[log_region] = min_log_mel + np.log(freq[log_region] / min_log_hertz) * logstep elif freq >= min_log_hertz: mels = min_log_mel + np.log(freq / min_log_hertz) * logstep return mels def mel_to_hertz(mels: Union[float, np.ndarray], mel_scale: str = "htk") -> Union[float, np.ndarray]: """ Convert frequency from mels to hertz. Args: mels (`float` or `np.ndarray`): The frequency, or multiple frequencies, in mels. mel_scale (`str`, *optional*, `"htk"`): The mel frequency scale to use, `"htk"`, `"kaldi"` or `"slaney"`. Returns: `float` or `np.ndarray`: The frequencies in hertz. """ if mel_scale not in ["slaney", "htk", "kaldi"]: raise ValueError('mel_scale should be one of "htk", "slaney" or "kaldi".') if mel_scale == "htk": return 700.0 * (np.power(10, mels / 2595.0) - 1.0) elif mel_scale == "kaldi": return 700.0 * (np.exp(mels / 1127.0) - 1.0) min_log_hertz = 1000.0 min_log_mel = 15.0 logstep = np.log(6.4) / 27.0 freq = 200.0 * mels / 3.0 if isinstance(mels, np.ndarray): log_region = mels >= min_log_mel freq[log_region] = min_log_hertz * np.exp(logstep * (mels[log_region] - min_log_mel)) elif mels >= min_log_mel: freq = min_log_hertz * np.exp(logstep * (mels - min_log_mel)) return freq def _create_triangular_filter_bank(fft_freqs: np.ndarray, filter_freqs: np.ndarray) -> np.ndarray: """ Creates a triangular filter bank. Adapted from *torchaudio* and *librosa*. Args: fft_freqs (`np.ndarray` of shape `(num_frequency_bins,)`): Discrete frequencies of the FFT bins in Hz. filter_freqs (`np.ndarray` of shape `(num_mel_filters,)`): Center frequencies of the triangular filters to create, in Hz. Returns: `np.ndarray` of shape `(num_frequency_bins, num_mel_filters)` """ filter_diff = np.diff(filter_freqs) slopes = np.expand_dims(filter_freqs, 0) - np.expand_dims(fft_freqs, 1) down_slopes = -slopes[:, :-2] / filter_diff[:-1] up_slopes = slopes[:, 2:] / filter_diff[1:] return np.maximum(np.zeros(1), np.minimum(down_slopes, up_slopes)) def mel_filter_bank( num_frequency_bins: int, num_mel_filters: int, min_frequency: float, max_frequency: float, sampling_rate: int, norm: Optional[str] = None, mel_scale: str = "htk", triangularize_in_mel_space: bool = False, ) -> np.ndarray: """ Creates a frequency bin conversion matrix used to obtain a mel spectrogram. This is called a *mel filter bank*, and various implementation exist, which differ in the number of filters, the shape of the filters, the way the filters are spaced, the bandwidth of the filters, and the manner in which the spectrum is warped. The goal of these features is to approximate the non-linear human perception of the variation in pitch with respect to the frequency. Different banks of mel filters were introduced in the literature. The following variations are supported: - MFCC FB-20: introduced in 1980 by Davis and Mermelstein, it assumes a sampling frequency of 10 kHz and a speech bandwidth of `[0, 4600]` Hz. - MFCC FB-24 HTK: from the Cambridge HMM Toolkit (HTK) (1995) uses a filter bank of 24 filters for a speech bandwidth of `[0, 8000]` Hz. This assumes sampling rate ≥ 16 kHz. - MFCC FB-40: from the Auditory Toolbox for MATLAB written by Slaney in 1998, assumes a sampling rate of 16 kHz and speech bandwidth of `[133, 6854]` Hz. This version also includes area normalization. - HFCC-E FB-29 (Human Factor Cepstral Coefficients) of Skowronski and Harris (2004), assumes a sampling rate of 12.5 kHz and speech bandwidth of `[0, 6250]` Hz. This code is adapted from *torchaudio* and *librosa*. Note that the default parameters of torchaudio's `melscale_fbanks` implement the `"htk"` filters while librosa uses the `"slaney"` implementation. Args: num_frequency_bins (`int`): Number of frequencies used to compute the spectrogram (should be the same as in `stft`). num_mel_filters (`int`): Number of mel filters to generate. min_frequency (`float`): Lowest frequency of interest in Hz. max_frequency (`float`): Highest frequency of interest in Hz. This should not exceed `sampling_rate / 2`. sampling_rate (`int`): Sample rate of the audio waveform. norm (`str`, *optional*): If `"slaney"`, divide the triangular mel weights by the width of the mel band (area normalization). mel_scale (`str`, *optional*, defaults to `"htk"`): The mel frequency scale to use, `"htk"`, `"kaldi"` or `"slaney"`. triangularize_in_mel_space (`bool`, *optional*, defaults to `False`): If this option is enabled, the triangular filter is applied in mel space rather than frequency space. This should be set to `true` in order to get the same results as `torchaudio` when computing mel filters. Returns: `np.ndarray` of shape (`num_frequency_bins`, `num_mel_filters`): Triangular filter bank matrix. This is a projection matrix to go from a spectrogram to a mel spectrogram. """ if norm is not None and norm != "slaney": raise ValueError('norm must be one of None or "slaney"') # center points of the triangular mel filters mel_min = hertz_to_mel(min_frequency, mel_scale=mel_scale) mel_max = hertz_to_mel(max_frequency, mel_scale=mel_scale) mel_freqs = np.linspace(mel_min, mel_max, num_mel_filters + 2) filter_freqs = mel_to_hertz(mel_freqs, mel_scale=mel_scale) if triangularize_in_mel_space: # frequencies of FFT bins in Hz, but filters triangularized in mel space fft_bin_width = sampling_rate / (num_frequency_bins * 2) fft_freqs = hertz_to_mel(fft_bin_width * np.arange(num_frequency_bins), mel_scale=mel_scale) filter_freqs = mel_freqs else: # frequencies of FFT bins in Hz fft_freqs = np.linspace(0, sampling_rate // 2, num_frequency_bins) mel_filters = _create_triangular_filter_bank(fft_freqs, filter_freqs) if norm is not None and norm == "slaney": # Slaney-style mel is scaled to be approx constant energy per channel enorm = 2.0 / (filter_freqs[2 : num_mel_filters + 2] - filter_freqs[:num_mel_filters]) mel_filters *= np.expand_dims(enorm, 0) if (mel_filters.max(axis=0) == 0.0).any(): warnings.warn( "At least one mel filter has all zero values. " f"The value for `num_mel_filters` ({num_mel_filters}) may be set too high. " f"Or, the value for `num_frequency_bins` ({num_frequency_bins}) may be set too low." ) return mel_filters def optimal_fft_length(window_length: int) -> int: """ Finds the best FFT input size for a given `window_length`. This function takes a given window length and, if not already a power of two, rounds it up to the next power or two. The FFT algorithm works fastest when the length of the input is a power of two, which may be larger than the size of the window or analysis frame. For example, if the window is 400 samples, using an FFT input size of 512 samples is more optimal than an FFT size of 400 samples. Using a larger FFT size does not affect the detected frequencies, it simply gives a higher frequency resolution (i.e. the frequency bins are smaller). """ return 2 ** int(np.ceil(np.log2(window_length))) def window_function( window_length: int, name: str = "hann", periodic: bool = True, frame_length: Optional[int] = None, center: bool = True, ) -> np.ndarray: """ Returns an array containing the specified window. This window is intended to be used with `stft`. The following window types are supported: - `"boxcar"`: a rectangular window - `"hamming"`: the Hamming window - `"hann"`: the Hann window - `"povey"`: the Povey window Args: window_length (`int`): The length of the window in samples. name (`str`, *optional*, defaults to `"hann"`): The name of the window function. periodic (`bool`, *optional*, defaults to `True`): Whether the window is periodic or symmetric. frame_length (`int`, *optional*): The length of the analysis frames in samples. Provide a value for `frame_length` if the window is smaller than the frame length, so that it will be zero-padded. center (`bool`, *optional*, defaults to `True`): Whether to center the window inside the FFT buffer. Only used when `frame_length` is provided. Returns: `np.ndarray` of shape `(window_length,)` or `(frame_length,)` containing the window. """ length = window_length + 1 if periodic else window_length if name == "boxcar": window = np.ones(length) elif name in ["hamming", "hamming_window"]: window = np.hamming(length) elif name in ["hann", "hann_window"]: window = np.hanning(length) elif name in ["povey"]: window = np.power(np.hanning(length), 0.85) else: raise ValueError(f"Unknown window function '{name}'") if periodic: window = window[:-1] if frame_length is None: return window if window_length > frame_length: raise ValueError( f"Length of the window ({window_length}) may not be larger than frame_length ({frame_length})" ) padded_window = np.zeros(frame_length) offset = (frame_length - window_length) // 2 if center else 0 padded_window[offset : offset + window_length] = window return padded_window # TODO This method does not support batching yet as we are mainly focused on inference. def spectrogram( waveform: np.ndarray, window: np.ndarray, frame_length: int, hop_length: int, fft_length: Optional[int] = None, power: Optional[float] = 1.0, center: bool = True, pad_mode: str = "reflect", onesided: bool = True, preemphasis: Optional[float] = None, mel_filters: Optional[np.ndarray] = None, mel_floor: float = 1e-10, log_mel: Optional[str] = None, reference: float = 1.0, min_value: float = 1e-10, db_range: Optional[float] = None, remove_dc_offset: Optional[bool] = None, dtype: np.dtype = np.float32, ) -> np.ndarray: """ Calculates a spectrogram over one waveform using the Short-Time Fourier Transform. This function can create the following kinds of spectrograms: - amplitude spectrogram (`power = 1.0`) - power spectrogram (`power = 2.0`) - complex-valued spectrogram (`power = None`) - log spectrogram (use `log_mel` argument) - mel spectrogram (provide `mel_filters`) - log-mel spectrogram (provide `mel_filters` and `log_mel`) How this works: 1. The input waveform is split into frames of size `frame_length` that are partially overlapping by `frame_length - hop_length` samples. 2. Each frame is multiplied by the window and placed into a buffer of size `fft_length`. 3. The DFT is taken of each windowed frame. 4. The results are stacked into a spectrogram. We make a distinction between the following "blocks" of sample data, each of which may have a different lengths: - The analysis frame. This is the size of the time slices that the input waveform is split into. - The window. Each analysis frame is multiplied by the window to avoid spectral leakage. - The FFT input buffer. The length of this determines how many frequency bins are in the spectrogram. In this implementation, the window is assumed to be zero-padded to have the same size as the analysis frame. A padded window can be obtained from `window_function()`. The FFT input buffer may be larger than the analysis frame, typically the next power of two. Note: This function is not optimized for speed yet. It should be mostly compatible with `librosa.stft` and `torchaudio.functional.transforms.Spectrogram`, although it is more flexible due to the different ways spectrograms can be constructed. Args: waveform (`np.ndarray` of shape `(length,)`): The input waveform. This must be a single real-valued, mono waveform. window (`np.ndarray` of shape `(frame_length,)`): The windowing function to apply, including zero-padding if necessary. The actual window length may be shorter than `frame_length`, but we're assuming the array has already been zero-padded. frame_length (`int`): The length of the analysis frames in samples. With librosa this is always equal to `fft_length` but we also allow smaller sizes. hop_length (`int`): The stride between successive analysis frames in samples. fft_length (`int`, *optional*): The size of the FFT buffer in samples. This determines how many frequency bins the spectrogram will have. For optimal speed, this should be a power of two. If `None`, uses `frame_length`. power (`float`, *optional*, defaults to 1.0): If 1.0, returns the amplitude spectrogram. If 2.0, returns the power spectrogram. If `None`, returns complex numbers. center (`bool`, *optional*, defaults to `True`): Whether to pad the waveform so that frame `t` is centered around time `t * hop_length`. If `False`, frame `t` will start at time `t * hop_length`. pad_mode (`str`, *optional*, defaults to `"reflect"`): Padding mode used when `center` is `True`. Possible values are: `"constant"` (pad with zeros), `"edge"` (pad with edge values), `"reflect"` (pads with mirrored values). onesided (`bool`, *optional*, defaults to `True`): If True, only computes the positive frequencies and returns a spectrogram containing `fft_length // 2 + 1` frequency bins. If False, also computes the negative frequencies and returns `fft_length` frequency bins. preemphasis (`float`, *optional*) Coefficient for a low-pass filter that applies pre-emphasis before the DFT. mel_filters (`np.ndarray` of shape `(num_freq_bins, num_mel_filters)`, *optional*): The mel filter bank. If supplied, applies a this filter bank to create a mel spectrogram. mel_floor (`float`, *optional*, defaults to 1e-10): Minimum value of mel frequency banks. log_mel (`str`, *optional*): How to convert the spectrogram to log scale. Possible options are: `None` (don't convert), `"log"` (take the natural logarithm) `"log10"` (take the base-10 logarithm), `"dB"` (convert to decibels). Can only be used when `power` is not `None`. reference (`float`, *optional*, defaults to 1.0): Sets the input spectrogram value that corresponds to 0 dB. For example, use `np.max(spectrogram)` to set the loudest part to 0 dB. Must be greater than zero. min_value (`float`, *optional*, defaults to `1e-10`): The spectrogram will be clipped to this minimum value before conversion to decibels, to avoid taking `log(0)`. For a power spectrogram, the default of `1e-10` corresponds to a minimum of -100 dB. For an amplitude spectrogram, the value `1e-5` corresponds to -100 dB. Must be greater than zero. db_range (`float`, *optional*): Sets the maximum dynamic range in decibels. For example, if `db_range = 80`, the difference between the peak value and the smallest value will never be more than 80 dB. Must be greater than zero. remove_dc_offset (`bool`, *optional*): Subtract mean from waveform on each frame, applied before pre-emphasis. This should be set to `true` in order to get the same results as `torchaudio.compliance.kaldi.fbank` when computing mel filters. dtype (`np.dtype`, *optional*, defaults to `np.float32`): Data type of the spectrogram tensor. If `power` is None, this argument is ignored and the dtype will be `np.complex64`. Returns: `nd.array` containing a spectrogram of shape `(num_frequency_bins, length)` for a regular spectrogram or shape `(num_mel_filters, length)` for a mel spectrogram. """ window_length = len(window) if fft_length is None: fft_length = frame_length if frame_length > fft_length: raise ValueError(f"frame_length ({frame_length}) may not be larger than fft_length ({fft_length})") if window_length != frame_length: raise ValueError(f"Length of the window ({window_length}) must equal frame_length ({frame_length})") if hop_length <= 0: raise ValueError("hop_length must be greater than zero") if waveform.ndim != 1: raise ValueError(f"Input waveform must have only one dimension, shape is {waveform.shape}") if np.iscomplexobj(waveform): raise ValueError("Complex-valued input waveforms are not currently supported") # center pad the waveform if center: padding = [(int(frame_length // 2), int(frame_length // 2))] waveform = np.pad(waveform, padding, mode=pad_mode) # promote to float64, since np.fft uses float64 internally waveform = waveform.astype(np.float64) window = window.astype(np.float64) # split waveform into frames of frame_length size num_frames = int(1 + np.floor((waveform.size - frame_length) / hop_length)) num_frequency_bins = (fft_length // 2) + 1 if onesided else fft_length spectrogram = np.empty((num_frames, num_frequency_bins), dtype=np.complex64) # rfft is faster than fft fft_func = np.fft.rfft if onesided else np.fft.fft buffer = np.zeros(fft_length) timestep = 0 for frame_idx in range(num_frames): buffer[:frame_length] = waveform[timestep : timestep + frame_length] if remove_dc_offset: buffer[:frame_length] = buffer[:frame_length] - buffer[:frame_length].mean() if preemphasis is not None: buffer[1:frame_length] -= preemphasis * buffer[: frame_length - 1] buffer[0] *= 1 - preemphasis buffer[:frame_length] *= window spectrogram[frame_idx] = fft_func(buffer) timestep += hop_length # note: ** is much faster than np.power if power is not None: spectrogram = np.abs(spectrogram, dtype=np.float64) ** power spectrogram = spectrogram.T if mel_filters is not None: spectrogram = np.maximum(mel_floor, np.dot(mel_filters.T, spectrogram)) if power is not None and log_mel is not None: if log_mel == "log": spectrogram = np.log(spectrogram) elif log_mel == "log10": spectrogram = np.log10(spectrogram) elif log_mel == "dB": if power == 1.0: spectrogram = amplitude_to_db(spectrogram, reference, min_value, db_range) elif power == 2.0: spectrogram = power_to_db(spectrogram, reference, min_value, db_range) else: raise ValueError(f"Cannot use log_mel option '{log_mel}' with power {power}") else: raise ValueError(f"Unknown log_mel option: {log_mel}") spectrogram = np.asarray(spectrogram, dtype) return spectrogram def power_to_db( spectrogram: np.ndarray, reference: float = 1.0, min_value: float = 1e-10, db_range: Optional[float] = None, ) -> np.ndarray: """ Converts a power spectrogram to the decibel scale. This computes `10 * log10(spectrogram / reference)`, using basic logarithm properties for numerical stability. The motivation behind applying the log function on the (mel) spectrogram is that humans do not hear loudness on a linear scale. Generally to double the perceived volume of a sound we need to put 8 times as much energy into it. This means that large variations in energy may not sound all that different if the sound is loud to begin with. This compression operation makes the (mel) spectrogram features match more closely what humans actually hear. Based on the implementation of `librosa.power_to_db`. Args: spectrogram (`np.ndarray`): The input power (mel) spectrogram. Note that a power spectrogram has the amplitudes squared! reference (`float`, *optional*, defaults to 1.0): Sets the input spectrogram value that corresponds to 0 dB. For example, use `np.max(spectrogram)` to set the loudest part to 0 dB. Must be greater than zero. min_value (`float`, *optional*, defaults to `1e-10`): The spectrogram will be clipped to this minimum value before conversion to decibels, to avoid taking `log(0)`. The default of `1e-10` corresponds to a minimum of -100 dB. Must be greater than zero. db_range (`float`, *optional*): Sets the maximum dynamic range in decibels. For example, if `db_range = 80`, the difference between the peak value and the smallest value will never be more than 80 dB. Must be greater than zero. Returns: `np.ndarray`: the spectrogram in decibels """ if reference <= 0.0: raise ValueError("reference must be greater than zero") if min_value <= 0.0: raise ValueError("min_value must be greater than zero") reference = max(min_value, reference) spectrogram = np.clip(spectrogram, a_min=min_value, a_max=None) spectrogram = 10.0 * (np.log10(spectrogram) - np.log10(reference)) if db_range is not None: if db_range <= 0.0: raise ValueError("db_range must be greater than zero") spectrogram = np.clip(spectrogram, a_min=spectrogram.max() - db_range, a_max=None) return spectrogram def amplitude_to_db( spectrogram: np.ndarray, reference: float = 1.0, min_value: float = 1e-5, db_range: Optional[float] = None, ) -> np.ndarray: """ Converts an amplitude spectrogram to the decibel scale. This computes `20 * log10(spectrogram / reference)`, using basic logarithm properties for numerical stability. The motivation behind applying the log function on the (mel) spectrogram is that humans do not hear loudness on a linear scale. Generally to double the perceived volume of a sound we need to put 8 times as much energy into it. This means that large variations in energy may not sound all that different if the sound is loud to begin with. This compression operation makes the (mel) spectrogram features match more closely what humans actually hear. Args: spectrogram (`np.ndarray`): The input amplitude (mel) spectrogram. reference (`float`, *optional*, defaults to 1.0): Sets the input spectrogram value that corresponds to 0 dB. For example, use `np.max(spectrogram)` to set the loudest part to 0 dB. Must be greater than zero. min_value (`float`, *optional*, defaults to `1e-5`): The spectrogram will be clipped to this minimum value before conversion to decibels, to avoid taking `log(0)`. The default of `1e-5` corresponds to a minimum of -100 dB. Must be greater than zero. db_range (`float`, *optional*): Sets the maximum dynamic range in decibels. For example, if `db_range = 80`, the difference between the peak value and the smallest value will never be more than 80 dB. Must be greater than zero. Returns: `np.ndarray`: the spectrogram in decibels """ if reference <= 0.0: raise ValueError("reference must be greater than zero") if min_value <= 0.0: raise ValueError("min_value must be greater than zero") reference = max(min_value, reference) spectrogram = np.clip(spectrogram, a_min=min_value, a_max=None) spectrogram = 20.0 * (np.log10(spectrogram) - np.log10(reference)) if db_range is not None: if db_range <= 0.0: raise ValueError("db_range must be greater than zero") spectrogram = np.clip(spectrogram, a_min=spectrogram.max() - db_range, a_max=None) return spectrogram ### deprecated functions below this line ### def get_mel_filter_banks( nb_frequency_bins: int, nb_mel_filters: int, frequency_min: float, frequency_max: float, sample_rate: int, norm: Optional[str] = None, mel_scale: str = "htk", ) -> np.array: warnings.warn( "The function `get_mel_filter_banks` is deprecated and will be removed in version 4.31.0 of Transformers", FutureWarning, ) return mel_filter_bank( num_frequency_bins=nb_frequency_bins, num_mel_filters=nb_mel_filters, min_frequency=frequency_min, max_frequency=frequency_max, sampling_rate=sample_rate, norm=norm, mel_scale=mel_scale, ) def fram_wave(waveform: np.array, hop_length: int = 160, fft_window_size: int = 400, center: bool = True): """ In order to compute the short time fourier transform, the waveform needs to be split in overlapping windowed segments called `frames`. The window length (window_length) defines how much of the signal is contained in each frame, while the hop length defines the step between the beginning of each new frame. Args: waveform (`np.array` of shape `(sample_length,)`): The raw waveform which will be split into smaller chunks. hop_length (`int`, *optional*, defaults to 160): Step between each window of the waveform. fft_window_size (`int`, *optional*, defaults to 400): Defines the size of the window. center (`bool`, defaults to `True`): Whether or not to center each frame around the middle of the frame. Centering is done by reflecting the waveform on the left and on the right. Return: framed_waveform (`np.array` of shape `(waveform.shape // hop_length , fft_window_size)`): The framed waveforms that can be fed to `np.fft`. """ warnings.warn( "The function `fram_wave` is deprecated and will be removed in version 4.31.0 of Transformers", FutureWarning, ) frames = [] for i in range(0, waveform.shape[0] + 1, hop_length): if center: half_window = (fft_window_size - 1) // 2 + 1 start = i - half_window if i > half_window else 0 end = i + half_window if i < waveform.shape[0] - half_window else waveform.shape[0] frame = waveform[start:end] if start == 0: padd_width = (-i + half_window, 0) frame = np.pad(frame, pad_width=padd_width, mode="reflect") elif end == waveform.shape[0]: padd_width = (0, (i - waveform.shape[0] + half_window)) frame = np.pad(frame, pad_width=padd_width, mode="reflect") else: frame = waveform[i : i + fft_window_size] frame_width = frame.shape[0] if frame_width < waveform.shape[0]: frame = np.lib.pad( frame, pad_width=(0, fft_window_size - frame_width), mode="constant", constant_values=0 ) frames.append(frame) frames = np.stack(frames, 0) return frames def stft(frames: np.array, windowing_function: np.array, fft_window_size: int = None): """ Calculates the complex Short-Time Fourier Transform (STFT) of the given framed signal. Should give the same results as `torch.stft`. Args: frames (`np.array` of dimension `(num_frames, fft_window_size)`): A framed audio signal obtained using `audio_utils.fram_wav`. windowing_function (`np.array` of dimension `(nb_frequency_bins, nb_mel_filters)`: A array reprensenting the function that will be used to reduces the amplitude of the discontinuities at the boundaries of each frame when computing the STFT. Each frame will be multiplied by the windowing_function. For more information on the discontinuities, called *Spectral leakage*, refer to [this tutorial]https://download.ni.com/evaluation/pxi/Understanding%20FFTs%20and%20Windowing.pdf fft_window_size (`int`, *optional*): Size of the window om which the Fourier transform is applied. This controls the frequency resolution of the spectrogram. 400 means that the fourrier transform is computed on windows of 400 samples. The number of frequency bins (`nb_frequency_bins`) used to divide the window into equal strips is equal to `(1+fft_window_size)//2`. An increase of the fft_window_size slows the calculus time proportionnally. Example: ```python >>> from transformers.audio_utils import stft, fram_wave >>> import numpy as np >>> audio = np.random.rand(50) >>> fft_window_size = 10 >>> hop_length = 2 >>> framed_audio = fram_wave(audio, hop_length, fft_window_size) >>> spectrogram = stft(framed_audio, np.hanning(fft_window_size + 1)) ``` Returns: spectrogram (`np.ndarray`): A spectrogram of shape `(num_frames, nb_frequency_bins)` obtained using the STFT algorithm """ warnings.warn( "The function `stft` is deprecated and will be removed in version 4.31.0 of Transformers", FutureWarning, ) frame_size = frames.shape[1] if fft_window_size is None: fft_window_size = frame_size if fft_window_size < frame_size: raise ValueError("FFT size must greater or equal the frame size") # number of FFT bins to store nb_frequency_bins = (fft_window_size >> 1) + 1 spectrogram = np.empty((len(frames), nb_frequency_bins), dtype=np.complex64) fft_signal = np.zeros(fft_window_size) for f, frame in enumerate(frames): if windowing_function is not None: np.multiply(frame, windowing_function, out=fft_signal[:frame_size]) else: fft_signal[:frame_size] = frame spectrogram[f] = np.fft.fft(fft_signal, axis=0)[:nb_frequency_bins] return spectrogram.T

transformers/src/transformers/audio_utils.py/0

{ "file_path": "transformers/src/transformers/audio_utils.py", "repo_id": "transformers", "token_count": 12205 }

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# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import os from argparse import ArgumentParser, Namespace from importlib import import_module import huggingface_hub import numpy as np from packaging import version from .. import ( FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoConfig, AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer, is_datasets_available, is_tf_available, is_torch_available, ) from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging from . import BaseTransformersCLICommand if is_tf_available(): import tensorflow as tf tf.config.experimental.enable_tensor_float_32_execution(False) if is_torch_available(): import torch if is_datasets_available(): from datasets import load_dataset MAX_ERROR = 5e-5 # larger error tolerance than in our internal tests, to avoid flaky user-facing errors def convert_command_factory(args: Namespace): """ Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint. Returns: ServeCommand """ return PTtoTFCommand( args.model_name, args.local_dir, args.max_error, args.new_weights, args.no_pr, args.push, args.extra_commit_description, args.override_model_class, ) class PTtoTFCommand(BaseTransformersCLICommand): @staticmethod def register_subcommand(parser: ArgumentParser): """ Register this command to argparse so it's available for the transformer-cli Args: parser: Root parser to register command-specific arguments """ train_parser = parser.add_parser( "pt-to-tf", help=( "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint." " Can also be used to validate existing weights without opening PRs, with --no-pr." ), ) train_parser.add_argument( "--model-name", type=str, required=True, help="The model name, including owner/organization, as seen on the hub.", ) train_parser.add_argument( "--local-dir", type=str, default="", help="Optional local directory of the model repository. Defaults to /tmp/{model_name}", ) train_parser.add_argument( "--max-error", type=float, default=MAX_ERROR, help=( f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk." ), ) train_parser.add_argument( "--new-weights", action="store_true", help="Optional flag to create new TensorFlow weights, even if they already exist.", ) train_parser.add_argument( "--no-pr", action="store_true", help="Optional flag to NOT open a PR with converted weights." ) train_parser.add_argument( "--push", action="store_true", help="Optional flag to push the weights directly to `main` (requires permissions)", ) train_parser.add_argument( "--extra-commit-description", type=str, default="", help="Optional additional commit description to use when opening a PR (e.g. to tag the owner).", ) train_parser.add_argument( "--override-model-class", type=str, default=None, help="If you think you know better than the auto-detector, you can specify the model class here. " "Can be either an AutoModel class or a specific model class like BertForSequenceClassification.", ) train_parser.set_defaults(func=convert_command_factory) @staticmethod def find_pt_tf_differences(pt_outputs, tf_outputs): """ Compares the TensorFlow and PyTorch outputs, returning a dictionary with all tensor differences. """ # 1. All output attributes must be the same pt_out_attrs = set(pt_outputs.keys()) tf_out_attrs = set(tf_outputs.keys()) if pt_out_attrs != tf_out_attrs: raise ValueError( f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:" f" {tf_out_attrs})" ) # 2. For each output attribute, computes the difference def _find_pt_tf_differences(pt_out, tf_out, differences, attr_name=""): # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in # recursivelly, keeping the name of the attribute. if isinstance(pt_out, torch.Tensor): tensor_difference = np.max(np.abs(pt_out.numpy() - tf_out.numpy())) differences[attr_name] = tensor_difference else: root_name = attr_name for i, pt_item in enumerate(pt_out): # If it is a named attribute, we keep the name. Otherwise, just its index. if isinstance(pt_item, str): branch_name = root_name + pt_item tf_item = tf_out[pt_item] pt_item = pt_out[pt_item] else: branch_name = root_name + f"[{i}]" tf_item = tf_out[i] differences = _find_pt_tf_differences(pt_item, tf_item, differences, branch_name) return differences return _find_pt_tf_differences(pt_outputs, tf_outputs, {}) def __init__( self, model_name: str, local_dir: str, max_error: float, new_weights: bool, no_pr: bool, push: bool, extra_commit_description: str, override_model_class: str, *args, ): self._logger = logging.get_logger("transformers-cli/pt_to_tf") self._model_name = model_name self._local_dir = local_dir if local_dir else os.path.join("/tmp", model_name) self._max_error = max_error self._new_weights = new_weights self._no_pr = no_pr self._push = push self._extra_commit_description = extra_commit_description self._override_model_class = override_model_class def get_inputs(self, pt_model, tf_dummy_inputs, config): """ Returns the right inputs for the model, based on its signature. """ def _get_audio_input(): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") speech_samples = ds.sort("id").select(range(2))[:2]["audio"] raw_samples = [x["array"] for x in speech_samples] return raw_samples model_config_class = type(pt_model.config) if model_config_class in PROCESSOR_MAPPING: processor = AutoProcessor.from_pretrained(self._local_dir) if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None: processor.tokenizer.pad_token = processor.tokenizer.eos_token elif model_config_class in IMAGE_PROCESSOR_MAPPING: processor = AutoImageProcessor.from_pretrained(self._local_dir) elif model_config_class in FEATURE_EXTRACTOR_MAPPING: processor = AutoFeatureExtractor.from_pretrained(self._local_dir) elif model_config_class in TOKENIZER_MAPPING: processor = AutoTokenizer.from_pretrained(self._local_dir) if processor.pad_token is None: processor.pad_token = processor.eos_token else: raise ValueError(f"Unknown data processing type (model config type: {model_config_class})") model_forward_signature = set(inspect.signature(pt_model.forward).parameters.keys()) processor_inputs = {} if "input_ids" in model_forward_signature: processor_inputs.update( { "text": ["Hi there!", "I am a batch with more than one row and different input lengths."], "padding": True, "truncation": True, } ) if "pixel_values" in model_forward_signature: sample_images = load_dataset("cifar10", "plain_text", split="test")[:2]["img"] processor_inputs.update({"images": sample_images}) if "input_features" in model_forward_signature: feature_extractor_signature = inspect.signature(processor.feature_extractor).parameters # Pad to the largest input length by default but take feature extractor default # padding value if it exists e.g. "max_length" and is not False or None if "padding" in feature_extractor_signature: default_strategy = feature_extractor_signature["padding"].default if default_strategy is not False and default_strategy is not None: padding_strategy = default_strategy else: padding_strategy = True else: padding_strategy = True processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy}) if "input_values" in model_forward_signature: # Wav2Vec2 audio input processor_inputs.update({"audio": _get_audio_input(), "padding": True}) pt_input = processor(**processor_inputs, return_tensors="pt") tf_input = processor(**processor_inputs, return_tensors="tf") # Extra input requirements, in addition to the input modality if ( config.is_encoder_decoder or (hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder")) or "decoder_input_ids" in tf_dummy_inputs ): decoder_input_ids = np.asarray([[1], [1]], dtype=int) * (pt_model.config.decoder_start_token_id or 0) pt_input.update({"decoder_input_ids": torch.tensor(decoder_input_ids)}) tf_input.update({"decoder_input_ids": tf.convert_to_tensor(decoder_input_ids)}) return pt_input, tf_input def run(self): # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens. if version.parse(huggingface_hub.__version__) < version.parse("0.9.0"): raise ImportError( "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub" " installation." ) else: from huggingface_hub import Repository, create_commit from huggingface_hub._commit_api import CommitOperationAdd # Fetch remote data repo = Repository(local_dir=self._local_dir, clone_from=self._model_name) # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights config = AutoConfig.from_pretrained(self._local_dir) architectures = config.architectures if self._override_model_class is not None: if self._override_model_class.startswith("TF"): architectures = [self._override_model_class[2:]] else: architectures = [self._override_model_class] try: pt_class = getattr(import_module("transformers"), architectures[0]) except AttributeError: raise ValueError(f"Model class {self._override_model_class} not found in transformers.") try: tf_class = getattr(import_module("transformers"), "TF" + architectures[0]) except AttributeError: raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers.") elif architectures is None: # No architecture defined -- use auto classes pt_class = getattr(import_module("transformers"), "AutoModel") tf_class = getattr(import_module("transformers"), "TFAutoModel") self._logger.warning("No detected architecture, using AutoModel/TFAutoModel") else: # Architecture defined -- use it if len(architectures) > 1: raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})") self._logger.warning(f"Detected architecture: {architectures[0]}") pt_class = getattr(import_module("transformers"), architectures[0]) try: tf_class = getattr(import_module("transformers"), "TF" + architectures[0]) except AttributeError: raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.") # Check the TF dummy inputs to see what keys we need in the forward pass tf_from_pt_model = tf_class.from_config(config) tf_dummy_inputs = tf_from_pt_model.dummy_inputs del tf_from_pt_model # Try to keep only one model in memory at a time # Load the model and get some basic inputs pt_model = pt_class.from_pretrained(self._local_dir) pt_model.eval() pt_input, tf_input = self.get_inputs(pt_model, tf_dummy_inputs, config) with torch.no_grad(): pt_outputs = pt_model(**pt_input, output_hidden_states=True) del pt_model # will no longer be used, and may have a large memory footprint tf_from_pt_model = tf_class.from_pretrained(self._local_dir, from_pt=True) tf_from_pt_outputs = tf_from_pt_model(**tf_input, output_hidden_states=True, training=False) # Confirms that cross loading PT weights into TF worked. crossload_differences = self.find_pt_tf_differences(pt_outputs, tf_from_pt_outputs) output_differences = {k: v for k, v in crossload_differences.items() if "hidden" not in k} hidden_differences = {k: v for k, v in crossload_differences.items() if "hidden" in k} if len(output_differences) == 0 and architectures is not None: raise ValueError( f"Something went wrong -- the config file has architectures ({architectures}), but no model head" " output was found. All outputs start with 'hidden'" ) max_crossload_output_diff = max(output_differences.values()) if output_differences else 0.0 max_crossload_hidden_diff = max(hidden_differences.values()) if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error: raise ValueError( "The cross-loaded TensorFlow model has different outputs, something went wrong!\n" + f"\nList of maximum output differences above the threshold ({self._max_error}):\n" + "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]) + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n" + "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]) ) # Save the weights in a TF format (if needed) and confirms that the results are still good tf_weights_path = os.path.join(self._local_dir, TF2_WEIGHTS_NAME) tf_weights_index_path = os.path.join(self._local_dir, TF2_WEIGHTS_INDEX_NAME) if (not os.path.exists(tf_weights_path) and not os.path.exists(tf_weights_index_path)) or self._new_weights: tf_from_pt_model.save_pretrained(self._local_dir) del tf_from_pt_model # will no longer be used, and may have a large memory footprint tf_model = tf_class.from_pretrained(self._local_dir) tf_outputs = tf_model(**tf_input, output_hidden_states=True) conversion_differences = self.find_pt_tf_differences(pt_outputs, tf_outputs) output_differences = {k: v for k, v in conversion_differences.items() if "hidden" not in k} hidden_differences = {k: v for k, v in conversion_differences.items() if "hidden" in k} if len(output_differences) == 0 and architectures is not None: raise ValueError( f"Something went wrong -- the config file has architectures ({architectures}), but no model head" " output was found. All outputs start with 'hidden'" ) max_conversion_output_diff = max(output_differences.values()) if output_differences else 0.0 max_conversion_hidden_diff = max(hidden_differences.values()) if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error: raise ValueError( "The converted TensorFlow model has different outputs, something went wrong!\n" + f"\nList of maximum output differences above the threshold ({self._max_error}):\n" + "\n".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]) + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n" + "\n".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]) ) commit_message = "Update TF weights" if self._new_weights else "Add TF weights" if self._push: repo.git_add(auto_lfs_track=True) repo.git_commit(commit_message) repo.git_push(blocking=True) # this prints a progress bar with the upload self._logger.warning(f"TF weights pushed into {self._model_name}") elif not self._no_pr: self._logger.warning("Uploading the weights into a new PR...") commit_descrition = ( "Model converted by the [`transformers`' `pt_to_tf`" " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). " "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n" f"Maximum crossload output difference={max_crossload_output_diff:.3e}; " f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n" f"Maximum conversion output difference={max_conversion_output_diff:.3e}; " f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n" ) if self._max_error > MAX_ERROR: commit_descrition += ( f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!" ) if self._extra_commit_description: commit_descrition += "\n\n" + self._extra_commit_description # sharded model -> adds all related files (index and .h5 shards) if os.path.exists(tf_weights_index_path): operations = [ CommitOperationAdd(path_in_repo=TF2_WEIGHTS_INDEX_NAME, path_or_fileobj=tf_weights_index_path) ] for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"): operations += [ CommitOperationAdd(path_in_repo=os.path.basename(shard_path), path_or_fileobj=shard_path) ] else: operations = [CommitOperationAdd(path_in_repo=TF2_WEIGHTS_NAME, path_or_fileobj=tf_weights_path)] hub_pr_url = create_commit( repo_id=self._model_name, operations=operations, commit_message=commit_message, commit_description=commit_descrition, repo_type="model", create_pr=True, ).pr_url self._logger.warning(f"PR open in {hub_pr_url}")

transformers/src/transformers/commands/pt_to_tf.py/0

{ "file_path": "transformers/src/transformers/commands/pt_to_tf.py", "repo_id": "transformers", "token_count": 8900 }

300

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import pickle import random import time import warnings from typing import Dict, List, Optional import torch from filelock import FileLock from torch.utils.data import Dataset from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) DEPRECATION_WARNING = ( "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets " "library. You can have a look at this example script for pointers: {0}" ) class TextDataset(Dataset): """ This will be superseded by a framework-agnostic approach soon. """ def __init__( self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False, cache_dir: Optional[str] = None, ): warnings.warn( DEPRECATION_WARNING.format( "https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py" ), FutureWarning, ) if os.path.isfile(file_path) is False: raise ValueError(f"Input file path {file_path} not found") block_size = block_size - tokenizer.num_special_tokens_to_add(pair=False) directory, filename = os.path.split(file_path) cached_features_file = os.path.join( cache_dir if cache_dir is not None else directory, f"cached_lm_{tokenizer.__class__.__name__}_{block_size}_{filename}", ) # Make sure only the first process in distributed training processes the dataset, # and the others will use the cache. lock_path = cached_features_file + ".lock" with FileLock(lock_path): if os.path.exists(cached_features_file) and not overwrite_cache: start = time.time() with open(cached_features_file, "rb") as handle: self.examples = pickle.load(handle) logger.info( f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start ) else: logger.info(f"Creating features from dataset file at {directory}") self.examples = [] with open(file_path, encoding="utf-8") as f: text = f.read() tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text)) for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size self.examples.append( tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size]) ) # Note that we are losing the last truncated example here for the sake of simplicity (no padding) # If your dataset is small, first you should look for a bigger one :-) and second you # can change this behavior by adding (model specific) padding. start = time.time() with open(cached_features_file, "wb") as handle: pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL) logger.info( f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" ) def __len__(self): return len(self.examples) def __getitem__(self, i) -> torch.Tensor: return torch.tensor(self.examples[i], dtype=torch.long) class LineByLineTextDataset(Dataset): """ This will be superseded by a framework-agnostic approach soon. """ def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int): warnings.warn( DEPRECATION_WARNING.format( "https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py" ), FutureWarning, ) if os.path.isfile(file_path) is False: raise ValueError(f"Input file path {file_path} not found") # Here, we do not cache the features, operating under the assumption # that we will soon use fast multithreaded tokenizers from the # `tokenizers` repo everywhere =) logger.info(f"Creating features from dataset file at {file_path}") with open(file_path, encoding="utf-8") as f: lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())] batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size) self.examples = batch_encoding["input_ids"] self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples] def __len__(self): return len(self.examples) def __getitem__(self, i) -> Dict[str, torch.tensor]: return self.examples[i] class LineByLineWithRefDataset(Dataset): """ This will be superseded by a framework-agnostic approach soon. """ def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str): warnings.warn( DEPRECATION_WARNING.format( "https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm_wwm.py" ), FutureWarning, ) if os.path.isfile(file_path) is False: raise ValueError(f"Input file path {file_path} not found") if os.path.isfile(ref_path) is False: raise ValueError(f"Ref file path {file_path} not found") # Here, we do not cache the features, operating under the assumption # that we will soon use fast multithreaded tokenizers from the # `tokenizers` repo everywhere =) logger.info(f"Creating features from dataset file at {file_path}") logger.info(f"Use ref segment results at {ref_path}") with open(file_path, encoding="utf-8") as f: data = f.readlines() # use this method to avoid delimiter '\u2029' to split a line data = [line.strip() for line in data if len(line) > 0 and not line.isspace()] # Get ref inf from file with open(ref_path, encoding="utf-8") as f: ref = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())] if len(data) != len(ref): raise ValueError( f"Length of Input file should be equal to Ref file. But the length of {file_path} is {len(data)} " f"while length of {ref_path} is {len(ref)}" ) batch_encoding = tokenizer(data, add_special_tokens=True, truncation=True, max_length=block_size) self.examples = batch_encoding["input_ids"] self.examples = [{"input_ids": torch.tensor(e, dtype=torch.long)} for e in self.examples] n = len(self.examples) for i in range(n): self.examples[i]["chinese_ref"] = torch.tensor(ref[i], dtype=torch.long) def __len__(self): return len(self.examples) def __getitem__(self, i) -> Dict[str, torch.tensor]: return self.examples[i] class LineByLineWithSOPTextDataset(Dataset): """ Dataset for sentence order prediction task, prepare sentence pairs for SOP task """ def __init__(self, tokenizer: PreTrainedTokenizer, file_dir: str, block_size: int): warnings.warn( DEPRECATION_WARNING.format( "https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py" ), FutureWarning, ) if os.path.isdir(file_dir) is False: raise ValueError(f"{file_dir} is not a directory") logger.info(f"Creating features from dataset file folder at {file_dir}") self.examples = [] # TODO: randomness could apply a random seed, ex. rng = random.Random(random_seed) # file path looks like ./dataset/wiki_1, ./dataset/wiki_2 for file_name in os.listdir(file_dir): file_path = os.path.join(file_dir, file_name) if os.path.isfile(file_path) is False: raise ValueError(f"{file_path} is not a file") article_open = False with open(file_path, encoding="utf-8") as f: original_lines = f.readlines() article_lines = [] for line in original_lines: if "<doc id=" in line: article_open = True elif "</doc>" in line: article_open = False document = [ tokenizer.convert_tokens_to_ids(tokenizer.tokenize(line)) for line in article_lines[1:] if (len(line) > 0 and not line.isspace()) ] examples = self.create_examples_from_document(document, block_size, tokenizer) self.examples.extend(examples) article_lines = [] else: if article_open: article_lines.append(line) logger.info("Dataset parse finished.") def create_examples_from_document(self, document, block_size, tokenizer, short_seq_prob=0.1): """Creates examples for a single document.""" # Account for special tokens max_num_tokens = block_size - tokenizer.num_special_tokens_to_add(pair=True) # We *usually* want to fill up the entire sequence since we are padding # to `block_size` anyways, so short sequences are generally wasted # computation. However, we *sometimes* # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter # sequences to minimize the mismatch between pretraining and fine-tuning. # The `target_seq_length` is just a rough target however, whereas # `block_size` is a hard limit. target_seq_length = max_num_tokens if random.random() < short_seq_prob: target_seq_length = random.randint(2, max_num_tokens) # We DON'T just concatenate all of the tokens from a document into a long # sequence and choose an arbitrary split point because this would make the # next sentence prediction task too easy. Instead, we split the input into # segments "A" and "B" based on the actual "sentences" provided by the user # input. examples = [] current_chunk = [] # a buffer stored current working segments current_length = 0 i = 0 while i < len(document): segment = document[i] # get a segment if not segment: i += 1 continue current_chunk.append(segment) # add a segment to current chunk current_length += len(segment) # overall token length # if current length goes to the target length or reaches the end of file, start building token a and b if i == len(document) - 1 or current_length >= target_seq_length: if current_chunk: # `a_end` is how many segments from `current_chunk` go into the `A` (first) sentence. a_end = 1 # if current chunk has more than 2 sentences, pick part of it `A` (first) sentence if len(current_chunk) >= 2: a_end = random.randint(1, len(current_chunk) - 1) # token a tokens_a = [] for j in range(a_end): tokens_a.extend(current_chunk[j]) # token b tokens_b = [] for j in range(a_end, len(current_chunk)): tokens_b.extend(current_chunk[j]) if len(tokens_a) == 0 or len(tokens_b) == 0: continue # switch tokens_a and tokens_b randomly if random.random() < 0.5: is_next = False tokens_a, tokens_b = tokens_b, tokens_a else: is_next = True def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens): """Truncates a pair of sequences to a maximum sequence length.""" while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_num_tokens: break trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b if not (len(trunc_tokens) >= 1): raise ValueError("Sequence length to be truncated must be no less than one") # We want to sometimes truncate from the front and sometimes from the # back to add more randomness and avoid biases. if random.random() < 0.5: del trunc_tokens[0] else: trunc_tokens.pop() truncate_seq_pair(tokens_a, tokens_b, max_num_tokens) if not (len(tokens_a) >= 1): raise ValueError(f"Length of sequence a is {len(tokens_a)} which must be no less than 1") if not (len(tokens_b) >= 1): raise ValueError(f"Length of sequence b is {len(tokens_b)} which must be no less than 1") # add special tokens input_ids = tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b) # add token type ids, 0 for sentence a, 1 for sentence b token_type_ids = tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b) example = { "input_ids": torch.tensor(input_ids, dtype=torch.long), "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long), "sentence_order_label": torch.tensor(0 if is_next else 1, dtype=torch.long), } examples.append(example) current_chunk = [] # clear current chunk current_length = 0 # reset current text length i += 1 # go to next line return examples def __len__(self): return len(self.examples) def __getitem__(self, i) -> Dict[str, torch.tensor]: return self.examples[i] class TextDatasetForNextSentencePrediction(Dataset): """ This will be superseded by a framework-agnostic approach soon. """ def __init__( self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False, short_seq_probability=0.1, nsp_probability=0.5, ): warnings.warn( DEPRECATION_WARNING.format( "https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py" ), FutureWarning, ) if not os.path.isfile(file_path): raise ValueError(f"Input file path {file_path} not found") self.short_seq_probability = short_seq_probability self.nsp_probability = nsp_probability directory, filename = os.path.split(file_path) cached_features_file = os.path.join( directory, f"cached_nsp_{tokenizer.__class__.__name__}_{block_size}_{filename}", ) self.tokenizer = tokenizer # Make sure only the first process in distributed training processes the dataset, # and the others will use the cache. lock_path = cached_features_file + ".lock" # Input file format: # (1) One sentence per line. These should ideally be actual sentences, not # entire paragraphs or arbitrary spans of text. (Because we use the # sentence boundaries for the "next sentence prediction" task). # (2) Blank lines between documents. Document boundaries are needed so # that the "next sentence prediction" task doesn't span between documents. # # Example: # I am very happy. # Here is the second sentence. # # A new document. with FileLock(lock_path): if os.path.exists(cached_features_file) and not overwrite_cache: start = time.time() with open(cached_features_file, "rb") as handle: self.examples = pickle.load(handle) logger.info( f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start ) else: logger.info(f"Creating features from dataset file at {directory}") self.documents = [[]] with open(file_path, encoding="utf-8") as f: while True: line = f.readline() if not line: break line = line.strip() # Empty lines are used as document delimiters if not line and len(self.documents[-1]) != 0: self.documents.append([]) tokens = tokenizer.tokenize(line) tokens = tokenizer.convert_tokens_to_ids(tokens) if tokens: self.documents[-1].append(tokens) logger.info(f"Creating examples from {len(self.documents)} documents.") self.examples = [] for doc_index, document in enumerate(self.documents): self.create_examples_from_document(document, doc_index, block_size) start = time.time() with open(cached_features_file, "wb") as handle: pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL) logger.info( f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" ) def create_examples_from_document(self, document: List[List[int]], doc_index: int, block_size: int): """Creates examples for a single document.""" max_num_tokens = block_size - self.tokenizer.num_special_tokens_to_add(pair=True) # We *usually* want to fill up the entire sequence since we are padding # to `block_size` anyways, so short sequences are generally wasted # computation. However, we *sometimes* # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter # sequences to minimize the mismatch between pretraining and fine-tuning. # The `target_seq_length` is just a rough target however, whereas # `block_size` is a hard limit. target_seq_length = max_num_tokens if random.random() < self.short_seq_probability: target_seq_length = random.randint(2, max_num_tokens) current_chunk = [] # a buffer stored current working segments current_length = 0 i = 0 while i < len(document): segment = document[i] current_chunk.append(segment) current_length += len(segment) if i == len(document) - 1 or current_length >= target_seq_length: if current_chunk: # `a_end` is how many segments from `current_chunk` go into the `A` # (first) sentence. a_end = 1 if len(current_chunk) >= 2: a_end = random.randint(1, len(current_chunk) - 1) tokens_a = [] for j in range(a_end): tokens_a.extend(current_chunk[j]) tokens_b = [] if len(current_chunk) == 1 or random.random() < self.nsp_probability: is_random_next = True target_b_length = target_seq_length - len(tokens_a) # This should rarely go for more than one iteration for large # corpora. However, just to be careful, we try to make sure that # the random document is not the same as the document # we're processing. for _ in range(10): random_document_index = random.randint(0, len(self.documents) - 1) if random_document_index != doc_index: break random_document = self.documents[random_document_index] random_start = random.randint(0, len(random_document) - 1) for j in range(random_start, len(random_document)): tokens_b.extend(random_document[j]) if len(tokens_b) >= target_b_length: break # We didn't actually use these segments so we "put them back" so # they don't go to waste. num_unused_segments = len(current_chunk) - a_end i -= num_unused_segments # Actual next else: is_random_next = False for j in range(a_end, len(current_chunk)): tokens_b.extend(current_chunk[j]) if not (len(tokens_a) >= 1): raise ValueError(f"Length of sequence a is {len(tokens_a)} which must be no less than 1") if not (len(tokens_b) >= 1): raise ValueError(f"Length of sequence b is {len(tokens_b)} which must be no less than 1") # add special tokens input_ids = self.tokenizer.build_inputs_with_special_tokens(tokens_a, tokens_b) # add token type ids, 0 for sentence a, 1 for sentence b token_type_ids = self.tokenizer.create_token_type_ids_from_sequences(tokens_a, tokens_b) example = { "input_ids": torch.tensor(input_ids, dtype=torch.long), "token_type_ids": torch.tensor(token_type_ids, dtype=torch.long), "next_sentence_label": torch.tensor(1 if is_random_next else 0, dtype=torch.long), } self.examples.append(example) current_chunk = [] current_length = 0 i += 1 def __len__(self): return len(self.examples) def __getitem__(self, i): return self.examples[i]

transformers/src/transformers/data/datasets/language_modeling.py/0

{ "file_path": "transformers/src/transformers/data/datasets/language_modeling.py", "repo_id": "transformers", "token_count": 11449 }

301

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ File utilities: utilities related to download and cache models This module should not be update anymore and is only left for backward compatibility. """ from huggingface_hub import get_full_repo_name # for backward compatibility from huggingface_hub.constants import HF_HUB_DISABLE_TELEMETRY as DISABLE_TELEMETRY # for backward compatibility from . import __version__ # Backward compatibility imports, to make sure all those objects can be found in file_utils from .utils import ( CLOUDFRONT_DISTRIB_PREFIX, CONFIG_NAME, DUMMY_INPUTS, DUMMY_MASK, ENV_VARS_TRUE_AND_AUTO_VALUES, ENV_VARS_TRUE_VALUES, FEATURE_EXTRACTOR_NAME, FLAX_WEIGHTS_NAME, HF_MODULES_CACHE, HUGGINGFACE_CO_PREFIX, HUGGINGFACE_CO_RESOLVE_ENDPOINT, MODEL_CARD_NAME, MULTIPLE_CHOICE_DUMMY_INPUTS, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, S3_BUCKET_PREFIX, SENTENCEPIECE_UNDERLINE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TORCH_FX_REQUIRED_VERSION, TRANSFORMERS_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, USE_JAX, USE_TF, USE_TORCH, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ContextManagers, DummyObject, EntryNotFoundError, ExplicitEnum, ModelOutput, PaddingStrategy, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, TensorType, _LazyModule, add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, cached_property, copy_func, default_cache_path, define_sagemaker_information, get_cached_models, get_file_from_repo, get_torch_version, has_file, http_user_agent, is_apex_available, is_bs4_available, is_coloredlogs_available, is_datasets_available, is_detectron2_available, is_faiss_available, is_flax_available, is_ftfy_available, is_g2p_en_available, is_in_notebook, is_ipex_available, is_librosa_available, is_offline_mode, is_onnx_available, is_pandas_available, is_phonemizer_available, is_protobuf_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_pytesseract_available, is_pytorch_quantization_available, is_rjieba_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_scipy_available, is_sentencepiece_available, is_seqio_available, is_sklearn_available, is_soundfile_availble, is_spacy_available, is_speech_available, is_tensor, is_tensorflow_probability_available, is_tf2onnx_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_bf16_available, is_torch_cuda_available, is_torch_fx_available, is_torch_fx_proxy, is_torch_mps_available, is_torch_tf32_available, is_torch_tpu_available, is_torchaudio_available, is_training_run_on_sagemaker, is_vision_available, replace_return_docstrings, requires_backends, to_numpy, to_py_obj, torch_only_method, )

transformers/src/transformers/file_utils.py/0

{ "file_path": "transformers/src/transformers/file_utils.py", "repo_id": "transformers", "token_count": 1564 }

302

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team. # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from .generation import GenerationMixin class GenerationMixin(GenerationMixin): # warning at import time warnings.warn( "Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will " "be removed in Transformers v4.40. Import as `from transformers import GenerationMixin` instead.", FutureWarning, )

transformers/src/transformers/generation_utils.py/0

{ "file_path": "transformers/src/transformers/generation_utils.py", "repo_id": "transformers", "token_count": 315 }

303

/*! ************************************************************************************************** * Deformable DETR * Copyright (c) 2020 SenseTime. All Rights Reserved. * Licensed under the Apache License, Version 2.0 [see LICENSE for details] ************************************************************************************************** * Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 ************************************************************************************************** */ #include <vector> #include <cuda.h> #include <cuda_runtime.h> #include <cstdio> #include <algorithm> #include <cstring> #include <ATen/ATen.h> #include <ATen/cuda/CUDAContext.h> #include <THC/THCAtomics.cuh> #define CUDA_KERNEL_LOOP(i, n) \ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ i < (n); \ i += blockDim.x * gridDim.x) at::Tensor ms_deform_attn_cuda_forward( const at::Tensor &value, const at::Tensor &spatial_shapes, const at::Tensor &level_start_index, const at::Tensor &sampling_loc, const at::Tensor &attn_weight, const int im2col_step) { AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); const int batch = value.size(0); const int spatial_size = value.size(1); const int num_heads = value.size(2); const int channels = value.size(3); const int num_levels = spatial_shapes.size(0); const int num_query = sampling_loc.size(1); const int num_point = sampling_loc.size(4); const int im2col_step_ = std::min(batch, im2col_step); AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); const int batch_n = im2col_step_; auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); auto per_value_size = spatial_size * num_heads * channels; auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; for (int n = 0; n < batch/im2col_step_; ++n) { auto columns = output_n.select(0, n); AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), value.data<scalar_t>() + n * im2col_step_ * per_value_size, spatial_shapes.data<int64_t>(), level_start_index.data<int64_t>(), sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size, batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, columns.data<scalar_t>()); })); } output = output.view({batch, num_query, num_heads*channels}); return output; } std::vector<at::Tensor> ms_deform_attn_cuda_backward( const at::Tensor &value, const at::Tensor &spatial_shapes, const at::Tensor &level_start_index, const at::Tensor &sampling_loc, const at::Tensor &attn_weight, const at::Tensor &grad_output, const int im2col_step) { AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); const int batch = value.size(0); const int spatial_size = value.size(1); const int num_heads = value.size(2); const int channels = value.size(3); const int num_levels = spatial_shapes.size(0); const int num_query = sampling_loc.size(1); const int num_point = sampling_loc.size(4); const int im2col_step_ = std::min(batch, im2col_step); AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); auto grad_value = at::zeros_like(value); auto grad_sampling_loc = at::zeros_like(sampling_loc); auto grad_attn_weight = at::zeros_like(attn_weight); const int batch_n = im2col_step_; auto per_value_size = spatial_size * num_heads * channels; auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); for (int n = 0; n < batch/im2col_step_; ++n) { auto grad_output_g = grad_output_n.select(0, n); AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), grad_output_g.data<scalar_t>(), value.data<scalar_t>() + n * im2col_step_ * per_value_size, spatial_shapes.data<int64_t>(), level_start_index.data<int64_t>(), sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size, batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size, grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size, grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size); })); } return { grad_value, grad_sampling_loc, grad_attn_weight }; } const int CUDA_NUM_THREADS = 1024; inline int GET_BLOCKS(const int N, const int num_threads) { return (N + num_threads - 1) / num_threads; } template <typename scalar_t> __device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, const int &height, const int &width, const int &nheads, const int &channels, const scalar_t &h, const scalar_t &w, const int &m, const int &c) { const int h_low = floor(h); const int w_low = floor(w); const int h_high = h_low + 1; const int w_high = w_low + 1; const scalar_t lh = h - h_low; const scalar_t lw = w - w_low; const scalar_t hh = 1 - lh, hw = 1 - lw; const int w_stride = nheads * channels; const int h_stride = width * w_stride; const int h_low_ptr_offset = h_low * h_stride; const int h_high_ptr_offset = h_low_ptr_offset + h_stride; const int w_low_ptr_offset = w_low * w_stride; const int w_high_ptr_offset = w_low_ptr_offset + w_stride; const int base_ptr = m * channels + c; scalar_t v1 = 0; if (h_low >= 0 && w_low >= 0) { const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; v1 = bottom_data[ptr1]; } scalar_t v2 = 0; if (h_low >= 0 && w_high <= width - 1) { const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; v2 = bottom_data[ptr2]; } scalar_t v3 = 0; if (h_high <= height - 1 && w_low >= 0) { const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; v3 = bottom_data[ptr3]; } scalar_t v4 = 0; if (h_high <= height - 1 && w_high <= width - 1) { const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; v4 = bottom_data[ptr4]; } const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); return val; } template <typename scalar_t> __device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, const int &height, const int &width, const int &nheads, const int &channels, const scalar_t &h, const scalar_t &w, const int &m, const int &c, const scalar_t &top_grad, const scalar_t &attn_weight, scalar_t* &grad_value, scalar_t* grad_sampling_loc, scalar_t* grad_attn_weight) { const int h_low = floor(h); const int w_low = floor(w); const int h_high = h_low + 1; const int w_high = w_low + 1; const scalar_t lh = h - h_low; const scalar_t lw = w - w_low; const scalar_t hh = 1 - lh, hw = 1 - lw; const int w_stride = nheads * channels; const int h_stride = width * w_stride; const int h_low_ptr_offset = h_low * h_stride; const int h_high_ptr_offset = h_low_ptr_offset + h_stride; const int w_low_ptr_offset = w_low * w_stride; const int w_high_ptr_offset = w_low_ptr_offset + w_stride; const int base_ptr = m * channels + c; const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; const scalar_t top_grad_value = top_grad * attn_weight; scalar_t grad_h_weight = 0, grad_w_weight = 0; scalar_t v1 = 0; if (h_low >= 0 && w_low >= 0) { const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; v1 = bottom_data[ptr1]; grad_h_weight -= hw * v1; grad_w_weight -= hh * v1; atomicAdd(grad_value+ptr1, w1*top_grad_value); } scalar_t v2 = 0; if (h_low >= 0 && w_high <= width - 1) { const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; v2 = bottom_data[ptr2]; grad_h_weight -= lw * v2; grad_w_weight += hh * v2; atomicAdd(grad_value+ptr2, w2*top_grad_value); } scalar_t v3 = 0; if (h_high <= height - 1 && w_low >= 0) { const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; v3 = bottom_data[ptr3]; grad_h_weight += hw * v3; grad_w_weight -= lh * v3; atomicAdd(grad_value+ptr3, w3*top_grad_value); } scalar_t v4 = 0; if (h_high <= height - 1 && w_high <= width - 1) { const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; v4 = bottom_data[ptr4]; grad_h_weight += lw * v4; grad_w_weight += lh * v4; atomicAdd(grad_value+ptr4, w4*top_grad_value); } const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); *grad_attn_weight = top_grad * val; *grad_sampling_loc = width * grad_w_weight * top_grad_value; *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; } template <typename scalar_t> __device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, const int &height, const int &width, const int &nheads, const int &channels, const scalar_t &h, const scalar_t &w, const int &m, const int &c, const scalar_t &top_grad, const scalar_t &attn_weight, scalar_t* &grad_value, scalar_t* grad_sampling_loc, scalar_t* grad_attn_weight) { const int h_low = floor(h); const int w_low = floor(w); const int h_high = h_low + 1; const int w_high = w_low + 1; const scalar_t lh = h - h_low; const scalar_t lw = w - w_low; const scalar_t hh = 1 - lh, hw = 1 - lw; const int w_stride = nheads * channels; const int h_stride = width * w_stride; const int h_low_ptr_offset = h_low * h_stride; const int h_high_ptr_offset = h_low_ptr_offset + h_stride; const int w_low_ptr_offset = w_low * w_stride; const int w_high_ptr_offset = w_low_ptr_offset + w_stride; const int base_ptr = m * channels + c; const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; const scalar_t top_grad_value = top_grad * attn_weight; scalar_t grad_h_weight = 0, grad_w_weight = 0; scalar_t v1 = 0; if (h_low >= 0 && w_low >= 0) { const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; v1 = bottom_data[ptr1]; grad_h_weight -= hw * v1; grad_w_weight -= hh * v1; atomicAdd(grad_value+ptr1, w1*top_grad_value); } scalar_t v2 = 0; if (h_low >= 0 && w_high <= width - 1) { const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; v2 = bottom_data[ptr2]; grad_h_weight -= lw * v2; grad_w_weight += hh * v2; atomicAdd(grad_value+ptr2, w2*top_grad_value); } scalar_t v3 = 0; if (h_high <= height - 1 && w_low >= 0) { const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; v3 = bottom_data[ptr3]; grad_h_weight += hw * v3; grad_w_weight -= lh * v3; atomicAdd(grad_value+ptr3, w3*top_grad_value); } scalar_t v4 = 0; if (h_high <= height - 1 && w_high <= width - 1) { const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; v4 = bottom_data[ptr4]; grad_h_weight += lw * v4; grad_w_weight += lh * v4; atomicAdd(grad_value+ptr4, w4*top_grad_value); } const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); atomicAdd(grad_attn_weight, top_grad * val); atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); } template <typename scalar_t> __global__ void ms_deformable_im2col_gpu_kernel(const int n, const scalar_t *data_value, const int64_t *data_spatial_shapes, const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t *data_col) { CUDA_KERNEL_LOOP(index, n) { int _temp = index; const int c_col = _temp % channels; _temp /= channels; const int sampling_index = _temp; const int m_col = _temp % num_heads; _temp /= num_heads; const int q_col = _temp % num_query; _temp /= num_query; const int b_col = _temp; scalar_t *data_col_ptr = data_col + index; int data_weight_ptr = sampling_index * num_levels * num_point; int data_loc_w_ptr = data_weight_ptr << 1; const int qid_stride = num_heads * channels; const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; scalar_t col = 0; for (int l_col=0; l_col < num_levels; ++l_col) { const int level_start_id = data_level_start_index[l_col]; const int spatial_h_ptr = l_col << 1; const int spatial_h = data_spatial_shapes[spatial_h_ptr]; const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); for (int p_col=0; p_col < num_point; ++p_col) { const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; const scalar_t weight = data_attn_weight[data_weight_ptr]; const scalar_t h_im = loc_h * spatial_h - 0.5; const scalar_t w_im = loc_w * spatial_w - 0.5; if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) { col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; } data_weight_ptr += 1; data_loc_w_ptr += 2; } } *data_col_ptr = col; } } template <typename scalar_t, unsigned int blockSize> __global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes, const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) { CUDA_KERNEL_LOOP(index, n) { __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; __shared__ scalar_t cache_grad_attn_weight[blockSize]; unsigned int tid = threadIdx.x; int _temp = index; const int c_col = _temp % channels; _temp /= channels; const int sampling_index = _temp; const int m_col = _temp % num_heads; _temp /= num_heads; const int q_col = _temp % num_query; _temp /= num_query; const int b_col = _temp; const scalar_t top_grad = grad_col[index]; int data_weight_ptr = sampling_index * num_levels * num_point; int data_loc_w_ptr = data_weight_ptr << 1; const int grad_sampling_ptr = data_weight_ptr; grad_sampling_loc += grad_sampling_ptr << 1; grad_attn_weight += grad_sampling_ptr; const int grad_weight_stride = 1; const int grad_loc_stride = 2; const int qid_stride = num_heads * channels; const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; for (int l_col=0; l_col < num_levels; ++l_col) { const int level_start_id = data_level_start_index[l_col]; const int spatial_h_ptr = l_col << 1; const int spatial_h = data_spatial_shapes[spatial_h_ptr]; const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; const scalar_t *data_value_ptr = data_value + value_ptr_offset; scalar_t *grad_value_ptr = grad_value + value_ptr_offset; for (int p_col=0; p_col < num_point; ++p_col) { const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; const scalar_t weight = data_attn_weight[data_weight_ptr]; const scalar_t h_im = loc_h * spatial_h - 0.5; const scalar_t w_im = loc_w * spatial_w - 0.5; *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; *(cache_grad_attn_weight+threadIdx.x)=0; if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) { ms_deform_attn_col2im_bilinear( data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, top_grad, weight, grad_value_ptr, cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); } __syncthreads(); if (tid == 0) { scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; int sid=2; for (unsigned int tid = 1; tid < blockSize; ++tid) { _grad_w += cache_grad_sampling_loc[sid]; _grad_h += cache_grad_sampling_loc[sid + 1]; _grad_a += cache_grad_attn_weight[tid]; sid += 2; } *grad_sampling_loc = _grad_w; *(grad_sampling_loc + 1) = _grad_h; *grad_attn_weight = _grad_a; } __syncthreads(); data_weight_ptr += 1; data_loc_w_ptr += 2; grad_attn_weight += grad_weight_stride; grad_sampling_loc += grad_loc_stride; } } } } template <typename scalar_t, unsigned int blockSize> __global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes, const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) { CUDA_KERNEL_LOOP(index, n) { __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; __shared__ scalar_t cache_grad_attn_weight[blockSize]; unsigned int tid = threadIdx.x; int _temp = index; const int c_col = _temp % channels; _temp /= channels; const int sampling_index = _temp; const int m_col = _temp % num_heads; _temp /= num_heads; const int q_col = _temp % num_query; _temp /= num_query; const int b_col = _temp; const scalar_t top_grad = grad_col[index]; int data_weight_ptr = sampling_index * num_levels * num_point; int data_loc_w_ptr = data_weight_ptr << 1; const int grad_sampling_ptr = data_weight_ptr; grad_sampling_loc += grad_sampling_ptr << 1; grad_attn_weight += grad_sampling_ptr; const int grad_weight_stride = 1; const int grad_loc_stride = 2; const int qid_stride = num_heads * channels; const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; for (int l_col=0; l_col < num_levels; ++l_col) { const int level_start_id = data_level_start_index[l_col]; const int spatial_h_ptr = l_col << 1; const int spatial_h = data_spatial_shapes[spatial_h_ptr]; const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; const scalar_t *data_value_ptr = data_value + value_ptr_offset; scalar_t *grad_value_ptr = grad_value + value_ptr_offset; for (int p_col=0; p_col < num_point; ++p_col) { const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; const scalar_t weight = data_attn_weight[data_weight_ptr]; const scalar_t h_im = loc_h * spatial_h - 0.5; const scalar_t w_im = loc_w * spatial_w - 0.5; *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; *(cache_grad_attn_weight+threadIdx.x)=0; if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) { ms_deform_attn_col2im_bilinear( data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, top_grad, weight, grad_value_ptr, cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); } __syncthreads(); for (unsigned int s=blockSize/2; s>0; s>>=1) { if (tid < s) { const unsigned int xid1 = tid << 1; const unsigned int xid2 = (tid + s) << 1; cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; } __syncthreads(); } if (tid == 0) { *grad_sampling_loc = cache_grad_sampling_loc[0]; *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; *grad_attn_weight = cache_grad_attn_weight[0]; } __syncthreads(); data_weight_ptr += 1; data_loc_w_ptr += 2; grad_attn_weight += grad_weight_stride; grad_sampling_loc += grad_loc_stride; } } } } template <typename scalar_t> __global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes, const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) { CUDA_KERNEL_LOOP(index, n) { extern __shared__ int _s[]; scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; unsigned int tid = threadIdx.x; int _temp = index; const int c_col = _temp % channels; _temp /= channels; const int sampling_index = _temp; const int m_col = _temp % num_heads; _temp /= num_heads; const int q_col = _temp % num_query; _temp /= num_query; const int b_col = _temp; const scalar_t top_grad = grad_col[index]; int data_weight_ptr = sampling_index * num_levels * num_point; int data_loc_w_ptr = data_weight_ptr << 1; const int grad_sampling_ptr = data_weight_ptr; grad_sampling_loc += grad_sampling_ptr << 1; grad_attn_weight += grad_sampling_ptr; const int grad_weight_stride = 1; const int grad_loc_stride = 2; const int qid_stride = num_heads * channels; const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; for (int l_col=0; l_col < num_levels; ++l_col) { const int level_start_id = data_level_start_index[l_col]; const int spatial_h_ptr = l_col << 1; const int spatial_h = data_spatial_shapes[spatial_h_ptr]; const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; const scalar_t *data_value_ptr = data_value + value_ptr_offset; scalar_t *grad_value_ptr = grad_value + value_ptr_offset; for (int p_col=0; p_col < num_point; ++p_col) { const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; const scalar_t weight = data_attn_weight[data_weight_ptr]; const scalar_t h_im = loc_h * spatial_h - 0.5; const scalar_t w_im = loc_w * spatial_w - 0.5; *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; *(cache_grad_attn_weight+threadIdx.x)=0; if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) { ms_deform_attn_col2im_bilinear( data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, top_grad, weight, grad_value_ptr, cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); } __syncthreads(); if (tid == 0) { scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; int sid=2; for (unsigned int tid = 1; tid < blockDim.x; ++tid) { _grad_w += cache_grad_sampling_loc[sid]; _grad_h += cache_grad_sampling_loc[sid + 1]; _grad_a += cache_grad_attn_weight[tid]; sid += 2; } *grad_sampling_loc = _grad_w; *(grad_sampling_loc + 1) = _grad_h; *grad_attn_weight = _grad_a; } __syncthreads(); data_weight_ptr += 1; data_loc_w_ptr += 2; grad_attn_weight += grad_weight_stride; grad_sampling_loc += grad_loc_stride; } } } } template <typename scalar_t> __global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes, const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) { CUDA_KERNEL_LOOP(index, n) { extern __shared__ int _s[]; scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; unsigned int tid = threadIdx.x; int _temp = index; const int c_col = _temp % channels; _temp /= channels; const int sampling_index = _temp; const int m_col = _temp % num_heads; _temp /= num_heads; const int q_col = _temp % num_query; _temp /= num_query; const int b_col = _temp; const scalar_t top_grad = grad_col[index]; int data_weight_ptr = sampling_index * num_levels * num_point; int data_loc_w_ptr = data_weight_ptr << 1; const int grad_sampling_ptr = data_weight_ptr; grad_sampling_loc += grad_sampling_ptr << 1; grad_attn_weight += grad_sampling_ptr; const int grad_weight_stride = 1; const int grad_loc_stride = 2; const int qid_stride = num_heads * channels; const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; for (int l_col=0; l_col < num_levels; ++l_col) { const int level_start_id = data_level_start_index[l_col]; const int spatial_h_ptr = l_col << 1; const int spatial_h = data_spatial_shapes[spatial_h_ptr]; const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; const scalar_t *data_value_ptr = data_value + value_ptr_offset; scalar_t *grad_value_ptr = grad_value + value_ptr_offset; for (int p_col=0; p_col < num_point; ++p_col) { const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; const scalar_t weight = data_attn_weight[data_weight_ptr]; const scalar_t h_im = loc_h * spatial_h - 0.5; const scalar_t w_im = loc_w * spatial_w - 0.5; *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; *(cache_grad_attn_weight+threadIdx.x)=0; if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) { ms_deform_attn_col2im_bilinear( data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, top_grad, weight, grad_value_ptr, cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); } __syncthreads(); for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) { if (tid < s) { const unsigned int xid1 = tid << 1; const unsigned int xid2 = (tid + s) << 1; cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; if (tid + (s << 1) < spre) { cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; } } __syncthreads(); } if (tid == 0) { *grad_sampling_loc = cache_grad_sampling_loc[0]; *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; *grad_attn_weight = cache_grad_attn_weight[0]; } __syncthreads(); data_weight_ptr += 1; data_loc_w_ptr += 2; grad_attn_weight += grad_weight_stride; grad_sampling_loc += grad_loc_stride; } } } } template <typename scalar_t> __global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes, const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) { CUDA_KERNEL_LOOP(index, n) { extern __shared__ int _s[]; scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; unsigned int tid = threadIdx.x; int _temp = index; const int c_col = _temp % channels; _temp /= channels; const int sampling_index = _temp; const int m_col = _temp % num_heads; _temp /= num_heads; const int q_col = _temp % num_query; _temp /= num_query; const int b_col = _temp; const scalar_t top_grad = grad_col[index]; int data_weight_ptr = sampling_index * num_levels * num_point; int data_loc_w_ptr = data_weight_ptr << 1; const int grad_sampling_ptr = data_weight_ptr; grad_sampling_loc += grad_sampling_ptr << 1; grad_attn_weight += grad_sampling_ptr; const int grad_weight_stride = 1; const int grad_loc_stride = 2; const int qid_stride = num_heads * channels; const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; for (int l_col=0; l_col < num_levels; ++l_col) { const int level_start_id = data_level_start_index[l_col]; const int spatial_h_ptr = l_col << 1; const int spatial_h = data_spatial_shapes[spatial_h_ptr]; const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; const scalar_t *data_value_ptr = data_value + value_ptr_offset; scalar_t *grad_value_ptr = grad_value + value_ptr_offset; for (int p_col=0; p_col < num_point; ++p_col) { const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; const scalar_t weight = data_attn_weight[data_weight_ptr]; const scalar_t h_im = loc_h * spatial_h - 0.5; const scalar_t w_im = loc_w * spatial_w - 0.5; *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; *(cache_grad_attn_weight+threadIdx.x)=0; if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) { ms_deform_attn_col2im_bilinear( data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, top_grad, weight, grad_value_ptr, cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); } __syncthreads(); for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) { if (tid < s) { const unsigned int xid1 = tid << 1; const unsigned int xid2 = (tid + s) << 1; cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; if (tid + (s << 1) < spre) { cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; } } __syncthreads(); } if (tid == 0) { atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); } __syncthreads(); data_weight_ptr += 1; data_loc_w_ptr += 2; grad_attn_weight += grad_weight_stride; grad_sampling_loc += grad_loc_stride; } } } } template <typename scalar_t> __global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes, const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) { CUDA_KERNEL_LOOP(index, n) { int _temp = index; const int c_col = _temp % channels; _temp /= channels; const int sampling_index = _temp; const int m_col = _temp % num_heads; _temp /= num_heads; const int q_col = _temp % num_query; _temp /= num_query; const int b_col = _temp; const scalar_t top_grad = grad_col[index]; int data_weight_ptr = sampling_index * num_levels * num_point; int data_loc_w_ptr = data_weight_ptr << 1; const int grad_sampling_ptr = data_weight_ptr; grad_sampling_loc += grad_sampling_ptr << 1; grad_attn_weight += grad_sampling_ptr; const int grad_weight_stride = 1; const int grad_loc_stride = 2; const int qid_stride = num_heads * channels; const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; for (int l_col=0; l_col < num_levels; ++l_col) { const int level_start_id = data_level_start_index[l_col]; const int spatial_h_ptr = l_col << 1; const int spatial_h = data_spatial_shapes[spatial_h_ptr]; const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; const scalar_t *data_value_ptr = data_value + value_ptr_offset; scalar_t *grad_value_ptr = grad_value + value_ptr_offset; for (int p_col=0; p_col < num_point; ++p_col) { const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; const scalar_t weight = data_attn_weight[data_weight_ptr]; const scalar_t h_im = loc_h * spatial_h - 0.5; const scalar_t w_im = loc_w * spatial_w - 0.5; if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) { ms_deform_attn_col2im_bilinear_gm( data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, top_grad, weight, grad_value_ptr, grad_sampling_loc, grad_attn_weight); } data_weight_ptr += 1; data_loc_w_ptr += 2; grad_attn_weight += grad_weight_stride; grad_sampling_loc += grad_loc_stride; } } } } template <typename scalar_t> void ms_deformable_im2col_cuda(cudaStream_t stream, const scalar_t* data_value, const int64_t* data_spatial_shapes, const int64_t* data_level_start_index, const scalar_t* data_sampling_loc, const scalar_t* data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t* data_col) { const int num_kernels = batch_size * num_query * num_heads * channels; const int num_actual_kernels = batch_size * num_query * num_heads * channels; const int num_threads = CUDA_NUM_THREADS; ms_deformable_im2col_gpu_kernel<scalar_t> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); } } template <typename scalar_t> void ms_deformable_col2im_cuda(cudaStream_t stream, const scalar_t* grad_col, const scalar_t* data_value, const int64_t * data_spatial_shapes, const int64_t * data_level_start_index, const scalar_t * data_sampling_loc, const scalar_t * data_attn_weight, const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels, const int num_query, const int num_point, scalar_t* grad_value, scalar_t* grad_sampling_loc, scalar_t* grad_attn_weight) { const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; const int num_kernels = batch_size * num_query * num_heads * channels; const int num_actual_kernels = batch_size * num_query * num_heads * channels; if (channels > 1024) { if ((channels & 1023) == 0) { ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, num_threads*3*sizeof(scalar_t), stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); } else { ms_deformable_col2im_gpu_kernel_gm<scalar_t> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); } } else{ switch(channels) { case 1: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 2: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 4: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 8: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 16: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 32: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 64: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 128: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 256: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 512: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; case 1024: ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); break; default: if (channels < 64) { ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, num_threads*3*sizeof(scalar_t), stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); } else { ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t> <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, num_threads*3*sizeof(scalar_t), stream>>>( num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight); } } } cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); } }

transformers/src/transformers/kernels/deformable_detr/cuda/ms_deform_attn_cuda.cuh/0

{ "file_path": "transformers/src/transformers/kernels/deformable_detr/cuda/ms_deform_attn_cuda.cuh", "repo_id": "transformers", "token_count": 34691 }

304

// File from https://github.com/mlpen/YOSO/blob/main/encoders/backbones/efficient_attentions/yoso/yoso_v1/cuda/fast_lsh_cumulation.cu #include <torch/extension.h> #include <ATen/ATen.h> #include "fast_lsh_cumulation.h" #include "fast_lsh_cumulation_cuda.h" #include "common_cuda.h" #include "common.h" #include <vector> ////////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////// std::vector<at::Tensor> fast_hash_ver1_kernel( at::Tensor query_mask, at::Tensor query_vector, at::Tensor key_mask, at::Tensor key_vector, int num_hash_f, int hash_code_len, bool use_cuda ) { int batch_size = query_vector.size(0); int num_query = query_vector.size(1); int num_key = key_vector.size(1); int vector_dim = query_vector.size(2); int num_hash_per_part = vector_dim / hash_code_len; int num_part = max(1, ceil_divide(num_hash_f, num_hash_per_part)); at::Tensor Dmat = 2 * at::randint(0, 2, {batch_size, 3, num_part, vector_dim}, query_mask.options()) - 1; at::Tensor query_hash_code = at::zeros({batch_size, num_query, num_hash_f}, query_mask.options()); at::Tensor key_hash_code = at::zeros({batch_size, num_key, num_hash_f}, key_mask.options()); int *query_mask_ptr = query_mask.data_ptr<int>(); float *query_vector_ptr = query_vector.data_ptr<float>(); int *key_mask_ptr = key_mask.data_ptr<int>(); float *key_vector_ptr = key_vector.data_ptr<float>(); int *Dmat_ptr = Dmat.data_ptr<int>(); int *query_hash_code_ptr = query_hash_code.data_ptr<int>(); int *key_hash_code_ptr = key_hash_code.data_ptr<int>(); if (use_cuda) { { dim3 threads(vector_dim); dim3 blocks(num_part, num_query, batch_size); int shared_mem = vector_dim * sizeof(float); fast_hash_ver1_cuda_kernel<<<blocks, threads, shared_mem>>>( query_mask_ptr, query_vector_ptr, Dmat_ptr, query_hash_code_ptr, batch_size, num_query, vector_dim, num_part, num_hash_f, hash_code_len ); } { dim3 threads(vector_dim); dim3 blocks(num_part, num_key, batch_size); int shared_mem = vector_dim * sizeof(float); fast_hash_ver1_cuda_kernel<<<blocks, threads, shared_mem>>>( key_mask_ptr, key_vector_ptr, Dmat_ptr, key_hash_code_ptr, batch_size, num_key, vector_dim, num_part, num_hash_f, hash_code_len ); } } return {query_hash_code, key_hash_code}; } at::Tensor lsh_cumulation_ver1_kernel( at::Tensor query_mask, at::Tensor query_hash_code, at::Tensor key_mask, at::Tensor key_hash_code, at::Tensor value, int hashtable_capacity, bool use_cuda ) { int batch_size = query_hash_code.size(0); int num_hash_f = query_hash_code.size(2); int num_query = query_hash_code.size(1); int num_key = key_hash_code.size(1); int value_dim = value.size(2); at::Tensor hashtable_value = at::empty({batch_size, num_hash_f, hashtable_capacity, WARP_SIZE}, value.options()); at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); if (use_cuda) { int threads_x = WARP_SIZE; int threads_y = OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE; int block_x_step1 = num_key / threads_y; int block_x_step2 = num_query / threads_y; int block_y = batch_size; dim3 threads(threads_x, threads_y); dim3 blocks_step1(block_x_step1, block_y); dim3 blocks_step2(block_x_step2, block_y); int *query_mask_ptr = query_mask.data_ptr<int>(); int *query_hash_code_ptr = query_hash_code.data_ptr<int>(); int *key_mask_ptr = key_mask.data_ptr<int>(); int *key_hash_code_ptr = key_hash_code.data_ptr<int>(); float *value_ptr = value.data_ptr<float>(); float *hashtable_value_ptr = hashtable_value.data_ptr<float>(); float *cumulation_value_ptr = cumulation_value.data_ptr<float>(); for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { cudaMemset(hashtable_value_ptr, 0, (batch_size * num_hash_f * hashtable_capacity * WARP_SIZE) * sizeof(float)); lsh_cumulation_ver1_step1_cuda_kernel<<<blocks_step1, threads>>>( key_mask_ptr, key_hash_code_ptr, value_ptr, hashtable_value_ptr, batch_size, num_hash_f, hashtable_capacity, num_key, value_dim, value_offset ); lsh_cumulation_ver1_step2_cuda_kernel<<<blocks_step2, threads>>>( query_mask_ptr, query_hash_code_ptr, hashtable_value_ptr, cumulation_value_ptr, batch_size, num_hash_f, hashtable_capacity, num_query, value_dim, value_offset ); } } return cumulation_value; } at::Tensor lsh_weighted_cumulation_ver1_kernel( at::Tensor query_mask, at::Tensor query_hash_code, at::Tensor query_weight, at::Tensor key_mask, at::Tensor key_hash_code, at::Tensor key_weight, at::Tensor value, int hashtable_capacity, bool use_cuda ) { int batch_size = query_hash_code.size(0); int num_hash_f = query_hash_code.size(2); int num_query = query_hash_code.size(1); int num_key = key_hash_code.size(1); int value_dim = value.size(2); int weight_dim = query_weight.size(2); at::Tensor hashtable_value = at::zeros({batch_size, num_hash_f, hashtable_capacity, WARP_SIZE}, value.options()); at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); if (use_cuda) { int threads_x = WARP_SIZE; int threads_y = OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE; int block_x_step1 = num_key / threads_y; int block_x_step2 = num_query / threads_y; int block_y = batch_size; dim3 threads(threads_x, threads_y); dim3 blocks_step1(block_x_step1, block_y); dim3 blocks_step2(block_x_step2, block_y); int *query_mask_ptr = query_mask.data_ptr<int>(); int *query_hash_code_ptr = query_hash_code.data_ptr<int>(); float *query_weight_ptr = query_weight.data_ptr<float>(); int *key_mask_ptr = key_mask.data_ptr<int>(); int *key_hash_code_ptr = key_hash_code.data_ptr<int>(); float *key_weight_ptr = key_weight.data_ptr<float>(); float *value_ptr = value.data_ptr<float>(); float *hashtable_value_ptr = hashtable_value.data_ptr<float>(); float *cumulation_value_ptr = cumulation_value.data_ptr<float>(); for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { for (int weight_idx = 0; weight_idx < weight_dim; weight_idx++) { cudaMemset(hashtable_value_ptr, 0, (batch_size * num_hash_f * hashtable_capacity * WARP_SIZE) * sizeof(float)); lsh_weighted_cumulation_ver1_step1_cuda_kernel<<<blocks_step1, threads>>>( key_mask_ptr, key_hash_code_ptr, key_weight_ptr, value_ptr, hashtable_value_ptr, batch_size, num_hash_f, hashtable_capacity, num_key, value_dim, weight_dim, value_offset, weight_idx ); lsh_weighted_cumulation_ver1_step2_cuda_kernel<<<blocks_step2, threads>>>( query_mask_ptr, query_hash_code_ptr, query_weight_ptr, hashtable_value_ptr, cumulation_value_ptr, batch_size, num_hash_f, hashtable_capacity, num_query, value_dim, weight_dim, value_offset, weight_idx ); } } } return cumulation_value; } at::Tensor lsh_weighted_cumulation_ver2_kernel( at::Tensor query_mask, at::Tensor query_hash_code, at::Tensor query_weight, at::Tensor key_mask, at::Tensor key_hash_code, at::Tensor key_weight, at::Tensor value, int hashtable_capacity, bool use_cuda ) { int batch_size = query_hash_code.size(0); int num_hash_f = query_hash_code.size(2); int num_query = query_hash_code.size(1); int num_key = key_hash_code.size(1); int value_dim = value.size(2); int weight_dim = query_weight.size(2); at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options()); at::Tensor key_sorted_idxes = at::zeros({batch_size, num_hash_f, num_key}, query_hash_code.options()); at::Tensor query_info = at::zeros({batch_size, num_query, 2, num_hash_f}, query_hash_code.options()); at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); if (use_cuda) { int *query_mask_ptr = query_mask.data_ptr<int>(); int *query_hash_code_ptr = query_hash_code.data_ptr<int>(); float *query_weight_ptr = query_weight.data_ptr<float>(); int *key_mask_ptr = key_mask.data_ptr<int>(); int *key_hash_code_ptr = key_hash_code.data_ptr<int>(); float *key_weight_ptr = key_weight.data_ptr<float>(); float *value_ptr = value.data_ptr<float>(); int *count_sort_table_ptr = count_sort_table.data_ptr<int>(); int *key_sorted_idxes_ptr = key_sorted_idxes.data_ptr<int>(); int *query_info_ptr = query_info.data_ptr<int>(); float *cumulation_value_ptr = cumulation_value.data_ptr<float>(); { dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); dim3 blocks_step13(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK)); dim3 blocks_step2(num_hash_f, batch_size); int shared_mem = hashtable_capacity * sizeof(float); count_sort_step1_cuda_kernel<<<blocks_step13, threads_step13>>>( key_mask_ptr, key_hash_code_ptr, count_sort_table_ptr, batch_size, num_hash_f, hashtable_capacity, num_key ); count_sort_step2_cuda_kernel<<<blocks_step2, threads_step2, shared_mem>>>( count_sort_table_ptr, batch_size, num_hash_f, hashtable_capacity ); count_sort_step3_cuda_kernel<<<blocks_step13, threads_step13>>>( key_mask_ptr, key_hash_code_ptr, count_sort_table_ptr, key_sorted_idxes_ptr, batch_size, num_hash_f, hashtable_capacity, num_key ); } { dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); dim3 blocks(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); extract_query_info_cuda_kernel<<<blocks, threads>>>( query_mask_ptr, query_hash_code_ptr, count_sort_table_ptr, query_info_ptr, batch_size, num_hash_f, hashtable_capacity, num_query ); } { dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE); dim3 blocks(num_query, num_hash_f, batch_size); int shared_mem = (weight_dim + WARP_SIZE) * sizeof(float); lsh_weighted_cumulation_ver2_step2_cuda_kernel<<<blocks, threads, shared_mem>>>( query_mask_ptr, query_info_ptr, key_sorted_idxes_ptr, query_weight_ptr, key_weight_ptr, value_ptr, cumulation_value_ptr, batch_size, num_hash_f, num_query, num_key, value_dim, weight_dim ); } } return cumulation_value; } at::Tensor lsh_weighted_cumulation_ver3_kernel( at::Tensor query_mask, at::Tensor query_hash_code, at::Tensor query_weight, at::Tensor key_mask, at::Tensor key_hash_code, at::Tensor key_weight, at::Tensor value, int hashtable_capacity, bool use_cuda ) { int batch_size = query_hash_code.size(0); int num_hash_f = query_hash_code.size(2); int num_query = query_hash_code.size(1); int num_key = key_hash_code.size(1); int value_dim = value.size(2); int weight_dim = query_weight.size(2); at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options()); at::Tensor query_sorted_idxes = at::zeros({batch_size, num_hash_f, num_query}, query_hash_code.options()); at::Tensor key_info = at::zeros({batch_size, num_key, 2, num_hash_f}, query_hash_code.options()); at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); if (use_cuda) { int *query_mask_ptr = query_mask.data_ptr<int>(); int *query_hash_code_ptr = query_hash_code.data_ptr<int>(); float *query_weight_ptr = query_weight.data_ptr<float>(); int *key_mask_ptr = key_mask.data_ptr<int>(); int *key_hash_code_ptr = key_hash_code.data_ptr<int>(); float *key_weight_ptr = key_weight.data_ptr<float>(); float *value_ptr = value.data_ptr<float>(); int *count_sort_table_ptr = count_sort_table.data_ptr<int>(); int *query_sorted_idxes_ptr = query_sorted_idxes.data_ptr<int>(); int *key_info_ptr = key_info.data_ptr<int>(); float *cumulation_value_ptr = cumulation_value.data_ptr<float>(); { dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); dim3 blocks_step13(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK)); dim3 blocks_step2(num_hash_f, batch_size); int shared_mem = hashtable_capacity * sizeof(float); count_sort_step1_cuda_kernel<<<blocks_step13, threads_step13>>>( query_mask_ptr, query_hash_code_ptr, count_sort_table_ptr, batch_size, num_hash_f, hashtable_capacity, num_query ); count_sort_step2_cuda_kernel<<<blocks_step2, threads_step2, shared_mem>>>( count_sort_table_ptr, batch_size, num_hash_f, hashtable_capacity ); count_sort_step3_cuda_kernel<<<blocks_step13, threads_step13>>>( query_mask_ptr, query_hash_code_ptr, count_sort_table_ptr, query_sorted_idxes_ptr, batch_size, num_hash_f, hashtable_capacity, num_query ); } { dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); dim3 blocks(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); extract_query_info_cuda_kernel<<<blocks, threads>>>( key_mask_ptr, key_hash_code_ptr, count_sort_table_ptr, key_info_ptr, batch_size, num_hash_f, hashtable_capacity, num_key ); } { dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE); dim3 blocks(num_key, num_hash_f, batch_size); int shared_mem = (weight_dim + value_dim + WARP_SIZE) * sizeof(float); lsh_weighted_cumulation_ver3_step2_cuda_kernel<<<blocks, threads, shared_mem>>>( query_sorted_idxes_ptr, key_mask_ptr, key_info_ptr, query_weight_ptr, key_weight_ptr, value_ptr, cumulation_value_ptr, batch_size, num_hash_f, num_query, num_key, value_dim, weight_dim ); } } return cumulation_value; } at::Tensor lsh_weighted_cumulation_ver4_kernel( at::Tensor query_mask, at::Tensor query_hash_code, at::Tensor query_weight, at::Tensor key_mask, at::Tensor key_hash_code, at::Tensor key_weight, at::Tensor value, int hashtable_capacity, bool use_cuda ) { int batch_size = query_hash_code.size(0); int num_hash_f = query_hash_code.size(2); int num_query = query_hash_code.size(1); int num_key = key_hash_code.size(1); int value_dim = value.size(2); int weight_dim = query_weight.size(2); at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options()); at::Tensor query_sorted_idxes = at::zeros({batch_size, num_hash_f, num_query}, query_hash_code.options()); at::Tensor key_info = at::zeros({batch_size, num_key, 2, num_hash_f}, query_hash_code.options()); at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); if (use_cuda) { int *query_mask_ptr = query_mask.data_ptr<int>(); int *query_hash_code_ptr = query_hash_code.data_ptr<int>(); float *query_weight_ptr = query_weight.data_ptr<float>(); int *key_mask_ptr = key_mask.data_ptr<int>(); int *key_hash_code_ptr = key_hash_code.data_ptr<int>(); float *key_weight_ptr = key_weight.data_ptr<float>(); float *value_ptr = value.data_ptr<float>(); int *count_sort_table_ptr = count_sort_table.data_ptr<int>(); int *query_sorted_idxes_ptr = query_sorted_idxes.data_ptr<int>(); int *key_info_ptr = key_info.data_ptr<int>(); float *cumulation_value_ptr = cumulation_value.data_ptr<float>(); { dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); dim3 blocks_step13(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK)); dim3 blocks_step2(num_hash_f, batch_size); int shared_mem = hashtable_capacity * sizeof(float); count_sort_step1_cuda_kernel<<<blocks_step13, threads_step13>>>( query_mask_ptr, query_hash_code_ptr, count_sort_table_ptr, batch_size, num_hash_f, hashtable_capacity, num_query ); count_sort_step2_cuda_kernel<<<blocks_step2, threads_step2, shared_mem>>>( count_sort_table_ptr, batch_size, num_hash_f, hashtable_capacity ); count_sort_step3_cuda_kernel<<<blocks_step13, threads_step13>>>( query_mask_ptr, query_hash_code_ptr, count_sort_table_ptr, query_sorted_idxes_ptr, batch_size, num_hash_f, hashtable_capacity, num_query ); } { dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); dim3 blocks(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); extract_query_info_cuda_kernel<<<blocks, threads>>>( key_mask_ptr, key_hash_code_ptr, count_sort_table_ptr, key_info_ptr, batch_size, num_hash_f, hashtable_capacity, num_key ); } { dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE); dim3 blocks(num_key, batch_size); int shared_mem = (weight_dim + value_dim + 2 * num_hash_f) * sizeof(float); lsh_weighted_cumulation_ver4_step2_cuda_kernel<<<blocks, threads, shared_mem>>>( query_sorted_idxes_ptr, key_mask_ptr, key_info_ptr, query_weight_ptr, key_weight_ptr, value_ptr, cumulation_value_ptr, batch_size, num_hash_f, num_query, num_key, value_dim, weight_dim ); } } return cumulation_value; }

transformers/src/transformers/kernels/yoso/fast_lsh_cumulation.cu/0

{ "file_path": "transformers/src/transformers/kernels/yoso/fast_lsh_cumulation.cu", "repo_id": "transformers", "token_count": 8662 }

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# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_albert"] = ["AlbertTokenizer"] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_albert_fast"] = ["AlbertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_albert"] = [ "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "AlbertForMaskedLM", "AlbertForMultipleChoice", "AlbertForPreTraining", "AlbertForQuestionAnswering", "AlbertForSequenceClassification", "AlbertForTokenClassification", "AlbertModel", "AlbertPreTrainedModel", "load_tf_weights_in_albert", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_albert"] = [ "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAlbertForMaskedLM", "TFAlbertForMultipleChoice", "TFAlbertForPreTraining", "TFAlbertForQuestionAnswering", "TFAlbertForSequenceClassification", "TFAlbertForTokenClassification", "TFAlbertMainLayer", "TFAlbertModel", "TFAlbertPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_albert"] = [ "FlaxAlbertForMaskedLM", "FlaxAlbertForMultipleChoice", "FlaxAlbertForPreTraining", "FlaxAlbertForQuestionAnswering", "FlaxAlbertForSequenceClassification", "FlaxAlbertForTokenClassification", "FlaxAlbertModel", "FlaxAlbertPreTrainedModel", ] if TYPE_CHECKING: from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_albert import AlbertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_albert_fast import AlbertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_albert import ( ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, AlbertForMaskedLM, AlbertForMultipleChoice, AlbertForPreTraining, AlbertForQuestionAnswering, AlbertForSequenceClassification, AlbertForTokenClassification, AlbertModel, AlbertPreTrainedModel, load_tf_weights_in_albert, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_albert import ( TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFAlbertForMaskedLM, TFAlbertForMultipleChoice, TFAlbertForPreTraining, TFAlbertForQuestionAnswering, TFAlbertForSequenceClassification, TFAlbertForTokenClassification, TFAlbertMainLayer, TFAlbertModel, TFAlbertPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_albert import ( FlaxAlbertForMaskedLM, FlaxAlbertForMultipleChoice, FlaxAlbertForPreTraining, FlaxAlbertForQuestionAnswering, FlaxAlbertForSequenceClassification, FlaxAlbertForTokenClassification, FlaxAlbertModel, FlaxAlbertPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/albert/__init__.py/0

{ "file_path": "transformers/src/transformers/models/albert/__init__.py", "repo_id": "transformers", "token_count": 2259 }

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# coding=utf-8 # Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json from typing import List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_bart import BartTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} # See all BART models at https://huggingface.co/models?filter=bart PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json", "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json", "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json", "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json", "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json", "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json", }, "merges_file": { "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt", "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt", "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt", "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt", "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt", "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt", }, "tokenizer_file": { "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json", "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json", "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json", "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json", "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json", "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/bart-base": 1024, "facebook/bart-large": 1024, "facebook/bart-large-mnli": 1024, "facebook/bart-large-cnn": 1024, "facebook/bart-large-xsum": 1024, "yjernite/bart_eli5": 1024, } class BartTokenizerFast(PreTrainedTokenizerFast): r""" Construct a "fast" BART tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ```python >>> from transformers import BartTokenizerFast >>> tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base") >>> tokenizer("Hello world")["input_ids"] [0, 31414, 232, 2] >>> tokenizer(" Hello world")["input_ids"] [0, 20920, 232, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (BART tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = BartTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, trim_offsets=True, **kwargs, ): # we have to specify that this tokens is special otherwise adding it will reset the normalized flag to `False` in `add_special_tokens` mask_token = ( AddedToken(mask_token, lstrip=True, normalized=True, special=True) if isinstance(mask_token, str) else mask_token ) super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__` tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if state.get("trim_offsets", trim_offsets) != trim_offsets: state["trim_offsets"] = trim_offsets changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) @property def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. BART tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily comprise the space before the *<mask>*. """ if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @mask_token.setter def mask_token(self, value): """ Overriding the default behavior of the mask token to have it eat the space before it. This is needed to preserve backward compatibility with all the previously used models based on Bart. """ # Mask token behave like a normal word, i.e. include the space before it # So we set lstrip to True value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value self._mask_token = value def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words and not self.add_prefix_space: raise ValueError( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words and not self.add_prefix_space: raise ValueError( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] if token_ids_1 is None: return output return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. BART does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

transformers/src/transformers/models/bart/tokenization_bart_fast.py/0

{ "file_path": "transformers/src/transformers/models/bart/tokenization_bart_fast.py", "repo_id": "transformers", "token_count": 5760 }

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# coding=utf-8 # Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team. # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tokenization classes for BERTweet""" import html import os import re from shutil import copyfile from typing import List, Optional, Tuple import regex from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.txt", "merges_file": "bpe.codes", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "vinai/bertweet-base": "https://huggingface.co/vinai/bertweet-base/resolve/main/vocab.txt", }, "merges_file": { "vinai/bertweet-base": "https://huggingface.co/vinai/bertweet-base/resolve/main/bpe.codes", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "vinai/bertweet-base": 128, } def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char pairs = set(pairs) return pairs class BertweetTokenizer(PreTrainedTokenizer): """ Constructs a BERTweet tokenizer, using Byte-Pair-Encoding. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. normalization (`bool`, *optional*, defaults to `False`): Whether or not to apply a normalization preprocess. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, merges_file, normalization=False, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", **kwargs, ): try: from emoji import demojize self.demojizer = demojize except ImportError: logger.warning( "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3" " install emoji==0.6.0" ) self.demojizer = None self.vocab_file = vocab_file self.merges_file = merges_file self.encoder = {} self.encoder[str(bos_token)] = 0 self.encoder[str(pad_token)] = 1 self.encoder[str(eos_token)] = 2 self.encoder[str(unk_token)] = 3 self.add_from_file(vocab_file) self.decoder = {v: k for k, v in self.encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: merges = merges_handle.read().split("\n")[:-1] merges = [tuple(merge.split()[:-1]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} self.normalization = normalization self.tweetPreprocessor = TweetTokenizer() self.special_puncts = {"’": "'", "…": "..."} super().__init__( normalization=normalization, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, **kwargs, ) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BERTweet sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. BERTweet does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] @property def vocab_size(self): return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) word = tuple(list(word[:-1]) + [word[-1] + "</w>"]) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = "@@ ".join(word) word = word[:-4] self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" if self.normalization: # Perform Tweet normalization before performing BPE text = self.normalizeTweet(text) split_tokens = [] words = re.findall(r"\S+\n?", text) for token in words: split_tokens.extend(list(self.bpe(token).split(" "))) return split_tokens def normalizeTweet(self, tweet): """ Normalize a raw Tweet """ for punct in self.special_puncts: tweet = tweet.replace(punct, self.special_puncts[punct]) tokens = self.tweetPreprocessor.tokenize(tweet) normTweet = " ".join([self.normalizeToken(token) for token in tokens]) normTweet = ( normTweet.replace("cannot ", "can not ") .replace("n't ", " n't ") .replace("n 't ", " n't ") .replace("ca n't", "can't") .replace("ai n't", "ain't") ) normTweet = ( normTweet.replace("'m ", " 'm ") .replace("'re ", " 're ") .replace("'s ", " 's ") .replace("'ll ", " 'll ") .replace("'d ", " 'd ") .replace("'ve ", " 've ") ) normTweet = ( normTweet.replace(" p . m .", " p.m.") .replace(" p . m ", " p.m ") .replace(" a . m .", " a.m.") .replace(" a . m ", " a.m ") ) return " ".join(normTweet.split()) def normalizeToken(self, token): """ Normalize tokens in a Tweet """ lowercased_token = token.lower() if token.startswith("@"): return "@USER" elif lowercased_token.startswith("http") or lowercased_token.startswith("www"): return "HTTPURL" elif len(token) == 1: if token in self.special_puncts: return self.special_puncts[token] if self.demojizer is not None: return self.demojizer(token) else: return token else: return token def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" out_string = " ".join(tokens).replace("@@ ", "").strip() return out_string def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) out_merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) if os.path.abspath(self.merges_file) != os.path.abspath(out_merge_file): copyfile(self.merges_file, out_merge_file) return out_vocab_file, out_merge_file # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True): # filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)) # tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens) # tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far) # return ''.join(tokens_generated_so_far) def add_from_file(self, f): """ Loads a pre-existing dictionary from a text file and adds its symbols to this instance. """ if isinstance(f, str): try: with open(f, "r", encoding="utf-8") as fd: self.add_from_file(fd) except FileNotFoundError as fnfe: raise fnfe except UnicodeError: raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset") return lines = f.readlines() for lineTmp in lines: line = lineTmp.strip() idx = line.rfind(" ") if idx == -1: raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'") word = line[:idx] self.encoder[word] = len(self.encoder) # Natural Language Toolkit: Twitter Tokenizer # # Copyright (C) 2001-2020 NLTK Project # Author: Christopher Potts <[email protected]> # Ewan Klein <[email protected]> (modifications) # Pierpaolo Pantone <> (modifications) # URL: http://nltk.org/ # For license information, see LICENSE.TXT # """ Twitter-aware tokenizer, designed to be flexible and easy to adapt to new domains and tasks. The basic logic is this: 1. The tuple regex_strings defines a list of regular expression strings. 2. The regex_strings strings are put, in order, into a compiled regular expression object called word_re. 3. The tokenization is done by word_re.findall(s), where s is the user-supplied string, inside the tokenize() method of the class Tokenizer. 4. When instantiating Tokenizer objects, there is a single option: preserve_case. By default, it is set to True. If it is set to False, then the tokenizer will lowercase everything except for emoticons. """ ###################################################################### # # import regex # https://github.com/nltk/nltk/issues/2409 # import html # ###################################################################### # The following strings are components in the regular expression # that is used for tokenizing. It's important that phone_number # appears first in the final regex (since it can contain whitespace). # It also could matter that tags comes after emoticons, due to the # possibility of having text like # # <:| and some text >:) # # Most importantly, the final element should always be last, since it # does a last ditch whitespace-based tokenization of whatever is left. # ToDo: Update with http://en.wikipedia.org/wiki/List_of_emoticons ? # This particular element is used in a couple ways, so we define it # with a name: # docstyle-ignore EMOTICONS = r""" (?: [<>]? [:;=8] # eyes [\-o\*\']? # optional nose [\)\]$\[dDpP/\:\}\{@\|\\] # mouth | [$\]$\[dDpP/\:\}\{@\|\\] # mouth [\-o\*\']? # optional nose [:;=8] # eyes [<>]? | <3 # heart )""" # URL pattern due to John Gruber, modified by Tom Winzig. See # https://gist.github.com/winzig/8894715 # docstyle-ignore URLS = r""" # Capture 1: entire matched URL (?: https?: # URL protocol and colon (?: /{1,3} # 1-3 slashes | # or [a-z0-9%] # Single letter or digit or '%' # (Trying not to match e.g. "URI::Escape") ) | # or # looks like domain name followed by a slash: [a-z0-9.\-]+[.] (?:[a-z]{2,13}) / ) (?: # One or more: [^\s()<>{}\[\]]+ # Run of non-space, non-()<>{}[] | # or \([^\s()]*?\([^\s()]+$[^\s()]*?\) # balanced parens, one level deep: (...(...)...) | $[^\s]+?$ # balanced parens, non-recursive: (...) )+ (?: # End with: $[^\s()]*?\([^\s()]+$[^\s()]*?\) # balanced parens, one level deep: (...(...)...) | $[^\s]+?$ # balanced parens, non-recursive: (...) | # or [^\s`!()\[\]{};:'".,<>?«»“”‘’] # not a space or one of these punct chars ) | # OR, the following to match naked domains: (?: (?<!@) # not preceded by a @, avoid matching foo@_gmail.com_ [a-z0-9]+ (?:[.\-][a-z0-9]+)* [.] (?:[a-z]{2,13}) \b /? (?!@) # not succeeded by a @, # avoid matching "foo.na" in "[email protected]" ) """ # docstyle-ignore # The components of the tokenizer: REGEXPS = ( URLS, # Phone numbers: r""" (?: (?: # (international) \+?[01] [ *\-.\)]* )? (?: # (area code) [$]? \d{3} [ *\-.$]* )? \d{3} # exchange [ *\-.\)]* \d{4} # base )""", # ASCII Emoticons EMOTICONS, # HTML tags: r"""<[^>\s]+>""", # ASCII Arrows r"""[\-]+>|<[\-]+""", # Twitter username: r"""(?:@[\w_]+)""", # Twitter hashtags: r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)""", # email addresses r"""[\w.+-]+@[\w-]+\.(?:[\w-]\.?)+[\w-]""", # docstyle-ignore # Remaining word types: r""" (?:[^\W\d_](?:[^\W\d_]|['\-_])+[^\W\d_]) # Words with apostrophes or dashes. | (?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals. | (?:[\w_]+) # Words without apostrophes or dashes. | (?:\.(?:\s*\.){1,}) # Ellipsis dots. | (?:\S) # Everything else that isn't whitespace. """, ) ###################################################################### # This is the core tokenizing regex: WORD_RE = regex.compile(r"""(%s)""" % "|".join(REGEXPS), regex.VERBOSE | regex.I | regex.UNICODE) # WORD_RE performs poorly on these patterns: HANG_RE = regex.compile(r"([^a-zA-Z0-9])\1{3,}") # The emoticon string gets its own regex so that we can preserve case for # them as needed: EMOTICON_RE = regex.compile(EMOTICONS, regex.VERBOSE | regex.I | regex.UNICODE) # These are for regularizing HTML entities to Unicode: ENT_RE = regex.compile(r"&(#?(x?))([^&;\s]+);") ###################################################################### # Functions for converting html entities ###################################################################### def _str_to_unicode(text, encoding=None, errors="strict"): if encoding is None: encoding = "utf-8" if isinstance(text, bytes): return text.decode(encoding, errors) return text def _replace_html_entities(text, keep=(), remove_illegal=True, encoding="utf-8"): """ Remove entities from text by converting them to their corresponding unicode character. Args: text: A unicode string or a byte string encoded in the given *encoding* (which defaults to 'utf-8'). keep (list): List of entity names which should not be replaced. This supports both numeric entities (`&#nnnn;` and `&#hhhh;`) and named entities (such as ` ` or `>`). remove_illegal (bool): If `True`, entities that can't be converted are removed. Otherwise, entities that can't be converted are kept "as is". Returns: A unicode string with the entities removed. See https://github.com/scrapy/w3lib/blob/master/w3lib/html.py Examples: ```python >>> from nltk.tokenize.casual import _replace_html_entities >>> _replace_html_entities(b"Price: £100") 'Price: \\xa3100' >>> print(_replace_html_entities(b"Price: £100")) Price: £100 ```""" def _convert_entity(match): entity_body = match.group(3) if match.group(1): try: if match.group(2): number = int(entity_body, 16) else: number = int(entity_body, 10) # Numeric character references in the 80-9F range are typically # interpreted by browsers as representing the characters mapped # to bytes 80-9F in the Windows-1252 encoding. For more info # see: https://en.wikipedia.org/wiki/ISO/IEC_8859-1#Similar_character_sets if 0x80 <= number <= 0x9F: return bytes((number,)).decode("cp1252") except ValueError: number = None else: if entity_body in keep: return match.group(0) else: number = html.entities.name2codepoint.get(entity_body) if number is not None: try: return chr(number) except (ValueError, OverflowError): pass return "" if remove_illegal else match.group(0) return ENT_RE.sub(_convert_entity, _str_to_unicode(text, encoding)) ###################################################################### class TweetTokenizer: r""" Examples: ```python >>> # Tokenizer for tweets. >>> from nltk.tokenize import TweetTokenizer >>> tknzr = TweetTokenizer() >>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--" >>> tknzr.tokenize(s0) ['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--'] >>> # Examples using *strip_handles* and *reduce_len parameters*: >>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True) >>> s1 = "@remy: This is waaaaayyyy too much for you!!!!!!" >>> tknzr.tokenize(s1) [':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!'] ```""" def __init__(self, preserve_case=True, reduce_len=False, strip_handles=False): self.preserve_case = preserve_case self.reduce_len = reduce_len self.strip_handles = strip_handles def tokenize(self, text): """ Args: text: str Returns: list(str) A tokenized list of strings; concatenating this list returns the original string if `preserve_case=False` """ # Fix HTML character entities: text = _replace_html_entities(text) # Remove username handles if self.strip_handles: text = remove_handles(text) # Normalize word lengthening if self.reduce_len: text = reduce_lengthening(text) # Shorten problematic sequences of characters safe_text = HANG_RE.sub(r"\1\1\1", text) # Tokenize: words = WORD_RE.findall(safe_text) # Possibly alter the case, but avoid changing emoticons like :D into :d: if not self.preserve_case: words = [x if EMOTICON_RE.search(x) else x.lower() for x in words] return words ###################################################################### # Normalization Functions ###################################################################### def reduce_lengthening(text): """ Replace repeated character sequences of length 3 or greater with sequences of length 3. """ pattern = regex.compile(r"(.)\1{2,}") return pattern.sub(r"\1\1\1", text) def remove_handles(text): """ Remove Twitter username handles from text. """ pattern = regex.compile( r"(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){20}(?!@))|(?<![A-Za-z0-9_!@#\$%&*])@(([A-Za-z0-9_]){1,19})(?![A-Za-z0-9_]*@)" ) # Substitute handles with ' ' to ensure that text on either side of removed handles are tokenized correctly return pattern.sub(" ", text) ###################################################################### # Tokenization Function ###################################################################### def casual_tokenize(text, preserve_case=True, reduce_len=False, strip_handles=False): """ Convenience function for wrapping the tokenizer. """ return TweetTokenizer(preserve_case=preserve_case, reduce_len=reduce_len, strip_handles=strip_handles).tokenize( text ) ###############################################################################

transformers/src/transformers/models/bertweet/tokenization_bertweet.py/0

{ "file_path": "transformers/src/transformers/models/bertweet/tokenization_bertweet.py", "repo_id": "transformers", "token_count": 12290 }

308

# coding=utf-8 # Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for BioGPT.""" import json import os from typing import List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json", }, "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"}, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/biogpt": 1024, } def get_pairs(word): """ Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class BioGptTokenizer(PreTrainedTokenizer): """ Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Merges file. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, merges_file, unk_token="<unk>", bos_token="<s>", eos_token="</s>", sep_token="</s>", pad_token="<pad>", **kwargs, ): try: import sacremoses except ImportError: raise ImportError( "You need to install sacremoses to use BioGptTokenizer. " "See https://pypi.org/project/sacremoses/ for installation." ) self.lang = "en" self.sm = sacremoses # cache of sm.MosesTokenizer instance self.cache_moses_tokenizer = {} self.cache_moses_detokenizer = {} """ Initialisation""" with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: merges = merges_handle.read().split("\n")[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} super().__init__( bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, unk_token=unk_token, pad_token=pad_token, **kwargs, ) @property def vocab_size(self): """Returns vocab size""" return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def moses_tokenize(self, text, lang): if lang not in self.cache_moses_tokenizer: moses_tokenizer = self.sm.MosesTokenizer(lang=lang) self.cache_moses_tokenizer[lang] = moses_tokenizer return self.cache_moses_tokenizer[lang].tokenize( text, aggressive_dash_splits=True, return_str=False, escape=True ) def moses_detokenize(self, tokens, lang): if lang not in self.cache_moses_detokenizer: moses_detokenizer = self.sm.MosesDetokenizer(lang=lang) self.cache_moses_detokenizer[lang] = moses_detokenizer return self.cache_moses_detokenizer[lang].detokenize(tokens) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + "</w>",) if token in self.cache: return self.cache[token] pairs = get_pairs(word) if not pairs: return token + "</w>" while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) if word == "\n </w>": word = "\n</w>" self.cache[token] = word return word def _tokenize(self, text, bypass_tokenizer=False): """Returns a tokenized string.""" if bypass_tokenizer: text = text.split() else: text = self.moses_tokenize(text, self.lang) split_tokens = [] for token in text: if token: split_tokens.extend(list(self.bpe(token).split(" "))) return split_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" # remove BPE tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens] tokens = "".join(tokens).split() # detokenize text = self.moses_detokenize(tokens, self.lang) return text def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A BioGPT sequence has the following format: - single sequence: `</s> X ` - pair of sequences: `</s> A </s> B ` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.sep_token_id] + token_ids_0 sep = [self.sep_token_id] return sep + token_ids_0 + sep + token_ids_1 def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) # no bos used in fairseq if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) return [1] + ([0] * len(token_ids_0)) def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A FAIRSEQ Transformer sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] # no bos used in fairseq if token_ids_1 is None: return len(token_ids_0 + sep) * [0] return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 with open(merge_file, "w", encoding="utf-8") as writer: for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file def __getstate__(self): state = self.__dict__.copy() state["sm"] = None return state def __setstate__(self, d): self.__dict__ = d try: import sacremoses except ImportError: raise ImportError( "You need to install sacremoses to use XLMTokenizer. " "See https://pypi.org/project/sacremoses/ for installation." ) self.sm = sacremoses

transformers/src/transformers/models/biogpt/tokenization_biogpt.py/0

{ "file_path": "transformers/src/transformers/models/biogpt/tokenization_biogpt.py", "repo_id": "transformers", "token_count": 6244 }

309

# coding=utf-8 # Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch BlenderbotSmall model.""" import copy import math from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_blenderbot_small import BlenderbotSmallConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "BlenderbotSmallConfig" BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/blenderbot_small-90M", # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small ] # Copied from transformers.models.bart.modeling_bart.shift_tokens_right def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): """ Shift input ids one token to the right. """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() shifted_input_ids[:, 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids # Copied from transformers.models.blenderbot.modeling_blenderbot.BlenderbotLearnedPositionalEmbedding with Blenderbot->BlenderbotSmall class BlenderbotSmallLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): super().__init__(num_embeddings, embedding_dim) def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0): """`input_ids_shape` is expected to be [bsz x seqlen].""" bsz, seq_len = input_ids_shape[:2] positions = torch.arange( past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device ) return super().forward(positions) # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BlenderbotSmall class BlenderbotSmallAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[BlenderbotSmallConfig] = None, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads self.config = config if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.is_causal = is_causal self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value # Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->BlenderbotSmall, BART->BLENDERBOT_SMALL class BlenderbotSmallEncoderLayer(nn.Module): def __init__(self, config: BlenderbotSmallConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation]( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, config=config, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) if hidden_states.dtype == torch.float16 and ( torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() ): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # TODO: Implement attention with SDPA for TimeSeriesTransformer. BLENDERBOT_SMALL_ATTENTION_CLASSES = { "eager": BlenderbotSmallAttention, } # Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->BlenderbotSmall, BART->BLENDERBOT_SMALL class BlenderbotSmallDecoderLayer(nn.Module): def __init__(self, config: BlenderbotSmallConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation]( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, is_causal=True, config=config, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation]( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, config=config, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_present_key_value = None cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # add cross-attn to positions 3,4 of present_key_value tuple present_key_value = present_key_value + cross_attn_present_key_value # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) if use_cache: outputs += (present_key_value,) return outputs class BlenderbotSmallPreTrainedModel(PreTrainedModel): config_class = BlenderbotSmallConfig base_model_prefix = "model" supports_gradient_checkpointing = True def _init_weights(self, module): std = self.config.init_std if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() @property def dummy_inputs(self): pad_token = self.config.pad_token_id input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) dummy_inputs = { "attention_mask": input_ids.ne(pad_token), "input_ids": input_ids, "decoder_input_ids": input_ids, } return dummy_inputs BLENDERBOT_SMALL_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`BlenderbotSmallConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ BLENDERBOT_SMALL_GENERATION_EXAMPLE = r""" Conversation example: ```python >>> from transformers import AutoTokenizer, BlenderbotSmallForConditionalGeneration >>> mname = "facebook/blenderbot_small-90M" >>> model = BlenderbotSmallForConditionalGeneration.from_pretrained(mname) >>> tokenizer = AutoTokenizer.from_pretrained(mname) >>> UTTERANCE = "My friends are cool but they eat too many carbs." >>> print("Human: ", UTTERANCE) Human: My friends are cool but they eat too many carbs. >>> inputs = tokenizer([UTTERANCE], return_tensors="pt") >>> reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]) Bot: what kind of carbs do they eat? i don't know much about carbs. >>> REPLY = "I'm not sure" >>> print("Human: ", REPLY) Human: I'm not sure >>> NEXT_UTTERANCE = ( ... "My friends are cool but they eat too many carbs.__end__ __start__what kind of carbs do they eat? " ... "i don't know much about carbs__end__ " ... "__start__ I'm not sure." ... ) >>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt") >>> next_reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0]) Bot: they eat a lot of carbs. carbs are high in fat, protein, and fats. ``` """ BLENDERBOT_SMALL_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) BlenderbotSmall uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class BlenderbotSmallEncoder(BlenderbotSmallPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`BlenderbotSmallEncoderLayer`]. Args: config: BlenderbotSmallConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config: BlenderbotSmallConfig, embed_tokens: Optional[nn.Embedding] = None): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop embed_dim = config.d_model self.padding_idx = config.pad_token_id self.max_source_positions = config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList([BlenderbotSmallEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm_embedding = nn.LayerNorm(embed_dim) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(input_shape) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None # check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.size()[0] != len(self.layers): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) to_drop = False if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: # skip the layer to_drop = True if to_drop: layer_outputs = (None, None) else: if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotSmallDecoderLayer`] Args: config: BlenderbotSmallConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config: BlenderbotSmallConfig, embed_tokens: Optional[nn.Embedding] = None): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 if embed_tokens is not None: self.embed_tokens = embed_tokens else: self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, ) self.layers = nn.ModuleList([BlenderbotSmallDecoderLayer(config) for _ in range(config.decoder_layers)]) self.layernorm_embedding = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale attention_mask = _prepare_4d_causal_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _prepare_4d_attention_mask( encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] ) # embed positions positions = self.embed_positions(input_shape, past_key_values_length) # BlenderbotSmall applies layer norm on hidden_states inputs_embeds = self.layernorm_embedding(inputs_embeds) hidden_states = inputs_embeds + positions hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None next_decoder_cache = () if use_cache else None # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != len(self.layers): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, head_mask[idx] if head_mask is not None else None, cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, None, output_attentions, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), cross_attn_layer_head_mask=( cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None ), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( "The bare BlenderbotSmall Model outputting raw hidden-states without any specific head on top.", BLENDERBOT_SMALL_START_DOCSTRING, ) class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel): _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"] def __init__(self, config: BlenderbotSmallConfig): super().__init__(config) padding_idx, vocab_size = config.pad_token_id, config.vocab_size self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) self.encoder = BlenderbotSmallEncoder(config, self.shared) self.decoder = BlenderbotSmallDecoder(config, self.shared) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, value): self.shared = value self.encoder.embed_tokens = self.shared self.decoder.embed_tokens = self.shared def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]: r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, BlenderbotSmallModel >>> model = BlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt") >>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt") # Batch size 1 >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 3, 512] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The BlenderbotSmall Model with a language modeling head. Can be used for summarization.", BLENDERBOT_SMALL_START_DOCSTRING, ) class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel): base_model_prefix = "model" _keys_to_ignore_on_load_missing = ["final_logits_bias"] _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: BlenderbotSmallConfig): super().__init__(config) self.model = BlenderbotSmallModel(config) self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding: new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of) self._resize_final_logits_bias(new_embeddings.weight.shape[0]) return new_embeddings def _resize_final_logits_bias(self, new_num_tokens: int) -> None: old_num_tokens = self.final_logits_bias.shape[-1] if new_num_tokens <= old_num_tokens: new_bias = self.final_logits_bias[:, :new_num_tokens] else: extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) self.register_buffer("final_logits_bias", new_bias) def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings @add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: if use_cache: logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") use_cache = False if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (lm_logits,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return Seq2SeqLMOutput( loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) def prepare_inputs_for_generation( self, decoder_input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs, ): # cut decoder_input_ids if past is used if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if decoder_input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = decoder_input_ids.shape[1] - 1 decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] return { "input_ids": None, # encoder_outputs is defined. input_ids not needed "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, # change this to avoid caching (presumably for debugging) } @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: # cached cross_attention states don't have to be reordered -> they are always the same reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past # Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->BlenderbotSmall class BlenderbotSmallDecoderWrapper(BlenderbotSmallPreTrainedModel): """ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the [`EncoderDecoderModel`] framework. """ def __init__(self, config): super().__init__(config) self.decoder = BlenderbotSmallDecoder(config) def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) # Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->BlenderbotSmall, facebook/bart-base->facebook/blenderbot_small-90M class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): config = copy.deepcopy(config) config.is_decoder = True config.is_encoder_decoder = False super().__init__(config) self.model = BlenderbotSmallDecoderWrapper(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.decoder.embed_tokens def set_input_embeddings(self, value): self.model.decoder.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model.decoder = decoder def get_decoder(self): return self.model.decoder @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: ```python >>> from transformers import AutoTokenizer, BlenderbotSmallForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") >>> model = BlenderbotSmallForCausalLM.from_pretrained("facebook/blenderbot_small-90M", add_cross_attention=False) >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size] >>> list(logits.shape) == expected_shape True ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.decoder( input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = self.lm_head(outputs[0]) loss = None if labels is not None: labels = labels.to(logits.device) loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs ): # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_ids.shape) if past_key_values: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] # first step, decoder_cached_states are empty return { "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": use_cache, } @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past

transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py/0

{ "file_path": "transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py", "repo_id": "transformers", "token_count": 32333 }

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert BLIP-2 checkpoints from the original repository. URL: https://github.com/salesforce/LAVIS/tree/main/projects/blip2 """ import argparse import requests import torch # pip3 install salesforce-lavis # I'm actually installing a slightly modified version: pip3 install -U git+https://github.com/nielsrogge/LAVIS.git@blip2_float32 # to make sure we can compare both original and HF implementation in float32 from lavis.models import load_model_and_preprocess from PIL import Image from transformers import ( AutoTokenizer, Blip2Config, Blip2ForConditionalGeneration, Blip2Processor, Blip2VisionConfig, BlipImageProcessor, OPTConfig, T5Config, set_seed, ) from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD def load_demo_image(): url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") return image # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config): rename_keys = [] # fmt: off # vision encoder rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")) rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")) rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")) rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")) rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")) rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")) for i in range(config.vision_config.num_hidden_layers): rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",)) rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias")) # QFormer rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")) rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")) # fmt: on return rename_keys def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val def read_in_q_v_bias(state_dict, config): for i in range(config.vision_config.num_hidden_layers): # read in original q and v biases q_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias") v_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias") # next, set bias in the state dict qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias)) state_dict[f"vision_model.encoder.layers.{i}.self_attn.qkv.bias"] = qkv_bias def get_blip2_config(model_name, eos_token_id): image_size = 364 if "coco" in model_name else 224 vision_config = Blip2VisionConfig(image_size=image_size).to_dict() # make sure the models have proper bos_token_id and eos_token_id set (important for generation) # seems like flan-T5 models don't have bos_token_id properly set? if "opt-2.7b" in model_name: text_config = OPTConfig.from_pretrained("facebook/opt-2.7b", eos_token_id=eos_token_id).to_dict() elif "opt-6.7b" in model_name: text_config = OPTConfig.from_pretrained("facebook/opt-6.7b", eos_token_id=eos_token_id).to_dict() elif "t5-xl" in model_name: text_config = T5Config.from_pretrained("google/flan-t5-xl", dense_act_fn="gelu", bos_token_id=1).to_dict() elif "t5-xxl" in model_name: text_config = T5Config.from_pretrained("google/flan-t5-xxl", dense_act_fn="gelu", bos_token_id=1).to_dict() config = Blip2Config(vision_config=vision_config, text_config=text_config) return config, image_size @torch.no_grad() def convert_blip2_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False): """ Copy/paste/tweak model's weights to Transformers design. """ tokenizer = ( AutoTokenizer.from_pretrained("facebook/opt-2.7b") if "opt" in model_name else AutoTokenizer.from_pretrained("google/flan-t5-xl") ) eos_token_id = tokenizer("\n", add_special_tokens=False).input_ids[0] config, image_size = get_blip2_config(model_name, eos_token_id=eos_token_id) hf_model = Blip2ForConditionalGeneration(config).eval() model_name_to_original = { "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"), "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"), "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"), "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"), "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"), "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"), "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"), } name, type = model_name_to_original[model_name] # note: this script is tested on 2 GPUs, as models are compared in float32, # which requires quite some memory. Hence loading both on a # separate device is the easiest to compare hf_model_device = "cuda:0" if torch.cuda.is_available() else "cpu" lavis_device = "cuda:1" if torch.cuda.is_available() else "cpu" # load original model print("Loading original model...") original_model, vis_processors, _ = load_model_and_preprocess( name=name, model_type=type, is_eval=True, device=lavis_device ) original_model.eval() print("Done!") # update state dict keys state_dict = original_model.state_dict() rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) # some keys can be renamed efficiently for key, val in state_dict.copy().items(): val = state_dict.pop(key) if key.startswith("Qformer.bert"): key = key.replace("Qformer.bert", "qformer") if "attention.self" in key: key = key.replace("self", "attention") if "opt_proj" in key: key = key.replace("opt_proj", "language_projection") if "t5_proj" in key: key = key.replace("t5_proj", "language_projection") if key.startswith("opt"): key = key.replace("opt", "language") if key.startswith("t5"): key = key.replace("t5", "language") state_dict[key] = val # read in qv biases read_in_q_v_bias(state_dict, config) missing_keys, unexpected_keys = hf_model.load_state_dict(state_dict, strict=False) assert len(missing_keys) == 0 assert unexpected_keys == ["qformer.embeddings.position_ids"] image = load_demo_image() original_pixel_values = vis_processors["eval"](image).unsqueeze(0).to(lavis_device) input_ids = tokenizer(["\n"], return_tensors="pt").input_ids.to(hf_model_device) # create processor image_processor = BlipImageProcessor( size={"height": image_size, "width": image_size}, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD ) processor = Blip2Processor(image_processor=image_processor, tokenizer=tokenizer) pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(hf_model_device) # make sure processor creates exact same pixel values assert torch.allclose(pixel_values, original_pixel_values.to(pixel_values.device)) original_model.to(lavis_device) hf_model.to(hf_model_device) with torch.no_grad(): if "opt" in model_name: original_logits = original_model({"image": original_pixel_values, "text_input": [""]}).logits logits = hf_model(pixel_values, input_ids).logits else: original_logits = original_model( {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits labels = input_ids.masked_fill(input_ids == tokenizer.pad_token_id, -100) logits = hf_model(pixel_values, input_ids, labels=labels).logits assert original_logits.shape == logits.shape print("First values of original logits:", original_logits[0, :3, :3]) print("First values of HF logits:", logits[0, :3, :3]) # assert values assert torch.allclose(original_logits.to(logits.device), logits, atol=1e-4) print("Looks ok!") print("Generating a caption...") prompt = "Question: what object is in this image? Answer:" input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(hf_model_device) set_seed(42) original_outputs = original_model.generate( {"image": original_pixel_values, "prompt": prompt}, use_nucleus_sampling=True ) outputs = hf_model.generate( pixel_values, input_ids, do_sample=True, num_beams=5, max_length=30, min_length=1, top_p=0.9, repetition_penalty=1.0, length_penalty=1.0, temperature=1, ) output_text = processor.batch_decode(outputs, skip_special_tokens=True) output_text = [text.strip() for text in output_text] print("Original generation:", original_outputs) print("HF generation:", output_text) if pytorch_dump_folder_path is not None: processor.save_pretrained(pytorch_dump_folder_path) hf_model.save_pretrained(pytorch_dump_folder_path) if push_to_hub: processor.push_to_hub(f"nielsr/{model_name}") hf_model.push_to_hub(f"nielsr/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() choices = [ "blip2-opt-2.7b", "blip2-opt-6.7b", "blip2-opt-2.7b-coco", "blip2-opt-6.7b-coco", "blip2-flan-t5-xl", "blip2-flan-t5-xl-coco", "blip2-flan-t5-xxl", ] parser.add_argument( "--model_name", default="blip2-opt-2.7b", choices=choices, type=str, help="Path to hf config.json of model to convert", ) parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument( "--push_to_hub", action="store_true", help="Whether to push the model and processor to the hub after converting", ) args = parser.parse_args() convert_blip2_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/blip_2/convert_blip_2_original_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/blip_2/convert_blip_2_original_to_pytorch.py", "repo_id": "transformers", "token_count": 5162 }

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Bros checkpoints.""" import argparse import bros # original repo import torch from transformers import BrosConfig, BrosModel, BrosProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_configs(model_name): bros_config = BrosConfig.from_pretrained(model_name) return bros_config def remove_ignore_keys_(state_dict): ignore_keys = [ "embeddings.bbox_sinusoid_emb.inv_freq", ] for k in ignore_keys: state_dict.pop(k, None) def rename_key(name): if name == "embeddings.bbox_projection.weight": name = "bbox_embeddings.bbox_projection.weight" if name == "embeddings.bbox_sinusoid_emb.x_pos_emb.inv_freq": name = "bbox_embeddings.bbox_sinusoid_emb.x_pos_emb.inv_freq" if name == "embeddings.bbox_sinusoid_emb.y_pos_emb.inv_freq": name = "bbox_embeddings.bbox_sinusoid_emb.y_pos_emb.inv_freq" return name def convert_state_dict(orig_state_dict, model): # rename keys for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) orig_state_dict[rename_key(key)] = val # remove ignore keys remove_ignore_keys_(orig_state_dict) return orig_state_dict def convert_bros_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False): # load original model original_model = bros.BrosModel.from_pretrained(model_name).eval() # load HuggingFace Model bros_config = get_configs(model_name) model = BrosModel.from_pretrained(model_name, config=bros_config) model.eval() state_dict = original_model.state_dict() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) # verify results # original BROS model require 4 points (8 float values) for each bbox, prepare bbox with [batch_size, seq_len, 8] shape bbox = torch.tensor( [ [ [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000], [0.4396, 0.6720, 0.4659, 0.6720, 0.4659, 0.6850, 0.4396, 0.6850], [0.4698, 0.6720, 0.4843, 0.6720, 0.4843, 0.6850, 0.4698, 0.6850], [0.4698, 0.6720, 0.4843, 0.6720, 0.4843, 0.6850, 0.4698, 0.6850], [0.2047, 0.6870, 0.2730, 0.6870, 0.2730, 0.7000, 0.2047, 0.7000], [0.2047, 0.6870, 0.2730, 0.6870, 0.2730, 0.7000, 0.2047, 0.7000], [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000], ] ] ) processor = BrosProcessor.from_pretrained(model_name) encoding = processor("His name is Rocco.", return_tensors="pt") encoding["bbox"] = bbox original_hidden_states = original_model(**encoding).last_hidden_state # pixel_values = processor(image, return_tensors="pt").pixel_values last_hidden_states = model(**encoding).last_hidden_state assert torch.allclose(original_hidden_states, last_hidden_states, atol=1e-4) if pytorch_dump_folder_path is not None: print(f"Saving model and processor to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: model.push_to_hub("jinho8345/" + model_name.split("/")[-1], commit_message="Update model") processor.push_to_hub("jinho8345/" + model_name.split("/")[-1], commit_message="Update model") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="jinho8345/bros-base-uncased", required=False, type=str, help="Name of the original model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, required=False, type=str, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model and processor to the 🤗 hub.", ) args = parser.parse_args() convert_bros_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/bros/convert_bros_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/bros/convert_bros_to_pytorch.py", "repo_id": "transformers", "token_count": 2040 }

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# coding=utf-8 # Copyright Google AI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for CANINE.""" from typing import Dict, List, Optional from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "nielsr/canine-s": 2048, } # Unicode defines 1,114,112 total “codepoints” UNICODE_VOCAB_SIZE = 1114112 # Below: Constants defining canonical codepoints for special, pseudo-characters. # Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py PAD = 0 CLS = 0xE000 SEP = 0xE001 BOS = 0xE002 MASK = 0xE003 RESERVED = 0xE004 # Maps special codepoints to human-readable names. SPECIAL_CODEPOINTS: Dict[int, str] = { # Special symbols are represented using codepoints values that are valid, # but designated as "Private Use", meaning that they will never be assigned # characters by the Unicode Consortium, and are thus safe for use here. # # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly # excluded and should fail with a hard error. CLS: "[CLS]", SEP: "[SEP]", BOS: "[BOS]", MASK: "[MASK]", PAD: "[PAD]", RESERVED: "[RESERVED]", } # Maps special codepoint human-readable names to their codepoint values. SPECIAL_CODEPOINTS_BY_NAME: Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()} class CanineTokenizer(PreTrainedTokenizer): r""" Construct a CANINE tokenizer (i.e. a character splitter). It turns text into a sequence of characters, and then converts each character into its Unicode code point. [`CanineTokenizer`] inherits from [`PreTrainedTokenizer`]. Refer to superclass [`PreTrainedTokenizer`] for usage examples and documentation concerning parameters. Args: model_max_length (`int`, *optional*, defaults to 2048): The maximum sentence length the model accepts. """ max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, bos_token=chr(CLS), eos_token=chr(SEP), sep_token=chr(SEP), cls_token=chr(CLS), pad_token=chr(PAD), mask_token=chr(MASK), add_prefix_space=False, model_max_length=2048, **kwargs, ): bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token # Creates a mapping for looking up the IDs of special symbols. self._special_codepoints: Dict[str, int] = {} for codepoint, name in SPECIAL_CODEPOINTS.items(): self._special_codepoints[name] = codepoint # Creates a mapping for looking up the string forms of special symbol IDs. self._special_codepoint_strings: Dict[int, str] = { codepoint: name for name, codepoint in self._special_codepoints.items() } self._unicode_vocab_size = UNICODE_VOCAB_SIZE self._num_special_tokens = len(self._special_codepoints) super().__init__( bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, model_max_length=model_max_length, **kwargs, ) @property def vocab_size(self) -> int: return self._unicode_vocab_size def get_vocab(self): vocab = {chr(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text: str) -> List[str]: """Tokenize a string (i.e. perform character splitting).""" return list(text) def _convert_token_to_id(self, token: str) -> int: """Converts a token (i.e. a Unicode character) in an id (i.e. its integer Unicode code point value).""" try: return ord(token) except TypeError: raise ValueError(f"invalid token: '{token}'") def _convert_id_to_token(self, index: int) -> str: """ Converts a Unicode code point (integer) in a token (str). In case it's a special code point, convert to human-readable format. """ try: if index in SPECIAL_CODEPOINTS: return SPECIAL_CODEPOINTS[index] return chr(index) except TypeError: raise ValueError(f"invalid id: {index}") def convert_tokens_to_string(self, tokens): return "".join(tokens) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A CANINE sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ sep = [self.sep_token_id] cls = [self.cls_token_id] result = cls + token_ids_0 + sep if token_ids_1 is not None: result += token_ids_1 + sep return result def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) result = [1] + ([0] * len(token_ids_0)) + [1] if token_ids_1 is not None: result += ([0] * len(token_ids_1)) + [1] return result def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A CANINE sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] result = len(cls + token_ids_0 + sep) * [0] if token_ids_1 is not None: result += len(token_ids_1 + sep) * [1] return result # CanineTokenizer has no vocab file def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None): return ()

transformers/src/transformers/models/canine/tokenization_canine.py/0

{ "file_path": "transformers/src/transformers/models/canine/tokenization_canine.py", "repo_id": "transformers", "token_count": 3965 }

313

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import torch from clip import load from transformers import CLIPConfig, CLIPModel def copy_attn_layer(hf_attn_layer, pt_attn_layer): q_proj, k_proj, v_proj = pt_attn_layer.in_proj_weight.chunk(3, dim=0) q_proj_bias, k_proj_bias, v_proj_bias = pt_attn_layer.in_proj_bias.chunk(3, dim=0) out_proj_weights = pt_attn_layer.out_proj.weight out_proj_bias = pt_attn_layer.out_proj.bias hf_attn_layer.q_proj.weight.data = q_proj hf_attn_layer.q_proj.bias.data = q_proj_bias hf_attn_layer.k_proj.weight.data = k_proj hf_attn_layer.k_proj.bias.data = k_proj_bias hf_attn_layer.v_proj.weight.data = v_proj hf_attn_layer.v_proj.bias.data = v_proj_bias hf_attn_layer.out_proj.weight = out_proj_weights hf_attn_layer.out_proj.bias = out_proj_bias def copy_mlp(hf_mlp, pt_mlp): copy_linear(hf_mlp.fc1, pt_mlp.c_fc) copy_linear(hf_mlp.fc2, pt_mlp.c_proj) def copy_linear(hf_linear, pt_linear): hf_linear.weight = pt_linear.weight hf_linear.bias = pt_linear.bias def copy_layer(hf_layer, pt_layer): # copy layer norms copy_linear(hf_layer.layer_norm1, pt_layer.ln_1) copy_linear(hf_layer.layer_norm2, pt_layer.ln_2) # copy MLP copy_mlp(hf_layer.mlp, pt_layer.mlp) # copy attn copy_attn_layer(hf_layer.self_attn, pt_layer.attn) def copy_layers(hf_layers, pt_layers): for hf_layer, pt_layer in zip(hf_layers, pt_layers): copy_layer(hf_layer, pt_layer) def copy_encoder(hf_encoder, pt_model): # copy embeds hf_encoder.embeddings.token_embedding.weight = pt_model.token_embedding.weight hf_encoder.embeddings.position_embedding.weight.data = pt_model.positional_embedding # copy layer norm copy_linear(hf_encoder.final_layer_norm, pt_model.ln_final) # copy hidden layers copy_layers(hf_encoder.encoder.layers, pt_model.transformer.resblocks) def copy_text_model_and_projection(hf_model, pt_model): # copy projection hf_model.text_projection.weight.data = pt_model.text_projection.data.T # copy text encoder copy_encoder(hf_model.text_model, pt_model) def copy_vison_model_and_projection(hf_model, pt_model): # copy projection hf_model.visual_projection.weight.data = pt_model.visual.proj.data.T # copy layer norms copy_linear(hf_model.vision_model.pre_layrnorm, pt_model.visual.ln_pre) copy_linear(hf_model.vision_model.post_layernorm, pt_model.visual.ln_post) # copy embeds hf_model.vision_model.embeddings.patch_embedding.weight.data = pt_model.visual.conv1.weight.data hf_model.vision_model.embeddings.class_embedding = pt_model.visual.class_embedding hf_model.vision_model.embeddings.position_embedding.weight.data = pt_model.visual.positional_embedding.data # copy encoder copy_layers(hf_model.vision_model.encoder.layers, pt_model.visual.transformer.resblocks) @torch.no_grad() def convert_clip_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None): """ Copy/paste/tweak model's weights to transformers design. """ if config_path is not None: config = CLIPConfig.from_pretrained(config_path) else: config = CLIPConfig(projection_dim=512, text_config={}, vision_config={}) hf_model = CLIPModel(config).eval() pt_model, _ = load(checkpoint_path, device="cpu", jit=False) pt_model = pt_model.eval() copy_text_model_and_projection(hf_model, pt_model) copy_vison_model_and_projection(hf_model, pt_model) hf_model.logit_scale = pt_model.logit_scale input_ids = torch.arange(0, 77).unsqueeze(0) pixel_values = torch.randn(1, 3, 224, 224) hf_outputs = hf_model(input_ids=input_ids, pixel_values=pixel_values, return_dict=True) hf_logits_per_image = hf_outputs.logits_per_image hf_logits_per_text = hf_outputs.logits_per_text pt_logits_per_image, pt_logits_per_text = pt_model(pixel_values, input_ids) assert torch.allclose(hf_logits_per_image, pt_logits_per_image, atol=1e-3) assert torch.allclose(hf_logits_per_text, pt_logits_per_text, atol=1e-3) hf_model.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") args = parser.parse_args() convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

transformers/src/transformers/models/clip/convert_clip_original_pytorch_to_hf.py/0

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314

# coding=utf-8 # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Weights conversion script for CLVP """ import argparse import os import torch from huggingface_hub import hf_hub_download from transformers import ClvpConfig, ClvpModelForConditionalGeneration _MODELS = { "clvp": "https://huggingface.co/jbetker/tortoise-tts-v2/blob/main/.models/clvp2.pth", "decoder": "https://huggingface.co/jbetker/tortoise-tts-v2/blob/main/.models/autoregressive.pth", } dim = 1024 sub_dim = dim // 16 CLVP_ENCODERS_MAPPING = { "text_transformer.transformer.attn_layers": "text_encoder_model", "speech_transformer.transformer.attn_layers": "speech_encoder_model", "text_transformer.transformer.norm": "text_encoder_model.final_layer_norm", "speech_transformer.transformer.norm": "speech_encoder_model.final_layer_norm", "to_text_latent": "text_encoder_model.projection", "to_speech_latent": "speech_encoder_model.projection", "text_emb": "text_encoder_model.token_embedding", "speech_emb": "speech_encoder_model.token_embedding", "1.wrap.net.0": "mlp.fc1", "1.wrap.net.3": "mlp.fc2", "1.wrap": "self_attn", "to_out": "out_proj", "to_q": "q_proj", "to_k": "k_proj", "to_v": "v_proj", "temperature": "logit_scale", } CLVP_DECODER_MAPPING = { "conditioning_encoder.init": "conditioning_encoder.mel_conv", "conditioning_encoder.attn": "conditioning_encoder.mel_attn_blocks", "mel_attn_blocks": "group_norms", ".norm.weight": ".weight", ".norm.bias": ".bias", "text_embedding": "conditioning_encoder.text_token_embedding", "text_pos_embedding.emb": "conditioning_encoder.text_position_embedding", "final_norm": "speech_decoder_model.final_norm", "mel_head": "speech_decoder_model.lm_head", "gpt.ln_f": "speech_decoder_model.model.decoder.layer_norm", "mel_embedding": "speech_decoder_model.model.decoder.input_embeds_layer", "mel_pos_embedding.emb": "speech_decoder_model.model.decoder.position_embeds_layer", "gpt.h": "speech_decoder_model.model.decoder.layers", "ln_1": "input_layernorm", "ln_2": "post_attention_layernorm", } def update_index(present_index): if present_index % 2 == 0: return int(present_index / 2) else: return int((present_index - 1) / 2) def convert_encoder_weights(original_weights): converted_weights = {} original_weights_keys = sorted(original_weights.keys()) for original_key in original_weights_keys: updated_key = original_key # for input_rmsnorm.weight and post_attention_rmsnorm.weight if "0.0.g" in updated_key: present_index = updated_key.split(".")[4] if int(present_index) % 2 == 0: updated_key = updated_key.replace("0.0.g", "input_rmsnorm.weight") else: updated_key = updated_key.replace("0.0.g", "post_attention_rmsnorm.weight") if "transformer.attn_layers.layers" in updated_key: present_index = updated_key.split(".")[4] updated_index = update_index(int(present_index)) updated_key = updated_key.replace( f"transformer.attn_layers.layers.{present_index}", f"transformer.attn_layers.layers.{updated_index}" ) for k, v in CLVP_ENCODERS_MAPPING.items(): if k in updated_key: updated_key = updated_key.replace(k, v) converted_weights[updated_key] = original_weights.pop(original_key) return converted_weights def convert_decoder_weights(original_weights): converted_weights = {} original_weights_keys = sorted(original_weights.keys()) for original_key in original_weights_keys: updated_key = original_key if len(updated_key.split(".")) > 3: index, attr = updated_key.split(".")[2], updated_key.split(".")[-1] # for decoder attention if "attn.c_attn" in updated_key: if attr == "weight": slice1, slice2, slice3 = original_weights[updated_key].squeeze(-1).T.split(split_size=dim, dim=0) else: slice1, slice2, slice3 = original_weights[updated_key].split(split_size=dim, dim=0) converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.q_proj.{attr}"] = slice1 converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.k_proj.{attr}"] = slice2 converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.v_proj.{attr}"] = slice3 continue if "attn.c_proj" in updated_key: converted_weights[f"speech_decoder_model.model.decoder.layers.{index}.attn.out_proj.{attr}"] = ( original_weights[updated_key].squeeze(-1).T ) continue if "attn.bias" in updated_key or "attn.masked_bias" in updated_key or "text_head" in updated_key: original_weights.pop(updated_key) continue # conditional encoder attention if "qkv" in updated_key: if attr == "weight": slice1, slice2, slice3 = original_weights[updated_key].squeeze(-1).split(split_size=dim, dim=0) else: slice1, slice2, slice3 = original_weights[updated_key].split(split_size=dim, dim=0) indices = torch.arange(dim) index1, index2, index3 = ( indices.unfold(0, sub_dim, sub_dim * 3).flatten(), indices[sub_dim:].unfold(0, sub_dim, sub_dim * 3).flatten(), indices[2 * sub_dim :].unfold(0, sub_dim, sub_dim * 3).flatten(), ) converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.q_proj.{attr}"] = torch.concatenate( [slice1[index1], slice2[index3], slice3[index2]], axis=0, ) converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.k_proj.{attr}"] = torch.concatenate( [slice1[index2], slice2[index1], slice3[index3]], axis=0, ) converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.v_proj.{attr}"] = torch.concatenate( [slice1[index3], slice2[index2], slice3[index1]], axis=0, ) continue if "proj_out" in updated_key: converted_weights[f"conditioning_encoder.mel_attn_blocks.{index}.out_proj.{attr}"] = original_weights[ updated_key ].squeeze(-1) continue for k, v in CLVP_DECODER_MAPPING.items(): if k in updated_key: updated_key = updated_key.replace(k, v) converted_weights[updated_key] = original_weights.pop(original_key) return converted_weights def _download(url: str, root: str): repo_id = f"{url.split('/')[3]}/{url.split('/')[4]}" filename = f"{url.split('/')[-2]}/{url.split('/')[-1]}" hf_hub_download( repo_id=repo_id, filename=filename, force_filename=root, local_dir_use_symlinks=False, ) def convert_clvp_weights(checkpoint_path, pytorch_dump_folder_path): converted_checkpoint = {} for each_model_name, each_model_url in _MODELS.items(): each_model_path = os.path.join(checkpoint_path, each_model_url.split("/")[-1]) if not os.path.exists(each_model_path): print(f"\n{each_model_name} was not found! Downloading it to {each_model_path}") _download(url=each_model_url, root=each_model_path) if each_model_name == "clvp": clvp_checkpoint = torch.load(each_model_path, map_location="cpu") else: decoder_checkpoint = torch.load(each_model_path, map_location="cpu") # Converting the weights converted_checkpoint.update(**convert_encoder_weights(clvp_checkpoint)) converted_checkpoint.update(**convert_decoder_weights(decoder_checkpoint)) config = ClvpConfig.from_pretrained("susnato/clvp_dev") model = ClvpModelForConditionalGeneration(config) model.load_state_dict(converted_checkpoint, strict=True) model.save_pretrained(pytorch_dump_folder_path) print(f"Model saved at {pytorch_dump_folder_path}!") if __name__ == "__main__": parser = argparse.ArgumentParser() # # Required parameters parser.add_argument( "--checkpoint_path", type=str, help="Path to the folder of downloaded checkpoints. (Please enter full path)" ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model. (Please enter full path)", ) args = parser.parse_args() convert_clvp_weights(args.checkpoint_path, args.pytorch_dump_folder_path)

transformers/src/transformers/models/clvp/convert_clvp_to_hf.py/0

{ "file_path": "transformers/src/transformers/models/clvp/convert_clvp_to_hf.py", "repo_id": "transformers", "token_count": 4095 }

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# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Conditional DETR checkpoints.""" import argparse import json from collections import OrderedDict from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( ConditionalDetrConfig, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrImageProcessor, ) from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) # here we list all keys to be renamed (original name on the left, our name on the right) rename_keys = [] for i in range(6): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append( (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight") ) rename_keys.append( (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias") ) rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias")) rename_keys.append( (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight") ) rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias")) # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms rename_keys.append( (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias") ) rename_keys.append( ( f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight", f"decoder.layers.{i}.encoder_attn.out_proj.weight", ) ) rename_keys.append( ( f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias", f"decoder.layers.{i}.encoder_attn.out_proj.bias", ) ) rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight") ) rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias") ) rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias")) # q, k, v projections in self/cross-attention in decoder for conditional DETR rename_keys.append( (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight") ) rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight") ) # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight") ) rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias") ) rename_keys.append( (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias") ) rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias") ) # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias") ) rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias")) rename_keys.append( (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias") ) # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads # for conditional DETR, also convert reference point head and query scale MLP rename_keys.extend( [ ("input_proj.weight", "input_projection.weight"), ("input_proj.bias", "input_projection.bias"), ("query_embed.weight", "query_position_embeddings.weight"), ("transformer.decoder.norm.weight", "decoder.layernorm.weight"), ("transformer.decoder.norm.bias", "decoder.layernorm.bias"), ("class_embed.weight", "class_labels_classifier.weight"), ("class_embed.bias", "class_labels_classifier.bias"), ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"), ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"), ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"), ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"), ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"), ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"), ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"), ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"), ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"), ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"), ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"), ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"), ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"), ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"), ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"), ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"), ] ) def rename_key(state_dict, old, new): val = state_dict.pop(old) state_dict[new] = val def rename_backbone_keys(state_dict): new_state_dict = OrderedDict() for key, value in state_dict.items(): if "backbone.0.body" in key: new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model") new_state_dict[new_key] = value else: new_state_dict[key] = value return new_state_dict def read_in_q_k_v(state_dict, is_panoptic=False): prefix = "" if is_panoptic: prefix = "conditional_detr." # first: transformer encoder for i in range(6): # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight") in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:] # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_conditional_detr_checkpoint(model_name, pytorch_dump_folder_path): """ Copy/paste/tweak model's weights to our CONDITIONAL_DETR structure. """ # load default config config = ConditionalDetrConfig() # set backbone and dilation attributes if "resnet101" in model_name: config.backbone = "resnet101" if "dc5" in model_name: config.dilation = True is_panoptic = "panoptic" in model_name if is_panoptic: config.num_labels = 250 else: config.num_labels = 91 repo_id = "huggingface/label-files" filename = "coco-detection-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} # load image processor format = "coco_panoptic" if is_panoptic else "coco_detection" image_processor = ConditionalDetrImageProcessor(format=format) # prepare image img = prepare_img() encoding = image_processor(images=img, return_tensors="pt") pixel_values = encoding["pixel_values"] logger.info(f"Converting model {model_name}...") # load original model from torch hub conditional_detr = torch.hub.load("DeppMeng/ConditionalDETR", model_name, pretrained=True).eval() state_dict = conditional_detr.state_dict() # rename keys for src, dest in rename_keys: if is_panoptic: src = "conditional_detr." + src rename_key(state_dict, src, dest) state_dict = rename_backbone_keys(state_dict) # query, key and value matrices need special treatment read_in_q_k_v(state_dict, is_panoptic=is_panoptic) # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them prefix = "conditional_detr.model." if is_panoptic else "model." for key in state_dict.copy().keys(): if is_panoptic: if ( key.startswith("conditional_detr") and not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor") ): val = state_dict.pop(key) state_dict["conditional_detr.model" + key[4:]] = val elif "class_labels_classifier" in key or "bbox_predictor" in key: val = state_dict.pop(key) state_dict["conditional_detr." + key] = val elif key.startswith("bbox_attention") or key.startswith("mask_head"): continue else: val = state_dict.pop(key) state_dict[prefix + key] = val else: if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"): val = state_dict.pop(key) state_dict[prefix + key] = val # finally, create HuggingFace model and load state dict model = ConditionalDetrForSegmentation(config) if is_panoptic else ConditionalDetrForObjectDetection(config) model.load_state_dict(state_dict) model.eval() model.push_to_hub(repo_id=model_name, organization="DepuMeng", commit_message="Add model") # verify our conversion original_outputs = conditional_detr(pixel_values) outputs = model(pixel_values) assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-4) assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-4) if is_panoptic: assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4) # Save model and image processor logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...") Path(pytorch_dump_folder_path).mkdir(exist_ok=True) model.save_pretrained(pytorch_dump_folder_path) image_processor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_name", default="conditional_detr_resnet50", type=str, help="Name of the CONDITIONAL_DETR model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model." ) args = parser.parse_args() convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

transformers/src/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py/0

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# coding=utf-8 # Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ConvNext model.""" from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ...utils.backbone_utils import BackboneMixin from .configuration_convnext import ConvNextConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "ConvNextConfig" # Base docstring _CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224" _EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "facebook/convnext-tiny-224" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/convnext-tiny-224", # See all ConvNext models at https://huggingface.co/models?filter=convnext ] # Copied from transformers.models.beit.modeling_beit.drop_path def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0.0 or not training: return input keep_prob = 1 - drop_prob shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) random_tensor.floor_() # binarize output = input.div(keep_prob) * random_tensor return output # Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->ConvNext class ConvNextDropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, drop_prob: Optional[float] = None) -> None: super().__init__() self.drop_prob = drop_prob def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: return drop_path(hidden_states, self.drop_prob, self.training) def extra_repr(self) -> str: return "p={}".format(self.drop_prob) class ConvNextLayerNorm(nn.Module): r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). """ def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): super().__init__() self.weight = nn.Parameter(torch.ones(normalized_shape)) self.bias = nn.Parameter(torch.zeros(normalized_shape)) self.eps = eps self.data_format = data_format if self.data_format not in ["channels_last", "channels_first"]: raise NotImplementedError(f"Unsupported data format: {self.data_format}") self.normalized_shape = (normalized_shape,) def forward(self, x: torch.Tensor) -> torch.Tensor: if self.data_format == "channels_last": x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) elif self.data_format == "channels_first": input_dtype = x.dtype x = x.float() u = x.mean(1, keepdim=True) s = (x - u).pow(2).mean(1, keepdim=True) x = (x - u) / torch.sqrt(s + self.eps) x = x.to(dtype=input_dtype) x = self.weight[:, None, None] * x + self.bias[:, None, None] return x class ConvNextEmbeddings(nn.Module): """This class is comparable to (and inspired by) the SwinEmbeddings class found in src/transformers/models/swin/modeling_swin.py. """ def __init__(self, config): super().__init__() self.patch_embeddings = nn.Conv2d( config.num_channels, config.hidden_sizes[0], kernel_size=config.patch_size, stride=config.patch_size ) self.layernorm = ConvNextLayerNorm(config.hidden_sizes[0], eps=1e-6, data_format="channels_first") self.num_channels = config.num_channels def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: num_channels = pixel_values.shape[1] if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embeddings = self.patch_embeddings(pixel_values) embeddings = self.layernorm(embeddings) return embeddings class ConvNextLayer(nn.Module): """This corresponds to the `Block` class in the original implementation. There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C, H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back The authors used (2) as they find it slightly faster in PyTorch. Args: config ([`ConvNextConfig`]): Model configuration class. dim (`int`): Number of input channels. drop_path (`float`): Stochastic depth rate. Default: 0.0. """ def __init__(self, config, dim, drop_path=0): super().__init__() self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv self.layernorm = ConvNextLayerNorm(dim, eps=1e-6) self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers self.act = ACT2FN[config.hidden_act] self.pwconv2 = nn.Linear(4 * dim, dim) self.layer_scale_parameter = ( nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True) if config.layer_scale_init_value > 0 else None ) self.drop_path = ConvNextDropPath(drop_path) if drop_path > 0.0 else nn.Identity() def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: input = hidden_states x = self.dwconv(hidden_states) x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) x = self.layernorm(x) x = self.pwconv1(x) x = self.act(x) x = self.pwconv2(x) if self.layer_scale_parameter is not None: x = self.layer_scale_parameter * x x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) x = input + self.drop_path(x) return x class ConvNextStage(nn.Module): """ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks. Args: config ([`ConvNextConfig`]): Model configuration class. in_channels (`int`): Number of input channels. out_channels (`int`): Number of output channels. depth (`int`): Number of residual blocks. drop_path_rates(`List[float]`): Stochastic depth rates for each layer. """ def __init__(self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None): super().__init__() if in_channels != out_channels or stride > 1: self.downsampling_layer = nn.Sequential( ConvNextLayerNorm(in_channels, eps=1e-6, data_format="channels_first"), nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride), ) else: self.downsampling_layer = nn.Identity() drop_path_rates = drop_path_rates or [0.0] * depth self.layers = nn.Sequential( *[ConvNextLayer(config, dim=out_channels, drop_path=drop_path_rates[j]) for j in range(depth)] ) def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: hidden_states = self.downsampling_layer(hidden_states) hidden_states = self.layers(hidden_states) return hidden_states class ConvNextEncoder(nn.Module): def __init__(self, config): super().__init__() self.stages = nn.ModuleList() drop_path_rates = [ x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths) ] prev_chs = config.hidden_sizes[0] for i in range(config.num_stages): out_chs = config.hidden_sizes[i] stage = ConvNextStage( config, in_channels=prev_chs, out_channels=out_chs, stride=2 if i > 0 else 1, depth=config.depths[i], drop_path_rates=drop_path_rates[i], ) self.stages.append(stage) prev_chs = out_chs def forward( self, hidden_states: torch.FloatTensor, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutputWithNoAttention]: all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.stages): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) hidden_states = layer_module(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) return BaseModelOutputWithNoAttention( last_hidden_state=hidden_states, hidden_states=all_hidden_states, ) class ConvNextPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ConvNextConfig base_model_prefix = "convnext" main_input_name = "pixel_values" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) CONVNEXT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CONVNEXT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConvNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ConvNext model outputting raw features without any specific head on top.", CONVNEXT_START_DOCSTRING, ) class ConvNextModel(ConvNextPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = ConvNextEmbeddings(config) self.encoder = ConvNextEncoder(config) # final layernorm layer self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: torch.FloatTensor = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") embedding_output = self.embeddings(pixel_values) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] # global average pooling, (N, C, H, W) -> (N, C) pooled_output = self.layernorm(last_hidden_state.mean([-2, -1])) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, ) @add_start_docstrings( """ ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, CONVNEXT_START_DOCSTRING, ) class ConvNextForImageClassification(ConvNextPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.convnext = ConvNextModel(config) # Classifier head self.classifier = ( nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity() ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: torch.FloatTensor = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.convnext(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention( loss=loss, logits=logits, hidden_states=outputs.hidden_states, ) @add_start_docstrings( """ ConvNeXt backbone, to be used with frameworks like DETR and MaskFormer. """, CONVNEXT_START_DOCSTRING, ) class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin): def __init__(self, config): super().__init__(config) super()._init_backbone(config) self.embeddings = ConvNextEmbeddings(config) self.encoder = ConvNextEncoder(config) self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes # Add layer norms to hidden states of out_features hidden_states_norms = {} for stage, num_channels in zip(self._out_features, self.channels): hidden_states_norms[stage] = ConvNextLayerNorm(num_channels, data_format="channels_first") self.hidden_states_norms = nn.ModuleDict(hidden_states_norms) # initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> BackboneOutput: """ Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224") >>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224") >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) embedding_output = self.embeddings(pixel_values) outputs = self.encoder( embedding_output, output_hidden_states=True, return_dict=return_dict, ) hidden_states = outputs.hidden_states if return_dict else outputs[1] feature_maps = () for stage, hidden_state in zip(self.stage_names, hidden_states): if stage in self.out_features: hidden_state = self.hidden_states_norms[stage](hidden_state) feature_maps += (hidden_state,) if not return_dict: output = (feature_maps,) if output_hidden_states: output += (hidden_states,) return output return BackboneOutput( feature_maps=feature_maps, hidden_states=hidden_states if output_hidden_states else None, attentions=None, )

transformers/src/transformers/models/convnext/modeling_convnext.py/0

{ "file_path": "transformers/src/transformers/models/convnext/modeling_convnext.py", "repo_id": "transformers", "token_count": 9129 }

317

# coding=utf-8 # Copyright 2018 Salesforce and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CTRL model.""" from typing import Optional, Tuple, Union import numpy as np import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_ctrl import CTRLConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "CTRLConfig" CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = [ "Salesforce/ctrl" # See all CTRL models at https://huggingface.co/models?filter=ctrl ] def angle_defn(pos, i, d_model_size): angle_rates = 1 / torch.pow(10000, (2 * (i // 2)) / d_model_size) return pos * angle_rates def positional_encoding(position, d_model_size, dtype): # create the sinusoidal pattern for the positional encoding angle_rads = angle_defn( torch.arange(position, dtype=torch.int64).to(dtype).unsqueeze(1), torch.arange(d_model_size, dtype=torch.int64).to(dtype).unsqueeze(0), d_model_size, ) sines = torch.sin(angle_rads[:, 0::2]) cosines = torch.cos(angle_rads[:, 1::2]) pos_encoding = torch.cat([sines, cosines], dim=-1) return pos_encoding def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None): # calculate attention matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2)) dk = k.shape[-1] scaled_attention_logits = matmul_qk / np.sqrt(dk) if mask is not None: nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1) scaled_attention_logits += mask[ns - nd : ns, :ns] * -1e4 if attention_mask is not None: # Apply the attention mask scaled_attention_logits = scaled_attention_logits + attention_mask attention_weights = torch.softmax(scaled_attention_logits, dim=-1) # Mask heads if we want to if head_mask is not None: attention_weights = attention_weights * head_mask output = torch.matmul(attention_weights, v) return output, attention_weights class MultiHeadAttention(nn.Module): def __init__(self, d_model_size, num_heads): super().__init__() self.num_heads = num_heads self.d_model_size = d_model_size self.depth = int(d_model_size / self.num_heads) self.Wq = nn.Linear(d_model_size, d_model_size) self.Wk = nn.Linear(d_model_size, d_model_size) self.Wv = nn.Linear(d_model_size, d_model_size) self.dense = nn.Linear(d_model_size, d_model_size) self.pruned_heads = set() def prune_heads(self, heads): attention_head_size = self.d_model_size // self.num_heads if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, attention_head_size, self.pruned_heads) # Prune linear layers self.Wq = prune_linear_layer(self.Wq, index) self.Wk = prune_linear_layer(self.Wk, index) self.Wv = prune_linear_layer(self.Wv, index) self.dense = prune_linear_layer(self.dense, index, dim=1) # Update hyper params self.num_heads = self.num_heads - len(heads) self.d_model_size = attention_head_size * self.num_heads self.pruned_heads = self.pruned_heads.union(heads) def split_into_heads(self, x, batch_size): x = x.reshape(batch_size, -1, self.num_heads, self.depth) return x.permute([0, 2, 1, 3]) def forward( self, v, k, q, mask, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False, ): batch_size = q.shape[0] q = self.Wq(q) k = self.Wk(k) v = self.Wv(v) q = self.split_into_heads(q, batch_size) k = self.split_into_heads(k, batch_size) v = self.split_into_heads(v, batch_size) if layer_past is not None: past_key, past_value = layer_past[0], layer_past[1] k = torch.cat((past_key, k), dim=-2) v = torch.cat((past_value, v), dim=-2) if use_cache is True: present = torch.stack((k, v)) else: present = (None,) output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask) scaled_attention = output[0].permute([0, 2, 1, 3]) attn = output[1] original_size_attention = scaled_attention.reshape(batch_size, -1, self.d_model_size) output = self.dense(original_size_attention) outputs = (output, present) if output_attentions: outputs = outputs + (attn,) return outputs def point_wise_feed_forward_network(d_model_size, dff): return nn.Sequential(nn.Linear(d_model_size, dff), nn.ReLU(), nn.Linear(dff, d_model_size)) class EncoderLayer(nn.Module): def __init__(self, d_model_size, num_heads, dff, rate=0.1): super().__init__() self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads) self.ffn = point_wise_feed_forward_network(d_model_size, dff) self.layernorm1 = nn.LayerNorm(d_model_size, eps=1e-6) self.layernorm2 = nn.LayerNorm(d_model_size, eps=1e-6) self.dropout1 = nn.Dropout(rate) self.dropout2 = nn.Dropout(rate) def forward( self, x, mask, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False ): normed = self.layernorm1(x) attn_outputs = self.multi_head_attention( normed, normed, normed, mask, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attn_outputs[0] attn_output = self.dropout1(attn_output) out1 = x + attn_output out2 = self.layernorm2(out1) ffn_output = self.ffn(out2) ffn_output = self.dropout2(ffn_output) out2 = out1 + ffn_output outputs = (out2,) + attn_outputs[1:] return outputs class CTRLPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CTRLConfig base_model_prefix = "transformer" def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear, Conv1D)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) CTRL_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CTRLConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CTRL_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0].shape[-2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only input IDs that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`Tuple[Tuple[torch.FloatTensor]]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as input ids as they have already been computed. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.", CTRL_START_DOCSTRING, ) class CTRLModel(CTRLPreTrainedModel): def __init__(self, config): super().__init__(config) self.d_model_size = config.n_embd self.num_layers = config.n_layer self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size, torch.float) self.w = nn.Embedding(config.vocab_size, config.n_embd) self.dropout = nn.Dropout(config.embd_pdrop) self.h = nn.ModuleList( [EncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop) for _ in range(config.n_layer)] ) self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.w def set_input_embeddings(self, new_embeddings): self.w = new_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ for layer, heads in heads_to_prune.items(): self.h[layer].multi_head_attention.prune_heads(heads) @add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]: r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, CTRLModel >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl") >>> model = CTRLModel.from_pretrained("Salesforce/ctrl") >>> # CTRL was trained with control codes as the first token >>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt") >>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values() >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 5, 1280] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions use_cache = use_cache if use_cache is not None else self.config.use_cache output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) batch_size = input_ids.shape[0] elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size = inputs_embeds.shape[0] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if past_key_values is None: past_length = 0 past_key_values = tuple([None] * len(self.h)) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) position_ids = position_ids.unsqueeze(0) # Attention mask. if attention_mask is not None: if batch_size <= 0: raise ValueError("batch_size has to be defined and > 0") attention_mask = attention_mask.view(batch_size, -1) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.n_layer) if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1]) token_type_embeds = self.w(token_type_ids) token_type_embeds *= np.sqrt(self.d_model_size) else: token_type_embeds = 0 if inputs_embeds is None: inputs_embeds = self.w(input_ids) # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded seq_len = input_shape[-1] mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(device) inputs_embeds *= np.sqrt(self.d_model_size) # `self.pos_encoding` won't be sent to the correct device along the model, so we do it manually. self.pos_encoding = self.pos_encoding.to(device) pos_embeds = self.pos_encoding[position_ids, :] hidden_states = inputs_embeds + pos_embeds + token_type_embeds hidden_states = self.dropout(hidden_states) presents = () if use_cache else None all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, (h, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = h( hidden_states, mask, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present = outputs[:2] if use_cache is True: presents = presents + (present,) if output_attentions: all_attentions += (outputs[2],) hidden_states = self.layernorm(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, ) @add_start_docstrings( """ The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, CTRL_START_DOCSTRING, ) class CTRLLMHeadModel(CTRLPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = CTRLModel(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, input_ids, past_key_values=None, use_cache=None, **kwargs): # only last tokens for inputs_ids if past is defined in kwargs if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] return {"input_ids": input_ids, "past_key_values": past_key_values, "use_cache": use_cache} @add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: Example: ```python >>> import torch >>> from transformers import AutoTokenizer, CTRLLMHeadModel >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl") >>> model = CTRLLMHeadModel.from_pretrained("Salesforce/ctrl") >>> # CTRL was trained with control codes as the first token >>> inputs = tokenizer("Wikipedia The llama is", return_tensors="pt") >>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values() >>> sequence_ids = model.generate(inputs["input_ids"]) >>> sequences = tokenizer.batch_decode(sequence_ids) >>> sequences ['Wikipedia The llama is a member of the family Bovidae. It is native to the Andes of Peru,'] >>> outputs = model(**inputs, labels=inputs["input_ids"]) >>> round(outputs.loss.item(), 2) 9.21 >>> list(outputs.logits.shape) [1, 5, 246534] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache( past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor ) -> Tuple[Tuple[torch.Tensor]]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. """ return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values ) @add_start_docstrings( """ The CTRL Model transformer with a sequence classification head on top (linear layer). [`CTRLForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, CTRL_START_DOCSTRING, ) class CTRLForSequenceClassification(CTRLPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = CTRLModel(config) self.classifier = nn.Linear(config.n_embd, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CTRL_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Example of single-label classification: ```python >>> import torch >>> from transformers import AutoTokenizer, CTRLForSequenceClassification >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl") >>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl") >>> # CTRL was trained with control codes as the first token >>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt") >>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values() >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_class_id = logits.argmax().item() >>> model.config.id2label[predicted_class_id] 'LABEL_0' ``` ```python >>> import torch >>> torch.manual_seed(42) # doctest: +IGNORE_RESULT >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)` >>> num_labels = len(model.config.id2label) >>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl", num_labels=num_labels) >>> labels = torch.tensor(1) >>> loss = model(**inputs, labels=labels).loss >>> round(loss.item(), 2) 0.35 ``` Example of multi-label classification: ```python >>> import torch >>> from transformers import AutoTokenizer, CTRLForSequenceClassification >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl") >>> model = CTRLForSequenceClassification.from_pretrained( ... "Salesforce/ctrl", problem_type="multi_label_classification" ... ) >>> # CTRL was trained with control codes as the first token >>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt") >>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values() >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_class_id = logits.argmax().item() >>> model.config.id2label[predicted_class_id] 'LABEL_0' ``` ```python >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)` >>> num_labels = len(model.config.id2label) >>> model = CTRLForSequenceClassification.from_pretrained("Salesforce/ctrl", num_labels=num_labels) >>> num_labels = len(model.config.id2label) >>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to( ... torch.float ... ) >>> loss = model(**inputs, labels=labels).loss >>> loss.backward() # doctest: +IGNORE_RESULT ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] logits = self.classifier(hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[range(batch_size), sequence_lengths] loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=pooled_logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )

transformers/src/transformers/models/ctrl/modeling_ctrl.py/0

{ "file_path": "transformers/src/transformers/models/ctrl/modeling_ctrl.py", "repo_id": "transformers", "token_count": 15197 }

318

# coding=utf-8 # Copyright 2022 The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Decision Transformer model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "edbeeching/decision-transformer-gym-hopper-medium": ( "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json" ), # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer } class DecisionTransformerConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`DecisionTransformerModel`]. It is used to instantiate a Decision Transformer model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the standard DecisionTransformer architecture. Many of the config options are used to instatiate the GPT2 model that is used as part of the architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: state_dim (`int`, *optional*, defaults to 17): The state size for the RL environment act_dim (`int`, *optional*, defaults to 4): The size of the output action space hidden_size (`int`, *optional*, defaults to 128): The size of the hidden layers max_ep_len (`int`, *optional*, defaults to 4096): The maximum length of an episode in the environment action_tanh (`bool`, *optional*, defaults to True): Whether to use a tanh activation on action prediction vocab_size (`int`, *optional*, defaults to 50257): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DecisionTransformerModel`]. n_positions (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_layer (`int`, *optional*, defaults to 3): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 1): Number of attention heads for each attention layer in the Transformer encoder. n_inner (`int`, *optional*): Dimensionality of the inner feed-forward layers. If unset, will default to 4 times `n_embd`. activation_function (`str`, *optional*, defaults to `"gelu"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_attn_weights (`bool`, *optional*, defaults to `True`): Scale attention weights by dividing by sqrt(hidden_size).. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): Whether to additionally scale attention weights by `1 / layer_idx + 1`. reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision. Example: ```python >>> from transformers import DecisionTransformerConfig, DecisionTransformerModel >>> # Initializing a DecisionTransformer configuration >>> configuration = DecisionTransformerConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = DecisionTransformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "decision_transformer" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "max_position_embeddings": "n_positions", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, state_dim=17, act_dim=4, hidden_size=128, max_ep_len=4096, action_tanh=True, vocab_size=1, n_positions=1024, n_layer=3, n_head=1, n_inner=None, activation_function="relu", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, scale_attn_weights=True, use_cache=True, bos_token_id=50256, eos_token_id=50256, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False, **kwargs, ): self.state_dim = state_dim self.act_dim = act_dim self.hidden_size = hidden_size self.max_ep_len = max_ep_len self.action_tanh = action_tanh self.vocab_size = vocab_size self.n_positions = n_positions self.n_layer = n_layer self.n_head = n_head self.n_inner = n_inner self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.scale_attn_weights = scale_attn_weights self.use_cache = use_cache self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx self.reorder_and_upcast_attn = reorder_and_upcast_attn self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

transformers/src/transformers/models/decision_transformer/configuration_decision_transformer.py/0

{ "file_path": "transformers/src/transformers/models/decision_transformer/configuration_decision_transformer.py", "repo_id": "transformers", "token_count": 2795 }

319

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ RetriBERT model """ import math from typing import Optional import torch import torch.utils.checkpoint as checkpoint from torch import nn from ....modeling_utils import PreTrainedModel from ....utils import add_start_docstrings, logging from ...bert.modeling_bert import BertModel from .configuration_retribert import RetriBertConfig logger = logging.get_logger(__name__) RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "yjernite/retribert-base-uncased", # See all RetriBert models at https://huggingface.co/models?filter=retribert ] # INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL # class RetriBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RetriBertConfig load_tf_weights = None base_model_prefix = "retribert" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) RETRIBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( """Bert Based model to embed queries or document for document retrieval.""", RETRIBERT_START_DOCSTRING, ) class RetriBertModel(RetriBertPreTrainedModel): def __init__(self, config: RetriBertConfig) -> None: super().__init__(config) self.projection_dim = config.projection_dim self.bert_query = BertModel(config) self.bert_doc = None if config.share_encoders else BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.project_query = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.project_doc = nn.Linear(config.hidden_size, config.projection_dim, bias=False) self.ce_loss = nn.CrossEntropyLoss(reduction="mean") # Initialize weights and apply final processing self.post_init() def embed_sentences_checkpointed( self, input_ids, attention_mask, sent_encoder, checkpoint_batch_size=-1, ): # reproduces BERT forward pass with checkpointing if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: return sent_encoder(input_ids, attention_mask=attention_mask)[1] else: # prepare implicit variables device = input_ids.device input_shape = input_ids.size() token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) head_mask = [None] * sent_encoder.config.num_hidden_layers extended_attention_mask: torch.Tensor = sent_encoder.get_extended_attention_mask( attention_mask, input_shape ) # define function for checkpointing def partial_encode(*inputs): encoder_outputs = sent_encoder.encoder( inputs[0], attention_mask=inputs[1], head_mask=head_mask, ) sequence_output = encoder_outputs[0] pooled_output = sent_encoder.pooler(sequence_output) return pooled_output # run embedding layer on everything at once embedding_output = sent_encoder.embeddings( input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None ) # run encoding and pooling on one mini-batch at a time pooled_output_list = [] for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) pooled_output_list.append(pooled_output) return torch.cat(pooled_output_list, dim=0) def embed_questions( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): q_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query, checkpoint_batch_size, ) return self.project_query(q_reps) def embed_answers( self, input_ids, attention_mask=None, checkpoint_batch_size=-1, ): a_reps = self.embed_sentences_checkpointed( input_ids, attention_mask, self.bert_query if self.bert_doc is None else self.bert_doc, checkpoint_batch_size, ) return self.project_doc(a_reps) def forward( self, input_ids_query: torch.LongTensor, attention_mask_query: Optional[torch.FloatTensor], input_ids_doc: torch.LongTensor, attention_mask_doc: Optional[torch.FloatTensor], checkpoint_batch_size: int = -1, ) -> torch.FloatTensor: r""" Args: input_ids_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the queries in a batch. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask_query (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) input_ids_doc (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary for the documents in a batch. attention_mask_doc (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on documents padding token indices. checkpoint_batch_size (`int`, *optional*, defaults to `-1`): If greater than 0, uses gradient checkpointing to only compute sequence representation on `checkpoint_batch_size` examples at a time on the GPU. All query representations are still compared to all document representations in the batch. Return: `torch.FloatTensor``: The bidirectional cross-entropy loss obtained while trying to match each query to its corresponding document and each document to its corresponding query in the batch """ device = input_ids_query.device q_reps = self.embed_questions(input_ids_query, attention_mask_query, checkpoint_batch_size) a_reps = self.embed_answers(input_ids_doc, attention_mask_doc, checkpoint_batch_size) compare_scores = torch.mm(q_reps, a_reps.t()) loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) loss = (loss_qa + loss_aq) / 2 return loss

transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py/0

{ "file_path": "transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py", "repo_id": "transformers", "token_count": 3872 }

320

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tokenization classes for Transformer XL model. Adapted from https://github.com/kimiyoung/transformer-xl. """ import glob import os import pickle import re from collections import Counter, OrderedDict from typing import List, Optional, Tuple import numpy as np from ....tokenization_utils import PreTrainedTokenizer from ....utils import ( cached_file, is_sacremoses_available, is_torch_available, logging, requires_backends, strtobool, torch_only_method, ) if is_sacremoses_available(): import sacremoses as sm if is_torch_available(): import torch logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "pretrained_vocab_file": "vocab.pkl", "pretrained_vocab_file_torch": "vocab.bin", "vocab_file": "vocab.txt", } PRETRAINED_VOCAB_FILES_MAP = { "pretrained_vocab_file": { "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/vocab.pkl", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "transfo-xl-wt103": None, } PRETRAINED_CORPUS_ARCHIVE_MAP = { "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/corpus.bin", } CORPUS_NAME = "corpus.bin" MATCH_NUMBERS = r"(?<=\d)[,.](?=\d)", r" @\g<0>@ " DETOKENIZE_NUMBERS = [(r" @\,@ ", r","), (r" @\.@ ", r".")] def tokenize_numbers(text_array: List[str]) -> List[str]: """ Splits large comma-separated numbers and floating point values. This is done by replacing commas with ' @,@ ' and dots with ' @.@ '. Args: text_array: An already tokenized text as list. Returns: A list of strings with tokenized numbers. Example: ```python >>> tokenize_numbers(["$", "5,000", "1.73", "m"]) ['$', '5', '@,@', '000', '1', '@.@', '73', 'm'] ```""" tokenized = [] for i in range(len(text_array)): reg, sub = MATCH_NUMBERS replaced = re.sub(reg, sub, text_array[i]).split() tokenized.extend(replaced) return tokenized def detokenize_numbers(text: str) -> str: """ Inverts the operation of *tokenize_numbers*. This is replacing ' @,@ ' and ' @.@' by ',' and '.'. Args: text: A string where the number should be detokenized. Returns: A detokenized string. Example: ```python >>> detokenize_numbers("$ 5 @,@ 000 1 @.@ 73 m") '$ 5,000 1.73 m' ```""" for reg, sub in DETOKENIZE_NUMBERS: text = re.sub(reg, sub, text) return text class TransfoXLTokenizer(PreTrainedTokenizer): """ Construct a Transformer-XL tokenizer adapted from Vocab class in [the original code](https://github.com/kimiyoung/transformer-xl). The Transformer-XL tokenizer is a word-level tokenizer (no sub-word tokenization). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: special (`List[str]`, *optional*): A list of special tokens (to be treated by the original implementation of this tokenizer). min_freq (`int`, *optional*, defaults to 0): The minimum number of times a token has to be present in order to be kept in the vocabulary (otherwise it will be mapped to `unk_token`). max_size (`int`, *optional*): The maximum size of the vocabulary. If left unset, it will default to the size of the vocabulary found after excluding the tokens according to the `min_freq` rule. lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the input when tokenizing. delimiter (`str`, *optional*): The delimiter used between tokens. vocab_file (`str`, *optional*): File containing the vocabulary (from the original implementation). pretrained_vocab_file (`str`, *optional*): File containing the vocabulary as saved with the `save_pretrained()` method. never_split (`List[str]`, *optional*): List of tokens that should never be split. If no list is specified, will simply use the existing special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. eos_token (`str`, *optional*, defaults to `"<eos>"`): The end of sequence token. additional_special_tokens (`List[str]`, *optional*, defaults to `['<formula>']`): A list of additional special tokens (for the HuggingFace functionality). language (`str`, *optional*, defaults to `"en"`): The language of this tokenizer (used for mose preprocessing). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids"] def __init__( self, special=None, min_freq=0, max_size=None, lower_case=False, delimiter=None, vocab_file=None, pretrained_vocab_file: str = None, never_split=None, unk_token="<unk>", eos_token="<eos>", additional_special_tokens=["<formula>"], language="en", **kwargs, ): logger.error( "`TransfoXL` was deprecated due to security issues linked to `pickle.load` in `TransfoXLTokenizer`. " "See more details on this model's documentation page: " "`https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/transfo-xl.md`." ) requires_backends(self, "sacremoses") if special is None: special = [] self.counter = Counter() self.special = special self.min_freq = min_freq self.max_size = max_size self.lower_case = lower_case self.delimiter = delimiter self.vocab_file = vocab_file self.punctuation_symbols = '!"#$%&()*+,-./\\:;<=>?@[\\]^_`{|}~' self.punction_without_space_before_pattern = re.compile(rf"[^\s][{self.punctuation_symbols}]") self.punctuation_with_space_around_pattern = self._compile_space_around_punctuation_pattern() self.language = language self.moses_punct_normalizer = sm.MosesPunctNormalizer(language) self.moses_tokenizer = sm.MosesTokenizer(language) self.moses_detokenizer = sm.MosesDetokenizer(language) self.idx2sym = [] self.sym2idx = OrderedDict() # This try... catch... is not beautiful but honestly this tokenizer was not made to be used # in a library like ours, at all. try: vocab_dict = None if pretrained_vocab_file is not None: # Priority on pickle files (support PyTorch and TF) if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")): raise ValueError( "This part uses `pickle.load` which is insecure and will execute arbitrary code that is " "potentially malicious. It's recommended to never unpickle data that could have come from an " "untrusted source, or that could have been tampered with. If you already verified the pickle " "data and decided to use it, you can set the environment variable " "`TRUST_REMOTE_CODE` to `True` to allow it." ) with open(pretrained_vocab_file, "rb") as f: vocab_dict = pickle.load(f) # Loading a torch-saved transfo-xl vocab dict with pickle results in an integer # Entering this if statement means that we tried to load a torch-saved file with pickle, and we failed. # We therefore load it with torch, if it's available. if isinstance(vocab_dict, int): if not is_torch_available(): raise ImportError( "Not trying to load dict with PyTorch as you need to install pytorch to load " "from a PyTorch pretrained vocabulary, " "or activate it with environment variables USE_TORCH=1 and USE_TF=0." ) vocab_dict = torch.load(pretrained_vocab_file) if vocab_dict is not None: for key, value in vocab_dict.items(): if key not in self.__dict__ or key in ["sym2idx", "idx2sym"]: self.__dict__[key] = value elif vocab_file is not None: self.build_vocab() except Exception as e: raise ValueError( f"Unable to parse file {pretrained_vocab_file}. Unknown format. " "If you tried to load a model saved through TransfoXLTokenizerFast, " "please note they are not compatible." ) from e if vocab_file is not None: self.build_vocab() super().__init__( special=special, min_freq=min_freq, max_size=max_size, lower_case=lower_case, delimiter=delimiter, vocab_file=vocab_file, pretrained_vocab_file=pretrained_vocab_file, never_split=never_split, unk_token=unk_token, eos_token=eos_token, additional_special_tokens=additional_special_tokens, language=language, **kwargs, ) # these are not required to initialize the parent class as only used when tokenizing. if never_split is None: never_split = self.all_special_tokens self.never_split = never_split @property def do_lower_case(self): return self.lower_case def _compile_space_around_punctuation_pattern(self): look_ahead_for_special_token = f"(?=[{self.punctuation_symbols}])" look_ahead_to_match_all_except_space = r"(?=[^\s])" return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space) def count_file(self, path, verbose=False, add_eos=False): if verbose: logger.info(f"counting file {path} ...") assert os.path.exists(path), f"Input file {path} not found" sents = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") symbols = self.tokenize(line, add_eos=add_eos) self.counter.update(symbols) sents.append(symbols) return sents def count_sents(self, sents, verbose=False): """ sents : a list of sentences, each a list of tokenized symbols """ if verbose: logger.info(f"counting {len(sents)} sents ...") for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") self.counter.update(symbols) def _build_from_file(self, vocab_file): self.idx2sym = [] self.sym2idx = OrderedDict() with open(vocab_file, "r", encoding="utf-8") as f: for line in f: symb = line.strip().split()[0] self.add_symbol(symb) if "<UNK>" in self.sym2idx: self.unk_idx = self.sym2idx["<UNK>"] elif "<unk>" in self.sym2idx: self.unk_idx = self.sym2idx["<unk>"] else: raise ValueError("Token not in vocabulary and no <unk> token in vocabulary for replacement.") def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if os.path.isdir(save_directory): vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["pretrained_vocab_file"], ) else: vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory with open(vocab_file, "wb") as f: pickle.dump(self.__dict__, f) return (vocab_file,) def build_vocab(self): if self.vocab_file: logger.info(f"building vocab from {self.vocab_file}") self._build_from_file(self.vocab_file) logger.info(f"Final vocab size {len(self.sym2idx)}") else: logger.info(f"building vocab with min_freq={self.min_freq}, max_size={self.max_size}") self.idx2sym = [] self.sym2idx = OrderedDict() for sym in self.special: self.add_special(sym) for sym, cnt in self.counter.most_common(self.max_size): if cnt < self.min_freq: break self.add_symbol(sym) logger.info(f"Final vocab size {len(self.sym2idx)} from {len(self.counter)} unique tokens") @torch_only_method def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False): if verbose: logger.info(f"encoding file {path} ...") assert os.path.exists(path), f"Output file {path} not found" encoded = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") symbols = self.tokenize(line, add_eos=add_eos, add_double_eos=add_double_eos) encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = torch.cat(encoded) return encoded @torch_only_method def encode_sents(self, sents, ordered=False, verbose=False): if verbose: logger.info(f"encoding {len(sents)} sents ...") encoded = [] for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0: logger.info(f" line {idx}") encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = torch.cat(encoded) return encoded def add_special(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 setattr(self, f"{sym.strip('<>')}_idx", self.sym2idx[sym]) def add_symbol(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 def move_added_token(self, token: str, target_idx: int): """ Moves an added token to a specific position in the vocab. This method should be used when resizing an embedding layer other than the last one in the `AdaptiveEmbedding` in order to move the token in the tokenizer from the default position (at the very end) to the desired one. Args: token: The token to move to a specific position in the vocab. target_idx: The position where the token should be moved to. """ assert token in self.added_tokens_encoder, "Token which should be moved has to be an added token" assert token not in self.idx2sym, "Token which should be moved is already in vocab" # Insert sym into vocab self.idx2sym.insert(target_idx, token) self.sym2idx[token] = target_idx # Shift following indices in sym2idx for idx in range(target_idx + 1, len(self.idx2sym)): current_sym = self.idx2sym[idx] self.sym2idx[current_sym] = idx # Delete token from added_tokens old_index = self._added_tokens_encoder.pop(token) self._added_tokens_decoder.pop(old_index) def moses_punct_norm(self, text): return self.moses_punct_normalizer.normalize(text) def moses_tokenize(self, text): return self.moses_tokenizer.tokenize( text, aggressive_dash_splits=True, return_str=False, escape=False, protected_patterns=self.never_split ) def moses_pipeline(self, text: str) -> List[str]: """ Does basic tokenization using [`sacremoses.MosesPunctNormalizer`] and [`sacremoses.MosesTokenizer`] with *aggressive_dash_splits=True* (see [`sacremoses.tokenize.MosesTokenizer.tokenize`]). Additionally, large comma-separated numbers and floating point values are split. E.g. "23,000 people are 1.80m tall" -> "23 @,@ 000 people are 1 @.@ 80m tall" Args: text: Text to be tokenize Returns: A list of tokenized string Example: ```python >>> tokenizer = TransfoXLTokenizer.from_pretrained("transfo-xl-wt103") >>> tokenizer.moses_pipeline("23,000 people are 1.80 m tall") ['23', '@,@', '000', 'people', 'are', '1', '@.@', '80', 'm', 'tall'] ```""" text = self.moses_punct_norm(text) text = self.moses_tokenize(text) text = tokenize_numbers(text) return text def _convert_id_to_token(self, idx): """Converts an id in a token (BPE) using the vocab.""" assert 0 <= idx < len(self), f"Index {idx} out of vocabulary range" return self.idx2sym[idx] def _convert_token_to_id(self, sym): """Converts a token (str) in an id using the vocab.""" if sym in self.sym2idx: return self.sym2idx[sym] else: # logger.info(f'encounter unk {sym}') # assert '<eos>' not in sym if hasattr(self, "unk_idx"): return self.sym2idx.get(sym, self.unk_idx) # Backward compatibility with pre-trained models elif "<unk>" in self.sym2idx: return self.sym2idx["<unk>"] elif "<UNK>" in self.sym2idx: return self.sym2idx["<UNK>"] else: raise ValueError("Token not in vocabulary and no <unk> token in vocabulary for replacement.") def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (string) in a single string. Additionally, the split numbers are converted back into it's original form. """ out_string = self.moses_detokenizer.detokenize(tokens) return detokenize_numbers(out_string).strip() @torch_only_method def convert_to_tensor(self, symbols): return torch.LongTensor(self.convert_tokens_to_ids(symbols)) @property def vocab_size(self): return len(self.idx2sym) def get_vocab(self): vocab = self.sym2idx.copy() vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, line, add_eos=False, add_double_eos=False): line = line.strip() # convert to lower case if self.lower_case: line = line.lower() # empty delimiter '' will evaluate False if self.delimiter == "": symbols = line else: symbols = self.moses_pipeline(line) if add_double_eos: # lm1b return ["<S>"] + symbols + ["<S>"] elif add_eos: return symbols + ["<eos>"] else: return symbols class LMOrderedIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None): """ data -- LongTensor -- the LongTensor is strictly ordered """ self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device # Work out how cleanly we can divide the dataset into bsz parts. self.n_step = data.size(0) // bsz # Trim off any extra elements that wouldn't cleanly fit (remainders). data = data.narrow(0, 0, self.n_step * bsz) # Evenly divide the data across the bsz batches. self.data = data.view(bsz, -1).t().contiguous().to(device) # Number of mini-batches self.n_batch = (self.n_step + self.bptt - 1) // self.bptt def get_batch(self, i, bptt=None): if bptt is None: bptt = self.bptt seq_len = min(bptt, self.data.size(0) - 1 - i) end_idx = i + seq_len beg_idx = max(0, i - self.ext_len) data = self.data[beg_idx:end_idx] target = self.data[i + 1 : i + 1 + seq_len] data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) return data_out, target_out, seq_len def get_fixlen_iter(self, start=0): for i in range(start, self.data.size(0) - 1, self.bptt): yield self.get_batch(i) def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3): max_len = self.bptt + max_deviation * std i = start while True: bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.0 bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std)))) data, target, seq_len = self.get_batch(i, bptt) i += seq_len yield data, target, seq_len if i >= self.data.size(0) - 2: break def __iter__(self): return self.get_fixlen_iter() class LMShuffledIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False): """ data -- list[LongTensor] -- there is no order among the LongTensors """ self.data = data self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self): # index iterator epoch_indices = np.random.permutation(len(self.data)) if self.shuffle else np.array(range(len(self.data))) # sentence iterator for idx in epoch_indices: yield self.data[idx] @torch_only_method def stream_iterator(self, sent_stream): # streams for each data in the batch streams = [None] * self.bsz data = torch.LongTensor(self.bptt, self.bsz) target = torch.LongTensor(self.bptt, self.bsz) n_retain = 0 while True: # data : [n_retain+bptt x bsz] # target : [bptt x bsz] data[n_retain:].fill_(-1) target.fill_(-1) valid_batch = True for i in range(self.bsz): n_filled = 0 try: while n_filled < self.bptt: if streams[i] is None or len(streams[i]) <= 1: streams[i] = next(sent_stream) # number of new tokens to fill in n_new = min(len(streams[i]) - 1, self.bptt - n_filled) # first n_retain tokens are retained from last batch data[n_retain + n_filled : n_retain + n_filled + n_new, i] = streams[i][:n_new] target[n_filled : n_filled + n_new, i] = streams[i][1 : n_new + 1] streams[i] = streams[i][n_new:] n_filled += n_new except StopIteration: valid_batch = False break if not valid_batch: return data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) yield data_out, target_out, self.bptt n_retain = min(data.size(0), self.ext_len) if n_retain > 0: data[:n_retain] = data[-n_retain:] data.resize_(n_retain + self.bptt, data.size(1)) def __iter__(self): # sent_stream is an iterator sent_stream = self.get_sent_stream() for batch in self.stream_iterator(sent_stream): yield batch class LMMultiFileIterator(LMShuffledIterator): def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False): self.paths = paths self.vocab = vocab self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self, path): sents = self.vocab.encode_file(path, add_double_eos=True) if self.shuffle: np.random.shuffle(sents) sent_stream = iter(sents) return sent_stream def __iter__(self): if self.shuffle: np.random.shuffle(self.paths) for path in self.paths: # sent_stream is an iterator sent_stream = self.get_sent_stream(path) for batch in self.stream_iterator(sent_stream): yield batch class TransfoXLCorpus(object): @classmethod @torch_only_method def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): """ Instantiate a pre-processed corpus. """ vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) is_local = os.path.isdir(pretrained_model_name_or_path) # redirect to the cache, if necessary try: resolved_corpus_file = cached_file(pretrained_model_name_or_path, CORPUS_NAME, cache_dir=cache_dir) except EnvironmentError: logger.error( f"Corpus '{pretrained_model_name_or_path}' was not found in corpus list" f" ({', '.join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys())}. We assumed '{pretrained_model_name_or_path}'" f" was a path or url but couldn't find files {CORPUS_NAME} at this path or url." ) return None if is_local: logger.info(f"loading corpus file {resolved_corpus_file}") else: logger.info(f"loading corpus file {CORPUS_NAME} from cache at {resolved_corpus_file}") # Instantiate tokenizer. corpus = cls(*inputs, **kwargs) corpus_dict = torch.load(resolved_corpus_file) for key, value in corpus_dict.items(): corpus.__dict__[key] = value corpus.vocab = vocab if corpus.train is not None: corpus.train = torch.tensor(corpus.train, dtype=torch.long) if corpus.valid is not None: corpus.valid = torch.tensor(corpus.valid, dtype=torch.long) if corpus.test is not None: corpus.test = torch.tensor(corpus.test, dtype=torch.long) return corpus def __init__(self, *args, **kwargs): self.vocab = TransfoXLTokenizer(*args, **kwargs) self.dataset = None self.train = None self.valid = None self.test = None def build_corpus(self, path, dataset): self.dataset = dataset if self.dataset in ["ptb", "wt2", "enwik8", "text8"]: self.vocab.count_file(os.path.join(path, "train.txt")) self.vocab.count_file(os.path.join(path, "valid.txt")) self.vocab.count_file(os.path.join(path, "test.txt")) elif self.dataset == "wt103": self.vocab.count_file(os.path.join(path, "train.txt")) elif self.dataset == "lm1b": train_path_pattern = os.path.join( path, "1-billion-word-language-modeling-benchmark-r13output", "training-monolingual.tokenized.shuffled", "news.en-*", ) train_paths = glob.glob(train_path_pattern) # the vocab will load from file when build_vocab() is called self.vocab.build_vocab() if self.dataset in ["ptb", "wt2", "wt103"]: self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True) self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True) self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True) elif self.dataset in ["enwik8", "text8"]: self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True, add_eos=False) self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True, add_eos=False) self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True, add_eos=False) elif self.dataset == "lm1b": self.train = train_paths self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=False, add_double_eos=True) self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=False, add_double_eos=True) def get_iterator(self, split, *args, **kwargs): if split == "train": if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: data_iter = LMOrderedIterator(self.train, *args, **kwargs) elif self.dataset == "lm1b": kwargs["shuffle"] = True data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs) elif split in ["valid", "test"]: data = self.valid if split == "valid" else self.test if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: data_iter = LMOrderedIterator(data, *args, **kwargs) elif self.dataset == "lm1b": data_iter = LMShuffledIterator(data, *args, **kwargs) else: data_iter = None raise ValueError(f"Split not recognized: {split}") return data_iter @torch_only_method def get_lm_corpus(datadir, dataset): fn = os.path.join(datadir, "cache.pt") fn_pickle = os.path.join(datadir, "cache.pkl") if os.path.exists(fn): logger.info("Loading cached dataset...") corpus = torch.load(fn_pickle) elif os.path.exists(fn): logger.info("Loading cached dataset from pickle...") if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")): raise ValueError( "This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially " "malicious. It's recommended to never unpickle data that could have come from an untrusted source, or " "that could have been tampered with. If you already verified the pickle data and decided to use it, " "you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it." ) with open(fn, "rb") as fp: corpus = pickle.load(fp) else: logger.info(f"Producing dataset {dataset}...") kwargs = {} if dataset in ["wt103", "wt2"]: kwargs["special"] = ["<eos>"] kwargs["lower_case"] = False elif dataset == "ptb": kwargs["special"] = ["<eos>"] kwargs["lower_case"] = True elif dataset == "lm1b": kwargs["special"] = [] kwargs["lower_case"] = False kwargs["vocab_file"] = os.path.join(datadir, "1b_word_vocab.txt") elif dataset in ["enwik8", "text8"]: pass corpus = TransfoXLCorpus(datadir, dataset, **kwargs) torch.save(corpus, fn) return corpus

transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py/0

{ "file_path": "transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py", "repo_id": "transformers", "token_count": 15131 }

321

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ DistilBERT model configuration""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json", "distilbert-base-uncased-distilled-squad": ( "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json" ), "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json", "distilbert-base-cased-distilled-squad": ( "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json" ), "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json", "distilbert-base-multilingual-cased": ( "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json" ), "distilbert-base-uncased-finetuned-sst-2-english": ( "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json" ), } class DistilBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DistilBertModel`] or a [`TFDistilBertModel`]. It is used to instantiate a DistilBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DistilBERT [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DistilBertModel`] or [`TFDistilBertModel`]. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). sinusoidal_pos_embds (`boolean`, *optional*, defaults to `False`): Whether to use sinusoidal positional embeddings. n_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. n_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. dim (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. hidden_dim (`int`, *optional*, defaults to 3072): The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. activation (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. qa_dropout (`float`, *optional*, defaults to 0.1): The dropout probabilities used in the question answering model [`DistilBertForQuestionAnswering`]. seq_classif_dropout (`float`, *optional*, defaults to 0.2): The dropout probabilities used in the sequence classification and the multiple choice model [`DistilBertForSequenceClassification`]. Examples: ```python >>> from transformers import DistilBertConfig, DistilBertModel >>> # Initializing a DistilBERT configuration >>> configuration = DistilBertConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = DistilBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "distilbert" attribute_map = { "hidden_size": "dim", "num_attention_heads": "n_heads", "num_hidden_layers": "n_layers", } def __init__( self, vocab_size=30522, max_position_embeddings=512, sinusoidal_pos_embds=False, n_layers=6, n_heads=12, dim=768, hidden_dim=4 * 768, dropout=0.1, attention_dropout=0.1, activation="gelu", initializer_range=0.02, qa_dropout=0.1, seq_classif_dropout=0.2, pad_token_id=0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.sinusoidal_pos_embds = sinusoidal_pos_embds self.n_layers = n_layers self.n_heads = n_heads self.dim = dim self.hidden_dim = hidden_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation = activation self.initializer_range = initializer_range self.qa_dropout = qa_dropout self.seq_classif_dropout = seq_classif_dropout super().__init__(**kwargs, pad_token_id=pad_token_id) class DistilBertOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} else: dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ] )

transformers/src/transformers/models/distilbert/configuration_distilbert.py/0

{ "file_path": "transformers/src/transformers/models/distilbert/configuration_distilbert.py", "repo_id": "transformers", "token_count": 2693 }

322

# coding=utf-8 # Copyright 2010, DPR authors, The Hugging Face Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ DPR model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DPR_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/dpr-ctx_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-reader-single-nq-base": ( "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json" ), "facebook/dpr-ctx_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-reader-multiset-base": ( "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json" ), } class DPRConfig(PretrainedConfig): r""" [`DPRConfig`] is the configuration class to store the configuration of a *DPRModel*. This is the configuration class to store the configuration of a [`DPRContextEncoder`], [`DPRQuestionEncoder`], or a [`DPRReader`]. It is used to instantiate the components of the DPR model according to the specified arguments, defining the model component architectures. Instantiating a configuration with the defaults will yield a similar configuration to that of the DPRContextEncoder [facebook/dpr-ctx_encoder-single-nq-base](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base) architecture. This class is a subclass of [`BertConfig`]. Please check the superclass for the documentation of all kwargs. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the DPR model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`BertModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the *token_type_ids* passed into [`BertModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). projection_dim (`int`, *optional*, defaults to 0): Dimension of the projection for the context and question encoders. If it is set to zero (default), then no projection is done. Example: ```python >>> from transformers import DPRConfig, DPRContextEncoder >>> # Initializing a DPR facebook/dpr-ctx_encoder-single-nq-base style configuration >>> configuration = DPRConfig() >>> # Initializing a model (with random weights) from the facebook/dpr-ctx_encoder-single-nq-base style configuration >>> model = DPRContextEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "dpr" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", projection_dim: int = 0, **kwargs, ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.projection_dim = projection_dim self.position_embedding_type = position_embedding_type

transformers/src/transformers/models/dpr/configuration_dpr.py/0

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# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) _import_structure = { "configuration_efficientformer": [ "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "EfficientFormerConfig", ] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["image_processing_efficientformer"] = ["EfficientFormerImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_efficientformer"] = [ "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "EfficientFormerForImageClassification", "EfficientFormerForImageClassificationWithTeacher", "EfficientFormerModel", "EfficientFormerPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_efficientformer"] = [ "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFEfficientFormerForImageClassification", "TFEfficientFormerForImageClassificationWithTeacher", "TFEfficientFormerModel", "TFEfficientFormerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_efficientformer import EfficientFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_efficientformer import ( EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, EfficientFormerForImageClassification, EfficientFormerForImageClassificationWithTeacher, EfficientFormerModel, EfficientFormerPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_efficientformer import ( TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFEfficientFormerForImageClassification, TFEfficientFormerForImageClassificationWithTeacher, TFEfficientFormerModel, TFEfficientFormerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/efficientformer/__init__.py/0

{ "file_path": "transformers/src/transformers/models/efficientformer/__init__.py", "repo_id": "transformers", "token_count": 1330 }

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# coding=utf-8 # Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF Electra model.""" from __future__ import annotations import math import warnings from dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFMaskedLMOutput, TFMultipleChoiceModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFMaskedLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFSequenceSummary, TFTokenClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_electra import ElectraConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/electra-small-discriminator" _CONFIG_FOR_DOC = "ElectraConfig" TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/electra-small-generator", "google/electra-base-generator", "google/electra-large-generator", "google/electra-small-discriminator", "google/electra-base-discriminator", "google/electra-large-discriminator", # See all ELECTRA models at https://huggingface.co/models?filter=electra ] # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Electra class TFElectraSelfAttention(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder self.config = config def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) key_layer = tf.concat([past_key_value[0], key_layer], axis=2) value_layer = tf.concat([past_key_value[1], value_layer], axis=2) else: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFElectraModel call() function) attention_scores = tf.add(attention_scores, attention_mask) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Electra class TFElectraSelfOutput(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Electra class TFElectraAttention(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFElectraSelfAttention(config, name="self") self.dense_output = TFElectraSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) # add attentions (possibly with past_key_value) if we output them outputs = (attention_output,) + self_outputs[1:] return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attention", None) is not None: with tf.name_scope(self.self_attention.name): self.self_attention.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Electra class TFElectraIntermediate(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Electra class TFElectraOutput(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Electra class TFElectraLayer(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.attention = TFElectraAttention(config, name="attention") self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = TFElectraAttention(config, name="crossattention") self.intermediate = TFElectraIntermediate(config, name="intermediate") self.bert_output = TFElectraOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_value: Tuple[tf.Tensor] | None, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( input_tensor=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=self_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( input_tensor=attention_output, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value intermediate_output = self.intermediate(hidden_states=attention_output) layer_output = self.bert_output( hidden_states=intermediate_output, input_tensor=attention_output, training=training ) outputs = (layer_output,) + outputs # add attentions if we output them # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "bert_output", None) is not None: with tf.name_scope(self.bert_output.name): self.bert_output.build(None) if getattr(self, "crossattention", None) is not None: with tf.name_scope(self.crossattention.name): self.crossattention.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Electra class TFElectraEncoder(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFElectraLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_values: Tuple[Tuple[tf.Tensor]] | None, use_cache: Optional[bool], output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) past_key_value = past_key_values[i] if past_key_values is not None else None layer_outputs = layer_module( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Electra class TFElectraPooler(keras.layers.Layer): def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.albert.modeling_tf_albert.TFAlbertEmbeddings with Albert->Electra class TFElectraEmbeddings(keras.layers.Layer): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config: ElectraConfig, **kwargs): super().__init__(**kwargs) self.config = config self.embedding_size = config.embedding_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape=None): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.config.vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.config.type_vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.embedding_size], initializer=get_initializer(self.initializer_range), ) if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.embedding_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call def call( self, input_ids: tf.Tensor = None, position_ids: tf.Tensor = None, token_type_ids: tf.Tensor = None, inputs_embeds: tf.Tensor = None, past_key_values_length=0, training: bool = False, ) -> tf.Tensor: """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ if input_ids is None and inputs_embeds is None: raise ValueError("Need to provide either `input_ids` or `input_embeds`.") if input_ids is not None: check_embeddings_within_bounds(input_ids, self.config.vocab_size) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: position_ids = tf.expand_dims( tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0 ) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings class TFElectraDiscriminatorPredictions(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense(config.hidden_size, name="dense") self.dense_prediction = keras.layers.Dense(1, name="dense_prediction") self.config = config def call(self, discriminator_hidden_states, training=False): hidden_states = self.dense(discriminator_hidden_states) hidden_states = get_tf_activation(self.config.hidden_act)(hidden_states) logits = tf.squeeze(self.dense_prediction(hidden_states), -1) return logits def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "dense_prediction", None) is not None: with tf.name_scope(self.dense_prediction.name): self.dense_prediction.build([None, None, self.config.hidden_size]) class TFElectraGeneratorPredictions(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dense = keras.layers.Dense(config.embedding_size, name="dense") self.config = config def call(self, generator_hidden_states, training=False): hidden_states = self.dense(generator_hidden_states) hidden_states = get_tf_activation("gelu")(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.embedding_size]) if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) class TFElectraPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ElectraConfig base_model_prefix = "electra" # When the model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"generator_lm_head.weight"] _keys_to_ignore_on_load_missing = [r"dropout"] @keras_serializable class TFElectraMainLayer(keras.layers.Layer): config_class = ElectraConfig def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.is_decoder = config.is_decoder self.embeddings = TFElectraEmbeddings(config, name="embeddings") if config.embedding_size != config.hidden_size: self.embeddings_project = keras.layers.Dense(config.hidden_size, name="embeddings_project") self.encoder = TFElectraEncoder(config, name="encoder") def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError def get_extended_attention_mask(self, attention_mask, input_shape, dtype, past_key_values_length=0): batch_size, seq_length = input_shape if attention_mask is None: attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) mask_seq_length = seq_length + past_key_values_length # Copied from `modeling_tf_t5.py` # Provided a padding mask of dimensions [batch_size, mask_seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] if self.is_decoder: seq_ids = tf.range(mask_seq_length) causal_mask = tf.less_equal( tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)), seq_ids[None, :, None], ) causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype) extended_attention_mask = causal_mask * attention_mask[:, None, :] attention_mask_shape = shape_list(extended_attention_mask) extended_attention_mask = tf.reshape( extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2]) ) if past_key_values_length > 0: extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :] else: extended_attention_mask = tf.reshape( attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=dtype) one_cst = tf.constant(1.0, dtype=dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) return extended_attention_mask def get_head_mask(self, head_mask): if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers return head_mask @unpack_inputs def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: if not self.config.is_decoder: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape if past_key_values is None: past_key_values_length = 0 past_key_values = [None] * len(self.encoder.layer) else: past_key_values_length = shape_list(past_key_values[0][0])[-2] if attention_mask is None: attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1) if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) hidden_states = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, training=training, ) extended_attention_mask = self.get_extended_attention_mask( attention_mask, input_shape, hidden_states.dtype, past_key_values_length ) # Copied from `modeling_tf_t5.py` with -1e9 -> -10000 if self.is_decoder and encoder_attention_mask is not None: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype) num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) if num_dims_encoder_attention_mask == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if num_dims_encoder_attention_mask == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 else: encoder_extended_attention_mask = None head_mask = self.get_head_mask(head_mask) if hasattr(self, "embeddings_project"): hidden_states = self.embeddings_project(hidden_states, training=training) hidden_states = self.encoder( hidden_states=hidden_states, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "embeddings_project", None) is not None: with tf.name_scope(self.embeddings_project.name): self.embeddings_project.build([None, None, self.config.embedding_size]) @dataclass class TFElectraForPreTrainingOutput(ModelOutput): """ Output type of [`TFElectraForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`): Total loss of the ELECTRA objective. logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Prediction scores of the head (scores for each token before SoftMax). hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None ELECTRA_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`ElectraConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ELECTRA_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare Electra Model transformer outputting raw hidden-states without any specific head on top. Identical to " "the BERT model except that it uses an additional linear layer between the embedding layer and the encoder if the " "hidden size and embedding size are different. " "" "Both the generator and discriminator checkpoints may be loaded into this model.", ELECTRA_START_DOCSTRING, ) class TFElectraModel(TFElectraPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.electra = TFElectraMainLayer(config, name="electra") @unpack_inputs @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation """ outputs = self.electra( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "electra", None) is not None: with tf.name_scope(self.electra.name): self.electra.build(None) @add_start_docstrings( """ Electra model with a binary classification head on top as used during pretraining for identifying generated tokens. Even though both the discriminator and generator may be loaded into this model, the discriminator is the only model of the two to have the correct classification head to be used for this model. """, ELECTRA_START_DOCSTRING, ) class TFElectraForPreTraining(TFElectraPreTrainedModel): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.electra = TFElectraMainLayer(config, name="electra") self.discriminator_predictions = TFElectraDiscriminatorPredictions(config, name="discriminator_predictions") @unpack_inputs @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFElectraForPreTrainingOutput, Tuple[tf.Tensor]]: r""" Returns: Examples: ```python >>> import tensorflow as tf >>> from transformers import AutoTokenizer, TFElectraForPreTraining >>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator") >>> model = TFElectraForPreTraining.from_pretrained("google/electra-small-discriminator") >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1 >>> outputs = model(input_ids) >>> scores = outputs[0] ```""" discriminator_hidden_states = self.electra( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) discriminator_sequence_output = discriminator_hidden_states[0] logits = self.discriminator_predictions(discriminator_sequence_output) if not return_dict: return (logits,) + discriminator_hidden_states[1:] return TFElectraForPreTrainingOutput( logits=logits, hidden_states=discriminator_hidden_states.hidden_states, attentions=discriminator_hidden_states.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "electra", None) is not None: with tf.name_scope(self.electra.name): self.electra.build(None) if getattr(self, "discriminator_predictions", None) is not None: with tf.name_scope(self.discriminator_predictions.name): self.discriminator_predictions.build(None) class TFElectraMaskedLMHead(keras.layers.Layer): def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.config = config self.embedding_size = config.embedding_size self.input_embeddings = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def get_output_embeddings(self): return self.input_embeddings def set_output_embeddings(self, value): self.input_embeddings.weight = value self.input_embeddings.vocab_size = shape_list(value)[0] def get_bias(self): return {"bias": self.bias} def set_bias(self, value): self.bias = value["bias"] self.config.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states): seq_length = shape_list(tensor=hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size]) hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states @add_start_docstrings( """ Electra model with a language modeling head on top. Even though both the discriminator and generator may be loaded into this model, the generator is the only model of the two to have been trained for the masked language modeling task. """, ELECTRA_START_DOCSTRING, ) class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLoss): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.config = config self.electra = TFElectraMainLayer(config, name="electra") self.generator_predictions = TFElectraGeneratorPredictions(config, name="generator_predictions") if isinstance(config.hidden_act, str): self.activation = get_tf_activation(config.hidden_act) else: self.activation = config.hidden_act self.generator_lm_head = TFElectraMaskedLMHead(config, self.electra.embeddings, name="generator_lm_head") def get_lm_head(self): return self.generator_lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.generator_lm_head.name @unpack_inputs @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="google/electra-small-generator", output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="[MASK]", expected_output="'paris'", expected_loss=1.22, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ generator_hidden_states = self.electra( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) generator_sequence_output = generator_hidden_states[0] prediction_scores = self.generator_predictions(generator_sequence_output, training=training) prediction_scores = self.generator_lm_head(prediction_scores, training=training) loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores) if not return_dict: output = (prediction_scores,) + generator_hidden_states[1:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=generator_hidden_states.hidden_states, attentions=generator_hidden_states.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "electra", None) is not None: with tf.name_scope(self.electra.name): self.electra.build(None) if getattr(self, "generator_predictions", None) is not None: with tf.name_scope(self.generator_predictions.name): self.generator_predictions.build(None) if getattr(self, "generator_lm_head", None) is not None: with tf.name_scope(self.generator_lm_head.name): self.generator_lm_head.build(None) class TFElectraClassificationHead(keras.layers.Layer): """Head for sentence-level classification tasks.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) classifier_dropout = ( config.classifhidden_dropout_probier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = keras.layers.Dropout(classifier_dropout) self.out_proj = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj" ) self.config = config def call(self, inputs, **kwargs): x = inputs[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = get_tf_activation("gelu")(x) # although BERT uses tanh here, it seems Electra authors used gelu here x = self.dropout(x) x = self.out_proj(x) return x def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "out_proj", None) is not None: with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ELECTRA_START_DOCSTRING, ) class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.electra = TFElectraMainLayer(config, name="electra") self.classifier = TFElectraClassificationHead(config, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="bhadresh-savani/electra-base-emotion", output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'joy'", expected_loss=0.06, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs = self.electra( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) logits = self.classifier(outputs[0]) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "electra", None) is not None: with tf.name_scope(self.electra.name): self.electra.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ ELECTRA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ELECTRA_START_DOCSTRING, ) class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.electra = TFElectraMainLayer(config, name="electra") self.sequence_summary = TFSequenceSummary( config, initializer_range=config.initializer_range, name="sequence_summary" ) self.classifier = keras.layers.Dense( 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ if input_ids is not None: num_choices = shape_list(input_ids)[1] seq_length = shape_list(input_ids)[2] else: num_choices = shape_list(inputs_embeds)[1] seq_length = shape_list(inputs_embeds)[2] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None flat_inputs_embeds = ( tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3])) if inputs_embeds is not None else None ) outputs = self.electra( input_ids=flat_input_ids, attention_mask=flat_attention_mask, token_type_ids=flat_token_type_ids, position_ids=flat_position_ids, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) logits = self.sequence_summary(outputs[0]) logits = self.classifier(logits) reshaped_logits = tf.reshape(logits, (-1, num_choices)) loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits) if not return_dict: output = (reshaped_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFMultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "electra", None) is not None: with tf.name_scope(self.electra.name): self.electra.build(None) if getattr(self, "sequence_summary", None) is not None: with tf.name_scope(self.sequence_summary.name): self.sequence_summary.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ Electra model with a token classification head on top. Both the discriminator and generator may be loaded into this model. """, ELECTRA_START_DOCSTRING, ) class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassificationLoss): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self.electra = TFElectraMainLayer(config, name="electra") classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = keras.layers.Dropout(classifier_dropout) self.classifier = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english", output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']", expected_loss=0.11, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ discriminator_hidden_states = self.electra( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) discriminator_sequence_output = discriminator_hidden_states[0] discriminator_sequence_output = self.dropout(discriminator_sequence_output) logits = self.classifier(discriminator_sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + discriminator_hidden_states[1:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=discriminator_hidden_states.hidden_states, attentions=discriminator_hidden_states.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "electra", None) is not None: with tf.name_scope(self.electra.name): self.electra.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ Electra Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ELECTRA_START_DOCSTRING, ) class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnsweringLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.electra = TFElectraMainLayer(config, name="electra") self.qa_outputs = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="bhadresh-savani/electra-base-squad2", output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, qa_target_start_index=11, qa_target_end_index=12, expected_output="'a nice puppet'", expected_loss=2.64, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ discriminator_hidden_states = self.electra( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) discriminator_sequence_output = discriminator_hidden_states[0] logits = self.qa_outputs(discriminator_sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions} labels["end_position"] = end_positions loss = self.hf_compute_loss(labels, (start_logits, end_logits)) if not return_dict: output = ( start_logits, end_logits, ) + discriminator_hidden_states[1:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=discriminator_hidden_states.hidden_states, attentions=discriminator_hidden_states.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "electra", None) is not None: with tf.name_scope(self.electra.name): self.electra.build(None) if getattr(self, "qa_outputs", None) is not None: with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])

transformers/src/transformers/models/electra/modeling_tf_electra.py/0

{ "file_path": "transformers/src/transformers/models/electra/modeling_tf_electra.py", "repo_id": "transformers", "token_count": 33552 }

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# Copyright 2023 The HuggingFace and Baidu Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available _import_structure = { "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_ernie_m"] = ["ErnieMTokenizer"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_ernie_m"] = [ "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST", "ErnieMForMultipleChoice", "ErnieMForQuestionAnswering", "ErnieMForSequenceClassification", "ErnieMForTokenClassification", "ErnieMModel", "ErnieMPreTrainedModel", "ErnieMForInformationExtraction", ] if TYPE_CHECKING: from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_ernie_m import ErnieMTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_ernie_m import ( ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST, ErnieMForInformationExtraction, ErnieMForMultipleChoice, ErnieMForQuestionAnswering, ErnieMForSequenceClassification, ErnieMForTokenClassification, ErnieMModel, ErnieMPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/ernie_m/__init__.py/0

{ "file_path": "transformers/src/transformers/models/ernie_m/__init__.py", "repo_id": "transformers", "token_count": 1020 }

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# Copyright 2021 AlQuraishi Laboratory # Copyright 2021 DeepMind Technologies Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Constants used in AlphaFold.""" import collections import copy import functools from importlib import resources from typing import Dict, List, Mapping, Sequence, Tuple import numpy as np # Internal import (35fd). # Distance from one CA to next CA [trans configuration: omega = 180]. ca_ca = 3.80209737096 # Format: The list for each AA type contains chi1, chi2, chi3, chi4 in # this order (or a relevant subset from chi1 onwards). ALA and GLY don't have # chi angles so their chi angle lists are empty. chi_angles_atoms: Dict[str, List[List[str]]] = { "ALA": [], # Chi5 in arginine is always 0 +- 5 degrees, so ignore it. "ARG": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "NE"], ["CG", "CD", "NE", "CZ"]], "ASN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], "ASP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], "CYS": [["N", "CA", "CB", "SG"]], "GLN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"]], "GLU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"]], "GLY": [], "HIS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "ND1"]], "ILE": [["N", "CA", "CB", "CG1"], ["CA", "CB", "CG1", "CD1"]], "LEU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "LYS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "CE"], ["CG", "CD", "CE", "NZ"]], "MET": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "SD"], ["CB", "CG", "SD", "CE"]], "PHE": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "PRO": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"]], "SER": [["N", "CA", "CB", "OG"]], "THR": [["N", "CA", "CB", "OG1"]], "TRP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "TYR": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "VAL": [["N", "CA", "CB", "CG1"]], } # If chi angles given in fixed-length array, this matrix determines how to mask # them for each AA type. The order is as per restype_order (see below). chi_angles_mask: List[List[float]] = [ [0.0, 0.0, 0.0, 0.0], # ALA [1.0, 1.0, 1.0, 1.0], # ARG [1.0, 1.0, 0.0, 0.0], # ASN [1.0, 1.0, 0.0, 0.0], # ASP [1.0, 0.0, 0.0, 0.0], # CYS [1.0, 1.0, 1.0, 0.0], # GLN [1.0, 1.0, 1.0, 0.0], # GLU [0.0, 0.0, 0.0, 0.0], # GLY [1.0, 1.0, 0.0, 0.0], # HIS [1.0, 1.0, 0.0, 0.0], # ILE [1.0, 1.0, 0.0, 0.0], # LEU [1.0, 1.0, 1.0, 1.0], # LYS [1.0, 1.0, 1.0, 0.0], # MET [1.0, 1.0, 0.0, 0.0], # PHE [1.0, 1.0, 0.0, 0.0], # PRO [1.0, 0.0, 0.0, 0.0], # SER [1.0, 0.0, 0.0, 0.0], # THR [1.0, 1.0, 0.0, 0.0], # TRP [1.0, 1.0, 0.0, 0.0], # TYR [1.0, 0.0, 0.0, 0.0], # VAL ] # The following chi angles are pi periodic: they can be rotated by a multiple # of pi without affecting the structure. chi_pi_periodic: List[List[float]] = [ [0.0, 0.0, 0.0, 0.0], # ALA [0.0, 0.0, 0.0, 0.0], # ARG [0.0, 0.0, 0.0, 0.0], # ASN [0.0, 1.0, 0.0, 0.0], # ASP [0.0, 0.0, 0.0, 0.0], # CYS [0.0, 0.0, 0.0, 0.0], # GLN [0.0, 0.0, 1.0, 0.0], # GLU [0.0, 0.0, 0.0, 0.0], # GLY [0.0, 0.0, 0.0, 0.0], # HIS [0.0, 0.0, 0.0, 0.0], # ILE [0.0, 0.0, 0.0, 0.0], # LEU [0.0, 0.0, 0.0, 0.0], # LYS [0.0, 0.0, 0.0, 0.0], # MET [0.0, 1.0, 0.0, 0.0], # PHE [0.0, 0.0, 0.0, 0.0], # PRO [0.0, 0.0, 0.0, 0.0], # SER [0.0, 0.0, 0.0, 0.0], # THR [0.0, 0.0, 0.0, 0.0], # TRP [0.0, 1.0, 0.0, 0.0], # TYR [0.0, 0.0, 0.0, 0.0], # VAL [0.0, 0.0, 0.0, 0.0], # UNK ] # Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi, # psi and chi angles: # 0: 'backbone group', # 1: 'pre-omega-group', (empty) # 2: 'phi-group', (currently empty, because it defines only hydrogens) # 3: 'psi-group', # 4,5,6,7: 'chi1,2,3,4-group' # The atom positions are relative to the axis-end-atom of the corresponding # rotation axis. The x-axis is in direction of the rotation axis, and the y-axis # is defined such that the dihedral-angle-definiting atom (the last entry in # chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate). # format: [atomname, group_idx, rel_position] rigid_group_atom_positions: Dict[str, List[Tuple[str, int, Tuple[float, float, float]]]] = { "ALA": [ ("N", 0, (-0.525, 1.363, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.526, -0.000, -0.000)), ("CB", 0, (-0.529, -0.774, -1.205)), ("O", 3, (0.627, 1.062, 0.000)), ], "ARG": [ ("N", 0, (-0.524, 1.362, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.525, -0.000, -0.000)), ("CB", 0, (-0.524, -0.778, -1.209)), ("O", 3, (0.626, 1.062, 0.000)), ("CG", 4, (0.616, 1.390, -0.000)), ("CD", 5, (0.564, 1.414, 0.000)), ("NE", 6, (0.539, 1.357, -0.000)), ("NH1", 7, (0.206, 2.301, 0.000)), ("NH2", 7, (2.078, 0.978, -0.000)), ("CZ", 7, (0.758, 1.093, -0.000)), ], "ASN": [ ("N", 0, (-0.536, 1.357, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.526, -0.000, -0.000)), ("CB", 0, (-0.531, -0.787, -1.200)), ("O", 3, (0.625, 1.062, 0.000)), ("CG", 4, (0.584, 1.399, 0.000)), ("ND2", 5, (0.593, -1.188, 0.001)), ("OD1", 5, (0.633, 1.059, 0.000)), ], "ASP": [ ("N", 0, (-0.525, 1.362, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.527, 0.000, -0.000)), ("CB", 0, (-0.526, -0.778, -1.208)), ("O", 3, (0.626, 1.062, -0.000)), ("CG", 4, (0.593, 1.398, -0.000)), ("OD1", 5, (0.610, 1.091, 0.000)), ("OD2", 5, (0.592, -1.101, -0.003)), ], "CYS": [ ("N", 0, (-0.522, 1.362, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.524, 0.000, 0.000)), ("CB", 0, (-0.519, -0.773, -1.212)), ("O", 3, (0.625, 1.062, -0.000)), ("SG", 4, (0.728, 1.653, 0.000)), ], "GLN": [ ("N", 0, (-0.526, 1.361, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.526, 0.000, 0.000)), ("CB", 0, (-0.525, -0.779, -1.207)), ("O", 3, (0.626, 1.062, -0.000)), ("CG", 4, (0.615, 1.393, 0.000)), ("CD", 5, (0.587, 1.399, -0.000)), ("NE2", 6, (0.593, -1.189, -0.001)), ("OE1", 6, (0.634, 1.060, 0.000)), ], "GLU": [ ("N", 0, (-0.528, 1.361, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.526, -0.000, -0.000)), ("CB", 0, (-0.526, -0.781, -1.207)), ("O", 3, (0.626, 1.062, 0.000)), ("CG", 4, (0.615, 1.392, 0.000)), ("CD", 5, (0.600, 1.397, 0.000)), ("OE1", 6, (0.607, 1.095, -0.000)), ("OE2", 6, (0.589, -1.104, -0.001)), ], "GLY": [ ("N", 0, (-0.572, 1.337, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.517, -0.000, -0.000)), ("O", 3, (0.626, 1.062, -0.000)), ], "HIS": [ ("N", 0, (-0.527, 1.360, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.525, 0.000, 0.000)), ("CB", 0, (-0.525, -0.778, -1.208)), ("O", 3, (0.625, 1.063, 0.000)), ("CG", 4, (0.600, 1.370, -0.000)), ("CD2", 5, (0.889, -1.021, 0.003)), ("ND1", 5, (0.744, 1.160, -0.000)), ("CE1", 5, (2.030, 0.851, 0.002)), ("NE2", 5, (2.145, -0.466, 0.004)), ], "ILE": [ ("N", 0, (-0.493, 1.373, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.527, -0.000, -0.000)), ("CB", 0, (-0.536, -0.793, -1.213)), ("O", 3, (0.627, 1.062, -0.000)), ("CG1", 4, (0.534, 1.437, -0.000)), ("CG2", 4, (0.540, -0.785, -1.199)), ("CD1", 5, (0.619, 1.391, 0.000)), ], "LEU": [ ("N", 0, (-0.520, 1.363, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.525, -0.000, -0.000)), ("CB", 0, (-0.522, -0.773, -1.214)), ("O", 3, (0.625, 1.063, -0.000)), ("CG", 4, (0.678, 1.371, 0.000)), ("CD1", 5, (0.530, 1.430, -0.000)), ("CD2", 5, (0.535, -0.774, 1.200)), ], "LYS": [ ("N", 0, (-0.526, 1.362, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.526, 0.000, 0.000)), ("CB", 0, (-0.524, -0.778, -1.208)), ("O", 3, (0.626, 1.062, -0.000)), ("CG", 4, (0.619, 1.390, 0.000)), ("CD", 5, (0.559, 1.417, 0.000)), ("CE", 6, (0.560, 1.416, 0.000)), ("NZ", 7, (0.554, 1.387, 0.000)), ], "MET": [ ("N", 0, (-0.521, 1.364, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.525, 0.000, 0.000)), ("CB", 0, (-0.523, -0.776, -1.210)), ("O", 3, (0.625, 1.062, -0.000)), ("CG", 4, (0.613, 1.391, -0.000)), ("SD", 5, (0.703, 1.695, 0.000)), ("CE", 6, (0.320, 1.786, -0.000)), ], "PHE": [ ("N", 0, (-0.518, 1.363, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.524, 0.000, -0.000)), ("CB", 0, (-0.525, -0.776, -1.212)), ("O", 3, (0.626, 1.062, -0.000)), ("CG", 4, (0.607, 1.377, 0.000)), ("CD1", 5, (0.709, 1.195, -0.000)), ("CD2", 5, (0.706, -1.196, 0.000)), ("CE1", 5, (2.102, 1.198, -0.000)), ("CE2", 5, (2.098, -1.201, -0.000)), ("CZ", 5, (2.794, -0.003, -0.001)), ], "PRO": [ ("N", 0, (-0.566, 1.351, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.527, -0.000, 0.000)), ("CB", 0, (-0.546, -0.611, -1.293)), ("O", 3, (0.621, 1.066, 0.000)), ("CG", 4, (0.382, 1.445, 0.0)), # ('CD', 5, (0.427, 1.440, 0.0)), ("CD", 5, (0.477, 1.424, 0.0)), # manually made angle 2 degrees larger ], "SER": [ ("N", 0, (-0.529, 1.360, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.525, -0.000, -0.000)), ("CB", 0, (-0.518, -0.777, -1.211)), ("O", 3, (0.626, 1.062, -0.000)), ("OG", 4, (0.503, 1.325, 0.000)), ], "THR": [ ("N", 0, (-0.517, 1.364, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.526, 0.000, -0.000)), ("CB", 0, (-0.516, -0.793, -1.215)), ("O", 3, (0.626, 1.062, 0.000)), ("CG2", 4, (0.550, -0.718, -1.228)), ("OG1", 4, (0.472, 1.353, 0.000)), ], "TRP": [ ("N", 0, (-0.521, 1.363, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.525, -0.000, 0.000)), ("CB", 0, (-0.523, -0.776, -1.212)), ("O", 3, (0.627, 1.062, 0.000)), ("CG", 4, (0.609, 1.370, -0.000)), ("CD1", 5, (0.824, 1.091, 0.000)), ("CD2", 5, (0.854, -1.148, -0.005)), ("CE2", 5, (2.186, -0.678, -0.007)), ("CE3", 5, (0.622, -2.530, -0.007)), ("NE1", 5, (2.140, 0.690, -0.004)), ("CH2", 5, (3.028, -2.890, -0.013)), ("CZ2", 5, (3.283, -1.543, -0.011)), ("CZ3", 5, (1.715, -3.389, -0.011)), ], "TYR": [ ("N", 0, (-0.522, 1.362, 0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.524, -0.000, -0.000)), ("CB", 0, (-0.522, -0.776, -1.213)), ("O", 3, (0.627, 1.062, -0.000)), ("CG", 4, (0.607, 1.382, -0.000)), ("CD1", 5, (0.716, 1.195, -0.000)), ("CD2", 5, (0.713, -1.194, -0.001)), ("CE1", 5, (2.107, 1.200, -0.002)), ("CE2", 5, (2.104, -1.201, -0.003)), ("OH", 5, (4.168, -0.002, -0.005)), ("CZ", 5, (2.791, -0.001, -0.003)), ], "VAL": [ ("N", 0, (-0.494, 1.373, -0.000)), ("CA", 0, (0.000, 0.000, 0.000)), ("C", 0, (1.527, -0.000, -0.000)), ("CB", 0, (-0.533, -0.795, -1.213)), ("O", 3, (0.627, 1.062, -0.000)), ("CG1", 4, (0.540, 1.429, -0.000)), ("CG2", 4, (0.533, -0.776, 1.203)), ], } # A list of atoms (excluding hydrogen) for each AA type. PDB naming convention. residue_atoms: Dict[str, List[str]] = { "ALA": ["C", "CA", "CB", "N", "O"], "ARG": ["C", "CA", "CB", "CG", "CD", "CZ", "N", "NE", "O", "NH1", "NH2"], "ASP": ["C", "CA", "CB", "CG", "N", "O", "OD1", "OD2"], "ASN": ["C", "CA", "CB", "CG", "N", "ND2", "O", "OD1"], "CYS": ["C", "CA", "CB", "N", "O", "SG"], "GLU": ["C", "CA", "CB", "CG", "CD", "N", "O", "OE1", "OE2"], "GLN": ["C", "CA", "CB", "CG", "CD", "N", "NE2", "O", "OE1"], "GLY": ["C", "CA", "N", "O"], "HIS": ["C", "CA", "CB", "CG", "CD2", "CE1", "N", "ND1", "NE2", "O"], "ILE": ["C", "CA", "CB", "CG1", "CG2", "CD1", "N", "O"], "LEU": ["C", "CA", "CB", "CG", "CD1", "CD2", "N", "O"], "LYS": ["C", "CA", "CB", "CG", "CD", "CE", "N", "NZ", "O"], "MET": ["C", "CA", "CB", "CG", "CE", "N", "O", "SD"], "PHE": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O"], "PRO": ["C", "CA", "CB", "CG", "CD", "N", "O"], "SER": ["C", "CA", "CB", "N", "O", "OG"], "THR": ["C", "CA", "CB", "CG2", "N", "O", "OG1"], "TRP": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE2", "CE3", "CZ2", "CZ3", "CH2", "N", "NE1", "O"], "TYR": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O", "OH"], "VAL": ["C", "CA", "CB", "CG1", "CG2", "N", "O"], } # Naming swaps for ambiguous atom names. # Due to symmetries in the amino acids the naming of atoms is ambiguous in # 4 of the 20 amino acids. # (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities # in LEU, VAL and ARG can be resolved by using the 3d constellations of # the 'ambiguous' atoms and their neighbours) # TODO: ^ interpret this residue_atom_renaming_swaps: Dict[str, Dict[str, str]] = { "ASP": {"OD1": "OD2"}, "GLU": {"OE1": "OE2"}, "PHE": {"CD1": "CD2", "CE1": "CE2"}, "TYR": {"CD1": "CD2", "CE1": "CE2"}, } # Van der Waals radii [Angstroem] of the atoms (from Wikipedia) van_der_waals_radius: Dict[str, float] = { "C": 1.7, "N": 1.55, "O": 1.52, "S": 1.8, } Bond = collections.namedtuple("Bond", ["atom1_name", "atom2_name", "length", "stddev"]) BondAngle = collections.namedtuple( "BondAngle", ["atom1_name", "atom2_name", "atom3name", "angle_rad", "stddev"], ) def map_structure_with_atom_order(in_list: list, first_call: bool = True) -> list: # Maps strings in a nested list structure to their corresponding index in atom_order if first_call: in_list = copy.deepcopy(in_list) for i in range(len(in_list)): if isinstance(in_list[i], list): in_list[i] = map_structure_with_atom_order(in_list[i], first_call=False) elif isinstance(in_list[i], str): in_list[i] = atom_order[in_list[i]] else: raise ValueError("Unexpected type when mapping nested lists!") return in_list @functools.lru_cache(maxsize=None) def load_stereo_chemical_props() -> ( Tuple[ Mapping[str, List[Bond]], Mapping[str, List[Bond]], Mapping[str, List[BondAngle]], ] ): """Load stereo_chemical_props.txt into a nice structure. Load literature values for bond lengths and bond angles and translate bond angles into the length of the opposite edge of the triangle ("residue_virtual_bonds"). Returns: residue_bonds: dict that maps resname --> list of Bond tuples residue_virtual_bonds: dict that maps resname --> list of Bond tuples residue_bond_angles: dict that maps resname --> list of BondAngle tuples """ # TODO: this file should be downloaded in a setup script stereo_chemical_props = resources.read_text("openfold.resources", "stereo_chemical_props.txt") lines_iter = iter(stereo_chemical_props.splitlines()) # Load bond lengths. residue_bonds: Dict[str, List[Bond]] = {} next(lines_iter) # Skip header line. for line in lines_iter: if line.strip() == "-": break bond, resname, bond_length, stddev = line.split() atom1, atom2 = bond.split("-") if resname not in residue_bonds: residue_bonds[resname] = [] residue_bonds[resname].append(Bond(atom1, atom2, float(bond_length), float(stddev))) residue_bonds["UNK"] = [] # Load bond angles. residue_bond_angles: Dict[str, List[BondAngle]] = {} next(lines_iter) # Skip empty line. next(lines_iter) # Skip header line. for line in lines_iter: if line.strip() == "-": break bond, resname, angle_degree, stddev_degree = line.split() atom1, atom2, atom3 = bond.split("-") if resname not in residue_bond_angles: residue_bond_angles[resname] = [] residue_bond_angles[resname].append( BondAngle( atom1, atom2, atom3, float(angle_degree) / 180.0 * np.pi, float(stddev_degree) / 180.0 * np.pi, ) ) residue_bond_angles["UNK"] = [] def make_bond_key(atom1_name: str, atom2_name: str) -> str: """Unique key to lookup bonds.""" return "-".join(sorted([atom1_name, atom2_name])) # Translate bond angles into distances ("virtual bonds"). residue_virtual_bonds: Dict[str, List[Bond]] = {} for resname, bond_angles in residue_bond_angles.items(): # Create a fast lookup dict for bond lengths. bond_cache: Dict[str, Bond] = {} for b in residue_bonds[resname]: bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b residue_virtual_bonds[resname] = [] for ba in bond_angles: bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)] bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)] # Compute distance between atom1 and atom3 using the law of cosines # c^2 = a^2 + b^2 - 2ab*cos(gamma). gamma = ba.angle_rad length = np.sqrt(bond1.length**2 + bond2.length**2 - 2 * bond1.length * bond2.length * np.cos(gamma)) # Propagation of uncertainty assuming uncorrelated errors. dl_outer = 0.5 / length dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer stddev = np.sqrt( (dl_dgamma * ba.stddev) ** 2 + (dl_db1 * bond1.stddev) ** 2 + (dl_db2 * bond2.stddev) ** 2 ) residue_virtual_bonds[resname].append(Bond(ba.atom1_name, ba.atom3name, length, stddev)) return (residue_bonds, residue_virtual_bonds, residue_bond_angles) # Between-residue bond lengths for general bonds (first element) and for Proline # (second element). between_res_bond_length_c_n: Tuple[float, float] = (1.329, 1.341) between_res_bond_length_stddev_c_n: Tuple[float, float] = (0.014, 0.016) # Between-residue cos_angles. between_res_cos_angles_c_n_ca: Tuple[float, float] = (-0.5203, 0.0353) # degrees: 121.352 +- 2.315 between_res_cos_angles_ca_c_n: Tuple[float, float] = (-0.4473, 0.0311) # degrees: 116.568 +- 1.995 # This mapping is used when we need to store atom data in a format that requires # fixed atom data size for every residue (e.g. a numpy array). atom_types: List[str] = [ "N", "CA", "C", "CB", "O", "CG", "CG1", "CG2", "OG", "OG1", "SG", "CD", "CD1", "CD2", "ND1", "ND2", "OD1", "OD2", "SD", "CE", "CE1", "CE2", "CE3", "NE", "NE1", "NE2", "OE1", "OE2", "CH2", "NH1", "NH2", "OH", "CZ", "CZ2", "CZ3", "NZ", "OXT", ] atom_order: Dict[str, int] = {atom_type: i for i, atom_type in enumerate(atom_types)} atom_type_num = len(atom_types) # := 37. # A compact atom encoding with 14 columns # pylint: disable=line-too-long # pylint: disable=bad-whitespace restype_name_to_atom14_names: Dict[str, List[str]] = { "ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""], "ARG": ["N", "CA", "C", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2", "", "", ""], "ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", ""], "ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", ""], "CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""], "GLN": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2", "", "", "", "", ""], "GLU": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2", "", "", "", "", ""], "GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""], "HIS": ["N", "CA", "C", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2", "", "", "", ""], "ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", ""], "LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", ""], "LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", ""], "MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", ""], "PHE": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "", "", ""], "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""], "SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""], "THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", ""], "TRP": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "NE1", "CE2", "CE3", "CZ2", "CZ3", "CH2"], "TYR": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH", "", ""], "VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", ""], "UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""], } # pylint: enable=line-too-long # pylint: enable=bad-whitespace # This is the standard residue order when coding AA type as a number. # Reproduce it by taking 3-letter AA codes and sorting them alphabetically. restypes: List[str] = [ "A", "R", "N", "D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V", ] restype_order: Dict[str, int] = {restype: i for i, restype in enumerate(restypes)} restype_num = len(restypes) # := 20. unk_restype_index = restype_num # Catch-all index for unknown restypes. restypes_with_x: List[str] = restypes + ["X"] restype_order_with_x: Dict[str, int] = {restype: i for i, restype in enumerate(restypes_with_x)} def sequence_to_onehot(sequence: str, mapping: Mapping[str, int], map_unknown_to_x: bool = False) -> np.ndarray: """Maps the given sequence into a one-hot encoded matrix. Args: sequence: An amino acid sequence. mapping: A dictionary mapping amino acids to integers. map_unknown_to_x: If True, any amino acid that is not in the mapping will be mapped to the unknown amino acid 'X'. If the mapping doesn't contain amino acid 'X', an error will be thrown. If False, any amino acid not in the mapping will throw an error. Returns: A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of the sequence. Raises: ValueError: If the mapping doesn't contain values from 0 to num_unique_aas - 1 without any gaps. """ num_entries = max(mapping.values()) + 1 if sorted(set(mapping.values())) != list(range(num_entries)): raise ValueError( "The mapping must have values from 0 to num_unique_aas-1 without any gaps. Got: %s" % sorted(mapping.values()) ) one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32) for aa_index, aa_type in enumerate(sequence): if map_unknown_to_x: if aa_type.isalpha() and aa_type.isupper(): aa_id = mapping.get(aa_type, mapping["X"]) else: raise ValueError(f"Invalid character in the sequence: {aa_type}") else: aa_id = mapping[aa_type] one_hot_arr[aa_index, aa_id] = 1 return one_hot_arr restype_1to3: Dict[str, str] = { "A": "ALA", "R": "ARG", "N": "ASN", "D": "ASP", "C": "CYS", "Q": "GLN", "E": "GLU", "G": "GLY", "H": "HIS", "I": "ILE", "L": "LEU", "K": "LYS", "M": "MET", "F": "PHE", "P": "PRO", "S": "SER", "T": "THR", "W": "TRP", "Y": "TYR", "V": "VAL", } # NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple # 1-to-1 mapping of 3 letter names to one letter names. The latter contains # many more, and less common, three letter names as keys and maps many of these # to the same one letter name (including 'X' and 'U' which we don't use here). restype_3to1: Dict[str, str] = {v: k for k, v in restype_1to3.items()} # Define a restype name for all unknown residues. unk_restype = "UNK" resnames: List[str] = [restype_1to3[r] for r in restypes] + [unk_restype] resname_to_idx: Dict[str, int] = {resname: i for i, resname in enumerate(resnames)} # The mapping here uses hhblits convention, so that B is mapped to D, J and O # are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the # remaining 20 amino acids are kept in alphabetical order. # There are 2 non-amino acid codes, X (representing any amino acid) and # "-" representing a missing amino acid in an alignment. The id for these # codes is put at the end (20 and 21) so that they can easily be ignored if # desired. HHBLITS_AA_TO_ID: Dict[str, int] = { "A": 0, "B": 2, "C": 1, "D": 2, "E": 3, "F": 4, "G": 5, "H": 6, "I": 7, "J": 20, "K": 8, "L": 9, "M": 10, "N": 11, "O": 20, "P": 12, "Q": 13, "R": 14, "S": 15, "T": 16, "U": 1, "V": 17, "W": 18, "X": 20, "Y": 19, "Z": 3, "-": 21, } # Partial inversion of HHBLITS_AA_TO_ID. ID_TO_HHBLITS_AA: Dict[int, str] = { 0: "A", 1: "C", # Also U. 2: "D", # Also B. 3: "E", # Also Z. 4: "F", 5: "G", 6: "H", 7: "I", 8: "K", 9: "L", 10: "M", 11: "N", 12: "P", 13: "Q", 14: "R", 15: "S", 16: "T", 17: "V", 18: "W", 19: "Y", 20: "X", # Includes J and O. 21: "-", } restypes_with_x_and_gap: List[str] = restypes + ["X", "-"] MAP_HHBLITS_AATYPE_TO_OUR_AATYPE: Tuple[int, ...] = tuple( restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i]) for i in range(len(restypes_with_x_and_gap)) ) def _make_standard_atom_mask() -> np.ndarray: """Returns [num_res_types, num_atom_types] mask array.""" # +1 to account for unknown (all 0s). mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32) for restype, restype_letter in enumerate(restypes): restype_name = restype_1to3[restype_letter] atom_names = residue_atoms[restype_name] for atom_name in atom_names: atom_type = atom_order[atom_name] mask[restype, atom_type] = 1 return mask STANDARD_ATOM_MASK = _make_standard_atom_mask() # A one hot representation for the first and second atoms defining the axis # of rotation for each chi-angle in each residue. def chi_angle_atom(atom_index: int) -> np.ndarray: """Define chi-angle rigid groups via one-hot representations.""" chi_angles_index = {} one_hots = [] for k, v in chi_angles_atoms.items(): indices = [atom_types.index(s[atom_index]) for s in v] indices.extend([-1] * (4 - len(indices))) chi_angles_index[k] = indices for r in restypes: res3 = restype_1to3[r] one_hot = np.eye(atom_type_num)[chi_angles_index[res3]] one_hots.append(one_hot) one_hots.append(np.zeros([4, atom_type_num])) # Add zeros for residue `X`. one_hot = np.stack(one_hots, axis=0) one_hot = np.transpose(one_hot, [0, 2, 1]) return one_hot chi_atom_1_one_hot = chi_angle_atom(1) chi_atom_2_one_hot = chi_angle_atom(2) # An array like chi_angles_atoms but using indices rather than names. chi_angles_atom_indices_list: List[List[List[str]]] = [chi_angles_atoms[restype_1to3[r]] for r in restypes] chi_angles_atom_indices_ours: list = map_structure_with_atom_order(chi_angles_atom_indices_list) chi_angles_atom_indices = np.array( [chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms))) for chi_atoms in chi_angles_atom_indices_list] ) # Mapping from (res_name, atom_name) pairs to the atom's chi group index # and atom index within that group. chi_groups_for_atom: Dict[Tuple[str, str], List[Tuple[int, int]]] = collections.defaultdict(list) for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items(): for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res): for atom_i, atom in enumerate(chi_group): chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i)) chi_groups_for_atom = dict(chi_groups_for_atom) def _make_rigid_transformation_4x4(ex: np.ndarray, ey: np.ndarray, translation: np.ndarray) -> np.ndarray: """Create a rigid 4x4 transformation matrix from two axes and transl.""" # Normalize ex. ex_normalized = ex / np.linalg.norm(ex) # make ey perpendicular to ex ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized ey_normalized /= np.linalg.norm(ey_normalized) # compute ez as cross product eznorm = np.cross(ex_normalized, ey_normalized) m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose() m = np.concatenate([m, [[0.0, 0.0, 0.0, 1.0]]], axis=0) return m # create an array with (restype, atomtype) --> rigid_group_idx # and an array with (restype, atomtype, coord) for the atom positions # and compute affine transformation matrices (4,4) from one rigid group to the # previous group restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int) restype_atom37_mask = np.zeros([21, 37], dtype=np.float32) restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32) restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int) restype_atom14_mask = np.zeros([21, 14], dtype=np.float32) restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32) restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32) def _make_rigid_group_constants() -> None: """Fill the arrays above.""" for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] for atomname, group_idx, atom_position in rigid_group_atom_positions[resname]: atomtype = atom_order[atomname] restype_atom37_to_rigid_group[restype, atomtype] = group_idx restype_atom37_mask[restype, atomtype] = 1 restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position atom14idx = restype_name_to_atom14_names[resname].index(atomname) restype_atom14_to_rigid_group[restype, atom14idx] = group_idx restype_atom14_mask[restype, atom14idx] = 1 restype_atom14_rigid_group_positions[restype, atom14idx, :] = atom_position for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] atom_positions: Dict[str, np.ndarray] = { name: np.array(pos) for name, _, pos in rigid_group_atom_positions[resname] } # backbone to backbone is the identity transform restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4) # pre-omega-frame to backbone (currently dummy identity matrix) restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4) # phi-frame to backbone mat = _make_rigid_transformation_4x4( ex=atom_positions["N"] - atom_positions["CA"], ey=np.array([1.0, 0.0, 0.0]), translation=atom_positions["N"], ) restype_rigid_group_default_frame[restype, 2, :, :] = mat # psi-frame to backbone mat = _make_rigid_transformation_4x4( ex=atom_positions["C"] - atom_positions["CA"], ey=atom_positions["CA"] - atom_positions["N"], translation=atom_positions["C"], ) restype_rigid_group_default_frame[restype, 3, :, :] = mat # chi1-frame to backbone if chi_angles_mask[restype][0]: base_atom_names = chi_angles_atoms[resname][0] base_atom_positions = [atom_positions[name] for name in base_atom_names] mat = _make_rigid_transformation_4x4( ex=base_atom_positions[2] - base_atom_positions[1], ey=base_atom_positions[0] - base_atom_positions[1], translation=base_atom_positions[2], ) restype_rigid_group_default_frame[restype, 4, :, :] = mat # chi2-frame to chi1-frame # chi3-frame to chi2-frame # chi4-frame to chi3-frame # luckily all rotation axes for the next frame start at (0,0,0) of the # previous frame for chi_idx in range(1, 4): if chi_angles_mask[restype][chi_idx]: axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2] axis_end_atom_position = atom_positions[axis_end_atom_name] mat = _make_rigid_transformation_4x4( ex=axis_end_atom_position, ey=np.array([-1.0, 0.0, 0.0]), translation=axis_end_atom_position, ) restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat _make_rigid_group_constants() def make_atom14_dists_bounds( overlap_tolerance: float = 1.5, bond_length_tolerance_factor: int = 15, ) -> Dict[str, np.ndarray]: """compute upper and lower bounds for bonds to assess violations.""" restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32) restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32) restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32) residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props() for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] atom_list = restype_name_to_atom14_names[resname] # create lower and upper bounds for clashes for atom1_idx, atom1_name in enumerate(atom_list): if not atom1_name: continue atom1_radius = van_der_waals_radius[atom1_name[0]] for atom2_idx, atom2_name in enumerate(atom_list): if (not atom2_name) or atom1_idx == atom2_idx: continue atom2_radius = van_der_waals_radius[atom2_name[0]] lower = atom1_radius + atom2_radius - overlap_tolerance upper = 1e10 restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper # overwrite lower and upper bounds for bonds and angles for b in residue_bonds[resname] + residue_virtual_bonds[resname]: atom1_idx = atom_list.index(b.atom1_name) atom2_idx = atom_list.index(b.atom2_name) lower = b.length - bond_length_tolerance_factor * b.stddev upper = b.length + bond_length_tolerance_factor * b.stddev restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev return { "lower_bound": restype_atom14_bond_lower_bound, # shape (21,14,14) "upper_bound": restype_atom14_bond_upper_bound, # shape (21,14,14) "stddev": restype_atom14_bond_stddev, # shape (21,14,14) } restype_atom14_ambiguous_atoms = np.zeros((21, 14), dtype=np.float32) restype_atom14_ambiguous_atoms_swap_idx: np.ndarray = np.tile(np.arange(14, dtype=int), (21, 1)) def _make_atom14_ambiguity_feats() -> None: for res, pairs in residue_atom_renaming_swaps.items(): res_idx = restype_order[restype_3to1[res]] for atom1, atom2 in pairs.items(): atom1_idx = restype_name_to_atom14_names[res].index(atom1) atom2_idx = restype_name_to_atom14_names[res].index(atom2) restype_atom14_ambiguous_atoms[res_idx, atom1_idx] = 1 restype_atom14_ambiguous_atoms[res_idx, atom2_idx] = 1 restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom1_idx] = atom2_idx restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom2_idx] = atom1_idx _make_atom14_ambiguity_feats() def aatype_to_str_sequence(aatype: Sequence[int]) -> str: return "".join([restypes_with_x[aatype[i]] for i in range(len(aatype))])

transformers/src/transformers/models/esm/openfold_utils/residue_constants.py/0

{ "file_path": "transformers/src/transformers/models/esm/openfold_utils/residue_constants.py", "repo_id": "transformers", "token_count": 19142 }

327

# coding=utf-8 # Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flaubert configuration""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json", "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json", "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json", "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json", } class FlaubertConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`FlaubertModel`] or a [`TFFlaubertModel`]. It is used to instantiate a FlauBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FlauBERT [flaubert/flaubert_base_uncased](https://huggingface.co/flaubert/flaubert_base_uncased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: pre_norm (`bool`, *optional*, defaults to `False`): Whether to apply the layer normalization before or after the feed forward layer following the attention in each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018) layerdrop (`float`, *optional*, defaults to 0.0): Probability to drop layers during training (Fan et al., Reducing Transformer Depth on Demand with Structured Dropout. ICLR 2020) vocab_size (`int`, *optional*, defaults to 30145): Vocabulary size of the FlauBERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FlaubertModel`] or [`TFFlaubertModel`]. emb_dim (`int`, *optional*, defaults to 2048): Dimensionality of the encoder layers and the pooler layer. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the attention mechanism gelu_activation (`bool`, *optional*, defaults to `True`): Whether or not to use a *gelu* activation instead of *relu*. sinusoidal_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings. causal (`bool`, *optional*, defaults to `False`): Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in order to only attend to the left-side context instead if a bidirectional context. asm (`bool`, *optional*, defaults to `False`): Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction layer. n_langs (`int`, *optional*, defaults to 1): The number of languages the model handles. Set to 1 for monolingual models. use_lang_emb (`bool`, *optional*, defaults to `True`) Whether to use language embeddings. Some models use additional language embeddings, see [the multilingual models page](http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings) for information on how to use them. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). embed_init_std (`float`, *optional*, defaults to 2048^-0.5): The standard deviation of the truncated_normal_initializer for initializing the embedding matrices. init_std (`int`, *optional*, defaults to 50257): The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the embedding matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. bos_index (`int`, *optional*, defaults to 0): The index of the beginning of sentence token in the vocabulary. eos_index (`int`, *optional*, defaults to 1): The index of the end of sentence token in the vocabulary. pad_index (`int`, *optional*, defaults to 2): The index of the padding token in the vocabulary. unk_index (`int`, *optional*, defaults to 3): The index of the unknown token in the vocabulary. mask_index (`int`, *optional*, defaults to 5): The index of the masking token in the vocabulary. is_encoder(`bool`, *optional*, defaults to `True`): Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al. summary_type (`string`, *optional*, defaults to "first"): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in the sequence classification and multiple choice models. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Used in the sequence classification and multiple choice models. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Used in the sequence classification and multiple choice models. The dropout ratio to be used after the projection and activation. start_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. end_n_top (`int`, *optional*, defaults to 5): Used in the SQuAD evaluation script. mask_token_id (`int`, *optional*, defaults to 0): Model agnostic parameter to identify masked tokens when generating text in an MLM context. lang_id (`int`, *optional*, defaults to 1): The ID of the language used by the model. This parameter is used when generating text in a given language. """ model_type = "flaubert" attribute_map = { "hidden_size": "emb_dim", "num_attention_heads": "n_heads", "num_hidden_layers": "n_layers", "n_words": "vocab_size", # For backward compatibility } def __init__( self, pre_norm=False, layerdrop=0.0, vocab_size=30145, emb_dim=2048, n_layers=12, n_heads=16, dropout=0.1, attention_dropout=0.1, gelu_activation=True, sinusoidal_embeddings=False, causal=False, asm=False, n_langs=1, use_lang_emb=True, max_position_embeddings=512, embed_init_std=2048**-0.5, layer_norm_eps=1e-12, init_std=0.02, bos_index=0, eos_index=1, pad_index=2, unk_index=3, mask_index=5, is_encoder=True, summary_type="first", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, start_n_top=5, end_n_top=5, mask_token_id=0, lang_id=0, pad_token_id=2, bos_token_id=0, **kwargs, ): """Constructs FlaubertConfig.""" self.pre_norm = pre_norm self.layerdrop = layerdrop self.vocab_size = vocab_size self.emb_dim = emb_dim self.n_layers = n_layers self.n_heads = n_heads self.dropout = dropout self.attention_dropout = attention_dropout self.gelu_activation = gelu_activation self.sinusoidal_embeddings = sinusoidal_embeddings self.causal = causal self.asm = asm self.n_langs = n_langs self.use_lang_emb = use_lang_emb self.layer_norm_eps = layer_norm_eps self.bos_index = bos_index self.eos_index = eos_index self.pad_index = pad_index self.unk_index = unk_index self.mask_index = mask_index self.is_encoder = is_encoder self.max_position_embeddings = max_position_embeddings self.embed_init_std = embed_init_std self.init_std = init_std self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_proj_to_labels = summary_proj_to_labels self.summary_first_dropout = summary_first_dropout self.start_n_top = start_n_top self.end_n_top = end_n_top self.mask_token_id = mask_token_id self.lang_id = lang_id if "n_words" in kwargs: self.n_words = kwargs["n_words"] super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs) class FlaubertOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} else: dynamic_axis = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ] )

transformers/src/transformers/models/flaubert/configuration_flaubert.py/0

{ "file_path": "transformers/src/transformers/models/flaubert/configuration_flaubert.py", "repo_id": "transformers", "token_count": 4615 }

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# coding=utf-8 # Copyright 2021 Google Research, Google AI, Google Brain and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tokenization classes for FNet model.""" import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model", "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "google/fnet-base": 512, "google/fnet-large": 512, } SPIECE_UNDERLINE = "▁" class FNetTokenizer(PreTrainedTokenizer): """ Construct an FNet tokenizer. Adapted from [`AlbertTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the input when tokenizing. remove_space (`bool`, *optional*, defaults to `True`): Whether or not to strip the text when tokenizing (removing excess spaces before and after the string). keep_accents (`bool`, *optional*, defaults to `True`): Whether or not to keep accents when tokenizing. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Attributes: sp_model (`SentencePieceProcessor`): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "token_type_ids"] def __init__( self, vocab_file, do_lower_case=False, remove_space=True, keep_accents=True, unk_token="<unk>", sep_token="[SEP]", pad_token="<pad>", cls_token="[CLS]", mask_token="[MASK]", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: # Mask token behave like a normal word, i.e. include the space before it and # is included in the raw text, there should be a match in a non-normalized sentence. mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) super().__init__( do_lower_case=do_lower_case, remove_space=remove_space, keep_accents=keep_accents, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) @property def vocab_size(self): return len(self.sp_model) def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def preprocess_text(self, inputs): if self.remove_space: outputs = " ".join(inputs.strip().split()) else: outputs = inputs outputs = outputs.replace("``", '"').replace("''", '"') if not self.keep_accents: outputs = unicodedata.normalize("NFKD", outputs) outputs = "".join([c for c in outputs if not unicodedata.combining(c)]) if self.do_lower_case: outputs = outputs.lower() return outputs def _tokenize(self, text: str) -> List[str]: """Tokenize a string.""" text = self.preprocess_text(text) pieces = self.sp_model.encode(text, out_type=str) new_pieces = [] for piece in pieces: if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, "")) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0]) == 1: cur_pieces = cur_pieces[1:] else: cur_pieces[0] = cur_pieces[0][1:] cur_pieces.append(piece[-1]) new_pieces.extend(cur_pieces) else: new_pieces.append(piece) return new_pieces def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.sp_model.PieceToId(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.sp_model.IdToPiece(index) # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.convert_tokens_to_string def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" prev_is_special = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, spaces_between_special_tokens: bool = False, **kwargs, ) -> str: text = super()._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, spaces_between_special_tokens=spaces_between_special_tokens, **kwargs, ) # Mimic the behavior of the Rust tokenizer: # No space after <unk> if not spaces_between_special_tokens: text = text.replace("<unk> ", "<unk>") return text def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An FNet sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return cls + token_ids_0 + sep return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. An FNet sequence pair mask has the following format: : ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,)

transformers/src/transformers/models/fnet/tokenization_fnet.py/0

{ "file_path": "transformers/src/transformers/models/fnet/tokenization_fnet.py", "repo_id": "transformers", "token_count": 6513 }

329

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ GroupViT model configuration""" import os from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging if TYPE_CHECKING: from ...processing_utils import ProcessorMixin from ...utils import TensorType logger = logging.get_logger(__name__) GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "nvidia/groupvit-gcc-yfcc": "https://huggingface.co/nvidia/groupvit-gcc-yfcc/resolve/main/config.json", } class GroupViTTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`GroupViTTextModel`]. It is used to instantiate an GroupViT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the GroupViT [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 49408): Vocabulary size of the GroupViT text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GroupViTModel`]. hidden_size (`int`, *optional*, defaults to 256): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 1024): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (`int`, *optional*, defaults to 77): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import GroupViTTextConfig, GroupViTTextModel >>> # Initializing a GroupViTTextModel with nvidia/groupvit-gcc-yfcc style configuration >>> configuration = GroupViTTextConfig() >>> model = GroupViTTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "groupvit_text_model" def __init__( self, vocab_size=49408, hidden_size=256, intermediate_size=1024, num_hidden_layers=12, num_attention_heads=4, max_position_embeddings=77, hidden_act="quick_gelu", layer_norm_eps=1e-5, dropout=0.0, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, pad_token_id=1, bos_token_id=49406, eos_token_id=49407, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.dropout = dropout self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_position_embeddings = max_position_embeddings self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the text config dict if we are loading from GroupViTConfig if config_dict.get("model_type") == "groupvit": config_dict = config_dict["text_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class GroupViTVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`GroupViTVisionModel`]. It is used to instantiate an GroupViT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the GroupViT [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 384): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. depths (`List[int]`, *optional*, defaults to [6, 3, 3]): The number of layers in each encoder block. num_group_tokens (`List[int]`, *optional*, defaults to [64, 8, 0]): The number of group tokens for each stage. num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 8]): The number of output groups for each stage, 0 means no group. num_attention_heads (`int`, *optional*, defaults to 6): Number of attention heads for each attention layer in the Transformer encoder. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import GroupViTVisionConfig, GroupViTVisionModel >>> # Initializing a GroupViTVisionModel with nvidia/groupvit-gcc-yfcc style configuration >>> configuration = GroupViTVisionConfig() >>> model = GroupViTVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "groupvit_vision_model" def __init__( self, hidden_size=384, intermediate_size=1536, depths=[6, 3, 3], num_hidden_layers=12, num_group_tokens=[64, 8, 0], num_output_groups=[64, 8, 8], num_attention_heads=6, image_size=224, patch_size=16, num_channels=3, hidden_act="gelu", layer_norm_eps=1e-5, dropout=0.0, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, assign_eps=1.0, assign_mlp_ratio=[0.5, 4], **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.depths = depths if num_hidden_layers != sum(depths): logger.warning( f"Manually setting num_hidden_layers to {num_hidden_layers}, but we expect num_hidden_layers =" f" sum(depth) = {sum(depths)}" ) self.num_hidden_layers = num_hidden_layers self.num_group_tokens = num_group_tokens self.num_output_groups = num_output_groups self.num_attention_heads = num_attention_heads self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.hidden_act = hidden_act self.layer_norm_eps = layer_norm_eps self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.assign_eps = assign_eps self.assign_mlp_ratio = assign_mlp_ratio @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the vision config dict if we are loading from GroupViTConfig if config_dict.get("model_type") == "groupvit": config_dict = config_dict["vision_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class GroupViTConfig(PretrainedConfig): r""" [`GroupViTConfig`] is the configuration class to store the configuration of a [`GroupViTModel`]. It is used to instantiate a GroupViT model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the GroupViT [nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`GroupViTTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`GroupViTVisionConfig`]. projection_dim (`int`, *optional*, defaults to 256): Dimentionality of text and vision projection layers. projection_intermediate_dim (`int`, *optional*, defaults to 4096): Dimentionality of intermediate layer of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* parameter. Default is used as per the original GroupViT implementation. kwargs (*optional*): Dictionary of keyword arguments. """ model_type = "groupvit" def __init__( self, text_config=None, vision_config=None, projection_dim=256, projection_intermediate_dim=4096, logit_scale_init_value=2.6592, **kwargs, ): # If `_config_dict` exist, we use them for the backward compatibility. # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot # of confusion!). text_config_dict = kwargs.pop("text_config_dict", None) vision_config_dict = kwargs.pop("vision_config_dict", None) super().__init__(**kwargs) # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`. if text_config_dict is not None: if text_config is None: text_config = {} # This is the complete result when using `text_config_dict`. _text_config_dict = GroupViTTextConfig(**text_config_dict).to_dict() # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different. for key, value in _text_config_dict.items(): if key in text_config and value != text_config[key] and key not in ["transformers_version"]: # If specified in `text_config_dict` if key in text_config_dict: message = ( f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. " f'The value `text_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`text_config_dict` is provided which will be used to initialize `GroupViTTextConfig`. " f'The value `text_config["{key}"]` will be overriden.' ) logger.info(message) # Update all values in `text_config` with the ones in `_text_config_dict`. text_config.update(_text_config_dict) if vision_config_dict is not None: if vision_config is None: vision_config = {} # This is the complete result when using `vision_config_dict`. _vision_config_dict = GroupViTVisionConfig(**vision_config_dict).to_dict() # convert keys to string instead of integer if "id2label" in _vision_config_dict: _vision_config_dict["id2label"] = { str(key): value for key, value in _vision_config_dict["id2label"].items() } # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different. for key, value in _vision_config_dict.items(): if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]: # If specified in `vision_config_dict` if key in vision_config_dict: message = ( f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different " f'values. The value `vision_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`vision_config_dict` is provided which will be used to initialize `GroupViTVisionConfig`." f' The value `vision_config["{key}"]` will be overriden.' ) logger.info(message) # Update all values in `vision_config` with the ones in `_vision_config_dict`. vision_config.update(_vision_config_dict) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `GroupViTTextConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. initializing the `GroupViTVisionConfig` with default values.") self.text_config = GroupViTTextConfig(**text_config) self.vision_config = GroupViTVisionConfig(**vision_config) self.projection_dim = projection_dim self.projection_intermediate_dim = projection_intermediate_dim self.logit_scale_init_value = logit_scale_init_value self.initializer_range = 0.02 self.initializer_factor = 1.0 self.output_segmentation = False @classmethod def from_text_vision_configs(cls, text_config: GroupViTTextConfig, vision_config: GroupViTVisionConfig, **kwargs): r""" Instantiate a [`GroupViTConfig`] (or a derived class) from groupvit text model configuration and groupvit vision model configuration. Returns: [`GroupViTConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) class GroupViTOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ("attention_mask", {0: "batch", 1: "sequence"}), ] ) @property def outputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("logits_per_image", {0: "batch"}), ("logits_per_text", {0: "batch"}), ("text_embeds", {0: "batch"}), ("image_embeds", {0: "batch"}), ] ) @property def atol_for_validation(self) -> float: return 1e-4 def generate_dummy_inputs( self, processor: "ProcessorMixin", batch_size: int = -1, seq_length: int = -1, framework: Optional["TensorType"] = None, ) -> Mapping[str, Any]: text_input_dict = super().generate_dummy_inputs( processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework ) image_input_dict = super().generate_dummy_inputs( processor.image_processor, batch_size=batch_size, framework=framework ) return {**text_input_dict, **image_input_dict} @property def default_onnx_opset(self) -> int: return 14

transformers/src/transformers/models/groupvit/configuration_groupvit.py/0

{ "file_path": "transformers/src/transformers/models/groupvit/configuration_groupvit.py", "repo_id": "transformers", "token_count": 8505 }

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# coding=utf-8 # Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao, # Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team. # Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch I-BERT model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import gelu from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_ibert import IBertConfig from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "kssteven/ibert-roberta-base" _CONFIG_FOR_DOC = "IBertConfig" IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "kssteven/ibert-roberta-base", "kssteven/ibert-roberta-large", "kssteven/ibert-roberta-large-mnli", ] class IBertEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.embedding_bit = 8 self.embedding_act_bit = 16 self.act_bit = 8 self.ln_input_bit = 22 self.ln_output_bit = 32 self.word_embeddings = QuantEmbedding( config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id, weight_bit=self.embedding_bit, quant_mode=self.quant_mode, ) self.token_type_embeddings = QuantEmbedding( config.type_vocab_size, config.hidden_size, weight_bit=self.embedding_bit, quant_mode=self.quant_mode ) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") # End copy self.padding_idx = config.pad_token_id self.position_embeddings = QuantEmbedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx, weight_bit=self.embedding_bit, quant_mode=self.quant_mode, ) # Integer-only addition between embeddings self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode) self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids( input_ids, self.padding_idx, past_key_values_length ).to(input_ids.device) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids) else: inputs_embeds_scaling_factor = None token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids) embeddings, embeddings_scaling_factor = self.embeddings_act1( inputs_embeds, inputs_embeds_scaling_factor, identity=token_type_embeddings, identity_scaling_factor=token_type_embeddings_scaling_factor, ) if self.position_embedding_type == "absolute": position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(position_ids) embeddings, embeddings_scaling_factor = self.embeddings_act1( embeddings, embeddings_scaling_factor, identity=position_embeddings, identity_scaling_factor=position_embeddings_scaling_factor, ) embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor) embeddings = self.dropout(embeddings) embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor) return embeddings, embeddings_scaling_factor def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) class IBertSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.quant_mode = config.quant_mode self.weight_bit = 8 self.bias_bit = 32 self.act_bit = 8 self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size # Q, K, V Linear layers self.query = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.key = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.value = QuantLinear( config.hidden_size, self.all_head_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) # Requantization (32bit -> 8bit) for Q, K, V activations self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type != "absolute": raise ValueError("I-BERT only supports 'absolute' for `config.position_embedding_type`") self.softmax = IntSoftmax(self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): # Projection mixed_query_layer, mixed_query_layer_scaling_factor = self.query(hidden_states, hidden_states_scaling_factor) mixed_key_layer, mixed_key_layer_scaling_factor = self.key(hidden_states, hidden_states_scaling_factor) mixed_value_layer, mixed_value_layer_scaling_factor = self.value(hidden_states, hidden_states_scaling_factor) # Requantization query_layer, query_layer_scaling_factor = self.query_activation( mixed_query_layer, mixed_query_layer_scaling_factor ) key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor) value_layer, value_layer_scaling_factor = self.value_activation( mixed_value_layer, mixed_value_layer_scaling_factor ) # Transpose query_layer = self.transpose_for_scores(query_layer) key_layer = self.transpose_for_scores(key_layer) value_layer = self.transpose_for_scores(value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) scale = math.sqrt(self.attention_head_size) attention_scores = attention_scores / scale if self.quant_mode: attention_scores_scaling_factor = query_layer_scaling_factor * key_layer_scaling_factor / scale else: attention_scores_scaling_factor = None if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in IBertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs, attention_probs_scaling_factor = self.softmax( attention_scores, attention_scores_scaling_factor ) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) if attention_probs_scaling_factor is not None: context_layer_scaling_factor = attention_probs_scaling_factor * value_layer_scaling_factor else: context_layer_scaling_factor = None context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) # requantization: 32-bit -> 8-bit context_layer, context_layer_scaling_factor = self.output_activation( context_layer, context_layer_scaling_factor ) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) output_scaling_factor = ( (context_layer_scaling_factor, attention_probs_scaling_factor) if output_attentions else (context_layer_scaling_factor,) ) return outputs, output_scaling_factor class IBertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.ln_input_bit = 22 self.ln_output_bit = 32 self.dense = QuantLinear( config.hidden_size, config.hidden_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode) self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states = self.dropout(hidden_states) hidden_states, hidden_states_scaling_factor = self.ln_input_act( hidden_states, hidden_states_scaling_factor, identity=input_tensor, identity_scaling_factor=input_tensor_scaling_factor, ) hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertAttention(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.self = IBertSelfAttention(config) self.output = IBertSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): self_outputs, self_outputs_scaling_factor = self.self( hidden_states, hidden_states_scaling_factor, attention_mask, head_mask, output_attentions, ) attention_output, attention_output_scaling_factor = self.output( self_outputs[0], self_outputs_scaling_factor[0], hidden_states, hidden_states_scaling_factor ) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[1:] return outputs, outputs_scaling_factor class IBertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.dense = QuantLinear( config.hidden_size, config.intermediate_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) if config.hidden_act != "gelu": raise ValueError("I-BERT only supports 'gelu' for `config.hidden_act`") self.intermediate_act_fn = IntGELU(quant_mode=self.quant_mode, force_dequant=config.force_dequant) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) def forward(self, hidden_states, hidden_states_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn( hidden_states, hidden_states_scaling_factor ) # Requantization: 32bit -> 8-bit hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertOutput(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.weight_bit = 8 self.bias_bit = 32 self.ln_input_bit = 22 self.ln_output_bit = 32 self.dense = QuantLinear( config.intermediate_size, config.hidden_size, bias=True, weight_bit=self.weight_bit, bias_bit=self.bias_bit, quant_mode=self.quant_mode, per_channel=True, ) self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode) self.LayerNorm = IntLayerNorm( config.hidden_size, eps=config.layer_norm_eps, output_bit=self.ln_output_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant, ) self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor): hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor) hidden_states = self.dropout(hidden_states) hidden_states, hidden_states_scaling_factor = self.ln_input_act( hidden_states, hidden_states_scaling_factor, identity=input_tensor, identity_scaling_factor=input_tensor_scaling_factor, ) hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor) hidden_states, hidden_states_scaling_factor = self.output_activation( hidden_states, hidden_states_scaling_factor ) return hidden_states, hidden_states_scaling_factor class IBertLayer(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.act_bit = 8 self.seq_len_dim = 1 self.attention = IBertAttention(config) self.intermediate = IBertIntermediate(config) self.output = IBertOutput(config) self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode) self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, ): self_attention_outputs, self_attention_outputs_scaling_factor = self.attention( hidden_states, hidden_states_scaling_factor, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] attention_output_scaling_factor = self_attention_outputs_scaling_factor[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output, layer_output_scaling_factor = self.feed_forward_chunk( attention_output, attention_output_scaling_factor ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output, attention_output_scaling_factor): attention_output, attention_output_scaling_factor = self.pre_intermediate_act( attention_output, attention_output_scaling_factor ) intermediate_output, intermediate_output_scaling_factor = self.intermediate( attention_output, attention_output_scaling_factor ) intermediate_output, intermediate_output_scaling_factor = self.pre_output_act( intermediate_output, intermediate_output_scaling_factor ) layer_output, layer_output_scaling_factor = self.output( intermediate_output, intermediate_output_scaling_factor, attention_output, attention_output_scaling_factor ) return layer_output, layer_output_scaling_factor class IBertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.quant_mode = config.quant_mode self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)]) def forward( self, hidden_states, hidden_states_scaling_factor, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = None # `config.add_cross_attention` is not supported next_decoder_cache = None # `config.use_cache` is not supported for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None layer_outputs = layer_module( hidden_states, hidden_states_scaling_factor, attention_mask, layer_head_mask, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class IBertPooler(nn.Module): def __init__(self, config): super().__init__() self.quant_mode = config.quant_mode self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class IBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = IBertConfig base_model_prefix = "ibert" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (QuantLinear, nn.Linear)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, (QuantEmbedding, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, (IntLayerNorm, nn.LayerNorm)): module.bias.data.zero_() module.weight.data.fill_(1.0) def resize_token_embeddings(self, new_num_tokens=None): raise NotImplementedError("`resize_token_embeddings` is not supported for I-BERT.") IBERT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`IBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ IBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare I-BERT Model transformer outputting raw hidden-states without any specific head on top.", IBERT_START_DOCSTRING, ) class IBertModel(IBertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. """ def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.quant_mode = config.quant_mode self.embeddings = IBertEmbeddings(config) self.encoder = IBertEncoder(config) self.pooler = IBertPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[BaseModelOutputWithPoolingAndCrossAttentions, Tuple[torch.FloatTensor]]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output, embedding_output_scaling_factor = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, embedding_output_scaling_factor, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings("""I-BERT Model with a `language modeling` head on top.""", IBERT_START_DOCSTRING) class IBertForMaskedLM(IBertPreTrainedModel): _tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"] def __init__(self, config): super().__init__(config) self.ibert = IBertModel(config, add_pooling_layer=False) self.lm_head = IBertLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head.decoder def set_output_embeddings(self, new_embeddings): self.lm_head.decoder = new_embeddings @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class IBertLMHead(nn.Module): """I-BERT Head for masked language modeling.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.decoder = nn.Linear(config.hidden_size, config.vocab_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.decoder.bias = self.bias def forward(self, features, **kwargs): x = self.dense(features) x = gelu(x) x = self.layer_norm(x) # project back to size of vocabulary with bias x = self.decoder(x) return x def _tie_weights(self): # To tie those two weights if they get disconnected (on TPU or when the bias is resized) self.bias = self.decoder.bias @add_start_docstrings( """ I-BERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, IBERT_START_DOCSTRING, ) class IBertForSequenceClassification(IBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.classifier = IBertClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ I-BERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, IBERT_START_DOCSTRING, ) class IBertForMultipleChoice(IBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.ibert = IBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.ibert( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, head_mask=head_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ I-BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, IBERT_START_DOCSTRING, ) class IBertForTokenClassification(IBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class IBertClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): hidden_states = features[:, 0, :] # take <s> token (equiv. to [CLS]) hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states @add_start_docstrings( """ I-BERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, IBERT_START_DOCSTRING, ) class IBertForQuestionAnswering(IBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.ibert = IBertModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[QuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.ibert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's *utils.make_positions*. Args: input_ids (`torch.LongTensor`): Indices of input sequence tokens in the vocabulary. Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

transformers/src/transformers/models/ibert/modeling_ibert.py/0

{ "file_path": "transformers/src/transformers/models/ibert/modeling_ibert.py", "repo_id": "transformers", "token_count": 24475 }

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Informer model configuration""" from typing import List, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "huggingface/informer-tourism-monthly": ( "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json" ), # See all Informer models at https://huggingface.co/models?filter=informer } class InformerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of an [`InformerModel`]. It is used to instantiate an Informer model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Informer [huggingface/informer-tourism-monthly](https://huggingface.co/huggingface/informer-tourism-monthly) architecture. Configuration objects inherit from [`PretrainedConfig`] can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: prediction_length (`int`): The prediction length for the decoder. In other words, the prediction horizon of the model. This value is typically dictated by the dataset and we recommend to set it appropriately. context_length (`int`, *optional*, defaults to `prediction_length`): The context length for the encoder. If `None`, the context length will be the same as the `prediction_length`. distribution_output (`string`, *optional*, defaults to `"student_t"`): The distribution emission head for the model. Could be either "student_t", "normal" or "negative_binomial". loss (`string`, *optional*, defaults to `"nll"`): The loss function for the model corresponding to the `distribution_output` head. For parametric distributions it is the negative log likelihood (nll) - which currently is the only supported one. input_size (`int`, *optional*, defaults to 1): The size of the target variable which by default is 1 for univariate targets. Would be > 1 in case of multivariate targets. scaling (`string` or `bool`, *optional* defaults to `"mean"`): Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the scaler is set to "mean". lags_sequence (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 5, 6, 7]`): The lags of the input time series as covariates often dictated by the frequency of the data. Default is `[1, 2, 3, 4, 5, 6, 7]` but we recommend to change it based on the dataset appropriately. num_time_features (`int`, *optional*, defaults to 0): The number of time features in the input time series. num_dynamic_real_features (`int`, *optional*, defaults to 0): The number of dynamic real valued features. num_static_categorical_features (`int`, *optional*, defaults to 0): The number of static categorical features. num_static_real_features (`int`, *optional*, defaults to 0): The number of static real valued features. cardinality (`list[int]`, *optional*): The cardinality (number of different values) for each of the static categorical features. Should be a list of integers, having the same length as `num_static_categorical_features`. Cannot be `None` if `num_static_categorical_features` is > 0. embedding_dimension (`list[int]`, *optional*): The dimension of the embedding for each of the static categorical features. Should be a list of integers, having the same length as `num_static_categorical_features`. Cannot be `None` if `num_static_categorical_features` is > 0. d_model (`int`, *optional*, defaults to 64): Dimensionality of the transformer layers. encoder_layers (`int`, *optional*, defaults to 2): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 2): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 2): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 2): Number of attention heads for each attention layer in the Transformer decoder. encoder_ffn_dim (`int`, *optional*, defaults to 32): Dimension of the "intermediate" (often named feed-forward) layer in encoder. decoder_ffn_dim (`int`, *optional*, defaults to 32): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and decoder. If string, `"gelu"` and `"relu"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the encoder, and decoder. encoder_layerdrop (`float`, *optional*, defaults to 0.1): The dropout probability for the attention and fully connected layers for each encoder layer. decoder_layerdrop (`float`, *optional*, defaults to 0.1): The dropout probability for the attention and fully connected layers for each decoder layer. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout probability for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.1): The dropout probability used between the two layers of the feed-forward networks. num_parallel_samples (`int`, *optional*, defaults to 100): The number of samples to generate in parallel for each time step of inference. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated normal weight initialization distribution. use_cache (`bool`, *optional*, defaults to `True`): Whether to use the past key/values attentions (if applicable to the model) to speed up decoding. attention_type (`str`, *optional*, defaults to "prob"): Attention used in encoder. This can be set to "prob" (Informer's ProbAttention) or "full" (vanilla transformer's canonical self-attention). sampling_factor (`int`, *optional*, defaults to 5): ProbSparse sampling factor (only makes affect when `attention_type`="prob"). It is used to control the reduced query matrix (Q_reduce) input length. distil (`bool`, *optional*, defaults to `True`): Whether to use distilling in encoder. Example: ```python >>> from transformers import InformerConfig, InformerModel >>> # Initializing an Informer configuration with 12 time steps for prediction >>> configuration = InformerConfig(prediction_length=12) >>> # Randomly initializing a model (with random weights) from the configuration >>> model = InformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "informer" attribute_map = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", "num_hidden_layers": "encoder_layers", } def __init__( self, prediction_length: Optional[int] = None, context_length: Optional[int] = None, distribution_output: str = "student_t", loss: str = "nll", input_size: int = 1, lags_sequence: List[int] = None, scaling: Optional[Union[str, bool]] = "mean", num_dynamic_real_features: int = 0, num_static_real_features: int = 0, num_static_categorical_features: int = 0, num_time_features: int = 0, cardinality: Optional[List[int]] = None, embedding_dimension: Optional[List[int]] = None, d_model: int = 64, encoder_ffn_dim: int = 32, decoder_ffn_dim: int = 32, encoder_attention_heads: int = 2, decoder_attention_heads: int = 2, encoder_layers: int = 2, decoder_layers: int = 2, is_encoder_decoder: bool = True, activation_function: str = "gelu", dropout: float = 0.05, encoder_layerdrop: float = 0.1, decoder_layerdrop: float = 0.1, attention_dropout: float = 0.1, activation_dropout: float = 0.1, num_parallel_samples: int = 100, init_std: float = 0.02, use_cache=True, # Informer arguments attention_type: str = "prob", sampling_factor: int = 5, distil: bool = True, **kwargs, ): # time series specific configuration self.prediction_length = prediction_length self.context_length = context_length or prediction_length self.distribution_output = distribution_output self.loss = loss self.input_size = input_size self.num_time_features = num_time_features self.lags_sequence = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7] self.scaling = scaling self.num_dynamic_real_features = num_dynamic_real_features self.num_static_real_features = num_static_real_features self.num_static_categorical_features = num_static_categorical_features # set cardinality if cardinality and num_static_categorical_features > 0: if len(cardinality) != num_static_categorical_features: raise ValueError( "The cardinality should be a list of the same length as `num_static_categorical_features`" ) self.cardinality = cardinality else: self.cardinality = [0] # set embedding_dimension if embedding_dimension and num_static_categorical_features > 0: if len(embedding_dimension) != num_static_categorical_features: raise ValueError( "The embedding dimension should be a list of the same length as `num_static_categorical_features`" ) self.embedding_dimension = embedding_dimension else: self.embedding_dimension = [min(50, (cat + 1) // 2) for cat in self.cardinality] self.num_parallel_samples = num_parallel_samples # Transformer architecture configuration self.feature_size = input_size * len(self.lags_sequence) + self._number_of_features self.d_model = d_model self.encoder_attention_heads = encoder_attention_heads self.decoder_attention_heads = decoder_attention_heads self.encoder_ffn_dim = encoder_ffn_dim self.decoder_ffn_dim = decoder_ffn_dim self.encoder_layers = encoder_layers self.decoder_layers = decoder_layers self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.activation_function = activation_function self.init_std = init_std self.use_cache = use_cache # Informer self.attention_type = attention_type self.sampling_factor = sampling_factor self.distil = distil super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) @property def _number_of_features(self) -> int: return ( sum(self.embedding_dimension) + self.num_dynamic_real_features + self.num_time_features + self.num_static_real_features + self.input_size * 2 # the log1p(abs(loc)) and log(scale) features )

transformers/src/transformers/models/informer/configuration_informer.py/0

{ "file_path": "transformers/src/transformers/models/informer/configuration_informer.py", "repo_id": "transformers", "token_count": 4821 }

332

# coding=utf-8 # Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Processor class for KOSMOS-2.""" import copy import math import re from typing import List, Optional, Tuple, Union from ...image_processing_utils import BatchFeature from ...image_utils import ImageInput, is_batched from ...processing_utils import ProcessorMixin from ...tokenization_utils import AddedToken from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy from ...utils import TensorType BboxInput = Union[ List[Tuple[int, int]], List[Tuple[float, float, float, float]], List[List[Tuple[int, int]]], List[List[Tuple[float, float, float]]], ] class Kosmos2Processor(ProcessorMixin): r""" Constructs an KOSMOS-2 processor which wraps a KOSMOS-2 image processor and a KOSMOS-2 tokenizer into a single processor. [`Kosmos2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and some functionalities of [`XLMRobertaTokenizerFast`]. See the docstring of [`~Kosmos2Processor.__call__`] and [`~Kosmos2Processor.decode`] for more information. Args: image_processor (`CLIPImageProcessor`): An instance of [`CLIPImageProcessor`]. The image processor is a required input. tokenizer (`XLMRobertaTokenizerFast`): An instance of ['XLMRobertaTokenizerFast`]. The tokenizer is a required input. num_patch_index_tokens (`int`, *optional*, defaults to 1024): The number of tokens that represent patch indices. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "CLIPImageProcessor" tokenizer_class = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast") def __init__(self, image_processor, tokenizer, num_patch_index_tokens=1024): tokenizer.return_token_type_ids = False self.eod_token = "</doc>" self.boi_token = "<image>" self.eoi_token = "</image>" self.eoc_token = "</chunk>" self.eol_token = "</line>" self.bop_token = "<phrase>" self.eop_token = "</phrase>" self.boo_token = "<object>" self.eoo_token = "</object>" self.dom_token = "</delimiter_of_multi_objects/>" self.grd_token = "<grounding>" self.tag_tokens = [ self.eod_token, self.boi_token, self.eoi_token, self.eoc_token, self.eol_token, self.bop_token, self.eop_token, self.boo_token, self.eoo_token, self.dom_token, self.grd_token, ] self.num_patch_index_tokens = num_patch_index_tokens patch_index_tokens = [f"<patch_index_{str(x).zfill(4)}>" for x in range(self.num_patch_index_tokens)] tokens_to_add = [] for token in self.tag_tokens + patch_index_tokens: tokens_to_add.append(AddedToken(token, lstrip=True, rstrip=False, normalized=False)) tokenizer.add_tokens(tokens_to_add) super().__init__(image_processor, tokenizer) def __call__( self, images: ImageInput = None, text: Union[TextInput, List[TextInput]] = None, bboxes: BboxInput = None, num_image_tokens: Optional[int] = 64, first_image_token_id: Optional[int] = None, add_special_tokens: bool = True, add_eos_token: bool = False, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_length: bool = False, verbose: bool = True, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs, ) -> BatchFeature: """ This method uses [`CLIPImageProcessor.__call__`] method to prepare image(s) for the model, and [`XLMRobertaTokenizerFast.__call__`] to prepare text for the model. Please refer to the docstring of the above two methods for more information. The rest of this documentation shows the arguments specific to `Kosmos2Processor`. Args: bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*): The bounding bboxes associated to `texts`. num_image_tokens (`int`, defaults to 64): The number of (consecutive) places that are used to mark the placeholders to store image information. This should be the same as `latent_query_num` in the instance of `Kosmos2Config` you are using. first_image_token_id (`int`, *optional*): The token id that will be used for the first place of the subsequence that is reserved to store image information. If unset, will default to `self.tokenizer.unk_token_id + 1`. add_eos_token (`bool`, defaults to `False`): Whether or not to include `EOS` token id in the encoding when `add_special_tokens=True`. """ if images is None and text is None: raise ValueError("You have to specify either images or text.") encoding = BatchFeature() if images is not None: image_encoding = self.image_processor(images, return_tensors=return_tensors) encoding.update(image_encoding) if text is not None: text = self.preprocess_examples(text, images, bboxes, num_image_tokens=num_image_tokens) if add_special_tokens and not add_eos_token: if isinstance(text, str): text = f"{self.tokenizer.bos_token}{text}" elif isinstance(text, list): text = [f"{self.tokenizer.bos_token}{s}" for s in text] text_encoding = self.tokenizer( text=text, add_special_tokens=(add_special_tokens and add_eos_token), padding=padding and images is None, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of if images is None else pad_to_multiple_of, return_attention_mask=return_attention_mask, verbose=verbose, return_tensors=return_tensors if images is None else None, **kwargs, ) encoding.update(text_encoding) if text is not None and images is not None: # Use the id of the first token after <unk> if first_image_token_id is None: first_image_token_id = self.tokenizer.unk_token_id + 1 # To see if we need one more `0` (for `<s>`) at the beginning of `image_embeds_position_mask`. with_bos = add_special_tokens # The first (actual) `<image>` token is always at the 1st or 2nd place (after `<s>` if any). Here we look # for the second `<image>` token (which indicate the first image token). start_index = int(with_bos) + 1 # Add `image_embeds_position_mask`: the leading and trailing `0` are for `boi` and `eoi` tokens. The `1` indicates # the places of image tokens. image_token_ids = list(range(first_image_token_id, first_image_token_id + num_image_tokens)) base_image_embeds_position_mask = [0] + [1] * num_image_tokens + [0] # loop over `encoding["input_ids"]` input_ids = [] image_embeds_position_mask = [] all_input_ids = encoding["input_ids"] # not batched -> (changed to) batch of size 1 if isinstance(text, str): all_input_ids = [all_input_ids] encoding["attention_mask"] = [encoding["attention_mask"]] for text_ids in all_input_ids: # change the ids for the fake `<image>` tokens in `input_ids` text_ids = text_ids[:start_index] + image_token_ids + text_ids[start_index + num_image_tokens :] input_ids.append(text_ids) mask = copy.copy(base_image_embeds_position_mask) if with_bos: # for `<s>` mask = [0] + mask # trailing part (which are not related to the image) mask += [0] * (len(text_ids) - len(mask)) image_embeds_position_mask.append(mask) if isinstance(text, list): sorted_length = sorted( [(idx, len(x)) for idx, x in enumerate(text_encoding.input_ids)], key=lambda x: x[-1] ) _, min_len_not_padded = sorted_length[0] idx, _ = sorted_length[-1] text_encoding = self.tokenizer( text=[text[idx]], add_special_tokens=(add_special_tokens and add_eos_token), padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, return_tensors=None, **kwargs, ) max_len_padded = len(text_encoding.input_ids[0]) if min_len_not_padded != max_len_padded: if self.tokenizer.padding_side == "right": input_ids = [x + [self.tokenizer.pad_token_id] * (max_len_padded - len(x)) for x in input_ids] image_embeds_position_mask = [ x + [0] * (max_len_padded - len(x)) for x in image_embeds_position_mask ] encoding["attention_mask"] = [ x + [0] * (max_len_padded - len(x)) for x in encoding["attention_mask"] ] elif self.tokenizer.padding_side == "left": input_ids = [[self.tokenizer.pad_token_id] * (max_len_padded - len(x)) + x for x in input_ids] image_embeds_position_mask = [ [0] * (max_len_padded - len(x)) + x for x in image_embeds_position_mask ] encoding["attention_mask"] = [ [0] * (max_len_padded - len(x)) + x for x in encoding["attention_mask"] ] # un-batch if necessary if isinstance(text, str) and return_tensors is None: input_ids = input_ids[0] encoding["attention_mask"] = encoding["attention_mask"][0] image_embeds_position_mask = image_embeds_position_mask[0] # update (with the target tensor type if specified) encoding.update( BatchEncoding( data={ "input_ids": input_ids, "attention_mask": encoding["attention_mask"], "image_embeds_position_mask": image_embeds_position_mask, }, tensor_type=return_tensors, ) ) return encoding def _check_bboxes_for_single_text(self, bboxes): """ Check `bboxes` for a single text example. It could be - `None`: no bounding box associated to a text. - A list with each element being the bounding boxes associated to one `<phrase> ... </phrase>` pair found in a text. This could be: - `None`: no bounding box associated to a `<phrase> ... </phrase>` pair. - A tuple of 2 integers: A single bounding box specified by patch indices. - A tuple of 4 float point number: A single bounding box specified by (normalized) coordinates. - A list containing the above 2 tuple types: Multiple bounding boxes for a `<phrase> ... </phrase>` pair. """ if bboxes is None: return elif not isinstance(bboxes, list): raise ValueError("`bboxes` (for a single text example) should be `None` or a list.") # `bbox` is the bounding boxes for a single <phrase> </phrase> pair for bbox in bboxes: if bbox is None: continue elif not isinstance(bbox, list): bbox = [bbox] for element in bbox: if not isinstance(element, tuple) or not ( (len(element) == 2 and all(isinstance(x, int) for x in element)) or (len(element) == 4 and all(isinstance(x, float) for x in element)) ): raise ValueError( "Each element in `bboxes` (for a single text example) should be either `None`, a tuple containing " "2 integers or 4 float point numbers, or a list containing such tuples. Also " "make sure the arguments `texts` and `bboxes` passed to `preprocess_text` are both in " "batches or both for a single example." ) def _preprocess_single_example(self, text, image, bboxes, img_info_tokens): text = text.strip() if image is not None: # Add `<image> ... (fake) image tokens ... </image>` text = f"{img_info_tokens} {text}" # Add `<object> <patch_idx_xxxx> <patch_idx_yyy> </object>` after `<phrase> phrase text </phrase>` text = self._insert_patch_index_tokens(text, bboxes) return text def preprocess_examples( self, texts: Union[TextInput, List[TextInput]], images: ImageInput = None, bboxes: BboxInput = None, num_image_tokens: Optional[int] = 64, ) -> Union[str, List[str]]: """Add image and bounding box information to `texts` as image and patch index tokens. Args: texts (`Union[TextInput, List[TextInput]]`): The texts to be processed. images (`ImageInput`, *optional*): The images associated to `texts`. bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*): The bounding bboxes associated to `texts`. num_image_tokens (`int`, *optional*, defaults to 64): The number of image tokens (used as latent queries). This should corresponds to the `latent_query_num` attribute in `Kosmos2Config`. Returns: `Union[TextInput, List[TextInput]]`: The processed texts with image and patch index tokens. """ # These are fake `<image>` tokens enclosed between (the actual) `<image>` token and `</image>`. img_tokens = [self.boi_token] * num_image_tokens img_info_tokens = " ".join([self.boi_token] + img_tokens + [self.eoi_token]) # make batch to simplify processing logic batched = True if isinstance(texts, str): batched = False texts = [texts] if images is None: images = [None] * len(texts) elif not is_batched(images): images = [images] if len(texts) != len(images): raise ValueError( f"The number of examples in `texts` and `images` should be the same. Got {len(texts)} v.s. {len(images)} instead." ) if not batched: self._check_bboxes_for_single_text(bboxes) bboxes = [bboxes] elif bboxes is not None: if not isinstance(bboxes, list): raise ValueError("`bboxes` should be `None` or a list (as a batch) when `texts` is passed as a batch.") for x in bboxes: self._check_bboxes_for_single_text(x) else: bboxes = [None] * len(texts) if len(bboxes) != len(texts): raise ValueError( f"The number of examples in `texts` and `bboxes` should be the same. Got {len(texts)} v.s. {len(bboxes)} instead." ) result = [ self._preprocess_single_example(text, image, bbox, img_info_tokens) for text, image, bbox in zip(texts, images, bboxes) ] # un-batch if necessary if not batched: result = result[0] return result # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer def decode(self, *args, **kwargs): """ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) def post_process_generation(self, text, cleanup_and_extract=True): caption = text.split(self.eoi_token)[-1] if cleanup_and_extract: return clean_text_and_extract_entities_with_bboxes(caption) return caption @property # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) def _insert_patch_index_tokens(self, text: str, bboxes: Union[List[Tuple[int]], List[Tuple[float]]]) -> str: if bboxes is None or len(bboxes) == 0: return text matched_phrases = list(re.finditer(r"<phrase>.+?</phrase>", string=text)) if len(matched_phrases) != len(bboxes): raise ValueError( f"The number of elements in `bboxes` should be the same as the number of `<phrase> ... </phrase>` pairs in `text`. Got {len(matched_phrases)} v.s. {len(bboxes)} instead." ) # insert object's patch index tokens # the found `<phrase> ... </phrase>` pairs. curr_pos = 0 buffer = [] for matched, bbox in zip(matched_phrases, bboxes): _, end = matched.span() buffer.append(text[curr_pos:end]) curr_pos = end # A phrase without bbox if bbox is None: continue # A phrase with a single bbox if isinstance(bbox, tuple): bbox = [bbox] patch_index_strings = [] # A phrase could have multiple bboxes if not all(box is not None for box in bbox): raise ValueError( "The multiple bounding boxes for a single phrase should not contain any `None` value." ) for box in bbox: patch_index_1, patch_index_2 = self._convert_bbox_to_patch_index_tokens(box) patch_index_strings.append(f"{patch_index_1} {patch_index_2}") # `bbox` being an empty list if len(patch_index_strings) == 0: continue position_str = " </delimiter_of_multi_objects/> ".join(patch_index_strings) buffer.append(f"<object> {position_str} </object>") # remaining if curr_pos < len(text): buffer.append(text[curr_pos:]) text = "".join(buffer) return text def _convert_bbox_to_patch_index_tokens( self, bbox: Union[Tuple[int, int], Tuple[float, float, float, float]] ) -> Tuple[str, str]: # already computed patch indices if len(bbox) == 2: idx_1, idx_2 = bbox # bbox specified with (normalized) coordinates else: # use `self.tokenizer` to get `num_patches_per_side` num_patches_per_side = int(math.sqrt(self.num_patch_index_tokens)) idx_1, idx_2 = coordinate_to_patch_index(bbox, num_patches_per_side) token_1 = f"<patch_index_{str(idx_1).zfill(4)}>" token_2 = f"<patch_index_{str(idx_2).zfill(4)}>" return token_1, token_2 def coordinate_to_patch_index(bbox: Tuple[float, float, float, float], num_patches_per_side: int) -> Tuple[int, int]: """Convert a bounding box to a pair of patch indices. Args: bbox (`Tuple[float, float, float, float]`): The 4 coordinates of the bounding box, with the format being (x1, y1, x2, y2) specifying the upper-left and lower-right corners of the box. It should have x2 > x1 and y2 > y1. num_patches_per_side (`int`): the number of patches along each side. Returns: `Tuple[int, int]`: A pair of patch indices representing the upper-left patch and lower-right patch. """ (x1, y1, x2, y2) = bbox if not (x2 > x1 and y2 > y1): raise ValueError("The coordinates in `bbox` should be `(x1, y1, x2, y2)` with `x2 > x1` and `y2 > y1`.") ul_x = math.floor(x1 * num_patches_per_side) ul_y = math.floor(y1 * num_patches_per_side) lr_x = math.ceil(x2 * num_patches_per_side - 1) lr_y = math.ceil(y2 * num_patches_per_side - 1) ul_idx = ul_y * num_patches_per_side + ul_x lr_idx = lr_y * num_patches_per_side + lr_x return ul_idx, lr_idx # copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L35C1-L75C38 # (with format modifications) def patch_index_to_coordinate(ul_idx: int, lr_idx: int, num_patches_per_side: int): """ Given a grid of length `num_patches_per_side` and the indices of the upper-left and lower-right corners of a bounding box, returns the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2). Args: ul_idx (`int`): the index of the grid cell that corresponds to the upper-left corner of the bounding box. lr_idx (`int`): the index of the grid cell that corresponds to the lower-right corner of the bounding box. num_patches_per_side (`int`): the number of patches along each side. Returns: `Tuple[float]`: the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2). """ # Compute the size of each cell in the grid cell_size = 1.0 / num_patches_per_side # Compute the x and y indices of the upper-left and lower-right corners of the bounding box ul_x = ul_idx % num_patches_per_side ul_y = ul_idx // num_patches_per_side lr_x = lr_idx % num_patches_per_side lr_y = lr_idx // num_patches_per_side # Compute the normalized coordinates of the bounding box if ul_idx == lr_idx: x1 = ul_x * cell_size y1 = ul_y * cell_size x2 = lr_x * cell_size + cell_size y2 = lr_y * cell_size + cell_size elif ul_x == lr_x or ul_y == lr_y: x1 = ul_x * cell_size y1 = ul_y * cell_size x2 = lr_x * cell_size + cell_size y2 = lr_y * cell_size + cell_size else: x1 = ul_x * cell_size + cell_size / 2 y1 = ul_y * cell_size + cell_size / 2 x2 = lr_x * cell_size + cell_size / 2 y2 = lr_y * cell_size + cell_size / 2 return x1, y1, x2, y2 # copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L4-L33 # (with format modifications) def extract_entities_with_patch_indices(text): """Extract entities contained in `text`. The bounding bboxes is given in the form of patch indices. This functioin is only intended to be used within `clean_text_and_extract_entities_with_bboxes` where further processing happens, including converting to normalized coordinates and whitespace character cleaning up. Examples: ```python >>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>." >>> entities = extract_entities_with_patch_indices(text) >>> entities [(' a snowman', (31, 41), [(44, 863)]), (' a fire', (130, 137), [(5, 911)])] ```""" # The regular expression pattern for matching the required formats pattern = r"(?:(<phrase>([^<]+)</phrase>))?<object>((?:<patch_index_\d+><patch_index_\d+></delimiter_of_multi_objects/>)*<patch_index_\d+><patch_index_\d+>)</object>" # Find all matches in the given string matches = re.finditer(pattern, text) # Initialize an empty list to store the valid patch_index combinations entities_with_patch_indices = [] for match in matches: # span of a `phrase` that is between <phrase> and </phrase> span = match.span(2) phrase_tag, phrase, match_content = match.groups() if not phrase_tag: phrase = None # We take the starting position of `<object>` span = (match.span(0)[0], match.span(0)[0]) # Split the match_content by the delimiter to get individual patch_index pairs patch_index_pairs = match_content.split("</delimiter_of_multi_objects/>") entity_bboxes = [] for pair in patch_index_pairs: # Extract the xxxx and yyyy values from the patch_index pair x = re.search(r"<patch_index_(\d+)>", pair) y = re.search(r"<patch_index_(\d+)>", pair[1:]) if x and y: if phrase: entity_bboxes.append((int(x.group(1)), int(y.group(1)))) else: entity_bboxes.append((int(x.group(1)), int(y.group(1)))) if phrase: entities_with_patch_indices.append((phrase, span, entity_bboxes)) else: for bbox in entity_bboxes: # fake entity name entity = f"<patch_index_{bbox[0]}><patch_index_{bbox[1]}>" entities_with_patch_indices.append((entity, span, [bbox])) return entities_with_patch_indices def adjust_entity_positions(entity, text): """Adjust the positions of the entities in `text` to be relative to the text with special fields removed.""" entity_name, (start, end) = entity # computed the length of strings with special fields (tag tokens, patch index tokens, etc.) removed adjusted_start = len(re.sub("<.*?>", "", text[:start])) adjusted_end = len(re.sub("<.*?>", "", text[:end])) adjusted_entity = (entity_name, (adjusted_start, adjusted_end)) return adjusted_entity def _cleanup_spaces(text, entities): """Remove the spaces around the text and the entities in it.""" new_text = text.strip() leading_spaces = len(text) - len(text.lstrip()) new_entities = [] for entity_name, (start, end), bboxes in entities: entity_name_leading_spaces = len(entity_name) - len(entity_name.lstrip()) entity_name_trailing_spaces = len(entity_name) - len(entity_name.rstrip()) start = start - leading_spaces + entity_name_leading_spaces end = end - leading_spaces - entity_name_trailing_spaces entity_name = entity_name.strip() new_entities.append((entity_name, (start, end), bboxes)) return new_text, new_entities # copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L77-L87 # (with format modifications) def clean_text_and_extract_entities_with_bboxes(text, num_patches_per_side=32): """Remove the tag tokens from `text`, extract entities in it with some cleaning up of white characters. Examples: ```python >>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>." >>> clean_text, entities = clean_text_and_extract_entities_with_bboxes(text) >>> clean_text 'An image of a snowman warming himself by a fire.' >>> entities [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])] ```""" # remove special fields (tag tokens, patch index tokens, etc.) processed_text = re.sub("<.*?>", "", text) entities_with_patch_indices = extract_entities_with_patch_indices(text) entities = [] for item in entities_with_patch_indices: entity, bboxes = item[0:2], item[2] adjusted_entity = adjust_entity_positions(entity, text) bboxes_in_coords = [patch_index_to_coordinate(bbox[0], bbox[1], num_patches_per_side) for bbox in bboxes] entities.append(adjusted_entity + (bboxes_in_coords,)) return _cleanup_spaces(processed_text, entities)

transformers/src/transformers/models/kosmos2/processing_kosmos2.py/0

{ "file_path": "transformers/src/transformers/models/kosmos2/processing_kosmos2.py", "repo_id": "transformers", "token_count": 13353 }

333

# coding=utf-8 # Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ LayoutLMv3 model configuration""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...utils import logging if TYPE_CHECKING: from ...processing_utils import ProcessorMixin from ...utils import TensorType logger = logging.get_logger(__name__) LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json", } class LayoutLMv3Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an LayoutLMv3 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LayoutLMv3 [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50265): Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`LayoutLMv3Model`]. hidden_size (`int`, *optional*, defaults to 768): Dimension of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv3Model`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. max_2d_position_embeddings (`int`, *optional*, defaults to 1024): The maximum value that the 2D position embedding might ever be used with. Typically set this to something large just in case (e.g., 1024). coordinate_size (`int`, *optional*, defaults to `128`): Dimension of the coordinate embeddings. shape_size (`int`, *optional*, defaults to `128`): Dimension of the width and height embeddings. has_relative_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a relative attention bias in the self-attention mechanism. rel_pos_bins (`int`, *optional*, defaults to 32): The number of relative position bins to be used in the self-attention mechanism. max_rel_pos (`int`, *optional*, defaults to 128): The maximum number of relative positions to be used in the self-attention mechanism. max_rel_2d_pos (`int`, *optional*, defaults to 256): The maximum number of relative 2D positions in the self-attention mechanism. rel_2d_pos_bins (`int`, *optional*, defaults to 64): The number of 2D relative position bins in the self-attention mechanism. has_spatial_attention_bias (`bool`, *optional*, defaults to `True`): Whether or not to use a spatial attention bias in the self-attention mechanism. visual_embed (`bool`, *optional*, defaults to `True`): Whether or not to add patch embeddings. input_size (`int`, *optional*, defaults to `224`): The size (resolution) of the images. num_channels (`int`, *optional*, defaults to `3`): The number of channels of the images. patch_size (`int`, *optional*, defaults to `16`) The size (resolution) of the patches. classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. Example: ```python >>> from transformers import LayoutLMv3Config, LayoutLMv3Model >>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration >>> configuration = LayoutLMv3Config() >>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration >>> model = LayoutLMv3Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "layoutlmv3" def __init__( self, vocab_size=50265, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-5, pad_token_id=1, bos_token_id=0, eos_token_id=2, max_2d_position_embeddings=1024, coordinate_size=128, shape_size=128, has_relative_attention_bias=True, rel_pos_bins=32, max_rel_pos=128, rel_2d_pos_bins=64, max_rel_2d_pos=256, has_spatial_attention_bias=True, text_embed=True, visual_embed=True, input_size=224, num_channels=3, patch_size=16, classifier_dropout=None, **kwargs, ): super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, intermediate_size=intermediate_size, hidden_act=hidden_act, hidden_dropout_prob=hidden_dropout_prob, attention_probs_dropout_prob=attention_probs_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, initializer_range=initializer_range, layer_norm_eps=layer_norm_eps, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) self.max_2d_position_embeddings = max_2d_position_embeddings self.coordinate_size = coordinate_size self.shape_size = shape_size self.has_relative_attention_bias = has_relative_attention_bias self.rel_pos_bins = rel_pos_bins self.max_rel_pos = max_rel_pos self.has_spatial_attention_bias = has_spatial_attention_bias self.rel_2d_pos_bins = rel_2d_pos_bins self.max_rel_2d_pos = max_rel_2d_pos self.text_embed = text_embed self.visual_embed = visual_embed self.input_size = input_size self.num_channels = num_channels self.patch_size = patch_size self.classifier_dropout = classifier_dropout class LayoutLMv3OnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.12") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: # The order of inputs is different for question answering and sequence classification if self.task in ["question-answering", "sequence-classification"]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("attention_mask", {0: "batch", 1: "sequence"}), ("bbox", {0: "batch", 1: "sequence"}), ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) else: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("bbox", {0: "batch", 1: "sequence"}), ("attention_mask", {0: "batch", 1: "sequence"}), ("pixel_values", {0: "batch", 1: "num_channels"}), ] ) @property def atol_for_validation(self) -> float: return 1e-5 @property def default_onnx_opset(self) -> int: return 12 def generate_dummy_inputs( self, processor: "ProcessorMixin", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional["TensorType"] = None, num_channels: int = 3, image_width: int = 40, image_height: int = 40, ) -> Mapping[str, Any]: """ Generate inputs to provide to the ONNX exporter for the specific framework Args: processor ([`ProcessorMixin`]): The processor associated with this model configuration. batch_size (`int`, *optional*, defaults to -1): The batch size to export the model for (-1 means dynamic axis). seq_length (`int`, *optional*, defaults to -1): The sequence length to export the model for (-1 means dynamic axis). is_pair (`bool`, *optional*, defaults to `False`): Indicate if the input is a pair (sentence 1, sentence 2). framework (`TensorType`, *optional*, defaults to `None`): The framework (PyTorch or TensorFlow) that the processor will generate tensors for. num_channels (`int`, *optional*, defaults to 3): The number of channels of the generated images. image_width (`int`, *optional*, defaults to 40): The width of the generated images. image_height (`int`, *optional*, defaults to 40): The height of the generated images. Returns: Mapping[str, Any]: holding the kwargs to provide to the model's forward function """ # A dummy image is used so OCR should not be applied setattr(processor.image_processor, "apply_ocr", False) # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = processor.tokenizer.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence dummy_text = [[" ".join([processor.tokenizer.unk_token]) * seq_length]] * batch_size # Generate dummy bounding boxes dummy_bboxes = [[[48, 84, 73, 128]]] * batch_size # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch) dummy_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width) inputs = dict( processor( dummy_image, text=dummy_text, boxes=dummy_bboxes, return_tensors=framework, ) ) return inputs

transformers/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py/0

{ "file_path": "transformers/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py", "repo_id": "transformers", "token_count": 5486 }

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# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for LLaMA.""" import os from shutil import copyfile from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...convert_slow_tokenizer import import_protobuf from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from ...tokenization_utils_base import TextInput logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model", }, "tokenizer_file": { "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "hf-internal-testing/llama-tokenizer": 2048, } SPIECE_UNDERLINE = "▁" B_INST, E_INST = "[INST]", "[/INST]" B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n" # fmt: off DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \ answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\ that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \ correct. If you don't know the answer to a question, please don't share false information.""" # fmt: on class LlamaTokenizer(PreTrainedTokenizer): """ Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is no padding token in the original model. Args: vocab_file (`str`): Path to the vocabulary file. unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`): The end of sequence token. pad_token (`str` or `tokenizers.AddedToken`, *optional*): A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. add_bos_token (`bool`, *optional*, defaults to `True`): Whether or not to add an `bos_token` at the start of sequences. add_eos_token (`bool`, *optional*, defaults to `False`): Whether or not to add an `eos_token` at the end of sequences. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces. use_default_system_prompt (`bool`, *optional*, defaults to `False`): Whether or not the default system prompt for Llama should be used. spaces_between_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to add spaces between special tokens. legacy (`bool`, *optional*): Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622 and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple example: - `legacy=True`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True) >>> tokenizer.encode("Hello <extra_id_0>.") [8774, 32099, 3, 5, 1] ``` - `legacy=False`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False) >>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here [8774, 32099, 5, 1] ``` Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, unk_token="<unk>", bos_token="<s>", eos_token="</s>", pad_token=None, sp_model_kwargs: Optional[Dict[str, Any]] = None, add_bos_token=True, add_eos_token=False, clean_up_tokenization_spaces=False, use_default_system_prompt=False, spaces_between_special_tokens=False, legacy=None, **kwargs, ): self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token if legacy is None: logger.warning_once( f"You are using the default legacy behaviour of the {self.__class__}. This is" " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you." " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it" " means, and thoroughly read the reason why this was added as explained in" " https://github.com/huggingface/transformers/pull/24565" ) legacy = True self.legacy = legacy self.vocab_file = vocab_file self.add_bos_token = add_bos_token self.add_eos_token = add_eos_token self.use_default_system_prompt = use_default_system_prompt self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False)) super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, add_bos_token=add_bos_token, add_eos_token=add_eos_token, sp_model_kwargs=self.sp_model_kwargs, clean_up_tokenization_spaces=clean_up_tokenization_spaces, use_default_system_prompt=use_default_system_prompt, spaces_between_special_tokens=spaces_between_special_tokens, legacy=legacy, **kwargs, ) @property def unk_token_length(self): return len(self.sp_model.encode(str(self.unk_token))) # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor def get_spm_processor(self, from_slow=False): tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs) if self.legacy or from_slow: # no dependency on protobuf tokenizer.Load(self.vocab_file) return tokenizer with open(self.vocab_file, "rb") as f: sp_model = f.read() model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)") model = model_pb2.ModelProto.FromString(sp_model) normalizer_spec = model_pb2.NormalizerSpec() normalizer_spec.add_dummy_prefix = False model.normalizer_spec.MergeFrom(normalizer_spec) sp_model = model.SerializeToString() tokenizer.LoadFromSerializedProto(sp_model) return tokenizer def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d): self.__dict__ = d self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @property def vocab_size(self): """Returns vocab size""" return self.sp_model.get_piece_size() def get_vocab(self): """Returns vocab as a dict""" vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]: """ Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the first token is special. """ if self.legacy or len(text) == 0: return super().tokenize(text, **kwargs) tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs) if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: tokens = tokens[1:] return tokens # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize def _tokenize(self, text, **kwargs): """ Returns a tokenized string. We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`. `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`. """ tokens = self.sp_model.encode(text, out_type=str) if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")): return tokens # 1. Encode string + prefix ex: "<unk> Hey" tokens = self.sp_model.encode(self.unk_token + text, out_type=str) # 2. Remove self.unk_token from ['<','unk','>', '▁Hey'] return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" token = self.sp_model.IdToPiece(index) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" # since we manually add the prefix space, we have to remove it when decoding if tokens[0].startswith(SPIECE_UNDERLINE): tokens[0] = tokens[0][1:] current_sub_tokens = [] out_string = "" prev_is_special = False for i, token in enumerate(tokens): # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special and i != 0 and self.legacy: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: """ Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory in which to save the vocabulary. Returns: `Tuple(str)`: Paths to the files saved. """ if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): bos_token_id = [self.bos_token_id] if self.add_bos_token else [] eos_token_id = [self.eos_token_id] if self.add_eos_token else [] output = bos_token_id + token_ids_0 + eos_token_id if token_ids_1 is not None: output = output + bos_token_id + token_ids_1 + eos_token_id return output def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) bos_token_id = [1] if self.add_bos_token else [] eos_token_id = [1] if self.add_eos_token else [] if token_ids_1 is None: return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id return ( bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + ([0] * len(token_ids_1)) + eos_token_id ) def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` if token_ids_1 is None, only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of ids. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ bos_token_id = [self.bos_token_id] if self.add_bos_token else [] eos_token_id = [self.eos_token_id] if self.add_eos_token else [] output = [0] * len(bos_token_id + token_ids_0 + eos_token_id) if token_ids_1 is not None: output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) return output @property def default_chat_template(self): """ LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages. Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering rather than needing special tokens. The system message is partly 'embedded' in the first user message, which results in an unusual token ordering when it is present. This template should definitely be changed if you wish to fine-tune a model with more flexible role ordering! The output should look something like: <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos> <bos>[INST] Prompt [/INST] The reference for this chat template is [this code snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362) in the original repository. """ logger.warning_once( "\nNo chat template is defined for this tokenizer - using the default template " f"for the {self.__class__.__name__} class. If the default is not appropriate for " "your model, please set `tokenizer.chat_template` to an appropriate template. " "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n" ) template = ( "{% if messages[0]['role'] == 'system' %}" "{% set loop_messages = messages[1:] %}" # Extract system message if it's present "{% set system_message = messages[0]['content'] %}" "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}" "{% set loop_messages = messages %}" # Or use the default system message if the flag is set "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}" "{% else %}" "{% set loop_messages = messages %}" "{% set system_message = false %}" "{% endif %}" "{% for message in loop_messages %}" # Loop over all non-system messages "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}" "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}" "{% endif %}" "{% if loop.index0 == 0 and system_message != false %}" # Embed system message in first message "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}" "{% else %}" "{% set content = message['content'] %}" "{% endif %}" "{% if message['role'] == 'user' %}" # After all of that, handle messages/roles in a fairly normal way "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}" "{% elif message['role'] == 'system' %}" "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}" "{% elif message['role'] == 'assistant' %}" "{{ ' ' + content.strip() + ' ' + eos_token }}" "{% endif %}" "{% endfor %}" ) template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false") default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'") template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message) return template

transformers/src/transformers/models/llama/tokenization_llama.py/0

{ "file_path": "transformers/src/transformers/models/llama/tokenization_llama.py", "repo_id": "transformers", "token_count": 9293 }

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# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert T5/LongT5X checkpoints from the original repository to JAX/FLAX model. This script is an extension of 'src/transformers/models/t5/convert_t5x_checkpoint_to_flax. """ import argparse from t5x import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path): config = AutoConfig.from_pretrained(config_name) flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=config) t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path) split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"] if config.model_type == "t5": encoder_attn_name = "SelfAttention" if config.model_type == "longt5" and config.encoder_attention_type == "local": encoder_attn_name = "LocalSelfAttention" elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": encoder_attn_name = "TransientGlobalSelfAttention" else: raise ValueError( "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`" " attribute with a value from ['local', 'transient-global]." ) # Encoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"] # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_global_layer_norm = t5x_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"] # Layer Normalization t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"] if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_encoder_layer_block = flax_model.params["encoder"]["block"][str(layer_index)]["layer"] flax_model_encoder_layer_block["0"][encoder_attn_name]["k"]["kernel"] = t5x_attention_key flax_model_encoder_layer_block["0"][encoder_attn_name]["o"]["kernel"] = t5x_attention_out flax_model_encoder_layer_block["0"][encoder_attn_name]["q"]["kernel"] = t5x_attention_query flax_model_encoder_layer_block["0"][encoder_attn_name]["v"]["kernel"] = t5x_attention_value flax_model_encoder_layer_block["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": flax_model_encoder_layer_block["0"][encoder_attn_name]["global_input_layer_norm"][ "weight" ] = t5x_global_layer_norm if split_mlp_wi: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_encoder_layer_block["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_encoder_layer_block["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm flax_model.params["encoder"]["block"][str(layer_index)]["layer"] = flax_model_encoder_layer_block # Only for layer 0: t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["relative_attention_bias"][ "embedding" ] = t5x_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_encoder_global_rel_embedding = t5x_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["global_relative_attention_bias"][ "embedding" ] = t5x_encoder_global_rel_embedding # Assigning t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"] flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm # Decoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"] # Layer Normalization t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][ "scale" ] # Encoder-Decoder-Attention t5x_enc_dec_attention_module = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"] t5x_enc_dec_attention_key = t5x_enc_dec_attention_module["key"]["kernel"] t5x_enc_dec_attention_out = t5x_enc_dec_attention_module["out"]["kernel"] t5x_enc_dec_attention_query = t5x_enc_dec_attention_module["query"]["kernel"] t5x_enc_dec_attention_value = t5x_enc_dec_attention_module["value"]["kernel"] # Layer Normalization t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"] # MLP if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_decoder_layer_block = flax_model.params["decoder"]["block"][str(layer_index)]["layer"] flax_model_decoder_layer_block["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key flax_model_decoder_layer_block["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out flax_model_decoder_layer_block["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query flax_model_decoder_layer_block["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value flax_model_decoder_layer_block["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm flax_model_decoder_layer_block["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key flax_model_decoder_layer_block["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out flax_model_decoder_layer_block["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query flax_model_decoder_layer_block["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value flax_model_decoder_layer_block["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm if split_mlp_wi: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_decoder_layer_block["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_decoder_layer_block["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm flax_model.params["decoder"]["block"][str(layer_index)]["layer"] = flax_model_decoder_layer_block # Decoder Normalization tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"] flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm # Only for layer 0: t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][ "embedding" ] = t5x_decoder_rel_embedding # Token Embeddings tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"] flax_model.params["shared"]["embedding"] = tx5_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in t5x_model["target"]["decoder"]: flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"] flax_model.save_pretrained(flax_dump_folder_path) print("T5X Model was sucessfully converted!") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint." ) parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.") parser.add_argument( "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model." ) args = parser.parse_args() convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path)

transformers/src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py/0

{ "file_path": "transformers/src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py", "repo_id": "transformers", "token_count": 4985 }

336

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Processor class for MarkupLM. """ from typing import Optional, Union from ...file_utils import TensorType from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy class MarkupLMProcessor(ProcessorMixin): r""" Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single processor. [`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model. It first uses [`MarkupLMFeatureExtractor`] to extract nodes and corresponding xpaths from one or more HTML strings. Next, these are provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which turns them into token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_subs_seq`. Args: feature_extractor (`MarkupLMFeatureExtractor`): An instance of [`MarkupLMFeatureExtractor`]. The feature extractor is a required input. tokenizer (`MarkupLMTokenizer` or `MarkupLMTokenizerFast`): An instance of [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]. The tokenizer is a required input. parse_html (`bool`, *optional*, defaults to `True`): Whether or not to use `MarkupLMFeatureExtractor` to parse HTML strings into nodes and corresponding xpaths. """ feature_extractor_class = "MarkupLMFeatureExtractor" tokenizer_class = ("MarkupLMTokenizer", "MarkupLMTokenizerFast") parse_html = True def __call__( self, html_strings=None, nodes=None, xpaths=None, node_labels=None, questions=None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs, ) -> BatchEncoding: """ This method first forwards the `html_strings` argument to [`~MarkupLMFeatureExtractor.__call__`]. Next, it passes the `nodes` and `xpaths` along with the additional arguments to [`~MarkupLMTokenizer.__call__`] and returns the output. Optionally, one can also provide a `text` argument which is passed along as first sequence. Please refer to the docstring of the above two methods for more information. """ # first, create nodes and xpaths if self.parse_html: if html_strings is None: raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`") if nodes is not None or xpaths is not None or node_labels is not None: raise ValueError( "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`" ) features = self.feature_extractor(html_strings) nodes = features["nodes"] xpaths = features["xpaths"] else: if html_strings is not None: raise ValueError("You have passed HTML strings but `parse_html` is set to `False`.") if nodes is None or xpaths is None: raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`") # # second, apply the tokenizer if questions is not None and self.parse_html: if isinstance(questions, str): questions = [questions] # add batch dimension (as the feature extractor always adds a batch dimension) encoded_inputs = self.tokenizer( text=questions if questions is not None else nodes, text_pair=nodes if questions is not None else None, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, return_tensors=return_tensors, **kwargs, ) return encoded_inputs def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names return tokenizer_input_names

transformers/src/transformers/models/markuplm/processing_markuplm.py/0

{ "file_path": "transformers/src/transformers/models/markuplm/processing_markuplm.py", "repo_id": "transformers", "token_count": 2520 }

337

# coding=utf-8 # Copyright 2022 Meta Platforms, Inc.s and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch MaskFormer model.""" import math from dataclasses import dataclass from numbers import Number from typing import Dict, List, Optional, Tuple import numpy as np import torch from torch import Tensor, nn from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_attention_mask from ...modeling_outputs import BaseModelOutputWithCrossAttentions from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_scipy_available, logging, replace_return_docstrings, requires_backends, ) from ...utils.backbone_utils import load_backbone from ..detr import DetrConfig from .configuration_maskformer import MaskFormerConfig from .configuration_maskformer_swin import MaskFormerSwinConfig if is_scipy_available(): from scipy.optimize import linear_sum_assignment logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "MaskFormerConfig" _CHECKPOINT_FOR_DOC = "facebook/maskformer-swin-base-ade" MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/maskformer-swin-base-ade", # See all MaskFormer models at https://huggingface.co/models?filter=maskformer ] @dataclass # Copied from transformers.models.detr.modeling_detr.DetrDecoderOutput class DetrDecoderOutput(BaseModelOutputWithCrossAttentions): """ Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. """ intermediate_hidden_states: Optional[torch.FloatTensor] = None @dataclass class MaskFormerPixelLevelModuleOutput(ModelOutput): """ MaskFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the `encoder` and `decoder`. By default, the `encoder` is a MaskFormerSwin Transformer and the `decoder` is a Feature Pyramid Network (FPN). The `encoder_last_hidden_state` are referred on the paper as **images features**, while `decoder_last_hidden_state` as **pixel embeddings** Args: encoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`): Last hidden states (final feature map) of the last stage of the encoder. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage. decoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`): Last hidden states (final feature map) of the last stage of the decoder. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage. """ encoder_last_hidden_state: Optional[torch.FloatTensor] = None decoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MaskFormerPixelDecoderOutput(ModelOutput): """ MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state and (optionally) the hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Last hidden states (final feature map) of the last stage of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights from Detr's decoder after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MaskFormerModelOutput(ModelOutput): """ Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits. Args: encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Last hidden states (final feature map) of the last stage of the encoder model (backbone). pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN). transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Last hidden states (final feature map) of the last stage of the transformer decoder model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder model at the output of each stage. pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel decoder model at the output of each stage. transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the transformer decoder at the output of each stage. hidden_states `tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and `decoder_hidden_states` attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights from Detr's decoder after the attention softmax, used to compute the weighted average in the self-attention heads. """ encoder_last_hidden_state: Optional[torch.FloatTensor] = None pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None pixel_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None transformer_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MaskFormerForInstanceSegmentationOutput(ModelOutput): """ Class for outputs of [`MaskFormerForInstanceSegmentation`]. This output can be directly passed to [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or or [`~MaskFormerImageProcessor.post_process_instance_segmentation`] or [`~MaskFormerImageProcessor.post_process_panoptic_segmentation`] depending on the task. Please, see [`~MaskFormerImageProcessor] for details regarding usage. Args: loss (`torch.Tensor`, *optional*): The computed loss, returned when labels are present. class_queries_logits (`torch.FloatTensor`): A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each query. Note the `+ 1` is needed because we incorporate the null class. masks_queries_logits (`torch.FloatTensor`): A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each query. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Last hidden states (final feature map) of the last stage of the encoder model (backbone). pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN). transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Last hidden states (final feature map) of the last stage of the transformer decoder model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder model at the output of each stage. pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel decoder model at the output of each stage. transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the transformer decoder at the output of each stage. hidden_states `tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and `decoder_hidden_states`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights from Detr's decoder after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None class_queries_logits: torch.FloatTensor = None masks_queries_logits: torch.FloatTensor = None auxiliary_logits: torch.FloatTensor = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None pixel_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None transformer_decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None def upsample_like(pixel_values: Tensor, like: Tensor, mode: str = "bilinear") -> Tensor: """ An utility function that upsamples `pixel_values` to match the dimension of `like`. Args: pixel_values (`torch.Tensor`): The tensor we wish to upsample. like (`torch.Tensor`): The tensor we wish to use as size target. mode (str, *optional*, defaults to `"bilinear"`): The interpolation mode. Returns: `torch.Tensor`: The upsampled tensor """ _, _, height, width = like.shape upsampled = nn.functional.interpolate(pixel_values, size=(height, width), mode=mode, align_corners=False) return upsampled # refactored from original implementation def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor: r""" Compute the DICE loss, similar to generalized IOU for masks as follows: $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$ In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$ Args: inputs (`torch.Tensor`): A tensor representing a mask. labels (`torch.Tensor`): A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs (0 for the negative class and 1 for the positive class). num_masks (`int`): The number of masks present in the current batch, used for normalization. Returns: `torch.Tensor`: The computed loss. """ probs = inputs.sigmoid().flatten(1) numerator = 2 * (probs * labels).sum(-1) denominator = probs.sum(-1) + labels.sum(-1) loss = 1 - (numerator + 1) / (denominator + 1) loss = loss.sum() / num_masks return loss # refactored from original implementation def sigmoid_focal_loss( inputs: Tensor, labels: Tensor, num_masks: int, alpha: float = 0.25, gamma: float = 2 ) -> Tensor: r""" Focal loss proposed in [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) originally used in RetinaNet. The loss is computed as follows: $$ \mathcal{L}_{\text{focal loss} = -(1 - p_t)^{\gamma}\log{(p_t)} $$ where \$CE(p_t) = -\log{(p_t)}}\$, CE is the standard Cross Entropy Loss Please refer to equation (1,2,3) of the paper for a better understanding. Args: inputs (`torch.Tensor`): A float tensor of arbitrary shape. labels (`torch.Tensor`): A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs (0 for the negative class and 1 for the positive class). num_masks (`int`): The number of masks present in the current batch, used for normalization. alpha (float, *optional*, defaults to 0.25): Weighting factor in range (0,1) to balance positive vs negative examples. gamma (float, *optional*, defaults to 2.0): Exponent of the modulating factor \$1 - p_t\$ to balance easy vs hard examples. Returns: `torch.Tensor`: The computed loss. """ criterion = nn.BCEWithLogitsLoss(reduction="none") probs = inputs.sigmoid() cross_entropy_loss = criterion(inputs, labels) p_t = probs * labels + (1 - probs) * (1 - labels) loss = cross_entropy_loss * ((1 - p_t) ** gamma) if alpha >= 0: alpha_t = alpha * labels + (1 - alpha) * (1 - labels) loss = alpha_t * loss loss = loss.mean(1).sum() / num_masks return loss # refactored from original implementation def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor: """ A pair wise version of the dice loss, see `dice_loss` for usage. Args: inputs (`torch.Tensor`): A tensor representing a mask labels (`torch.Tensor`): A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs (0 for the negative class and 1 for the positive class). Returns: `torch.Tensor`: The computed loss between each pairs. """ inputs = inputs.sigmoid().flatten(1) numerator = 2 * torch.matmul(inputs, labels.T) # using broadcasting to get a [num_queries, NUM_CLASSES] matrix denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :] loss = 1 - (numerator + 1) / (denominator + 1) return loss # refactored from original implementation def pair_wise_sigmoid_focal_loss(inputs: Tensor, labels: Tensor, alpha: float = 0.25, gamma: float = 2.0) -> Tensor: r""" A pair wise version of the focal loss, see `sigmoid_focal_loss` for usage. Args: inputs (`torch.Tensor`): A tensor representing a mask. labels (`torch.Tensor`): A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs (0 for the negative class and 1 for the positive class). alpha (float, *optional*, defaults to 0.25): Weighting factor in range (0,1) to balance positive vs negative examples. gamma (float, *optional*, defaults to 2.0): Exponent of the modulating factor \$1 - p_t\$ to balance easy vs hard examples. Returns: `torch.Tensor`: The computed loss between each pairs. """ if alpha < 0: raise ValueError("alpha must be positive") height_and_width = inputs.shape[1] criterion = nn.BCEWithLogitsLoss(reduction="none") prob = inputs.sigmoid() cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs)) focal_pos = ((1 - prob) ** gamma) * cross_entropy_loss_pos focal_pos *= alpha cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs)) focal_neg = (prob**gamma) * cross_entropy_loss_neg focal_neg *= 1 - alpha loss = torch.matmul(focal_pos, labels.T) + torch.matmul(focal_neg, (1 - labels).T) return loss / height_and_width # Copied from transformers.models.detr.modeling_detr.DetrAttention class DetrAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and keys (as explained in the DETR paper). """ def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if self.head_dim * num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {num_heads})." ) self.scaling = self.head_dim**-0.5 self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs): position_embeddings = kwargs.pop("position_embeddings", None) if kwargs: raise ValueError(f"Unexpected arguments {kwargs.keys()}") if position_embeddings is not None and object_queries is not None: raise ValueError( "Cannot specify both position_embeddings and object_queries. Please use just object_queries" ) if position_embeddings is not None: logger.warning_once( "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" ) object_queries = position_embeddings return tensor if object_queries is None else tensor + object_queries def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, object_queries: Optional[torch.Tensor] = None, key_value_states: Optional[torch.Tensor] = None, spatial_position_embeddings: Optional[torch.Tensor] = None, output_attentions: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" position_embeddings = kwargs.pop("position_ebmeddings", None) key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None) if kwargs: raise ValueError(f"Unexpected arguments {kwargs.keys()}") if position_embeddings is not None and object_queries is not None: raise ValueError( "Cannot specify both position_embeddings and object_queries. Please use just object_queries" ) if key_value_position_embeddings is not None and spatial_position_embeddings is not None: raise ValueError( "Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings" ) if position_embeddings is not None: logger.warning_once( "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" ) object_queries = position_embeddings if key_value_position_embeddings is not None: logger.warning_once( "key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead" ) spatial_position_embeddings = key_value_position_embeddings # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size, target_len, embed_dim = hidden_states.size() # add position embeddings to the hidden states before projecting to queries and keys if object_queries is not None: hidden_states_original = hidden_states hidden_states = self.with_pos_embed(hidden_states, object_queries) # add key-value position embeddings to the key value states if spatial_position_embeddings is not None: key_value_states_original = key_value_states key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings) # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) proj_shape = (batch_size * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) source_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): raise ValueError( f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, target_len, source_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" f" {attention_mask.size()}" ) attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(batch_size, target_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer class DetrDecoderLayer(nn.Module): def __init__(self, config: DetrConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = DetrAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = DetrAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, object_queries: Optional[torch.Tensor] = None, query_position_embeddings: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, **kwargs, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. object_queries (`torch.FloatTensor`, *optional*): object_queries that are added to the hidden states in the cross-attention layer. query_position_embeddings (`torch.FloatTensor`, *optional*): position embeddings that are added to the queries and keys in the self-attention layer. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ position_embeddings = kwargs.pop("position_embeddings", None) if kwargs: raise ValueError(f"Unexpected arguments {kwargs.keys()}") if position_embeddings is not None and object_queries is not None: raise ValueError( "Cannot specify both position_embeddings and object_queries. Please use just object_queries" ) if position_embeddings is not None: logger.warning_once( "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" ) object_queries = position_embeddings residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, object_queries=query_position_embeddings, attention_mask=attention_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, object_queries=query_position_embeddings, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, spatial_position_embeddings=object_queries, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class DetrDecoder(nn.Module): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`]. The decoder updates the query embeddings through multiple self-attention and cross-attention layers. Some small tweaks for DETR: - object_queries and query_position_embeddings are added to the forward pass. - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. Args: config: DetrConfig """ def __init__(self, config: DetrConfig): super().__init__() self.config = config self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)]) # in DETR, the decoder uses layernorm after the last decoder layer output self.layernorm = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False def forward( self, inputs_embeds=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, object_queries=None, query_position_embeddings=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The query embeddings that are passed into the decoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: - 1 for queries that are **not masked**, - 0 for queries that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Position embeddings that are added to the queries and keys in each cross-attention layer. query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ position_embeddings = kwargs.pop("position_embeddings", None) if kwargs: raise ValueError(f"Unexpected arguments {kwargs.keys()}") if position_embeddings is not None and object_queries is not None: raise ValueError( "Cannot specify both position_embeddings and object_queries. Please use just object_queries" ) if position_embeddings is not None: logger.warning_once( "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" ) object_queries = position_embeddings output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is not None: hidden_states = inputs_embeds input_shape = inputs_embeds.size()[:-1] # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _prepare_4d_attention_mask( encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] ) # optional intermediate hidden states intermediate = () if self.config.auxiliary_loss else None # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, None, encoder_hidden_states, encoder_attention_mask, None, output_attentions, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=None, object_queries=object_queries, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if self.config.auxiliary_loss: hidden_states = self.layernorm(hidden_states) intermediate += (hidden_states,) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # finally, apply layernorm hidden_states = self.layernorm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) # stack intermediate decoder activations if self.config.auxiliary_loss: intermediate = torch.stack(intermediate) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate] if v is not None ) return DetrDecoderOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, intermediate_hidden_states=intermediate, ) # refactored from original implementation class MaskFormerHungarianMatcher(nn.Module): """This class computes an assignment between the labels and the predictions of the network. For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (and thus treated as non-objects). """ def __init__(self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0): """Creates the matcher Params: cost_class (float, *optional*, defaults to 1.0): This is the relative weight of the classification error in the matching cost. cost_mask (float, *optional*, defaults to 1.0): This is the relative weight of the focal loss of the binary mask in the matching cost. cost_dice (float, *optional*, defaults to 1.0): This is the relative weight of the dice loss of the binary mask in the matching cost """ super().__init__() if cost_class == 0 and cost_mask == 0 and cost_dice == 0: raise ValueError("All costs cant be 0") self.cost_class = cost_class self.cost_mask = cost_mask self.cost_dice = cost_dice @torch.no_grad() def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]: """Performs the matching Params: masks_queries_logits (`torch.Tensor`): A tensor` of dim `batch_size, num_queries, num_labels` with the classification logits. class_queries_logits (`torch.Tensor`): A tensor` of dim `batch_size, num_queries, height, width` with the predicted masks. class_labels (`torch.Tensor`): A tensor` of dim `num_target_boxes` (where num_target_boxes is the number of ground-truth objects in the target) containing the class labels. mask_labels (`torch.Tensor`): A tensor` of dim `num_target_boxes, height, width` containing the target masks. Returns: `List[Tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where: - index_i is the indices of the selected predictions (in order) - index_j is the indices of the corresponding selected labels (in order) For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes). """ indices: List[Tuple[np.array]] = [] preds_masks = masks_queries_logits preds_probs = class_queries_logits # iterate through batch size for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels): # downsample the target mask, save memory target_mask = nn.functional.interpolate(target_mask[:, None], size=pred_mask.shape[-2:], mode="nearest") pred_probs = pred_probs.softmax(-1) # Compute the classification cost. Contrary to the loss, we don't use the NLL, # but approximate it in 1 - proba[target class]. # The 1 is a constant that doesn't change the matching, it can be ommitted. cost_class = -pred_probs[:, labels] # flatten spatial dimension "q h w -> q (h w)" pred_mask_flat = pred_mask.flatten(1) # [num_queries, height*width] # same for target_mask "c h w -> c (h w)" target_mask_flat = target_mask[:, 0].flatten(1) # [num_total_labels, height*width] # compute the focal loss between each mask pairs -> shape (num_queries, num_labels) cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat) # Compute the dice loss betwen each mask pairs -> shape (num_queries, num_labels) cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat) # final cost matrix cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice # do the assigmented using the hungarian algorithm in scipy assigned_indices: Tuple[np.array] = linear_sum_assignment(cost_matrix.cpu()) indices.append(assigned_indices) # It could be stacked in one tensor matched_indices = [ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices ] return matched_indices def __repr__(self): head = "Matcher " + self.__class__.__name__ body = [ f"cost_class: {self.cost_class}", f"cost_mask: {self.cost_mask}", f"cost_dice: {self.cost_dice}", ] _repr_indent = 4 lines = [head] + [" " * _repr_indent + line for line in body] return "\n".join(lines) # copied and adapted from original implementation class MaskFormerLoss(nn.Module): def __init__( self, num_labels: int, matcher: MaskFormerHungarianMatcher, weight_dict: Dict[str, float], eos_coef: float, ): """ The MaskFormer Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we compute hungarian assignment between ground truth masks and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and mask) Args: num_labels (`int`): The number of classes. matcher (`MaskFormerHungarianMatcher`): A torch module that computes the assigments between the predictions and labels. weight_dict (`Dict[str, float]`): A dictionary of weights to be applied to the different losses. eos_coef (`float`): Weight to apply to the null class. """ super().__init__() requires_backends(self, ["scipy"]) self.num_labels = num_labels self.matcher = matcher self.weight_dict = weight_dict self.eos_coef = eos_coef empty_weight = torch.ones(self.num_labels + 1) empty_weight[-1] = self.eos_coef self.register_buffer("empty_weight", empty_weight) def _max_by_axis(self, the_list: List[List[int]]) -> List[int]: maxes = the_list[0] for sublist in the_list[1:]: for index, item in enumerate(sublist): maxes[index] = max(maxes[index], item) return maxes def _pad_images_to_max_in_batch(self, tensors: List[Tensor]) -> Tuple[Tensor, Tensor]: # get the maximum size in the batch max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors]) batch_size = len(tensors) # compute finel size batch_shape = [batch_size] + max_size b, _, h, w = batch_shape # get metadata dtype = tensors[0].dtype device = tensors[0].device padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device) padding_masks = torch.ones((b, h, w), dtype=torch.bool, device=device) # pad the tensors to the size of the biggest one for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks): padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor) padding_mask[: tensor.shape[1], : tensor.shape[2]] = False return padded_tensors, padding_masks def loss_labels( self, class_queries_logits: Tensor, class_labels: List[Tensor], indices: Tuple[np.array] ) -> Dict[str, Tensor]: """Compute the losses related to the labels using cross entropy. Args: class_queries_logits (`torch.Tensor`): A tensor of shape `batch_size, num_queries, num_labels` class_labels (`List[torch.Tensor]`): List of class labels of shape `(labels)`. indices (`Tuple[np.array])`: The indices computed by the Hungarian matcher. Returns: `Dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key: - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels. """ pred_logits = class_queries_logits batch_size, num_queries, _ = pred_logits.shape criterion = nn.CrossEntropyLoss(weight=self.empty_weight) idx = self._get_predictions_permutation_indices(indices) # shape = (batch_size, num_queries) target_classes_o = torch.cat([target[j] for target, (_, j) in zip(class_labels, indices)]) # shape = (batch_size, num_queries) target_classes = torch.full( (batch_size, num_queries), fill_value=self.num_labels, dtype=torch.int64, device=pred_logits.device ) target_classes[idx] = target_classes_o # target_classes is a (batch_size, num_labels, num_queries), we need to permute pred_logits "b q c -> b c q" pred_logits_transposed = pred_logits.transpose(1, 2) loss_ce = criterion(pred_logits_transposed, target_classes) losses = {"loss_cross_entropy": loss_ce} return losses def loss_masks( self, masks_queries_logits: Tensor, mask_labels: List[Tensor], indices: Tuple[np.array], num_masks: int ) -> Dict[str, Tensor]: """Compute the losses related to the masks using focal and dice loss. Args: masks_queries_logits (`torch.Tensor`): A tensor of shape `batch_size, num_queries, height, width` mask_labels (`torch.Tensor`): List of mask labels of shape `(labels, height, width)`. indices (`Tuple[np.array])`: The indices computed by the Hungarian matcher. num_masks (`int)`: The number of masks, used for normalization. Returns: `Dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys: - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks. - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth masks. """ src_idx = self._get_predictions_permutation_indices(indices) tgt_idx = self._get_targets_permutation_indices(indices) # shape (batch_size * num_queries, height, width) pred_masks = masks_queries_logits[src_idx] # shape (batch_size, num_queries, height, width) # pad all and stack the targets to the num_labels dimension target_masks, _ = self._pad_images_to_max_in_batch(mask_labels) target_masks = target_masks[tgt_idx] # upsample predictions to the target size, we have to add one dim to use interpolate pred_masks = nn.functional.interpolate( pred_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False ) pred_masks = pred_masks[:, 0].flatten(1) target_masks = target_masks.flatten(1) losses = { "loss_mask": sigmoid_focal_loss(pred_masks, target_masks, num_masks), "loss_dice": dice_loss(pred_masks, target_masks, num_masks), } return losses def _get_predictions_permutation_indices(self, indices): # permute predictions following indices batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) predictions_indices = torch.cat([src for (src, _) in indices]) return batch_indices, predictions_indices def _get_targets_permutation_indices(self, indices): # permute labels following indices batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) target_indices = torch.cat([tgt for (_, tgt) in indices]) return batch_indices, target_indices def forward( self, masks_queries_logits: Tensor, class_queries_logits: Tensor, mask_labels: List[Tensor], class_labels: List[Tensor], auxiliary_predictions: Optional[Dict[str, Tensor]] = None, ) -> Dict[str, Tensor]: """ This performs the loss computation. Args: masks_queries_logits (`torch.Tensor`): A tensor of shape `batch_size, num_queries, height, width` class_queries_logits (`torch.Tensor`): A tensor of shape `batch_size, num_queries, num_labels` mask_labels (`torch.Tensor`): List of mask labels of shape `(labels, height, width)`. class_labels (`List[torch.Tensor]`): List of class labels of shape `(labels)`. auxiliary_predictions (`Dict[str, torch.Tensor]`, *optional*): if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], then it contains the logits from the inner layers of the Detr's Decoder. Returns: `Dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys: - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels. - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks. - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth masks. if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], the dictionary contains addional losses for each auxiliary predictions. """ # retrieve the matching between the outputs of the last layer and the labels indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels) # compute the average number of target masks for normalization purposes num_masks: Number = self.get_num_masks(class_labels, device=class_labels[0].device) # get all the losses losses: Dict[str, Tensor] = { **self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks), **self.loss_labels(class_queries_logits, class_labels, indices), } # in case of auxiliary losses, we repeat this process with the output of each intermediate layer. if auxiliary_predictions is not None: for idx, aux_outputs in enumerate(auxiliary_predictions): masks_queries_logits = aux_outputs["masks_queries_logits"] class_queries_logits = aux_outputs["class_queries_logits"] loss_dict = self.forward(masks_queries_logits, class_queries_logits, mask_labels, class_labels) loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()} losses.update(loss_dict) return losses def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor: """ Computes the average number of target masks across the batch, for normalization purposes. """ num_masks = sum([len(classes) for classes in class_labels]) num_masks_pt = torch.as_tensor(num_masks, dtype=torch.float, device=device) return num_masks_pt class MaskFormerFPNConvLayer(nn.Module): def __init__(self, in_features: int, out_features: int, kernel_size: int = 3, padding: int = 1): """ A basic module that executes conv - norm - in sequence used in MaskFormer. Args: in_features (`int`): The number of input features (channels). out_features (`int`): The number of outputs features (channels). """ super().__init__() self.layers = [ nn.Conv2d(in_features, out_features, kernel_size=kernel_size, padding=padding, bias=False), nn.GroupNorm(32, out_features), nn.ReLU(inplace=True), ] for i, layer in enumerate(self.layers): # Provide backwards compatibility from when the class inherited from nn.Sequential # In nn.Sequential subclasses, the name given to the layer is its index in the sequence. # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g. # self.my_layer_name = Layer() # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register # explicitly self.add_module(str(i), layer) def forward(self, input: Tensor) -> Tensor: hidden_state = input for layer in self.layers: hidden_state = layer(hidden_state) return hidden_state class MaskFormerFPNLayer(nn.Module): def __init__(self, in_features: int, lateral_features: int): """ A Feature Pyramid Network Layer (FPN) layer. It creates a feature map by aggregating features from the previous and backbone layer. Due to the spatial mismatch, the tensor coming from the previous layer is upsampled. Args: in_features (`int`): The number of input features (channels). lateral_features (`int`): The number of lateral features (channels). """ super().__init__() self.proj = nn.Sequential( nn.Conv2d(lateral_features, in_features, kernel_size=1, padding=0, bias=False), nn.GroupNorm(32, in_features), ) self.block = MaskFormerFPNConvLayer(in_features, in_features) def forward(self, down: Tensor, left: Tensor) -> Tensor: left = self.proj(left) down = nn.functional.interpolate(down, size=left.shape[-2:], mode="nearest") down += left down = self.block(down) return down class MaskFormerFPNModel(nn.Module): def __init__(self, in_features: int, lateral_widths: List[int], feature_size: int = 256): """ Feature Pyramid Network, given an input tensor and a set of feature map of different feature/spatial size, it creates a list of feature maps with the same feature size. Args: in_features (`int`): The number of input features (channels). lateral_widths (`List[int]`): A list with the features (channels) size of each lateral connection. feature_size (int, *optional*, defaults to 256): The features (channels) of the resulting feature maps. """ super().__init__() self.stem = MaskFormerFPNConvLayer(in_features, feature_size) self.layers = nn.Sequential( *[MaskFormerFPNLayer(feature_size, lateral_width) for lateral_width in lateral_widths[::-1]] ) def forward(self, features: List[Tensor]) -> List[Tensor]: fpn_features = [] last_feature = features[-1] other_features = features[:-1] output = self.stem(last_feature) for layer, left in zip(self.layers, other_features[::-1]): output = layer(output, left) fpn_features.append(output) return fpn_features class MaskFormerPixelDecoder(nn.Module): def __init__(self, *args, feature_size: int = 256, mask_feature_size: int = 256, **kwargs): r""" Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278). It first runs the backbone's features into a Feature Pyramid Network creating a list of feature maps. Then, it projects the last one to the correct `mask_size`. Args: feature_size (`int`, *optional*, defaults to 256): The feature size (channel dimension) of the FPN feature maps. mask_feature_size (`int`, *optional*, defaults to 256): The features (channels) of the target masks size \$C_{\epsilon}\$ in the paper. """ super().__init__() self.fpn = MaskFormerFPNModel(*args, feature_size=feature_size, **kwargs) self.mask_projection = nn.Conv2d(feature_size, mask_feature_size, kernel_size=3, padding=1) def forward( self, features: List[Tensor], output_hidden_states: bool = False, return_dict: bool = True ) -> MaskFormerPixelDecoderOutput: fpn_features = self.fpn(features) # we use the last feature map last_feature_projected = self.mask_projection(fpn_features[-1]) if not return_dict: return (last_feature_projected, tuple(fpn_features)) if output_hidden_states else (last_feature_projected,) return MaskFormerPixelDecoderOutput( last_hidden_state=last_feature_projected, hidden_states=tuple(fpn_features) if output_hidden_states else () ) # copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding class MaskFormerSinePositionEmbedding(nn.Module): """ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized to work on images. """ def __init__( self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: Optional[float] = None ): super().__init__() if scale is not None and normalize is False: raise ValueError("normalize should be True if scale is passed") self.num_pos_feats = num_pos_feats self.temperature = temperature self.normalize = normalize self.scale = 2 * math.pi if scale is None else scale def forward(self, x: Tensor, mask: Optional[Tensor] = None) -> Tensor: if mask is None: mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) not_mask = (~mask).to(x.dtype) y_embed = not_mask.cumsum(1) x_embed = not_mask.cumsum(2) if self.normalize: eps = 1e-6 y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=x.device).type_as(x) dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos class PredictionBlock(nn.Module): def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None: super().__init__() self.layers = [nn.Linear(in_dim, out_dim), activation] # Maintain submodule indexing as if part of a Sequential block for i, layer in enumerate(self.layers): self.add_module(str(i), layer) def forward(self, input: Tensor) -> Tensor: hidden_state = input for layer in self.layers: hidden_state = layer(hidden_state) return hidden_state class MaskformerMLPPredictionHead(nn.Module): def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3): """ A classic Multi Layer Perceptron (MLP). Args: input_dim (`int`): The input dimensions. hidden_dim (`int`): The hidden dimensions. output_dim (`int`): The output dimensions. num_layers (int, *optional*, defaults to 3): The number of layers. """ super().__init__() in_dims = [input_dim] + [hidden_dim] * (num_layers - 1) out_dims = [hidden_dim] * (num_layers - 1) + [output_dim] self.layers = [] for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)): activation = nn.ReLU() if i < num_layers - 1 else nn.Identity() layer = PredictionBlock(in_dim, out_dim, activation=activation) self.layers.append(layer) # Provide backwards compatibility from when the class inherited from nn.Sequential # In nn.Sequential subclasses, the name given to the layer is its index in the sequence. # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g. # self.my_layer_name = Layer() # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register # explicitly self.add_module(str(i), layer) def forward(self, input: Tensor) -> Tensor: hidden_state = input for layer in self.layers: hidden_state = layer(hidden_state) return hidden_state class MaskFormerPixelLevelModule(nn.Module): def __init__(self, config: MaskFormerConfig): """ Pixel Level Module proposed in [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278). It runs the input image through a backbone and a pixel decoder, generating an image feature map and pixel embeddings. Args: config ([`MaskFormerConfig`]): The configuration used to instantiate this model. """ super().__init__() if hasattr(config, "backbone_config") and config.backbone_config.model_type == "swin": # for backwards compatibility backbone_config = config.backbone_config backbone_config = MaskFormerSwinConfig.from_dict(backbone_config.to_dict()) backbone_config.out_features = ["stage1", "stage2", "stage3", "stage4"] config.backbone_config = backbone_config self.encoder = load_backbone(config) feature_channels = self.encoder.channels self.decoder = MaskFormerPixelDecoder( in_features=feature_channels[-1], feature_size=config.fpn_feature_size, mask_feature_size=config.mask_feature_size, lateral_widths=feature_channels[:-1], ) def forward( self, pixel_values: Tensor, output_hidden_states: bool = False, return_dict: bool = True ) -> MaskFormerPixelLevelModuleOutput: features = self.encoder(pixel_values).feature_maps decoder_output = self.decoder(features, output_hidden_states, return_dict=return_dict) if not return_dict: last_hidden_state = decoder_output[0] outputs = (features[-1], last_hidden_state) if output_hidden_states: hidden_states = decoder_output[1] outputs = outputs + (tuple(features),) + (hidden_states,) return outputs return MaskFormerPixelLevelModuleOutput( # the last feature is actually the output from the last layer encoder_last_hidden_state=features[-1], decoder_last_hidden_state=decoder_output.last_hidden_state, encoder_hidden_states=tuple(features) if output_hidden_states else (), decoder_hidden_states=decoder_output.hidden_states if output_hidden_states else (), ) class MaskFormerTransformerModule(nn.Module): """ The MaskFormer's transformer module. """ def __init__(self, in_features: int, config: MaskFormerConfig): super().__init__() hidden_size = config.decoder_config.hidden_size should_project = in_features != hidden_size self.position_embedder = MaskFormerSinePositionEmbedding(num_pos_feats=hidden_size // 2, normalize=True) self.queries_embedder = nn.Embedding(config.decoder_config.num_queries, hidden_size) self.input_projection = nn.Conv2d(in_features, hidden_size, kernel_size=1) if should_project else None self.decoder = DetrDecoder(config=config.decoder_config) def forward( self, image_features: Tensor, output_hidden_states: bool = False, output_attentions: bool = False, return_dict: Optional[bool] = None, ) -> DetrDecoderOutput: if self.input_projection is not None: image_features = self.input_projection(image_features) object_queries = self.position_embedder(image_features) # repeat the queries "q c -> b q c" batch_size = image_features.shape[0] queries_embeddings = self.queries_embedder.weight.unsqueeze(0).repeat(batch_size, 1, 1) inputs_embeds = torch.zeros_like(queries_embeddings, requires_grad=True) batch_size, num_channels, height, width = image_features.shape # rearrange both image_features and object_queries "b c h w -> b (h w) c" image_features = image_features.view(batch_size, num_channels, height * width).permute(0, 2, 1) object_queries = object_queries.view(batch_size, num_channels, height * width).permute(0, 2, 1) decoder_output: DetrDecoderOutput = self.decoder( inputs_embeds=inputs_embeds, attention_mask=None, encoder_hidden_states=image_features, encoder_attention_mask=None, object_queries=object_queries, query_position_embeddings=queries_embeddings, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) return decoder_output MASKFORMER_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MaskFormerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MASKFORMER_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`MaskFormerImageProcessor.__call__`] for details. pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of Detr's decoder attention layers. return_dict (`bool`, *optional*): Whether or not to return a [`~MaskFormerModelOutput`] instead of a plain tuple. """ class MaskFormerPreTrainedModel(PreTrainedModel): config_class = MaskFormerConfig base_model_prefix = "model" main_input_name = "pixel_values" def _init_weights(self, module: nn.Module): xavier_std = self.config.init_xavier_std std = self.config.init_std if isinstance(module, MaskFormerTransformerModule): if module.input_projection is not None: nn.init.xavier_uniform_(module.input_projection.weight, gain=xavier_std) nn.init.constant_(module.input_projection.bias, 0) # FPN elif isinstance(module, MaskFormerFPNModel): nn.init.xavier_uniform_(module.stem.get_submodule("0").weight, gain=xavier_std) elif isinstance(module, MaskFormerFPNLayer): nn.init.xavier_uniform_(module.proj[0].weight, gain=xavier_std) elif isinstance(module, MaskFormerFPNConvLayer): nn.init.xavier_uniform_(module.get_submodule("0").weight, gain=xavier_std) # The MLP head elif isinstance(module, MaskformerMLPPredictionHead): # I was not able to find the correct initializer in the original implementation # we'll use xavier for submodule in module.modules(): if isinstance(submodule, nn.Linear): nn.init.xavier_uniform_(submodule.weight, gain=xavier_std) nn.init.constant_(submodule.bias, 0) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) # copied from DETR if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() @add_start_docstrings( "The bare MaskFormer Model outputting raw hidden-states without any specific head on top.", MASKFORMER_START_DOCSTRING, ) class MaskFormerModel(MaskFormerPreTrainedModel): def __init__(self, config: MaskFormerConfig): super().__init__(config) self.pixel_level_module = MaskFormerPixelLevelModule(config) self.transformer_module = MaskFormerTransformerModule( in_features=self.pixel_level_module.encoder.channels[-1], config=config ) self.post_init() @add_start_docstrings_to_model_forward(MASKFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MaskFormerModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Tensor, pixel_mask: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> MaskFormerModelOutput: r""" Returns: Examples: ```python >>> from transformers import AutoImageProcessor, MaskFormerModel >>> from PIL import Image >>> import requests >>> # load MaskFormer fine-tuned on ADE20k semantic segmentation >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-ade") >>> model = MaskFormerModel.from_pretrained("facebook/maskformer-swin-base-ade") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = image_processor(image, return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> # the decoder of MaskFormer outputs hidden states of shape (batch_size, num_queries, hidden_size) >>> transformer_decoder_last_hidden_state = outputs.transformer_decoder_last_hidden_state >>> list(transformer_decoder_last_hidden_state.shape) [1, 100, 256] ```""" if pixel_values is None: raise ValueError("You have to specify pixel_values") output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, _, height, width = pixel_values.shape if pixel_mask is None: pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device) pixel_level_module_output = self.pixel_level_module( pixel_values, output_hidden_states, return_dict=return_dict ) image_features = pixel_level_module_output[0] pixel_embeddings = pixel_level_module_output[1] transformer_module_output = self.transformer_module(image_features, output_hidden_states, output_attentions) queries = transformer_module_output.last_hidden_state encoder_hidden_states = None pixel_decoder_hidden_states = None transformer_decoder_hidden_states = None hidden_states = None if output_hidden_states: encoder_hidden_states = pixel_level_module_output[2] pixel_decoder_hidden_states = pixel_level_module_output[3] transformer_decoder_hidden_states = transformer_module_output[1] hidden_states = encoder_hidden_states + pixel_decoder_hidden_states + transformer_decoder_hidden_states output = MaskFormerModelOutput( encoder_last_hidden_state=image_features, pixel_decoder_last_hidden_state=pixel_embeddings, transformer_decoder_last_hidden_state=queries, encoder_hidden_states=encoder_hidden_states, pixel_decoder_hidden_states=pixel_decoder_hidden_states, transformer_decoder_hidden_states=transformer_decoder_hidden_states, hidden_states=hidden_states, attentions=transformer_module_output.attentions, ) if not return_dict: output = tuple(v for v in output.values()) return output class MaskFormerForInstanceSegmentation(MaskFormerPreTrainedModel): def __init__(self, config: MaskFormerConfig): super().__init__(config) self.model = MaskFormerModel(config) hidden_size = config.decoder_config.hidden_size # + 1 because we add the "null" class self.class_predictor = nn.Linear(hidden_size, config.num_labels + 1) self.mask_embedder = MaskformerMLPPredictionHead(hidden_size, hidden_size, config.mask_feature_size) self.matcher = MaskFormerHungarianMatcher( cost_class=1.0, cost_dice=config.dice_weight, cost_mask=config.mask_weight ) self.weight_dict: Dict[str, float] = { "loss_cross_entropy": config.cross_entropy_weight, "loss_mask": config.mask_weight, "loss_dice": config.dice_weight, } self.criterion = MaskFormerLoss( config.num_labels, matcher=self.matcher, weight_dict=self.weight_dict, eos_coef=config.no_object_weight, ) self.post_init() def get_loss_dict( self, masks_queries_logits: Tensor, class_queries_logits: Tensor, mask_labels: Tensor, class_labels: Tensor, auxiliary_logits: Dict[str, Tensor], ) -> Dict[str, Tensor]: loss_dict: Dict[str, Tensor] = self.criterion( masks_queries_logits, class_queries_logits, mask_labels, class_labels, auxiliary_logits ) # weight each loss by `self.weight_dict[<LOSS_NAME>]` including auxiliary losses for key, weight in self.weight_dict.items(): for loss_key, loss in loss_dict.items(): if key in loss_key: loss *= weight return loss_dict def get_loss(self, loss_dict: Dict[str, Tensor]) -> Tensor: return sum(loss_dict.values()) def get_logits(self, outputs: MaskFormerModelOutput) -> Tuple[Tensor, Tensor, Dict[str, Tensor]]: pixel_embeddings = outputs.pixel_decoder_last_hidden_state # get the auxiliary predictions (one for each decoder's layer) auxiliary_logits: List[str, Tensor] = [] # This code is a little bit cumbersome, an improvement can be to return a list of predictions. If we have auxiliary loss then we are going to return more than one element in the list if self.config.use_auxiliary_loss: stacked_transformer_decoder_outputs = torch.stack(outputs.transformer_decoder_hidden_states) classes = self.class_predictor(stacked_transformer_decoder_outputs) class_queries_logits = classes[-1] # get the masks mask_embeddings = self.mask_embedder(stacked_transformer_decoder_outputs) # Equivalent to einsum('lbqc, bchw -> lbqhw') but jit friendly num_embeddings, batch_size, num_queries, num_channels = mask_embeddings.shape _, _, height, width = pixel_embeddings.shape binaries_masks = torch.zeros( (num_embeddings, batch_size, num_queries, height, width), device=mask_embeddings.device ) for c in range(num_channels): binaries_masks += mask_embeddings[..., c][..., None, None] * pixel_embeddings[None, :, None, c] masks_queries_logits = binaries_masks[-1] # go til [:-1] because the last one is always used for aux_binary_masks, aux_classes in zip(binaries_masks[:-1], classes[:-1]): auxiliary_logits.append( {"masks_queries_logits": aux_binary_masks, "class_queries_logits": aux_classes} ) else: transformer_decoder_hidden_states = outputs.transformer_decoder_last_hidden_state classes = self.class_predictor(transformer_decoder_hidden_states) class_queries_logits = classes # get the masks mask_embeddings = self.mask_embedder(transformer_decoder_hidden_states) # sum up over the channels # Equivalent to einsum('bqc, bchw -> bqhw') but jit friendly batch_size, num_queries, num_channels = mask_embeddings.shape _, _, height, width = pixel_embeddings.shape masks_queries_logits = torch.zeros((batch_size, num_queries, height, width), device=mask_embeddings.device) for c in range(num_channels): masks_queries_logits += mask_embeddings[..., c][..., None, None] * pixel_embeddings[:, None, c] return class_queries_logits, masks_queries_logits, auxiliary_logits @add_start_docstrings_to_model_forward(MASKFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MaskFormerForInstanceSegmentationOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Tensor, mask_labels: Optional[List[Tensor]] = None, class_labels: Optional[List[Tensor]] = None, pixel_mask: Optional[Tensor] = None, output_auxiliary_logits: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> MaskFormerForInstanceSegmentationOutput: r""" mask_labels (`List[torch.Tensor]`, *optional*): List of mask labels of shape `(num_labels, height, width)` to be fed to a model class_labels (`List[torch.LongTensor]`, *optional*): list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`. Returns: Examples: Semantic segmentation example: ```python >>> from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation >>> from PIL import Image >>> import requests >>> # load MaskFormer fine-tuned on ADE20k semantic segmentation >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-ade") >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade") >>> url = ( ... "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" ... ) >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)` >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` >>> class_queries_logits = outputs.class_queries_logits >>> masks_queries_logits = outputs.masks_queries_logits >>> # you can pass them to image_processor for postprocessing >>> predicted_semantic_map = image_processor.post_process_semantic_segmentation( ... outputs, target_sizes=[image.size[::-1]] ... )[0] >>> # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs) >>> list(predicted_semantic_map.shape) [512, 683] ``` Panoptic segmentation example: ```python >>> from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation >>> from PIL import Image >>> import requests >>> # load MaskFormer fine-tuned on COCO panoptic segmentation >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-coco") >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-coco") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)` >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` >>> class_queries_logits = outputs.class_queries_logits >>> masks_queries_logits = outputs.masks_queries_logits >>> # you can pass them to image_processor for postprocessing >>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] >>> # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs) >>> predicted_panoptic_map = result["segmentation"] >>> list(predicted_panoptic_map.shape) [480, 640] ``` """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict raw_outputs = self.model( pixel_values, pixel_mask, output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss, return_dict=return_dict, output_attentions=output_attentions, ) # We need to have raw_outputs optionally be returned as a dict to use torch.compile. For backwards # compatibility we convert to a dataclass for the rest of the model logic outputs = MaskFormerModelOutput( encoder_last_hidden_state=raw_outputs[0], pixel_decoder_last_hidden_state=raw_outputs[1], transformer_decoder_last_hidden_state=raw_outputs[2], encoder_hidden_states=raw_outputs[3] if output_hidden_states else None, pixel_decoder_hidden_states=raw_outputs[4] if output_hidden_states else None, transformer_decoder_hidden_states=raw_outputs[5] if output_hidden_states else None, hidden_states=raw_outputs[6] if output_hidden_states else None, attentions=raw_outputs[-1] if output_attentions else None, ) loss, loss_dict, auxiliary_logits = None, None, None class_queries_logits, masks_queries_logits, auxiliary_logits = self.get_logits(outputs) if mask_labels is not None and class_labels is not None: loss_dict: Dict[str, Tensor] = self.get_loss_dict( masks_queries_logits, class_queries_logits, mask_labels, class_labels, auxiliary_logits ) loss = self.get_loss(loss_dict) output_auxiliary_logits = ( self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits ) if not output_auxiliary_logits: auxiliary_logits = None if not return_dict: output = tuple( v for v in (loss, class_queries_logits, masks_queries_logits, auxiliary_logits, *outputs.values()) if v is not None ) return output return MaskFormerForInstanceSegmentationOutput( loss=loss, **outputs, class_queries_logits=class_queries_logits, masks_queries_logits=masks_queries_logits, auxiliary_logits=auxiliary_logits, )

transformers/src/transformers/models/maskformer/modeling_maskformer.py/0

{ "file_path": "transformers/src/transformers/models/maskformer/modeling_maskformer.py", "repo_id": "transformers", "token_count": 38532 }

338

# coding=utf-8 # Copyright 2023 The Mega Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch MEGA model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import ALL_LAYERNORM_LAYERS from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mega import MegaConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "mnaylor/mega-base-wikitext" _CONFIG_FOR_DOC = "MegaConfig" MEGA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "mnaylor/mega-base-wikitext", # See all Mega models at https://huggingface.co/models?filter=mega ] class MegaEmbeddings(nn.Module): """ Mega's basic implementation does not incorporate token type embeddings, so this is a stripped-down version of RoBERTa's embeddings which optionally includes token types """ def __init__(self, config: MegaConfig): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.use_token_types = config.add_token_type_embeddings if self.use_token_types: self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # registering a buffer here allows model tracing when not passing optional token type IDs # more info at transformers issue #5664 self.register_buffer( "token_type_ids", torch.zeros(config.max_positions, dtype=torch.long).expand((1, -1)), persistent=False ) self.padding_idx = config.pad_token_id def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None): if (input_ids is None) and (inputs_embeds is None): raise ValueError("Must provide one of input_ids or inputs_embeds") elif input_ids is not None: input_shape = input_ids.size() device = input_ids.device # get the word embeddings if only IDs are provided inputs_embeds = self.word_embeddings(input_ids) else: input_shape = inputs_embeds.size()[:-1] device = inputs_embeds.device # the original Mega implementation did not include token type embeddings, so we add # an option to use them if desired; if embeddings are present and token type IDs are # not provided, we will use a registered buffer (which helps with tracing) if self.use_token_types: if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, : input_shape[1]] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], input_shape[1]) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # access token type embeddings token_type_embeddings = self.token_type_embeddings(token_type_ids) # add the token type embeddings to the word embeddings embeddings = inputs_embeds + token_type_embeddings else: embeddings = inputs_embeds return embeddings class MegaSimpleRelativePositionalBias(nn.Module): """ Simple relative positional embeddings copied from the Mega repo; renamed variables for better readability """ def __init__(self, config: MegaConfig): super().__init__() self.config = config self.max_positions = self.config.max_positions if self.config.chunk_size < 0 else self.config.chunk_size self.rel_pos_bias = nn.Parameter(torch.Tensor(2 * config.max_positions - 1)) def forward(self, seq_len): if seq_len > self.max_positions: raise ValueError("Sequence length {} going beyond max length {}".format(seq_len, self.max_positions)) # seq_len * 2 - 1 bias = self.rel_pos_bias[(self.max_positions - seq_len) : (self.max_positions + seq_len - 1)] # seq_len * 3 - 1 tile = F.pad(bias, (0, seq_len)) # (seq_len * 3 - 1) * seq_len tile = torch.tile(tile, (seq_len,)) tile = tile[:-seq_len] # seq_len x (3 * seq_len - 2) tile = tile.view(seq_len, 3 * seq_len - 2) start = (2 * seq_len - 1) // 2 end = tile.size(1) - start tile = tile[:, start:end] return tile class MegaRotaryRelativePositionalBias(nn.Module): """ Rotary relative bias for positional information; similar in concept to RoPE (i.e. RoFormer) but taken from the Mega repo due to differences in implementation. When initialized, produces a positional bias which ranges from position 0 to config.max_positions, but can extrapolate to longer sequences. Can be indexed according to input position IDs """ def __init__(self, config: MegaConfig): super().__init__() if config.hidden_size % 2 != 0: raise RuntimeError("Rotary positional bias requires `hidden_size` to be a multiple of 2") self.config = config self.embed_dim = config.shared_representation_size self.max_positions = self.config.max_positions if self.config.chunk_size < 0 else self.config.chunk_size self.sine, self.cosine = MegaRotaryRelativePositionalBias.get_sinusoid_embeddings( config.max_positions, self.embed_dim ) # alpha and beta parameters for the rotary bias; beta renamed to b_param to avoid clashes with tf/flax weight handling # in loading pretrained weights self.alpha = nn.Parameter(torch.Tensor(1, self.embed_dim)) self.b_param = nn.Parameter(torch.Tensor(1, self.embed_dim)) self.register_buffer("_float_tensor", torch.FloatTensor([0.0])) @staticmethod def get_sinusoid_embeddings(max_positions: int, embedding_dim: int): half_dim = embedding_dim // 2 emb = math.log(10000) / half_dim emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) emb = torch.arange(max_positions, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0) return torch.sin(emb), torch.cos(emb) def rotary(self, input): seq_len, embed_dim = input.size() chunk_1, chunk_2 = torch.chunk(input, 2, dim=-1) if self.sine is None or seq_len > self.sine.size(0): self.sine, self.cosine = MegaRotaryRelativePositionalBias.get_sinusoid_embeddings(seq_len, embed_dim) self.max_positions = seq_len self.sine = self.sine.to(self._float_tensor) self.cosine = self.cosine.to(self._float_tensor) sin = self.sine[:seq_len] cos = self.cosine[:seq_len] return torch.cat([chunk_1 * cos - chunk_2 * sin, chunk_2 * cos + chunk_1 * sin], dim=1) def forward(self, seq_len): rotary_alpha = self.rotary(self.alpha.expand(seq_len, self.embed_dim)) rotary_beta = self.rotary(self.b_param.expand(seq_len, self.embed_dim)) bias = torch.einsum("mk,nk->mn", rotary_alpha, rotary_beta) return bias class MegaDropout(nn.Module): """ A unified class for standard dropout functionality and featurewise dropout. The original fairseq Mega repo used 2 classes for these, which included some unnecessary handling of training logic and an unused `inplace` option. The original implementation used torch.nn.functional instead of submodules, which is retained here as well. """ def __init__(self, dropout_probability, is_featurewise=False): super().__init__() self.dropout_probability = dropout_probability self.is_featurewise = is_featurewise def forward(self, input, batch_first: bool = False): if self.is_featurewise: if batch_first: # (batch_size X sequence_length X feature_dimension) # -> (batch_size X feature_dimension X sequence_length) # -> (batch_size X sequence_length X feature_dimension) return F.dropout2d( input.transpose(-1, -2), p=self.dropout_probability, training=self.training ).transpose(-1, -2) else: if input.dim() != 3: raise ValueError( "Feature dropout inputs must be exactly 3-dimensional if inputs are ordered [sequence length, batch size, hidden dimension]" ) # (sequence_length X batch_size X feature_dimension) # -> (batch_size X feature_dimension X sequence_length) # -> (sequence_length X batch_size X feature_dimension) return F.dropout2d(input.permute(1, 2, 0), p=self.dropout_probability, training=self.training).permute( 2, 0, 1 ) else: return F.dropout(input, p=self.dropout_probability, training=self.training) class MegaRMSNorm(nn.Module): """ RMSNorm used in Mega implementation. Differs from T5's RMSNorm by applying the weight prior to taking the square root (as opposed to after in T5) """ def __init__(self, number_features, eps=1e-6, affine=True): super().__init__() self.num_features = number_features self.eps = eps self.affine = affine if affine: self.weight = nn.Parameter(torch.Tensor(self.num_features)) else: self.register_parameter("weight", None) def forward(self, input): mean_square = torch.mean(torch.square(input), dim=-1, keepdim=True) if self.weight is not None: input = input * self.weight input * torch.rsqrt(mean_square + self.eps) return input class MegaScaleNorm(nn.Module): """ Scale normalization introduced in MEGA which is similar to RMSNorm, but uses a single parameter for scalar multiplication instead of a vector, and applies over a specified dimension """ def __init__(self, dim, eps=1e-6, affine=True): super().__init__() self.dim = dim self.eps = eps self.affine = affine if affine: self.scalar = nn.Parameter(torch.Tensor(1)) else: self.register_parameter("scalar", None) def forward(self, input): mean_square = torch.mean(torch.square(input), dim=self.dim, keepdim=True) if self.scalar is not None: input = self.scalar * input output = input * torch.rsqrt(mean_square + self.eps) return output class MegaSequenceNorm(nn.Module): """ A wrapper class for various layer normalization options used in Mega. Used to handle differences in expectations on input axis locations for different normalization methods. """ def __init__(self, norm_type, embedding_dim, eps=1e-5, affine=True, export=False): super().__init__() if norm_type == "layernorm": self.norm = nn.LayerNorm(embedding_dim, eps, elementwise_affine=affine) elif norm_type == "scalenorm": self.norm = MegaScaleNorm(dim=-1, eps=eps, affine=affine) elif norm_type == "rmsnorm": self.norm = MegaRMSNorm(embedding_dim, eps=eps, affine=affine) elif norm_type == "batchnorm": self.norm = nn.BatchNorm1d(embedding_dim, eps=eps, affine=affine) elif norm_type == "syncbatchnorm": self.norm = nn.SyncBatchNorm(embedding_dim, eps=eps, affine=affine) else: raise ValueError("Unknown norm type: {}".format(norm_type)) def forward(self, input): if isinstance(self.norm, nn.modules.batchnorm._BatchNorm): if input.dim() != 3: raise ValueError("BatchNorm inputs must be exactly 3-dimensional") input = input.permute(1, 2, 0) input = self.norm(input) return input.permute(2, 0, 1) else: return self.norm(input) # add this layernorm class to ALL_LAYERNORM_LAYERS ALL_LAYERNORM_LAYERS.append(MegaSequenceNorm) class MegaMultiDimensionDampedEma(nn.Module): """ Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of variable names and moving away from the stateful representation of incremental decoding state. See "https://arxiv.org/abs/2209.10655" for more details. """ def __init__(self, config: MegaConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.ndim = config.ema_projection_size self.bidirectional = config.bidirectional self.truncation = config.truncation self.scale = math.sqrt(1.0 / self.ndim) kernel_dim = 2 * config.hidden_size if self.bidirectional else config.hidden_size # renamed delta (damping_factor) and alpha (decay_factor) to be more descriptive of what the parameters are doing self.damping_factor = nn.Parameter(torch.Tensor(kernel_dim, self.ndim, 1)) self.decay_factor = nn.Parameter(torch.Tensor(kernel_dim, self.ndim, 1)) # renamed gamma (kernel_projection_matrix) and beta (ema_expansion_matrix) respectively to avoid HF renaming # things and align with the paper's description of these params' behavior self.ema_expansion_matrix = nn.Parameter(torch.Tensor(kernel_dim, self.ndim, 1)) self.kernel_projection_matrix = nn.Parameter(torch.Tensor(kernel_dim, self.ndim)) # renamed omega to residual_weight to describe what it's doing self.residual_weight = nn.Parameter(torch.Tensor(config.hidden_size)) self._kernel = None self._coeffs = None def _compute_ema_coefficients(self): self._coeffs = None # convert the alpha and delta parameters (kernel_dim x EMA projection size x 1) to [0, 1] with sigmoid damping_factor = torch.sigmoid(self.damping_factor) decay_factor = torch.sigmoid(self.decay_factor) previous_timestep_weight = 1.0 - damping_factor * decay_factor return damping_factor, previous_timestep_weight def _compute_efficient_ema_kernel(self, length: int): # computes the kernel used for efficient damped EMA applied via FFT convolution self._kernel = None # p and q have shape (kernel_dim x ema_projection_size x 1) damping_factor, previous_timestep_weight = self._compute_ema_coefficients() # extend the kernel to (kernel_dim X ema_projection_size X sequence_length) and # multiply q by sequential ints up to the sequence length vander = torch.arange(length).to(damping_factor).view(1, 1, length) * torch.log(previous_timestep_weight) kernel = (damping_factor * self.ema_expansion_matrix) * torch.exp(vander) # (kernel_dim X ema_projection_size X sequence_length) -> (kernel_dim, sequence_length) return torch.einsum("dnl,dn->dl", kernel, self.kernel_projection_matrix * self.scale) def get_ema_coefficients(self): if self.training: return self._compute_ema_coefficients() else: if self._coeffs is None: self._coeffs = self._compute_ema_coefficients() return self._coeffs def get_ema_kernel(self, length: int): kernel_size = length if self.truncation is None else min(self.truncation, length) if self.training: return self._compute_efficient_ema_kernel(kernel_size) else: if self._kernel is None or self._kernel.size(-1) < kernel_size: self._kernel = self._compute_efficient_ema_kernel(kernel_size) return self._kernel[..., :kernel_size] def fft_convolution(self, inputs, kernel, length): # this is a wrapper for repeated use of EMA calculation via FFT (fast Fourier transform) convolution inputs_fft = torch.fft.rfft(inputs.float(), n=2 * length) kernel_fft = torch.fft.rfft(kernel.float(), n=2 * length) convolved_sequence = torch.fft.irfft(inputs_fft * kernel_fft, n=2 * length) return convolved_sequence def ema_step(self, inputs, length, past_state=None): if length == 1: return self.one_ema_step(inputs, past_state=past_state) # (kernel_dim X ema_projection_size X 1) damping_factor, previous_timestep_weight = self.get_ema_coefficients() # (kernel_dim X ema_projection_size X 1+sequence_length) vander = torch.arange(length + 1).to(damping_factor).view(1, 1, length + 1) * torch.log( previous_timestep_weight ) vander = torch.exp(vander) if past_state is not None: # (kernel_dim X ema_projection_size X sequence_length) * (kernel_dim X ema_projection_size X 1) # -> (kernel_dim X ema_projection_size X sequence_length) past_ema_proj = vander[:, :, 1:] * (self.kernel_projection_matrix * self.scale).unsqueeze(-1) # past_state will be (batch_size, kernel_dim, ema_projection_size) past_ema_state = torch.einsum("bdn,dnl->bdl", past_state, past_ema_proj) # (kernel_dim X ema_projection_size) * (batch_size X kernel_dim X ema_projection_size) # -> (batch_size X kernel_dim X ema_projection_size) past_vandermonde = vander[:, :, -1] * past_state else: past_ema_state = None past_vandermonde = None # (kernel_dim X ema_projection_size X sequence_length) vander = vander[:, :, :-1] kernel = (damping_factor * self.ema_expansion_matrix) * vander kernel_proj = torch.einsum("dnl,dn->dl", kernel, self.kernel_projection_matrix * self.scale) ema_output = self.fft_convolution(inputs, kernel_proj, length=length)[..., 0:length] ema_output = ema_output.type_as(inputs) if past_ema_state is not None: ema_output = ema_output + past_ema_state updated_hidden_state = torch.einsum("bdl,dnl->bdn", inputs, torch.flip(kernel, dims=[2])) if past_vandermonde is not None: updated_hidden_state = updated_hidden_state + past_vandermonde # return a tuple: # (sequence_length, batch_size, kernel_dim) # (batch_size, kernel_dim, ema_projection_size) return ema_output.permute(2, 0, 1), updated_hidden_state def one_ema_step(self, inputs, past_state=None): damping_factor, previous_timestep_weight = self.get_ema_coefficients() # (kernel_dim X ema_projection_size) x (batch_size X kernel_dim X 1) # -> (batch_size X kernel_dim X ema_projection_size) updated_state = (damping_factor * self.ema_expansion_matrix).squeeze(-1) * inputs if past_state is not None: updated_state = updated_state + previous_timestep_weight.squeeze(-1) * past_state # (batch_size X kernel_dim) out = torch.einsum("bdn,dn->bd", updated_state, self.kernel_projection_matrix * self.scale) # (1 X batch_size X kernel_dim), (batch_size X kernel_dim X ema_projection_size) return out.unsqueeze(0), updated_state def forward( self, inputs, attention_mask: Optional[torch.Tensor] = None, prev_state: Optional[torch.Tensor] = None, use_cache: bool = False, ) -> torch.Tensor: """ Mega's exponential moving average (EMA) sub-layer applied prior to single-headed (traditional) self-attention Args: inputs (`torch.Tensor` of shape `(sequence_length, batch_size, hidden_size)`): Hidden state / embedding input to update via EMA based on FFT convolution attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indicates which inputs are to be ignored (mostly due to padding), where elements are either 1 for *not masked* or 0 for *masked* prev_state (`torch.Tensor` of shape `(batch_size, config.ndim)`, *optional*): The hidden state returned from the previous timestep during incremental decoding. use_cache (`bool`, default `False`): Whether to perfom incremental decoding; uses `prev_state` as the prior timestep, and returns the updated EMA hidden state for use in the next step Returns: `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and inputs: - **hidden_states** (`torch.FloatTensor` of shape `(sequence_length, batch_size, hidden_size)`) -- Hidden states updated by EMA, with same shapes as inputs - **updated_state** (*optional*, returned when `use_cache=True`) `torch.FloatTensor of shape `(batch_size, config.ndim)` -- The incremental EMA state for use in the next step of incremental decoding """ seq_len, bsz, embed_dim = inputs.size() if embed_dim != self.embed_dim: raise ValueError( f"Unexpected embedding dimension received: input is {embed_dim}, model expects {self.embed_dim}" ) # sequence_length X batch_size X hidden_size residual = inputs * self.residual_weight # (sequence_length x batch_size x hidden_size) -> (batch_size x hidden_size x sequence_length) inputs = inputs.permute(1, 2, 0) # mask the input: output is a tensor with 0 in the masked positions if attention_mask is not None: inputs = inputs * (attention_mask.unsqueeze(1).type_as(inputs)) if self.bidirectional and use_cache: raise RuntimeError("Bidirectional EMA does not support incremental state") if use_cache: out, updated_state = self.ema_step(inputs, seq_len, past_state=prev_state) # (batch_size X hidden_size) -> (1 x batch_size x hidden_size) out = F.silu(out + residual) # if incremental decoding, return the new state along with the output return out, updated_state else: # (hidden_size x sequence_length) kernel = self.get_ema_kernel(seq_len) fft_len = seq_len s_index = 0 kernel_size = kernel.size(1) if self.bidirectional: # split the kernel for each direction of EMA k1, k2 = torch.split(kernel, [self.embed_dim, self.embed_dim], dim=0) # (hidden_size X 2*sequence_length - 1) kernel = F.pad(k1, (kernel_size - 1, 0)) + F.pad(k2.flip(-1), (0, kernel_size - 1)) inputs = F.pad(inputs, (kernel_size - 1, 0)) fft_len = fft_len + kernel_size - 1 s_index = 2 * kernel_size - 2 ema_output = self.fft_convolution(inputs, kernel, length=fft_len)[..., s_index : s_index + seq_len] ema_output = ema_output.type_as(inputs) # (batch_size X hidden_size X sequence_length) -> (sequence_length X batch_size X hidden_size) gated_ema_output = F.silu(ema_output.permute(2, 0, 1) + residual) return gated_ema_output, None class MegaGatedCrossAttention(nn.Module): """ Gated Structured State Attention for use in encoder-decoder model. See Mega paper for more details. Only modifications from original implementation are variable names, removing the unnecessary `before_attn_fn` and `static_kv` arguments, and the stateful representation of incremental decoder state. """ def __init__(self, config: MegaConfig): super().__init__() self.config = config self.activation = ACT2FN[self.config.activation] self.attention_activation = self.config.attention_activation self.scaling = self.config.shared_representation_size**-0.5 if self.attention_activation == "softmax" else None self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout) self.hidden_dropout = MegaDropout( self.config.hidden_dropout_prob, is_featurewise=self.config.use_feature_dropout ) # Attention dropout is standard dropout self.attention_dropout = MegaDropout(self.config.attention_probs_dropout_prob, is_featurewise=False) self.prenorm = self.config.normalize_before_mega self.norm = MegaSequenceNorm( self.config.normalization_type, self.config.hidden_size, affine=self.config.norm_affine ) self.k_proj = nn.Linear(self.config.hidden_size, self.config.shared_representation_size) self.v_proj = nn.Linear(self.config.hidden_size, self.config.hidden_size) self.q_proj = nn.Linear( self.config.hidden_size, 2 * self.config.hidden_size + self.config.shared_representation_size ) self.h_proj = nn.Linear(self.config.hidden_size, self.config.hidden_size) if self.config.relative_positional_bias == "simple": self.rel_pos_bias = MegaSimpleRelativePositionalBias(config) elif self.config.relative_positional_bias == "rotary": self.rel_pos_bias = MegaRotaryRelativePositionalBias(config) else: raise ValueError("unknown relative position bias: {}".format(self.config.relative_positional_bias)) self.softmax = nn.Softmax(dim=-1) def element_attention(self, query, key, key_padding_mask, pidx): bsz, src_len, _ = key.size() tgt_len = query.size(1) if pidx is None else pidx + 1 if key_padding_mask is not None: # (batch_size X source_sequence_length) --> (batch_size X 1 X 1) lengths = key_padding_mask.sum(dim=-1).view(bsz, 1, 1) else: lengths = src_len # (target_sequence_length X source_sequence_length) bias = self.rel_pos_bias(max(tgt_len, src_len))[:, :src_len] if pidx is not None: if query.size(1) != 1: raise ValueError("Position offset provided with queries longer than 1 token") # source_sequence_length bias = bias[pidx] else: # (target_sequence_length X source_sequence_length) bias = bias[:tgt_len] # (batch_size X target_sequence_length X source_sequence_length) qk = torch.bmm(query, key.transpose(1, 2)) / lengths + bias attn_weights = ACT2FN[self.attention_activation](qk).type_as(qk) if key_padding_mask is not None: attn_weights = attn_weights * key_padding_mask.unsqueeze(1) return attn_weights def softmax_attention(self, query, key, key_padding_mask, pidx): bsz, src_len, _ = key.size() tgt_len = query.size(1) if pidx is None else pidx + 1 # (target_sequence_length X source_sequence_length) bias = self.rel_pos_bias(max(tgt_len, src_len))[:, :src_len] if pidx is not None: if query.size(1) != 1: raise ValueError("Position offset provided with queries longer than 1 token") # source_sequence_length bias = bias[pidx] else: # (target_sequence_length X source_sequence_length) bias = bias[:tgt_len] # scaled attention query = query * self.scaling # (batch_size X target_sequence_length X source_sequence_length) qk = torch.bmm(query, key.transpose(1, 2)) + bias if key_padding_mask is not None: qk = qk.masked_fill((1 - key_padding_mask).unsqueeze(1).to(torch.bool), float("-inf")) attn_weights = self.softmax(qk).type_as(qk) return attn_weights def forward( self, query, key: Optional[torch.Tensor], value: Optional[torch.Tensor], key_padding_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """ Gated cross-attention used in Mega Args: query (`torch.Tensor` of shape `(target_sequence_length, batch_size, hidden_size)`): The self (or target) sequence input used as query inputs for cross-attention key (`torch.Tensor` of shape `(source_sequence_length, batch_size, hidden_size)`): The cross (or source) sequence input with shape used as keys in cross-attention value (`torch.Tensor` of shape `(source_sequence_length, batch_size, hidden_size)`): The cross (or source) sequence input with shape used as values in cross-attention key_padding_mask (`torch.LongTensor` of shape `(batch_size, source_sequence_length)`, *optional*): Padding mask corresponding to the source sequence, where entries are 1 for *not masked* and 0 for *masked* tokens past_key_values (`tuple(torch.FloatTensor)`, *optional*): If provided, the hidden state returned from the previous timestep during incremental decoding; expects that prior cross-attention keys and values will be the last two items in the tuple output_attentions (`bool`, defaults to `False`): Whether or not to return the cross-attention weights. use_cache (`bool`, defaults to `False`): Whether to perfom incremental decoding; uses `prev_state` as the prior timestep, and returns the updated EMA hidden state for use in the next step Returns: `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and inputs: - **hidden_states** (`torch.FloatTensor` of shape `(target_sequence_length, batch_size, hidden_size)`) -- Hidden states from target sequence updated by gated cross-attention - **attn_weights** (*optional*, returned when `output_attentions=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length, target_sequence_length)` -- The pairwise cross-attention weights corresponding to each token in the source and target sequences - **cross_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.shared_representation_size)` -- The cross-attention key state for use in the next step of incremental decoding - **cross_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.hidden_size)` -- The cross-attention value state for use in the next step of incremental decoding """ seq_len, bsz, embed_dim = query.size() if embed_dim != self.config.hidden_size: raise ValueError( f"Unexpected embedding dimension received: input is {embed_dim} but expected {self.config.hidden_size}" ) if past_key_values is not None: # make sure the inputs only have a sequence length of 1 if we're doing incremental decoding if seq_len != 1: raise ValueError(f"Incremental decoding requested with self-sequence length > 1: {seq_len}") # expect past_key_values to have (self_key, self_value, self_ema, cross_key, cross_value) prev_cross_key, prev_cross_value = past_key_values[-2:] key = value = None # use the self-attention cache to get the position id of the current step prev_self_key = past_key_values[0] num_incremental_steps = prev_self_key.size(1) + 1 else: prev_cross_key = prev_cross_value = None # we still need the position id if we're doing incremental decoding (past_key_values will be None for the first step) num_incremental_steps = 0 if use_cache and (seq_len == 1) else None full_query = query if self.prenorm: full_query = self.norm(full_query) # (target_sequence_length X batch_size X 2*hidden_size + shared_representation_size) query_projected = self.q_proj(full_query) # split the query projections into separate components # - residual_weight is passed through sigmoid and sent through elementwise multiplication to the gated/weighted targets prior to being added to the query directly # - target_gate is a silu-gated tensor that is multiplied by the attention-weighted target below prior to residual connection # - attention_query is the part that is passed to the attention function residual_weight, target_gate, attention_query = torch.split( query_projected, [self.config.hidden_size, self.config.hidden_size, self.config.shared_representation_size], dim=-1, ) # (target_sequence_length X batch_size X hidden_size) residual_weight = torch.sigmoid(residual_weight) target_gate = F.silu(target_gate) if key is None: if value is not None: raise ValueError("Key and value must be `None` simultaneously") projected_key = projected_value = None else: # (source_sequence_length X batch_size X shared_representation_size) projected_key = self.k_proj(key) # (source_sequence_length X batch_size X hidden_size) projected_value = self.activation(self.v_proj(key)) # (target_sequence_length X batch_size X shared_representation_size) # -> (batch_size X target_sequence_length X shared_representation_size) attention_query = attention_query.transpose(0, 1) if projected_key is not None: projected_key = projected_key.transpose(0, 1) if projected_value is not None: projected_value = projected_value.transpose(0, 1) # if we're doing incremental decoding, k and v are None and need to be overwritten with past values if past_key_values is not None: projected_key = prev_cross_key projected_value = prev_cross_value # if we're returning the cache for later use, store these now for later return (can be done without having past_key_values provided) if use_cache: updated_cross_key = projected_key updated_cross_value = projected_value ctx_len = projected_key.size(1) # This is part of a workaround to get around fork/join parallelism # not supporting Optional types. if key_padding_mask is not None and key_padding_mask.dim() == 0: key_padding_mask = None if key_padding_mask is not None: if key_padding_mask.size(0) != bsz: raise ValueError("Key padding mask does not align on the batch dimension") if key_padding_mask.size(1) != ctx_len: raise ValueError("Key padding mask does not align on the sequence length dimension") if self.attention_activation == "softmax": attn_weights = self.softmax_attention( attention_query, projected_key, key_padding_mask, num_incremental_steps ) else: attn_weights = self.element_attention( attention_query, projected_key, key_padding_mask, num_incremental_steps ) projected_value = self.hidden_dropout(projected_value, batch_first=True) kernel = self.attention_dropout(attn_weights) # (batch_size X target_sequence_length X hidden_size) # -> (target_sequence_length X batch_size X hidden_size) weighted_targets = torch.bmm(kernel, projected_value).transpose(0, 1) # (target_sequence_length X batch_size X hidden_size) weighted_targets = self.activation(self.h_proj(weighted_targets * target_gate)) weighted_targets = self.dropout(weighted_targets) out = torch.addcmul(query, residual_weight, weighted_targets - query) if not self.prenorm: out = self.norm(out) outputs = (out, attn_weights) if output_attentions else (out,) if use_cache: outputs = outputs + (updated_cross_key, updated_cross_value) return outputs class MegaMovingAverageGatedAttention(nn.Module): """ Pure PyTorch implementation of Mega block; see https://arxiv.org/abs/2209.10655 and original fairseq implementation at https://github.com/facebookresearch/mega (copyright Meta Research, licensed under MIT License) Differences from original implementation include hidden state refactor and fixed inconsistency with additive / multiplicative attention masks """ def __init__(self, config: MegaConfig): super().__init__() self.config = config self.activation = ACT2FN[self.config.activation] self.scaling = ( self.config.shared_representation_size**-0.5 if self.config.attention_activation == "softmax" else None ) self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout) self.hidden_dropout = MegaDropout( self.config.hidden_dropout_prob, is_featurewise=self.config.use_feature_dropout ) # attention dropout is standard dropout self.attention_dropout = MegaDropout(self.config.attention_probs_dropout_prob, is_featurewise=False) self.norm = MegaSequenceNorm( self.config.normalization_type, self.config.hidden_size, affine=self.config.norm_affine ) self.ema_gate = MegaMultiDimensionDampedEma(config) self.v_proj = nn.Linear(self.config.hidden_size, self.config.intermediate_size) self.mx_proj = nn.Linear( self.config.hidden_size, self.config.shared_representation_size + self.config.intermediate_size + 2 * self.config.hidden_size, ) self.h_proj = nn.Linear(self.config.intermediate_size, self.config.hidden_size) self.qk_weight = nn.Parameter(torch.Tensor(2, self.config.shared_representation_size)) self.qk_bias = nn.Parameter(torch.Tensor(2, self.config.shared_representation_size)) if self.config.relative_positional_bias == "simple": self.rel_pos_bias = MegaSimpleRelativePositionalBias(config) elif self.config.relative_positional_bias == "rotary": self.rel_pos_bias = MegaRotaryRelativePositionalBias(config) else: raise ValueError(f"Unknown relative positional bias: {self.config.relative_positional_bias}") self.softmax = nn.Softmax(dim=-1) self.attention_function = ( self.softmax_attention if self.config.attention_activation == "softmax" else self.element_attention ) def element_attention(self, query, key, padding_mask, causal_mask): """ Apply element-wise attention via relu^2 or laplace. Same as original implementation but with standardized causal attention mask. Expects the Hugging Face standard attention mask paradigm: 1 for not masked, and 0 for masked. """ seq_len = key.size(2) if padding_mask is not None: # (batch_size X number of chunks X 1) lengths = padding_mask.sum(-1, keepdim=True) # (batch_size X number of chunks X 1 X 1) lengths = lengths.clamp(min=1.0).unsqueeze(-1) else: lengths = seq_len if causal_mask is not None: lengths = causal_mask.sum(dim=-1, keepdim=True) # (sequence_length X sequence_length) bias = self.rel_pos_bias(seq_len) if seq_len != query.size(2): if query.size(2) != 1: raise ValueError("Size mismatch between Q and K in element attention") # (1 X sequence_length) bias = bias[-1:] # (batch_size X number of chunks X sequence_length X sequence_length) qk = torch.matmul(query, key.transpose(2, 3)) / lengths + bias attn_weights = ACT2FN[self.config.attention_activation](qk).type_as(qk) if padding_mask is not None: attn_weights = attn_weights * padding_mask.unsqueeze(2) if causal_mask is not None: attn_weights = attn_weights * causal_mask return attn_weights def softmax_attention(self, query, key, padding_mask, causal_mask): "Standard softmax self-attention, as in the original Transformer paper" seq_len = key.size(2) # (sequence_length X sequence_length) bias = self.rel_pos_bias(seq_len) if seq_len != query.size(2): if query.size(2) != 1: raise ValueError("Size mismatch between Q and K in softmax attention") # (1 X sequence_length) bias = bias[-1:] # scaled attention query = query * self.scaling # (batch_size x number of chunks x chunk_size x chunk_size) if chunking # (batch_size x 1 x sequence_length x sequence_length) otherwise qk = torch.matmul(query, key.transpose(2, 3)) + bias # apply causal mask (presumed to be 1/0 for not masked / masked) # additive, but convert to 0/-inf (which is not explicitly in the Mega source code) if causal_mask is not None: additive_causal_mask = torch.zeros_like(causal_mask, dtype=qk.dtype) additive_causal_mask = additive_causal_mask.masked_fill((1 - causal_mask).bool(), float("-inf")) qk = qk + additive_causal_mask if padding_mask is not None: # 1 for tokens which are *not masked* # 0 for tokens which are *masked* # replace masked tokens with -inf to make softmax ignore them # need to invert the padding mask to match what mega original did padding_mask = 1 - padding_mask padding_mask_all = padding_mask.all(dim=-1, keepdim=True) padding_mask = torch.logical_and(padding_mask, ~padding_mask_all) qk = qk.masked_fill(padding_mask.unsqueeze(2).to(torch.bool), float("-inf")) attn_weights = self.softmax(qk).type_as(qk) return attn_weights def forward( self, input, padding_mask: Optional[torch.Tensor] = None, causal_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.Tensor]] = None, output_attentions=False, use_cache=False, ): """ Mega's self-attention block, which combines multi-headed EMA with traditional self-attention Args: input (`torch.Tensor` of shape `(sequence_length, batch_size, hidden_size)`): Hidden states to be updated by Mega's self-attention padding_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* or 0 for *masked* causal_mask (`torch.LongTensor` of shape `(sequence_length, sequence_length)`, *optional*): Indicates which inputs are to be ignored due to causal attention, where elements are either 1 for *not masked* or 0 for *masked* past_key_values (`tuple(torch.Tensor)`, *optional*): The hidden states returned from the previous timestep during incremental decoding; expects that self-attention key, value, and EMA states are the first 3 entries in the tuple output_attentions (`bool`, default `False`): Whether to return self-attention weights use_cache (`bool`, default `False`): Whether to perfom incremental decoding; uses `past_key_values` as prior state, and returns the updated states for use in the next step Returns: `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and inputs: - **hidden_states** (`torch.FloatTensor` of shape `(sequence_length, batch_size, hidden_size)`) -- Hidden states from target sequence updated by Mega's self-attention - **attn_weights** (*optional*, returned when `output_attentions=True`) `torch.FloatTensor` of shape `(batch_size, 1, sequence_length, sequence_length)` -- The self-attention weights corresponding to how each token in the input sequence attends to every other token - **self_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, sequence_length, config.shared_representation_size)` -- The self-attention key state for use in the next step of incremental decoding - **self_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, sequence_length, config.hidden_size)` -- The self-attention value state for use in the next step of incremental decoding - **self_ema_state** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, config.ndim)` The incremental EMA state for use in the next step of incremental decoding. """ seq_len, bsz, embed_dim = input.size() if embed_dim != self.config.hidden_size: raise ValueError(f"Input embedding dimension should be {self.config.hidden_size}; received {embed_dim}") # store inputs for residual connection and handle pre-norm if requested residual = input if self.config.normalize_before_mega: input = self.norm(input) # (sequence_length X batch_size X hidden_size) -> (sequence_length X batch_size X intermediate_size) value = self.activation(self.v_proj(input)) # unpack the incremental state if provided # assumed to be (self K, self V, self EMA state, cross K, cross V) # also assumes that incremental decoding is working one token at a time, so input sequence length must be 1 if self.config.is_decoder and (past_key_values is not None): if seq_len > 1: raise ValueError(f"Incremental decoding only supports self sequence length of 1; received {seq_len}") # the first 3 items in the saved states will be these regardless of whether cross-attention is present prev_self_key, prev_self_value, prev_ema_state = past_key_values[0:3] else: prev_self_key = prev_self_value = prev_ema_state = None # ema output is (sequence_length x batch_size x hidden_size) # updated_ema_state will be None if use_cache=False; otherwise (batch_size, config.ndim) ema_out, updated_ema_state = self.ema_gate( input, attention_mask=padding_mask, prev_state=prev_ema_state, use_cache=use_cache ) ema_out = self.dropout(ema_out) # (sequence_length X batch_size X hidden_size) # -> (sequence_length X batch_size X 2*hidden_size + config.shared_representation_size + config.intermediate_size) # - residual_weight -> sigmoid -> applied to residual connection in torch.addcmul # - query_key_gates -> split into two components: query_key becomes query and key for attention input, gates becomes gating for self-attention output # - intermediate_state -> added to weighted attention output, sent through activation, and has inputs subtracted during # torch.addcmul to create the final layer output base = self.mx_proj(ema_out) residual_weight, query_key_gates, intermediate_state = torch.split( base, [ self.config.hidden_size, self.config.shared_representation_size + self.config.intermediate_size, self.config.hidden_size, ], dim=-1, ) # (sequence_length X batch_size X hidden_size) residual_weight = torch.sigmoid(residual_weight) # (sequence_length X batch_size X shared_representation_size + intermediate_size) query_key_gates = F.silu(query_key_gates) # split into two different tensors: one for Q/K usage and the other for gating self-attention query_key, attention_gate = torch.split( query_key_gates, [self.config.shared_representation_size, self.config.intermediate_size], dim=-1 ) # (sequence_length X batch_size X shared_representation_size) # -> (sequence_length X batch_size X 1 X shared_representation_size) # -> (sequence_length X batch_size X 2 X shared_representation_size) query_key = query_key.unsqueeze(2) * self.qk_weight + self.qk_bias # (sequence_length X batch_size X 2 X shared_representation_size) # -> 2 tensors of (sequence_length X batch_size X shared_representation_size) query, key = torch.unbind(query_key, dim=2) # (sequence_length X batch_size X dimension) # -> (batch_size X sequence_length X dimension) # where `dimension` is either shared_representation_size (queries and keys) or intermediate_size (values) query = query.transpose(0, 1) key = key.transpose(0, 1) value = value.transpose(0, 1) if self.config.is_decoder: # combine history and current to save updated state (if history is provided) # when chunking is applied, the past states will be None at the end of the chunk, in # which case, proceed as if no K/V history had been provided # saved states are stored with shape (batch_size X sequence_length X dimension) if prev_self_key is not None: key = torch.cat([prev_self_key, key], dim=1) if prev_self_value is not None: value = torch.cat([prev_self_value, value], dim=1) # if not chunking, store as-is if not self.config.use_chunking: updated_self_key = key updated_self_value = value else: curr_len = key.size(1) % self.config.chunk_size if curr_len == 0: # if we're chunking and have reached the end of a chunk, wipe out the saved state updated_self_key = None updated_self_value = None else: updated_self_key = key updated_self_value = value ctx_len = key.size(1) # potentially differs from seq_len because of incremental decoding if not self.config.use_chunking: # if we're not chunking, treat the entire sequence as one long chunk # (batch_size X sequence_length X dimension) -> (batch_size X 1 X sequence_length X dimension) query = query.unsqueeze(1) key = key.unsqueeze(1) value = value.unsqueeze(1) if padding_mask is not None: # (batch_size X sequence_length) -> (batch_size X 1 X sequence_length) padding_mask = padding_mask.unsqueeze(1) else: # otherwise, split the sequences in the batch into `n_chunks` chunks of size `chunk_size` if seq_len < self.config.chunk_size: query = query.unsqueeze(1) else: # (batch_size X sequence_length X dimension) -> (batch_size X n_chunks X chunk_size X dimension) n_chunks = seq_len // self.config.chunk_size query = query.reshape(bsz, n_chunks, self.config.chunk_size, self.config.shared_representation_size) if ctx_len < self.config.chunk_size: key = key.unsqueeze(1) value = value.unsqueeze(1) if padding_mask is not None: padding_mask = padding_mask.unsqueeze(1) else: # (batch_size X sequence_length X dimension) -> (batch_size X n_chunks X chunk_size X dimension) n_chunks = ctx_len // self.config.chunk_size key = key.reshape(bsz, n_chunks, self.config.chunk_size, self.config.shared_representation_size) value = value.reshape(bsz, n_chunks, self.config.chunk_size, self.config.intermediate_size) if padding_mask is not None: padding_mask = padding_mask.view(bsz, n_chunks, self.config.chunk_size) # this is in the original Mega implementation to work around fork/join parallelism not supporting optional types if padding_mask is not None and padding_mask.dim() == 0: padding_mask = None attn_weights = self.attention_function(query, key, padding_mask=padding_mask, causal_mask=causal_mask) value = self.hidden_dropout(value, batch_first=True) kernel = self.attention_dropout(attn_weights) # (batch_size x n_chunks x chunk_size x intermediate_size) -> (sequence_length X batch_size X intermediate_size) weighted_self_output = ( torch.matmul(kernel, value).view(bsz, seq_len, self.config.intermediate_size).transpose(0, 1) ) # (sequence_length X batch_size X intermediate_size) -> (sequence_length X batch_size X hidden_size) weighted_self_output = self.activation(intermediate_state + self.h_proj(weighted_self_output * attention_gate)) weighted_self_output = self.dropout(weighted_self_output) # (sequence_length X batch_size X hidden_size) out = torch.addcmul(residual, residual_weight, weighted_self_output - residual) if not self.config.normalize_before_mega: out = self.norm(out) return_values = (out, attn_weights) if output_attentions else (out,) if self.config.is_decoder: return_values = return_values + (updated_self_key, updated_self_value, updated_ema_state) return return_values class MegaNormalizedFeedForwardNetwork(nn.Module): """ Normalized feed-forward network used in Mega blocks. Left as-is from original Mega repo aside from retrieving args from Hugging Face config """ def __init__(self, config: MegaConfig): super().__init__() self.config = config self.hidden_dim = config.nffn_hidden_size self.act_fn = config.activation self.activation = ACT2FN[config.activation] self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout) self.hidden_dropout = MegaDropout( self.config.nffn_activation_dropout_prob, is_featurewise=self.config.use_feature_dropout ) self.prenorm = self.config.normalize_before_ffn self.norm = MegaSequenceNorm( self.config.normalization_type, self.config.hidden_size, affine=self.config.norm_affine ) self.fc1 = nn.Linear(self.config.hidden_size, self.config.nffn_hidden_size) self.fc2 = nn.Linear(self.config.nffn_hidden_size, self.config.hidden_size) def forward(self, inputs): residual = inputs if self.prenorm: inputs = self.norm(inputs) hidden = self.activation(self.fc1(inputs)) hidden = self.hidden_dropout(hidden) output = self.fc2(hidden) output = self.dropout(output) output = output + residual if not self.prenorm: output = self.norm(output) return output class MegaBlock(nn.Module): def __init__(self, config: MegaConfig): super().__init__() self.seq_len_dim = 1 self.mega_layer = MegaMovingAverageGatedAttention(config) self.nffn = MegaNormalizedFeedForwardNetwork(config) if config.use_normalized_ffn else None self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.cross_attn = MegaGatedCrossAttention(config) else: self.cross_attn = None def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, causal_mask: Optional[torch.LongTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[torch.FloatTensor]] = None, output_attentions: Optional[bool] = False, use_cache: bool = False, ) -> Tuple[torch.Tensor]: """ A single Mega layer: either encoder or decoder, with optional cross-attention and optional normalized feed-forward layer Args: hidden_states (`torch.Tensor` of shape `(target_sequence_length, batch_size, hidden_size)`): Hidden states to be updated by the Mega block attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indicates which entries in the self/target sequence are to be ignored (mostly due to padding), where elements are either 1 for *not masked* or 0 for *masked*. Causal attention is enforced internally. causal_mask (`torch.LongTensor` of shape `(sequence_length, sequence_length)`, *optional*): Indicates which inputs are to be ignored due to causal attention, where elements are either 1 for *not masked* or 0 for *masked* encoder_hidden_states (`torch.Tensor`, of shape `(source_sequence_length, batch_size, hidden_size)`, *optional*): Encoder hidden states to be used for cross-attention (and required for encoder-decoder model setup) encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, source_sequence_length)`, *optional*): Indicates which entries in the cross/source sequence are to be ignored (mostly due to padding), where elements are either 1 for *not masked* or 0 for *masked*. past_key_value (`tuple(torch.Tensor)`, *optional*): The hidden states returned from the previous timestep during incremental decoding; expects that self-attention key, value, and EMA states are the first 3 entries in the tuple, and (if doing cross-attention) cross-attention key and value are the last 2 entries in the tuple output_attentions (`bool`, default `False`): Whether to return self-attention weights use_cache (`bool`, default `False`): Whether to perfom incremental decoding; uses `past_key_value` as prior state, and returns the updated states for use in the next step Returns: `tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and inputs: - **hidden_states** (`torch.FloatTensor` of shape `(target_sequence_length, batch_size, hidden_size)`) -- Hidden states from target sequence updated by Mega - **self_attn_weights** (*optional*, returned when `output_attentions=True`) `torch.FloatTensor` of shape `(batch_size, 1, target_sequence_length, target_sequence_length)` -- The self-attention weights corresponding to how each token in the input sequence attends to every other token - **cross_attn_weights** (*optional*, returned when `output_attentions=True` and `config.add_cross_attention=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length, target_sequence_length)` -- Pairwise cross-attention weights between every entry in the source sequence and target sequence - **self_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, sequence_length, config.shared_representation_size)` -- The self-attention key state for use in the next step of incremental decoding - **self_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, sequence_length, config.hidden_size)` -- The self-attention value state for use in the next step of incremental decoding - **self_ema_state** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size, config.ndim)` The incremental EMA state for use in the next step of incremental decoding. - **cross_key** (*optional*, returned when `use_cache=True` and `config.is_decoder=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.shared_representation_size)` -- The cross-attention key state for use in the next step of incremental decoding - **cross_value** (*optional*, returned when `use_cache=True` and `config.is_decoder=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.hidden_size)` -- The cross-attention value state for use in the next step of incremental decoding """ # incremental decoding in the MegaMultiDimensionDampedEma module requires that the attention mask has the same # sequence length as the input tensor; if we're caching incremental states, we assume the input # sequence length is 1 (Mega will break otherwise), so we take the padding mask for the final # token in the input (mask is received as [batch X sequence length]) if use_cache and (past_key_value is not None) and (attention_mask is not None): mega_padding_mask = attention_mask[:, -1].unsqueeze(-1) else: mega_padding_mask = attention_mask mega_outputs = self.mega_layer( input=hidden_states, padding_mask=mega_padding_mask, causal_mask=causal_mask, past_key_values=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) new_hidden_states = mega_outputs[0] self_key, self_value, self_ema_state = mega_outputs[-3:] if use_cache else (None, None, None) self_attention_weights = mega_outputs[1] if output_attentions else None # optional cross attention if self.cross_attn is not None: if encoder_hidden_states is None: raise ValueError("Requested cross-attention without providing encoder hidden states") cross_attn_outputs = self.cross_attn( query=new_hidden_states, key=encoder_hidden_states, value=encoder_hidden_states, key_padding_mask=encoder_attention_mask, past_key_values=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) # update the hidden state from cross attention new_hidden_states = cross_attn_outputs[0] # store cross-attention k/v if caching cross_key, cross_value = cross_attn_outputs[-2:] if use_cache else (None, None) cross_attention_weights = cross_attn_outputs[1] if output_attentions else None # optional NFFN follows cross attention if self.nffn is not None: new_hidden_states = self.nffn(new_hidden_states) outs = (new_hidden_states,) if output_attentions: outs = outs + (self_attention_weights,) if self.cross_attn is not None: outs = outs + (cross_attention_weights,) if use_cache: new_key_values = ( self_key, self_value, self_ema_state, ) if self.cross_attn is not None: new_key_values = new_key_values + (cross_key, cross_value) outs = outs + (new_key_values,) return outs # copied from transformers.models.roberta.modeling_roberta.RobertaPooler with Roberta->Mega class MegaPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class MegaPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MegaConfig base_model_prefix = "mega" supports_gradient_checkpointing = False _no_split_modules = ["MegaMovingAverageGatedAttention"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, MegaMultiDimensionDampedEma): with torch.no_grad(): # delta & alpha nn.init.normal_(module.damping_factor, mean=0.0, std=self.config.ema_delta_alpha_range) nn.init.normal_(module.decay_factor, mean=0.0, std=self.config.ema_delta_alpha_range) # beta [1, -1, 1, -1, ...] seems more stable. val = torch.ones(self.config.ema_projection_size, 1) if self.config.ema_projection_size > 1: idx = torch.tensor(list(range(1, self.config.ema_projection_size, 2))) val.index_fill_(0, idx, -1.0) module.ema_expansion_matrix.normal_(mean=0.0, std=self.config.ema_beta_range).add_(val) # gamma & omega nn.init.normal_(module.kernel_projection_matrix, mean=0.0, std=self.config.ema_gamma_omega_range) nn.init.normal_(module.residual_weight, mean=0.0, std=self.config.ema_gamma_omega_range) elif isinstance(module, MegaSimpleRelativePositionalBias): nn.init.normal_(module.rel_pos_bias, mean=0.0, std=self.config.initializer_range) elif isinstance(module, MegaRotaryRelativePositionalBias): nn.init.normal_(module.alpha, mean=0.0, std=self.config.initializer_range) nn.init.normal_(module.b_param, mean=0.0, std=self.config.initializer_range) elif isinstance(module, MegaScaleNorm): if self.config.norm_affine: nn.init.constant_(module.scalar, 1.0) elif isinstance(module, MegaRMSNorm): if self.config.norm_affine: nn.init.constant_(module.weight, 1.0) elif isinstance(module, MegaMovingAverageGatedAttention): # linear layers covered separately by the generic nn.Linear init below nn.init.normal_(module.qk_weight, mean=0.0, std=self.config.initializer_range) nn.init.constant_(module.qk_bias, 0.0) elif isinstance(module, nn.Linear): # initializes all linear layers in the entire network module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) MEGA_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MegaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MEGA_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. This parameter can only be used when the model is initialized with `add_token_type_embeddings` parameter set to `True`. All the value in this tensor should be always < config.type_vocab_size. [What are token type IDs?](../glossary#token-type-ids) inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare MEGA Model transformer outputting raw hidden-states without any specific head on top.", MEGA_START_DOCSTRING, ) class MegaModel(MegaPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added after self-attention, following the architecture described in *Mega: Moving Average Equipped Gated Attention*_ by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer To behave as a decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True` and `bidirectional` set to `False`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder=True` and `bidirectional=False` argument as well as `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Mega: Moving Average Equipped Gated Attention*: https://arxiv.org/abs/2209.10655 """ def __init__(self, config: MegaConfig, add_pooling_layer=True): super().__init__(config) self.config = config self.embedding_layer = MegaEmbeddings(config) self.layers = nn.ModuleList([MegaBlock(config) for _ in range(config.num_hidden_layers)]) self.pooler = MegaPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing (retained from RoBERTa code) self.post_init() def get_input_embeddings(self): return self.embedding_layer.word_embeddings def set_input_embeddings(self, value): self.embedding_layer.word_embeddings = value @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() device = input_ids.device elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] device = inputs_embeds.device else: raise ValueError("You have to specify either input_ids or inputs_embeds") if self.config.use_chunking: input_shape = torch.tensor([input_shape[0], self.config.chunk_size]) batch_size, sequence_length = input_shape if self.config.use_chunking and (sequence_length > self.config.chunk_size): if sequence_length % self.config.chunk_size != 0: raise ValueError( f"config.use_chunking is activated; input sequence length must be shorter than or a multiple of config.chunk_size\nreceived sequence length of {sequence_length} with chunk size {self.config.chunk_size}" ) if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache # Mega expects the causal mask to be a 2D square matrix of (from) x (to) over the input sequence length # the HF utility function generates a 3D causal mask which includes batch size, so we'll create a dummy # mask with the correct device and all ones temp_mask_for_extension = torch.ones((1, sequence_length), dtype=torch.long, device=device) causal_mask = self.create_extended_attention_mask_for_decoder(input_shape, temp_mask_for_extension) # get rid of batch dimension in the generated mask; result is (sequence_length X sequence_length) causal_mask = causal_mask.squeeze(0) else: use_cache = False causal_mask = None # if using cache, make sure we have a tuple of tuples which matches the length of our hidden layers if (past_key_values is not None) and (len(past_key_values) != self.config.num_hidden_layers): raise ValueError( f"Received past key/value cache with size mismatch; expected {self.config.num_hidden_layers}, received {len(past_key_values)}" ) # get embeddings (batch X sequence length X embed dim) embedding_output = self.embedding_layer( input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) # transpose for Mega --> (seq len X batch X embed dim) hidden_states = embedding_output.transpose(0, 1) # we expect encoder hidden states to also have batch first in line # with typical Hugging Face behavior (which is also how we return them) # Mega expects sequence length first, so do the same transpose here if encoder_hidden_states is not None: encoder_hidden_states = encoder_hidden_states.transpose(0, 1) # pass through mega layers all_hidden_states = (embedding_output,) if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, mega_layer in enumerate(self.layers): current_decoder_cache = past_key_values[i] if past_key_values is not None else None mega_outputs = mega_layer( hidden_states=hidden_states, attention_mask=attention_mask, causal_mask=causal_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=current_decoder_cache, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = mega_outputs[0] if output_hidden_states: # store layer-wise hidden states in the way that the user expects # (seq len X batch X embed dim) --> (batch X seq len X embed dim) all_hidden_states += (hidden_states.transpose(0, 1),) if output_attentions: self_attn_weights = mega_outputs[1] all_self_attentions += (self_attn_weights,) if self.config.add_cross_attention: cross_attn_weights = mega_outputs[2] all_cross_attentions += (cross_attn_weights,) if use_cache: updated_cache = mega_outputs[-1] next_decoder_cache += (updated_cache,) # transpose final hidden states hidden_states = hidden_states.transpose(0, 1) # optional pooling layer pooled_output = self.pooler(hidden_states) if self.pooler is not None else None if not return_dict: return (hidden_states, pooled_output) + ( all_hidden_states, next_decoder_cache, all_self_attentions, all_cross_attentions, ) return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=hidden_states, pooler_output=pooled_output, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) @add_start_docstrings( """MEGA Model with a `language modeling` head on top for CLM fine-tuning.""", MEGA_START_DOCSTRING ) class MegaForCausalLM(MegaPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: MegaConfig): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `MegaForCausalLM` as a standalone, add `is_decoder=True.`") self.mega = MegaModel(config, add_pooling_layer=False) if config.add_lm_hidden_dense_layer: self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.hidden_activation = nn.Tanh() else: self.dense = None self.hidden_activation = None self.lm_head = nn.Linear(config.hidden_size, config.vocab_size) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Returns: Example: ```python >>> from transformers import AutoTokenizer, MegaForCausalLM, AutoConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext") >>> config = AutoConfig.from_pretrained("mnaylor/mega-base-wikitext") >>> config.is_decoder = True >>> config.bidirectional = False >>> model = MegaForCausalLM.from_pretrained( ... "mnaylor/mega-base-wikitext", config=config, ignore_mismatched_sizes=True ... ) >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False outputs = self.mega( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] if self.dense is not None: sequence_output = self.dense(sequence_output) sequence_output = self.hidden_activation(sequence_output) prediction_scores = self.lm_head(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past_key_values is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values} def _reorder_cache(self, past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past @add_start_docstrings("""MEGA Model with a `language modeling` head on top.""", MEGA_START_DOCSTRING) class MegaForMaskedLM(MegaPreTrainedModel): _tied_weights_keys = ["mlm_head.weight"] def __init__(self, config: MegaConfig): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `MegaForMaskedLM`, set `config.is_decoder=False` for " "bi-directional self-attention." ) self.mega = MegaModel(config, add_pooling_layer=False) if config.add_lm_hidden_dense_layer: self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.hidden_activation = nn.Tanh() else: self.dense = None self.hidden_activation = None self.mlm_head = nn.Linear(config.hidden_size, config.vocab_size) self.dropout = nn.Dropout(config.dropout_prob) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.mlm_head def set_output_embeddings(self, new_embeddings): self.mlm_head = new_embeddings @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` kwargs (`Dict[str, any]`, optional, defaults to *{}*): Used to hide legacy arguments that have been deprecated. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mega( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] if self.dense is not None: sequence_output = self.dense(sequence_output) sequence_output = self.hidden_activation(sequence_output) prediction_scores = self.mlm_head(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MEGA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MEGA_START_DOCSTRING, ) class MegaForSequenceClassification(MegaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.mega = MegaModel(config, add_pooling_layer=False) self.classifier = MegaClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mega( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MEGA Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, MEGA_START_DOCSTRING, ) class MegaForMultipleChoice(MegaPreTrainedModel): def __init__(self, config): super().__init__(config) self.mega = MegaModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.mega( flat_input_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ MEGA Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, MEGA_START_DOCSTRING, ) class MegaForTokenClassification(MegaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mega = MegaModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mega( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Mega class MegaClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = torch.tanh(x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """ MEGA Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, MEGA_START_DOCSTRING, ) class MegaForQuestionAnswering(MegaPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mega = MegaModel(config, add_pooling_layer=False) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mega( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/mega/modeling_mega.py/0

{ "file_path": "transformers/src/transformers/models/mega/modeling_mega.py", "repo_id": "transformers", "token_count": 45877 }

339

# coding=utf-8 # Copyright 2024 Mistral AI and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax Mistral model.""" from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxBaseModelOutputWithPast, FlaxCausalLMOutput, FlaxCausalLMOutputWithCrossAttentions, ) from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, logging from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward from .configuration_mistral import MistralConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "MistralConfig" _REAL_CHECKPOINT_FOR_DOC = "mistralai/Mistral-7B-v0.1" _CHECKPOINT_FOR_DOC = "ksmcg/Mistral-tiny" MISTRAL_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`MistralConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16`, or `jax.numpy.bfloat16`. This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ MISTRAL_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaRMSNorm with Llama->Mistral class FlaxMistralRMSNorm(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.epsilon = self.config.rms_norm_eps self.weight = self.param("weight", lambda _, shape: jnp.ones(shape), self.config.hidden_size) def __call__(self, hidden_states): variance = jnp.asarray(hidden_states, dtype=jnp.float32) variance = jnp.power(variance, 2) variance = variance.mean(-1, keepdims=True) # use `jax.numpy.sqrt` as `jax.lax.rsqrt` does not match `torch.rsqrt` hidden_states = hidden_states / jnp.sqrt(variance + self.epsilon) return self.weight * jnp.asarray(hidden_states, dtype=self.dtype) # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaRotaryEmbedding with Llama->Mistral class FlaxMistralRotaryEmbedding(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): head_dim = self.config.hidden_size // self.config.num_attention_heads self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim) def __call__(self, key, query, position_ids): sincos = self.sincos[position_ids] sin_pos, cos_pos = jnp.split(sincos, 2, axis=-1) key = apply_rotary_pos_emb(key, sin_pos, cos_pos) query = apply_rotary_pos_emb(query, sin_pos, cos_pos) key = jnp.asarray(key, dtype=self.dtype) query = jnp.asarray(query, dtype=self.dtype) return key, query # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaMLP with Llama->Mistral class FlaxMistralMLP(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): embed_dim = self.config.hidden_size inner_dim = self.config.intermediate_size if self.config.intermediate_size is not None else 4 * embed_dim kernel_init = jax.nn.initializers.normal(self.config.initializer_range) self.act = ACT2FN[self.config.hidden_act] self.gate_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init) self.down_proj = nn.Dense(embed_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init) self.up_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init) def __call__(self, hidden_states): up_proj_states = self.up_proj(hidden_states) gate_states = self.act(self.gate_proj(hidden_states)) hidden_states = self.down_proj(up_proj_states * gate_states) return hidden_states # Copied from transformers.models.llama.modeling_flax_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(tensor, sin_pos, cos_pos): return (tensor * cos_pos) + (rotate_half(tensor) * sin_pos) # Copied from transformers.models.llama.modeling_flax_llama.create_sinusoidal_positions def create_sinusoidal_positions(num_pos, dim): inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim)) freqs = np.einsum("i , j -> i j", np.arange(num_pos), inv_freq).astype("float32") emb = np.concatenate((freqs, freqs), axis=-1) out = np.concatenate((np.sin(emb)[:, None, :], np.cos(emb)[:, None, :]), axis=-1) return jnp.array(out[:, :, :num_pos]) # Copied from transformers.models.llama.modeling_flax_llama.rotate_half def rotate_half(tensor): """Rotates half the hidden dims of the input.""" rotate_half_tensor = jnp.concatenate( (-tensor[..., tensor.shape[-1] // 2 :], tensor[..., : tensor.shape[-1] // 2]), axis=-1 ) return rotate_half_tensor class FlaxMistralAttention(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): config = self.config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.attention_softmax_in_fp32 = self.dtype is not jnp.float32 self.rope_theta = config.rope_theta if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads})." ) self.q_proj = nn.Dense(self.num_heads * self.head_dim, use_bias=False, dtype=self.dtype) self.k_proj = nn.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, dtype=self.dtype) self.v_proj = nn.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, dtype=self.dtype) self.o_proj = nn.Dense(self.hidden_size, use_bias=False, dtype=self.dtype) casual_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool") self.causal_mask = jnp.triu(casual_mask, k=-config.sliding_window) self.rotary_emb = FlaxMistralRotaryEmbedding(config, dtype=self.dtype) def _split_heads(self, hidden_states, num_heads): return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.hidden_size,)) @nn.compact # Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoSelfAttention._concatenate_to_cache def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, deterministic: bool = True, output_attentions: bool = False, init_cache: bool = False, ) -> Tuple[jnp.ndarray, jnp.ndarray]: query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states, self.num_heads) key_states = self._split_heads(key_states, self.num_key_value_heads) value_states = self._split_heads(value_states, self.num_key_value_heads) key_states, query_states = self.rotary_emb(key_states, query_states, position_ids) query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] batch_size = hidden_states.shape[0] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) if self.has_variable("cache", "cached_key") or init_cache: key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) key_states = jnp.repeat(key_states, self.num_key_value_groups, axis=2) value_states = jnp.repeat(value_states, self.num_key_value_groups, axis=2) attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) # usual dot product attention attention_dtype = jnp.float32 if self.attention_softmax_in_fp32 else self.dtype attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, deterministic=deterministic, dropout_rate=self.config.attention_dropout, dtype=attention_dtype, ) if self.attention_softmax_in_fp32: attn_weights = attn_weights.astype(self.dtype) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.o_proj(attn_output) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaDecoderLayer with Llama->Mistral class FlaxMistralDecoderLayer(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.input_layernorm = FlaxMistralRMSNorm(self.config, dtype=self.dtype) self.self_attn = FlaxMistralAttention(self.config, dtype=self.dtype) self.post_attention_layernorm = FlaxMistralRMSNorm(self.config, dtype=self.dtype) self.mlp = FlaxMistralMLP(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, ): residual = hidden_states hidden_states = self.input_layernorm(hidden_states) outputs = self.self_attn( hidden_states, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, ) # residual connection attn_output = outputs[0] hidden_states = residual + attn_output residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) # residual connection hidden_states = residual + hidden_states return (hidden_states,) + outputs[1:] # Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoPreTrainedModel with GPTNeo->Mistral, GPT_NEO->MISTRAL, transformer->model class FlaxMistralPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MistralConfig base_model_prefix = "model" module_class: nn.Module = None def __init__( self, config: MistralConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length)) attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) def __call__( self, input_ids, attention_mask=None, position_ids=None, params: dict = None, past_key_values: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_ids.shape if position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.") position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxMistralAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, False, output_attentions, output_hidden_states, return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] return outputs # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaLayerCollection with Llama->Mistral class FlaxMistralLayerCollection(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.blocks = [ FlaxMistralDecoderLayer(self.config, dtype=self.dtype, name=str(i)) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = False, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for block in self.blocks: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = block( hidden_states, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) # this contains possible `None` values - `FlaxMistralModule` will filter them out outputs = (hidden_states, all_hidden_states, all_attentions) return outputs # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaModule with Llama->Mistral class FlaxMistralModule(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.hidden_size = self.config.hidden_size embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range) self.embed_tokens = nn.Embed( self.config.vocab_size, self.hidden_size, embedding_init=embedding_init, dtype=self.dtype, ) self.layers = FlaxMistralLayerCollection(self.config, dtype=self.dtype) self.norm = FlaxMistralRMSNorm(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask=None, position_ids=None, deterministic=True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): input_embeds = self.embed_tokens(input_ids.astype("i4")) outputs = self.layers( input_embeds, position_ids=position_ids, attention_mask=attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states = outputs[1] + (hidden_states,) outputs = (hidden_states, all_hidden_states) + outputs[2:] else: outputs = (hidden_states,) + outputs[1:] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=outputs[1], attentions=outputs[-1], ) @add_start_docstrings( "The bare Mistral Model transformer outputting raw hidden-states without any specific head on top.", MISTRAL_START_DOCSTRING, ) class FlaxMistralModel(FlaxMistralPreTrainedModel): module_class = FlaxMistralModule append_call_sample_docstring( FlaxMistralModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPast, _CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, ) # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaForCausalLMModule with Llama->Mistral class FlaxMistralForCausalLMModule(nn.Module): config: MistralConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.model = FlaxMistralModule(self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) def __call__( self, input_ids, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): outputs = self.model( input_ids, position_ids=position_ids, attention_mask=attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + outputs[1:] return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) @add_start_docstrings( """ The Mistral Model transformer with a language modeling head (linear layer) on top. """, MISTRAL_START_DOCSTRING, ) # Copied from transformers.models.gptj.modeling_flax_gptj.FlaxGPTJForCausalLM with GPTJ->Mistral class FlaxMistralForCausalLM(FlaxMistralPreTrainedModel): module_class = FlaxMistralForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since Mistral uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxMistralForCausalLM, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, )

transformers/src/transformers/models/mistral/modeling_flax_mistral.py/0

{ "file_path": "transformers/src/transformers/models/mistral/modeling_flax_mistral.py", "repo_id": "transformers", "token_count": 13485 }

340

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for MobileViT.""" from typing import Dict, List, Optional, Tuple, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import flip_channel_order, get_resize_output_image_size, resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging if is_vision_available(): import PIL if is_torch_available(): import torch logger = logging.get_logger(__name__) class MobileViTImageProcessor(BaseImageProcessor): r""" Constructs a MobileViT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): Controls the size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 256, "width": 256}`): Desired output size `(size["height"], size["width"])` when applying center-cropping. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_flip_channel_order (`bool`, *optional*, defaults to `True`): Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_flip_channel_order: bool = True, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"shortest_edge": 224} size = get_size_dict(size, default_to_square=False) crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256} crop_size = get_size_dict(crop_size, param_name="crop_size") self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_flip_channel_order = do_flip_channel_order # Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize with PILImageResampling.BICUBIC->PILImageResampling.BILINEAR def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BILINEAR, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ default_to_square = True if "shortest_edge" in size: size = size["shortest_edge"] default_to_square = False elif "height" in size and "width" in size: size = (size["height"], size["width"]) else: raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") output_size = get_resize_output_image_size( image, size=size, default_to_square=default_to_square, input_data_format=input_data_format, ) return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) def flip_channel_order( self, image: np.ndarray, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.ndarray: """ Flip the color channels from RGB to BGR or vice versa. Args: image (`np.ndarray`): The image, represented as a numpy array. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ return flip_channel_order(image, data_format=data_format, input_data_format=input_data_format) def __call__(self, images, segmentation_maps=None, **kwargs): """ Preprocesses a batch of images and optionally segmentation maps. Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be passed in as positional arguments. """ return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs) def _preprocess( self, image: ImageInput, do_resize: bool, do_rescale: bool, do_center_crop: bool, do_flip_channel_order: bool, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, rescale_factor: Optional[float] = None, crop_size: Optional[Dict[str, int]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): if do_resize: image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) if do_rescale: image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) if do_center_crop: image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) if do_flip_channel_order: image = self.flip_channel_order(image, input_data_format=input_data_format) return image def _preprocess_image( self, image: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: bool = None, rescale_factor: float = None, do_center_crop: bool = None, crop_size: Dict[str, int] = None, do_flip_channel_order: bool = None, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.ndarray: """Preprocesses a single image.""" # All transformations expect numpy arrays. image = to_numpy_array(image) if is_scaled_image(image) and do_rescale: logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: input_data_format = infer_channel_dimension_format(image) image = self._preprocess( image=image, do_resize=do_resize, size=size, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_center_crop=do_center_crop, crop_size=crop_size, do_flip_channel_order=do_flip_channel_order, input_data_format=input_data_format, ) image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) return image def _preprocess_mask( self, segmentation_map: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, do_center_crop: bool = None, crop_size: Dict[str, int] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.ndarray: """Preprocesses a single mask.""" segmentation_map = to_numpy_array(segmentation_map) # Add channel dimension if missing - needed for certain transformations if segmentation_map.ndim == 2: added_channel_dim = True segmentation_map = segmentation_map[None, ...] input_data_format = ChannelDimension.FIRST else: added_channel_dim = False if input_data_format is None: input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1) segmentation_map = self._preprocess( image=segmentation_map, do_resize=do_resize, size=size, resample=PILImageResampling.NEAREST, do_rescale=False, do_center_crop=do_center_crop, crop_size=crop_size, do_flip_channel_order=False, input_data_format=input_data_format, ) # Remove extra channel dimension if added for processing if added_channel_dim: segmentation_map = segmentation_map.squeeze(0) segmentation_map = segmentation_map.astype(np.int64) return segmentation_map def preprocess( self, images: ImageInput, segmentation_maps: Optional[ImageInput] = None, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: bool = None, rescale_factor: float = None, do_center_crop: bool = None, crop_size: Dict[str, int] = None, do_flip_channel_order: bool = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. segmentation_maps (`ImageInput`, *optional*): Segmentation map to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image by rescale factor. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the center crop if `do_center_crop` is set to `True`. do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`): Whether to flip the channel order of the image. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ do_resize = do_resize if do_resize is not None else self.do_resize resample = resample if resample is not None else self.resample do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop do_flip_channel_order = ( do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order ) size = size if size is not None else self.size size = get_size_dict(size, default_to_square=False) crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size") images = make_list_of_images(images) if segmentation_maps is not None: segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if segmentation_maps is not None and not valid_images(segmentation_maps): raise ValueError( "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if do_resize and size is None: raise ValueError("Size must be specified if do_resize is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_center_crop and crop_size is None: raise ValueError("Crop size must be specified if do_center_crop is True.") images = [ self._preprocess_image( image=img, do_resize=do_resize, size=size, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_center_crop=do_center_crop, crop_size=crop_size, do_flip_channel_order=do_flip_channel_order, data_format=data_format, input_data_format=input_data_format, ) for img in images ] data = {"pixel_values": images} if segmentation_maps is not None: segmentation_maps = [ self._preprocess_mask( segmentation_map=segmentation_map, do_resize=do_resize, size=size, do_center_crop=do_center_crop, crop_size=crop_size, input_data_format=input_data_format, ) for segmentation_map in segmentation_maps ] data["labels"] = segmentation_maps return BatchFeature(data=data, tensor_type=return_tensors) # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileViT def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None): """ Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`MobileViTForSemanticSegmentation`]): Raw outputs of the model. target_sizes (`List[Tuple]` of length `batch_size`, *optional*): List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, predictions will not be resized. Returns: semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each `torch.Tensor` correspond to a semantic class id. """ # TODO: add support for other frameworks logits = outputs.logits # Resize logits and compute semantic segmentation maps if target_sizes is not None: if len(logits) != len(target_sizes): raise ValueError( "Make sure that you pass in as many target sizes as the batch dimension of the logits" ) if is_torch_tensor(target_sizes): target_sizes = target_sizes.numpy() semantic_segmentation = [] for idx in range(len(logits)): resized_logits = torch.nn.functional.interpolate( logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False ) semantic_map = resized_logits[0].argmax(dim=0) semantic_segmentation.append(semantic_map) else: semantic_segmentation = logits.argmax(dim=1) semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] return semantic_segmentation

transformers/src/transformers/models/mobilevit/image_processing_mobilevit.py/0

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# coding=utf-8 # Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ OpenAI GPT configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"} class OpenAIGPTConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a [`TFOpenAIGPTModel`]. It is used to instantiate a GPT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the GPT [openai-gpt](https://huggingface.co/openai-gpt) architecture from OpenAI. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 40478): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OpenAIGPTModel`] or [`TFOpenAIGPTModel`]. n_positions (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_embd (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. afn (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`int`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. summary_type (`str`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and [`OpenAIGPTDoubleHeadsModel`]. The dropout ratio to be used after the projection and activation. Examples: ```python >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel >>> # Initializing a GPT configuration >>> configuration = OpenAIGPTConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = OpenAIGPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "openai-gpt" attribute_map = { "max_position_embeddings": "n_positions", "hidden_size": "n_embd", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=40478, n_positions=512, n_embd=768, n_layer=12, n_head=12, afn="gelu", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, **kwargs, ): self.vocab_size = vocab_size self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.afn = afn self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels super().__init__(**kwargs)

transformers/src/transformers/models/openai/configuration_openai.py/0

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# coding=utf-8 # Copyright 2023 Google AI and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch OWLv2 model.""" import warnings from dataclasses import dataclass from typing import Any, Dict, Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import Tensor, nn from ...activations import ACT2FN from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_vision_available, logging, replace_return_docstrings, ) from .configuration_owlv2 import Owlv2Config, Owlv2TextConfig, Owlv2VisionConfig if is_vision_available(): from transformers.image_transforms import center_to_corners_format logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/owlv2-base-patch16-ensemble" # See all Owlv2 models at https://huggingface.co/models?filter=owlv2 OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/owlv2-base-patch16-ensemble", # See all OWLv2 models at https://huggingface.co/models?filter=owlv2 ] # Copied from transformers.models.clip.modeling_clip.contrastive_loss with clip->owlv2 def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) # Copied from transformers.models.clip.modeling_clip.clip_loss with clip->owlv2 def owlv2_loss(similarity: torch.Tensor) -> torch.Tensor: caption_loss = contrastive_loss(similarity) image_loss = contrastive_loss(similarity.t()) return (caption_loss + image_loss) / 2.0 @dataclass class Owlv2Output(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds (`torch.FloatTensor` of shape `(batch_size * num_max_text_queries, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`Owlv2TextModel`]. image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`Owlv2VisionModel`]. text_model_output (Tuple[`BaseModelOutputWithPooling`]): The output of the [`Owlv2TextModel`]. vision_model_output (`BaseModelOutputWithPooling`): The output of the [`Owlv2VisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits_per_image: torch.FloatTensor = None logits_per_text: torch.FloatTensor = None text_embeds: torch.FloatTensor = None image_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) # Copied from transformers.models.detr.modeling_detr._upcast def _upcast(t: Tensor) -> Tensor: # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type if t.is_floating_point(): return t if t.dtype in (torch.float32, torch.float64) else t.float() else: return t if t.dtype in (torch.int32, torch.int64) else t.int() # Copied from transformers.models.detr.modeling_detr.box_area def box_area(boxes: Tensor) -> Tensor: """ Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. Args: boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 < x2` and `0 <= y1 < y2`. Returns: `torch.FloatTensor`: a tensor containing the area for each box. """ boxes = _upcast(boxes) return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) # Copied from transformers.models.detr.modeling_detr.box_iou def box_iou(boxes1, boxes2): area1 = box_area(boxes1) area2 = box_area(boxes2) left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] union = area1[:, None] + area2 - inter iou = inter / union return iou, union # Copied from transformers.models.detr.modeling_detr.generalized_box_iou def generalized_box_iou(boxes1, boxes2): """ Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. Returns: `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) """ # degenerate boxes gives inf / nan results # so do an early check if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") iou, union = box_iou(boxes1, boxes2) top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] area = width_height[:, :, 0] * width_height[:, :, 1] return iou - (area - union) / area @dataclass class Owlv2ObjectDetectionOutput(ModelOutput): """ Output type of [`Owlv2ForObjectDetection`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized scale-invariant IoU loss. loss_dict (`Dict`, *optional*): A dictionary containing the individual losses. Useful for logging. logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`): Classification logits (including no-object) for all queries. objectness_logits (`torch.FloatTensor` of shape `(batch_size, num_patches, 1)`): The objectness logits of all image patches. OWL-ViT represents images as a set of image patches where the total number of patches is (image_size / patch_size)**2. pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. text_embeds (`torch.FloatTensor` of shape `(batch_size, num_max_text_queries, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`Owlv2TextModel`]. image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes image embeddings for each patch. class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): Class embeddings of all image patches. OWLv2 represents images as a set of image patches where the total number of patches is (image_size / patch_size)**2. text_model_output (Tuple[`BaseModelOutputWithPooling`]): The output of the [`Owlv2TextModel`]. vision_model_output (`BaseModelOutputWithPooling`): The output of the [`Owlv2VisionModel`]. """ loss: Optional[torch.FloatTensor] = None loss_dict: Optional[Dict] = None logits: torch.FloatTensor = None objectness_logits: torch.FloatTensor = None pred_boxes: torch.FloatTensor = None text_embeds: torch.FloatTensor = None image_embeds: torch.FloatTensor = None class_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTImageGuidedObjectDetectionOutput with OwlViT->Owlv2,OWL-ViT->OWLv2 class Owlv2ImageGuidedObjectDetectionOutput(ModelOutput): """ Output type of [`Owlv2ForObjectDetection.image_guided_detection`]. Args: logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`): Classification logits (including no-object) for all queries. target_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual target image in the batch (disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. query_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual query image in the batch (disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes image embeddings for each patch. query_image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes image embeddings for each patch. class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): Class embeddings of all image patches. OWLv2 represents images as a set of image patches where the total number of patches is (image_size / patch_size)**2. text_model_output (Tuple[`BaseModelOutputWithPooling`]): The output of the [`Owlv2TextModel`]. vision_model_output (`BaseModelOutputWithPooling`): The output of the [`Owlv2VisionModel`]. """ logits: torch.FloatTensor = None image_embeds: torch.FloatTensor = None query_image_embeds: torch.FloatTensor = None target_pred_boxes: torch.FloatTensor = None query_pred_boxes: torch.FloatTensor = None class_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionEmbeddings with OwlViT->Owlv2 class Owlv2VisionEmbeddings(nn.Module): def __init__(self, config: Owlv2VisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.class_embedding = nn.Parameter(torch.randn(config.hidden_size)) self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=config.patch_size, stride=config.patch_size, bias=False, ) self.num_patches = (config.image_size // config.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: batch_size = pixel_values.shape[0] patch_embeds = self.patch_embedding(pixel_values) # shape = [batch_size, num_channels, height, width] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextEmbeddings with OwlViT->Owlv2 class Owlv2TextEmbeddings(nn.Module): def __init__(self, config: Owlv2TextConfig): super().__init__() self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size) self.position_embedding = nn.Embedding(config.max_position_embeddings, config.hidden_size) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if inputs_embeds is None: inputs_embeds = self.token_embedding(input_ids) position_embeddings = self.position_embedding(position_ids) embeddings = inputs_embeds + position_embeddings return embeddings # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTAttention with OwlViT->Owlv2 class Owlv2Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scale key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) # apply the causal_attention_mask first if causal_attention_mask is not None: if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {causal_attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit akward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) # For int8 compatibility, sometimes the `attn_probs` are in `fp32` attn_probs = attn_probs.to(value_states.dtype) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Owlv2 class Owlv2MLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Owlv2 class Owlv2EncoderLayer(nn.Module): def __init__(self, config: Owlv2Config): super().__init__() self.embed_dim = config.hidden_size self.self_attn = Owlv2Attention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = Owlv2MLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTPreTrainedModel with OwlViT->Owlv2,owlvit->owlv2 class Owlv2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Owlv2Config base_model_prefix = "owlv2" supports_gradient_checkpointing = True _no_split_modules = ["Owlv2EncoderLayer"] def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor if isinstance(module, Owlv2TextEmbeddings): module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) elif isinstance(module, Owlv2VisionEmbeddings): factor = self.config.initializer_factor nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) elif isinstance(module, Owlv2Attention): factor = self.config.initializer_factor in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor out_proj_std = (module.embed_dim**-0.5) * factor nn.init.normal_(module.q_proj.weight, std=in_proj_std) nn.init.normal_(module.k_proj.weight, std=in_proj_std) nn.init.normal_(module.v_proj.weight, std=in_proj_std) nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, Owlv2MLP): factor = self.config.initializer_factor in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) elif isinstance(module, Owlv2Model): nn.init.normal_( module.text_projection.weight, std=module.text_embed_dim**-0.5 * self.config.initializer_factor, ) nn.init.normal_( module.visual_projection.weight, std=module.vision_embed_dim**-0.5 * self.config.initializer_factor, ) if isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() OWLV2_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`Owvl2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ OWLV2_TEXT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, num_max_text_queries, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ OWLV2_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ OWLV2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_base_image_embeds (`bool`, *optional*): Whether or not to return the base image embeddings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ OWLV2_OBJECT_DETECTION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`, *optional*): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids). attention_mask (`torch.Tensor` of shape `(batch_size, num_max_text_queries, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_hidden_states (`bool`, *optional*): Whether or not to return the last hidden state. See `text_model_last_hidden_state` and `vision_model_last_hidden_state` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ OWLV2_IMAGE_GUIDED_OBJECT_DETECTION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. query_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values of query image(s) to be detected. Pass in one query image per target image. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTEncoder with OwlViT->Owlv2 class Owlv2Encoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Owlv2EncoderLayer`]. Args: config: Owlv2Config """ def __init__(self, config: Owlv2Config): super().__init__() self.layers = nn.ModuleList([Owlv2EncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`). attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Causal mask for the text model. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for encoder_layer in self.layers: if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, causal_attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextTransformer with OWLVIT->OWLV2,OwlViT->Owlv2 class Owlv2TextTransformer(nn.Module): def __init__(self, config: Owlv2TextConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = Owlv2TextEmbeddings(config) self.encoder = Owlv2Encoder(config) self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) @add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2TextConfig) def forward( self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) # num_samples, seq_len = input_shape where num_samples = batch_size * num_max_text_queries # OWLV2's text model uses causal mask, prepare it here. # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324 causal_attention_mask = _create_4d_causal_attention_mask( input_shape, hidden_states.dtype, device=hidden_states.device ) # expand attention_mask if attention_mask is not None: # [num_samples, seq_len] -> [num_samples, 1, tgt_seq_len, src_seq_len] attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.final_layer_norm(last_hidden_state) # take features from the end of tokens embedding (end of token is the highest number in each sequence) # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 pooled_output = last_hidden_state[ torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), input_ids.to(torch.int).argmax(dim=-1).to(last_hidden_state.device), ] if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextModel with google/owlvit-base-patch32->google/owlv2-base-patch16, OWLVIT->OWLV2,OwlViT->Owlv2 class Owlv2TextModel(Owlv2PreTrainedModel): config_class = Owlv2TextConfig def __init__(self, config: Owlv2TextConfig): super().__init__(config) self.text_model = Owlv2TextTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.text_model.embeddings.token_embedding def set_input_embeddings(self, value): self.text_model.embeddings.token_embedding = value @add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2TextConfig) def forward( self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from transformers import AutoProcessor, Owlv2TextModel >>> model = Owlv2TextModel.from_pretrained("google/owlv2-base-patch16") >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16") >>> inputs = processor( ... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt" ... ) >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled (EOS token) states ```""" # Get embeddings for all text queries in all batch samples return self.text_model( input_ids=input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionTransformer with OWLVIT->OWLV2,OwlViT->Owlv2 class Owlv2VisionTransformer(nn.Module): def __init__(self, config: Owlv2VisionConfig): super().__init__() self.config = config self.embeddings = Owlv2VisionEmbeddings(config) self.pre_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.encoder = Owlv2Encoder(config) self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) @add_start_docstrings_to_model_forward(OWLV2_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2VisionConfig) def forward( self, pixel_values: torch.FloatTensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Cast the input to the expected `dtype` expected_input_dtype = self.embeddings.patch_embedding.weight.dtype pixel_values = pixel_values.to(expected_input_dtype) hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layernorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionModel with OWLVIT->OWLV2,OwlViT->Owlv2,google/owlvit-base-patch32->google/owlv2-base-patch16 class Owlv2VisionModel(Owlv2PreTrainedModel): config_class = Owlv2VisionConfig main_input_name = "pixel_values" def __init__(self, config: Owlv2VisionConfig): super().__init__(config) self.vision_model = Owlv2VisionTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding @add_start_docstrings_to_model_forward(OWLV2_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2VisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, Owlv2VisionModel >>> model = Owlv2VisionModel.from_pretrained("google/owlv2-base-patch16") >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled CLS states ```""" return self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) @add_start_docstrings(OWLV2_START_DOCSTRING) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTModel with google/owlvit-base-patch32->google/owlv2-base-patch16-ensemble, OWLVIT->OWLV2,OwlViT->Owlv2,owlvit->owlv2,OWL-ViT->OWLv2 class Owlv2Model(Owlv2PreTrainedModel): config_class = Owlv2Config def __init__(self, config: Owlv2Config): super().__init__(config) if not isinstance(config.text_config, Owlv2TextConfig): raise ValueError( "config.text_config is expected to be of type Owlv2TextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.vision_config, Owlv2VisionConfig): raise ValueError( "config.vision_config is expected to be of type Owlv2VisionConfig but is of type" f" {type(config.vision_config)}." ) text_config = config.text_config vision_config = config.vision_config self.projection_dim = config.projection_dim self.text_embed_dim = text_config.hidden_size self.vision_embed_dim = vision_config.hidden_size self.text_model = Owlv2TextTransformer(text_config) self.vision_model = Owlv2VisionTransformer(vision_config) self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) self.logit_scale = nn.Parameter(torch.tensor(config.logit_scale_init_value)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`Owlv2TextModel`]. Examples: ```python >>> from transformers import AutoProcessor, Owlv2Model >>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble") >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") >>> inputs = processor( ... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt" ... ) >>> text_features = model.get_text_features(**inputs) ```""" # Use OWLv2 model's config for some fields (if specified) instead of those of vision & text components. return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Get embeddings for all text queries in all batch samples text_output = self.text_model(input_ids=input_ids, attention_mask=attention_mask, return_dict=return_dict) pooled_output = text_output[1] text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(OWLV2_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`Owlv2VisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, Owlv2Model >>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble") >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""" # Use OWLv2 model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = vision_outputs[1] image_features = self.visual_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward(OWLV2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Owlv2Output, config_class=Owlv2Config) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_base_image_embeds: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, Owlv2Output]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, Owlv2Model >>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble") >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ```""" # Use OWLv2 model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Get embeddings for all text queries in all batch samples text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) # normalized features image_embeds = image_embeds / torch.linalg.norm(image_embeds, ord=2, dim=-1, keepdim=True) text_embeds_norm = text_embeds / torch.linalg.norm(text_embeds, ord=2, dim=-1, keepdim=True) # cosine similarity as logits and set it on the correct device logit_scale = self.logit_scale.exp().to(image_embeds.device) logits_per_text = torch.matmul(text_embeds_norm, image_embeds.t()) * logit_scale logits_per_image = logits_per_text.t() loss = None if return_loss: loss = owlv2_loss(logits_per_text) if return_base_image_embeds: warnings.warn( "`return_base_image_embeds` is deprecated and will be removed in v4.27 of Transformers, one can" " obtain the base (unprojected) image embeddings from outputs.vision_model_output.", FutureWarning, ) last_hidden_state = vision_outputs[0] image_embeds = self.vision_model.post_layernorm(last_hidden_state) else: text_embeds = text_embeds_norm if not return_dict: output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return ((loss,) + output) if loss is not None else output return Owlv2Output( loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTBoxPredictionHead with OwlViT->Owlv2 class Owlv2BoxPredictionHead(nn.Module): def __init__(self, config: Owlv2Config, out_dim: int = 4): super().__init__() width = config.vision_config.hidden_size self.dense0 = nn.Linear(width, width) self.dense1 = nn.Linear(width, width) self.gelu = nn.GELU() self.dense2 = nn.Linear(width, out_dim) def forward(self, image_features: torch.Tensor) -> torch.FloatTensor: output = self.dense0(image_features) output = self.gelu(output) output = self.dense1(output) output = self.gelu(output) output = self.dense2(output) return output # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTClassPredictionHead with OwlViT->Owlv2 class Owlv2ClassPredictionHead(nn.Module): def __init__(self, config: Owlv2Config): super().__init__() out_dim = config.text_config.hidden_size self.query_dim = config.vision_config.hidden_size self.dense0 = nn.Linear(self.query_dim, out_dim) self.logit_shift = nn.Linear(self.query_dim, 1) self.logit_scale = nn.Linear(self.query_dim, 1) self.elu = nn.ELU() def forward( self, image_embeds: torch.FloatTensor, query_embeds: Optional[torch.FloatTensor], query_mask: Optional[torch.Tensor], ) -> Tuple[torch.FloatTensor]: image_class_embeds = self.dense0(image_embeds) if query_embeds is None: device = image_class_embeds.device batch_size, num_patches = image_class_embeds.shape[:2] pred_logits = torch.zeros((batch_size, num_patches, self.query_dim)).to(device) return (pred_logits, image_class_embeds) # Normalize image and text features image_class_embeds = image_class_embeds / (torch.linalg.norm(image_class_embeds, dim=-1, keepdim=True) + 1e-6) query_embeds = query_embeds / (torch.linalg.norm(query_embeds, dim=-1, keepdim=True) + 1e-6) # Get class predictions pred_logits = torch.einsum("...pd,...qd->...pq", image_class_embeds, query_embeds) # Apply a learnable shift and scale to logits logit_shift = self.logit_shift(image_embeds) logit_scale = self.logit_scale(image_embeds) logit_scale = self.elu(logit_scale) + 1 pred_logits = (pred_logits + logit_shift) * logit_scale if query_mask is not None: if query_mask.ndim > 1: query_mask = torch.unsqueeze(query_mask, dim=-2) pred_logits = pred_logits.to(torch.float64) pred_logits = torch.where(query_mask == 0, -1e6, pred_logits) pred_logits = pred_logits.to(torch.float32) return (pred_logits, image_class_embeds) class Owlv2ForObjectDetection(Owlv2PreTrainedModel): config_class = Owlv2Config def __init__(self, config: Owlv2Config): super().__init__(config) self.owlv2 = Owlv2Model(config) self.class_head = Owlv2ClassPredictionHead(config) self.box_head = Owlv2BoxPredictionHead(config) self.objectness_head = Owlv2BoxPredictionHead(config, out_dim=1) self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps) self.sigmoid = nn.Sigmoid() # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.normalize_grid_corner_coordinates def normalize_grid_corner_coordinates(self, feature_map: torch.FloatTensor): # Computes normalized xy corner coordinates from feature_map. if not feature_map.ndim == 4: raise ValueError("Expected input shape is [batch_size, num_patches, num_patches, hidden_dim]") device = feature_map.device num_patches = feature_map.shape[1] box_coordinates = np.stack( np.meshgrid(np.arange(1, num_patches + 1), np.arange(1, num_patches + 1)), axis=-1 ).astype(np.float32) box_coordinates /= np.array([num_patches, num_patches], np.float32) # Flatten (h, w, 2) -> (h*w, 2) box_coordinates = box_coordinates.reshape( box_coordinates.shape[0] * box_coordinates.shape[1], box_coordinates.shape[2] ) box_coordinates = torch.from_numpy(box_coordinates).to(device) return box_coordinates def objectness_predictor(self, image_features: torch.FloatTensor) -> torch.FloatTensor: """Predicts the probability that each image feature token is an object. Args: image_features (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_dim)`)): Features extracted from the image. Returns: Objectness scores. """ image_features = image_features.detach() objectness_logits = self.objectness_head(image_features) objectness_logits = objectness_logits[..., 0] return objectness_logits # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.compute_box_bias def compute_box_bias(self, feature_map: torch.FloatTensor) -> torch.FloatTensor: # The box center is biased to its position on the feature grid box_coordinates = self.normalize_grid_corner_coordinates(feature_map) box_coordinates = torch.clip(box_coordinates, 0.0, 1.0) # Unnormalize xy box_coord_bias = torch.log(box_coordinates + 1e-4) - torch.log1p(-box_coordinates + 1e-4) # The box size is biased to the patch size box_size = torch.full_like(box_coord_bias, 1.0 / feature_map.shape[-2]) box_size_bias = torch.log(box_size + 1e-4) - torch.log1p(-box_size + 1e-4) # Compute box bias box_bias = torch.cat([box_coord_bias, box_size_bias], dim=-1) return box_bias # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.box_predictor def box_predictor( self, image_feats: torch.FloatTensor, feature_map: torch.FloatTensor, ) -> torch.FloatTensor: """ Args: image_feats: Features extracted from the image, returned by the `image_text_embedder` method. feature_map: A spatial re-arrangement of image_features, also returned by the `image_text_embedder` method. Returns: pred_boxes: List of predicted boxes (cxcywh normalized to 0, 1) nested within a dictionary. """ # Bounding box detection head [batch_size, num_boxes, 4]. pred_boxes = self.box_head(image_feats) # Compute the location of each token on the grid and use it to compute a bias for the bbox prediction pred_boxes += self.compute_box_bias(feature_map) pred_boxes = self.sigmoid(pred_boxes) return pred_boxes # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.class_predictor def class_predictor( self, image_feats: torch.FloatTensor, query_embeds: Optional[torch.FloatTensor] = None, query_mask: Optional[torch.Tensor] = None, ) -> Tuple[torch.FloatTensor]: """ Args: image_feats: Features extracted from the `image_text_embedder`. query_embeds: Text query embeddings. query_mask: Must be provided with query_embeddings. A mask indicating which query embeddings are valid. """ (pred_logits, image_class_embeds) = self.class_head(image_feats, query_embeds, query_mask) return (pred_logits, image_class_embeds) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_text_embedder with owlvit->owlv2 def image_text_embedder( self, input_ids: torch.Tensor, pixel_values: torch.FloatTensor, attention_mask: torch.Tensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> Tuple[torch.FloatTensor]: # Encode text and image outputs = self.owlv2( pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, ) # Get image embeddings last_hidden_state = outputs.vision_model_output[0] image_embeds = self.owlv2.vision_model.post_layernorm(last_hidden_state) # Resize class token new_size = tuple(np.array(image_embeds.shape) - np.array((0, 1, 0))) class_token_out = torch.broadcast_to(image_embeds[:, :1, :], new_size) # Merge image embedding with class tokens image_embeds = image_embeds[:, 1:, :] * class_token_out image_embeds = self.layer_norm(image_embeds) # Resize to [batch_size, num_patches, num_patches, hidden_size] new_size = ( image_embeds.shape[0], int(np.sqrt(image_embeds.shape[1])), int(np.sqrt(image_embeds.shape[1])), image_embeds.shape[-1], ) image_embeds = image_embeds.reshape(new_size) text_embeds = outputs[-4] return (text_embeds, image_embeds, outputs) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_embedder with owlvit->owlv2, OwlViTModel->Owlv2Model def image_embedder( self, pixel_values: torch.FloatTensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> Tuple[torch.FloatTensor]: # Get Owlv2Model vision embeddings (same as CLIP) vision_outputs = self.owlv2.vision_model(pixel_values=pixel_values, return_dict=True) # Apply post_layernorm to last_hidden_state, return non-projected output last_hidden_state = vision_outputs[0] image_embeds = self.owlv2.vision_model.post_layernorm(last_hidden_state) # Resize class token new_size = tuple(np.array(image_embeds.shape) - np.array((0, 1, 0))) class_token_out = torch.broadcast_to(image_embeds[:, :1, :], new_size) # Merge image embedding with class tokens image_embeds = image_embeds[:, 1:, :] * class_token_out image_embeds = self.layer_norm(image_embeds) # Resize to [batch_size, num_patches, num_patches, hidden_size] new_size = ( image_embeds.shape[0], int(np.sqrt(image_embeds.shape[1])), int(np.sqrt(image_embeds.shape[1])), image_embeds.shape[-1], ) image_embeds = image_embeds.reshape(new_size) return (image_embeds, vision_outputs) # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.embed_image_query def embed_image_query( self, query_image_features: torch.FloatTensor, query_feature_map: torch.FloatTensor ) -> torch.FloatTensor: _, class_embeds = self.class_predictor(query_image_features) pred_boxes = self.box_predictor(query_image_features, query_feature_map) pred_boxes_as_corners = center_to_corners_format(pred_boxes) # Loop over query images best_class_embeds = [] best_box_indices = [] pred_boxes_device = pred_boxes_as_corners.device for i in range(query_image_features.shape[0]): each_query_box = torch.tensor([[0, 0, 1, 1]], device=pred_boxes_device) each_query_pred_boxes = pred_boxes_as_corners[i] ious, _ = box_iou(each_query_box, each_query_pred_boxes) # If there are no overlapping boxes, fall back to generalized IoU if torch.all(ious[0] == 0.0): ious = generalized_box_iou(each_query_box, each_query_pred_boxes) # Use an adaptive threshold to include all boxes within 80% of the best IoU iou_threshold = torch.max(ious) * 0.8 selected_inds = (ious[0] >= iou_threshold).nonzero() if selected_inds.numel(): selected_embeddings = class_embeds[i][selected_inds.squeeze(1)] mean_embeds = torch.mean(class_embeds[i], axis=0) mean_sim = torch.einsum("d,id->i", mean_embeds, selected_embeddings) best_box_ind = selected_inds[torch.argmin(mean_sim)] best_class_embeds.append(class_embeds[i][best_box_ind]) best_box_indices.append(best_box_ind) if best_class_embeds: query_embeds = torch.stack(best_class_embeds) box_indices = torch.stack(best_box_indices) else: query_embeds, box_indices = None, None return query_embeds, box_indices, pred_boxes @add_start_docstrings_to_model_forward(OWLV2_IMAGE_GUIDED_OBJECT_DETECTION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Owlv2ImageGuidedObjectDetectionOutput, config_class=Owlv2Config) def image_guided_detection( self, pixel_values: torch.FloatTensor, query_pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Owlv2ImageGuidedObjectDetectionOutput: r""" Returns: Examples: ```python >>> import requests >>> from PIL import Image >>> import torch >>> import numpy as np >>> from transformers import AutoProcessor, Owlv2ForObjectDetection >>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") >>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> query_url = "http://images.cocodataset.org/val2017/000000001675.jpg" >>> query_image = Image.open(requests.get(query_url, stream=True).raw) >>> inputs = processor(images=image, query_images=query_image, return_tensors="pt") >>> # forward pass >>> with torch.no_grad(): ... outputs = model.image_guided_detection(**inputs) >>> # Note: boxes need to be visualized on the padded, unnormalized image >>> # hence we'll set the target image sizes (height, width) based on that >>> def get_preprocessed_image(pixel_values): ... pixel_values = pixel_values.squeeze().numpy() ... unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None] ... unnormalized_image = (unnormalized_image * 255).astype(np.uint8) ... unnormalized_image = np.moveaxis(unnormalized_image, 0, -1) ... unnormalized_image = Image.fromarray(unnormalized_image) ... return unnormalized_image >>> unnormalized_image = get_preprocessed_image(inputs.pixel_values) >>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]]) >>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) >>> results = processor.post_process_image_guided_detection( ... outputs=outputs, threshold=0.9, nms_threshold=0.3, target_sizes=target_sizes ... ) >>> i = 0 # Retrieve predictions for the first image >>> boxes, scores = results[i]["boxes"], results[i]["scores"] >>> for box, score in zip(boxes, scores): ... box = [round(i, 2) for i in box.tolist()] ... print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}") Detected similar object with confidence 0.938 at location [490.96, 109.89, 821.09, 536.11] Detected similar object with confidence 0.959 at location [8.67, 721.29, 928.68, 732.78] Detected similar object with confidence 0.902 at location [4.27, 720.02, 941.45, 761.59] Detected similar object with confidence 0.985 at location [265.46, -58.9, 1009.04, 365.66] Detected similar object with confidence 1.0 at location [9.79, 28.69, 937.31, 941.64] Detected similar object with confidence 0.998 at location [869.97, 58.28, 923.23, 978.1] Detected similar object with confidence 0.985 at location [309.23, 21.07, 371.61, 932.02] Detected similar object with confidence 0.947 at location [27.93, 859.45, 969.75, 915.44] Detected similar object with confidence 0.996 at location [785.82, 41.38, 880.26, 966.37] Detected similar object with confidence 0.998 at location [5.08, 721.17, 925.93, 998.41] Detected similar object with confidence 0.969 at location [6.7, 898.1, 921.75, 949.51] Detected similar object with confidence 0.966 at location [47.16, 927.29, 981.99, 942.14] Detected similar object with confidence 0.924 at location [46.4, 936.13, 953.02, 950.78] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # Compute feature maps for the input and query images query_feature_map = self.image_embedder(pixel_values=query_pixel_values)[0] feature_map, vision_outputs = self.image_embedder( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) batch_size, num_patches, num_patches, hidden_dim = feature_map.shape image_feats = torch.reshape(feature_map, (batch_size, num_patches * num_patches, hidden_dim)) batch_size, num_patches, num_patches, hidden_dim = query_feature_map.shape query_image_feats = torch.reshape(query_feature_map, (batch_size, num_patches * num_patches, hidden_dim)) # Get top class embedding and best box index for each query image in batch query_embeds, best_box_indices, query_pred_boxes = self.embed_image_query(query_image_feats, query_feature_map) # Predict object classes [batch_size, num_patches, num_queries+1] (pred_logits, class_embeds) = self.class_predictor(image_feats=image_feats, query_embeds=query_embeds) # Predict object boxes target_pred_boxes = self.box_predictor(image_feats, feature_map) if not return_dict: output = ( feature_map, query_feature_map, target_pred_boxes, query_pred_boxes, pred_logits, class_embeds, vision_outputs.to_tuple(), ) output = tuple(x for x in output if x is not None) return output return Owlv2ImageGuidedObjectDetectionOutput( image_embeds=feature_map, query_image_embeds=query_feature_map, target_pred_boxes=target_pred_boxes, query_pred_boxes=query_pred_boxes, logits=pred_logits, class_embeds=class_embeds, text_model_output=None, vision_model_output=vision_outputs, ) @add_start_docstrings_to_model_forward(OWLV2_OBJECT_DETECTION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Owlv2ObjectDetectionOutput, config_class=Owlv2Config) def forward( self, input_ids: torch.Tensor, pixel_values: torch.FloatTensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Owlv2ObjectDetectionOutput: r""" Returns: Examples: ```python >>> import requests >>> from PIL import Image >>> import numpy as np >>> import torch >>> from transformers import AutoProcessor, Owlv2ForObjectDetection >>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") >>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> texts = [["a photo of a cat", "a photo of a dog"]] >>> inputs = processor(text=texts, images=image, return_tensors="pt") >>> # forward pass >>> with torch.no_grad(): ... outputs = model(**inputs) >>> # Note: boxes need to be visualized on the padded, unnormalized image >>> # hence we'll set the target image sizes (height, width) based on that >>> def get_preprocessed_image(pixel_values): ... pixel_values = pixel_values.squeeze().numpy() ... unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None] ... unnormalized_image = (unnormalized_image * 255).astype(np.uint8) ... unnormalized_image = np.moveaxis(unnormalized_image, 0, -1) ... unnormalized_image = Image.fromarray(unnormalized_image) ... return unnormalized_image >>> unnormalized_image = get_preprocessed_image(inputs.pixel_values) >>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]]) >>> # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores >>> results = processor.post_process_object_detection( ... outputs=outputs, threshold=0.2, target_sizes=target_sizes ... ) >>> i = 0 # Retrieve predictions for the first image for the corresponding text queries >>> text = texts[i] >>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"] >>> for box, score, label in zip(boxes, scores, labels): ... box = [round(i, 2) for i in box.tolist()] ... print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}") Detected a photo of a cat with confidence 0.614 at location [512.5, 35.08, 963.48, 557.02] Detected a photo of a cat with confidence 0.665 at location [10.13, 77.94, 489.93, 709.69] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # Embed images and text queries query_embeds, feature_map, outputs = self.image_text_embedder( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) # Text and vision model outputs text_outputs = outputs.text_model_output vision_outputs = outputs.vision_model_output batch_size, num_patches, num_patches, hidden_dim = feature_map.shape image_feats = torch.reshape(feature_map, (batch_size, num_patches * num_patches, hidden_dim)) # Reshape from [batch_size * max_text_queries, hidden_dim] -> [batch_size, max_text_queries, hidden_dim] max_text_queries = input_ids.shape[0] // batch_size query_embeds = query_embeds.reshape(batch_size, max_text_queries, query_embeds.shape[-1]) # If first token is 0, then this is a padded query [batch_size, num_queries]. input_ids = input_ids.reshape(batch_size, max_text_queries, input_ids.shape[-1]) query_mask = input_ids[..., 0] > 0 # Predict object classes [batch_size, num_patches, num_queries+1] (pred_logits, class_embeds) = self.class_predictor(image_feats, query_embeds, query_mask) # Predict objectness objectness_logits = self.objectness_predictor(image_feats) # Predict object boxes pred_boxes = self.box_predictor(image_feats, feature_map) if not return_dict: output = ( pred_logits, objectness_logits, pred_boxes, query_embeds, feature_map, class_embeds, text_outputs.to_tuple(), vision_outputs.to_tuple(), ) output = tuple(x for x in output if x is not None) return output return Owlv2ObjectDetectionOutput( image_embeds=feature_map, text_embeds=query_embeds, pred_boxes=pred_boxes, logits=pred_logits, objectness_logits=objectness_logits, class_embeds=class_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, )

transformers/src/transformers/models/owlv2/modeling_owlv2.py/0

{ "file_path": "transformers/src/transformers/models/owlv2/modeling_owlv2.py", "repo_id": "transformers", "token_count": 35173 }

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# coding=utf-8 # Copyright 2021, Google and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PEGASUS model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json", # See all PEGASUS models at https://huggingface.co/models?filter=pegasus } class PegasusConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PegasusModel`]. It is used to instantiate an PEGASUS model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the PEGASUS [google/pegasus-large](https://huggingface.co/google/pegasus-large) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50265): Vocabulary size of the PEGASUS model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PegasusModel`] or [`TFPegasusModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. max_position_embeddings (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models) forced_eos_token_id (`int`, *optional*, defaults to 1): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. Example: ```python >>> from transformers import PegasusConfig, PegasusModel >>> # Initializing a PEGASUS google/pegasus-large style configuration >>> configuration = PegasusConfig() >>> # Initializing a model (with random weights) from the google/pegasus-large style configuration >>> model = PegasusModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "pegasus" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=50265, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=0, scale_embedding=False, pad_token_id=0, eos_token_id=1, forced_eos_token_id=1, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) @property def num_attention_heads(self) -> int: return self.encoder_attention_heads @property def hidden_size(self) -> int: return self.d_model

transformers/src/transformers/models/pegasus/configuration_pegasus.py/0

{ "file_path": "transformers/src/transformers/models/pegasus/configuration_pegasus.py", "repo_id": "transformers", "token_count": 2966 }

344

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tokenization class for Perceiver.""" from typing import Dict, List, Optional, Tuple from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) class PerceiverTokenizer(PreTrainedTokenizer): """ Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: pad_token (`str`, *optional*, defaults to `"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. bos_token (`str`, *optional*, defaults to `"[BOS]"`): The BOS token (reserved in the vocab, but not actually used). eos_token (`str`, *optional*, defaults to `"[EOS]"`): The end of sequence token (reserved in the vocab, but not actually used). <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> mask_token (`str`, *optional*, defaults to `"[MASK]"`): The MASK token, useful for masked language modeling. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The CLS token (reserved in the vocab, but not actually used). sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from two sequences. """ model_input_names = ["input_ids", "attention_mask"] def __init__( self, pad_token="[PAD]", bos_token="[BOS]", eos_token="[EOS]", mask_token="[MASK]", cls_token="[CLS]", sep_token="[SEP]", model_max_length=2048, **kwargs, ) -> None: pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token self._utf_vocab_size = 2**8 # utf is 8 bits # Since these tokens are not part of the vocabulary, we manually add them self._added_tokens_decoder: Dict[str, int] = { 0: pad_token, 1: bos_token, 2: eos_token, 3: mask_token, 4: cls_token, 5: sep_token, } self._num_special_tokens = len(self._added_tokens_decoder) super().__init__( pad_token=pad_token, bos_token=bos_token, eos_token=eos_token, mask_token=mask_token, cls_token=cls_token, sep_token=sep_token, model_max_length=model_max_length, **kwargs, ) def get_vocab(self) -> Dict[str, int]: vocab = {} for i in range(self._utf_vocab_size): token = chr(i) vocab[token] = i + self._num_special_tokens vocab.update(self.added_tokens_encoder) return vocab @property def vocab_size(self): return self._utf_vocab_size def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) # normal case: some special tokens if token_ids_1 is None: return [1] + [0] * len(token_ids_0) + [1] return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks. A sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] else: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.sep_token_id] def _tokenize(self, text: str) -> List[str]: """Take as input a string and return a list of strings (tokens) for words/sub-words""" tokens = [chr(i) for i in text.encode("utf-8")] return tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if len(token) != 1: token_id = self.unk_token_id else: token_id = ord(token) + self._num_special_tokens return token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" token = chr(index - self._num_special_tokens) return token # TODO @ArthurZ refactor this as well.... def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" bstring = b"" for token in tokens: if token in self.added_tokens_encoder: tok_string = str(token).encode("utf-8") else: tok_string = bytes([ord(token)]) bstring += tok_string string = bstring.decode("utf-8", errors="replace") return string # PerceiverTokenizer has no vocab file def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: return ()

transformers/src/transformers/models/perceiver/tokenization_perceiver.py/0

{ "file_path": "transformers/src/transformers/models/perceiver/tokenization_perceiver.py", "repo_id": "transformers", "token_count": 3437 }

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Processor class for Pix2Struct. """ from typing import List, Optional, Union from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType class Pix2StructProcessor(ProcessorMixin): r""" Constructs a PIX2STRUCT processor which wraps a BERT tokenizer and PIX2STRUCT image processor into a single processor. [`Pix2StructProcessor`] offers all the functionalities of [`Pix2StructImageProcessor`] and [`T5TokenizerFast`]. See the docstring of [`~Pix2StructProcessor.__call__`] and [`~Pix2StructProcessor.decode`] for more information. Args: image_processor (`Pix2StructImageProcessor`): An instance of [`Pix2StructImageProcessor`]. The image processor is a required input. tokenizer (Union[`T5TokenizerFast`, `T5Tokenizer`]): An instance of ['T5TokenizerFast`] or ['T5Tokenizer`]. The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "Pix2StructImageProcessor" tokenizer_class = ("T5Tokenizer", "T5TokenizerFast") def __init__(self, image_processor, tokenizer): tokenizer.return_token_type_ids = False super().__init__(image_processor, tokenizer) def __call__( self, images=None, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, max_patches: Optional[int] = 2048, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_token_type_ids: bool = False, return_length: bool = False, verbose: bool = True, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs, ) -> BatchEncoding: """ This method uses [`Pix2StructImageProcessor.preprocess`] method to prepare image(s) for the model, and [`T5TokenizerFast.__call__`] to prepare text for the model. Please refer to the docstring of the above two methods for more information. """ if images is None and text is None: raise ValueError("You have to specify either images or text.") # Get only text if images is None and not self.image_processor.is_vqa: self.current_processor = self.tokenizer text_encoding = self.tokenizer( text=text, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_token_type_ids=return_token_type_ids, return_length=return_length, verbose=verbose, return_tensors=return_tensors, **kwargs, ) return text_encoding if not self.image_processor.is_vqa: # add pixel_values encoding_image_processor = self.image_processor( images, return_tensors=return_tensors, max_patches=max_patches, **kwargs ) else: # add pixel_values and bbox encoding_image_processor = self.image_processor( images, return_tensors=return_tensors, max_patches=max_patches, header_text=text, **kwargs ) if text is not None and not self.image_processor.is_vqa: text_encoding = self.tokenizer( text=text, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_token_type_ids=return_token_type_ids, return_length=return_length, verbose=verbose, return_tensors=return_tensors, **kwargs, ) if "attention_mask" in text_encoding: text_encoding["decoder_attention_mask"] = text_encoding.pop("attention_mask") if "input_ids" in text_encoding: text_encoding["decoder_input_ids"] = text_encoding.pop("input_ids") else: text_encoding = None if text_encoding is not None: encoding_image_processor.update(text_encoding) return encoding_image_processor def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to Pix2StructTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to Pix2StructTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

transformers/src/transformers/models/pix2struct/processing_pix2struct.py/0

{ "file_path": "transformers/src/transformers/models/pix2struct/processing_pix2struct.py", "repo_id": "transformers", "token_count": 2993 }

346

# coding=utf-8 # Copyright 2023 The Pop2Piano Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Pop2Piano model.""" import copy import math from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import CrossEntropyLoss from transformers.generation import GenerationConfig from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, replace_return_docstrings, ) from .configuration_pop2piano import Pop2PianoConfig logger = logging.get_logger(__name__) _load_pop2piano_layer_norm = True try: from apex.normalization import FusedRMSNorm _load_pop2piano_layer_norm = False logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of Pop2PianoLayerNorm") except ImportError: # using the normal Pop2PianoLayerNorm pass except Exception: logger.warning("Discovered apex but it failed to load, falling back to Pop2PianoLayerNorm") pass _CONFIG_FOR_DOC = "Pop2PianoConfig" _CHECKPOINT_FOR_DOC = "sweetcocoa/pop2piano" POP2PIANO_PRETRAINED_MODEL_ARCHIVE_LIST = [ "sweetcocoa/pop2piano", # See all Pop2Piano models at https://huggingface.co/models?filter=pop2piano ] POP2PIANO_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Pop2Piano is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [Pop2Pianp Training](./Pop2Piano#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Pop2Piano uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Does the same task as `inputs_embeds`. If `inputs_embeds` is not present but `input_features` is present then `input_features` will be considered as `inputs_embeds`. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->Pop2Piano class Pop2PianoLayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Construct a layernorm module in the Pop2Piano style. No bias and no subtraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # Pop2Piano uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states if not _load_pop2piano_layer_norm: Pop2PianoLayerNorm = FusedRMSNorm # noqa ALL_LAYERNORM_LAYERS.append(Pop2PianoLayerNorm) # Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->Pop2Piano,t5->pop2piano class Pop2PianoDenseActDense(nn.Module): def __init__(self, config: Pop2PianoConfig): super().__init__() self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_states = self.wi(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dropout(hidden_states) if ( isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8 ): hidden_states = hidden_states.to(self.wo.weight.dtype) hidden_states = self.wo(hidden_states) return hidden_states # Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->Pop2Piano class Pop2PianoDenseGatedActDense(nn.Module): def __init__(self, config: Pop2PianoConfig): super().__init__() self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_gelu = self.act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32. # See https://github.com/huggingface/transformers/issues/20287 # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None`` if ( isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8 ): hidden_states = hidden_states.to(self.wo.weight.dtype) hidden_states = self.wo(hidden_states) return hidden_states # Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->Pop2Piano class Pop2PianoLayerFF(nn.Module): def __init__(self, config: Pop2PianoConfig): super().__init__() if config.is_gated_act: self.DenseReluDense = Pop2PianoDenseGatedActDense(config) else: self.DenseReluDense = Pop2PianoDenseActDense(config) self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states): forwarded_states = self.layer_norm(hidden_states) forwarded_states = self.DenseReluDense(forwarded_states) hidden_states = hidden_states + self.dropout(forwarded_states) return hidden_states # Copied from transformers.models.t5.modeling_t5.T5Attention with T5->Pop2Piano,t5->pop2piano class Pop2PianoAttention(nn.Module): def __init__(self, config: Pop2PianoConfig, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.relative_attention_num_buckets = config.relative_attention_num_buckets self.relative_attention_max_distance = config.relative_attention_max_distance self.d_model = config.d_model self.key_value_proj_dim = config.d_kv self.n_heads = config.num_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.key_value_proj_dim # Mesh TensorFlow initialization to avoid scaling before softmax self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) self.pruned_heads = set() self.gradient_checkpointing = False def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads ) # Prune linear layers self.q = prune_linear_layer(self.q, index) self.k = prune_linear_layer(self.k, index) self.v = prune_linear_layer(self.v, index) self.o = prune_linear_layer(self.o, index, dim=1) # Update hyper params self.n_heads = self.n_heads - len(heads) self.inner_dim = self.key_value_proj_dim * self.n_heads self.pruned_heads = self.pruned_heads.union(heads) @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets //= 2 relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) # now relative_position is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = relative_position < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) ) relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) return relative_buckets def compute_bias(self, query_length, key_length, device=None): """Compute binned relative position bias""" if device is None: device = self.relative_attention_bias.weight.device context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] relative_position = memory_position - context_position # shape (query_length, key_length) relative_position_bucket = self._relative_position_bucket( relative_position, # shape (query_length, key_length) bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) return values def forward( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False, ): """ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). """ # Input is (batch_size, seq_length, dim) # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head) batch_size, seq_length = hidden_states.shape[:2] real_seq_length = seq_length if past_key_value is not None: if len(past_key_value) != 2: raise ValueError( f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states" ) real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length key_length = real_seq_length if key_value_states is None else key_value_states.shape[1] def shape(states): """projection""" return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) def unshape(states): """reshape""" return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) def project(hidden_states, proj_layer, key_value_states, past_key_value): """projects hidden states correctly to key/query states""" if key_value_states is None: # self-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(hidden_states)) elif past_key_value is None: # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) if past_key_value is not None: if key_value_states is None: # self-attn # (batch_size, n_heads, key_length, dim_per_head) hidden_states = torch.cat([past_key_value, hidden_states], dim=2) elif past_key_value.shape[2] != key_value_states.shape[1]: # checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) else: # cross-attn hidden_states = past_key_value return hidden_states # get query states query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, seq_length, dim_per_head) # get key/value states key_states = project( hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None ) value_states = project( hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None ) # compute scores scores = torch.matmul( query_states, key_states.transpose(3, 2) ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 if position_bias is None: if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype ) if self.gradient_checkpointing and self.training: position_bias.requires_grad = True else: position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device) # if key and values are already calculated # we want only the last query position bias if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :] if mask is not None: position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length) if self.pruned_heads: mask = torch.ones(position_bias.shape[1]) mask[list(self.pruned_heads)] = 0 position_bias_masked = position_bias[:, mask.bool()] else: position_bias_masked = position_bias scores += position_bias_masked attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as( scores ) # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.dropout( attn_weights, p=self.dropout, training=self.training ) # (batch_size, n_heads, seq_length, key_length) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = unshape(torch.matmul(attn_weights, value_states)) # (batch_size, seq_length, dim) attn_output = self.o(attn_output) present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) if output_attentions: outputs = outputs + (attn_weights,) return outputs # Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->Pop2Piano,t5->pop2piano class Pop2PianoLayerSelfAttention(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.SelfAttention = Pop2PianoAttention(config, has_relative_attention_bias=has_relative_attention_bias) self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.SelfAttention( normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = hidden_states + self.dropout(attention_output[0]) outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them return outputs # Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->Pop2Piano,t5->pop2piano class Pop2PianoLayerCrossAttention(nn.Module): def __init__(self, config): super().__init__() self.EncDecAttention = Pop2PianoAttention(config, has_relative_attention_bias=False) self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.EncDecAttention( normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions, ) layer_output = hidden_states + self.dropout(attention_output[0]) outputs = (layer_output,) + attention_output[1:] # add attentions if we output them return outputs # Copied from transformers.models.t5.modeling_t5.T5Block with T5->Pop2Piano,t5->pop2piano class Pop2PianoBlock(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.layer = nn.ModuleList() self.layer.append(Pop2PianoLayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)) if self.is_decoder: self.layer.append(Pop2PianoLayerCrossAttention(config)) self.layer.append(Pop2PianoLayerFF(config)) def forward( self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True, ): if past_key_value is not None: if not self.is_decoder: logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.") expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 if len(past_key_value) != expected_num_past_key_values: raise ValueError( f"There should be {expected_num_past_key_values} past states. " f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}" f"Got {len(past_key_value)} past key / value states" ) self_attn_past_key_value = past_key_value[:2] cross_attn_past_key_value = past_key_value[2:] else: self_attn_past_key_value, cross_attn_past_key_value = None, None self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present_key_value_state = self_attention_outputs[:2] attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) do_cross_attention = self.is_decoder and encoder_hidden_states is not None if do_cross_attention: # the actual query length is unknown for cross attention # if using past key value states. Need to inject it here if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2] else: query_length = None cross_attention_outputs = self.layer[1]( hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = cross_attention_outputs[0] # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) # Combine self attn and cross attn key value states if present_key_value_state is not None: present_key_value_state = present_key_value_state + cross_attention_outputs[1] # Keep cross-attention outputs and relative position weights attention_outputs = attention_outputs + cross_attention_outputs[2:] # Apply Feed Forward layer hidden_states = self.layer[-1](hidden_states) # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16: clamp_value = torch.where( torch.isinf(hidden_states).any(), torch.finfo(hidden_states.dtype).max - 1000, torch.finfo(hidden_states.dtype).max, ) hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs else: outputs = outputs + attention_outputs return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) class Pop2PianoPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Pop2PianoConfig base_model_prefix = "transformer" is_parallelizable = False supports_gradient_checkpointing = True _no_split_modules = ["Pop2PianoBlock"] _keep_in_fp32_modules = ["wo"] def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor # Used for testing weights initialization if isinstance(module, Pop2PianoLayerNorm): module.weight.data.fill_(factor * 1.0) elif isinstance(module, Pop2PianoConcatEmbeddingToMel): module.embedding.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, Pop2PianoForConditionalGeneration): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0) if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, Pop2PianoDenseActDense): # Mesh TensorFlow FF initialization # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, Pop2PianoDenseGatedActDense): module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: module.wi_0.bias.data.zero_() module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: module.wi_1.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, Pop2PianoAttention): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model key_value_proj_dim = self.config.d_kv n_heads = self.config.num_heads module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5)) def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id if decoder_start_token_id is None: raise ValueError( "self.model.config.decoder_start_token_id has to be defined. In Pop2Piano it is usually set to the pad_token_id." ) # shift inputs to the right if is_torch_fx_proxy(input_ids): # Item assignment is not supported natively for proxies. shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id) shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) else: shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids class Pop2PianoStack(Pop2PianoPreTrainedModel): # Copied from transformers.models.t5.modeling_t5.T5Stack.__init__ with T5->Pop2Piano,t5->pop2piano def __init__(self, config, embed_tokens=None): super().__init__(config) self.embed_tokens = embed_tokens self.is_decoder = config.is_decoder self.block = nn.ModuleList( [Pop2PianoBlock(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)] ) self.final_layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) # Initialize weights and apply final processing self.post_init() # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False # Copied from transformers.models.t5.modeling_t5.T5Stack.get_input_embeddings def get_input_embeddings(self): return self.embed_tokens # Copied from transformers.models.t5.modeling_t5.T5Stack.set_input_embeddings def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): use_cache = use_cache if use_cache is not None else self.config.use_cache output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds") if inputs_embeds is None: if self.embed_tokens is None: raise ValueError("You have to initialize the model with valid token embeddings") inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape # required mask seq length can be calculated via length of past mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length if use_cache is True: if not self.is_decoder: raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder") if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None: encoder_seq_length = encoder_hidden_states.shape[1] encoder_attention_mask = torch.ones( batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long ) # initialize past_key_values with `None` if past does not exist if past_key_values is None: past_key_values = [None] * len(self.block) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.num_layers) cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers) present_key_value_states = () if use_cache else None all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if (output_attentions and self.is_decoder) else None position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(inputs_embeds) for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)): layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.forward, hidden_states, extended_attention_mask, position_bias, encoder_hidden_states, encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, # past_key_value is always None with gradient checkpointing use_cache, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask, cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) # layer_outputs is a tuple with: # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, present_key_value_state = layer_outputs[:2] # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3] # append next layer key value states if use_cache: present_key_value_states = present_key_value_states + (present_key_value_state,) if output_attentions: all_attentions = all_attentions + (layer_outputs[3],) if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],) hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) class Pop2PianoConcatEmbeddingToMel(nn.Module): """Embedding Matrix for `composer` tokens.""" def __init__(self, config): super().__init__() self.embedding = nn.Embedding(num_embeddings=config.composer_vocab_size, embedding_dim=config.d_model) def forward(self, feature, index_value, embedding_offset): index_shifted = index_value - embedding_offset composer_embedding = self.embedding(index_shifted).unsqueeze(1) inputs_embeds = torch.cat([composer_embedding, feature], dim=1) return inputs_embeds Pop2Piano_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`Pop2PianoConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings("""Pop2Piano Model with a `language modeling` head on top.""", Pop2Piano_START_DOCSTRING) class Pop2PianoForConditionalGeneration(Pop2PianoPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: Pop2PianoConfig): super().__init__(config) self.config = config self.model_dim = config.d_model self.shared = nn.Embedding(config.vocab_size, config.d_model) self.mel_conditioner = Pop2PianoConcatEmbeddingToMel(config) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = Pop2PianoStack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = Pop2PianoStack(decoder_config, self.shared) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def get_output_embeddings(self): return self.lm_head def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def get_mel_conditioner_outputs( self, input_features: torch.FloatTensor, composer: str, generation_config: GenerationConfig, attention_mask: torch.FloatTensor = None, ): """ This method is used to concatenate mel conditioner tokens at the front of the input_features in order to control the type of MIDI token generated by the model. Args: input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): input features extracted from the feature extractor. composer (`str`): composer token which determines the type of MIDI tokens to be generated. generation_config (`~generation.GenerationConfig`): The generation is used to get the composer-feature_token pair. attention_mask (``, *optional*): For batched generation `input_features` are padded to have the same shape across all examples. `attention_mask` helps to determine which areas were padded and which were not. - 1 for tokens that are **not padded**, - 0 for tokens that are **padded**. """ composer_to_feature_token = generation_config.composer_to_feature_token if composer not in composer_to_feature_token.keys(): raise ValueError( f"Please choose a composer from {list(composer_to_feature_token.keys())}. Composer received - {composer}" ) composer_value = composer_to_feature_token[composer] composer_value = torch.tensor(composer_value, device=self.device) composer_value = composer_value.repeat(input_features.shape[0]) embedding_offset = min(composer_to_feature_token.values()) input_features = self.mel_conditioner( feature=input_features, index_value=composer_value, embedding_offset=embedding_offset, ) if attention_mask is not None: input_features[~attention_mask[:, 0].bool()] = 0.0 # since self.mel_conditioner adds a new array at the front of inputs_embeds we need to do the same for attention_mask to keep the shapes same attention_mask = torch.concatenate([attention_mask[:, 0].view(-1, 1), attention_mask], axis=1) return input_features, attention_mask return input_features, None @add_start_docstrings_to_model_forward(POP2PIANO_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, input_features: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: """ use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is not None and input_features is not None: raise ValueError("Both `inputs_embeds` and `input_features` received! Please provide only one of them") elif input_features is not None and inputs_embeds is None: inputs_embeds = input_features # Encode if needed (training, first prediction pass) if encoder_outputs is None: # Convert encoder inputs in embeddings if needed encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) hidden_states = encoder_outputs[0] if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: # get decoder inputs from shifting lm labels to the right decoder_input_ids = self._shift_right(labels) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = decoder_outputs[0] if self.config.tie_word_embeddings: # Rescale output before projecting on vocab # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 sequence_output = sequence_output * (self.model_dim**-0.5) lm_logits = self.lm_head(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-100) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) if not return_dict: output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs return ((loss,) + output) if loss is not None else output return Seq2SeqLMOutput( loss=loss, logits=lm_logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @torch.no_grad() def generate( self, input_features, attention_mask=None, composer="composer1", generation_config=None, **kwargs, ): """ Generates token ids for midi outputs. <Tip warning={true}> Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the model's default generation configuration. You can override any `generation_config` by passing the corresponding parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. For an overview of generation strategies and code examples, check out the [following guide](./generation_strategies). </Tip> Parameters: input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): This is the featurized version of audio generated by `Pop2PianoFeatureExtractor`. attention_mask: For batched generation `input_features` are padded to have the same shape across all examples. `attention_mask` helps to determine which areas were padded and which were not. - 1 for tokens that are **not padded**, - 0 for tokens that are **padded**. composer (`str`, *optional*, defaults to `"composer1"`): This value is passed to `Pop2PianoConcatEmbeddingToMel` to generate different embeddings for each `"composer"`. Please make sure that the composet value is present in `composer_to_feature_token` in `generation_config`. For an example please see https://huggingface.co/sweetcocoa/pop2piano/blob/main/generation_config.json . generation_config (`~generation.GenerationConfig`, *optional*): The generation configuration to be used as base parametrization for the generation call. `**kwargs` passed to generate matching the attributes of `generation_config` will override them. If `generation_config` is not provided, the default will be used, which had the following loading priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s default values, whose documentation should be checked to parameterize generation. kwargs: Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. Return: [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. Since Pop2Piano is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible [`~utils.ModelOutput`] types are: - [`~generation.GenerateEncoderDecoderOutput`], - [`~generation.GenerateBeamEncoderDecoderOutput`] """ if generation_config is None: generation_config = self.generation_config generation_config.update(**kwargs) # check for composer_to_feature_token if not hasattr(generation_config, "composer_to_feature_token"): raise ValueError( "`composer_to_feature_token` was not found! Please refer to " "https://huggingface.co/sweetcocoa/pop2piano/blob/main/generation_config.json" "and parse a dict like that." ) if len(generation_config.composer_to_feature_token) != self.config.composer_vocab_size: raise ValueError( "config.composer_vocab_size must be same as the number of keys in " f"generation_config.composer_to_feature_token! " f"Found {self.config.composer_vocab_size} vs {len(generation_config.composer_to_feature_token)}." ) # to control the variation of generated MIDI tokens we concatenate mel-conditioner tokens(which depends on composer_token) # at the front of input_features. input_features, attention_mask = self.get_mel_conditioner_outputs( input_features=input_features, attention_mask=attention_mask, composer=composer, generation_config=generation_config, ) return super().generate( inputs=None, inputs_embeds=input_features, attention_mask=attention_mask, generation_config=generation_config, **kwargs, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs, ): # cut decoder_input_ids if past is used if past_key_values is not None: input_ids = input_ids[:, -1:] return { "decoder_input_ids": input_ids, "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) def _reorder_cache(self, past_key_values, beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder if past_key_values is None: logger.warning("You might want to consider setting `use_cache=True` to speed up decoding") return past_key_values reordered_decoder_past = () for layer_past_states in past_key_values: # get the correct batch idx from layer past batch dim # batch dim of `past` is at 2nd position reordered_layer_past_states = () for layer_past_state in layer_past_states: # need to set correct `past` for each of the four key / value states reordered_layer_past_states = reordered_layer_past_states + ( layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)), ) if reordered_layer_past_states[0].shape != layer_past_states[0].shape: raise ValueError( f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched" ) if len(reordered_layer_past_states) != len(layer_past_states): raise ValueError( f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched" ) reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,) return reordered_decoder_past

transformers/src/transformers/models/pop2piano/modeling_pop2piano.py/0

{ "file_path": "transformers/src/transformers/models/pop2piano/modeling_pop2piano.py", "repo_id": "transformers", "token_count": 28646 }

347

# Copyright 2024 The Qwen Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_qwen2": ["QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Qwen2Config"], "tokenization_qwen2": ["Qwen2Tokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_qwen2_fast"] = ["Qwen2TokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_qwen2"] = [ "Qwen2ForCausalLM", "Qwen2Model", "Qwen2PreTrainedModel", "Qwen2ForSequenceClassification", ] if TYPE_CHECKING: from .configuration_qwen2 import QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP, Qwen2Config from .tokenization_qwen2 import Qwen2Tokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_qwen2_fast import Qwen2TokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_qwen2 import ( Qwen2ForCausalLM, Qwen2ForSequenceClassification, Qwen2Model, Qwen2PreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/qwen2/__init__.py/0

{ "file_path": "transformers/src/transformers/models/qwen2/__init__.py", "repo_id": "transformers", "token_count": 894 }

348

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert RemBERT checkpoint.""" import argparse import torch from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert from transformers.utils import logging logging.set_verbosity_info() def convert_rembert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path): # Initialise PyTorch model config = RemBertConfig.from_json_file(bert_config_file) print("Building PyTorch model from configuration: {}".format(str(config))) model = RemBertModel(config) # Load weights from tf checkpoint load_tf_weights_in_rembert(model, config, tf_checkpoint_path) # Save pytorch-model print("Save PyTorch model to {}".format(pytorch_dump_path)) torch.save(model.state_dict(), pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--rembert_config_file", default=None, type=str, required=True, help=( "The config json file corresponding to the pre-trained RemBERT model. \n" "This specifies the model architecture." ), ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) args = parser.parse_args() convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)

transformers/src/transformers/models/rembert/convert_rembert_tf_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/rembert/convert_rembert_tf_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 775 }

349

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 RoBERTa model.""" from __future__ import annotations import math import warnings from typing import Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFBaseModelOutputWithPoolingAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFMaskedLMOutput, TFMultipleChoiceModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFMaskedLanguageModelingLoss, TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFTokenClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_roberta import RobertaConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "roberta-base" _CONFIG_FOR_DOC = "RobertaConfig" TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "roberta-base", "roberta-large", "roberta-large-mnli", "distilroberta-base", # See all RoBERTa models at https://huggingface.co/models?filter=roberta ] class TFRobertaEmbeddings(keras.layers.Layer): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__(self, config, **kwargs): super().__init__(**kwargs) self.padding_idx = 1 self.config = config self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape=None): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.config.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.config.type_vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: input_ids: tf.Tensor Returns: tf.Tensor """ mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype) incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask return incremental_indices + self.padding_idx def call( self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, past_key_values_length=0, training=False, ): """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: check_embeddings_within_bounds(input_ids, self.config.vocab_size) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = self.create_position_ids_from_input_ids( input_ids=input_ids, past_key_values_length=past_key_values_length ) else: position_ids = tf.expand_dims( tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0 ) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Roberta class TFRobertaPooler(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Roberta class TFRobertaSelfAttention(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder self.config = config def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) key_layer = tf.concat([past_key_value[0], key_layer], axis=2) value_layer = tf.concat([past_key_value[1], value_layer], axis=2) else: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFRobertaModel call() function) attention_scores = tf.add(attention_scores, attention_mask) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Roberta class TFRobertaSelfOutput(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Roberta class TFRobertaAttention(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFRobertaSelfAttention(config, name="self") self.dense_output = TFRobertaSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) # add attentions (possibly with past_key_value) if we output them outputs = (attention_output,) + self_outputs[1:] return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attention", None) is not None: with tf.name_scope(self.self_attention.name): self.self_attention.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Roberta class TFRobertaIntermediate(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Roberta class TFRobertaOutput(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Roberta class TFRobertaLayer(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.attention = TFRobertaAttention(config, name="attention") self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = TFRobertaAttention(config, name="crossattention") self.intermediate = TFRobertaIntermediate(config, name="intermediate") self.bert_output = TFRobertaOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_value: Tuple[tf.Tensor] | None, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( input_tensor=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=self_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( input_tensor=attention_output, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value intermediate_output = self.intermediate(hidden_states=attention_output) layer_output = self.bert_output( hidden_states=intermediate_output, input_tensor=attention_output, training=training ) outputs = (layer_output,) + outputs # add attentions if we output them # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "bert_output", None) is not None: with tf.name_scope(self.bert_output.name): self.bert_output.build(None) if getattr(self, "crossattention", None) is not None: with tf.name_scope(self.crossattention.name): self.crossattention.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Roberta class TFRobertaEncoder(keras.layers.Layer): def __init__(self, config: RobertaConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFRobertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_values: Tuple[Tuple[tf.Tensor]] | None, use_cache: Optional[bool], output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) past_key_value = past_key_values[i] if past_key_values is not None else None layer_outputs = layer_module( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFRobertaMainLayer(keras.layers.Layer): config_class = RobertaConfig def __init__(self, config, add_pooling_layer=True, **kwargs): super().__init__(**kwargs) self.config = config self.is_decoder = config.is_decoder self.num_hidden_layers = config.num_hidden_layers self.initializer_range = config.initializer_range self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.encoder = TFRobertaEncoder(config, name="encoder") self.pooler = TFRobertaPooler(config, name="pooler") if add_pooling_layer else None # The embeddings must be the last declaration in order to follow the weights order self.embeddings = TFRobertaEmbeddings(config, name="embeddings") # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings def set_input_embeddings(self, value: tf.Variable): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: if not self.config.is_decoder: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape if past_key_values is None: past_key_values_length = 0 past_key_values = [None] * len(self.encoder.layer) else: past_key_values_length = shape_list(past_key_values[0][0])[-2] if attention_mask is None: attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1) if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, training=training, ) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) mask_seq_length = seq_length + past_key_values_length # Copied from `modeling_tf_t5.py` # Provided a padding mask of dimensions [batch_size, mask_seq_length] # - if the model is a decoder, apply a causal mask in addition to the padding mask # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] if self.is_decoder: seq_ids = tf.range(mask_seq_length) causal_mask = tf.less_equal( tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)), seq_ids[None, :, None], ) causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype) extended_attention_mask = causal_mask * attention_mask[:, None, :] attention_mask_shape = shape_list(extended_attention_mask) extended_attention_mask = tf.reshape( extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2]) ) if past_key_values[0] is not None: # attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length] extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :] else: extended_attention_mask = tf.reshape( attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]) ) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) # Copied from `modeling_tf_t5.py` with -1e9 -> -10000 if self.is_decoder and encoder_attention_mask is not None: # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=extended_attention_mask.dtype) num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) if num_dims_encoder_attention_mask == 3: encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] if num_dims_encoder_attention_mask == 2: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, # tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( hidden_states=embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None if not return_dict: return ( sequence_output, pooled_output, ) + encoder_outputs[1:] return TFBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "pooler", None) is not None: with tf.name_scope(self.pooler.name): self.pooler.build(None) if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) class TFRobertaPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RobertaConfig base_model_prefix = "roberta" ROBERTA_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`RobertaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ROBERTA_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", ROBERTA_START_DOCSTRING, ) class TFRobertaModel(TFRobertaPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, name="roberta") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[Tuple, TFBaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation """ outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta", None) is not None: with tf.name_scope(self.roberta.name): self.roberta.build(None) class TFRobertaLMHead(keras.layers.Layer): """Roberta Head for masked language modeling.""" def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.config = config self.hidden_size = config.hidden_size self.dense = keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm") self.act = get_tf_activation("gelu") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = input_embeddings def build(self, input_shape=None): self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "layer_norm", None) is not None: with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.config.hidden_size]) def get_output_embeddings(self): return self.decoder def set_output_embeddings(self, value): self.decoder.weight = value self.decoder.vocab_size = shape_list(value)[0] def get_bias(self): return {"bias": self.bias} def set_bias(self, value): self.bias = value["bias"] self.config.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.layer_norm(hidden_states) # project back to size of vocabulary with bias seq_length = shape_list(tensor=hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top.""", ROBERTA_START_DOCSTRING) class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head") def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="<mask>", expected_output="' Paris'", expected_loss=0.1, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] prediction_scores = self.lm_head(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta", None) is not None: with tf.name_scope(self.roberta.name): self.roberta.build(None) if getattr(self, "lm_head", None) is not None: with tf.name_scope(self.lm_head.name): self.lm_head.build(None) class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"] def __init__(self, config: RobertaConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) if not config.is_decoder: logger.warning("If you want to use `TFRobertaLMHeadModel` as a standalone, add `is_decoder=True.`") self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.lm_head = TFRobertaLMHead(config, input_embeddings=self.roberta.embeddings, name="lm_head") def get_lm_head(self): return self.lm_head def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.lm_head.name # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.prepare_inputs_for_generation def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = tf.ones(input_shape) # cut decoder_input_ids if past is used if past_key_values is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values} @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]: r""" encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. """ outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.lm_head(hidden_states=sequence_output, training=training) loss = None if labels is not None: # shift labels to the left and cut last logit token shifted_logits = logits[:, :-1] labels = labels[:, 1:] loss = self.hf_compute_loss(labels=labels, logits=shifted_logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta", None) is not None: with tf.name_scope(self.roberta.name): self.roberta.build(None) if getattr(self, "lm_head", None) is not None: with tf.name_scope(self.lm_head.name): self.lm_head.build(None) class TFRobertaClassificationHead(keras.layers.Layer): """Head for sentence-level classification tasks.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = keras.layers.Dropout(classifier_dropout) self.out_proj = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj" ) self.config = config def call(self, features, training=False): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x, training=training) x = self.dense(x) x = self.dropout(x, training=training) x = self.out_proj(x) return x def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "out_proj", None) is not None: with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.classifier = TFRobertaClassificationHead(config, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="cardiffnlp/twitter-roberta-base-emotion", output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="'optimism'", expected_loss=0.08, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.classifier(sequence_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta", None) is not None: with tf.name_scope(self.roberta.name): self.roberta.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.roberta = TFRobertaMainLayer(config, name="roberta") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.classifier = keras.layers.Dense( 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ if input_ids is not None: num_choices = shape_list(input_ids)[1] seq_length = shape_list(input_ids)[2] else: num_choices = shape_list(inputs_embeds)[1] seq_length = shape_list(inputs_embeds)[2] flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None outputs = self.roberta( flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output, training=training) logits = self.classifier(pooled_output) reshaped_logits = tf.reshape(logits, (-1, num_choices)) loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta", None) is not None: with tf.name_scope(self.roberta.name): self.roberta.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ROBERTA_START_DOCSTRING, ) class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = keras.layers.Dropout(classifier_dropout) self.classifier = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="ydshieh/roberta-large-ner-english", output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", expected_loss=0.01, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output, training=training) logits = self.classifier(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta", None) is not None: with tf.name_scope(self.roberta.name): self.roberta.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROBERTA_START_DOCSTRING, ) class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnsweringLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta") self.qa_outputs = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="ydshieh/roberta-base-squad2", output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="' puppet'", expected_loss=0.86, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ outputs = self.roberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions} labels["end_position"] = end_positions loss = self.hf_compute_loss(labels, (start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "roberta", None) is not None: with tf.name_scope(self.roberta.name): self.roberta.build(None) if getattr(self, "qa_outputs", None) is not None: with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])

transformers/src/transformers/models/roberta/modeling_tf_roberta.py/0

{ "file_path": "transformers/src/transformers/models/roberta/modeling_tf_roberta.py", "repo_id": "transformers", "token_count": 34373 }

350

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax RoFormer model.""" from typing import Callable, Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxMaskedLMOutput, FlaxMultipleChoiceModelOutput, FlaxQuestionAnsweringModelOutput, FlaxSequenceClassifierOutput, FlaxTokenClassifierOutput, ) from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, overwrite_call_docstring from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_roformer import RoFormerConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "junnyu/roformer_chinese_base" _CONFIG_FOR_DOC = "RoFormerConfig" FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "junnyu/roformer_chinese_small", "junnyu/roformer_chinese_base", "junnyu/roformer_chinese_char_small", "junnyu/roformer_chinese_char_base", "junnyu/roformer_small_discriminator", "junnyu/roformer_small_generator", # See all RoFormer models at https://huggingface.co/models?filter=roformer ] ROFORMER_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a [flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`RoFormerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ ROFORMER_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`numpy.ndarray` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`numpy.ndarray` of shape `({0})`, `optional): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.marian.modeling_flax_marian.create_sinusoidal_positions def create_sinusoidal_positions(n_pos, dim): position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]) sentinel = dim // 2 + dim % 2 out = np.zeros_like(position_enc) out[:, 0:sentinel] = np.sin(position_enc[:, 0::2]) out[:, sentinel:] = np.cos(position_enc[:, 1::2]) return jnp.array(out) class FlaxRoFormerEmbeddings(nn.Module): """Construct the embeddings from word and token_type embeddings.""" config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.word_embeddings = nn.Embed( self.config.vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.token_type_embeddings = nn.Embed( self.config.type_vocab_size, self.config.hidden_size, embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, input_ids, token_type_ids, attention_mask, deterministic: bool = True): # Embed inputs_embeds = self.word_embeddings(input_ids.astype("i4")) token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4")) # Sum all embeddings hidden_states = inputs_embeds + token_type_embeddings # Layer Norm hidden_states = self.LayerNorm(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states class FlaxRoFormerSelfAttention(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: if self.config.hidden_size % self.config.num_attention_heads != 0: raise ValueError( "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` " " : {self.config.num_attention_heads}" ) self.query = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.key = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.value = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.rotary_value = self.config.rotary_value def __call__( self, hidden_states, attention_mask, sinusoidal_pos, layer_head_mask, deterministic=True, output_attentions: bool = False, ): head_dim = self.config.hidden_size // self.config.num_attention_heads query_states = self.query(hidden_states).reshape( hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim) ) value_states = self.value(hidden_states).reshape( hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim) ) key_states = self.key(hidden_states).reshape( hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim) ) if sinusoidal_pos is not None: if self.rotary_value: query_states, key_states, value_states = self.apply_rotary_position_embeddings( sinusoidal_pos, query_states, key_states, value_states ) else: query_states, key_states = self.apply_rotary_position_embeddings( sinusoidal_pos, query_states, key_states ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.config.attention_probs_dropout_prob > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_probs_dropout_prob, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) # Mask heads if we want to if layer_head_mask is not None: attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,)) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs @staticmethod def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, value_layer=None): sin, cos = sinusoidal_pos.split(2, axis=-1) sin_pos = jnp.stack([sin, sin], axis=-1).reshape(sinusoidal_pos.shape) cos_pos = jnp.stack([cos, cos], axis=-1).reshape(sinusoidal_pos.shape) def rotate_layer(layer, sin_pos, cos_pos): rotate_half_layer = jnp.stack([-layer[..., 1::2], layer[..., ::2]], axis=-1).reshape(layer.shape) rotary_matrix_cos = jnp.einsum("bslh,...sh->bslh", layer, cos_pos) rotary_matrix_sin = jnp.einsum("bslh,...sh->bslh", rotate_half_layer, sin_pos) return rotary_matrix_cos + rotary_matrix_sin query_layer = rotate_layer(query_layer, sin_pos, cos_pos) key_layer = rotate_layer(key_layer, sin_pos, cos_pos) if value_layer is not None: value_layer = rotate_layer(value_layer, sin_pos, cos_pos) return query_layer, key_layer, value_layer return query_layer, key_layer # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->RoFormer class FlaxRoFormerSelfOutput(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) def __call__(self, hidden_states, input_tensor, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class FlaxRoFormerAttention(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.self = FlaxRoFormerSelfAttention(self.config, dtype=self.dtype) self.output = FlaxRoFormerSelfOutput(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, sinusoidal_pos, layer_head_mask, deterministic=True, output_attentions: bool = False, ): # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length) # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length) attn_outputs = self.self( hidden_states, attention_mask, sinusoidal_pos, layer_head_mask=layer_head_mask, deterministic=deterministic, output_attentions=output_attentions, ) attn_output = attn_outputs[0] hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attn_outputs[1],) return outputs # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->RoFormer class FlaxRoFormerIntermediate(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.activation = ACT2FN[self.config.hidden_act] def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->RoFormer class FlaxRoFormerOutput(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_output, deterministic: bool = True): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.LayerNorm(hidden_states + attention_output) return hidden_states class FlaxRoFormerLayer(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.attention = FlaxRoFormerAttention(self.config, dtype=self.dtype) self.intermediate = FlaxRoFormerIntermediate(self.config, dtype=self.dtype) self.output = FlaxRoFormerOutput(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, sinusiodal_pos, layer_head_mask, deterministic: bool = True, output_attentions: bool = False, ): attention_outputs = self.attention( hidden_states, attention_mask, sinusiodal_pos, layer_head_mask=layer_head_mask, deterministic=deterministic, output_attentions=output_attentions, ) attention_output = attention_outputs[0] hidden_states = self.intermediate(attention_output) hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic) outputs = (hidden_states,) if output_attentions: outputs += (attention_outputs[1],) return outputs class FlaxRoFormerLayerCollection(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxRoFormerLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask, sinusoidal_pos, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None # Check if head_mask has a correct number of layers specified if desired if head_mask is not None: if head_mask.shape[0] != (len(self.layers)): raise ValueError( f"The head_mask should be specified for {len(self.layers)} layers, but it is for " f" {head_mask.shape[0]}." ) for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, sinusoidal_pos, layer_head_mask=head_mask[i] if head_mask is not None else None, deterministic=deterministic, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states,) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxRoFormerEncoder(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.embed_positions = create_sinusoidal_positions( self.config.max_position_embeddings, self.config.hidden_size // self.config.num_attention_heads ) self.layer = FlaxRoFormerLayerCollection(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): sinusoidal_pos = self.embed_positions[: hidden_states.shape[1], :] return self.layer( hidden_states, attention_mask, sinusoidal_pos, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPredictionHeadTransform with Bert->RoFormer class FlaxRoFormerPredictionHeadTransform(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype) self.activation = ACT2FN[self.config.hidden_act] self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) return self.LayerNorm(hidden_states) # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLMPredictionHead with Bert->RoFormer class FlaxRoFormerLMPredictionHead(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros def setup(self): self.transform = FlaxRoFormerPredictionHeadTransform(self.config, dtype=self.dtype) self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False) self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,)) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.transform(hidden_states) if shared_embedding is not None: hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: hidden_states = self.decoder(hidden_states) bias = jnp.asarray(self.bias, self.dtype) hidden_states += bias return hidden_states # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyMLMHead with Bert->RoFormer class FlaxRoFormerOnlyMLMHead(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.predictions = FlaxRoFormerLMPredictionHead(self.config, dtype=self.dtype) def __call__(self, hidden_states, shared_embedding=None): hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding) return hidden_states class FlaxRoFormerClassificationHead(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense( self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.out_proj = nn.Dense( self.config.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.activation = ACT2FN[self.config.hidden_act] def __call__(self, hidden_states, deterministic=True): hidden_states = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS]) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.out_proj(hidden_states) return hidden_states class FlaxRoFormerPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RoFormerConfig base_model_prefix = "roformer" module_class: nn.Module = None def __init__( self, config: RoFormerConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") token_type_ids = jnp.zeros_like(input_ids) attention_mask = jnp.ones_like(input_ids) head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, input_ids, attention_mask, token_type_ids, head_mask, return_dict=False )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) def __call__( self, input_ids, attention_mask=None, token_type_ids=None, head_mask=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # init input tensors if not passed if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if head_mask is None: head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), jnp.array(head_mask, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) class FlaxRoFormerModule(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.embeddings = FlaxRoFormerEmbeddings(self.config, dtype=self.dtype) self.encoder = FlaxRoFormerEncoder(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): hidden_states = self.embeddings(input_ids, token_type_ids, attention_mask, deterministic=deterministic) outputs = self.encoder( hidden_states, attention_mask, head_mask=head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if not return_dict: return (hidden_states,) + outputs[1:] return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( "The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top.", ROFORMER_START_DOCSTRING, ) class FlaxRoFormerModel(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerModule append_call_sample_docstring(FlaxRoFormerModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC) class FlaxRoFormerForMaskedLMModule(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype) self.cls = FlaxRoFormerOnlyMLMHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roformer( input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.roformer.variables["params"]["embeddings"]["word_embeddings"]["embedding"] else: shared_embedding = None # Compute the prediction scores logits = self.cls(hidden_states, shared_embedding=shared_embedding) if not return_dict: return (logits,) + outputs[1:] return FlaxMaskedLMOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings("""RoFormer Model with a `language modeling` head on top.""", ROFORMER_START_DOCSTRING) class FlaxRoFormerForMaskedLM(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForMaskedLMModule append_call_sample_docstring( FlaxRoFormerForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC, mask="<mask>", ) class FlaxRoFormerForSequenceClassificationModule(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype) self.classifier = FlaxRoFormerClassificationHead(config=self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roformer( input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output, deterministic=deterministic) if not return_dict: return (logits,) + outputs[1:] return FlaxSequenceClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROFORMER_START_DOCSTRING, ) class FlaxRoFormerForSequenceClassification(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForSequenceClassificationModule append_call_sample_docstring( FlaxRoFormerForSequenceClassification, _CHECKPOINT_FOR_DOC, FlaxSequenceClassifierOutput, _CONFIG_FOR_DOC, ) class FlaxRoFormerForMultipleChoiceModule(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(1, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): num_choices = input_ids.shape[1] input_ids = input_ids.reshape(-1, input_ids.shape[-1]) attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) # Model outputs = self.roformer( input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # Equivalent to sequence_summary call in the PyTorch implementation hidden_states = outputs[0] pooled_output = hidden_states[:, -1] pooled_output = self.dropout(pooled_output, deterministic=deterministic) logits = self.classifier(pooled_output) reshaped_logits = logits.reshape(-1, num_choices) if not return_dict: return (reshaped_logits,) + outputs[2:] return FlaxMultipleChoiceModelOutput( logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ RoFormer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROFORMER_START_DOCSTRING, ) class FlaxRoFormerForMultipleChoice(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForMultipleChoiceModule overwrite_call_docstring( FlaxRoFormerForMultipleChoice, ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) append_call_sample_docstring( FlaxRoFormerForMultipleChoice, _CHECKPOINT_FOR_DOC, FlaxMultipleChoiceModelOutput, _CONFIG_FOR_DOC, ) class FlaxRoFormerForTokenClassificationModule(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob) self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roformer( input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.classifier(hidden_states) if not return_dict: return (logits,) + outputs[1:] return FlaxTokenClassifierOutput( logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ RoFormer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, ROFORMER_START_DOCSTRING, ) class FlaxRoFormerForTokenClassification(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForTokenClassificationModule append_call_sample_docstring( FlaxRoFormerForTokenClassification, _CHECKPOINT_FOR_DOC, FlaxTokenClassifierOutput, _CONFIG_FOR_DOC, ) class FlaxRoFormerForQuestionAnsweringModule(nn.Module): config: RoFormerConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype) self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype) def __call__( self, input_ids, attention_mask, token_type_ids, head_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # Model outputs = self.roformer( input_ids, attention_mask, token_type_ids, head_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.qa_outputs(hidden_states) start_logits, end_logits = logits.split(self.config.num_labels, axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: return (start_logits, end_logits) + outputs[1:] return FlaxQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ RoFormer Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROFORMER_START_DOCSTRING, ) class FlaxRoFormerForQuestionAnswering(FlaxRoFormerPreTrainedModel): module_class = FlaxRoFormerForQuestionAnsweringModule append_call_sample_docstring( FlaxRoFormerForQuestionAnswering, _CHECKPOINT_FOR_DOC, FlaxQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, )

transformers/src/transformers/models/roformer/modeling_flax_roformer.py/0

{ "file_path": "transformers/src/transformers/models/roformer/modeling_flax_roformer.py", "repo_id": "transformers", "token_count": 17227 }

351

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Processor class for SAM. """ from copy import deepcopy from typing import Optional, Union import numpy as np from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding from ...utils import TensorType, is_tf_available, is_torch_available if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf class SamProcessor(ProcessorMixin): r""" Constructs a SAM processor which wraps a SAM image processor and an 2D points & Bounding boxes processor into a single processor. [`SamProcessor`] offers all the functionalities of [`SamImageProcessor`]. See the docstring of [`~SamImageProcessor.__call__`] for more information. Args: image_processor (`SamImageProcessor`): An instance of [`SamImageProcessor`]. The image processor is a required input. """ attributes = ["image_processor"] image_processor_class = "SamImageProcessor" def __init__(self, image_processor): super().__init__(image_processor) self.current_processor = self.image_processor self.point_pad_value = -10 self.target_size = self.image_processor.size["longest_edge"] def __call__( self, images=None, segmentation_maps=None, input_points=None, input_labels=None, input_boxes=None, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs, ) -> BatchEncoding: """ This method uses [`SamImageProcessor.__call__`] method to prepare image(s) for the model. It also prepares 2D points and bounding boxes for the model if they are provided. """ encoding_image_processor = self.image_processor( images, segmentation_maps=segmentation_maps, return_tensors=return_tensors, **kwargs, ) # pop arguments that are not used in the foward but used nevertheless original_sizes = encoding_image_processor["original_sizes"] if hasattr(original_sizes, "numpy"): # Checks if Torch or TF tensor original_sizes = original_sizes.numpy() input_points, input_labels, input_boxes = self._check_and_preprocess_points( input_points=input_points, input_labels=input_labels, input_boxes=input_boxes, ) encoding_image_processor = self._normalize_and_convert( encoding_image_processor, original_sizes, input_points=input_points, input_labels=input_labels, input_boxes=input_boxes, return_tensors=return_tensors, ) return encoding_image_processor def _normalize_and_convert( self, encoding_image_processor, original_sizes, input_points=None, input_labels=None, input_boxes=None, return_tensors="pt", ): if input_points is not None: if len(original_sizes) != len(input_points): input_points = [ self._normalize_coordinates(self.target_size, point, original_sizes[0]) for point in input_points ] else: input_points = [ self._normalize_coordinates(self.target_size, point, original_size) for point, original_size in zip(input_points, original_sizes) ] # check that all arrays have the same shape if not all(point.shape == input_points[0].shape for point in input_points): if input_labels is not None: input_points, input_labels = self._pad_points_and_labels(input_points, input_labels) input_points = np.array(input_points) if input_labels is not None: input_labels = np.array(input_labels) if input_boxes is not None: if len(original_sizes) != len(input_boxes): input_boxes = [ self._normalize_coordinates(self.target_size, box, original_sizes[0], is_bounding_box=True) for box in input_boxes ] else: input_boxes = [ self._normalize_coordinates(self.target_size, box, original_size, is_bounding_box=True) for box, original_size in zip(input_boxes, original_sizes) ] input_boxes = np.array(input_boxes) if input_boxes is not None: if return_tensors == "pt": input_boxes = torch.from_numpy(input_boxes) # boxes batch size of 1 by default input_boxes = input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes elif return_tensors == "tf": input_boxes = tf.convert_to_tensor(input_boxes) # boxes batch size of 1 by default input_boxes = tf.expand_dims(input_boxes, 1) if len(input_boxes.shape) != 3 else input_boxes encoding_image_processor.update({"input_boxes": input_boxes}) if input_points is not None: if return_tensors == "pt": input_points = torch.from_numpy(input_points) # point batch size of 1 by default input_points = input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points elif return_tensors == "tf": input_points = tf.convert_to_tensor(input_points) # point batch size of 1 by default input_points = tf.expand_dims(input_points, 1) if len(input_points.shape) != 4 else input_points encoding_image_processor.update({"input_points": input_points}) if input_labels is not None: if return_tensors == "pt": input_labels = torch.from_numpy(input_labels) # point batch size of 1 by default input_labels = input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels elif return_tensors == "tf": input_labels = tf.convert_to_tensor(input_labels) # point batch size of 1 by default input_labels = tf.expand_dims(input_labels, 1) if len(input_labels.shape) != 3 else input_labels encoding_image_processor.update({"input_labels": input_labels}) return encoding_image_processor def _pad_points_and_labels(self, input_points, input_labels): r""" The method pads the 2D points and labels to the maximum number of points in the batch. """ expected_nb_points = max([point.shape[0] for point in input_points]) processed_input_points = [] for i, point in enumerate(input_points): if point.shape[0] != expected_nb_points: point = np.concatenate( [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value], axis=0 ) input_labels[i] = np.append(input_labels[i], [self.point_pad_value]) processed_input_points.append(point) input_points = processed_input_points return input_points, input_labels def _normalize_coordinates( self, target_size: int, coords: np.ndarray, original_size, is_bounding_box=False ) -> np.ndarray: """ Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format. """ old_h, old_w = original_size new_h, new_w = self.image_processor._get_preprocess_shape(original_size, longest_edge=target_size) coords = deepcopy(coords).astype(float) if is_bounding_box: coords = coords.reshape(-1, 2, 2) coords[..., 0] = coords[..., 0] * (new_w / old_w) coords[..., 1] = coords[..., 1] * (new_h / old_h) if is_bounding_box: coords = coords.reshape(-1, 4) return coords def _check_and_preprocess_points( self, input_points=None, input_labels=None, input_boxes=None, ): r""" Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they are, it converts the coordinates of the points and bounding boxes. If a user passes directly a `torch.Tensor`, it is converted to a `numpy.ndarray` and then to a `list`. """ if input_points is not None: if hasattr(input_points, "numpy"): # Checks for TF or Torch tensor input_points = input_points.numpy().tolist() if not isinstance(input_points, list) or not isinstance(input_points[0], list): raise ValueError("Input points must be a list of list of floating points.") input_points = [np.array(input_point) for input_point in input_points] else: input_points = None if input_labels is not None: if hasattr(input_labels, "numpy"): input_labels = input_labels.numpy().tolist() if not isinstance(input_labels, list) or not isinstance(input_labels[0], list): raise ValueError("Input labels must be a list of list integers.") input_labels = [np.array(label) for label in input_labels] else: input_labels = None if input_boxes is not None: if hasattr(input_boxes, "numpy"): input_boxes = input_boxes.numpy().tolist() if ( not isinstance(input_boxes, list) or not isinstance(input_boxes[0], list) or not isinstance(input_boxes[0][0], list) ): raise ValueError("Input boxes must be a list of list of list of floating points.") input_boxes = [np.array(box).astype(np.float32) for box in input_boxes] else: input_boxes = None return input_points, input_labels, input_boxes @property def model_input_names(self): image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(image_processor_input_names)) def post_process_masks(self, *args, **kwargs): return self.image_processor.post_process_masks(*args, **kwargs)

transformers/src/transformers/models/sam/processing_sam.py/0

{ "file_path": "transformers/src/transformers/models/sam/processing_sam.py", "repo_id": "transformers", "token_count": 4814 }

352

# coding=utf-8 # Copyright 2024 Google AI and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Siglip model.""" import math import warnings from dataclasses import dataclass from typing import Any, Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn.init import _calculate_fan_in_and_fan_out from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_attention_mask from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224" SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/siglip-base-patch16-224", # See all SigLIP models at https://huggingface.co/models?filter=siglip ] def _trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn( "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2, ) # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * l - 1, 2 * u - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.0)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) def trunc_normal_tf_( tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0 ) -> torch.Tensor: """Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \\leq \text{mean} \\leq b`. NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 and the result is subsquently scaled and shifted by the mean and std args. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value """ with torch.no_grad(): _trunc_normal_(tensor, 0, 1.0, a, b) tensor.mul_(std).add_(mean) def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == "fan_in": denom = fan_in elif mode == "fan_out": denom = fan_out elif mode == "fan_avg": denom = (fan_in + fan_out) / 2 variance = scale / denom if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) elif distribution == "normal": with torch.no_grad(): tensor.normal_(std=math.sqrt(variance)) elif distribution == "uniform": bound = math.sqrt(3 * variance) with torch.no_grad(): tensor.uniform_(-bound, bound) else: raise ValueError(f"invalid distribution {distribution}") def lecun_normal_(tensor): variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") def default_flax_embed_init(tensor): variance_scaling_(tensor, mode="fan_in", distribution="normal") @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip class SiglipVisionModelOutput(ModelOutput): """ Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. Args: image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The image embeddings obtained by applying the projection layer to the pooler_output. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ image_embeds: Optional[torch.FloatTensor] = None last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Siglip class SiglipTextModelOutput(ModelOutput): """ Base class for text model's outputs that also contains a pooling of the last hidden states. Args: text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The text embeddings obtained by applying the projection layer to the pooler_output. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ text_embeds: Optional[torch.FloatTensor] = None last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip class SiglipOutput(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`SiglipTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): The output of the [`SiglipVisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits_per_image: torch.FloatTensor = None logits_per_text: torch.FloatTensor = None text_embeds: torch.FloatTensor = None image_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) class SiglipVisionEmbeddings(nn.Module): def __init__(self, config: SiglipVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, padding="valid", ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid] embeddings = patch_embeds.flatten(2).transpose(1, 2) embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Siglip class SiglipTextEmbeddings(nn.Module): def __init__(self, config: SiglipTextConfig): super().__init__() embed_dim = config.hidden_size self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if inputs_embeds is None: inputs_embeds = self.token_embedding(input_ids) position_embeddings = self.position_embedding(position_ids) embeddings = inputs_embeds + position_embeddings return embeddings class SiglipAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__ def __init__(self, config): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" batch_size, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) k_v_seq_len = key_states.shape[-2] attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len): raise ValueError( f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip class SiglipMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip class SiglipEncoderLayer(nn.Module): def __init__(self, config: SiglipConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = SiglipAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = SiglipMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) # Ignore copy def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): Input to the layer of shape `(batch, seq_len, embed_dim)`. attention_mask (`torch.FloatTensor`): Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*, defaults to `False`): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class SiglipPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = SiglipConfig base_model_prefix = "siglip" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, SiglipVisionEmbeddings): width = ( self.config.vision_config.hidden_size if isinstance(self.config, SiglipConfig) else self.config.hidden_size ) nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) elif isinstance(module, nn.Embedding): default_flax_embed_init(module.weight) elif isinstance(module, SiglipAttention): nn.init.xavier_uniform_(module.q_proj.weight) nn.init.xavier_uniform_(module.k_proj.weight) nn.init.xavier_uniform_(module.v_proj.weight) nn.init.xavier_uniform_(module.out_proj.weight) nn.init.zeros_(module.q_proj.bias) nn.init.zeros_(module.k_proj.bias) nn.init.zeros_(module.v_proj.bias) nn.init.zeros_(module.out_proj.bias) elif isinstance(module, SiglipMLP): nn.init.xavier_uniform_(module.fc1.weight) nn.init.xavier_uniform_(module.fc2.weight) nn.init.normal_(module.fc1.bias, std=1e-6) nn.init.normal_(module.fc2.bias, std=1e-6) elif isinstance(module, SiglipMultiheadAttentionPoolingHead): nn.init.xavier_uniform_(module.probe.data) nn.init.xavier_uniform_(module.attention.in_proj_weight.data) nn.init.zeros_(module.attention.in_proj_bias.data) elif isinstance(module, SiglipModel): logit_scale_init = torch.log(torch.tensor(1.0)) module.logit_scale.data.fill_(logit_scale_init) module.logit_bias.data.zero_() elif isinstance(module, (nn.Linear, nn.Conv2d)): lecun_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) SIGLIP_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`SiglipConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ SIGLIP_TEXT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ SIGLIP_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ SIGLIP_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip class SiglipEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`SiglipEncoderLayer`]. Args: config: SiglipConfig """ def __init__(self, config: SiglipConfig): super().__init__() self.config = config self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False # Ignore copy def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for encoder_layer in self.layers: if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class SiglipTextTransformer(nn.Module): def __init__(self, config: SiglipTextConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = SiglipTextEmbeddings(config) self.encoder = SiglipEncoder(config) self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.head = nn.Linear(embed_dim, embed_dim) @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify input_ids") input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) # note: SigLIP's text model does not use a causal mask, unlike the original CLIP model. # expand attention_mask if attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len] attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.final_layer_norm(last_hidden_state) # Assuming "sticky" EOS tokenization, last token is always EOS. pooled_output = last_hidden_state[:, -1, :] pooled_output = self.head(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """The text model from SigLIP without any head or projection on top.""", SIGLIP_START_DOCSTRING, ) class SiglipTextModel(SiglipPreTrainedModel): config_class = SiglipTextConfig _no_split_modules = ["SiglipTextEmbeddings", "SiglipEncoderLayer"] def __init__(self, config: SiglipTextConfig): super().__init__(config) self.text_model = SiglipTextTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.text_model.embeddings.token_embedding def set_input_embeddings(self, value): self.text_model.embeddings.token_embedding = value @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from transformers import AutoTokenizer, SiglipTextModel >>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224") >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") >>> # important: make sure to set padding="max_length" as that's how the model was trained >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled (EOS token) states ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class SiglipVisionTransformer(nn.Module): def __init__(self, config: SiglipVisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = SiglipVisionEmbeddings(config) self.encoder = SiglipEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.head = SiglipMultiheadAttentionPoolingHead(config) @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig) def forward( self, pixel_values, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = self.embeddings(pixel_values) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.post_layernorm(last_hidden_state) pooled_output = self.head(last_hidden_state) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class SiglipMultiheadAttentionPoolingHead(nn.Module): """Multihead Attention Pooling.""" def __init__(self, config: SiglipVisionConfig): super().__init__() self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.mlp = SiglipMLP(config) def forward(self, hidden_state): batch_size = hidden_state.shape[0] probe = self.probe.repeat(batch_size, 1, 1) hidden_state = self.attention(probe, hidden_state, hidden_state)[0] residual = hidden_state hidden_state = self.layernorm(hidden_state) hidden_state = residual + self.mlp(hidden_state) return hidden_state[:, 0] @add_start_docstrings( """The vision model from SigLIP without any head or projection on top.""", SIGLIP_START_DOCSTRING, ) class SiglipVisionModel(SiglipPreTrainedModel): config_class = SiglipVisionConfig main_input_name = "pixel_values" def __init__(self, config: SiglipVisionConfig): super().__init__(config) self.vision_model = SiglipVisionTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig) def forward( self, pixel_values, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, SiglipVisionModel >>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224") >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled features ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) @add_start_docstrings(SIGLIP_START_DOCSTRING) class SiglipModel(SiglipPreTrainedModel): config_class = SiglipConfig def __init__(self, config: SiglipConfig): super().__init__(config) if not isinstance(config.text_config, SiglipTextConfig): raise ValueError( "config.text_config is expected to be of type SiglipTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.vision_config, SiglipVisionConfig): raise ValueError( "config.vision_config is expected to be of type SiglipVisionConfig but is of type" f" {type(config.vision_config)}." ) text_config = config.text_config vision_config = config.vision_config self.text_model = SiglipTextTransformer(text_config) self.vision_model = SiglipVisionTransformer(vision_config) self.logit_scale = nn.Parameter(torch.randn(1)) self.logit_bias = nn.Parameter(torch.randn(1)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`]. Examples: ```python >>> from transformers import AutoTokenizer, AutoModel >>> import torch >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") >>> # important: make sure to set padding="max_length" as that's how the model was trained >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") >>> with torch.no_grad(): ... text_features = model.get_text_features(**inputs) ```""" # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[1] return pooled_output @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AutoModel >>> import torch >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... image_features = model.get_image_features(**inputs) ```""" # Use SiglipModel's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = vision_outputs[1] return pooled_output @add_start_docstrings_to_model_forward(SIGLIP_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SiglipOutput, config_class=SiglipConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SiglipOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AutoModel >>> import torch >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"] >>> # important: we pass `padding=max_length` since the model was trained with this >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") 31.9% that image 0 is 'a photo of 2 cats' ```""" # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] text_embeds = text_outputs[1] # normalized features image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) # cosine similarity as logits logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * self.logit_scale.exp() + self.logit_bias logits_per_image = logits_per_text.t() loss = None if return_loss: raise NotImplementedError("SigLIP loss to be implemented") if not return_dict: output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return ((loss,) + output) if loss is not None else output return SiglipOutput( loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, )

transformers/src/transformers/models/siglip/modeling_siglip.py/0

{ "file_path": "transformers/src/transformers/models/siglip/modeling_siglip.py", "repo_id": "transformers", "token_count": 20966 }

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# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for Speech2Text.""" import json import os from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "spm_file": "sentencepiece.bpe.model", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/s2t-small-librispeech-asr": ( "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json" ), }, "spm_file": { "facebook/s2t-small-librispeech-asr": ( "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model" ) }, } MAX_MODEL_INPUT_SIZES = { "facebook/s2t-small-librispeech-asr": 1024, } MUSTC_LANGS = ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"] LANGUAGES = {"mustc": MUSTC_LANGS} class Speech2TextTokenizer(PreTrainedTokenizer): """ Construct an Speech2Text tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. spm_file (`str`): Path to the [SentencePiece](https://github.com/google/sentencepiece) model file bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. do_upper_case (`bool`, *optional*, defaults to `False`): Whether or not to uppercase the output when decoding. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the input when tokenizing. tgt_lang (`str`, *optional*): A string representing the target language. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = MAX_MODEL_INPUT_SIZES model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] def __init__( self, vocab_file, spm_file, bos_token="<s>", eos_token="</s>", pad_token="<pad>", unk_token="<unk>", do_upper_case=False, do_lower_case=False, tgt_lang=None, lang_codes=None, additional_special_tokens=None, sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.do_upper_case = do_upper_case self.do_lower_case = do_lower_case self.encoder = load_json(vocab_file) self.decoder = {v: k for k, v in self.encoder.items()} self.spm_file = spm_file self.sp_model = load_spm(spm_file, self.sp_model_kwargs) if lang_codes is not None: self.lang_codes = lang_codes self.langs = LANGUAGES[lang_codes] self.lang_tokens = [f"<lang:{lang}>" for lang in self.langs] self.lang_code_to_id = {lang: self.sp_model.PieceToId(f"<lang:{lang}>") for lang in self.langs} if additional_special_tokens is not None: additional_special_tokens = self.lang_tokens + additional_special_tokens else: additional_special_tokens = self.lang_tokens self._tgt_lang = tgt_lang if tgt_lang is not None else self.langs[0] self.set_tgt_lang_special_tokens(self._tgt_lang) else: self.lang_code_to_id = {} super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, do_upper_case=do_upper_case, do_lower_case=do_lower_case, tgt_lang=tgt_lang, lang_codes=lang_codes, sp_model_kwargs=self.sp_model_kwargs, additional_special_tokens=additional_special_tokens, **kwargs, ) @property def vocab_size(self) -> int: return len(self.encoder) def get_vocab(self) -> Dict: vocab = self.encoder.copy() vocab.update(self.added_tokens_encoder) return vocab @property def tgt_lang(self) -> str: return self._tgt_lang @tgt_lang.setter def tgt_lang(self, new_tgt_lang) -> None: self._tgt_lang = new_tgt_lang self.set_tgt_lang_special_tokens(new_tgt_lang) def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. prefix=[eos, tgt_lang_code] and suffix=[eos].""" lang_code_id = self.lang_code_to_id[tgt_lang] self.prefix_tokens = [lang_code_id] def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): return self.encoder.get(token, self.encoder[self.unk_token]) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the decoder.""" return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens: List[str]) -> str: """Converts a sequence of tokens (strings for sub-words) in a single string.""" current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: decoded = self.sp_model.decode(current_sub_tokens) out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " " current_sub_tokens = [] else: current_sub_tokens.append(token) decoded = self.sp_model.decode(current_sub_tokens) out_string += decoded.upper() if self.do_upper_case else decoded return out_string.strip() def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]: """Build model inputs from a sequence by appending eos_token_id.""" if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_0 + token_ids_1 + [self.eos_token_id] def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) prefix_ones = [1] * len(self.prefix_tokens) suffix_ones = [1] if token_ids_1 is None: return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones def __getstate__(self) -> Dict: state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d: Dict) -> None: self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: save_dir = Path(save_directory) assert save_dir.is_dir(), f"{save_directory} should be a directory" vocab_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"] ) spm_save_path = save_dir / ( (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"] ) save_json(self.encoder, vocab_save_path) if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file): copyfile(self.spm_file, spm_save_path) elif not os.path.isfile(self.spm_file): with open(spm_save_path, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (str(vocab_save_path), str(spm_save_path)) def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor: spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs) spm.Load(str(path)) return spm def load_json(path: str) -> Union[Dict, List]: with open(path, "r") as f: return json.load(f) def save_json(data, path: str) -> None: with open(path, "w") as f: json.dump(data, f, indent=2)

transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py/0

{ "file_path": "transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py", "repo_id": "transformers", "token_count": 5268 }

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import argparse import json import requests import timm import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification def get_swin_config(swin_name): config = SwinConfig() name_split = swin_name.split("_") model_size = name_split[1] img_size = int(name_split[4]) window_size = int(name_split[3][-1]) if model_size == "tiny": embed_dim = 96 depths = (2, 2, 6, 2) num_heads = (3, 6, 12, 24) elif model_size == "small": embed_dim = 96 depths = (2, 2, 18, 2) num_heads = (3, 6, 12, 24) elif model_size == "base": embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) else: embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) if "in22k" in swin_name: num_classes = 21841 else: num_classes = 1000 repo_id = "huggingface/label-files" filename = "imagenet-1k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} config.image_size = img_size config.num_labels = num_classes config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads config.window_size = window_size return config def rename_key(name): if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "embeddings.norm") if "layers" in name: name = "encoder." + name if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if name == "norm.weight": name = "layernorm.weight" if name == "norm.bias": name = "layernorm.bias" if "head" in name: name = name.replace("head", "classifier") else: name = "swin." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "mask" in key: continue elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[1]) block_num = int(key_split[3]) dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[ dim : dim * 2, : ] orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[ :dim ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[ -dim: ] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swin_checkpoint(swin_name, pytorch_dump_folder_path): timm_model = timm.create_model(swin_name, pretrained=True) timm_model.eval() config = get_swin_config(swin_name) model = SwinForImageClassification(config) model.eval() new_state_dict = convert_state_dict(timm_model.state_dict(), model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image_processor = AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_", "-"))) image = Image.open(requests.get(url, stream=True).raw) inputs = image_processor(images=image, return_tensors="pt") timm_outs = timm_model(inputs["pixel_values"]) hf_outs = model(**inputs).logits assert torch.allclose(timm_outs, hf_outs, atol=1e-3) print(f"Saving model {swin_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--swin_name", default="swin_tiny_patch4_window7_224", type=str, help="Name of the Swin timm model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)

transformers/src/transformers/models/swin/convert_swin_timm_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/swin/convert_swin_timm_to_pytorch.py", "repo_id": "transformers", "token_count": 2718 }

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# coding=utf-8 # Copyright 2022 SwitchTransformers Authors and HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch SwitchTransformers model.""" import copy import math import warnings from typing import Optional, Tuple, Union import torch import torch.nn as nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...modeling_outputs import ( MoEModelOutput, MoEModelOutputWithPastAndCrossAttentions, Seq2SeqMoEModelOutput, Seq2SeqMoEOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, replace_return_docstrings, ) from .configuration_switch_transformers import SwitchTransformersConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "SwitchTransformersConfig" _CHECKPOINT_FOR_DOC = "google/switch-base-8" #################################################### # This dict contains ids and associated url # for the pretrained weights provided with the models #################################################### SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/switch-base-8", "google/switch-base-16", "google/switch-base-32", "google/switch-base-64", "google/switch-base-128", "google/switch-base-256", "google/switch-large-128", "google/switch-xxl-128", "google/switch-c-2048", # See all SwitchTransformers models at https://huggingface.co/models?filter=switch_transformers ] def router_z_loss_func(router_logits: torch.Tensor) -> float: r""" Compute the router z-loss implemented in PyTorch. The router z-loss was introduced in [Designing Effective Sparse Expert Models](https://arxiv.org/abs/2202.08906). It encourages router logits to remain small in an effort to improve stability. Args: router_logits (`float`): Input logits of shape [batch_size, sequence_length, num_experts] Returns: Scalar router z-loss. """ num_groups, tokens_per_group, _ = router_logits.shape log_z = torch.logsumexp(router_logits, dim=-1) z_loss = log_z**2 return torch.sum(z_loss) / (num_groups * tokens_per_group) def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float: r""" Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: router_probs (`torch.Tensor`): Probability assigned to each expert per token. Shape: [batch_size, seqeunce_length, num_experts]. expert_indices (`torch.Tensor`): Indices tensor of shape [batch_size, seqeunce_length] identifying the selected expert for a given token. Returns: The auxiliary loss. """ num_experts = router_probs.shape[-1] # cast the expert indices to int64, otherwise one-hot encoding will fail if expert_indices.dtype != torch.int64: expert_indices = expert_indices.to(torch.int64) if len(expert_indices.shape) == 2: expert_indices = expert_indices.unsqueeze(2) expert_mask = torch.nn.functional.one_hot(expert_indices, num_experts) # For a given token, determine if it was routed to a given expert. expert_mask = torch.max(expert_mask, axis=-2).values # cast to float32 otherwise mean will fail expert_mask = expert_mask.to(torch.float32) tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2) router_prob_per_group_and_expert = torch.mean(router_probs, axis=-2) return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert) * (num_experts**2) class SwitchTransformersTop1Router(nn.Module): """ Router using tokens choose top-1 experts assignment. This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE (https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by router_probs and then routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee that each token is processed by an expert**, or that each expert receives at least one token. """ def __init__(self, config: SwitchTransformersConfig): super().__init__() self.num_experts = config.num_experts self.expert_capacity = config.expert_capacity self.classifier = nn.Linear(config.hidden_size, self.num_experts, bias=config.router_bias) self.jitter_noise = config.router_jitter_noise self.ignore_padding_tokens = config.router_ignore_padding_tokens self.dtype = getattr(torch, config.router_dtype) def _compute_router_probabilities(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: r""" Computes router probabilities from input hidden states. Args: hidden_states (`torch.Tensor`): (batch_size, sequence_length, hidden_dim) from which router probabilities are computed. Returns: router_probabilities (`torch.Tensor`): Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each token and expert. Used for routing tokens to experts. router_logits (`torch.Tensor`): Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits. This is used later for computing router z-loss. """ # float32 is used to ensure stability. See the discussion of "selective precision" in # https://arxiv.org/abs/2101.03961. # We also store the previous dtype to cast back the output to the previous dtype self.input_dtype = hidden_states.dtype hidden_states = hidden_states.to(self.dtype) if self.training and self.jitter_noise > 0: # Multiply the token inputs by the uniform distribution - adding some noise hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise) # Shape: [num_groups, tokens_per_group, num_experts] self._cast_classifier() router_logits = self.classifier(hidden_states) # Apply Softmax and cast back to the original `dtype` router_probabilities = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype) return router_probabilities, router_logits def _cast_classifier(self): r""" `bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an instance of the `Linear8bitLt` class by checking special attributes. """ if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")): self.classifier = self.classifier.to(self.dtype) def forward(self, hidden_states: torch.Tensor) -> Tuple: r""" Generic forward function for every Router class. Each Router expects to have the same input hidden states (`hidden_states`) corresponding to the hidden states for each token, the `expert_capacity` corresponding to the number of tokens the Router will send to each expert, some Routers can send up to few tokens to each expert. Each Router works as the following: it expects the hidden states for each token, gets the `router_probs` and `router_logits` from the `router_weights`. This will assign for each token, the raw probability to be assigned to an expert. Then each Router class will have to define its own `_compute_routing_instructions`. Args: hidden_states (`torch.Tensor`) : [num_groups, tokens_per_group, hidden_dim] inputs to send to experts. Returns: Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`] Tuple containing the expert index, the router probs and the router logits. The router probabilities and logits are required to compute the loss. """ router_probs, router_logits = self._compute_router_probabilities(hidden_states) expert_index = torch.argmax(router_probs, dim=-1) expert_index = torch.nn.functional.one_hot(expert_index, num_classes=self.num_experts) # Mask tokens outside expert capacity. Sum over each sequence token_priority = torch.cumsum(expert_index, dim=-2) # mask if the token routed to to the expert will overflow expert_capacity_mask = token_priority <= self.expert_capacity expert_index = expert_index * expert_capacity_mask router_probs = torch.max(router_probs, dim=-1).values.unsqueeze(-1) return expert_index, router_probs, router_logits # Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->SwitchTransformers class SwitchTransformersLayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Construct a layernorm module in the SwitchTransformers style. No bias and no subtraction of mean. """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): # SwitchTransformers uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for # half-precision inputs is done in fp32 variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states ALL_LAYERNORM_LAYERS.append(SwitchTransformersLayerNorm) # Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->SwitchTransformers class SwitchTransformersDenseActDense(nn.Module): def __init__(self, config: SwitchTransformersConfig): super().__init__() self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.act = ACT2FN[config.dense_act_fn] def forward(self, hidden_states): hidden_states = self.wi(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dropout(hidden_states) if ( isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8 ): hidden_states = hidden_states.to(self.wo.weight.dtype) hidden_states = self.wo(hidden_states) return hidden_states class SwitchTransformersSparseMLP(nn.Module): r""" Implementation of the Switch Transformers Sparse MLP module. """ def __init__(self, config: SwitchTransformersConfig, expert_class: nn.Module = SwitchTransformersDenseActDense): super().__init__() # Step 1: Get the correct router according to its class self.router = SwitchTransformersTop1Router(config) # Step 2: Get the experts self.experts = nn.ModuleDict() for idx in range(config.num_experts): self.experts[f"expert_{idx}"] = expert_class(config) def forward(self, hidden_states): r""" Hold on, this will be slightly tricky to understand In the correct order, a MoE layer does the following: 1- Gets the `router_mask` from the router. The shape of the mask is `(batch_size, sequence_length, num_expert)` and corresponds to the argmax of the `router_probs`. The probabilities are needed in the computation of the hidden states : they are broadcasted to the hidden states values (can be interpreted as a scaling factor). 2- Dispatch the tokens to its associated experts. We do a classic for loop over the experts and assign for each expert the corresponding hidden states. """ # Step 1: Get the router_mask from the router as wel as the probabilities router_mask, router_probs, router_logits = self.router(hidden_states) expert_index = torch.argmax(router_mask, dim=-1) # The routers introduced might not always map all the tokens, to a router, which means that some hidden states # can be unchanged from one layer to another. That is why the hidden states are cloned before updating only the seleced ones. next_states = hidden_states.clone() for idx, expert in enumerate(self.experts.values()): token_indices = router_mask[:, :, idx].bool() next_states[token_indices] = expert(hidden_states[token_indices]).to(next_states.dtype) hidden_states = router_probs * next_states return hidden_states, (router_logits, expert_index) class SwitchTransformersLayerFF(nn.Module): r""" Switch Transformers Feed Forward layer module. This is a wrapper around the Mixture of Experts module. Parameters: config : ([`SwitchTransformersConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. is_sparse (`bool`): Whether the MLP layer is a `Sparse` layer (contains a Mixture of Experts) or not """ def __init__(self, config: SwitchTransformersConfig, is_sparse=False): super().__init__() self.is_sparse = is_sparse # Check if it is a sparse layer, if not then it is a dense layer if not self.is_sparse: self.mlp = SwitchTransformersDenseActDense(config) else: self.mlp = SwitchTransformersSparseMLP(config) self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward(self, hidden_states, output_router_logits): forwarded_states = self.layer_norm(hidden_states) forwarded_states = self.mlp(forwarded_states) if isinstance(forwarded_states, tuple): forwarded_states, router_tuple = forwarded_states else: router_tuple = None output = hidden_states + self.dropout(forwarded_states) if output_router_logits and router_tuple is not None: output = (output, router_tuple) return output # Copied from transformers.models.t5.modeling_t5.T5Attention with T5->SwitchTransformers class SwitchTransformersAttention(nn.Module): def __init__(self, config: SwitchTransformersConfig, has_relative_attention_bias=False): super().__init__() self.is_decoder = config.is_decoder self.has_relative_attention_bias = has_relative_attention_bias self.relative_attention_num_buckets = config.relative_attention_num_buckets self.relative_attention_max_distance = config.relative_attention_max_distance self.d_model = config.d_model self.key_value_proj_dim = config.d_kv self.n_heads = config.num_heads self.dropout = config.dropout_rate self.inner_dim = self.n_heads * self.key_value_proj_dim # Mesh TensorFlow initialization to avoid scaling before softmax self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) self.pruned_heads = set() self.gradient_checkpointing = False def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads ) # Prune linear layers self.q = prune_linear_layer(self.q, index) self.k = prune_linear_layer(self.k, index) self.v = prune_linear_layer(self.v, index) self.o = prune_linear_layer(self.o, index, dim=1) # Update hyper params self.n_heads = self.n_heads - len(heads) self.inner_dim = self.key_value_proj_dim * self.n_heads self.pruned_heads = self.pruned_heads.union(heads) @staticmethod def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): """ Adapted from Mesh Tensorflow: https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should allow for more graceful generalization to longer sequences than the model has been trained on Args: relative_position: an int32 Tensor bidirectional: a boolean - whether the attention is bidirectional num_buckets: an integer max_distance: an integer Returns: a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) """ relative_buckets = 0 if bidirectional: num_buckets //= 2 relative_buckets += (relative_position > 0).to(torch.long) * num_buckets relative_position = torch.abs(relative_position) else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) # now relative_position is in the range [0, inf) # half of the buckets are for exact increments in positions max_exact = num_buckets // 2 is_small = relative_position < max_exact # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance relative_position_if_large = max_exact + ( torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) ).to(torch.long) relative_position_if_large = torch.min( relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) ) relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) return relative_buckets def compute_bias(self, query_length, key_length, device=None): """Compute binned relative position bias""" if device is None: device = self.relative_attention_bias.weight.device context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] relative_position = memory_position - context_position # shape (query_length, key_length) relative_position_bucket = self._relative_position_bucket( relative_position, # shape (query_length, key_length) bidirectional=(not self.is_decoder), num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance, ) values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) return values def forward( self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False, ): """ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). """ # Input is (batch_size, seq_length, dim) # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head) batch_size, seq_length = hidden_states.shape[:2] real_seq_length = seq_length if past_key_value is not None: if len(past_key_value) != 2: raise ValueError( f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states" ) real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length key_length = real_seq_length if key_value_states is None else key_value_states.shape[1] def shape(states): """projection""" return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) def unshape(states): """reshape""" return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) def project(hidden_states, proj_layer, key_value_states, past_key_value): """projects hidden states correctly to key/query states""" if key_value_states is None: # self-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(hidden_states)) elif past_key_value is None: # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) if past_key_value is not None: if key_value_states is None: # self-attn # (batch_size, n_heads, key_length, dim_per_head) hidden_states = torch.cat([past_key_value, hidden_states], dim=2) elif past_key_value.shape[2] != key_value_states.shape[1]: # checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning # cross-attn # (batch_size, n_heads, seq_length, dim_per_head) hidden_states = shape(proj_layer(key_value_states)) else: # cross-attn hidden_states = past_key_value return hidden_states # get query states query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, seq_length, dim_per_head) # get key/value states key_states = project( hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None ) value_states = project( hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None ) # compute scores scores = torch.matmul( query_states, key_states.transpose(3, 2) ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 if position_bias is None: if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype ) if self.gradient_checkpointing and self.training: position_bias.requires_grad = True else: position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device) # if key and values are already calculated # we want only the last query position bias if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :] if mask is not None: position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length) if self.pruned_heads: mask = torch.ones(position_bias.shape[1]) mask[list(self.pruned_heads)] = 0 position_bias_masked = position_bias[:, mask.bool()] else: position_bias_masked = position_bias scores += position_bias_masked attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as( scores ) # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.dropout( attn_weights, p=self.dropout, training=self.training ) # (batch_size, n_heads, seq_length, key_length) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = unshape(torch.matmul(attn_weights, value_states)) # (batch_size, seq_length, dim) attn_output = self.o(attn_output) present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None outputs = (attn_output,) + (present_key_value_state,) + (position_bias,) if output_attentions: outputs = outputs + (attn_weights,) return outputs # Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->SwitchTransformers class SwitchTransformersLayerSelfAttention(nn.Module): def __init__(self, config, has_relative_attention_bias=False): super().__init__() self.SelfAttention = SwitchTransformersAttention( config, has_relative_attention_bias=has_relative_attention_bias ) self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.SelfAttention( normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = hidden_states + self.dropout(attention_output[0]) outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them return outputs # Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->SwitchTransformers class SwitchTransformersLayerCrossAttention(nn.Module): def __init__(self, config): super().__init__() self.EncDecAttention = SwitchTransformersAttention(config, has_relative_attention_bias=False) self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) def forward( self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, query_length=None, output_attentions=False, ): normed_hidden_states = self.layer_norm(hidden_states) attention_output = self.EncDecAttention( normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions, ) layer_output = hidden_states + self.dropout(attention_output[0]) outputs = (layer_output,) + attention_output[1:] # add attentions if we output them return outputs class SwitchTransformersBlock(nn.Module): def __init__(self, config, has_relative_attention_bias=False, is_sparse=False): super().__init__() self.is_decoder = config.is_decoder self.is_sparse = is_sparse self.layer = nn.ModuleList() self.layer.append( SwitchTransformersLayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias) ) if self.is_decoder: self.layer.append(SwitchTransformersLayerCrossAttention(config)) self.layer.append(SwitchTransformersLayerFF(config, is_sparse=self.is_sparse)) def forward( self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None, layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, output_router_logits=True, return_dict=True, ): if past_key_value is not None: if not self.is_decoder: logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.") expected_num_past_key_values = 2 if encoder_hidden_states is None else 4 if len(past_key_value) != expected_num_past_key_values: raise ValueError( f"There should be {expected_num_past_key_values} past states. " f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}" f"Got {len(past_key_value)} past key / value states" ) self_attn_past_key_value = past_key_value[:2] cross_attn_past_key_value = past_key_value[2:] else: self_attn_past_key_value, cross_attn_past_key_value = None, None self_attention_outputs = self.layer[0]( hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present_key_value_state = self_attention_outputs[:2] attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) do_cross_attention = self.is_decoder and encoder_hidden_states is not None if do_cross_attention: # the actual query length is unknown for cross attention # if using past key value states. Need to inject it here if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2] else: query_length = None cross_attention_outputs = self.layer[1]( hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, position_bias=encoder_decoder_position_bias, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = cross_attention_outputs[0] # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) # Combine self attn and cross attn key value states if present_key_value_state is not None: present_key_value_state = present_key_value_state + cross_attention_outputs[1] # Keep cross-attention outputs and relative position weights attention_outputs = attention_outputs + cross_attention_outputs[2:] # Apply Feed Forward layer hidden_states = self.layer[-1](hidden_states, output_router_logits) if isinstance(hidden_states, tuple): hidden_states, router_tuple = hidden_states else: router_tuple = (torch.zeros((1,), device=hidden_states.device, dtype=torch.int64),) # clamp inf values to enable fp16 training if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs + (router_tuple,) else: outputs = outputs + attention_outputs + (router_tuple,) return outputs # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights), (router_tuple) class SwitchTransformersPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = SwitchTransformersConfig base_model_prefix = "switch_transformers" supports_gradient_checkpointing = True _no_split_modules = ["SwitchTransformersBlock"] @property def dummy_inputs(self): input_ids = torch.tensor(DUMMY_INPUTS) input_mask = torch.tensor(DUMMY_MASK) dummy_inputs = { "decoder_input_ids": input_ids, "input_ids": input_ids, "decoder_attention_mask": input_mask, } return dummy_inputs def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor # Used for testing weights initialization if isinstance(module, SwitchTransformersLayerNorm): module.weight.data.fill_(factor * 1.0) elif isinstance( module, (SwitchTransformersModel, SwitchTransformersForConditionalGeneration, SwitchTransformersEncoderModel), ): # Mesh TensorFlow embeddings initialization # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624 module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0) if hasattr(module, "lm_head") and not self.config.tie_word_embeddings: module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0) elif isinstance(module, SwitchTransformersDenseActDense): # Mesh TensorFlow FF initialization # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56 # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89 module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) if hasattr(module.wi, "bias") and module.wi.bias is not None: module.wi.bias.data.zero_() module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_() elif isinstance(module, SwitchTransformersAttention): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model key_value_proj_dim = self.config.d_kv n_heads = self.config.num_heads module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5)) elif isinstance(module, SwitchTransformersSparseMLP): # Mesh TensorFlow attention initialization to avoid scaling before softmax # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136 d_model = self.config.d_model key_value_proj_dim = self.config.d_kv n_heads = self.config.num_heads module.router.classifier.weight.data.normal_(mean=0.0, std=factor * 1) for idx in range(self.config.num_experts): module.experts[f"expert_{idx}"].wi.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) module.experts[f"expert_{idx}"].wo.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5)) def _shift_right(self, input_ids): decoder_start_token_id = self.config.decoder_start_token_id pad_token_id = self.config.pad_token_id if decoder_start_token_id is None: raise ValueError( "self.model.config.decoder_start_token_id has to be defined. In SwitchTransformers it is usually set" " to the pad_token_id. See SwitchTransformers docs for more information" ) # shift inputs to the right if is_torch_fx_proxy(input_ids): # Item assignment is not supported natively for proxies. shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id) shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1) else: shifted_input_ids = input_ids.new_zeros(input_ids.shape) shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() shifted_input_ids[..., 0] = decoder_start_token_id if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) return shifted_input_ids class SwitchTransformersStack(SwitchTransformersPreTrainedModel): def __init__(self, config, embed_tokens=None): super().__init__(config) self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model) if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight self.is_decoder = config.is_decoder sparse_step = config.decoder_sparse_step if self.is_decoder else config.encoder_sparse_step config.num_layers = config.num_decoder_layers if self.is_decoder else config.num_layers self.block = nn.ModuleList() for i in range(config.num_layers): is_sparse = (i % sparse_step == 1 or sparse_step == 1) if sparse_step > 0 else False self.block.append( SwitchTransformersBlock(config, has_relative_attention_bias=bool(i == 0), is_sparse=is_sparse) ) self.final_layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon) self.dropout = nn.Dropout(config.dropout_rate) # Initialize weights and apply final processing self.post_init() self.device_map = None self.gradient_checkpointing = False def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings def forward( self, input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, inputs_embeds=None, head_mask=None, cross_attn_head_mask=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, output_router_logits=True, return_dict=None, ): use_cache = use_cache if use_cache is not None else self.config.use_cache output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError( f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: err_msg_prefix = "decoder_" if self.is_decoder else "" raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds") if inputs_embeds is None: if self.embed_tokens is None: raise ValueError("You have to initialize the model with valid token embeddings") inputs_embeds = self.embed_tokens(input_ids) batch_size, seq_length = input_shape # required mask seq length can be calculated via length of past mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length if use_cache is True: if not self.is_decoder: raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder") if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None: encoder_seq_length = encoder_hidden_states.shape[1] encoder_attention_mask = torch.ones( batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long ) # initialize past_key_values with `None` if past does not exist if past_key_values is None: past_key_values = [None] * len(self.block) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.num_layers) cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers) present_key_value_states = () if use_cache else None all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_router_probs = () if output_router_logits else None all_cross_attentions = () if (output_attentions and self.is_decoder) else None position_bias = None encoder_decoder_position_bias = None hidden_states = self.dropout(inputs_embeds) for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)): layer_head_mask = head_mask[i] cross_attn_layer_head_mask = cross_attn_head_mask[i] if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.forward, hidden_states, extended_attention_mask, position_bias, encoder_hidden_states, encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, # past_key_value is always None with gradient checkpointing use_cache, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask, cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions, output_router_logits=output_router_logits, ) router_probs = layer_outputs[-1] layer_outputs = layer_outputs[:-1] # layer_outputs is a tuple with: # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:] hidden_states, present_key_value_state = layer_outputs[:2] # We share the position biases between the layers - the first layer store them # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), # (cross-attention position bias), (cross-attention weights) position_bias = layer_outputs[2] if self.is_decoder and encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3] # append next layer key value states if use_cache: present_key_value_states = present_key_value_states + (present_key_value_state,) if output_attentions: all_attentions = all_attentions + (layer_outputs[3],) if self.is_decoder: all_cross_attentions = all_cross_attentions + (layer_outputs[5],) if output_router_logits: all_router_probs = all_router_probs + (router_probs,) hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.dropout(hidden_states) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions, all_router_probs, ] if v is not None ) return MoEModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=present_key_value_states, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, router_probs=all_router_probs, ) SWITCH_TRANSFORMERS_START_DOCSTRING = r""" The SWITCH_TRANSFORMERS model was proposed in [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by [William Fedus](https://arxiv.org/search/cs?searchtype=author&query=Fedus%2C+W), [Barret Zoph](https://arxiv.org/search/cs?searchtype=author&query=Zoph%2C+B), and [Noam Shazeer](https://arxiv.org/search/cs?searchtype=author&query=Shazeer%2C+N). It's an encoder-decoder T5-like model with sparse Feed Forward that stands for Mixture of Experts (MoE) architecture. This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`SwitchTransformersConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ SWITCH_TRANSFORMERS_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining take a look a [SWITCH_TRANSFORMERS Training](./switch_transformers#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) SWITCH_TRANSFORMERS uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare `decoder_input_ids` for pretraining take a look at [SWITCH_TRANSFORMERS Training](./switch_transformers#training). decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ SWITCH_TRANSFORMERS_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position embeddings so you should be able to pad the inputs on both the right and the left. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. To know more on how to prepare `input_ids` for pretraining take a look a [SWITCH_TRANSFORMERS Training](./switch_transformers#training). attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask __HEAD_MASK_WARNING_MSG = """ The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently, `decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions. If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers, num_heads)`. """ @add_start_docstrings( "The bare SWITCH_TRANSFORMERS Model transformer outputting raw hidden-states without any specific head on top.", SWITCH_TRANSFORMERS_START_DOCSTRING, ) class SwitchTransformersModel(SwitchTransformersPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: SwitchTransformersConfig): super().__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = SwitchTransformersStack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False self.decoder = SwitchTransformersStack(decoder_config, self.shared) # Initialize weights and apply final processing self.post_init() # Model parallel self.device_map = None def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def _tie_weights(self): if self.config.tie_word_embeddings: self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared) self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared) def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqMoEModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.Tensor] = None, decoder_inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqMoEModelOutput]: r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, SwitchTransformersModel >>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8") >>> model = SwitchTransformersModel.from_pretrained("google/switch-base-8") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for SwitchTransformersModel. >>> # This is not needed for torch's SwitchTransformersForConditionalGeneration as it does this internally using labels arg. >>> decoder_input_ids = model._shift_right(decoder_input_ids) >>> # forward pass >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) >>> last_hidden_states = outputs.last_hidden_state ```""" use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask if head_mask is not None and decoder_head_mask is None: if self.config.num_layers == self.config.num_decoder_layers: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) decoder_head_mask = head_mask if ( output_router_logits and self.config.num_sparse_encoder_layers == 0 and self.config.num_sparse_encoder_layers == 0 ): raise ValueError( "You asked to return `output_router_logits` but the transformer in dense, and does " " not contain any sparse MLP Layers. Set `output_router_logits = False` and restart" ) # Encode if needed (training, first prediction pass) if encoder_outputs is None: encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, MoEModelOutput): encoder_outputs = MoEModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None, ) hidden_states = encoder_outputs[0] # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return Seq2SeqMoEModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, decoder_router_logits=decoder_outputs.router_probs, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, encoder_router_logits=encoder_outputs.router_probs, ) @add_start_docstrings( """SWITCH_TRANSFORMERS Model with a `language modeling` head on top.""", SWITCH_TRANSFORMERS_START_DOCSTRING ) class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: SwitchTransformersConfig): super().__init__(config) self.model_dim = config.d_model self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.is_decoder = False encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = SwitchTransformersStack(encoder_config, self.shared) decoder_config = copy.deepcopy(config) decoder_config.is_decoder = True decoder_config.is_encoder_decoder = False decoder_config.num_layers = config.num_decoder_layers self.decoder = SwitchTransformersStack(decoder_config, self.shared) self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) self.router_z_loss_coef = config.router_z_loss_coef self.router_aux_loss_coef = config.router_aux_loss_coef # Initialize weights and apply final processing self.post_init() # Model parallel self.device_map = None def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) self.decoder.set_input_embeddings(new_embeddings) def _tie_weights(self): if self.config.tie_word_embeddings: self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared) self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared) def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def get_output_embeddings(self): return self.lm_head def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqMoEOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = True, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], Seq2SeqMoEOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` Returns: Examples: ```python >>> from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration >>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8") >>> model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8") >>> # training >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids >>> outputs = model(input_ids=input_ids, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits >>> # inference >>> input_ids = tokenizer( ... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model.generate(input_ids) >>> # . To, let’s say you have a dog. To summarize: >>> # Since the model has been trained on MLM, this will output gibberish ```""" use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask if head_mask is not None and decoder_head_mask is None: if self.config.num_layers == self.config.num_decoder_layers: warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning) decoder_head_mask = head_mask # Encode if needed (training, first prediction pass) if encoder_outputs is None: # Convert encoder inputs in embeddings if needed encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, return_dict=return_dict, ) elif return_dict and not isinstance(encoder_outputs, MoEModelOutput): encoder_outputs = MoEModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None, ) hidden_states = encoder_outputs[0] if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: # get decoder inputs from shifting lm labels to the right decoder_input_ids = self._shift_right(labels) # Decode decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, return_dict=return_dict, ) sequence_output = decoder_outputs[0] if self.config.tie_word_embeddings: # Rescale output before projecting on vocab # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 sequence_output = sequence_output * (self.model_dim**-0.5) lm_logits = self.lm_head(sequence_output) loss = None encoder_z_loss = None encoder_aux_loss = None decoder_z_loss = None decoder_aux_loss = None if output_router_logits: # Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder if self.encoder.config.encoder_sparse_step > 1: encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(encoder_outputs[-1]) encoder_z_loss = router_z_loss_func(encoder_router_logits) encoder_router_probs = nn.Softmax(dim=-1)(encoder_router_logits) encoder_aux_loss = load_balancing_loss_func(encoder_router_probs, encoder_expert_indexes) else: encoder_z_loss = 0 encoder_aux_loss = 0 if self.decoder.config.decoder_sparse_step > 1: decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(decoder_outputs[-1]) decoder_z_loss = router_z_loss_func(decoder_router_logits) decoder_router_probs = nn.Softmax(dim=-1)(decoder_router_logits) decoder_aux_loss = load_balancing_loss_func(decoder_router_probs, decoder_expert_indexes) else: decoder_z_loss = 0 decoder_aux_loss = 0 if labels is not None: loss_fct = CrossEntropyLoss(ignore_index=-100) # move labels to correct device to enable PP labels = labels.to(lm_logits.device) loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) if output_router_logits: z_loss = self.router_z_loss_coef * (encoder_z_loss + decoder_z_loss) aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss) loss = loss + z_loss + aux_loss if not return_dict: output = (lm_logits,) if output_router_logits: output += (encoder_z_loss, encoder_aux_loss, decoder_z_loss, decoder_aux_loss) output += (*decoder_outputs[1:], *encoder_outputs) return ((loss,) + output) if loss is not None else output return Seq2SeqMoEOutput( loss=loss, logits=lm_logits, encoder_z_loss=encoder_z_loss, encoder_aux_loss=encoder_aux_loss, decoder_z_loss=decoder_z_loss, decoder_aux_loss=decoder_aux_loss, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, decoder_router_logits=decoder_outputs.router_probs, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, encoder_router_logits=encoder_outputs.router_probs, ) def _unpack_router_logits(self, router_outputs): total_router_logits = [] total_expert_indexes = [] for router_output in router_outputs: if len(router_output[0].shape) > 1: router_logits, expert_indexes = router_output total_router_logits.append(router_logits) total_expert_indexes.append(expert_indexes) return torch.cat(total_router_logits, dim=1), torch.cat(total_expert_indexes, dim=1) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs, ): # cut decoder_input_ids if past_key_values is used if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] return { "decoder_input_ids": input_ids, "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache, } def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): return self._shift_right(labels) def _reorder_cache(self, past_key_values, beam_idx): # if decoder past is not included in output # speedy decoding is disabled and no need to reorder if past_key_values is None: logger.warning("You might want to consider setting `use_cache=True` to speed up decoding") return past_key_values reordered_decoder_past = () for layer_past_states in past_key_values: # get the correct batch idx from layer past batch dim # batch dim of `past` is at 2nd position reordered_layer_past_states = () for layer_past_state in layer_past_states: # need to set correct `past` for each of the four key / value states reordered_layer_past_states = reordered_layer_past_states + ( layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)), ) if reordered_layer_past_states[0].shape != layer_past_states[0].shape: raise ValueError( "expected reordered_layer_past_states to have the same shape than layer_past_states, " f"but got {reordered_layer_past_states[0].shape} and {layer_past_states[0].shape}" ) if len(reordered_layer_past_states) != len(layer_past_states): raise ValueError( "expected layer_past_states to have the same length as reordered_layer_past_states, " f"but got {len(layer_past_states)} and {len(reordered_layer_past_states)}" ) reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,) return reordered_decoder_past @add_start_docstrings( "The bare SWITCH_TRANSFORMERS Model transformer outputting encoder's raw hidden-states without any specific head" " on top.", SWITCH_TRANSFORMERS_START_DOCSTRING, ) class SwitchTransformersEncoderModel(SwitchTransformersPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight"] def __init__(self, config: SwitchTransformersConfig): super().__init__(config) self.shared = nn.Embedding(config.vocab_size, config.d_model) encoder_config = copy.deepcopy(config) encoder_config.use_cache = False encoder_config.is_encoder_decoder = False self.encoder = SwitchTransformersStack(encoder_config, self.shared) # Initialize weights and apply final processing self.post_init() # Model parallel self.device_map = None def get_input_embeddings(self): return self.shared def set_input_embeddings(self, new_embeddings): self.shared = new_embeddings self.encoder.set_input_embeddings(new_embeddings) def _tie_weights(self): if self.config.tie_word_embeddings: self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared) def get_encoder(self): return self.encoder def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads) @add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_ENCODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MoEModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = True, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], MoEModelOutput]: r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, SwitchTransformersEncoderModel >>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8") >>> model = SwitchTransformersEncoderModel.from_pretrained("google/switch-base-8") >>> input_ids = tokenizer( ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" ... ).input_ids # Batch size 1 >>> outputs = model(input_ids=input_ids) >>> last_hidden_states = outputs.last_hidden_state ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, return_dict=return_dict, ) return encoder_outputs

transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py/0

{ "file_path": "transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py", "repo_id": "transformers", "token_count": 37581 }

356

# coding=utf-8 # Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Table Transformer model.""" import math from dataclasses import dataclass from typing import Dict, List, Optional, Tuple, Union import torch from torch import Tensor, nn from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_attention_mask from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_scipy_available, is_timm_available, is_vision_available, logging, replace_return_docstrings, requires_backends, ) from ...utils.backbone_utils import load_backbone from .configuration_table_transformer import TableTransformerConfig if is_scipy_available(): from scipy.optimize import linear_sum_assignment if is_timm_available(): from timm import create_model if is_vision_available(): from transformers.image_transforms import center_to_corners_format logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "TableTransformerConfig" _CHECKPOINT_FOR_DOC = "microsoft/table-transformer-detection" TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/table-transformer-detection", # See all Table Transformer models at https://huggingface.co/models?filter=table-transformer ] @dataclass # Copied from transformers.models.detr.modeling_detr.DetrDecoderOutput with DETR->TABLE_TRANSFORMER,Detr->TableTransformer class TableTransformerDecoderOutput(BaseModelOutputWithCrossAttentions): """ Base class for outputs of the TABLE_TRANSFORMER decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. """ intermediate_hidden_states: Optional[torch.FloatTensor] = None @dataclass # Copied from transformers.models.detr.modeling_detr.DetrModelOutput with DETR->TABLE_TRANSFORMER,Detr->TableTransformer class TableTransformerModelOutput(Seq2SeqModelOutput): """ Base class for outputs of the TABLE_TRANSFORMER encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding losses. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a layernorm. """ intermediate_hidden_states: Optional[torch.FloatTensor] = None @dataclass # Copied from transformers.models.detr.modeling_detr.DetrObjectDetectionOutput with Detr->TableTransformer,DetrImageProcessor->DetrImageProcessor class TableTransformerObjectDetectionOutput(ModelOutput): """ Output type of [`TableTransformerForObjectDetection`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized scale-invariant IoU loss. loss_dict (`Dict`, *optional*): A dictionary containing the individual losses. Useful for logging. logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): Classification logits (including no-object) for all queries. pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). You can use [`~TableTransformerImageProcessor.post_process_object_detection`] to retrieve the unnormalized bounding boxes. auxiliary_outputs (`list[Dict]`, *optional*): Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and `pred_boxes`) for each decoder layer. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the decoder of the model. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None loss_dict: Optional[Dict] = None logits: torch.FloatTensor = None pred_boxes: torch.FloatTensor = None auxiliary_outputs: Optional[List[Dict]] = None last_hidden_state: Optional[torch.FloatTensor] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None # Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->TableTransformer class TableTransformerFrozenBatchNorm2d(nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super().__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ): num_batches_tracked_key = prefix + "num_batches_tracked" if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ) def forward(self, x): # move reshapes to the beginning # to make it user-friendly weight = self.weight.reshape(1, -1, 1, 1) bias = self.bias.reshape(1, -1, 1, 1) running_var = self.running_var.reshape(1, -1, 1, 1) running_mean = self.running_mean.reshape(1, -1, 1, 1) epsilon = 1e-5 scale = weight * (running_var + epsilon).rsqrt() bias = bias - running_mean * scale return x * scale + bias # Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->TableTransformer def replace_batch_norm(model): r""" Recursively replace all `torch.nn.BatchNorm2d` with `TableTransformerFrozenBatchNorm2d`. Args: model (torch.nn.Module): input model """ for name, module in model.named_children(): if isinstance(module, nn.BatchNorm2d): new_module = TableTransformerFrozenBatchNorm2d(module.num_features) if not module.weight.device == torch.device("meta"): new_module.weight.data.copy_(module.weight) new_module.bias.data.copy_(module.bias) new_module.running_mean.data.copy_(module.running_mean) new_module.running_var.data.copy_(module.running_var) model._modules[name] = new_module if len(list(module.children())) > 0: replace_batch_norm(module) # Copied from transformers.models.detr.modeling_detr.DetrConvEncoder with Detr->TableTransformer class TableTransformerConvEncoder(nn.Module): """ Convolutional backbone, using either the AutoBackbone API or one from the timm library. nn.BatchNorm2d layers are replaced by TableTransformerFrozenBatchNorm2d as defined above. """ def __init__(self, config): super().__init__() self.config = config if config.use_timm_backbone: requires_backends(self, ["timm"]) kwargs = {} if config.dilation: kwargs["output_stride"] = 16 backbone = create_model( config.backbone, pretrained=config.use_pretrained_backbone, features_only=True, out_indices=(1, 2, 3, 4), in_chans=config.num_channels, **kwargs, ) else: backbone = load_backbone(config) # replace batch norm by frozen batch norm with torch.no_grad(): replace_batch_norm(backbone) self.model = backbone self.intermediate_channel_sizes = ( self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels ) backbone_model_type = config.backbone if config.use_timm_backbone else config.backbone_config.model_type if "resnet" in backbone_model_type: for name, parameter in self.model.named_parameters(): if config.use_timm_backbone: if "layer2" not in name and "layer3" not in name and "layer4" not in name: parameter.requires_grad_(False) else: if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name: parameter.requires_grad_(False) def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): # send pixel_values through the model to get list of feature maps features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps out = [] for feature_map in features: # downsample pixel_mask to match shape of corresponding feature_map mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] out.append((feature_map, mask)) return out # Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->TableTransformer class TableTransformerConvModel(nn.Module): """ This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder. """ def __init__(self, conv_encoder, position_embedding): super().__init__() self.conv_encoder = conv_encoder self.position_embedding = position_embedding def forward(self, pixel_values, pixel_mask): # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples out = self.conv_encoder(pixel_values, pixel_mask) pos = [] for feature_map, mask in out: # position encoding pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype)) return out, pos # Copied from transformers.models.detr.modeling_detr.DetrSinePositionEmbedding with Detr->TableTransformer class TableTransformerSinePositionEmbedding(nn.Module): """ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized to work on images. """ def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None): super().__init__() self.embedding_dim = embedding_dim self.temperature = temperature self.normalize = normalize if scale is not None and normalize is False: raise ValueError("normalize should be True if scale is passed") if scale is None: scale = 2 * math.pi self.scale = scale def forward(self, pixel_values, pixel_mask): if pixel_mask is None: raise ValueError("No pixel mask provided") y_embed = pixel_mask.cumsum(1, dtype=torch.float32) x_embed = pixel_mask.cumsum(2, dtype=torch.float32) if self.normalize: y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale dim_t = torch.arange(self.embedding_dim, dtype=torch.int64, device=pixel_values.device).float() dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos # Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding with Detr->TableTransformer class TableTransformerLearnedPositionEmbedding(nn.Module): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, embedding_dim=256): super().__init__() self.row_embeddings = nn.Embedding(50, embedding_dim) self.column_embeddings = nn.Embedding(50, embedding_dim) def forward(self, pixel_values, pixel_mask=None): height, width = pixel_values.shape[-2:] width_values = torch.arange(width, device=pixel_values.device) height_values = torch.arange(height, device=pixel_values.device) x_emb = self.column_embeddings(width_values) y_emb = self.row_embeddings(height_values) pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) pos = pos.permute(2, 0, 1) pos = pos.unsqueeze(0) pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) return pos # Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->TableTransformer def build_position_encoding(config): n_steps = config.d_model // 2 if config.position_embedding_type == "sine": # TODO find a better way of exposing other arguments position_embedding = TableTransformerSinePositionEmbedding(n_steps, normalize=True) elif config.position_embedding_type == "learned": position_embedding = TableTransformerLearnedPositionEmbedding(n_steps) else: raise ValueError(f"Not supported {config.position_embedding_type}") return position_embedding # Copied from transformers.models.detr.modeling_detr.DetrAttention with DETR->TABLE_TRANSFORMER,Detr->TableTransformer class TableTransformerAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and keys (as explained in the TABLE_TRANSFORMER paper). """ def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if self.head_dim * num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {num_heads})." ) self.scaling = self.head_dim**-0.5 self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs): position_embeddings = kwargs.pop("position_embeddings", None) if kwargs: raise ValueError(f"Unexpected arguments {kwargs.keys()}") if position_embeddings is not None and object_queries is not None: raise ValueError( "Cannot specify both position_embeddings and object_queries. Please use just object_queries" ) if position_embeddings is not None: logger.warning_once( "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" ) object_queries = position_embeddings return tensor if object_queries is None else tensor + object_queries def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, object_queries: Optional[torch.Tensor] = None, key_value_states: Optional[torch.Tensor] = None, spatial_position_embeddings: Optional[torch.Tensor] = None, output_attentions: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" position_embeddings = kwargs.pop("position_ebmeddings", None) key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None) if kwargs: raise ValueError(f"Unexpected arguments {kwargs.keys()}") if position_embeddings is not None and object_queries is not None: raise ValueError( "Cannot specify both position_embeddings and object_queries. Please use just object_queries" ) if key_value_position_embeddings is not None and spatial_position_embeddings is not None: raise ValueError( "Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings" ) if position_embeddings is not None: logger.warning_once( "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" ) object_queries = position_embeddings if key_value_position_embeddings is not None: logger.warning_once( "key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead" ) spatial_position_embeddings = key_value_position_embeddings # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size, target_len, embed_dim = hidden_states.size() # add position embeddings to the hidden states before projecting to queries and keys if object_queries is not None: hidden_states_original = hidden_states hidden_states = self.with_pos_embed(hidden_states, object_queries) # add key-value position embeddings to the key value states if spatial_position_embeddings is not None: key_value_states_original = key_value_states key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings) # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj if is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) proj_shape = (batch_size * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) source_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): raise ValueError( f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, target_len, source_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" f" {attention_mask.size()}" ) attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(batch_size, target_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped class TableTransformerEncoderLayer(nn.Module): # Copied from transformers.models.detr.modeling_detr.DetrEncoderLayer.__init__ with Detr->TableTransformer def __init__(self, config: TableTransformerConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = TableTransformerAttention( embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, ) self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, object_queries: torch.Tensor = None, output_attentions: bool = False, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. object_queries (`torch.FloatTensor`, *optional*): object queries, to be added to hidden_states. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, object_queries=object_queries, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states if self.training: if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any(): clamp_value = torch.finfo(hidden_states.dtype).max - 1000 hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class TableTransformerDecoderLayer(nn.Module): # Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer.__init__ with Detr->TableTransformer def __init__(self, config: TableTransformerConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = TableTransformerAttention( embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, ) self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.encoder_attn = TableTransformerAttention( self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, object_queries: Optional[torch.Tensor] = None, query_position_embeddings: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ): """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. object_queries (`torch.FloatTensor`, *optional*): object queries that are added to the queries and keys in the cross-attention layer. query_position_embeddings (`torch.FloatTensor`, *optional*): object queries that are added to the queries and keys in the self-attention layer. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, object_queries=query_position_embeddings, attention_mask=attention_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, object_queries=query_position_embeddings, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, spatial_position_embeddings=object_queries, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) # Fully Connected hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs # Copied from transformers.models.detr.modeling_detr.DetrClassificationHead with Detr->TableTransformer class TableTransformerClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float): super().__init__() self.dense = nn.Linear(input_dim, inner_dim) self.dropout = nn.Dropout(p=pooler_dropout) self.out_proj = nn.Linear(inner_dim, num_classes) def forward(self, hidden_states: torch.Tensor): hidden_states = self.dropout(hidden_states) hidden_states = self.dense(hidden_states) hidden_states = torch.tanh(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.out_proj(hidden_states) return hidden_states class TableTransformerPreTrainedModel(PreTrainedModel): config_class = TableTransformerConfig base_model_prefix = "model" main_input_name = "pixel_values" _no_split_modules = [ r"TableTransformerConvEncoder", r"TableTransformerEncoderLayer", r"TableTransformerDecoderLayer", ] def _init_weights(self, module): std = self.config.init_std if isinstance(module, TableTransformerLearnedPositionEmbedding): nn.init.uniform_(module.row_embeddings.weight) nn.init.uniform_(module.column_embeddings.weight) if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() TABLE_TRANSFORMER_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`TableTransformerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ TABLE_TRANSFORMER_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`DetrImageProcessor`]. See [`DetrImageProcessor.__call__`] for details. pixel_mask (`torch.FloatTensor` of shape `(batch_size, height, width)`, *optional*): Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): Not used by default. Can be used to mask object queries. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you can choose to directly pass a flattened representation of an image. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an embedded representation. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class TableTransformerEncoder(TableTransformerPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`TableTransformerEncoderLayer`]. The encoder updates the flattened feature map through multiple self-attention layers. Small tweak for Table Transformer: - object_queries are added to the forward pass. Args: config: TableTransformerConfig """ def __init__(self, config: TableTransformerConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.encoder_layerdrop self.layers = nn.ModuleList([TableTransformerEncoderLayer(config) for _ in range(config.encoder_layers)]) self.layernorm = nn.LayerNorm(config.d_model) # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, attention_mask=None, object_queries=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: - 1 for pixel features that are real (i.e. **not masked**), - 0 for pixel features that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Position embeddings that are added to the queries and keys in each self-attention layer. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = inputs_embeds hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) # expand attention_mask if attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for encoder_layer in self.layers: if output_hidden_states: encoder_states = encoder_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) to_drop = False if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: # skip the layer to_drop = True if to_drop: layer_outputs = (None, None) else: # we add object_queries as extra input to the encoder_layer layer_outputs = encoder_layer( hidden_states, attention_mask, object_queries=object_queries, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) hidden_states = self.layernorm(hidden_states) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) # Copied from transformers.models.detr.modeling_detr.DetrDecoder with DETR->TABLE_TRANSFORMER,Detr->TableTransformer class TableTransformerDecoder(TableTransformerPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TableTransformerDecoderLayer`]. The decoder updates the query embeddings through multiple self-attention and cross-attention layers. Some small tweaks for TABLE_TRANSFORMER: - object_queries and query_position_embeddings are added to the forward pass. - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. Args: config: TableTransformerConfig """ def __init__(self, config: TableTransformerConfig): super().__init__(config) self.dropout = config.dropout self.layerdrop = config.decoder_layerdrop self.layers = nn.ModuleList([TableTransformerDecoderLayer(config) for _ in range(config.decoder_layers)]) # in TABLE_TRANSFORMER, the decoder uses layernorm after the last decoder layer output self.layernorm = nn.LayerNorm(config.d_model) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, object_queries=None, query_position_embeddings=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): The query embeddings that are passed into the decoder. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: - 1 for queries that are **not masked**, - 0 for queries that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Object queries that are added to the queries and keys in each cross-attention layer. query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): , *optional*): Position embeddings that are added to the values and keys in each self-attention layer. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ position_embeddings = kwargs.pop("position_embeddings", None) if kwargs: raise ValueError(f"Unexpected arguments {kwargs.keys()}") if position_embeddings is not None and object_queries is not None: raise ValueError( "Cannot specify both position_embeddings and object_queries. Please use just object_queries" ) if position_embeddings is not None: logger.warning_once( "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" ) object_queries = position_embeddings output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if inputs_embeds is not None: hidden_states = inputs_embeds input_shape = inputs_embeds.size()[:-1] combined_attention_mask = None if attention_mask is not None and combined_attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] combined_attention_mask = combined_attention_mask + _prepare_4d_attention_mask( attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] ) # expand encoder attention mask if encoder_hidden_states is not None and encoder_attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] encoder_attention_mask = _prepare_4d_attention_mask( encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] ) # optional intermediate hidden states intermediate = () if self.config.auxiliary_loss else None # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, combined_attention_mask, encoder_hidden_states, encoder_attention_mask, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=combined_attention_mask, object_queries=object_queries, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if self.config.auxiliary_loss: hidden_states = self.layernorm(hidden_states) intermediate += (hidden_states,) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # finally, apply layernorm hidden_states = self.layernorm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) # stack intermediate decoder activations if self.config.auxiliary_loss: intermediate = torch.stack(intermediate) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate] if v is not None ) return TableTransformerDecoderOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, intermediate_hidden_states=intermediate, ) @add_start_docstrings( """ The bare Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without any specific head on top. """, TABLE_TRANSFORMER_START_DOCSTRING, ) class TableTransformerModel(TableTransformerPreTrainedModel): # Copied from transformers.models.detr.modeling_detr.DetrModel.__init__ with Detr->TableTransformer def __init__(self, config: TableTransformerConfig): super().__init__(config) # Create backbone + positional encoding backbone = TableTransformerConvEncoder(config) object_queries = build_position_encoding(config) self.backbone = TableTransformerConvModel(backbone, object_queries) # Create projection layer self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1) self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model) self.encoder = TableTransformerEncoder(config) self.decoder = TableTransformerDecoder(config) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.encoder def get_decoder(self): return self.decoder def freeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(False) def unfreeze_backbone(self): for name, param in self.backbone.conv_encoder.model.named_parameters(): param.requires_grad_(True) @add_start_docstrings_to_model_forward(TABLE_TRANSFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TableTransformerModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, pixel_mask: Optional[torch.FloatTensor] = None, decoder_attention_mask: Optional[torch.FloatTensor] = None, encoder_outputs: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], TableTransformerModelOutput]: r""" Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TableTransformerModel >>> from huggingface_hub import hf_hub_download >>> from PIL import Image >>> file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png") >>> image = Image.open(file_path).convert("RGB") >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection") >>> model = TableTransformerModel.from_pretrained("microsoft/table-transformer-detection") >>> # prepare image for the model >>> inputs = image_processor(images=image, return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> # the last hidden states are the final query embeddings of the Transformer decoder >>> # these are of shape (batch_size, num_queries, hidden_size) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 15, 256] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, num_channels, height, width = pixel_values.shape device = pixel_values.device if pixel_mask is None: pixel_mask = torch.ones(((batch_size, height, width)), device=device) # First, sent pixel_values + pixel_mask through Backbone to obtain the features # pixel_values should be of shape (batch_size, num_channels, height, width) # pixel_mask should be of shape (batch_size, height, width) features, position_embeddings_list = self.backbone(pixel_values, pixel_mask) # get final feature map and downsampled mask feature_map, mask = features[-1] if mask is None: raise ValueError("Backbone does not return downsampled pixel mask") # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) projected_feature_map = self.input_projection(feature_map) # Third, flatten the feature map + object queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC # In other words, turn their shape into (batch_size, sequence_length, hidden_size) flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) object_queries = position_embeddings_list[-1].flatten(2).permute(0, 2, 1) flattened_mask = mask.flatten(1) # Fourth, sent flattened_features + flattened_mask + object queries through encoder # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) # flattened_mask is a Tensor of shape (batch_size, heigth*width) if encoder_outputs is None: encoder_outputs = self.encoder( inputs_embeds=flattened_features, attention_mask=flattened_mask, object_queries=object_queries, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput( last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, ) # Fifth, sent query embeddings + object queries through the decoder (which is conditioned on the encoder output) query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1) queries = torch.zeros_like(query_position_embeddings) # decoder outputs consists of (dec_features, dec_hidden, dec_attn) decoder_outputs = self.decoder( inputs_embeds=queries, attention_mask=None, object_queries=object_queries, query_position_embeddings=query_position_embeddings, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=flattened_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs + encoder_outputs return TableTransformerModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, ) @add_start_docstrings( """ Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection. """, TABLE_TRANSFORMER_START_DOCSTRING, ) class TableTransformerForObjectDetection(TableTransformerPreTrainedModel): # Copied from transformers.models.detr.modeling_detr.DetrForObjectDetection.__init__ with Detr->TableTransformer def __init__(self, config: TableTransformerConfig): super().__init__(config) # DETR encoder-decoder model self.model = TableTransformerModel(config) # Object detection heads self.class_labels_classifier = nn.Linear( config.d_model, config.num_labels + 1 ) # We add one for the "no object" class self.bbox_predictor = TableTransformerMLPPredictionHead( input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3 ) # Initialize weights and apply final processing self.post_init() @torch.jit.unused # Copied from transformers.models.detr.modeling_detr.DetrForObjectDetection._set_aux_loss def _set_aux_loss(self, outputs_class, outputs_coord): # this is a workaround to make torchscript happy, as torchscript # doesn't support dictionary with non-homogeneous values, such # as a dict having both a Tensor and a list. return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] @add_start_docstrings_to_model_forward(TABLE_TRANSFORMER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TableTransformerObjectDetectionOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, pixel_mask: Optional[torch.FloatTensor] = None, decoder_attention_mask: Optional[torch.FloatTensor] = None, encoder_outputs: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[List[Dict]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], TableTransformerObjectDetectionOutput]: r""" labels (`List[Dict]` of len `(batch_size,)`, *optional*): Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. Returns: Examples: ```python >>> from huggingface_hub import hf_hub_download >>> from transformers import AutoImageProcessor, TableTransformerForObjectDetection >>> import torch >>> from PIL import Image >>> file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png") >>> image = Image.open(file_path).convert("RGB") >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection") >>> model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection") >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) >>> target_sizes = torch.tensor([image.size[::-1]]) >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[ ... 0 ... ] >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): ... box = [round(i, 2) for i in box.tolist()] ... print( ... f"Detected {model.config.id2label[label.item()]} with confidence " ... f"{round(score.item(), 3)} at location {box}" ... ) Detected table with confidence 1.0 at location [202.1, 210.59, 1119.22, 385.09] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict # First, sent images through TABLE_TRANSFORMER base model to obtain encoder + decoder outputs outputs = self.model( pixel_values, pixel_mask=pixel_mask, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # class logits + predicted bounding boxes logits = self.class_labels_classifier(sequence_output) pred_boxes = self.bbox_predictor(sequence_output).sigmoid() loss, loss_dict, auxiliary_outputs = None, None, None if labels is not None: # First: create the matcher matcher = TableTransformerHungarianMatcher( class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost ) # Second: create the criterion losses = ["labels", "boxes", "cardinality"] criterion = TableTransformerLoss( matcher=matcher, num_classes=self.config.num_labels, eos_coef=self.config.eos_coefficient, losses=losses, ) criterion.to(self.device) # Third: compute the losses, based on outputs and labels outputs_loss = {} outputs_loss["logits"] = logits outputs_loss["pred_boxes"] = pred_boxes if self.config.auxiliary_loss: intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4] outputs_class = self.class_labels_classifier(intermediate) outputs_coord = self.bbox_predictor(intermediate).sigmoid() auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) outputs_loss["auxiliary_outputs"] = auxiliary_outputs loss_dict = criterion(outputs_loss, labels) # Fourth: compute total loss, as a weighted sum of the various losses weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient} weight_dict["loss_giou"] = self.config.giou_loss_coefficient if self.config.auxiliary_loss: aux_weight_dict = {} for i in range(self.config.decoder_layers - 1): aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) weight_dict.update(aux_weight_dict) loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) if not return_dict: if auxiliary_outputs is not None: output = (logits, pred_boxes) + auxiliary_outputs + outputs else: output = (logits, pred_boxes) + outputs return ((loss, loss_dict) + output) if loss is not None else output return TableTransformerObjectDetectionOutput( loss=loss, loss_dict=loss_dict, logits=logits, pred_boxes=pred_boxes, auxiliary_outputs=auxiliary_outputs, last_hidden_state=outputs.last_hidden_state, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) # Copied from transformers.models.detr.modeling_detr.dice_loss def dice_loss(inputs, targets, num_boxes): """ Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). """ inputs = inputs.sigmoid() inputs = inputs.flatten(1) numerator = 2 * (inputs * targets).sum(1) denominator = inputs.sum(-1) + targets.sum(-1) loss = 1 - (numerator + 1) / (denominator + 1) return loss.sum() / num_boxes # Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): """ Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (`torch.FloatTensor` of arbitrary shape): The predictions for each example. targets (`torch.FloatTensor` with the same shape as `inputs`) A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class and 1 for the positive class). alpha (`float`, *optional*, defaults to `0.25`): Optional weighting factor in the range (0,1) to balance positive vs. negative examples. gamma (`int`, *optional*, defaults to `2`): Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. Returns: Loss tensor """ prob = inputs.sigmoid() ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") # add modulating factor p_t = prob * targets + (1 - prob) * (1 - targets) loss = ce_loss * ((1 - p_t) ** gamma) if alpha >= 0: alpha_t = alpha * targets + (1 - alpha) * (1 - targets) loss = alpha_t * loss return loss.mean(1).sum() / num_boxes # Copied from transformers.models.detr.modeling_detr.DetrLoss with Detr->TableTransformer,detr->table_transformer class TableTransformerLoss(nn.Module): """ This class computes the losses for TableTransformerForObjectDetection/TableTransformerForSegmentation. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and box). A note on the `num_classes` argument (copied from original repo in table_transformer.py): "the naming of the `num_classes` parameter of the criterion is somewhat misleading. It indeed corresponds to `max_obj_id` + 1, where `max_obj_id` is the maximum id for a class in your dataset. For example, COCO has a `max_obj_id` of 90, so we pass `num_classes` to be 91. As another example, for a dataset that has a single class with `id` 1, you should pass `num_classes` to be 2 (`max_obj_id` + 1). For more details on this, check the following discussion https://github.com/facebookresearch/table_transformer/issues/108#issuecomment-650269223" Args: matcher (`TableTransformerHungarianMatcher`): Module able to compute a matching between targets and proposals. num_classes (`int`): Number of object categories, omitting the special no-object category. eos_coef (`float`): Relative classification weight applied to the no-object category. losses (`List[str]`): List of all the losses to be applied. See `get_loss` for a list of all available losses. """ def __init__(self, matcher, num_classes, eos_coef, losses): super().__init__() self.matcher = matcher self.num_classes = num_classes self.eos_coef = eos_coef self.losses = losses empty_weight = torch.ones(self.num_classes + 1) empty_weight[-1] = self.eos_coef self.register_buffer("empty_weight", empty_weight) # removed logging parameter, which was part of the original implementation def loss_labels(self, outputs, targets, indices, num_boxes): """ Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim [nb_target_boxes] """ if "logits" not in outputs: raise KeyError("No logits were found in the outputs") source_logits = outputs["logits"] idx = self._get_source_permutation_idx(indices) target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)]) target_classes = torch.full( source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device ) target_classes[idx] = target_classes_o loss_ce = nn.functional.cross_entropy(source_logits.transpose(1, 2), target_classes, self.empty_weight) losses = {"loss_ce": loss_ce} return losses @torch.no_grad() def loss_cardinality(self, outputs, targets, indices, num_boxes): """ Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes. This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients. """ logits = outputs["logits"] device = logits.device target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device) # Count the number of predictions that are NOT "no-object" (which is the last class) card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1) card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float()) losses = {"cardinality_error": card_err} return losses def loss_boxes(self, outputs, targets, indices, num_boxes): """ Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss. Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. """ if "pred_boxes" not in outputs: raise KeyError("No predicted boxes found in outputs") idx = self._get_source_permutation_idx(indices) source_boxes = outputs["pred_boxes"][idx] target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none") losses = {} losses["loss_bbox"] = loss_bbox.sum() / num_boxes loss_giou = 1 - torch.diag( generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes)) ) losses["loss_giou"] = loss_giou.sum() / num_boxes return losses def loss_masks(self, outputs, targets, indices, num_boxes): """ Compute the losses related to the masks: the focal loss and the dice loss. Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]. """ if "pred_masks" not in outputs: raise KeyError("No predicted masks found in outputs") source_idx = self._get_source_permutation_idx(indices) target_idx = self._get_target_permutation_idx(indices) source_masks = outputs["pred_masks"] source_masks = source_masks[source_idx] masks = [t["masks"] for t in targets] # TODO use valid to mask invalid areas due to padding in loss target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() target_masks = target_masks.to(source_masks) target_masks = target_masks[target_idx] # upsample predictions to the target size source_masks = nn.functional.interpolate( source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False ) source_masks = source_masks[:, 0].flatten(1) target_masks = target_masks.flatten(1) target_masks = target_masks.view(source_masks.shape) losses = { "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes), "loss_dice": dice_loss(source_masks, target_masks, num_boxes), } return losses def _get_source_permutation_idx(self, indices): # permute predictions following indices batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)]) source_idx = torch.cat([source for (source, _) in indices]) return batch_idx, source_idx def _get_target_permutation_idx(self, indices): # permute targets following indices batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)]) target_idx = torch.cat([target for (_, target) in indices]) return batch_idx, target_idx def get_loss(self, loss, outputs, targets, indices, num_boxes): loss_map = { "labels": self.loss_labels, "cardinality": self.loss_cardinality, "boxes": self.loss_boxes, "masks": self.loss_masks, } if loss not in loss_map: raise ValueError(f"Loss {loss} not supported") return loss_map[loss](outputs, targets, indices, num_boxes) def forward(self, outputs, targets): """ This performs the loss computation. Args: outputs (`dict`, *optional*): Dictionary of tensors, see the output specification of the model for the format. targets (`List[dict]`, *optional*): List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the losses applied, see each loss' doc. """ outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"} # Retrieve the matching between the outputs of the last layer and the targets indices = self.matcher(outputs_without_aux, targets) # Compute the average number of target boxes across all nodes, for normalization purposes num_boxes = sum(len(t["class_labels"]) for t in targets) num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device) # (Niels): comment out function below, distributed training to be added # if is_dist_avail_and_initialized(): # torch.distributed.all_reduce(num_boxes) # (Niels) in original implementation, num_boxes is divided by get_world_size() num_boxes = torch.clamp(num_boxes, min=1).item() # Compute all the requested losses losses = {} for loss in self.losses: losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. if "auxiliary_outputs" in outputs: for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]): indices = self.matcher(auxiliary_outputs, targets) for loss in self.losses: if loss == "masks": # Intermediate masks losses are too costly to compute, we ignore them. continue l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes) l_dict = {k + f"_{i}": v for k, v in l_dict.items()} losses.update(l_dict) return losses # Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with Detr->TableTransformer,detr->table_transformer class TableTransformerMLPPredictionHead(nn.Module): """ Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, height and width of a bounding box w.r.t. an image. Copied from https://github.com/facebookresearch/table_transformer/blob/master/models/table_transformer.py """ def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x # Copied from transformers.models.detr.modeling_detr.DetrHungarianMatcher with Detr->TableTransformer class TableTransformerHungarianMatcher(nn.Module): """ This class computes an assignment between the targets and the predictions of the network. For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are un-matched (and thus treated as non-objects). Args: class_cost: The relative weight of the classification error in the matching cost. bbox_cost: The relative weight of the L1 error of the bounding box coordinates in the matching cost. giou_cost: The relative weight of the giou loss of the bounding box in the matching cost. """ def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1): super().__init__() requires_backends(self, ["scipy"]) self.class_cost = class_cost self.bbox_cost = bbox_cost self.giou_cost = giou_cost if class_cost == 0 and bbox_cost == 0 and giou_cost == 0: raise ValueError("All costs of the Matcher can't be 0") @torch.no_grad() def forward(self, outputs, targets): """ Args: outputs (`dict`): A dictionary that contains at least these entries: * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates. targets (`List[dict]`): A list of targets (len(targets) = batch_size), where each target is a dict containing: * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth objects in the target) containing the class labels * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates. Returns: `List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where: - index_i is the indices of the selected predictions (in order) - index_j is the indices of the corresponding selected targets (in order) For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes) """ batch_size, num_queries = outputs["logits"].shape[:2] # We flatten to compute the cost matrices in a batch out_prob = outputs["logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes] out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] # Also concat the target labels and boxes target_ids = torch.cat([v["class_labels"] for v in targets]) target_bbox = torch.cat([v["boxes"] for v in targets]) # Compute the classification cost. Contrary to the loss, we don't use the NLL, # but approximate it in 1 - proba[target class]. # The 1 is a constant that doesn't change the matching, it can be ommitted. class_cost = -out_prob[:, target_ids] # Compute the L1 cost between boxes bbox_cost = torch.cdist(out_bbox, target_bbox, p=1) # Compute the giou cost between boxes giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox)) # Final cost matrix cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu() sizes = [len(v["boxes"]) for v in targets] indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))] return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] # Copied from transformers.models.detr.modeling_detr._upcast def _upcast(t: Tensor) -> Tensor: # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type if t.is_floating_point(): return t if t.dtype in (torch.float32, torch.float64) else t.float() else: return t if t.dtype in (torch.int32, torch.int64) else t.int() # Copied from transformers.models.detr.modeling_detr.box_area def box_area(boxes: Tensor) -> Tensor: """ Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. Args: boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 < x2` and `0 <= y1 < y2`. Returns: `torch.FloatTensor`: a tensor containing the area for each box. """ boxes = _upcast(boxes) return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) # Copied from transformers.models.detr.modeling_detr.box_iou def box_iou(boxes1, boxes2): area1 = box_area(boxes1) area2 = box_area(boxes2) left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] union = area1[:, None] + area2 - inter iou = inter / union return iou, union # Copied from transformers.models.detr.modeling_detr.generalized_box_iou def generalized_box_iou(boxes1, boxes2): """ Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. Returns: `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) """ # degenerate boxes gives inf / nan results # so do an early check if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") iou, union = box_iou(boxes1, boxes2) top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] area = width_height[:, :, 0] * width_height[:, :, 1] return iou - (area - union) / area # Copied from transformers.models.detr.modeling_detr._max_by_axis def _max_by_axis(the_list): # type: (List[List[int]]) -> List[int] maxes = the_list[0] for sublist in the_list[1:]: for index, item in enumerate(sublist): maxes[index] = max(maxes[index], item) return maxes # Copied from transformers.models.detr.modeling_detr.NestedTensor class NestedTensor(object): def __init__(self, tensors, mask: Optional[Tensor]): self.tensors = tensors self.mask = mask def to(self, device): cast_tensor = self.tensors.to(device) mask = self.mask if mask is not None: cast_mask = mask.to(device) else: cast_mask = None return NestedTensor(cast_tensor, cast_mask) def decompose(self): return self.tensors, self.mask def __repr__(self): return str(self.tensors) # Copied from transformers.models.detr.modeling_detr.nested_tensor_from_tensor_list def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): if tensor_list[0].ndim == 3: max_size = _max_by_axis([list(img.shape) for img in tensor_list]) batch_shape = [len(tensor_list)] + max_size batch_size, num_channels, height, width = batch_shape dtype = tensor_list[0].dtype device = tensor_list[0].device tensor = torch.zeros(batch_shape, dtype=dtype, device=device) mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device) for img, pad_img, m in zip(tensor_list, tensor, mask): pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) m[: img.shape[1], : img.shape[2]] = False else: raise ValueError("Only 3-dimensional tensors are supported") return NestedTensor(tensor, mask)

transformers/src/transformers/models/table_transformer/modeling_table_transformer.py/0

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, Tuple, Union import torch from ...modeling_outputs import BackboneOutput from ...modeling_utils import PreTrainedModel from ...utils import is_timm_available, is_torch_available, requires_backends from ...utils.backbone_utils import BackboneMixin from .configuration_timm_backbone import TimmBackboneConfig if is_timm_available(): import timm if is_torch_available(): from torch import Tensor class TimmBackbone(PreTrainedModel, BackboneMixin): """ Wrapper class for timm models to be used as backbones. This enables using the timm models interchangeably with the other models in the library keeping the same API. """ main_input_name = "pixel_values" supports_gradient_checkpointing = False config_class = TimmBackboneConfig def __init__(self, config, **kwargs): requires_backends(self, "timm") super().__init__(config) self.config = config if config.backbone is None: raise ValueError("backbone is not set in the config. Please set it to a timm model name.") if config.backbone not in timm.list_models(): raise ValueError(f"backbone {config.backbone} is not supported by timm.") if hasattr(config, "out_features") and config.out_features is not None: raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead.") pretrained = getattr(config, "use_pretrained_backbone", None) if pretrained is None: raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False.") # We just take the final layer by default. This matches the default for the transformers models. out_indices = config.out_indices if getattr(config, "out_indices", None) is not None else (-1,) self._backbone = timm.create_model( config.backbone, pretrained=pretrained, # This is currently not possible for transformer architectures. features_only=config.features_only, in_chans=config.num_channels, out_indices=out_indices, **kwargs, ) # Converts all `BatchNorm2d` and `SyncBatchNorm` or `BatchNormAct2d` and `SyncBatchNormAct2d` layers of provided module into `FrozenBatchNorm2d` or `FrozenBatchNormAct2d` respectively if getattr(config, "freeze_batch_norm_2d", False): self.freeze_batch_norm_2d() # These are used to control the output of the model when called. If output_hidden_states is True, then # return_layers is modified to include all layers. self._return_layers = self._backbone.return_layers self._all_layers = {layer["module"]: str(i) for i, layer in enumerate(self._backbone.feature_info.info)} super()._init_backbone(config) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): requires_backends(cls, ["vision", "timm"]) from ...models.timm_backbone import TimmBackboneConfig config = kwargs.pop("config", TimmBackboneConfig()) use_timm = kwargs.pop("use_timm_backbone", True) if not use_timm: raise ValueError("use_timm_backbone must be True for timm backbones") num_channels = kwargs.pop("num_channels", config.num_channels) features_only = kwargs.pop("features_only", config.features_only) use_pretrained_backbone = kwargs.pop("use_pretrained_backbone", config.use_pretrained_backbone) out_indices = kwargs.pop("out_indices", config.out_indices) config = TimmBackboneConfig( backbone=pretrained_model_name_or_path, num_channels=num_channels, features_only=features_only, use_pretrained_backbone=use_pretrained_backbone, out_indices=out_indices, ) return super()._from_config(config, **kwargs) def freeze_batch_norm_2d(self): timm.layers.freeze_batch_norm_2d(self._backbone) def unfreeze_batch_norm_2d(self): timm.layers.unfreeze_batch_norm_2d(self._backbone) def _init_weights(self, module): """ Empty init weights function to ensure compatibility of the class in the library. """ pass def forward( self, pixel_values: torch.FloatTensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[BackboneOutput, Tuple[Tensor, ...]]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions if output_attentions: raise ValueError("Cannot output attentions for timm backbones at the moment") if output_hidden_states: # We modify the return layers to include all the stages of the backbone self._backbone.return_layers = self._all_layers hidden_states = self._backbone(pixel_values, **kwargs) self._backbone.return_layers = self._return_layers feature_maps = tuple(hidden_states[i] for i in self.out_indices) else: feature_maps = self._backbone(pixel_values, **kwargs) hidden_states = None feature_maps = tuple(feature_maps) hidden_states = tuple(hidden_states) if hidden_states is not None else None if not return_dict: output = (feature_maps,) if output_hidden_states: output = output + (hidden_states,) return output return BackboneOutput(feature_maps=feature_maps, hidden_states=hidden_states, attentions=None)

transformers/src/transformers/models/timm_backbone/modeling_timm_backbone.py/0

{ "file_path": "transformers/src/transformers/models/timm_backbone/modeling_timm_backbone.py", "repo_id": "transformers", "token_count": 2548 }

358

# coding=utf-8 # Copyright 2023 The Intel AIA Team Authors, and HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License=, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing=, software # distributed under the License is distributed on an "AS IS" BASIS=, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND=, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Processor class for TVP. """ from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class TvpProcessor(ProcessorMixin): r""" Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor. [`TvpProcessor`] offers all the functionalities of [`TvpImageProcessor`] and [`BertTokenizerFast`]. See the [`~TvpProcessor.__call__`] and [`~TvpProcessor.decode`] for more information. Args: image_processor ([`TvpImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "TvpImageProcessor" tokenizer_class = ("BertTokenizer", "BertTokenizerFast") def __init__(self, image_processor=None, tokenizer=None, **kwargs): if image_processor is None: raise ValueError("You need to specify an `image_processor`.") if tokenizer is None: raise ValueError("You need to specify a `tokenizer`.") super().__init__(image_processor, tokenizer) def __call__(self, text=None, videos=None, return_tensors=None, **kwargs): """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `videos` and `kwargs` arguments to TvpImageProcessor's [`~TvpImageProcessor.__call__`] if `videos` is not `None`. Please refer to the doctsring of the above two methods for more information. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). videos (`List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, `List[List[PIL.Image.Image]]`, `List[List[np.ndarrray]]`,: `List[List[torch.Tensor]]`): The video or batch of videos to be prepared. Each video should be a list of frames, which can be either PIL images or NumPy arrays. In case of NumPy arrays/PyTorch tensors, each frame should be of shape (H, W, C), where H and W are frame height and width, and C is a number of channels. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `videos` is not `None`. """ max_text_length = kwargs.pop("max_text_length", None) if text is None and videos is None: raise ValueError("You have to specify either text or videos. Both cannot be none.") encoding = {} if text is not None: textual_input = self.tokenizer.batch_encode_plus( text, truncation=True, padding="max_length", max_length=max_text_length, pad_to_max_length=True, return_tensors=return_tensors, return_token_type_ids=False, **kwargs, ) encoding.update(textual_input) if videos is not None: image_features = self.image_processor(videos, return_tensors=return_tensors, **kwargs) encoding.update(image_features) return BatchEncoding(data=encoding, tensor_type=return_tensors) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) def post_process_video_grounding(self, logits, video_durations): """ Compute the time of the video. Args: logits (`torch.Tensor`): The logits output of TvpForVideoGrounding. video_durations (`float`): The video's duration. Returns: start (`float`): The start time of the video. end (`float`): The end time of the video. """ start, end = ( round(logits.tolist()[0][0] * video_durations, 1), round(logits.tolist()[0][1] * video_durations, 1), ) return start, end @property # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

transformers/src/transformers/models/tvp/processing_tvp.py/0

{ "file_path": "transformers/src/transformers/models/tvp/processing_tvp.py", "repo_id": "transformers", "token_count": 2826 }

359

# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import torch from transformers import UnivNetConfig, UnivNetModel, logging logging.set_verbosity_info() logger = logging.get_logger("transformers.models.univnet") def get_kernel_predictor_key_mapping(config: UnivNetConfig, old_prefix: str = "", new_prefix: str = ""): mapping = {} # Initial conv layer mapping[f"{old_prefix}.input_conv.0.weight_g"] = f"{new_prefix}.input_conv.weight_g" mapping[f"{old_prefix}.input_conv.0.weight_v"] = f"{new_prefix}.input_conv.weight_v" mapping[f"{old_prefix}.input_conv.0.bias"] = f"{new_prefix}.input_conv.bias" # Kernel predictor resnet blocks for i in range(config.kernel_predictor_num_blocks): mapping[f"{old_prefix}.residual_convs.{i}.1.weight_g"] = f"{new_prefix}.resblocks.{i}.conv1.weight_g" mapping[f"{old_prefix}.residual_convs.{i}.1.weight_v"] = f"{new_prefix}.resblocks.{i}.conv1.weight_v" mapping[f"{old_prefix}.residual_convs.{i}.1.bias"] = f"{new_prefix}.resblocks.{i}.conv1.bias" mapping[f"{old_prefix}.residual_convs.{i}.3.weight_g"] = f"{new_prefix}.resblocks.{i}.conv2.weight_g" mapping[f"{old_prefix}.residual_convs.{i}.3.weight_v"] = f"{new_prefix}.resblocks.{i}.conv2.weight_v" mapping[f"{old_prefix}.residual_convs.{i}.3.bias"] = f"{new_prefix}.resblocks.{i}.conv2.bias" # Kernel output conv mapping[f"{old_prefix}.kernel_conv.weight_g"] = f"{new_prefix}.kernel_conv.weight_g" mapping[f"{old_prefix}.kernel_conv.weight_v"] = f"{new_prefix}.kernel_conv.weight_v" mapping[f"{old_prefix}.kernel_conv.bias"] = f"{new_prefix}.kernel_conv.bias" # Bias output conv mapping[f"{old_prefix}.bias_conv.weight_g"] = f"{new_prefix}.bias_conv.weight_g" mapping[f"{old_prefix}.bias_conv.weight_v"] = f"{new_prefix}.bias_conv.weight_v" mapping[f"{old_prefix}.bias_conv.bias"] = f"{new_prefix}.bias_conv.bias" return mapping def get_key_mapping(config: UnivNetConfig): mapping = {} # NOTE: inital conv layer keys are the same # LVC Residual blocks for i in range(len(config.resblock_stride_sizes)): # LVCBlock initial convt layer mapping[f"res_stack.{i}.convt_pre.1.weight_g"] = f"resblocks.{i}.convt_pre.weight_g" mapping[f"res_stack.{i}.convt_pre.1.weight_v"] = f"resblocks.{i}.convt_pre.weight_v" mapping[f"res_stack.{i}.convt_pre.1.bias"] = f"resblocks.{i}.convt_pre.bias" # Kernel predictor kernel_predictor_mapping = get_kernel_predictor_key_mapping( config, old_prefix=f"res_stack.{i}.kernel_predictor", new_prefix=f"resblocks.{i}.kernel_predictor" ) mapping.update(kernel_predictor_mapping) # LVC Residual blocks for j in range(len(config.resblock_dilation_sizes[i])): mapping[f"res_stack.{i}.conv_blocks.{j}.1.weight_g"] = f"resblocks.{i}.resblocks.{j}.conv.weight_g" mapping[f"res_stack.{i}.conv_blocks.{j}.1.weight_v"] = f"resblocks.{i}.resblocks.{j}.conv.weight_v" mapping[f"res_stack.{i}.conv_blocks.{j}.1.bias"] = f"resblocks.{i}.resblocks.{j}.conv.bias" # Output conv layer mapping["conv_post.1.weight_g"] = "conv_post.weight_g" mapping["conv_post.1.weight_v"] = "conv_post.weight_v" mapping["conv_post.1.bias"] = "conv_post.bias" return mapping def rename_state_dict(state_dict, keys_to_modify, keys_to_remove): model_state_dict = {} for key, value in state_dict.items(): if key in keys_to_remove: continue if key in keys_to_modify: new_key = keys_to_modify[key] model_state_dict[new_key] = value else: model_state_dict[key] = value return model_state_dict def convert_univnet_checkpoint( checkpoint_path, pytorch_dump_folder_path, config_path=None, repo_id=None, safe_serialization=False, ): model_state_dict_base = torch.load(checkpoint_path, map_location="cpu") # Get the generator's state dict state_dict = model_state_dict_base["model_g"] if config_path is not None: config = UnivNetConfig.from_pretrained(config_path) else: config = UnivNetConfig() keys_to_modify = get_key_mapping(config) keys_to_remove = set() hf_state_dict = rename_state_dict(state_dict, keys_to_modify, keys_to_remove) model = UnivNetModel(config) # Apply weight norm since the original checkpoint has weight norm applied model.apply_weight_norm() model.load_state_dict(hf_state_dict) # Remove weight norm in preparation for inference model.remove_weight_norm() model.save_pretrained(pytorch_dump_folder_path, safe_serialization=safe_serialization) if repo_id: print("Pushing to the hub...") model.push_to_hub(repo_id) def main(): parser = argparse.ArgumentParser() parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." ) parser.add_argument( "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." ) parser.add_argument( "--safe_serialization", action="store_true", help="Whether to save the model using `safetensors`." ) args = parser.parse_args() convert_univnet_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, args.safe_serialization, ) if __name__ == "__main__": main()

transformers/src/transformers/models/univnet/convert_univnet.py/0

{ "file_path": "transformers/src/transformers/models/univnet/convert_univnet.py", "repo_id": "transformers", "token_count": 2613 }

360

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert ViLT checkpoints from the original Github repository.""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( BertTokenizer, ViltConfig, ViltForImageAndTextRetrieval, ViltForImagesAndTextClassification, ViltForMaskedLM, ViltForQuestionAnswering, ViltImageProcessor, ViltProcessor, ) from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config, vqa_model=False, nlvr_model=False, irtr_model=False): rename_keys = [] for i in range(config.num_hidden_layers): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append((f"transformer.blocks.{i}.norm1.weight", f"vilt.encoder.layer.{i}.layernorm_before.weight")) rename_keys.append((f"transformer.blocks.{i}.norm1.bias", f"vilt.encoder.layer.{i}.layernorm_before.bias")) rename_keys.append( (f"transformer.blocks.{i}.attn.proj.weight", f"vilt.encoder.layer.{i}.attention.output.dense.weight") ) rename_keys.append( (f"transformer.blocks.{i}.attn.proj.bias", f"vilt.encoder.layer.{i}.attention.output.dense.bias") ) rename_keys.append((f"transformer.blocks.{i}.norm2.weight", f"vilt.encoder.layer.{i}.layernorm_after.weight")) rename_keys.append((f"transformer.blocks.{i}.norm2.bias", f"vilt.encoder.layer.{i}.layernorm_after.bias")) rename_keys.append( (f"transformer.blocks.{i}.mlp.fc1.weight", f"vilt.encoder.layer.{i}.intermediate.dense.weight") ) rename_keys.append((f"transformer.blocks.{i}.mlp.fc1.bias", f"vilt.encoder.layer.{i}.intermediate.dense.bias")) rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.weight", f"vilt.encoder.layer.{i}.output.dense.weight")) rename_keys.append((f"transformer.blocks.{i}.mlp.fc2.bias", f"vilt.encoder.layer.{i}.output.dense.bias")) # embeddings rename_keys.extend( [ # text embeddings ("text_embeddings.word_embeddings.weight", "vilt.embeddings.text_embeddings.word_embeddings.weight"), ( "text_embeddings.position_embeddings.weight", "vilt.embeddings.text_embeddings.position_embeddings.weight", ), ("text_embeddings.position_ids", "vilt.embeddings.text_embeddings.position_ids"), ( "text_embeddings.token_type_embeddings.weight", "vilt.embeddings.text_embeddings.token_type_embeddings.weight", ), ("text_embeddings.LayerNorm.weight", "vilt.embeddings.text_embeddings.LayerNorm.weight"), ("text_embeddings.LayerNorm.bias", "vilt.embeddings.text_embeddings.LayerNorm.bias"), # patch embeddings ("transformer.cls_token", "vilt.embeddings.cls_token"), ("transformer.patch_embed.proj.weight", "vilt.embeddings.patch_embeddings.projection.weight"), ("transformer.patch_embed.proj.bias", "vilt.embeddings.patch_embeddings.projection.bias"), ("transformer.pos_embed", "vilt.embeddings.position_embeddings"), # token type embeddings ("token_type_embeddings.weight", "vilt.embeddings.token_type_embeddings.weight"), ] ) # final layernorm + pooler rename_keys.extend( [ ("transformer.norm.weight", "vilt.layernorm.weight"), ("transformer.norm.bias", "vilt.layernorm.bias"), ("pooler.dense.weight", "vilt.pooler.dense.weight"), ("pooler.dense.bias", "vilt.pooler.dense.bias"), ] ) # classifier head(s) if vqa_model: # classification head rename_keys.extend( [ ("vqa_classifier.0.weight", "classifier.0.weight"), ("vqa_classifier.0.bias", "classifier.0.bias"), ("vqa_classifier.1.weight", "classifier.1.weight"), ("vqa_classifier.1.bias", "classifier.1.bias"), ("vqa_classifier.3.weight", "classifier.3.weight"), ("vqa_classifier.3.bias", "classifier.3.bias"), ] ) elif nlvr_model: # classification head rename_keys.extend( [ ("nlvr2_classifier.0.weight", "classifier.0.weight"), ("nlvr2_classifier.0.bias", "classifier.0.bias"), ("nlvr2_classifier.1.weight", "classifier.1.weight"), ("nlvr2_classifier.1.bias", "classifier.1.bias"), ("nlvr2_classifier.3.weight", "classifier.3.weight"), ("nlvr2_classifier.3.bias", "classifier.3.bias"), ] ) else: pass return rename_keys # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict, config): for i in range(config.num_hidden_layers): prefix = "vilt." # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"transformer.blocks.{i}.attn.qkv.weight") in_proj_bias = state_dict.pop(f"transformer.blocks.{i}.attn.qkv.bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ : config.hidden_size, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ -config.hidden_size :, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] def remove_classification_head_(state_dict): ignore_keys = ["head.weight", "head.bias"] for k in ignore_keys: state_dict.pop(k, None) def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val @torch.no_grad() def convert_vilt_checkpoint(checkpoint_url, pytorch_dump_folder_path): """ Copy/paste/tweak model's weights to our ViLT structure. """ # define configuration and initialize HuggingFace model config = ViltConfig(image_size=384, patch_size=32, tie_word_embeddings=False) mlm_model = False vqa_model = False nlvr_model = False irtr_model = False if "vqa" in checkpoint_url: vqa_model = True config.num_labels = 3129 repo_id = "huggingface/label-files" filename = "vqa2-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} model = ViltForQuestionAnswering(config) elif "nlvr" in checkpoint_url: nlvr_model = True config.num_labels = 2 config.id2label = {0: "False", 1: "True"} config.label2id = {v: k for k, v in config.id2label.items()} config.modality_type_vocab_size = 3 model = ViltForImagesAndTextClassification(config) elif "irtr" in checkpoint_url: irtr_model = True model = ViltForImageAndTextRetrieval(config) elif "mlm_itm" in checkpoint_url: mlm_model = True model = ViltForMaskedLM(config) else: raise ValueError("Unknown model type") # load state_dict of original model, remove and rename some keys state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["state_dict"] rename_keys = create_rename_keys(config, vqa_model, nlvr_model, irtr_model) for src, dest in rename_keys: rename_key(state_dict, src, dest) read_in_q_k_v(state_dict, config) if mlm_model or irtr_model: ignore_keys = ["itm_score.fc.weight", "itm_score.fc.bias"] for k in ignore_keys: state_dict.pop(k, None) # load state dict into HuggingFace model model.eval() if mlm_model: missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) assert missing_keys == ["mlm_score.decoder.bias"] else: model.load_state_dict(state_dict) # Define processor image_processor = ViltImageProcessor(size=384) tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") processor = ViltProcessor(image_processor, tokenizer) # Forward pass on example inputs (image + text) if nlvr_model: image1 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw) image2 = Image.open(requests.get("https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg", stream=True).raw) text = ( "The left image contains twice the number of dogs as the right image, and at least two dogs in total are" " standing." ) encoding_1 = processor(image1, text, return_tensors="pt") encoding_2 = processor(image2, text, return_tensors="pt") outputs = model( input_ids=encoding_1.input_ids, pixel_values=encoding_1.pixel_values, pixel_values_2=encoding_2.pixel_values, ) else: image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) if mlm_model: text = "a bunch of [MASK] laying on a [MASK]." else: text = "How many cats are there?" encoding = processor(image, text, return_tensors="pt") outputs = model(**encoding) # Verify outputs if mlm_model: expected_shape = torch.Size([1, 11, 30522]) expected_slice = torch.tensor([-12.5061, -12.5123, -12.5174]) assert outputs.logits.shape == expected_shape assert torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4) # verify masked token prediction equals "cats" predicted_id = outputs.logits[0, 4, :].argmax(-1).item() assert tokenizer.decode([predicted_id]) == "cats" elif vqa_model: expected_shape = torch.Size([1, 3129]) expected_slice = torch.tensor([-15.9495, -18.1472, -10.3041]) assert torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4) assert outputs.logits.shape == expected_shape assert torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4) # verify vqa prediction equals "2" predicted_idx = outputs.logits.argmax(-1).item() assert model.config.id2label[predicted_idx] == "2" elif nlvr_model: expected_shape = torch.Size([1, 2]) expected_slice = torch.tensor([-2.8721, 2.1291]) assert torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4) assert outputs.logits.shape == expected_shape Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model and processor to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) processor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt", type=str, help="URL of the checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

transformers/src/transformers/models/vilt/convert_vilt_original_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/vilt/convert_vilt_original_to_pytorch.py", "repo_id": "transformers", "token_count": 5690 }

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# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax VisionTextDualEncoder model.""" from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring from ...utils import add_start_docstrings, logging from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded via the [`~FlaxAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and should be fine-tuned on a downstream task, like contrastive image-text modeling. In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on new zero-shot vision tasks such as image classification or retrieval. After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`VisionTextDualEncoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using an image processor (e.g. if you use ViT as the encoder, you should use [`AutoImageProcessor`]). See [`ViTImageProcessor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxVisionTextDualEncoderModule(nn.Module): config: VisionTextDualEncoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): vision_config = self.config.vision_config text_config = self.config.text_config self.vision_embed_dim = vision_config.hidden_size self.text_embed_dim = text_config.hidden_size self.projection_dim = self.config.projection_dim vision_module = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__, FlaxCLIPVisionModel).module_class text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class self.vision_model = vision_module(vision_config, dtype=self.dtype) self.text_model = text_module(text_config, dtype=self.dtype) self.visual_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.text_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.logit_scale = self.param( "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] ) def __call__( self, input_ids=None, pixel_values=None, attention_mask=None, position_ids=None, token_type_ids=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = jnp.exp(self.logit_scale) logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale logits_per_image = logits_per_text.T if not return_dict: return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return FlaxCLIPOutput( logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) @add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel): config_class = VisionTextDualEncoderConfig module_class = FlaxVisionTextDualEncoderModule def __init__( self, config: VisionTextDualEncoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): if not _do_init: raise ValueError( "`FlaxVisionTextDualEncoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if input_shape is None: input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape[0], dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) token_type_ids = jnp.ones_like(input_ids) attention_mask = jnp.ones_like(input_ids) pixel_values = jax.random.normal(rng, input_shape[1]) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids)[ "params" ] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, pixel_values, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(pixel_values, dtype=jnp.float32), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def get_text_features( self, input_ids, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False, ): r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Returns: text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of text model. """ if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic): text_outputs = module.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, deterministic=deterministic, ) pooled_output = text_outputs[1] text_features = module.text_projection(pooled_output) return text_features return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, method=_get_features, rngs=rngs, ) def get_image_features( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False ): r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`ImageFeatureExtractionMixin`]. See [`ImageFeatureExtractionMixin.__call__`] for details. Returns: image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of vision model. """ # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, pixel_values, deterministic): vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) pooled_output = vision_outputs[1] # pooled_output image_features = module.visual_projection(pooled_output) return image_features return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, method=_get_features, rngs=rngs, ) @classmethod def from_vision_text_pretrained( cls, vision_model_name_or_path: str = None, text_model_name_or_path: str = None, *model_args, **kwargs, ) -> FlaxPreTrainedModel: """ Params: vision_model_name_or_path (`str`, *optional*, defaults to `None`): Information necessary to initiate the vision model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. text_model_name_or_path (`str`, *optional*): Information necessary to initiate the text model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the text configuration, use the prefix *text_* for each configuration parameter. - To update the vision configuration, use the prefix *vision_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxVisionTextDualEncoderModel >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-bert") >>> # load fine-tuned model >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("./vit-bert") ```""" kwargs_vision = { argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") } kwargs_text = { argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") } # remove text, vision kwargs from kwargs for key in kwargs_vision.keys(): del kwargs["vision_" + key] for key in kwargs_text.keys(): del kwargs["text_" + key] # Load and initialize the text and vision model vision_model = kwargs_vision.pop("model", None) if vision_model is None: if vision_model_name_or_path is None: raise ValueError( "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" ) if "config" not in kwargs_vision: vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) if vision_config.model_type == "clip": kwargs_vision["config"] = vision_config.vision_config vision_model = FlaxCLIPVisionModel.from_pretrained( vision_model_name_or_path, *model_args, **kwargs_vision ) else: kwargs_vision["config"] = vision_config vision_model = FlaxAutoModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) text_model = kwargs_text.pop("model", None) if text_model is None: if text_model_name_or_path is None: raise ValueError( "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" ) if "config" not in kwargs_text: text_config = AutoConfig.from_pretrained(text_model_name_or_path) kwargs_text["config"] = text_config text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) # init model model = cls(config, *model_args, dtype=dtype, **kwargs) model.params["vision_model"] = vision_model.params model.params["text_model"] = text_model.params # the projection layers are always newly initialized when loading the model # using pre-trained vision and text model. logger.warning( "The projection layer and logit scale weights `[('visual_projection', 'kernel'), ('text_projection'," " 'kernel'), ('logit_scale',)]` are newly initialized. You should probably TRAIN this model on a" " down-stream task to be able to use it for predictions and inference." ) return model VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING = r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> import jax >>> from transformers import ( ... FlaxVisionTextDualEncoderModel, ... VisionTextDualEncoderProcessor, ... AutoImageProcessor, ... AutoTokenizer, ... ) >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") >>> image_processor = AutoImageProcesor.from_pretrained("google/vit-base-patch16-224") >>> processor = VisionTextDualEncoderProcessor(image_processor, tokenizer) >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # contrastive training >>> urls = [ ... "http://images.cocodataset.org/val2017/000000039769.jpg", ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", ... ] >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="np", padding=True ... ) >>> outputs = model( ... input_ids=inputs.input_ids, ... attention_mask=inputs.attention_mask, ... pixel_values=inputs.pixel_values, ... ) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> # save and load from pretrained >>> model.save_pretrained("vit-bert") >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("vit-bert") >>> # inference >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ``` """ overwrite_call_docstring( FlaxVisionTextDualEncoderModel, VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING + VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxVisionTextDualEncoderModel, output_type=FlaxCLIPOutput, config_class=_CONFIG_FOR_DOC )

transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py/0

{ "file_path": "transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py", "repo_id": "transformers", "token_count": 11230 }

362

# coding=utf-8 # Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ViT model.""" import collections.abc import math from typing import Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedImageModelingOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_vit import ViTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "ViTConfig" # Base docstring _CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k" _EXPECTED_OUTPUT_SHAPE = [1, 197, 768] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224" _IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat" VIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/vit-base-patch16-224", # See all ViT models at https://huggingface.co/models?filter=vit ] class ViTEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None: super().__init__() self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size)) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = ViTPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 """ num_patches = embeddings.shape[1] - 1 num_positions = self.position_embeddings.shape[1] - 1 if num_patches == num_positions and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] h0 = height // self.config.patch_size w0 = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 h0, w0 = h0 + 0.1, w0 + 0.1 patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)), mode="bicubic", align_corners=False, ) assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1] patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) if bool_masked_pos is not None: seq_length = embeddings.shape[1] mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings class ViTPatchEmbeddings(nn.Module): """ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. """ def __init__(self, config): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." f" Expected {self.num_channels} but got {num_channels}." ) if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings class ViTSelfAttention(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class ViTSelfOutput(nn.Module): """ The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class ViTAttention(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.attention = ViTSelfAttention(config) self.output = ViTSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class ViTIntermediate(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class ViTOutput(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class ViTLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: ViTConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = ViTAttention(config) self.intermediate = ViTIntermediate(config) self.output = ViTOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class ViTEncoder(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, layer_head_mask, output_attentions, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class ViTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ViTConfig base_model_prefix = "vit" main_input_name = "pixel_values" supports_gradient_checkpointing = True _no_split_modules = ["ViTEmbeddings", "ViTLayer"] def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid # `trunc_normal_cpu` not implemented in `half` issues module.weight.data = nn.init.trunc_normal_( module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range ).to(module.weight.dtype) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, ViTEmbeddings): module.position_embeddings.data = nn.init.trunc_normal_( module.position_embeddings.data.to(torch.float32), mean=0.0, std=self.config.initializer_range, ).to(module.position_embeddings.dtype) module.cls_token.data = nn.init.trunc_normal_( module.cls_token.data.to(torch.float32), mean=0.0, std=self.config.initializer_range, ).to(module.cls_token.dtype) VIT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.", VIT_START_DOCSTRING, ) class ViTModel(ViTPreTrainedModel): def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False): super().__init__(config) self.config = config self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token) self.encoder = ViTEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = ViTPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> ViTPatchEmbeddings: return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype if pixel_values.dtype != expected_dtype: pixel_values = pixel_values.to(expected_dtype) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class ViTPooler(nn.Module): def __init__(self, config: ViTConfig): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output @add_start_docstrings( """ViT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://arxiv.org/abs/2111.09886). <Tip> Note that we provide a script to pre-train this model on custom data in our [examples directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining). </Tip> """, VIT_START_DOCSTRING, ) class ViTForMaskedImageModeling(ViTPreTrainedModel): def __init__(self, config: ViTConfig) -> None: super().__init__(config) self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True) self.decoder = nn.Sequential( nn.Conv2d( in_channels=config.hidden_size, out_channels=config.encoder_stride**2 * config.num_channels, kernel_size=1, ), nn.PixelShuffle(config.encoder_stride), ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, MaskedImageModelingOutput]: r""" bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, ViTForMaskedImageModeling >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") >>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k") >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values >>> # create random boolean mask of shape (batch_size, num_patches) >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction >>> list(reconstructed_pixel_values.shape) [1, 3, 224, 224] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict if bool_masked_pos is not None and (self.config.patch_size != self.config.encoder_stride): raise ValueError( "When `bool_masked_pos` is provided, `patch_size` must be equal to `encoder_stride` to ensure that " "the reconstructed image has the same dimensions as the input. " f"Got `patch_size` = {self.config.patch_size} and `encoder_stride` = {self.config.encoder_stride}." ) outputs = self.vit( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) sequence_output = outputs[0] # Reshape to (batch_size, num_channels, height, width) sequence_output = sequence_output[:, 1:] batch_size, sequence_length, num_channels = sequence_output.shape height = width = math.floor(sequence_length**0.5) sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) # Reconstruct pixel values reconstructed_pixel_values = self.decoder(sequence_output) masked_im_loss = None if bool_masked_pos is not None: size = self.config.image_size // self.config.patch_size bool_masked_pos = bool_masked_pos.reshape(-1, size, size) mask = ( bool_masked_pos.repeat_interleave(self.config.patch_size, 1) .repeat_interleave(self.config.patch_size, 2) .unsqueeze(1) .contiguous() ) reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none") masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels if not return_dict: output = (reconstructed_pixel_values,) + outputs[1:] return ((masked_im_loss,) + output) if masked_im_loss is not None else output return MaskedImageModelingOutput( loss=masked_im_loss, reconstruction=reconstructed_pixel_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. <Tip> Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained position embeddings to the higher resolution. </Tip> """, VIT_START_DOCSTRING, ) class ViTForImageClassification(ViTPreTrainedModel): def __init__(self, config: ViTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.vit = ViTModel(config, add_pooling_layer=False) # Classifier head self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, ImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.vit( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output[:, 0, :]) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(logits.device) if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/vit/modeling_vit.py/0

{ "file_path": "transformers/src/transformers/models/vit/modeling_vit.py", "repo_id": "transformers", "token_count": 15053 }

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ VitDet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices logger = logging.get_logger(__name__) VITDET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/vit-det-base": "https://huggingface.co/facebook/vit-det-base/resolve/main/config.json", } class VitDetConfig(BackboneConfigMixin, PretrainedConfig): r""" This is the configuration class to store the configuration of a [`VitDetModel`]. It is used to instantiate an VitDet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the VitDet [google/vitdet-base-patch16-224](https://huggingface.co/google/vitdet-base-patch16-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. mlp_ratio (`int`, *optional*, defaults to 4): Ratio of mlp hidden dim to embedding dim. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. pretrain_image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image during pretraining. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. drop_path_rate (`float`, *optional*, defaults to 0.0): Stochastic depth rate. window_block_indices (`List[int]`, *optional*, defaults to `[]`): List of indices of blocks that should have window attention instead of regular global self-attention. residual_block_indices (`List[int]`, *optional*, defaults to `[]`): List of indices of blocks that should have an extra residual block after the MLP. use_absolute_position_embeddings (`bool`, *optional*, defaults to `True`): Whether to add absolute position embeddings to the patch embeddings. use_relative_position_embeddings (`bool`, *optional*, defaults to `False`): Whether to add relative position embeddings to the attention maps. window_size (`int`, *optional*, defaults to 0): The size of the attention window. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. Example: ```python >>> from transformers import VitDetConfig, VitDetModel >>> # Initializing a VitDet configuration >>> configuration = VitDetConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = VitDetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "vitdet" def __init__( self, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, mlp_ratio=4, hidden_act="gelu", dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-6, image_size=224, pretrain_image_size=224, patch_size=16, num_channels=3, qkv_bias=True, drop_path_rate=0.0, window_block_indices=[], residual_block_indices=[], use_absolute_position_embeddings=True, use_relative_position_embeddings=False, window_size=0, out_features=None, out_indices=None, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mlp_ratio = mlp_ratio self.hidden_act = hidden_act self.dropout_prob = dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.pretrain_image_size = pretrain_image_size self.patch_size = patch_size self.num_channels = num_channels self.qkv_bias = qkv_bias self.drop_path_rate = drop_path_rate self.window_block_indices = window_block_indices self.residual_block_indices = residual_block_indices self.use_absolute_position_embeddings = use_absolute_position_embeddings self.use_relative_position_embeddings = use_relative_position_embeddings self.window_size = window_size self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, self.num_hidden_layers + 1)] self._out_features, self._out_indices = get_aligned_output_features_output_indices( out_features=out_features, out_indices=out_indices, stage_names=self.stage_names )

transformers/src/transformers/models/vitdet/configuration_vitdet.py/0

{ "file_path": "transformers/src/transformers/models/vitdet/configuration_vitdet.py", "repo_id": "transformers", "token_count": 2892 }

364

# coding=utf-8 # Copyright 2023 Google AI and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ViViT model.""" import math from typing import Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_vivit import VivitConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/vivit-b-16x2-kinetics400" _CONFIG_FOR_DOC = "VivitConfig" VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/vivit-b-16x2-kinetics400", # See all Vivit models at https://huggingface.co/models?filter=vivit ] class VivitTubeletEmbeddings(nn.Module): """ Construct Vivit Tubelet embeddings. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. The seq_len (the number of patches) equals (number of frames // tubelet_size[0]) * (height // tubelet_size[1]) * (width // tubelet_size[2]). """ def __init__(self, config): super().__init__() self.num_frames = config.num_frames self.image_size = config.image_size self.patch_size = config.tubelet_size self.num_patches = ( (self.image_size // self.patch_size[2]) * (self.image_size // self.patch_size[1]) * (self.num_frames // self.patch_size[0]) ) self.embed_dim = config.hidden_size self.projection = nn.Conv3d( config.num_channels, config.hidden_size, kernel_size=config.tubelet_size, stride=config.tubelet_size ) def forward(self, pixel_values): batch_size, num_frames, num_channels, height, width = pixel_values.shape if height != self.image_size or width != self.image_size: raise ValueError( f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})." ) # permute to (batch_size, num_channels, num_frames, height, width) pixel_values = pixel_values.permute(0, 2, 1, 3, 4) x = self.projection(pixel_values) # out_batch_size, out_num_channels, out_num_frames, out_height, out_width = x.shape x = self.projection(pixel_values).flatten(2).transpose(1, 2) return x class VivitEmbeddings(nn.Module): """ Vivit Embeddings. Creates embeddings from a video using VivitTubeletEmbeddings, adds CLS token and positional embeddings. """ def __init__(self, config): super().__init__() self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.patch_embeddings = VivitTubeletEmbeddings(config) self.position_embeddings = nn.Parameter( torch.zeros(1, self.patch_embeddings.num_patches + 1, config.hidden_size) ) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def forward(self, pixel_values): batch_size = pixel_values.shape[0] embeddings = self.patch_embeddings(pixel_values) cls_tokens = self.cls_token.tile([batch_size, 1, 1]) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings # Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Vivit class VivitSelfAttention(nn.Module): def __init__(self, config: VivitConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Vivit class VivitSelfOutput(nn.Module): """ The residual connection is defined in VivitLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: VivitConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Vivit class VivitAttention(nn.Module): def __init__(self, config: VivitConfig) -> None: super().__init__() self.attention = VivitSelfAttention(config) self.output = VivitSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class VivitIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class VivitOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class VivitLayer(nn.Module): """This corresponds to the EncoderBlock class in the scenic/vivit implementation.""" def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = VivitAttention(config) self.intermediate = VivitIntermediate(config) self.output = VivitOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states, head_mask=None, output_attentions=False): self_attention_outputs = self.attention( # in Vivit, layernorm is applied before self-attention self.layernorm_before(hidden_states), head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] # add self attentions if we output attention weights outputs = self_attention_outputs[1:] # first residual connection hidden_states = attention_output + hidden_states # in Vivit, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class VivitEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([VivitLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, layer_head_mask, output_attentions, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class VivitPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class VivitPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = VivitConfig base_model_prefix = "vivit" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv3d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, nn.Parameter): module.data.normal_(mean=0.0, std=self.config.initializer_range) VIVIT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VivitConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIVIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`VivitImageProcessor`]. See [`VivitImageProcessor.preprocess`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ViViT Transformer model outputting raw hidden-states without any specific head on top.", VIVIT_START_DOCSTRING, ) class VivitModel(VivitPreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = VivitEmbeddings(config) self.encoder = VivitEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = VivitPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. Args: heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIVIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> import av >>> import numpy as np >>> from transformers import VivitImageProcessor, VivitModel >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def read_video_pyav(container, indices): ... ''' ... Decode the video with PyAV decoder. ... Args: ... container (`av.container.input.InputContainer`): PyAV container. ... indices (`List[int]`): List of frame indices to decode. ... Returns: ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). ... ''' ... frames = [] ... container.seek(0) ... start_index = indices[0] ... end_index = indices[-1] ... for i, frame in enumerate(container.decode(video=0)): ... if i > end_index: ... break ... if i >= start_index and i in indices: ... frames.append(frame) ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... ''' ... Sample a given number of frame indices from the video. ... Args: ... clip_len (`int`): Total number of frames to sample. ... frame_sample_rate (`int`): Sample every n-th frame. ... seg_len (`int`): Maximum allowed index of sample's last frame. ... Returns: ... indices (`List[int]`): List of sampled frame indices ... ''' ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> container = av.open(file_path) >>> # sample 32 frames >>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=1, seg_len=container.streams.video[0].frames) >>> video = read_video_pyav(container=container, indices=indices) >>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") >>> model = VivitModel.from_pretrained("google/vivit-b-16x2-kinetics400") >>> # prepare video for the model >>> inputs = image_processor(list(video), return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 3137, 768] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """ViViT Transformer model with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for Kinetics-400.""", VIVIT_START_DOCSTRING, ) class VivitForVideoClassification(VivitPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.vivit = VivitModel(config, add_pooling_layer=False) # Classifier head self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIVIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.FloatTensor], ImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> import av >>> import numpy as np >>> import torch >>> from transformers import VivitImageProcessor, VivitForVideoClassification >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def read_video_pyav(container, indices): ... ''' ... Decode the video with PyAV decoder. ... Args: ... container (`av.container.input.InputContainer`): PyAV container. ... indices (`List[int]`): List of frame indices to decode. ... Returns: ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). ... ''' ... frames = [] ... container.seek(0) ... start_index = indices[0] ... end_index = indices[-1] ... for i, frame in enumerate(container.decode(video=0)): ... if i > end_index: ... break ... if i >= start_index and i in indices: ... frames.append(frame) ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... ''' ... Sample a given number of frame indices from the video. ... Args: ... clip_len (`int`): Total number of frames to sample. ... frame_sample_rate (`int`): Sample every n-th frame. ... seg_len (`int`): Maximum allowed index of sample's last frame. ... Returns: ... indices (`List[int]`): List of sampled frame indices ... ''' ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> container = av.open(file_path) >>> # sample 32 frames >>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=4, seg_len=container.streams.video[0].frames) >>> video = read_video_pyav(container=container, indices=indices) >>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") >>> model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400") >>> inputs = image_processor(list(video), return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... logits = outputs.logits >>> # model predicts one of the 400 Kinetics-400 classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) LABEL_116 ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.vivit( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output[:, 0, :]) loss = None if labels is not None: if self.num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/vivit/modeling_vivit.py/0

{ "file_path": "transformers/src/transformers/models/vivit/modeling_vivit.py", "repo_id": "transformers", "token_count": 12679 }

365

# Copyright 2022 The OpenAI team and The HuggingFace Team. All rights reserved. # Most of the code is copy pasted from the original whisper repository # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re import unicodedata from fractions import Fraction from typing import Iterator, List, Match, Optional, Union import regex # non-ASCII letters that are not separated by "NFKD" normalization ADDITIONAL_DIACRITICS = { "œ": "oe", "Œ": "OE", "ø": "o", "Ø": "O", "æ": "ae", "Æ": "AE", "ß": "ss", "ẞ": "SS", "đ": "d", "Đ": "D", "ð": "d", "Ð": "D", "þ": "th", "Þ": "th", "ł": "l", "Ł": "L", } def remove_symbols_and_diacritics(s: str, keep=""): """ Replace any other markers, symbols, and punctuations with a space, and drop any diacritics (category 'Mn' and some manual mappings) """ def replace_character(char): if char in keep: return char elif char in ADDITIONAL_DIACRITICS: return ADDITIONAL_DIACRITICS[char] elif unicodedata.category(char) == "Mn": return "" elif unicodedata.category(char)[0] in "MSP": return " " return char return "".join(replace_character(c) for c in unicodedata.normalize("NFKD", s)) def remove_symbols(s: str): """ Replace any other markers, symbols, punctuations with a space, keeping diacritics """ return "".join(" " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)) class BasicTextNormalizer: def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols self.split_letters = split_letters def __call__(self, s: str): s = s.lower() s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets s = re.sub(r"$([^)]+?)$", "", s) # remove words between parenthesis s = self.clean(s).lower() if self.split_letters: s = " ".join(regex.findall(r"\X", s, regex.U)) s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space return s class EnglishNumberNormalizer: """ Convert any spelled-out numbers into arabic numbers, while handling: - remove any commas - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` - spell out `one` and `ones` - interpret successive single-digit numbers as nominal: `one oh one` -> `101` """ def __init__(self): super().__init__() self.zeros = {"o", "oh", "zero"} # fmt: off self.ones = { name: i for i, name in enumerate( ["one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen"], start=1, ) } # fmt: on self.ones_plural = { "sixes" if name == "six" else name + "s": (value, "s") for name, value in self.ones.items() } self.ones_ordinal = { "zeroth": (0, "th"), "first": (1, "st"), "second": (2, "nd"), "third": (3, "rd"), "fifth": (5, "th"), "twelfth": (12, "th"), **{ name + ("h" if name.endswith("t") else "th"): (value, "th") for name, value in self.ones.items() if value > 3 and value != 5 and value != 12 }, } self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} self.tens = { "twenty": 20, "thirty": 30, "forty": 40, "fifty": 50, "sixty": 60, "seventy": 70, "eighty": 80, "ninety": 90, } self.tens_plural = {name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()} self.tens_ordinal = {name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()} self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} self.multipliers = { "hundred": 100, "thousand": 1_000, "million": 1_000_000, "billion": 1_000_000_000, "trillion": 1_000_000_000_000, "quadrillion": 1_000_000_000_000_000, "quintillion": 1_000_000_000_000_000_000, "sextillion": 1_000_000_000_000_000_000_000, "septillion": 1_000_000_000_000_000_000_000_000, "octillion": 1_000_000_000_000_000_000_000_000_000, "nonillion": 1_000_000_000_000_000_000_000_000_000_000, "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, } self.multipliers_plural = {name + "s": (value, "s") for name, value in self.multipliers.items()} self.multipliers_ordinal = {name + "th": (value, "th") for name, value in self.multipliers.items()} self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal} self.decimals = {*self.ones, *self.tens, *self.zeros} self.preceding_prefixers = { "minus": "-", "negative": "-", "plus": "+", "positive": "+", } self.following_prefixers = { "pound": "£", "pounds": "£", "euro": "€", "euros": "€", "dollar": "$", "dollars": "$", "cent": "¢", "cents": "¢", } self.prefixes = set(list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())) self.suffixers = { "per": {"cent": "%"}, "percent": "%", } self.specials = {"and", "double", "triple", "point"} self.words = { key for mapping in [ self.zeros, self.ones, self.ones_suffixed, self.tens, self.tens_suffixed, self.multipliers, self.multipliers_suffixed, self.preceding_prefixers, self.following_prefixers, self.suffixers, self.specials, ] for key in mapping } self.literal_words = {"one", "ones"} def process_words(self, words: List[str]) -> Iterator[str]: prefix: Optional[str] = None value: Optional[Union[str, int]] = None skip = False def to_fraction(s: str): try: return Fraction(s) except ValueError: return None def output(result: Union[str, int]): nonlocal prefix, value result = str(result) if prefix is not None: result = prefix + result value = None prefix = None return result if len(words) == 0: return for i, current in enumerate(words): prev = words[i - 1] if i != 0 else None next = words[i + 1] if i != len(words) - 1 else None if skip: skip = False continue next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) has_prefix = current[0] in self.prefixes current_without_prefix = current[1:] if has_prefix else current if re.match(r"^\d+(\.\d+)?$", current_without_prefix): # arabic numbers (potentially with signs and fractions) f = to_fraction(current_without_prefix) if f is None: raise ValueError("Converting the fraction failed") if value is not None: if isinstance(value, str) and value.endswith("."): # concatenate decimals / ip address components value = str(value) + str(current) continue else: yield output(value) prefix = current[0] if has_prefix else prefix if f.denominator == 1: value = f.numerator # store integers as int else: value = current_without_prefix elif current not in self.words: # non-numeric words if value is not None: yield output(value) yield output(current) elif current in self.zeros: value = str(value or "") + "0" elif current in self.ones: ones = self.ones[current] if value is None: value = ones elif isinstance(value, str) or prev in self.ones: if prev in self.tens and ones < 10: # replace the last zero with the digit value = value[:-1] + str(ones) else: value = str(value) + str(ones) elif ones < 10: if value % 10 == 0: value += ones else: value = str(value) + str(ones) else: # eleven to nineteen if value % 100 == 0: value += ones else: value = str(value) + str(ones) elif current in self.ones_suffixed: # ordinal or cardinal; yield the number right away ones, suffix = self.ones_suffixed[current] if value is None: yield output(str(ones) + suffix) elif isinstance(value, str) or prev in self.ones: if prev in self.tens and ones < 10: yield output(value[:-1] + str(ones) + suffix) else: yield output(str(value) + str(ones) + suffix) elif ones < 10: if value % 10 == 0: yield output(str(value + ones) + suffix) else: yield output(str(value) + str(ones) + suffix) else: # eleven to nineteen if value % 100 == 0: yield output(str(value + ones) + suffix) else: yield output(str(value) + str(ones) + suffix) value = None elif current in self.tens: tens = self.tens[current] if value is None: value = tens elif isinstance(value, str): value = str(value) + str(tens) else: if value % 100 == 0: value += tens else: value = str(value) + str(tens) elif current in self.tens_suffixed: # ordinal or cardinal; yield the number right away tens, suffix = self.tens_suffixed[current] if value is None: yield output(str(tens) + suffix) elif isinstance(value, str): yield output(str(value) + str(tens) + suffix) else: if value % 100 == 0: yield output(str(value + tens) + suffix) else: yield output(str(value) + str(tens) + suffix) elif current in self.multipliers: multiplier = self.multipliers[current] if value is None: value = multiplier elif isinstance(value, str) or value == 0: f = to_fraction(value) p = f * multiplier if f is not None else None if f is not None and p.denominator == 1: value = p.numerator else: yield output(value) value = multiplier else: before = value // 1000 * 1000 residual = value % 1000 value = before + residual * multiplier elif current in self.multipliers_suffixed: multiplier, suffix = self.multipliers_suffixed[current] if value is None: yield output(str(multiplier) + suffix) elif isinstance(value, str): f = to_fraction(value) p = f * multiplier if f is not None else None if f is not None and p.denominator == 1: yield output(str(p.numerator) + suffix) else: yield output(value) yield output(str(multiplier) + suffix) else: # int before = value // 1000 * 1000 residual = value % 1000 value = before + residual * multiplier yield output(str(value) + suffix) value = None elif current in self.preceding_prefixers: # apply prefix (positive, minus, etc.) if it precedes a number if value is not None: yield output(value) if next in self.words or next_is_numeric: prefix = self.preceding_prefixers[current] else: yield output(current) elif current in self.following_prefixers: # apply prefix (dollars, cents, etc.) only after a number if value is not None: prefix = self.following_prefixers[current] yield output(value) else: yield output(current) elif current in self.suffixers: # apply suffix symbols (percent -> '%') if value is not None: suffix = self.suffixers[current] if isinstance(suffix, dict): if next in suffix: yield output(str(value) + suffix[next]) skip = True else: yield output(value) yield output(current) else: yield output(str(value) + suffix) else: yield output(current) elif current in self.specials: if next not in self.words and not next_is_numeric: # apply special handling only if the next word can be numeric if value is not None: yield output(value) yield output(current) elif current == "and": # ignore "and" after hundreds, thousands, etc. if prev not in self.multipliers: if value is not None: yield output(value) yield output(current) elif current == "double" or current == "triple": if next in self.ones or next in self.zeros: repeats = 2 if current == "double" else 3 ones = self.ones.get(next, 0) value = str(value or "") + str(ones) * repeats skip = True else: if value is not None: yield output(value) yield output(current) elif current == "point": if next in self.decimals or next_is_numeric: value = str(value or "") + "." else: # should all have been covered at this point raise ValueError(f"Unexpected token: {current}") else: # all should have been covered at this point raise ValueError(f"Unexpected token: {current}") if value is not None: yield output(value) def preprocess(self, s: str): # replace "<number> and a half" with "<number> point five" results = [] segments = re.split(r"\band\s+a\s+half\b", s) for i, segment in enumerate(segments): if len(segment.strip()) == 0: continue if i == len(segments) - 1: results.append(segment) else: results.append(segment) last_word = segment.rsplit(maxsplit=2)[-1] if last_word in self.decimals or last_word in self.multipliers: results.append("point five") else: results.append("and a half") s = " ".join(results) # put a space at number/letter boundary s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) # but remove spaces which could be a suffix s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) return s def postprocess(self, s: str): def combine_cents(m: Match): try: currency = m.group(1) integer = m.group(2) cents = int(m.group(3)) return f"{currency}{integer}.{cents:02d}" except ValueError: return m.string def extract_cents(m: Match): try: return f"¢{int(m.group(1))}" except ValueError: return m.string # apply currency postprocessing; "$2 and ¢7" -> "$2.07" s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) # write "one(s)" instead of "1(s)", just for the readability s = re.sub(r"\b1(s?)\b", r"one\1", s) return s def __call__(self, s: str): s = self.preprocess(s) s = " ".join(word for word in self.process_words(s.split()) if word is not None) s = self.postprocess(s) return s class EnglishSpellingNormalizer: """ Applies British-American spelling mappings as listed in [1]. [1] https://www.tysto.com/uk-us-spelling-list.html """ def __init__(self, english_spelling_mapping): self.mapping = english_spelling_mapping def __call__(self, s: str): return " ".join(self.mapping.get(word, word) for word in s.split()) class EnglishTextNormalizer: def __init__(self, english_spelling_mapping): self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" self.replacers = { # common contractions r"\bwon't\b": "will not", r"\bcan't\b": "can not", r"\blet's\b": "let us", r"\bain't\b": "aint", r"\by'all\b": "you all", r"\bwanna\b": "want to", r"\bgotta\b": "got to", r"\bgonna\b": "going to", r"\bi'ma\b": "i am going to", r"\bimma\b": "i am going to", r"\bwoulda\b": "would have", r"\bcoulda\b": "could have", r"\bshoulda\b": "should have", r"\bma'am\b": "madam", # contractions in titles/prefixes r"\bmr\b": "mister ", r"\bmrs\b": "missus ", r"\bst\b": "saint ", r"\bdr\b": "doctor ", r"\bprof\b": "professor ", r"\bcapt\b": "captain ", r"\bgov\b": "governor ", r"\bald\b": "alderman ", r"\bgen\b": "general ", r"\bsen\b": "senator ", r"\brep\b": "representative ", r"\bpres\b": "president ", r"\brev\b": "reverend ", r"\bhon\b": "honorable ", r"\basst\b": "assistant ", r"\bassoc\b": "associate ", r"\blt\b": "lieutenant ", r"\bcol\b": "colonel ", r"\bjr\b": "junior ", r"\bsr\b": "senior ", r"\besq\b": "esquire ", # prefect tenses, ideally it should be any past participles, but it's harder.. r"'d been\b": " had been", r"'s been\b": " has been", r"'d gone\b": " had gone", r"'s gone\b": " has gone", r"'d done\b": " had done", # "'s done" is ambiguous r"'s got\b": " has got", # general contractions r"n't\b": " not", r"'re\b": " are", r"'s\b": " is", r"'d\b": " would", r"'ll\b": " will", r"'t\b": " not", r"'ve\b": " have", r"'m\b": " am", } self.standardize_numbers = EnglishNumberNormalizer() self.standardize_spellings = EnglishSpellingNormalizer(english_spelling_mapping) def __call__(self, s: str): s = s.lower() s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets s = re.sub(r"$([^)]+?)$", "", s) # remove words between parenthesis s = re.sub(self.ignore_patterns, "", s) s = re.sub(r"\s+'", "'", s) # standardize when there's a space before an apostrophe for pattern, replacement in self.replacers.items(): s = re.sub(pattern, replacement, s) s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep some symbols for numerics s = self.standardize_numbers(s) s = self.standardize_spellings(s) # now remove prefix/suffix symbols that are not preceded/followed by numbers s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) s = re.sub(r"([^0-9])%", r"\1 ", s) s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space return s

transformers/src/transformers/models/whisper/english_normalizer.py/0

{ "file_path": "transformers/src/transformers/models/whisper/english_normalizer.py", "repo_id": "transformers", "token_count": 12164 }

366

import argparse from argparse import Namespace import torch from torch import nn from transformers import XGLMConfig, XGLMForCausalLM def remove_ignore_keys_(state_dict): ignore_keys = [ "decoder.version", "decoder.output_projection.weight", "_float_tensor", "decoder.embed_positions._float_tensor", ] for k in ignore_keys: state_dict.pop(k, None) def make_linear_from_emb(emb): vocab_size, emb_size = emb.weight.shape lin_layer = nn.Linear(vocab_size, emb_size, bias=False) lin_layer.weight.data = emb.weight.data return lin_layer def convert_fairseq_xglm_checkpoint_from_disk(checkpoint_path): checkpoint = torch.load(checkpoint_path, map_location="cpu") args = Namespace(**checkpoint["cfg"]["model"]) state_dict = checkpoint["model"] remove_ignore_keys_(state_dict) vocab_size = state_dict["decoder.embed_tokens.weight"].shape[0] state_dict = {key.replace("decoder", "model"): val for key, val in state_dict.items()} config = XGLMConfig( vocab_size=vocab_size, max_position_embeddings=args.max_target_positions, num_layers=args.decoder_layers, attention_heads=args.decoder_attention_heads, ffn_dim=args.decoder_ffn_embed_dim, d_model=args.decoder_embed_dim, layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function="gelu", scale_embedding=not args.no_scale_embedding, tie_word_embeddings=args.share_decoder_input_output_embed, ) model = XGLMForCausalLM(config) missing = model.load_state_dict(state_dict, strict=False) print(missing) model.lm_head = make_linear_from_emb(model.model.embed_tokens) return model if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.") parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") args = parser.parse_args() model = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path) model.save_pretrained(args.pytorch_dump_folder_path)

transformers/src/transformers/models/xglm/convert_xglm_original_ckpt_to_trfms.py/0

{ "file_path": "transformers/src/transformers/models/xglm/convert_xglm_original_ckpt_to_trfms.py", "repo_id": "transformers", "token_count": 938 }

367

# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tokenization classes for XLNet model.""" import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "xlnet-base-cased": None, "xlnet-large-cased": None, } # Segments (not really needed) SEG_ID_A = 0 SEG_ID_B = 1 SEG_ID_CLS = 2 SEG_ID_SEP = 3 SEG_ID_PAD = 4 class XLNetTokenizer(PreTrainedTokenizer): """ Construct an XLNet tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary necessary to instantiate a tokenizer. do_lower_case (`bool`, *optional*, defaults to `False`): Whether to lowercase the input when tokenizing. remove_space (`bool`, *optional*, defaults to `True`): Whether to strip the text when tokenizing (removing excess spaces before and after the string). keep_accents (`bool`, *optional*, defaults to `False`): Whether to keep accents when tokenizing. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (`str`, *optional*, defaults to `"<sep>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. cls_token (`str`, *optional*, defaults to `"<cls>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. additional_special_tokens (`List[str]`, *optional*, defaults to `['<eop>', '<eod>']`): Additional special tokens used by the tokenizer. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Attributes: sp_model (`SentencePieceProcessor`): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES padding_side = "left" def __init__( self, vocab_file, do_lower_case=False, remove_space=True, keep_accents=False, bos_token="<s>", eos_token="</s>", unk_token="<unk>", sep_token="<sep>", pad_token="<pad>", cls_token="<cls>", mask_token="<mask>", additional_special_tokens=["<eop>", "<eod>"], sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) super().__init__( do_lower_case=do_lower_case, remove_space=remove_space, keep_accents=keep_accents, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, additional_special_tokens=additional_special_tokens, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) self._pad_token_type_id = 3 @property def vocab_size(self): return len(self.sp_model) def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def preprocess_text(self, inputs): if self.remove_space: outputs = " ".join(inputs.strip().split()) else: outputs = inputs outputs = outputs.replace("``", '"').replace("''", '"') if not self.keep_accents: outputs = unicodedata.normalize("NFKD", outputs) outputs = "".join([c for c in outputs if not unicodedata.combining(c)]) if self.do_lower_case: outputs = outputs.lower() return outputs def _tokenize(self, text: str) -> List[str]: """Tokenize a string.""" text = self.preprocess_text(text) pieces = self.sp_model.encode(text, out_type=str) new_pieces = [] for piece in pieces: if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, "")) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0]) == 1: cur_pieces = cur_pieces[1:] else: cur_pieces[0] = cur_pieces[0][1:] cur_pieces.append(piece[-1]) new_pieces.extend(cur_pieces) else: new_pieces.append(piece) return new_pieces def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.sp_model.PieceToId(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.sp_model.IdToPiece(index) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, spaces_between_special_tokens: bool = True, **kwargs, ) -> str: self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False) filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) # To avoid mixing byte-level and unicode for byte-level BPT # we need to build string separately for added tokens and byte-level tokens # cf. https://github.com/huggingface/transformers/issues/1133 sub_texts = [] current_sub_text = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue if token in self.added_tokens_encoder: if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) current_sub_text = [] sub_texts.append(token) else: current_sub_text.append(token) if current_sub_text: sub_texts.append(self.convert_tokens_to_string(current_sub_text)) # Mimic the behavior of the Rust tokenizer: # By default, there are no spaces between special tokens text = "".join(sub_texts) clean_up_tokenization_spaces = ( clean_up_tokenization_spaces if clean_up_tokenization_spaces is not None else self.clean_up_tokenization_spaces ) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLNet sequence has the following format: - single sequence: `X <sep> <cls>` - pair of sequences: `A <sep> B <sep> <cls>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return token_ids_0 + sep + cls return token_ids_0 + sep + token_ids_1 + sep + cls def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is not None: return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1] return ([0] * len(token_ids_0)) + [1, 1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls_segment_id = [2] if token_ids_1 is None: return len(token_ids_0 + sep) * [0] + cls_segment_id return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,)

transformers/src/transformers/models/xlnet/tokenization_xlnet.py/0

{ "file_path": "transformers/src/transformers/models/xlnet/tokenization_xlnet.py", "repo_id": "transformers", "token_count": 7058 }

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# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ..utils import _LazyModule _import_structure = { "config": [ "EXTERNAL_DATA_FORMAT_SIZE_LIMIT", "OnnxConfig", "OnnxConfigWithPast", "OnnxSeq2SeqConfigWithPast", "PatchingSpec", ], "convert": ["export", "validate_model_outputs"], "features": ["FeaturesManager"], "utils": ["ParameterFormat", "compute_serialized_parameters_size"], } if TYPE_CHECKING: from .config import ( EXTERNAL_DATA_FORMAT_SIZE_LIMIT, OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast, PatchingSpec, ) from .convert import export, validate_model_outputs from .features import FeaturesManager from .utils import ParameterFormat, compute_serialized_parameters_size else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/onnx/__init__.py/0

{ "file_path": "transformers/src/transformers/onnx/__init__.py", "repo_id": "transformers", "token_count": 548 }

369

from typing import Dict from ..utils import add_end_docstrings from .base import GenericTensor, Pipeline, build_pipeline_init_args @add_end_docstrings( build_pipeline_init_args(has_tokenizer=True, supports_binary_output=False), r""" tokenize_kwargs (`dict`, *optional*): Additional dictionary of keyword arguments passed along to the tokenizer. return_tensors (`bool`, *optional*): If `True`, returns a tensor according to the specified framework, otherwise returns a list.""", ) class FeatureExtractionPipeline(Pipeline): """ Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base transformer, which can be used as features in downstream tasks. Example: ```python >>> from transformers import pipeline >>> extractor = pipeline(model="bert-base-uncased", task="feature-extraction") >>> result = extractor("This is a simple test.", return_tensors=True) >>> result.shape # This is a tensor of shape [1, sequence_lenth, hidden_dimension] representing the input string. torch.Size([1, 8, 768]) ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier: `"feature-extraction"`. All models may be used for this pipeline. See a list of all models, including community-contributed models on [huggingface.co/models](https://huggingface.co/models). """ def _sanitize_parameters(self, truncation=None, tokenize_kwargs=None, return_tensors=None, **kwargs): if tokenize_kwargs is None: tokenize_kwargs = {} if truncation is not None: if "truncation" in tokenize_kwargs: raise ValueError( "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)" ) tokenize_kwargs["truncation"] = truncation preprocess_params = tokenize_kwargs postprocess_params = {} if return_tensors is not None: postprocess_params["return_tensors"] = return_tensors return preprocess_params, {}, postprocess_params def preprocess(self, inputs, **tokenize_kwargs) -> Dict[str, GenericTensor]: model_inputs = self.tokenizer(inputs, return_tensors=self.framework, **tokenize_kwargs) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs, return_tensors=False): # [0] is the first available tensor, logits or last_hidden_state. if return_tensors: return model_outputs[0] if self.framework == "pt": return model_outputs[0].tolist() elif self.framework == "tf": return model_outputs[0].numpy().tolist() def __call__(self, *args, **kwargs): """ Extract the features of the input(s). Args: args (`str` or `List[str]`): One or several texts (or one list of texts) to get the features of. Return: A nested list of `float`: The features computed by the model. """ return super().__call__(*args, **kwargs)

transformers/src/transformers/pipelines/feature_extraction.py/0

{ "file_path": "transformers/src/transformers/pipelines/feature_extraction.py", "repo_id": "transformers", "token_count": 1276 }

370

from io import BytesIO from typing import List, Union import requests from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends from .base import Pipeline, build_pipeline_init_args if is_decord_available(): import numpy as np from decord import VideoReader if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES logger = logging.get_logger(__name__) @add_end_docstrings(build_pipeline_init_args(has_image_processor=True)) class VideoClassificationPipeline(Pipeline): """ Video classification pipeline using any `AutoModelForVideoClassification`. This pipeline predicts the class of a video. This video classification pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"video-classification"`. See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=video-classification). """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) requires_backends(self, "decord") self.check_model_type(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES) def _sanitize_parameters(self, top_k=None, num_frames=None, frame_sampling_rate=None): preprocess_params = {} if frame_sampling_rate is not None: preprocess_params["frame_sampling_rate"] = frame_sampling_rate if num_frames is not None: preprocess_params["num_frames"] = num_frames postprocess_params = {} if top_k is not None: postprocess_params["top_k"] = top_k return preprocess_params, {}, postprocess_params def __call__(self, videos: Union[str, List[str]], **kwargs): """ Assign labels to the video(s) passed as inputs. Args: videos (`str`, `List[str]`): The pipeline handles three types of videos: - A string containing a http link pointing to a video - A string containing a local path to a video The pipeline accepts either a single video or a batch of videos, which must then be passed as a string. Videos in a batch must all be in the same format: all as http links or all as local paths. top_k (`int`, *optional*, defaults to 5): The number of top labels that will be returned by the pipeline. If the provided number is higher than the number of labels available in the model configuration, it will default to the number of labels. num_frames (`int`, *optional*, defaults to `self.model.config.num_frames`): The number of frames sampled from the video to run the classification on. If not provided, will default to the number of frames specified in the model configuration. frame_sampling_rate (`int`, *optional*, defaults to 1): The sampling rate used to select frames from the video. If not provided, will default to 1, i.e. every frame will be used. Return: A dictionary or a list of dictionaries containing result. If the input is a single video, will return a dictionary, if the input is a list of several videos, will return a list of dictionaries corresponding to the videos. The dictionaries contain the following keys: - **label** (`str`) -- The label identified by the model. - **score** (`int`) -- The score attributed by the model for that label. """ return super().__call__(videos, **kwargs) def preprocess(self, video, num_frames=None, frame_sampling_rate=1): if num_frames is None: num_frames = self.model.config.num_frames if video.startswith("http://") or video.startswith("https://"): video = BytesIO(requests.get(video).content) videoreader = VideoReader(video) videoreader.seek(0) start_idx = 0 end_idx = num_frames * frame_sampling_rate - 1 indices = np.linspace(start_idx, end_idx, num=num_frames, dtype=np.int64) video = videoreader.get_batch(indices).asnumpy() video = list(video) model_inputs = self.image_processor(video, return_tensors=self.framework) return model_inputs def _forward(self, model_inputs): model_outputs = self.model(**model_inputs) return model_outputs def postprocess(self, model_outputs, top_k=5): if top_k > self.model.config.num_labels: top_k = self.model.config.num_labels if self.framework == "pt": probs = model_outputs.logits.softmax(-1)[0] scores, ids = probs.topk(top_k) else: raise ValueError(f"Unsupported framework: {self.framework}") scores = scores.tolist() ids = ids.tolist() return [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]

transformers/src/transformers/pipelines/video_classification.py/0

{ "file_path": "transformers/src/transformers/pipelines/video_classification.py", "repo_id": "transformers", "token_count": 1985 }

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import json import uuid from typing import Optional import requests from huggingface_hub import Discussion, HfApi, get_repo_discussions from .utils import cached_file, logging logger = logging.get_logger(__name__) def previous_pr(api: HfApi, model_id: str, pr_title: str, token: str) -> Optional["Discussion"]: main_commit = api.list_repo_commits(model_id, token=token)[0].commit_id for discussion in get_repo_discussions(repo_id=model_id, token=token): if discussion.title == pr_title and discussion.status == "open" and discussion.is_pull_request: commits = api.list_repo_commits(model_id, revision=discussion.git_reference, token=token) if main_commit == commits[1].commit_id: return discussion return None def spawn_conversion(token: str, private: bool, model_id: str): logger.info("Attempting to convert .bin model on the fly to safetensors.") safetensors_convert_space_url = "https://safetensors-convert.hf.space" sse_url = f"{safetensors_convert_space_url}/queue/join" sse_data_url = f"{safetensors_convert_space_url}/queue/data" # The `fn_index` is necessary to indicate to gradio that we will use the `run` method of the Space. hash_data = {"fn_index": 1, "session_hash": str(uuid.uuid4())} def start(_sse_connection, payload): for line in _sse_connection.iter_lines(): line = line.decode() if line.startswith("data:"): resp = json.loads(line[5:]) logger.debug(f"Safetensors conversion status: {resp['msg']}") if resp["msg"] == "queue_full": raise ValueError("Queue is full! Please try again.") elif resp["msg"] == "send_data": event_id = resp["event_id"] response = requests.post( sse_data_url, stream=True, params=hash_data, json={"event_id": event_id, **payload, **hash_data}, ) response.raise_for_status() elif resp["msg"] == "process_completed": return with requests.get(sse_url, stream=True, params=hash_data) as sse_connection: data = {"data": [model_id, private, token]} try: logger.debug("Spawning safetensors automatic conversion.") start(sse_connection, data) except Exception as e: logger.warning(f"Error during conversion: {repr(e)}") def get_conversion_pr_reference(api: HfApi, model_id: str, **kwargs): private = api.model_info(model_id).private logger.info("Attempting to create safetensors variant") pr_title = "Adding `safetensors` variant of this model" token = kwargs.get("token") # This looks into the current repo's open PRs to see if a PR for safetensors was already open. If so, it # returns it. It checks that the PR was opened by the bot and not by another user so as to prevent # security breaches. pr = previous_pr(api, model_id, pr_title, token=token) if pr is None or (not private and pr.author != "SFConvertBot"): spawn_conversion(token, private, model_id) pr = previous_pr(api, model_id, pr_title, token=token) else: logger.info("Safetensors PR exists") sha = f"refs/pr/{pr.num}" return sha def auto_conversion(pretrained_model_name_or_path: str, **cached_file_kwargs): api = HfApi(token=cached_file_kwargs.get("token")) sha = get_conversion_pr_reference(api, pretrained_model_name_or_path, **cached_file_kwargs) if sha is None: return None, None cached_file_kwargs["revision"] = sha del cached_file_kwargs["_commit_hash"] # This is an additional HEAD call that could be removed if we could infer sharded/non-sharded from the PR # description. sharded = api.file_exists( pretrained_model_name_or_path, "model.safetensors.index.json", revision=sha, token=cached_file_kwargs.get("token"), ) filename = "model.safetensors.index.json" if sharded else "model.safetensors" resolved_archive_file = cached_file(pretrained_model_name_or_path, filename, **cached_file_kwargs) return resolved_archive_file, sha, sharded

transformers/src/transformers/safetensors_conversion.py/0

{ "file_path": "transformers/src/transformers/safetensors_conversion.py", "repo_id": "transformers", "token_count": 1821 }

372

#!/usr/bin/env python # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ..models.auto import AutoModelForVision2Seq from ..utils import requires_backends from .base import PipelineTool if TYPE_CHECKING: from PIL import Image class ImageCaptioningTool(PipelineTool): default_checkpoint = "Salesforce/blip-image-captioning-base" description = ( "This is a tool that generates a description of an image. It takes an input named `image` which should be the " "image to caption, and returns a text that contains the description in English." ) name = "image_captioner" model_class = AutoModelForVision2Seq inputs = ["image"] outputs = ["text"] def __init__(self, *args, **kwargs): requires_backends(self, ["vision"]) super().__init__(*args, **kwargs) def encode(self, image: "Image"): return self.pre_processor(images=image, return_tensors="pt") def forward(self, inputs): return self.model.generate(**inputs) def decode(self, outputs): return self.pre_processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()

transformers/src/transformers/tools/image_captioning.py/0

{ "file_path": "transformers/src/transformers/tools/image_captioning.py", "repo_id": "transformers", "token_count": 564 }

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# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import io import json import math import os import warnings from dataclasses import asdict, dataclass, field, fields from datetime import timedelta from enum import Enum from pathlib import Path from typing import Any, Dict, List, Optional, Union from huggingface_hub import get_full_repo_name from packaging import version from .debug_utils import DebugOption from .trainer_utils import ( EvaluationStrategy, FSDPOption, HubStrategy, IntervalStrategy, SchedulerType, ) from .utils import ( ACCELERATE_MIN_VERSION, ExplicitEnum, cached_property, is_accelerate_available, is_safetensors_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_torch_available, is_torch_bf16_cpu_available, is_torch_bf16_gpu_available, is_torch_neuroncore_available, is_torch_npu_available, is_torch_tf32_available, is_torch_tpu_available, is_torch_xpu_available, logging, requires_backends, ) from .utils.generic import strtobool from .utils.import_utils import is_optimum_neuron_available logger = logging.get_logger(__name__) log_levels = logging.get_log_levels_dict().copy() trainer_log_levels = dict(**log_levels, passive=-1) if is_torch_available(): import torch import torch.distributed as dist if is_accelerate_available(): from accelerate.state import AcceleratorState, PartialState from accelerate.utils import DistributedType if is_torch_tpu_available(check_device=False): import torch_xla.core.xla_model as xm if is_torch_neuroncore_available(check_device=False): # torchrun support # https://github.com/pytorch/xla/pull/3609 if os.environ.get("TORCHELASTIC_RUN_ID"): if is_optimum_neuron_available(): logger.info( "Make sure that you are performing the training with the TrainiumTrainer from optimum[neuron], this " "will fail otherwise." ) else: logger.warning( "Please use the TrainiumTrainer from optimum[neuron] instead of the Transformers library to perform " "training on AWS Trainium instances. More information here: " "https://github.com/huggingface/optimum-neuron" ) import torch_xla.distributed.xla_backend as xbn if not isinstance(dist.group.WORLD, xbn.ProcessGroupXla): dist.init_process_group(backend="xla") if not isinstance(dist.group.WORLD, xbn.ProcessGroupXla): raise AssertionError("Failed to initialize torch.distributed process group using XLA backend.") if is_sagemaker_mp_enabled(): import smdistributed.modelparallel.torch as smp smp.init() def default_logdir() -> str: """ Same default as PyTorch """ import socket from datetime import datetime current_time = datetime.now().strftime("%b%d_%H-%M-%S") return os.path.join("runs", current_time + "_" + socket.gethostname()) def get_int_from_env(env_keys, default): """Returns the first positive env value found in the `env_keys` list or the default.""" for e in env_keys: val = int(os.environ.get(e, -1)) if val >= 0: return val return default def get_xla_device_type(device: "torch.device") -> Optional[str]: """ Returns the xla device type (CPU|GPU|TPU) or None if the device is a non-xla device. """ if is_torch_tpu_available(): return xm.xla_real_devices([device])[0].split(":")[0] return None class OptimizerNames(ExplicitEnum): """ Stores the acceptable string identifiers for optimizers. """ ADAMW_HF = "adamw_hf" ADAMW_TORCH = "adamw_torch" ADAMW_TORCH_FUSED = "adamw_torch_fused" ADAMW_TORCH_XLA = "adamw_torch_xla" ADAMW_TORCH_NPU_FUSED = "adamw_torch_npu_fused" ADAMW_APEX_FUSED = "adamw_apex_fused" ADAFACTOR = "adafactor" ADAMW_ANYPRECISION = "adamw_anyprecision" SGD = "sgd" ADAGRAD = "adagrad" ADAMW_BNB = "adamw_bnb_8bit" ADAMW_8BIT = "adamw_8bit" # just an alias for adamw_bnb_8bit LION_8BIT = "lion_8bit" LION = "lion_32bit" PAGED_ADAMW = "paged_adamw_32bit" PAGED_ADAMW_8BIT = "paged_adamw_8bit" PAGED_LION = "paged_lion_32bit" PAGED_LION_8BIT = "paged_lion_8bit" RMSPROP = "rmsprop" # TODO: `TrainingArguments` users rely on it being fully mutable. In the future see if we can narrow this to a few keys: https://github.com/huggingface/transformers/pull/25903 @dataclass class TrainingArguments: """ TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop itself**. Using [`HfArgumentParser`] we can turn this class into [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the command line. Parameters: output_dir (`str`): The output directory where the model predictions and checkpoints will be written. overwrite_output_dir (`bool`, *optional*, defaults to `False`): If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir` points to a checkpoint directory. do_train (`bool`, *optional*, defaults to `False`): Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. do_eval (`bool`, *optional*): Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. do_predict (`bool`, *optional*, defaults to `False`): Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`): The evaluation strategy to adopt during training. Possible values are: - `"no"`: No evaluation is done during training. - `"steps"`: Evaluation is done (and logged) every `eval_steps`. - `"epoch"`: Evaluation is done at the end of each epoch. prediction_loss_only (`bool`, *optional*, defaults to `False`): When performing evaluation and generating predictions, only returns the loss. per_device_train_batch_size (`int`, *optional*, defaults to 8): The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for training. per_device_eval_batch_size (`int`, *optional*, defaults to 8): The batch size per GPU/XPU/TPU/MPS/NPU core/CPU for evaluation. gradient_accumulation_steps (`int`, *optional*, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. <Tip warning={true}> When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples. </Tip> eval_accumulation_steps (`int`, *optional*): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/NPU/TPU before being moved to the CPU (faster but requires more memory). eval_delay (`float`, *optional*): Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy. learning_rate (`float`, *optional*, defaults to 5e-5): The initial learning rate for [`AdamW`] optimizer. weight_decay (`float`, *optional*, defaults to 0): The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`] optimizer. adam_beta1 (`float`, *optional*, defaults to 0.9): The beta1 hyperparameter for the [`AdamW`] optimizer. adam_beta2 (`float`, *optional*, defaults to 0.999): The beta2 hyperparameter for the [`AdamW`] optimizer. adam_epsilon (`float`, *optional*, defaults to 1e-8): The epsilon hyperparameter for the [`AdamW`] optimizer. max_grad_norm (`float`, *optional*, defaults to 1.0): Maximum gradient norm (for gradient clipping). num_train_epochs(`float`, *optional*, defaults to 3.0): Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training). max_steps (`int`, *optional*, defaults to -1): If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until `max_steps` is reached. lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`): The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values. lr_scheduler_kwargs ('dict', *optional*, defaults to {}): The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values. warmup_ratio (`float`, *optional*, defaults to 0.0): Ratio of total training steps used for a linear warmup from 0 to `learning_rate`. warmup_steps (`int`, *optional*, defaults to 0): Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`. log_level (`str`, *optional*, defaults to `passive`): Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and keeps the current log level for the Transformers library (which will be `"warning"` by default). log_level_replica (`str`, *optional*, defaults to `"warning"`): Logger log level to use on replicas. Same choices as `log_level`" log_on_each_node (`bool`, *optional*, defaults to `True`): In multinode distributed training, whether to log using `log_level` once per node, or only on the main node. logging_dir (`str`, *optional*): [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***. logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The logging strategy to adopt during training. Possible values are: - `"no"`: No logging is done during training. - `"epoch"`: Logging is done at the end of each epoch. - `"steps"`: Logging is done every `logging_steps`. logging_first_step (`bool`, *optional*, defaults to `False`): Whether to log and evaluate the first `global_step` or not. logging_steps (`int` or `float`, *optional*, defaults to 500): Number of update steps between two logs if `logging_strategy="steps"`. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. logging_nan_inf_filter (`bool`, *optional*, defaults to `True`): Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan` or `inf` is filtered and the average loss of the current logging window is taken instead. <Tip> `logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model. </Tip> save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The checkpoint save strategy to adopt during training. Possible values are: - `"no"`: No save is done during training. - `"epoch"`: Save is done at the end of each epoch. - `"steps"`: Save is done every `save_steps`. save_steps (`int` or `float`, *optional*, defaults to 500): Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. save_total_limit (`int`, *optional*): If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the "best" checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end`, it is possible that two checkpoints are saved: the last one and the best one (if they are different). save_safetensors (`bool`, *optional*, defaults to `True`): Use [safetensors](https://huggingface.co/docs/safetensors) saving and loading for state dicts instead of default `torch.load` and `torch.save`. save_on_each_node (`bool`, *optional*, defaults to `False`): When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one. This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node. save_only_model (`bool`, *optional*, defaults to `False`): When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state. Note that when this is true, you won't be able to resume training from checkpoint. This enables you to save storage by not storing the optimizer, scheduler & rng state. You can only load the model using `from_pretrained` with this option set to `True`. use_cpu (`bool`, *optional*, defaults to `False`): Whether or not to use cpu. If set to False, we will use cuda or mps device if available. seed (`int`, *optional*, defaults to 42): Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the [`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters. data_seed (`int`, *optional*): Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model seed. jit_mode_eval (`bool`, *optional*, defaults to `False`): Whether or not to use PyTorch jit trace for inference. use_ipex (`bool`, *optional*, defaults to `False`): Use Intel extension for PyTorch when it is available. [IPEX installation](https://github.com/intel/intel-extension-for-pytorch). bf16 (`bool`, *optional*, defaults to `False`): Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change. fp16 (`bool`, *optional*, defaults to `False`): Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training. fp16_opt_level (`str`, *optional*, defaults to 'O1'): For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on the [Apex documentation](https://nvidia.github.io/apex/amp). fp16_backend (`str`, *optional*, defaults to `"auto"`): This argument is deprecated. Use `half_precision_backend` instead. half_precision_backend (`str`, *optional*, defaults to `"auto"`): The backend to use for mixed precision training. Must be one of `"auto", "apex", "cpu_amp"`. `"auto"` will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend. bf16_full_eval (`bool`, *optional*, defaults to `False`): Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. This is an experimental API and it may change. fp16_full_eval (`bool`, *optional*, defaults to `False`): Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. tf32 (`bool`, *optional*): Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends on PyTorch's version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to the [TF32](https://huggingface.co/docs/transformers/performance#tf32) documentation. This is an experimental API and it may change. local_rank (`int`, *optional*, defaults to -1): Rank of the process during distributed training. ddp_backend (`str`, *optional*): The backend to use for distributed training. Must be one of `"nccl"`, `"mpi"`, `"ccl"`, `"gloo"`, `"hccl"`. tpu_num_cores (`int`, *optional*): When training on TPU, the number of TPU cores (automatically passed by launcher script). dataloader_drop_last (`bool`, *optional*, defaults to `False`): Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not. eval_steps (`int` or `float`, *optional*): Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same value as `logging_steps` if not set. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps. dataloader_num_workers (`int`, *optional*, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. past_index (`int`, *optional*, defaults to -1): Some models like [TransformerXL](../model_doc/transformerxl) or [XLNet](../model_doc/xlnet) can make use of the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument `mems`. run_name (`str`, *optional*): A descriptor for the run. Typically used for [wandb](https://www.wandb.com/) and [mlflow](https://www.mlflow.org/) logging. disable_tqdm (`bool`, *optional*): Whether or not to disable the tqdm progress bars and table of metrics produced by [`~notebook.NotebookTrainingTracker`] in Jupyter Notebooks. Will default to `True` if the logging level is set to warn or lower (default), `False` otherwise. remove_unused_columns (`bool`, *optional*, defaults to `True`): Whether or not to automatically remove the columns unused by the model forward method. label_names (`List[str]`, *optional*): The list of keys in your dictionary of inputs that correspond to the labels. Will eventually default to the list of argument names accepted by the model that contain the word "label", except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the `["start_positions", "end_positions"]` keys. load_best_model_at_end (`bool`, *optional*, defaults to `False`): Whether or not to load the best model found during training at the end of training. When this option is enabled, the best checkpoint will always be saved. See [`save_total_limit`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments.save_total_limit) for more. <Tip> When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in the case it is "steps", `save_steps` must be a round multiple of `eval_steps`. </Tip> metric_for_best_model (`str`, *optional*): Use in conjunction with `load_best_model_at_end` to specify the metric to use to compare two different models. Must be the name of a metric returned by the evaluation with or without the prefix `"eval_"`. Will default to `"loss"` if unspecified and `load_best_model_at_end=True` (to use the evaluation loss). If you set this value, `greater_is_better` will default to `True`. Don't forget to set it to `False` if your metric is better when lower. greater_is_better (`bool`, *optional*): Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models should have a greater metric or not. Will default to: - `True` if `metric_for_best_model` is set to a value that isn't `"loss"` or `"eval_loss"`. - `False` if `metric_for_best_model` is not set, or set to `"loss"` or `"eval_loss"`. ignore_data_skip (`bool`, *optional*, defaults to `False`): When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have. fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `''`): Use PyTorch Distributed Parallel Training (in distributed training only). A list of options along the following: - `"full_shard"`: Shard parameters, gradients and optimizer states. - `"shard_grad_op"`: Shard optimizer states and gradients. - `"hybrid_shard"`: Apply `FULL_SHARD` within a node, and replicate parameters across nodes. - `"hybrid_shard_zero2"`: Apply `SHARD_GRAD_OP` within a node, and replicate parameters across nodes. - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and `"shard_grad_op"`). - `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`. fsdp_config (`str` or `dict`, *optional*): Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`. A List of config and its options: - min_num_params (`int`, *optional*, defaults to `0`): FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). - transformer_layer_cls_to_wrap (`List[str]`, *optional*): List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed). - backward_prefetch (`str`, *optional*) FSDP's backward prefetch mode. Controls when to prefetch next set of parameters (useful only when `fsdp` field is passed). A list of options along the following: - `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter's gradient computation. - `"backward_post"` : This prefetches the next set of parameters after the current set of parameter’s gradient computation. - forward_prefetch (`bool`, *optional*, defaults to `False`) FSDP's forward prefetch mode (useful only when `fsdp` field is passed). If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. - limit_all_gathers (`bool`, *optional*, defaults to `False`) FSDP's limit_all_gathers (useful only when `fsdp` field is passed). If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers. - use_orig_params (`bool`, *optional*, defaults to `True`) If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please refer this [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019 - sync_module_states (`bool`, *optional*, defaults to `True`) If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to ensure they are the same across all ranks after initialization - activation_checkpointing (`bool`, *optional*, defaults to `False`): If `"True"`, activation checkpointing is a technique to reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time for reduced memory usage. - xla (`bool`, *optional*, defaults to `False`): Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature and its API may evolve in the future. - xla_fsdp_settings (`dict`, *optional*) The value is a dictionary which stores the XLA FSDP wrapping parameters. For a complete list of options, please see [here]( https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py). - xla_fsdp_grad_ckpt (`bool`, *optional*, defaults to `False`): Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be used when the xla flag is set to true, and an auto wrapping policy is specified through fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap. deepspeed (`str` or `dict`, *optional*): Use [Deepspeed](https://github.com/microsoft/deepspeed). This is an experimental feature and its API may evolve in the future. The value is either the location of DeepSpeed json config file (e.g., `ds_config.json`) or an already loaded json file as a `dict`" label_smoothing_factor (`float`, *optional*, defaults to 0.0): The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor + label_smoothing_factor/num_labels` respectively. debug (`str` or list of [`~debug_utils.DebugOption`], *optional*, defaults to `""`): Enable one or more debug features. This is an experimental feature. Possible options are: - `"underflow_overflow"`: detects overflow in model's input/outputs and reports the last frames that led to the event - `"tpu_metrics_debug"`: print debug metrics on TPU The options should be separated by whitespaces. optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"`): The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or adafactor. optim_args (`str`, *optional*): Optional arguments that are supplied to AnyPrecisionAdamW. group_by_length (`bool`, *optional*, defaults to `False`): Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding. length_column_name (`str`, *optional*, defaults to `"length"`): Column name for precomputed lengths. If the column exists, grouping by length will use these values rather than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an instance of `Dataset`. report_to (`str` or `List[str]`, *optional*, defaults to `"all"`): The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`, `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`, `"neptune"`, `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no integrations. ddp_find_unused_parameters (`bool`, *optional*): When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise. ddp_bucket_cap_mb (`int`, *optional*): When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. ddp_broadcast_buffers (`bool`, *optional*): When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise. dataloader_pin_memory (`bool`, *optional*, defaults to `True`): Whether you want to pin memory in data loaders or not. Will default to `True`. dataloader_persistent_workers (`bool`, *optional*, defaults to `False`): If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to `False`. dataloader_prefetch_factor (`int`, *optional*): Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. skip_memory_metrics (`bool`, *optional*, defaults to `True`): Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows down the training and evaluation speed. push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push the model to the Hub every time the model is saved. If this is activated, `output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content will be pushed each time a save is triggered (depending on your `save_strategy`). Calling [`~Trainer.save_model`] will also trigger a push. <Tip warning={true}> If `output_dir` exists, it needs to be a local clone of the repository to which the [`Trainer`] will be pushed. </Tip> resume_from_checkpoint (`str`, *optional*): The path to a folder with a valid checkpoint for your model. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. hub_model_id (`str`, *optional*): The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. Will default to `user_name/output_dir_name` with *output_dir_name* being the name of `output_dir`. Will default to the name of `output_dir`. hub_strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`): Defines the scope of what is pushed to the Hub and when. Possible values are: - `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card when the [`~Trainer.save_model`] method is called. - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training. - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with `trainer.train(resume_from_checkpoint="last-checkpoint")`. - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository) hub_token (`str`, *optional*): The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`. hub_private_repo (`bool`, *optional*, defaults to `False`): If True, the Hub repo will be set to private. hub_always_push (`bool`, *optional*, defaults to `False`): Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not finished. gradient_checkpointing (`bool`, *optional*, defaults to `False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. gradient_checkpointing_kwargs (`dict`, *optional*, defaults to `None`): Key word arguments to be passed to the `gradient_checkpointing_enable` method. include_inputs_for_metrics (`bool`, *optional*, defaults to `False`): Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics that need inputs, predictions and references for scoring calculation in Metric class. auto_find_batch_size (`bool`, *optional*, defaults to `False`) Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`) full_determinism (`bool`, *optional*, defaults to `False`) If `True`, [`enable_full_determinism`] is called instead of [`set_seed`] to ensure reproducible results in distributed training. Important: this will negatively impact the performance, so only use it for debugging. torchdynamo (`str`, *optional*): If set, the backend compiler for TorchDynamo. Possible choices are `"eager"`, `"aot_eager"`, `"inductor"`, `"nvfuser"`, `"aot_nvfuser"`, `"aot_cudagraphs"`, `"ofi"`, `"fx2trt"`, `"onnxrt"` and `"ipex"`. ray_scope (`str`, *optional*, defaults to `"last"`): The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the [Ray documentation]( https://docs.ray.io/en/latest/tune/api_docs/analysis.html#ray.tune.ExperimentAnalysis.get_best_trial) for more options. ddp_timeout (`int`, *optional*, defaults to 1800): The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when performing slow operations in distributed runnings. Please refer the [PyTorch documentation] (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information. use_mps_device (`bool`, *optional*, defaults to `False`): This argument is deprecated.`mps` device will be used if it is available similar to `cuda` device. torch_compile (`bool`, *optional*, defaults to `False`): Whether or not to compile the model using PyTorch 2.0 [`torch.compile`](https://pytorch.org/get-started/pytorch-2.0/). This will use the best defaults for the [`torch.compile` API](https://pytorch.org/docs/stable/generated/torch.compile.html?highlight=torch+compile#torch.compile). You can customize the defaults with the argument `torch_compile_backend` and `torch_compile_mode` but we don't guarantee any of them will work as the support is progressively rolled in in PyTorch. This flag and the whole compile API is experimental and subject to change in future releases. torch_compile_backend (`str`, *optional*): The backend to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`. Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions. This flag is experimental and subject to change in future releases. torch_compile_mode (`str`, *optional*): The mode to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`. Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions. This flag is experimental and subject to change in future releases. split_batches (`bool`, *optional*): Whether or not the accelerator should split the batches yielded by the dataloaders across the devices during distributed training. If set to `True`, the actual batch size used will be the same on any kind of distributed processes, but it must be a round multiple of the number of processes you are using (such as GPUs). include_tokens_per_second (`bool`, *optional*): Whether or not to compute the number of tokens per second per device for training speed metrics. This will iterate over the entire training dataloader once beforehand, and will slow down the entire process. include_num_input_tokens_seen (`bool`, *optional*): Whether or not to track the number of input tokens seen throughout training. May be slower in distributed training as gather operations must be called. neftune_noise_alpha (`Optional[float]`): If not `None`, this will activate NEFTune noise embeddings. This can drastically improve model performance for instruction fine-tuning. Check out the [original paper](https://arxiv.org/abs/2310.05914) and the [original code](https://github.com/neelsjain/NEFTune). Support transformers `PreTrainedModel` and also `PeftModel` from peft. """ framework = "pt" output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."}) evaluation_strategy: Union[IntervalStrategy, str] = field( default="no", metadata={"help": "The evaluation strategy to use."}, ) prediction_loss_only: bool = field( default=False, metadata={"help": "When performing evaluation and predictions, only returns the loss."}, ) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU/MPS/NPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation."} ) per_gpu_train_batch_size: Optional[int] = field( default=None, metadata={ "help": ( "Deprecated, the use of `--per_device_train_batch_size` is preferred. " "Batch size per GPU/TPU core/CPU for training." ) }, ) per_gpu_eval_batch_size: Optional[int] = field( default=None, metadata={ "help": ( "Deprecated, the use of `--per_device_eval_batch_size` is preferred. " "Batch size per GPU/TPU core/CPU for evaluation." ) }, ) gradient_accumulation_steps: int = field( default=1, metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."}, ) eval_accumulation_steps: Optional[int] = field( default=None, metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."}, ) eval_delay: Optional[float] = field( default=0, metadata={ "help": ( "Number of epochs or steps to wait for before the first evaluation can be performed, depending on the" " evaluation_strategy." ) }, ) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."}) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) max_steps: int = field( default=-1, metadata={"help": "If > 0: set total number of training steps to perform. Override num_train_epochs."}, ) lr_scheduler_type: Union[SchedulerType, str] = field( default="linear", metadata={"help": "The scheduler type to use."}, ) lr_scheduler_kwargs: Optional[Dict] = field( default_factory=dict, metadata={ "help": ( "Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts" ) }, ) warmup_ratio: float = field( default=0.0, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."} ) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) log_level: Optional[str] = field( default="passive", metadata={ "help": ( "Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug'," " 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and" " lets the application set the level. Defaults to 'passive'." ), "choices": trainer_log_levels.keys(), }, ) log_level_replica: Optional[str] = field( default="warning", metadata={ "help": "Logger log level to use on replica nodes. Same choices and defaults as ``log_level``", "choices": trainer_log_levels.keys(), }, ) log_on_each_node: bool = field( default=True, metadata={ "help": ( "When doing a multinode distributed training, whether to log once per node or just once on the main" " node." ) }, ) logging_dir: Optional[str] = field(default=None, metadata={"help": "Tensorboard log dir."}) logging_strategy: Union[IntervalStrategy, str] = field( default="steps", metadata={"help": "The logging strategy to use."}, ) logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"}) logging_steps: float = field( default=500, metadata={ "help": ( "Log every X updates steps. Should be an integer or a float in range `[0,1)`. " "If smaller than 1, will be interpreted as ratio of total training steps." ) }, ) logging_nan_inf_filter: bool = field(default=True, metadata={"help": "Filter nan and inf losses for logging."}) save_strategy: Union[IntervalStrategy, str] = field( default="steps", metadata={"help": "The checkpoint save strategy to use."}, ) save_steps: float = field( default=500, metadata={ "help": ( "Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. " "If smaller than 1, will be interpreted as ratio of total training steps." ) }, ) save_total_limit: Optional[int] = field( default=None, metadata={ "help": ( "If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in" " `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to" " `metric_for_best_model` will always be retained in addition to the most recent ones. For example," " for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be" " retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`," " it is possible that two checkpoints are saved: the last one and the best one (if they are different)." " Default is unlimited checkpoints" ) }, ) save_safetensors: Optional[bool] = field( default=True, metadata={ "help": "Use safetensors saving and loading for state dicts instead of default torch.load and torch.save." }, ) save_on_each_node: bool = field( default=False, metadata={ "help": ( "When doing multi-node distributed training, whether to save models and checkpoints on each node, or" " only on the main one" ) }, ) save_only_model: bool = field( default=False, metadata={ "help": ( "When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state." "Note that when this is true, you won't be able to resume training from checkpoint." "This enables you to save storage by not storing the optimizer, scheduler & rng state." "You can only load the model using from_pretrained with this option set to True." ) }, ) no_cuda: bool = field( default=False, metadata={"help": "This argument is deprecated. It will be removed in version 5.0 of 🤗 Transformers."}, ) use_cpu: bool = field( default=False, metadata={ "help": " Whether or not to use cpu. If set to False, we will use cuda/tpu/mps/npu device if available." }, ) use_mps_device: bool = field( default=False, metadata={ "help": "This argument is deprecated. `mps` device will be used if available similar to `cuda` device." " It will be removed in version 5.0 of 🤗 Transformers" }, ) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) data_seed: Optional[int] = field(default=None, metadata={"help": "Random seed to be used with data samplers."}) jit_mode_eval: bool = field( default=False, metadata={"help": "Whether or not to use PyTorch jit trace for inference"} ) use_ipex: bool = field( default=False, metadata={ "help": ( "Use Intel extension for PyTorch when it is available, installation:" " 'https://github.com/intel/intel-extension-for-pytorch'" ) }, ) bf16: bool = field( default=False, metadata={ "help": ( "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA" " architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change." ) }, ) fp16: bool = field( default=False, metadata={"help": "Whether to use fp16 (mixed) precision instead of 32-bit"}, ) fp16_opt_level: str = field( default="O1", metadata={ "help": ( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. " "See details at https://nvidia.github.io/apex/amp.html" ) }, ) half_precision_backend: str = field( default="auto", metadata={ "help": "The backend to be used for half precision.", "choices": ["auto", "apex", "cpu_amp"], }, ) bf16_full_eval: bool = field( default=False, metadata={ "help": ( "Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may" " change." ) }, ) fp16_full_eval: bool = field( default=False, metadata={"help": "Whether to use full float16 evaluation instead of 32-bit"}, ) tf32: Optional[bool] = field( default=None, metadata={ "help": ( "Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental" " API and it may change." ) }, ) local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"}) ddp_backend: Optional[str] = field( default=None, metadata={ "help": "The backend to be used for distributed training", "choices": ["nccl", "gloo", "mpi", "ccl", "hccl"], }, ) tpu_num_cores: Optional[int] = field( default=None, metadata={"help": "TPU: Number of TPU cores (automatically passed by launcher script)"} ) tpu_metrics_debug: bool = field( default=False, metadata={ "help": ( "Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics" ) }, ) debug: Union[str, List[DebugOption]] = field( default="", metadata={ "help": ( "Whether or not to enable debug mode. Current options: " "`underflow_overflow` (Detect underflow and overflow in activations and weights), " "`tpu_metrics_debug` (print debug metrics on TPU)." ) }, ) dataloader_drop_last: bool = field( default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."} ) eval_steps: Optional[float] = field( default=None, metadata={ "help": ( "Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. " "If smaller than 1, will be interpreted as ratio of total training steps." ) }, ) dataloader_num_workers: int = field( default=0, metadata={ "help": ( "Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded" " in the main process." ) }, ) dataloader_prefetch_factor: int = field( default=None, metadata={ "help": ( "Number of batches loaded in advance by each worker. " "2 means there will be a total of 2 * num_workers batches prefetched across all workers. " "Default is unset" ) }, ) past_index: int = field( default=-1, metadata={"help": "If >=0, uses the corresponding part of the output as the past state for next step."}, ) run_name: Optional[str] = field( default=None, metadata={"help": "An optional descriptor for the run. Notably used for wandb logging."} ) disable_tqdm: Optional[bool] = field( default=None, metadata={"help": "Whether or not to disable the tqdm progress bars."} ) remove_unused_columns: Optional[bool] = field( default=True, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."} ) label_names: Optional[List[str]] = field( default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."} ) load_best_model_at_end: Optional[bool] = field( default=False, metadata={ "help": ( "Whether or not to load the best model found during training at the end of training. When this option" " is enabled, the best checkpoint will always be saved. See `save_total_limit` for more." ) }, ) metric_for_best_model: Optional[str] = field( default=None, metadata={"help": "The metric to use to compare two different models."} ) greater_is_better: Optional[bool] = field( default=None, metadata={"help": "Whether the `metric_for_best_model` should be maximized or not."} ) ignore_data_skip: bool = field( default=False, metadata={ "help": ( "When resuming training, whether or not to skip the first epochs and batches to get to the same" " training data." ) }, ) fsdp: Optional[Union[List[FSDPOption], str]] = field( default="", metadata={ "help": ( "Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training" " only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add" " CPU-offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op" " offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard" " auto_wrap` or `shard_grad_op auto_wrap`." ), }, ) fsdp_min_num_params: int = field( default=0, metadata={ "help": ( "This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful" " only when `fsdp` field is passed)." ) }, ) # Do not touch this type annotation or it will stop working in CLI fsdp_config: Optional[str] = field( default=None, metadata={ "help": ( "Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a " "fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`." ) }, ) fsdp_transformer_layer_cls_to_wrap: Optional[str] = field( default=None, metadata={ "help": ( "This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g," " `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed)." ) }, ) # Do not touch this type annotation or it will stop working in CLI deepspeed: Optional[str] = field( default=None, metadata={ "help": ( "Enable deepspeed and pass the path to deepspeed json config file (e.g. `ds_config.json`) or an already" " loaded json file as a dict" ) }, ) label_smoothing_factor: float = field( default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."} ) default_optim = "adamw_torch" # XXX: enable when pytorch==2.0.1 comes out - we want to give it time to get all the bugs sorted out # if is_torch_available() and version.parse(version.parse(torch.__version__).base_version) >= version.parse("2.1.0"): # default_optim = "adamw_torch_fused" # and update the doc above to: # optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch_fused"` (for torch<2.1.0 `"adamw_torch"`): optim: Union[OptimizerNames, str] = field( default=default_optim, metadata={"help": "The optimizer to use."}, ) optim_args: Optional[str] = field(default=None, metadata={"help": "Optional arguments to supply to optimizer."}) adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) group_by_length: bool = field( default=False, metadata={"help": "Whether or not to group samples of roughly the same length together when batching."}, ) length_column_name: Optional[str] = field( default="length", metadata={"help": "Column name with precomputed lengths to use when grouping by length."}, ) report_to: Optional[List[str]] = field( default=None, metadata={"help": "The list of integrations to report the results and logs to."} ) ddp_find_unused_parameters: Optional[bool] = field( default=None, metadata={ "help": ( "When using distributed training, the value of the flag `find_unused_parameters` passed to " "`DistributedDataParallel`." ) }, ) ddp_bucket_cap_mb: Optional[int] = field( default=None, metadata={ "help": ( "When using distributed training, the value of the flag `bucket_cap_mb` passed to " "`DistributedDataParallel`." ) }, ) ddp_broadcast_buffers: Optional[bool] = field( default=None, metadata={ "help": ( "When using distributed training, the value of the flag `broadcast_buffers` passed to " "`DistributedDataParallel`." ) }, ) dataloader_pin_memory: bool = field( default=True, metadata={"help": "Whether or not to pin memory for DataLoader."} ) dataloader_persistent_workers: bool = field( default=False, metadata={ "help": "If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage." }, ) skip_memory_metrics: bool = field( default=True, metadata={"help": "Whether or not to skip adding of memory profiler reports to metrics."} ) use_legacy_prediction_loop: bool = field( default=False, metadata={"help": "Whether or not to use the legacy prediction_loop in the Trainer."} ) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) resume_from_checkpoint: Optional[str] = field( default=None, metadata={"help": "The path to a folder with a valid checkpoint for your model."}, ) hub_model_id: Optional[str] = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_strategy: Union[HubStrategy, str] = field( default="every_save", metadata={"help": "The hub strategy to use when `--push_to_hub` is activated."}, ) hub_token: Optional[str] = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) hub_private_repo: bool = field(default=False, metadata={"help": "Whether the model repository is private or not."}) hub_always_push: bool = field( default=False, metadata={"help": "Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet."}, ) gradient_checkpointing: bool = field( default=False, metadata={ "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass." }, ) gradient_checkpointing_kwargs: Optional[dict] = field( default=None, metadata={ "help": "Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`." }, ) include_inputs_for_metrics: bool = field( default=False, metadata={"help": "Whether or not the inputs will be passed to the `compute_metrics` function."} ) # Deprecated arguments fp16_backend: str = field( default="auto", metadata={ "help": "Deprecated. Use half_precision_backend instead", "choices": ["auto", "apex", "cpu_amp"], }, ) push_to_hub_model_id: Optional[str] = field( default=None, metadata={"help": "The name of the repository to which push the `Trainer`."} ) push_to_hub_organization: Optional[str] = field( default=None, metadata={"help": "The name of the organization in with to which push the `Trainer`."} ) push_to_hub_token: Optional[str] = field( default=None, metadata={"help": "The token to use to push to the Model Hub."} ) _n_gpu: int = field(init=False, repr=False, default=-1) mp_parameters: str = field( default="", metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer"}, ) auto_find_batch_size: bool = field( default=False, metadata={ "help": ( "Whether to automatically decrease the batch size in half and rerun the training loop again each time" " a CUDA Out-of-Memory was reached" ) }, ) full_determinism: bool = field( default=False, metadata={ "help": ( "Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed" " training. Important: this will negatively impact the performance, so only use it for debugging." ) }, ) torchdynamo: Optional[str] = field( default=None, metadata={ "help": "This argument is deprecated, use `--torch_compile_backend` instead.", }, ) ray_scope: Optional[str] = field( default="last", metadata={ "help": ( 'The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray' " will then use the last checkpoint of all trials, compare those, and select the best one. However," " other options are also available. See the Ray documentation" " (https://docs.ray.io/en/latest/tune/api_docs/analysis.html" "#ray.tune.ExperimentAnalysis.get_best_trial)" " for more options." ) }, ) ddp_timeout: Optional[int] = field( default=1800, metadata={ "help": "Overrides the default timeout for distributed training (value should be given in seconds)." }, ) torch_compile: bool = field( default=False, metadata={"help": "If set to `True`, the model will be wrapped in `torch.compile`."} ) torch_compile_backend: Optional[str] = field( default=None, metadata={ "help": "Which backend to use with `torch.compile`, passing one will trigger a model compilation.", }, ) torch_compile_mode: Optional[str] = field( default=None, metadata={ "help": "Which mode to use with `torch.compile`, passing one will trigger a model compilation.", }, ) dispatch_batches: Optional[bool] = field( default=None, metadata={ "help": "Whether to dispatch batches across devices in distributed training. If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process " "and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose" "underlying dataset is an `IterableDataset`, `False` otherwise." }, ) split_batches: Optional[bool] = field( default=False, metadata={ "help": "Whether or not the accelerator should split the batches yielded by the dataloaders across the devices during distributed training. If" "set to `True`, the actual batch size used will be the same on any kind of distributed processes, but it must be a" "round multiple of the number of processes you are using (such as GPUs)." }, ) include_tokens_per_second: Optional[bool] = field( default=False, metadata={"help": "If set to `True`, the speed metrics will include `tgs` (tokens per second per device)."}, ) include_num_input_tokens_seen: Optional[bool] = field( default=False, metadata={ "help": "If set to `True`, will track the number of input tokens seen throughout training. (May be slower in distributed training)" }, ) neftune_noise_alpha: float = field( default=None, metadata={ "help": "Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instrcution fine-tuning. Check out the original paper here: https://arxiv.org/abs/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes." }, ) def __post_init__(self): # expand paths, if not os.makedirs("~/bar") will make directory # in the current directory instead of the actual home # see https://github.com/huggingface/transformers/issues/10628 if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) if self.logging_dir is None and self.output_dir is not None: self.logging_dir = os.path.join(self.output_dir, default_logdir()) if self.logging_dir is not None: self.logging_dir = os.path.expanduser(self.logging_dir) if self.disable_tqdm is None: self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN if isinstance(self.evaluation_strategy, EvaluationStrategy): warnings.warn( "using `EvaluationStrategy` for `evaluation_strategy` is deprecated and will be removed in version 5" " of 🤗 Transformers. Use `IntervalStrategy` instead", FutureWarning, ) # Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it. self.evaluation_strategy = self.evaluation_strategy.value if self.no_cuda: warnings.warn( "using `no_cuda` is deprecated and will be removed in version 5.0 of 🤗 Transformers. " "Use `use_cpu` instead", FutureWarning, ) self.use_cpu = self.no_cuda self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy) self.logging_strategy = IntervalStrategy(self.logging_strategy) self.save_strategy = IntervalStrategy(self.save_strategy) self.hub_strategy = HubStrategy(self.hub_strategy) self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type) if self.do_eval is False and self.evaluation_strategy != IntervalStrategy.NO: self.do_eval = True # eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0): if self.logging_steps > 0: logger.info(f"using `logging_steps` to initialize `eval_steps` to {self.logging_steps}") self.eval_steps = self.logging_steps else: raise ValueError( f"evaluation strategy {self.evaluation_strategy} requires either non-zero --eval_steps or" " --logging_steps" ) # logging_steps must be non-zero for logging_strategy that is other than 'no' if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps == 0: raise ValueError(f"logging strategy {self.logging_strategy} requires non-zero --logging_steps") if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps > 1: if self.logging_steps != int(self.logging_steps): raise ValueError(f"--logging_steps must be an integer if bigger than 1: {self.logging_steps}") self.logging_steps = int(self.logging_steps) if self.evaluation_strategy == IntervalStrategy.STEPS and self.eval_steps > 1: if self.eval_steps != int(self.eval_steps): raise ValueError(f"--eval_steps must be an integer if bigger than 1: {self.eval_steps}") self.eval_steps = int(self.eval_steps) if self.save_strategy == IntervalStrategy.STEPS and self.save_steps > 1: if self.save_steps != int(self.save_steps): raise ValueError(f"--save_steps must be an integer if bigger than 1: {self.save_steps}") self.save_steps = int(self.save_steps) # Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible. if self.load_best_model_at_end: if self.evaluation_strategy != self.save_strategy: raise ValueError( "--load_best_model_at_end requires the save and eval strategy to match, but found\n- Evaluation " f"strategy: {self.evaluation_strategy}\n- Save strategy: {self.save_strategy}" ) if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0: if self.eval_steps < 1 or self.save_steps < 1: if not (self.eval_steps < 1 and self.save_steps < 1): raise ValueError( "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation " "steps, which cannot get guaranteed when mixing ratio and absolute steps for save_steps " f"{self.save_steps} and eval_steps {self.eval_steps}." ) # Work around floating point precision issues LARGE_MULTIPLIER = 1_000_000 if (self.save_steps * LARGE_MULTIPLIER) % (self.eval_steps * LARGE_MULTIPLIER) != 0: raise ValueError( "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation " f"steps, but found {self.save_steps}, which is not a multiple of {self.eval_steps}." ) raise ValueError( "--load_best_model_at_end requires the saving steps to be a round multiple of the evaluation " f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}." ) safetensors_available = is_safetensors_available() if self.save_safetensors and not safetensors_available: raise ValueError(f"--save_safetensors={self.save_safetensors} requires safetensors to be installed!") if not self.save_safetensors and safetensors_available: logger.info( f"Found safetensors installation, but --save_safetensors={self.save_safetensors}. " f"Safetensors should be a preferred weights saving format due to security and performance reasons. " f"If your model cannot be saved by safetensors please feel free to open an issue at " f"https://github.com/huggingface/safetensors!" ) if ( self.load_best_model_at_end or self.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU ) and self.metric_for_best_model is None: self.metric_for_best_model = "loss" if self.greater_is_better is None and self.metric_for_best_model is not None: self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"] if self.run_name is None: self.run_name = self.output_dir if self.framework == "pt" and is_torch_available(): if self.fp16_backend and self.fp16_backend != "auto": warnings.warn( "`fp16_backend` is deprecated and will be removed in version 5 of 🤗 Transformers. Use" " `half_precision_backend` instead", FutureWarning, ) self.half_precision_backend = self.fp16_backend if self.bf16 or self.bf16_full_eval: if self.use_cpu and not is_torch_bf16_cpu_available() and not is_torch_tpu_available(): # cpu raise ValueError("Your setup doesn't support bf16/(cpu, tpu, neuroncore). You need torch>=1.10") elif not self.use_cpu: if torch.cuda.is_available() and not is_torch_bf16_gpu_available(): # gpu raise ValueError( "Your setup doesn't support bf16/gpu. You need torch>=1.10, using Ampere GPU with cuda>=11.0" ) elif not is_torch_xpu_available(): # xpu from .pytorch_utils import is_torch_greater_or_equal_than_1_12 if not is_torch_greater_or_equal_than_1_12: raise ValueError( "Your setup doesn't support bf16/xpu. You need torch>=1.12, using Intel XPU/GPU with IPEX installed" ) if self.fp16 and self.bf16: raise ValueError("At most one of fp16 and bf16 can be True, but not both") if self.fp16_full_eval and self.bf16_full_eval: raise ValueError("At most one of fp16 and bf16 can be True for full eval, but not both") if self.bf16: if self.half_precision_backend == "apex": raise ValueError(" `--half_precision_backend apex`: GPU bf16 is not supported by apex.") if self.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU: if self.evaluation_strategy == IntervalStrategy.NO: raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires an eval strategy") if not is_torch_available(): raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires torch>=0.2.0") self.optim = OptimizerNames(self.optim) if self.adafactor: warnings.warn( "`--adafactor` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--optim" " adafactor` instead", FutureWarning, ) self.optim = OptimizerNames.ADAFACTOR if self.optim == OptimizerNames.ADAMW_TORCH_FUSED and is_torch_available(): if version.parse(version.parse(torch.__version__).base_version) < version.parse("2.0.0"): raise ValueError("--optim adamw_torch_fused requires PyTorch 2.0 or higher") # there is a bug in fp16/AMP in pt-2.0.0 if version.parse(version.parse(torch.__version__).base_version) == version.parse("2.0.0") and self.fp16: raise ValueError("--optim adamw_torch_fused with --fp16 requires PyTorch>2.0") if ( self.framework == "pt" and is_torch_available() and (self.device.type != "cuda") and (self.device.type != "npu") and (self.device.type != "xpu") and (get_xla_device_type(self.device) != "GPU") and (self.fp16 or self.fp16_full_eval) ): raise ValueError( "FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation" " (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX)." ) if ( self.framework == "pt" and is_torch_available() and (self.device.type != "cuda") and (self.device.type != "npu") and (self.device.type != "xpu") and (get_xla_device_type(self.device) != "GPU") and (get_xla_device_type(self.device) != "TPU") and (self.device.type != "cpu") and (self.bf16 or self.bf16_full_eval) ): raise ValueError( "BF16 Mixed precision training with AMP (`--bf16`) and BF16 half precision evaluation" " (`--bf16_full_eval`) can only be used on CUDA, XPU (with IPEX), NPU or CPU/TPU/NeuronCore devices." ) if self.torchdynamo is not None: warnings.warn( "`torchdynamo` is deprecated and will be removed in version 5 of 🤗 Transformers. Use" " `torch_compile_backend` instead", FutureWarning, ) self.torch_compile_backend = self.torchdynamo if (self.torch_compile_mode is not None or self.torch_compile_backend is not None) and not self.torch_compile: self.torch_compile = True if self.torch_compile and self.torch_compile_backend is None: self.torch_compile_backend = "inductor" # accelerate integration for torch compile if self.torch_compile: # set env vars for accelerate prefix = "ACCELERATE_DYNAMO_" os.environ[prefix + "BACKEND"] = self.torch_compile_backend if self.torch_compile_mode is not None: os.environ[prefix + "MODE"] = self.torch_compile_mode if self.framework == "pt" and is_torch_available() and self.torch_compile: if is_torch_tf32_available(): if self.tf32 is None and not self.fp16 or self.bf16: logger.info( "Setting TF32 in CUDA backends to speedup torch compile, you won't see any improvement" " otherwise." ) torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True else: logger.warning( "The speedups for torchdynamo mostly come wih GPU Ampere or higher and which is not detected here." ) if self.framework == "pt" and is_torch_available() and self.tf32 is not None: if self.tf32: if is_torch_tf32_available(): torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True else: raise ValueError("--tf32 requires Ampere or a newer GPU arch, cuda>=11 and torch>=1.7") else: if is_torch_tf32_available(): torch.backends.cuda.matmul.allow_tf32 = False torch.backends.cudnn.allow_tf32 = False # no need to assert on else # if training args is specified, it will override the one specified in the accelerate config if self.half_precision_backend != "apex": mixed_precision_dtype = os.environ.get("ACCELERATE_MIXED_PRECISION", "no") if self.fp16: mixed_precision_dtype = "fp16" elif self.bf16: mixed_precision_dtype = "bf16" os.environ["ACCELERATE_MIXED_PRECISION"] = mixed_precision_dtype if self.report_to is None: logger.info( "The default value for the training argument `--report_to` will change in v5 (from all installed " "integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as " "now. You should start updating your code and make this info disappear :-)." ) self.report_to = "all" if self.report_to == "all" or self.report_to == ["all"]: # Import at runtime to avoid a circular import. from .integrations import get_available_reporting_integrations self.report_to = get_available_reporting_integrations() elif self.report_to == "none" or self.report_to == ["none"]: self.report_to = [] elif not isinstance(self.report_to, list): self.report_to = [self.report_to] if self.warmup_ratio < 0 or self.warmup_ratio > 1: raise ValueError("warmup_ratio must lie in range [0,1]") elif self.warmup_ratio > 0 and self.warmup_steps > 0: logger.info( "Both warmup_ratio and warmup_steps given, warmup_steps will override any effect of warmup_ratio" " during training" ) if isinstance(self.fsdp, bool): self.fsdp = "full_shard" if self.fsdp else "" if isinstance(self.fsdp, str): self.fsdp = [FSDPOption(s) for s in self.fsdp.split()] if self.fsdp == [FSDPOption.OFFLOAD]: raise ValueError( "`--fsdp offload` can't work on its own. It needs to be added to `--fsdp full_shard` or " '`--fsdp shard_grad_op`. For example, `--fsdp "full_shard offload"`.' ) elif FSDPOption.FULL_SHARD in self.fsdp and FSDPOption.SHARD_GRAD_OP in self.fsdp: raise ValueError("`--fsdp full_shard` is not compatible with `--fsdp shard_grad_op`.") if self.fsdp_config is None: self.fsdp_config = {} if isinstance(self.fsdp_config, str): if len(self.fsdp) == 0: warnings.warn("`--fsdp_config` is useful only when `--fsdp` is specified.") with io.open(self.fsdp_config, "r", encoding="utf-8") as f: self.fsdp_config = json.load(f) for k in list(self.fsdp_config.keys()): if k.startswith("fsdp_"): v = self.fsdp_config.pop(k) self.fsdp_config[k[5:]] = v if self.fsdp_min_num_params > 0: warnings.warn("using `--fsdp_min_num_params` is deprecated. Use fsdp_config instead ", FutureWarning) self.fsdp_config["min_num_params"] = max(self.fsdp_config.get("min_num_params", 0), self.fsdp_min_num_params) # if fsdp_config["transformer_layer_cls_to_wrap"] is specified as a string, convert it to a list with a single object if isinstance(self.fsdp_config.get("transformer_layer_cls_to_wrap", None), str): self.fsdp_config["transformer_layer_cls_to_wrap"] = [self.fsdp_config["transformer_layer_cls_to_wrap"]] if self.fsdp_transformer_layer_cls_to_wrap is not None: warnings.warn( "using `--fsdp_transformer_layer_cls_to_wrap` is deprecated. Use fsdp_config instead ", FutureWarning ) self.fsdp_config["transformer_layer_cls_to_wrap"] = self.fsdp_config.get( "transformer_layer_cls_to_wrap", [] ) + [self.fsdp_transformer_layer_cls_to_wrap] if len(self.fsdp) == 0 and self.fsdp_config["min_num_params"] > 0: warnings.warn("`min_num_params` is useful only when `--fsdp` is specified.") if len(self.fsdp) == 0 and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None: warnings.warn("`transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.") if ( len(self.fsdp) > 0 and self.fsdp_config["min_num_params"] > 0 and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None ): raise ValueError("`min_num_params` and `transformer_layer_cls_to_wrap` are mutually exclusive.") self.fsdp_config["xla"] = self.fsdp_config.get("xla", False) self.fsdp_config["xla_fsdp_grad_ckpt"] = self.fsdp_config.get("xla_fsdp_grad_ckpt", False) if self.fsdp_config["xla"]: if len(self.fsdp) > 0: # store XLA fsdp configuration parameters into a dictionary self.xla_fsdp_config = self.fsdp_config.get("xla_fsdp_settings", {}) # apply appropriate string to torch.dtype conversions for parameters if "compute_dtype" in self.xla_fsdp_config: self.xla_fsdp_config["compute_dtype"] = getattr(torch, self.xla_fsdp_config["compute_dtype"]) if "buffer_dtype" in self.xla_fsdp_config: self.xla_fsdp_config["buffer_dtype"] = getattr(torch, self.xla_fsdp_config["buffer_dtype"]) else: warnings.warn("XLA FSDP can be used only when `--fsdp` is specified.") else: if self.fsdp_config["xla_fsdp_grad_ckpt"]: warnings.warn("`--xla_fsdp_grad_ckpt` is useful only when `--xla` is set to true.") # accelerate integration for FSDP if len(self.fsdp) > 0 and not self.fsdp_config["xla"]: os.environ["ACCELERATE_USE_FSDP"] = "true" from accelerate.utils.constants import ( FSDP_AUTO_WRAP_POLICY, FSDP_SHARDING_STRATEGY, ) prefix = "FSDP_" for fsdp_option in self.fsdp: if fsdp_option.upper() in FSDP_SHARDING_STRATEGY: # set environment variable for FSDP sharding strategy os.environ[f"{prefix}SHARDING_STRATEGY"] = str( FSDP_SHARDING_STRATEGY.index(fsdp_option.upper()) + 1 ) elif fsdp_option == FSDPOption.OFFLOAD: os.environ[f"{prefix}OFFLOAD_PARAMS"] = "true" elif fsdp_option == FSDPOption.AUTO_WRAP: os.environ[f"{prefix}AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[0] if self.fsdp_config["min_num_params"] > 0: os.environ[f"{prefix}MIN_NUM_PARAMS"] = str(self.fsdp_config["min_num_params"]) os.environ[f"{prefix}AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[1] elif self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None: os.environ[f"{prefix}TRANSFORMER_CLS_TO_WRAP"] = ",".join( self.fsdp_config["transformer_layer_cls_to_wrap"] ) prefetch_policy = self.fsdp_config.get("fsdp_backward_prefetch", "NO_PREFETCH") os.environ[f"{prefix}BACKWARD_PREFETCH"] = prefetch_policy.upper() os.environ[f"{prefix}FORWARD_PREFETCH"] = self.fsdp_config.get("forward_prefect", "false") os.environ[f"{prefix}SYNC_MODULE_STATES"] = self.fsdp_config.get("sync_module_states", "true") os.environ[f"{prefix}USE_ORIG_PARAMS"] = self.fsdp_config.get("use_orig_params", "true") if self.tpu_metrics_debug: warnings.warn( "using `--tpu_metrics_debug` is deprecated and will be removed in version 5 of 🤗 Transformers. Use" " `--debug tpu_metrics_debug` instead", FutureWarning, ) if self.debug is None: self.debug = " tpu_metrics_debug" else: self.debug += " tpu_metrics_debug" self.tpu_metrics_debug = False if isinstance(self.debug, str): self.debug = [DebugOption(s) for s in self.debug.split()] elif self.debug is None: self.debug = [] self.deepspeed_plugin = None if self.deepspeed: # - must be run very last in arg parsing, since it will use a lot of these settings. # - must be run before the model is created. if not is_accelerate_available(): raise ValueError("--deepspeed requires Accelerate to be installed: `pip install accelerate`.") from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig # will be used later by the Trainer # note: leave self.deepspeed unmodified in case a user relies on it not to be modified) self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed) self.hf_deepspeed_config.trainer_config_process(self) # Accelerate DeepSpeed Plugin from accelerate.utils import DeepSpeedPlugin os.environ["ACCELERATE_USE_DEEPSPEED"] = "true" self.deepspeed_plugin = DeepSpeedPlugin(hf_ds_config=self.hf_deepspeed_config) elif strtobool(os.environ.get("ACCELERATE_USE_DEEPSPEED", "false")): # Accelerate DeepSpeed Plugin from accelerate.utils import DeepSpeedPlugin self.deepspeed_plugin = DeepSpeedPlugin() mixed_precision = os.environ.get("ACCELERATE_MIXED_PRECISION", "no") self.deepspeed_plugin.set_mixed_precision(mixed_precision) self.deepspeed_plugin.set_deepspeed_weakref() if self.use_cpu: self.dataloader_pin_memory = False if self.dataloader_num_workers == 0 and self.dataloader_prefetch_factor is not None: raise ValueError( "--dataloader_prefetch_factor can only be set when data is loaded in a different process, i.e." " when --dataloader_num_workers > 1." ) if self.push_to_hub_token is not None: warnings.warn( "`--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_token` instead.", FutureWarning, ) self.hub_token = self.push_to_hub_token if self.push_to_hub_model_id is not None: self.hub_model_id = get_full_repo_name( self.push_to_hub_model_id, organization=self.push_to_hub_organization, token=self.hub_token ) if self.push_to_hub_organization is not None: warnings.warn( "`--push_to_hub_model_id` and `--push_to_hub_organization` are deprecated and will be removed in " "version 5 of 🤗 Transformers. Use `--hub_model_id` instead and pass the full repo name to this " f"argument (in this case {self.hub_model_id}).", FutureWarning, ) else: warnings.warn( "`--push_to_hub_model_id` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_model_id` instead and pass the full repo name to this argument (in this case " f"{self.hub_model_id}).", FutureWarning, ) elif self.push_to_hub_organization is not None: self.hub_model_id = f"{self.push_to_hub_organization}/{Path(self.output_dir).name}" warnings.warn( "`--push_to_hub_organization` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_model_id` instead and pass the full repo name to this argument (in this case " f"{self.hub_model_id}).", FutureWarning, ) def __str__(self): self_as_dict = asdict(self) # Remove deprecated arguments. That code should be removed once # those deprecated arguments are removed from TrainingArguments. (TODO: v5) del self_as_dict["per_gpu_train_batch_size"] del self_as_dict["per_gpu_eval_batch_size"] self_as_dict = {k: f"<{k.upper()}>" if k.endswith("_token") else v for k, v in self_as_dict.items()} attrs_as_str = [f"{k}={v},\n" for k, v in sorted(self_as_dict.items())] return f"{self.__class__.__name__}(\n{''.join(attrs_as_str)})" __repr__ = __str__ @property def train_batch_size(self) -> int: """ The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training). """ if self.per_gpu_train_batch_size: logger.warning( "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future " "version. Using `--per_device_train_batch_size` is preferred." ) per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size train_batch_size = per_device_batch_size * max(1, self.n_gpu) return train_batch_size @property def eval_batch_size(self) -> int: """ The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training). """ if self.per_gpu_eval_batch_size: logger.warning( "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future " "version. Using `--per_device_eval_batch_size` is preferred." ) per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size eval_batch_size = per_device_batch_size * max(1, self.n_gpu) return eval_batch_size @property def ddp_timeout_delta(self) -> timedelta: """ The actual timeout for torch.distributed.init_process_group since it expects a timedelta variable. """ return timedelta(seconds=self.ddp_timeout) @cached_property def _setup_devices(self) -> "torch.device": requires_backends(self, ["torch"]) logger.info("PyTorch: setting up devices") if not is_sagemaker_mp_enabled(): if not is_accelerate_available(): raise ImportError( f"Using the `Trainer` with `PyTorch` requires `accelerate>={ACCELERATE_MIN_VERSION}`: " "Please run `pip install transformers[torch]` or `pip install accelerate -U`" ) AcceleratorState._reset_state(reset_partial_state=True) self.distributed_state = None if not self.use_ipex and "ACCELERATE_USE_IPEX" not in os.environ: os.environ["ACCELERATE_USE_IPEX"] = "false" if self.use_cpu or strtobool(os.environ.get("ACCELERATE_USE_CPU", "False")): self.distributed_state = PartialState(cpu=True, backend=self.ddp_backend) self._n_gpu = 0 elif is_sagemaker_mp_enabled(): local_rank = smp.local_rank() device = torch.device("cuda", local_rank) self._n_gpu = 1 torch.cuda.set_device(device) elif is_sagemaker_dp_enabled(): self.distributed_state = PartialState(_use_sagemaker_dp=True) self._n_gpu = 1 elif self.deepspeed: # Need to do similar for Accelerator init os.environ["ACCELERATE_USE_DEEPSPEED"] = "true" self.distributed_state = PartialState(timeout=timedelta(seconds=self.ddp_timeout)) del os.environ["ACCELERATE_USE_DEEPSPEED"] self._n_gpu = 1 else: self.distributed_state = PartialState( backend=self.ddp_backend, timeout=timedelta(seconds=self.ddp_timeout) ) self._n_gpu = 1 if not is_sagemaker_mp_enabled(): device = self.distributed_state.device self.local_rank = self.distributed_state.local_process_index if dist.is_available() and dist.is_initialized() and self.parallel_mode != ParallelMode.DISTRIBUTED: logger.warning( "torch.distributed process group is initialized, but parallel_mode != ParallelMode.DISTRIBUTED. " "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" ) if is_torch_tpu_available(): device = self.distributed_state.device self._n_gpu = 0 elif is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled(): # Already set _n_gpu pass elif self.distributed_state.distributed_type == DistributedType.NO: if self.use_mps_device: warnings.warn( "`use_mps_device` is deprecated and will be removed in version 5.0 of 🤗 Transformers. " "`mps` device will be used by default if available similar to the way `cuda` device is used." "Therefore, no action from user is required. " ) if device.type != "mps": raise ValueError( "Either you do not have an MPS-enabled device on this machine or MacOS version is not 12.3+ " "or current PyTorch install was not built with MPS enabled." ) if device.type == "mps": self._n_gpu = 1 elif self.use_cpu: device = torch.device("cpu") self._n_gpu = 0 elif is_torch_xpu_available(): device = torch.device("xpu:0") torch.xpu.set_device(device) self._n_gpu = 1 elif is_torch_npu_available(): device = torch.device("npu:0") torch.npu.set_device(device) self._n_gpu = 1 else: # if n_gpu is > 1 we'll use nn.DataParallel. # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0` # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will # trigger an error that a device index is missing. Index 0 takes into account the # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0` # will use the first GPU in that env, i.e. GPU#1 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at # the default value. self._n_gpu = torch.cuda.device_count() if device.type == "cuda": torch.cuda.set_device(device) return device @property def device(self) -> "torch.device": """ The device used by this process. """ requires_backends(self, ["torch"]) return self._setup_devices @property def n_gpu(self): """ The number of GPUs used by this process. Note: This will only be greater than one when you have multiple GPUs available but are not using distributed training. For distributed training, it will always be 1. """ requires_backends(self, ["torch"]) # Make sure `self._n_gpu` is properly setup. if not hasattr(self, "_n_gpu"): _ = self._setup_devices return self._n_gpu @property def parallel_mode(self): """ The current mode used for parallelism if multiple GPUs/TPU cores are available. One of: - `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU). - `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`). - `ParallelMode.DISTRIBUTED`: several GPUs, each having its own process (uses `torch.nn.DistributedDataParallel`). - `ParallelMode.TPU`: several TPU cores. """ requires_backends(self, ["torch"]) if is_torch_tpu_available(): return ParallelMode.TPU elif is_sagemaker_mp_enabled(): return ParallelMode.SAGEMAKER_MODEL_PARALLEL elif is_sagemaker_dp_enabled(): return ParallelMode.SAGEMAKER_DATA_PARALLEL elif ( self.distributed_state is not None and self.distributed_state.distributed_type != DistributedType.NO ) or (self.distributed_state is None and self.local_rank != -1): return ParallelMode.DISTRIBUTED elif self.n_gpu > 1: return ParallelMode.NOT_DISTRIBUTED else: return ParallelMode.NOT_PARALLEL @property def world_size(self): """ The number of processes used in parallel. """ requires_backends(self, ["torch"]) if self.distributed_state is not None: return self.distributed_state.num_processes elif is_sagemaker_mp_enabled(): return smp.dp_size() if not smp.state.cfg.prescaled_batch else smp.rdp_size() return 1 @property def process_index(self): """ The index of the current process used. """ requires_backends(self, ["torch"]) if self.distributed_state is not None: return self.distributed_state.process_index elif is_sagemaker_mp_enabled(): return smp.dp_rank() if not smp.state.cfg.prescaled_batch else smp.rdp_rank() return 0 @property def local_process_index(self): """ The index of the local process used. """ requires_backends(self, ["torch"]) if self.distributed_state is not None: return self.distributed_state.local_process_index elif is_sagemaker_mp_enabled(): return smp.local_rank() return 0 @property def should_log(self): """ Whether or not the current process should produce log. """ if self.log_on_each_node: return self.local_process_index == 0 else: if is_sagemaker_mp_enabled(): return smp.rank() == 0 else: return self.process_index == 0 @property def should_save(self): """ Whether or not the current process should write to disk, e.g., to save models and checkpoints. """ if self.save_on_each_node: return self.local_process_index == 0 else: if is_sagemaker_mp_enabled(): return smp.rank() == 0 else: return self.process_index == 0 def get_process_log_level(self): """ Returns the log level to be used depending on whether this process is the main process of node 0, main process of node non-0, or a non-main process. For the main process the log level defaults to the logging level set (`logging.WARNING` if you didn't do anything) unless overridden by `log_level` argument. For the replica processes the log level defaults to `logging.WARNING` unless overridden by `log_level_replica` argument. The choice between the main and replica process settings is made according to the return value of `should_log`. """ # convert to int log_level = trainer_log_levels[self.log_level] log_level_replica = trainer_log_levels[self.log_level_replica] log_level_main_node = logging.get_verbosity() if log_level == -1 else log_level log_level_replica_node = logging.get_verbosity() if log_level_replica == -1 else log_level_replica return log_level_main_node if self.should_log else log_level_replica_node @property def place_model_on_device(self): """ Can be subclassed and overridden for some specific integrations. """ return not is_sagemaker_mp_enabled() @property def _no_sync_in_gradient_accumulation(self): """ Whether or not to use no_sync for the gradients when doing gradient accumulation. """ return not ( self.deepspeed or is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled() or is_torch_neuroncore_available() ) @contextlib.contextmanager def main_process_first(self, local=True, desc="work"): """ A context manager for torch distributed environment where on needs to do something on the main process, while blocking replicas, and when it's finished releasing the replicas. One such use is for `datasets`'s `map` feature which to be efficient should be run once on the main process, which upon completion saves a cached version of results and which then automatically gets loaded by the replicas. Args: local (`bool`, *optional*, defaults to `True`): if `True` first means process of rank 0 of each node if `False` first means process of rank 0 of node rank 0 In multi-node environment with a shared filesystem you most likely will want to use `local=False` so that only the main process of the first node will do the processing. If however, the filesystem is not shared, then the main process of each node will need to do the processing, which is the default behavior. desc (`str`, *optional*, defaults to `"work"`): a work description to be used in debug logs """ if is_torch_available() and self.world_size > 1: main_process_desc = "main local process" if local else "main process" if self.distributed_state is not None: is_main_process = ( self.distributed_state.is_local_main_process if local else self.distributed_state.is_main_process ) elif is_sagemaker_mp_enabled(): is_main_process = smp.rank() == 0 try: if not is_main_process: # tell all replicas to wait logger.debug(f"{self.process_index}: waiting for the {main_process_desc} to perform {desc}") if is_torch_tpu_available(): xm.rendezvous(desc) else: dist.barrier() yield finally: if is_main_process: # the wait is over logger.debug(f"{self.process_index}: {main_process_desc} completed {desc}, releasing all replicas") if is_torch_tpu_available(): xm.rendezvous(desc) else: dist.barrier() else: yield def get_warmup_steps(self, num_training_steps: int): """ Get number of steps used for a linear warmup. """ warmup_steps = ( self.warmup_steps if self.warmup_steps > 0 else math.ceil(num_training_steps * self.warmup_ratio) ) return warmup_steps def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ # filter out fields that are defined as field(init=False) d = {field.name: getattr(self, field.name) for field in fields(self) if field.init} for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d def to_json_string(self): """ Serializes this instance to a JSON string. """ return json.dumps(self.to_dict(), indent=2) def to_sanitized_dict(self) -> Dict[str, Any]: """ Sanitized serialization to use with TensorBoard’s hparams """ d = self.to_dict() d = {**d, **{"train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size}} valid_types = [bool, int, float, str] if is_torch_available(): valid_types.append(torch.Tensor) return {k: v if type(v) in valid_types else str(v) for k, v in d.items()} # The following methods are there to simplify the instantiation of `TrainingArguments` def set_training( self, learning_rate: float = 5e-5, batch_size: int = 8, weight_decay: float = 0, num_epochs: float = 3, max_steps: int = -1, gradient_accumulation_steps: int = 1, seed: int = 42, gradient_checkpointing: bool = False, ): """ A method that regroups all basic arguments linked to the training. <Tip> Calling this method will automatically set `self.do_train` to `True`. </Tip> Args: learning_rate (`float`, *optional*, defaults to 5e-5): The initial learning rate for the optimizer. batch_size (`int` *optional*, defaults to 8): The batch size per device (GPU/TPU core/CPU...) used for training. weight_decay (`float`, *optional*, defaults to 0): The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in the optimizer. num_train_epochs(`float`, *optional*, defaults to 3.0): Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training). max_steps (`int`, *optional*, defaults to -1): If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until `max_steps` is reached. gradient_accumulation_steps (`int`, *optional*, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. <Tip warning={true}> When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples. </Tip> seed (`int`, *optional*, defaults to 42): Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the [`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters. gradient_checkpointing (`bool`, *optional*, defaults to `False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_training(learning_rate=1e-4, batch_size=32) >>> args.learning_rate 1e-4 ``` """ self.do_train = True self.learning_rate = learning_rate self.per_device_train_batch_size = batch_size self.weight_decay = weight_decay self.num_train_epochs = num_epochs self.max_steps = max_steps self.gradient_accumulation_steps = gradient_accumulation_steps self.seed = seed self.gradient_checkpointing = gradient_checkpointing return self def set_evaluate( self, strategy: Union[str, IntervalStrategy] = "no", steps: int = 500, batch_size: int = 8, accumulation_steps: Optional[int] = None, delay: Optional[float] = None, loss_only: bool = False, jit_mode: bool = False, ): """ A method that regroups all arguments linked to evaluation. Args: strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`): The evaluation strategy to adopt during training. Possible values are: - `"no"`: No evaluation is done during training. - `"steps"`: Evaluation is done (and logged) every `steps`. - `"epoch"`: Evaluation is done at the end of each epoch. Setting a `strategy` different from `"no"` will set `self.do_eval` to `True`. steps (`int`, *optional*, defaults to 500): Number of update steps between two evaluations if `strategy="steps"`. batch_size (`int` *optional*, defaults to 8): The batch size per device (GPU/TPU core/CPU...) used for evaluation. accumulation_steps (`int`, *optional*): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but requires more memory). delay (`float`, *optional*): Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy. loss_only (`bool`, *optional*, defaults to `False`): Ignores all outputs except the loss. jit_mode (`bool`, *optional*): Whether or not to use PyTorch jit trace for inference. Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_evaluate(strategy="steps", steps=100) >>> args.eval_steps 100 ``` """ self.evaluation_strategy = IntervalStrategy(strategy) if self.evaluation_strategy == IntervalStrategy.STEPS and steps == 0: raise ValueError("Setting `strategy` as 'steps' requires a positive value for `steps`.") self.do_eval = self.evaluation_strategy != IntervalStrategy.NO self.eval_steps = steps self.per_device_eval_batch_size = batch_size self.eval_accumulation_steps = accumulation_steps self.eval_delay = delay self.prediction_loss_only = loss_only self.jit_mode_eval = jit_mode return self def set_testing( self, batch_size: int = 8, loss_only: bool = False, jit_mode: bool = False, ): """ A method that regroups all basic arguments linked to testing on a held-out dataset. <Tip> Calling this method will automatically set `self.do_predict` to `True`. </Tip> Args: batch_size (`int` *optional*, defaults to 8): The batch size per device (GPU/TPU core/CPU...) used for testing. loss_only (`bool`, *optional*, defaults to `False`): Ignores all outputs except the loss. jit_mode (`bool`, *optional*): Whether or not to use PyTorch jit trace for inference. Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_testing(batch_size=32) >>> args.per_device_eval_batch_size 32 ``` """ self.do_predict = True self.per_device_eval_batch_size = batch_size self.prediction_loss_only = loss_only self.jit_mode_eval = jit_mode return self def set_save( self, strategy: Union[str, IntervalStrategy] = "steps", steps: int = 500, total_limit: Optional[int] = None, on_each_node: bool = False, ): """ A method that regroups all arguments linked to checkpoint saving. Args: strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The checkpoint save strategy to adopt during training. Possible values are: - `"no"`: No save is done during training. - `"epoch"`: Save is done at the end of each epoch. - `"steps"`: Save is done every `save_steps`. steps (`int`, *optional*, defaults to 500): Number of updates steps before two checkpoint saves if `strategy="steps"`. total_limit (`int`, *optional*): If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. on_each_node (`bool`, *optional*, defaults to `False`): When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one. This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node. Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_save(strategy="steps", steps=100) >>> args.save_steps 100 ``` """ self.save_strategy = IntervalStrategy(strategy) if self.save_strategy == IntervalStrategy.STEPS and steps == 0: raise ValueError("Setting `strategy` as 'steps' requires a positive value for `steps`.") self.save_steps = steps self.save_total_limit = total_limit self.save_on_each_node = on_each_node return self def set_logging( self, strategy: Union[str, IntervalStrategy] = "steps", steps: int = 500, report_to: Union[str, List[str]] = "none", level: str = "passive", first_step: bool = False, nan_inf_filter: bool = False, on_each_node: bool = False, replica_level: str = "passive", ): """ A method that regroups all arguments linked to logging. Args: strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The logging strategy to adopt during training. Possible values are: - `"no"`: No save is done during training. - `"epoch"`: Save is done at the end of each epoch. - `"steps"`: Save is done every `save_steps`. steps (`int`, *optional*, defaults to 500): Number of update steps between two logs if `strategy="steps"`. level (`str`, *optional*, defaults to `"passive"`): Logger log level to use on the main process. Possible choices are the log levels as strings: `"debug"`, `"info"`, `"warning"`, `"error"` and `"critical"`, plus a `"passive"` level which doesn't set anything and lets the application set the level. report_to (`str` or `List[str]`, *optional*, defaults to `"all"`): The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`, `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`, `"neptune"`, `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no integrations. first_step (`bool`, *optional*, defaults to `False`): Whether to log and evaluate the first `global_step` or not. nan_inf_filter (`bool`, *optional*, defaults to `True`): Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan` or `inf` is filtered and the average loss of the current logging window is taken instead. <Tip> `nan_inf_filter` only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model. </Tip> on_each_node (`bool`, *optional*, defaults to `True`): In multinode distributed training, whether to log using `log_level` once per node, or only on the main node. replica_level (`str`, *optional*, defaults to `"passive"`): Logger log level to use on replicas. Same choices as `log_level` Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_logging(strategy="steps", steps=100) >>> args.logging_steps 100 ``` """ self.logging_strategy = IntervalStrategy(strategy) if self.logging_strategy == IntervalStrategy.STEPS and steps == 0: raise ValueError("Setting `strategy` as 'steps' requires a positive value for `steps`.") self.logging_steps = steps self.report_to = report_to self.log_level = level self.logging_first_step = first_step self.logging_nan_inf_filter = nan_inf_filter self.log_on_each_node = on_each_node self.log_level_replica = replica_level return self def set_push_to_hub( self, model_id: str, strategy: Union[str, HubStrategy] = "every_save", token: Optional[str] = None, private_repo: bool = False, always_push: bool = False, ): """ A method that regroups all arguments linked to synchronizing checkpoints with the Hub. <Tip> Calling this method will set `self.push_to_hub` to `True`, which means the `output_dir` will begin a git directory synced with the repo (determined by `model_id`) and the content will be pushed each time a save is triggered (depending on`self.save_strategy`). Calling [`~Trainer.save_model`] will also trigger a push. </Tip> Args: model_id (`str`): The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`): Defines the scope of what is pushed to the Hub and when. Possible values are: - `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card when the [`~Trainer.save_model`] method is called. - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training. - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with `trainer.train(resume_from_checkpoint="last-checkpoint")`. - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository) token (`str`, *optional*): The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`. private_repo (`bool`, *optional*, defaults to `False`): If True, the Hub repo will be set to private. always_push (`bool`, *optional*, defaults to `False`): Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not finished. Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_push_to_hub("me/awesome-model") >>> args.hub_model_id 'me/awesome-model' ``` """ self.push_to_hub = True self.hub_model_id = model_id self.hub_strategy = HubStrategy(strategy) self.hub_token = token self.hub_private_repo = private_repo self.hub_always_push = always_push return self def set_optimizer( self, name: Union[str, OptimizerNames] = "adamw_torch", learning_rate: float = 5e-5, weight_decay: float = 0, beta1: float = 0.9, beta2: float = 0.999, epsilon: float = 1e-8, args: Optional[str] = None, ): """ A method that regroups all arguments linked to the optimizer and its hyperparameters. Args: name (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"`): The optimizer to use: `"adamw_hf"`, `"adamw_torch"`, `"adamw_torch_fused"`, `"adamw_apex_fused"`, `"adamw_anyprecision"` or `"adafactor"`. learning_rate (`float`, *optional*, defaults to 5e-5): The initial learning rate. weight_decay (`float`, *optional*, defaults to 0): The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights. beta1 (`float`, *optional*, defaults to 0.9): The beta1 hyperparameter for the adam optimizer or its variants. beta2 (`float`, *optional*, defaults to 0.999): The beta2 hyperparameter for the adam optimizer or its variants. epsilon (`float`, *optional*, defaults to 1e-8): The epsilon hyperparameter for the adam optimizer or its variants. args (`str`, *optional*): Optional arguments that are supplied to AnyPrecisionAdamW (only useful when `optim="adamw_anyprecision"`). Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_optimizer(name="adamw_torch", beta1=0.8) >>> args.optim 'adamw_torch' ``` """ self.optim = OptimizerNames(name) self.learning_rate = learning_rate self.weight_decay = weight_decay self.adam_beta1 = beta1 self.adam_beta2 = beta2 self.adam_epsilon = epsilon self.optim_args = args return self def set_lr_scheduler( self, name: Union[str, SchedulerType] = "linear", num_epochs: float = 3.0, max_steps: int = -1, warmup_ratio: float = 0, warmup_steps: int = 0, ): """ A method that regroups all arguments linked to the learning rate scheduler and its hyperparameters. Args: name (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`): The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values. num_epochs(`float`, *optional*, defaults to 3.0): Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training). max_steps (`int`, *optional*, defaults to -1): If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until `max_steps` is reached. warmup_ratio (`float`, *optional*, defaults to 0.0): Ratio of total training steps used for a linear warmup from 0 to `learning_rate`. warmup_steps (`int`, *optional*, defaults to 0): Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`. Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_lr_scheduler(name="cosine", warmup_ratio=0.05) >>> args.warmup_ratio 0.05 ``` """ self.lr_scheduler_type = SchedulerType(name) self.num_train_epochs = num_epochs self.max_steps = max_steps self.warmup_ratio = warmup_ratio self.warmup_steps = warmup_steps return self def set_dataloader( self, train_batch_size: int = 8, eval_batch_size: int = 8, drop_last: bool = False, num_workers: int = 0, pin_memory: bool = True, persistent_workers: bool = False, prefetch_factor: Optional[int] = None, auto_find_batch_size: bool = False, ignore_data_skip: bool = False, sampler_seed: Optional[int] = None, ): """ A method that regroups all arguments linked to the dataloaders creation. Args: drop_last (`bool`, *optional*, defaults to `False`): Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not. num_workers (`int`, *optional*, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. pin_memory (`bool`, *optional*, defaults to `True`): Whether you want to pin memory in data loaders or not. Will default to `True`. persistent_workers (`bool`, *optional*, defaults to `False`): If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to `False`. prefetch_factor (`int`, *optional*): Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers. auto_find_batch_size (`bool`, *optional*, defaults to `False`) Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`) ignore_data_skip (`bool`, *optional*, defaults to `False`): When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have. sampler_seed (`int`, *optional*): Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as `self.seed`. This can be used to ensure reproducibility of data sampling, independent of the model seed. Example: ```py >>> from transformers import TrainingArguments >>> args = TrainingArguments("working_dir") >>> args = args.set_dataloader(train_batch_size=16, eval_batch_size=64) >>> args.per_device_train_batch_size 16 ``` """ self.per_device_train_batch_size = train_batch_size self.per_device_eval_batch_size = eval_batch_size self.dataloader_drop_last = drop_last self.dataloader_num_workers = num_workers self.dataloader_pin_memory = pin_memory self.dataloader_persistent_workers = persistent_workers self.dataloader_prefetch_factor = prefetch_factor self.auto_find_batch_size = auto_find_batch_size self.ignore_data_skip = ignore_data_skip self.data_seed = sampler_seed return self class ParallelMode(Enum): NOT_PARALLEL = "not_parallel" NOT_DISTRIBUTED = "not_distributed" DISTRIBUTED = "distributed" SAGEMAKER_MODEL_PARALLEL = "sagemaker_model_parallel" SAGEMAKER_DATA_PARALLEL = "sagemaker_data_parallel" TPU = "tpu"

transformers/src/transformers/training_args.py/0

{ "file_path": "transformers/src/transformers/training_args.py", "repo_id": "transformers", "token_count": 58198 }

374

# This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends class ASTFeatureExtractor(metaclass=DummyObject): _backends = ["speech"] def __init__(self, *args, **kwargs): requires_backends(self, ["speech"]) class Speech2TextFeatureExtractor(metaclass=DummyObject): _backends = ["speech"] def __init__(self, *args, **kwargs): requires_backends(self, ["speech"])

transformers/src/transformers/utils/dummy_speech_objects.py/0

{ "file_path": "transformers/src/transformers/utils/dummy_speech_objects.py", "repo_id": "transformers", "token_count": 166 }

375

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. {% if cookiecutter.is_encoder_decoder_model == "False" %} import unittest from transformers import is_flax_available, {{cookiecutter.camelcase_modelname}}Config from transformers.testing_utils import require_flax, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import numpy as np from transformers import ( Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM, Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification, Flax{{cookiecutter.camelcase_modelname}}Model, ) class Flax{{cookiecutter.camelcase_modelname}}ModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = {{cookiecutter.camelcase_modelname}}Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, return_dict=True, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = Flax{{cookiecutter.camelcase_modelname}}Model(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} inputs = [input_ids, input_mask] result = model(*inputs) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_lm_head( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = Flax{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } prediction_scores = model(**inputs)["logits"] self.parent.assertListEqual( list(prediction_scores.shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(**inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(**inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config) multiple_choice_inputs_ids = np.tile(np.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = np.tile(np.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = np.tile(np.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(**inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(**inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(**inputs) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_flax class Flax{{cookiecutter.camelcase_modelname}}ModelTest(FlaxModelTesterMixin, unittest.TestCase): all_model_classes = ( ( Flax{{cookiecutter.camelcase_modelname}}Model, Flax{{cookiecutter.camelcase_modelname}}ForCausalLM, Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM, Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, Flax{{cookiecutter.camelcase_modelname}}ForTokenClassification, Flax{{cookiecutter.camelcase_modelname}}ForMultipleChoice, ) if is_flax_available() else () ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = Flax{{cookiecutter.camelcase_modelname}}ModelTester(self) self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model = Flax{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}") self.assertIsNotNone(model) def _assert_tensors_equal(a, b, atol=1e-12, prefix=""): """If tensors not close, or a and b arent both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if _assert_tensors_equal(a, b, atol=atol): return True raise except Exception: if len(prefix) > 0: prefix = f"{prefix}: " raise AssertionError(f"{prefix}{a} != {b}") @require_flax class Flax{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = Flax{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}") input_ids = np.array([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] # TODO Replace vocab size vocab_size = 32000 expected_shape = [1, 6, vocab_size] self.assertEqual(output.shape, expected_shape) print(output[:, :3, :3]) # TODO Replace values below with what was printed above. expected_slice = np.array( [ [ [-0.05243197, -0.04498899, 0.05512108], [-0.07444685, -0.01064632, 0.04352357], [-0.05020351, 0.05530146, 0.00700043], ] ] ) _assert_tensors_equal(output[:, :3, :3], expected_slice, atol=1e-4) {% else %} import unittest from transformers import ( is_flax_available, {{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer, ) from transformers.testing_utils import require_sentencepiece, require_flax, require_tokenizers, slow from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import numpy as np import jax.numpy as jnp from transformers import ( Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, Flax{{cookiecutter.camelcase_modelname}}Model, ) @require_flax class Flax{{cookiecutter.camelcase_modelname}}ModelTester: config_cls = {{cookiecutter.camelcase_modelname}}Config config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size).clip(3, self.vocab_size) eos_tensor = np.expand_dims(np.array([self.eos_token_id] * self.batch_size), 1) input_ids = np.concatenate([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, **self.config_updates, ) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4") decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=outputs_cache.past_key_values, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) decoder_attention_mask_cache = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask_cache, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=decoder_attention_mask_cache, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, ): if attention_mask is None: attention_mask = np.not_equal(input_ids, config.pad_token_id).astype(np.int8) if decoder_attention_mask is None: decoder_attention_mask = np.concatenate([np.ones(decoder_input_ids[:, :1].shape, dtype=np.int8), np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id).astype(np.int8)], axis=-1) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_flax class Flax{{cookiecutter.camelcase_modelname}}ModelTest(FlaxModelTesterMixin, unittest.TestCase): all_model_classes = ( ( Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, Flax{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassification, Flax{{cookiecutter.camelcase_modelname}}Model, ) if is_flax_available() else () ) all_generative_model_classes = (Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_flax_available() else () is_encoder_decoder = True test_pruning = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = Flax{{cookiecutter.camelcase_modelname}}ModelTester(self) self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config) def test_config(self): self.config_tester.run_common_tests() def test_use_cache_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(model_class, config, inputs_dict) def test_use_cache_forward_with_attn_mask(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict) def _assert_tensors_equal(a, b, atol=1e-12, prefix=""): """If tensors not close, or a and b arent both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if _assert_tensors_equal(a, b, atol=atol): return True raise except Exception: if len(prefix) > 0: prefix = f"{prefix}: " raise AssertionError(f"{prefix}{a} != {b}") def _long_tensor(tok_lst): return np.array(tok_lst, dtype=np.int32) TOLERANCE = 1e-4 @slow @require_sentencepiece @require_tokenizers @require_flax class Flax{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase): def test_inference_no_head(self): model = Flax{{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}') # change to intended input here input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = np.array( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) _assert_tensors_equal(output[:, :3, :3], expected_slice, atol=TOLERANCE) def test_inference_with_head(self): model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') # change to intended input here input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = np.array( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) _assert_tensors_equal(output[:, :3, :3], expected_slice, atol=TOLERANCE) def test_seq_to_seq_generation(self): hf = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') batch_input = [ # string 1, # string 2, # string 3, # string 4, ] # The below article tests that we don't add any hypotheses outside of the top n_beams dct = tok.batch_encode_plus( batch_input, max_length=512, padding="max_length", truncation_strategy="only_first", truncation=True, return_tensors="np", ) hypotheses_batch = hf.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], num_beams=2, ) EXPECTED = [ # here expected 1, # here expected 2, # here expected 3, # here expected 4, ] generated = tok.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated == EXPECTED {%- endif %}

transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_flax_{{cookiecutter.lowercase_modelname}}.py/0

{ "file_path": "transformers/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_flax_{{cookiecutter.lowercase_modelname}}.py", "repo_id": "transformers", "token_count": 12273 }

376

{ "modelname": "TemplateTF", "uppercase_modelname": "TEMPLATE_TF", "lowercase_modelname": "template_tf", "camelcase_modelname": "TemplateTf", "authors": "The HuggingFace Team", "checkpoint_identifier": "brand-new-bert-base-cased", "tokenizer_type": "Based on BERT", "generate_tensorflow_pytorch_and_flax": "TensorFlow", "is_encoder_decoder_model": "False" }

transformers/templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json/0

{ "file_path": "transformers/templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json", "repo_id": "transformers", "token_count": 148 }

377

{ "feature_extractor_type": "Wav2Vec2FeatureExtractor", "processor_class": "Wav2Vec2Processor" }

transformers/tests/fixtures/dummy_feature_extractor_config.json/0

{ "file_path": "transformers/tests/fixtures/dummy_feature_extractor_config.json", "repo_id": "transformers", "token_count": 42 }

378

# coding=utf-8 # Copyright 2020 The HuggingFace Team Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a clone of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from transformers.generation import ( MaxLengthCriteria, MaxNewTokensCriteria, MaxTimeCriteria, StoppingCriteriaList, validate_stopping_criteria, ) @require_torch class StoppingCriteriaTestCase(unittest.TestCase): def _get_tensors(self, length): batch_size = 3 vocab_size = 250 input_ids = ids_tensor((batch_size, length), vocab_size) scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length return input_ids, scores def test_list_criteria(self): input_ids, scores = self._get_tensors(5) criteria = StoppingCriteriaList( [ MaxLengthCriteria(max_length=10), MaxTimeCriteria(max_time=0.1), ] ) self.assertFalse(criteria(input_ids, scores)) input_ids, scores = self._get_tensors(9) self.assertFalse(criteria(input_ids, scores)) input_ids, scores = self._get_tensors(10) self.assertTrue(criteria(input_ids, scores)) def test_max_length_criteria(self): criteria = MaxLengthCriteria(max_length=10) input_ids, scores = self._get_tensors(5) self.assertFalse(criteria(input_ids, scores)) input_ids, scores = self._get_tensors(9) self.assertFalse(criteria(input_ids, scores)) input_ids, scores = self._get_tensors(10) self.assertTrue(criteria(input_ids, scores)) def test_max_new_tokens_criteria(self): criteria = MaxNewTokensCriteria(start_length=5, max_new_tokens=5) input_ids, scores = self._get_tensors(5) self.assertFalse(criteria(input_ids, scores)) input_ids, scores = self._get_tensors(9) self.assertFalse(criteria(input_ids, scores)) input_ids, scores = self._get_tensors(10) self.assertTrue(criteria(input_ids, scores)) criteria_list = StoppingCriteriaList([criteria]) self.assertEqual(criteria_list.max_length, 10) def test_max_time_criteria(self): input_ids, scores = self._get_tensors(5) criteria = MaxTimeCriteria(max_time=0.1) self.assertFalse(criteria(input_ids, scores)) criteria = MaxTimeCriteria(max_time=0.1, initial_timestamp=time.time() - 0.2) self.assertTrue(criteria(input_ids, scores)) def test_validate_stopping_criteria(self): validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 10) with self.assertWarns(UserWarning): validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)]), 11) stopping_criteria = validate_stopping_criteria(StoppingCriteriaList(), 11) self.assertEqual(len(stopping_criteria), 1)

transformers/tests/generation/test_stopping_criteria.py/0

{ "file_path": "transformers/tests/generation/test_stopping_criteria.py", "repo_id": "transformers", "token_count": 1434 }

379

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Bark model. """ import copy import inspect import tempfile import unittest import pytest from transformers import ( BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig, is_torch_available, ) from transformers.models.bark.generation_configuration_bark import ( BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkSemanticGenerationConfig, ) from transformers.testing_utils import ( require_flash_attn, require_torch, require_torch_fp16, require_torch_gpu, slow, torch_device, ) from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ..encodec.test_modeling_encodec import EncodecModelTester if is_torch_available(): import torch from transformers import ( BarkCausalModel, BarkCoarseModel, BarkFineModel, BarkModel, BarkProcessor, BarkSemanticModel, ) class BarkSemanticModelTester: def __init__( self, parent, batch_size=2, seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.output_vocab_size = output_vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) inputs_dict = { "input_ids": input_ids, "head_mask": head_mask, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkSemanticConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkSemanticModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkCoarseModelTester: def __init__( self, parent, batch_size=2, seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.output_vocab_size = output_vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) inputs_dict = { "input_ids": input_ids, "head_mask": head_mask, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkCoarseConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkCoarseModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkFineModelTester: def __init__( self, parent, batch_size=2, seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.output_vocab_size = output_vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length, self.n_codes_total], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) # randint between self.n_codes_given - 1 and self.n_codes_total - 1 codebook_idx = ids_tensor((1,), self.n_codes_total - self.n_codes_given).item() + self.n_codes_given inputs_dict = { "codebook_idx": codebook_idx, "input_ids": input_ids, "head_mask": head_mask, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkFineConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkFineModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkModelTester: def __init__( self, parent, semantic_kwargs=None, coarse_acoustics_kwargs=None, fine_acoustics_kwargs=None, codec_kwargs=None, is_training=False, # for now training is not supported ): if semantic_kwargs is None: semantic_kwargs = {} if coarse_acoustics_kwargs is None: coarse_acoustics_kwargs = {} if fine_acoustics_kwargs is None: fine_acoustics_kwargs = {} if codec_kwargs is None: codec_kwargs = {} self.parent = parent self.semantic_model_tester = BarkSemanticModelTester(parent, **semantic_kwargs) self.coarse_acoustics_model_tester = BarkCoarseModelTester(parent, **coarse_acoustics_kwargs) self.fine_acoustics_model_tester = BarkFineModelTester(parent, **fine_acoustics_kwargs) self.codec_model_tester = EncodecModelTester(parent, **codec_kwargs) self.is_training = is_training def get_config(self): return BarkConfig.from_sub_model_configs( self.semantic_model_tester.get_config(), self.coarse_acoustics_model_tester.get_config(), self.fine_acoustics_model_tester.get_config(), self.codec_model_tester.get_config(), ) def get_pipeline_config(self): config = self.get_config() # follow the `get_pipeline_config` of the sub component models config.semantic_config.vocab_size = 300 config.coarse_acoustics_config.vocab_size = 300 config.fine_acoustics_config.vocab_size = 300 config.semantic_config.output_vocab_size = 300 config.coarse_acoustics_config.output_vocab_size = 300 config.fine_acoustics_config.output_vocab_size = 300 return config @require_torch class BarkSemanticModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BarkSemanticModel,) if is_torch_available() else () all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else () is_encoder_decoder = False fx_compatible = False test_missing_keys = False test_pruning = False test_model_parallel = False # no model_parallel for now test_resize_embeddings = True def setUp(self): self.model_tester = BarkSemanticModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkSemanticConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings() inputs["input_embeds"] = wte(input_ids) with torch.no_grad(): model(**inputs)[0] @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = self.all_generative_model_classes[0](config).eval().to(torch_device) model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) @require_torch class BarkCoarseModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): # Same tester as BarkSemanticModelTest, except for model_class and config_class all_model_classes = (BarkCoarseModel,) if is_torch_available() else () all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else () is_encoder_decoder = False fx_compatible = False test_missing_keys = False test_pruning = False test_model_parallel = False # no model_parallel for now test_resize_embeddings = True def setUp(self): self.model_tester = BarkCoarseModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkCoarseConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings() inputs["input_embeds"] = wte(input_ids) with torch.no_grad(): model(**inputs)[0] @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = self.all_generative_model_classes[0](config).eval().to(torch_device) model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) @require_torch class BarkFineModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (BarkFineModel,) if is_torch_available() else () is_encoder_decoder = False fx_compatible = False test_missing_keys = False test_pruning = False # no model_parallel for now test_model_parallel = False # torchscript disabled for now because forward with an int test_torchscript = False test_resize_embeddings = True def setUp(self): self.model_tester = BarkFineModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkFineConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings()[inputs_dict["codebook_idx"]] inputs["input_embeds"] = wte(input_ids[:, :, inputs_dict["codebook_idx"]]) with torch.no_grad(): model(**inputs)[0] @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] # take first codebook channel model = self.all_model_classes[0](config).eval().to(torch_device) model.half() # toy generation_configs semantic_generation_config = BarkSemanticGenerationConfig(semantic_vocab_size=0) coarse_generation_config = BarkCoarseGenerationConfig(n_coarse_codebooks=config.n_codes_given) fine_generation_config = BarkFineGenerationConfig( max_fine_history_length=config.block_size // 2, max_fine_input_length=config.block_size, n_fine_codebooks=config.n_codes_total, ) codebook_size = config.vocab_size - 1 model.generate( input_ids, history_prompt=None, temperature=None, semantic_generation_config=semantic_generation_config, coarse_generation_config=coarse_generation_config, fine_generation_config=fine_generation_config, codebook_size=codebook_size, ) model.generate( input_ids, history_prompt=None, temperature=0.7, semantic_generation_config=semantic_generation_config, coarse_generation_config=coarse_generation_config, fine_generation_config=fine_generation_config, codebook_size=codebook_size, ) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["codebook_idx", "input_ids"] self.assertListEqual(arg_names[:2], expected_arg_names) def test_model_common_attributes(self): # one embedding layer per codebook config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings()[0], (torch.nn.Embedding)) model.set_input_embeddings( torch.nn.ModuleList([torch.nn.Embedding(10, 10) for _ in range(config.n_codes_total)]) ) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x[0], torch.nn.Linear)) def test_resize_tokens_embeddings(self): # resizing tokens_embeddings of a ModuleList original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed_list = model.resize_token_embeddings(model_vocab_size) cloned_embeddings_list = [model_embed.weight.clone() for model_embed in model_embed_list] # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed_list = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix for each codebook for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list): self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed_list = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list): self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. # only check for the first embedding matrix models_equal = True for p1, p2 in zip(cloned_embeddings_list[0], model_embed_list[0].weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_embeddings_untied(self): # resizing tokens_embeddings of a ModuleList original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return original_config.tie_word_embeddings = False for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds_list = model.get_output_embeddings() for output_embeds in output_embeds_list: self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds_list = model.get_output_embeddings() for output_embeds in output_embeds_list: self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16) model.to(torch_device) dummy_input = inputs_dict["input_ids"][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] dummy_attention_mask[:, 1:] = 1 dummy_attention_mask[:, :1] = 0 outputs = model(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2) other_inputs = {"output_hidden_states": True} if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(inputs_dict["codebook_idx"], dummy_input, **other_inputs) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, **other_inputs) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa[1:], logits[1:], atol=4e-2, rtol=4e-2) # check with inference + dropout model.train() _ = model_fa(inputs_dict["codebook_idx"], dummy_input, **other_inputs) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_padding_right(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, ) model.to(torch_device) dummy_input = inputs_dict["input_ids"][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] dummy_attention_mask[:, :-1] = 1 dummy_attention_mask[:, -1:] = 0 outputs = model(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2) other_inputs = { "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(inputs_dict["codebook_idx"], dummy_input, **other_inputs) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, **other_inputs) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa[:-1], logits[:-1], atol=4e-2, rtol=4e-2) @require_torch class BarkModelIntegrationTests(unittest.TestCase): @cached_property def model(self): return BarkModel.from_pretrained("suno/bark").to(torch_device) @cached_property def processor(self): return BarkProcessor.from_pretrained("suno/bark") @cached_property def inputs(self): input_ids = self.processor("In the light of the moon, a little egg lay on a leaf", voice_preset="en_speaker_6") input_ids = input_ids.to(torch_device) return input_ids @cached_property def semantic_generation_config(self): semantic_generation_config = BarkSemanticGenerationConfig(**self.model.generation_config.semantic_config) return semantic_generation_config @cached_property def coarse_generation_config(self): coarse_generation_config = BarkCoarseGenerationConfig(**self.model.generation_config.coarse_acoustics_config) return coarse_generation_config @cached_property def fine_generation_config(self): fine_generation_config = BarkFineGenerationConfig(**self.model.generation_config.fine_acoustics_config) return fine_generation_config @slow def test_generate_semantic(self): input_ids = self.inputs # check first ids expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # greedy decoding with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) @slow def test_generate_semantic_early_stop(self): input_ids = self.inputs min_eos_p = 0.01 # check first ids expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # Should be able to read min_eos_p from kwargs with torch.no_grad(): torch.manual_seed(0) output_ids_without_min_eos_p = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, ) torch.manual_seed(0) output_ids_kwargs = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, min_eos_p=min_eos_p, ) self.assertListEqual(output_ids_without_min_eos_p[0, : len(expected_output_ids)].tolist(), expected_output_ids) self.assertLess(len(output_ids_kwargs[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())) # Should be able to read min_eos_p from the semantic generation config self.semantic_generation_config.min_eos_p = min_eos_p with torch.no_grad(): torch.manual_seed(0) output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, ) self.assertEqual(output_ids.shape, output_ids_kwargs.shape) self.assertLess(len(output_ids[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) @slow def test_generate_coarse(self): input_ids = self.inputs history_prompt = input_ids["history_prompt"] # check first ids expected_output_ids = [11018, 11391, 10651, 11418, 10857, 11620, 10642, 11366, 10312, 11528, 10531, 11516, 10474, 11051, 10524, 11051, ] # fmt: skip with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) output_ids = self.model.coarse_acoustics.generate( output_ids, history_prompt=history_prompt, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, codebook_size=self.model.generation_config.codebook_size, ) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) @slow def test_generate_fine(self): input_ids = self.inputs history_prompt = input_ids["history_prompt"] # fmt: off expected_output_ids = [ [1018, 651, 857, 642, 312, 531, 474, 524, 524, 776,], [367, 394, 596, 342, 504, 492, 27, 27, 822, 822,], [961, 955, 221, 955, 955, 686, 939, 939, 479, 176,], [638, 365, 218, 944, 853, 363, 639, 22, 884, 456,], [302, 912, 524, 38, 174, 209, 879, 23, 910, 227,], [440, 673, 861, 666, 372, 558, 49, 172, 232, 342,], [244, 358, 123, 356, 586, 520, 499, 877, 542, 637,], [806, 685, 905, 848, 803, 810, 921, 208, 625, 203,], ] # fmt: on with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) output_ids = self.model.coarse_acoustics.generate( output_ids, history_prompt=history_prompt, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, codebook_size=self.model.generation_config.codebook_size, ) # greedy decoding output_ids = self.model.fine_acoustics.generate( output_ids, history_prompt=history_prompt, temperature=None, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, fine_generation_config=self.fine_generation_config, codebook_size=self.model.generation_config.codebook_size, ) self.assertListEqual(output_ids[0, :, : len(expected_output_ids[0])].tolist(), expected_output_ids) @slow def test_generate_end_to_end(self): input_ids = self.inputs with torch.no_grad(): self.model.generate(**input_ids) self.model.generate(**{key: val for (key, val) in input_ids.items() if key != "history_prompt"}) @slow def test_generate_end_to_end_with_args(self): input_ids = self.inputs with torch.no_grad(): self.model.generate(**input_ids, do_sample=True, temperature=0.6, penalty_alpha=0.6) self.model.generate(**input_ids, do_sample=True, temperature=0.6, num_beams=4) @slow def test_generate_batching(self): args = {"do_sample": False, "temperature": None} s1 = "I love HuggingFace" s2 = "In the light of the moon, a little egg lay on a leaf" voice_preset = "en_speaker_6" input_ids = self.processor([s1, s2], voice_preset=voice_preset).to(torch_device) # generate in batch outputs, audio_lengths = self.model.generate(**input_ids, **args, return_output_lengths=True) # generate one-by-one s1 = self.processor(s1, voice_preset=voice_preset).to(torch_device) s2 = self.processor(s2, voice_preset=voice_preset).to(torch_device) output1 = self.model.generate(**s1, **args) output2 = self.model.generate(**s2, **args) # up until the coarse acoustic model (included), results are the same # the fine acoustic model introduces small differences # first verify if same length (should be the same because it's decided in the coarse model) self.assertEqual(tuple(audio_lengths), (output1.shape[1], output2.shape[1])) # then assert almost equal self.assertTrue(torch.allclose(outputs[0, : audio_lengths[0]], output1.squeeze(), atol=2e-3)) self.assertTrue(torch.allclose(outputs[1, : audio_lengths[1]], output2.squeeze(), atol=2e-3)) # now test single input with return_output_lengths = True outputs, _ = self.model.generate(**s1, **args, return_output_lengths=True) self.assertTrue((outputs == output1).all().item()) @slow def test_generate_end_to_end_with_sub_models_args(self): input_ids = self.inputs with torch.no_grad(): torch.manual_seed(0) self.model.generate( **input_ids, do_sample=False, temperature=1.0, coarse_do_sample=True, coarse_temperature=0.7 ) output_ids_without_min_eos_p = self.model.generate( **input_ids, do_sample=True, temperature=0.9, coarse_do_sample=True, coarse_temperature=0.7, fine_temperature=0.3, ) output_ids_with_min_eos_p = self.model.generate( **input_ids, do_sample=True, temperature=0.9, coarse_temperature=0.7, fine_temperature=0.3, min_eos_p=0.1, ) self.assertLess( len(output_ids_with_min_eos_p[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist()) ) @require_torch_gpu @slow def test_generate_end_to_end_with_offload(self): input_ids = self.inputs with torch.no_grad(): # standard generation output_with_no_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0) torch.cuda.empty_cache() memory_before_offload = torch.cuda.memory_allocated() model_memory_footprint = self.model.get_memory_footprint() # activate cpu offload self.model.enable_cpu_offload() memory_after_offload = torch.cuda.memory_allocated() # checks if the model have been offloaded # CUDA memory usage after offload should be near 0, leaving room to small differences room_for_difference = 1.1 self.assertGreater( (memory_before_offload - model_memory_footprint) * room_for_difference, memory_after_offload ) # checks if device is the correct one self.assertEqual(self.model.device.type, torch_device) # checks if hooks exist self.assertTrue(hasattr(self.model.semantic, "_hf_hook")) # output with cpu offload output_with_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0) # checks if same output self.assertListEqual(output_with_no_offload.tolist(), output_with_offload.tolist())

transformers/tests/models/bark/test_modeling_bark.py/0

{ "file_path": "transformers/tests/models/bark/test_modeling_bark.py", "repo_id": "transformers", "token_count": 23249 }

380

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import tempfile import unittest from transformers import BertConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import CaptureLogger, require_torch, require_torch_accelerator, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLMHeadModel, BertModel, logging, ) from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST class BertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): """ Returns a tiny configuration by default. """ return BertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = BertModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = BertLMHeadModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_model_for_causal_lm_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = BertLMHeadModel(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, encoder_hidden_states=encoder_hidden_states, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = BertLMHeadModel(config=config).to(torch_device).eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_for_next_sequence_prediction( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForNextSentencePrediction(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, 2)) def create_and_check_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForPreTraining(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, next_sentence_label=sequence_labels, ) self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = BertForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class BertModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( BertModel, BertLMHeadModel, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (BertLMHeadModel,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": BertModel, "fill-mask": BertForMaskedLM, "question-answering": BertForQuestionAnswering, "text-classification": BertForSequenceClassification, "text-generation": BertLMHeadModel, "token-classification": BertForTokenClassification, "zero-shot": BertForSequenceClassification, } if is_torch_available() else {} ) fx_compatible = True # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["next_sentence_label"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = BertModelTester(self) self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_model_as_decoder_with_default_input_mask(self): # This regression test was failing with PyTorch < 1.3 ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_causal_lm_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs_relative_pos_emb(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() config_and_inputs[0].position_embedding_type = "relative_key" self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_next_sequence_prediction(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_warning_if_padding_and_no_attention_mask(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.model_tester.prepare_config_and_inputs() # Set pad tokens in the input_ids input_ids[0, 0] = config.pad_token_id # Check for warnings if the attention_mask is missing. logger = logging.get_logger("transformers.modeling_utils") # clear cache so we can test the warning is emitted (from `warning_once`). logger.warning_once.cache_clear() with CaptureLogger(logger) as cl: model = BertModel(config=config) model.to(torch_device) model.eval() model(input_ids, attention_mask=None, token_type_ids=token_type_ids) self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out) @slow def test_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BertModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow @require_torch_accelerator def test_torchscript_device_change(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # BertForMultipleChoice behaves incorrectly in JIT environments. if model_class == BertForMultipleChoice: return config.torchscript = True model = model_class(config=config) inputs_dict = self._prepare_for_class(inputs_dict, model_class) traced_model = torch.jit.trace( model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu")) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(traced_model, os.path.join(tmp, "bert.pt")) loaded = torch.jit.load(os.path.join(tmp, "bert.pt"), map_location=torch_device) loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device)) @require_torch class BertModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = BertModel.from_pretrained("bert-base-uncased") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]]) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)) @slow def test_inference_no_head_relative_embedding_key(self): model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.0756, 0.3142, -0.5128], [0.3761, 0.3462, -0.5477], [0.2052, 0.3760, -0.1240]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)) @slow def test_inference_no_head_relative_embedding_key_query(self): model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key-query") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.6496, 0.3784, 0.8203], [0.8148, 0.5656, 0.2636], [-0.0681, 0.5597, 0.7045]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

transformers/tests/models/bert/test_modeling_bert.py/0

{ "file_path": "transformers/tests/models/bert/test_modeling_bert.py", "repo_id": "transformers", "token_count": 12408 }

381

#!/usr/bin/env python3 # coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for the Blenderbot small tokenizer.""" import json import os import unittest from transformers.models.blenderbot_small.tokenization_blenderbot_small import ( VOCAB_FILES_NAMES, BlenderbotSmallTokenizer, ) from ...test_tokenization_common import TokenizerTesterMixin class BlenderbotSmallTokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = BlenderbotSmallTokenizer test_rust_tokenizer = False def setUp(self): super().setUp() vocab = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""] self.special_tokens_map = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "adapt act apte" output_text = "adapt act apte" return input_text, output_text def test_full_blenderbot_small_tokenizer(self): tokenizer = BlenderbotSmallTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) text = "adapt act apte" bpe_tokens = ["adapt", "act", "ap@@", "te"] tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, bpe_tokens) input_tokens = [tokenizer.bos_token] + tokens + [tokenizer.eos_token] input_bpe_tokens = [0, 1, 2, 3, 4, 5] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_special_tokens_small_tok(self): tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M") assert tok("sam").input_ids == [1384] src_text = "I am a small frog." encoded = tok([src_text], padding=False, truncation=False)["input_ids"] decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] assert src_text != decoded # I wish it did! assert decoded == "i am a small frog ." def test_empty_word_small_tok(self): tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M") src_text = "I am a small frog ." src_text_dot = "." encoded = tok(src_text)["input_ids"] encoded_dot = tok(src_text_dot)["input_ids"] assert encoded[-1] == encoded_dot[0]

transformers/tests/models/blenderbot_small/test_tokenization_blenderbot_small.py/0

{ "file_path": "transformers/tests/models/blenderbot_small/test_tokenization_blenderbot_small.py", "repo_id": "transformers", "token_count": 1491 }

382

# coding=utf-8 # Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from typing import Dict, List, Optional, Union from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from PIL import Image from transformers import BridgeTowerImageProcessor class BridgeTowerImageProcessingTester(unittest.TestCase): def __init__( self, parent, do_resize: bool = True, size: Dict[str, int] = None, size_divisor: int = 32, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, do_center_crop: bool = True, image_mean: Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073], image_std: Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711], do_pad: bool = True, batch_size=7, min_resolution=30, max_resolution=400, num_channels=3, ): self.parent = parent self.do_resize = do_resize self.size = size if size is not None else {"shortest_edge": 288} self.size_divisor = size_divisor self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.do_center_crop = do_center_crop self.image_mean = image_mean self.image_std = image_std self.do_pad = do_pad self.batch_size = batch_size self.num_channels = num_channels self.min_resolution = min_resolution self.max_resolution = max_resolution def prepare_image_processor_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, "size_divisor": self.size_divisor, } def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to BridgeTowerImageProcessor, assuming do_resize is set to True with a scalar size and size_divisor. """ if not batched: size = self.size["shortest_edge"] image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size else: h, w = image.shape[1], image.shape[2] scale = size / min(w, h) if h < w: newh, neww = size, scale * w else: newh, neww = scale * h, size max_size = int((1333 / 800) * size) if max(newh, neww) > max_size: scale = max_size / max(newh, neww) newh = newh * scale neww = neww * scale newh, neww = int(newh + 0.5), int(neww + 0.5) expected_height, expected_width = ( newh // self.size_divisor * self.size_divisor, neww // self.size_divisor * self.size_divisor, ) else: expected_values = [] for image in image_inputs: expected_height, expected_width = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) expected_height = max(expected_values, key=lambda item: item[0])[0] expected_width = max(expected_values, key=lambda item: item[1])[1] return expected_height, expected_width def expected_output_image_shape(self, images): height, width = self.get_expected_values(images, batched=True) return self.num_channels, height, width def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class BridgeTowerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = BridgeTowerImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = BridgeTowerImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "size_divisor"))

transformers/tests/models/bridgetower/test_image_processing_bridgetower.py/0

{ "file_path": "transformers/tests/models/bridgetower/test_image_processing_bridgetower.py", "repo_id": "transformers", "token_count": 2508 }

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