mohitsha/hf_code_dataset · Datasets at Hugging Face

# Decision Transformer ## Overview Decision Transformer モデルは、[Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) で提案されました。 Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch. 論文の要約は次のとおりです。 _強化学習（RL）をシーケンスモデリング問題として抽象化するフレームワークを紹介します。これにより、Transformer アーキテクチャのシンプルさとスケーラビリティ、および関連する進歩を活用できるようになります。 GPT-x や BERT などの言語モデリングで。特に、Decision Transformer というアーキテクチャを紹介します。 RL の問題を条件付きシーケンスモデリングとして投げかけます。値関数に適合する以前の RL アプローチとは異なり、ポリシー勾配を計算すると、Decision Transformer は因果的にマスクされたアルゴリズムを利用して最適なアクションを出力するだけです。変成器。望ましいリターン (報酬)、過去の状態、アクションに基づいて自己回帰モデルを条件付けすることにより、 Decision Transformer モデルは、望ましいリターンを達成する将来のアクションを生成できます。そのシンプルさにも関わらず、 Decision Transformer は、最先端のモデルフリーのオフライン RL ベースラインのパフォーマンスと同等、またはそれを超えています。 Atari、OpenAI Gym、Key-to-Door タスク_ このバージョンのモデルは、状態がベクトルであるタスク用です。このモデルは、[edbeeching](https://huggingface.co/edbeeching) によって提供されました。元のコードは [ここ](https://github.com/kzl/decision-transformer) にあります。 ## DecisionTransformerConfig [[autodoc]] DecisionTransformerConfig ## DecisionTransformerGPT2Model [[autodoc]] DecisionTransformerGPT2Model - forward ## DecisionTransformerModel [[autodoc]] DecisionTransformerModel - forward

transformers/docs/source/ja/model_doc/decision_transformer.md/0

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# Efficient Inference on a Multiple GPUs この文書には、複数のGPUで効率的に推論を行う方法に関する情報が含まれています。 <Tip> 注意: 複数のGPUセットアップは、[単一のGPUセクション](./perf_infer_gpu_one)で説明されているほとんどの戦略を使用できます。ただし、より良い使用法のために使用できる簡単なテクニックについても認識しておく必要があります。 </Tip> ## Flash Attention 2 Flash Attention 2の統合は、複数のGPUセットアップでも機能します。詳細については、[単一のGPUセクション](./perf_infer_gpu_one#Flash-Attention-2)の適切なセクションをご覧ください。 ## BetterTransformer [BetterTransformer](https://huggingface.co/docs/optimum/bettertransformer/overview)は、🤗 TransformersモデルをPyTorchネイティブの高速実行パスを使用するように変換し、その下でFlash Attentionなどの最適化されたカーネルを呼び出します。 BetterTransformerは、テキスト、画像、音声モデルの単一GPUおよび複数GPUでの高速推論もサポートしています。 <Tip> Flash Attentionは、fp16またはbf16 dtypeを使用しているモデルにのみ使用できます。BetterTransformerを使用する前に、モデルを適切なdtypeにキャストしてください。 </Tip> ### Decoder models テキストモデル、特にデコーダーベースのモデル（GPT、T5、Llamaなど）の場合、BetterTransformer APIはすべての注意操作を[`torch.nn.functional.scaled_dot_product_attention`オペレーター](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention)（SDPA）を使用するように変換します。これはPyTorch 2.0以降でのみ使用可能です。モデルをBetterTransformerに変換するには： ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") # convert the model to BetterTransformer model.to_bettertransformer() # Use it for training or inference ``` SDPAは、ハードウェアや問題のサイズなどの特定の設定で[Flash Attention](https://arxiv.org/abs/2205.14135)カーネルを呼び出すこともできます。Flash Attentionを有効にするか、特定の設定（ハードウェア、問題のサイズ）で利用可能かを確認するには、[`torch.nn.kernel.sdpa_kernel`](https://pytorch.org/docs/stable/generated/torch.nn.attention.sdpa_kernel.html)をコンテキストマネージャとして使用します。 ```diff import torch + from torch.nn.attention import SDPBackend, sdpa_kernel from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m").to("cuda") # convert the model to BetterTransformer model.to_bettertransformer() input_text = "Hello my dog is cute and" inputs = tokenizer(input_text, return_tensors="pt").to("cuda") + with sdpa_kernel(SDPBackend.FLASH_ATTENTION): outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` もしトレースバックで次のようなエラーメッセージが表示された場合： ```bash RuntimeError: No available kernel. Aborting execution. ``` 当日、Flash Attentionのカバレッジが広範囲である可能性があるPyTorch Nightlyバージョンを試すようにお勧めします。 ```bash pip3 install -U --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118 ``` [このブログ投稿](https://pytorch.org/blog/out-of-the-box-acceleration/)をチェックして、BetterTransformer + SDPA APIで可能なことについて詳しく学びましょう。 ### Encoder Models 推論中のエンコーダーモデルでは、BetterTransformerはエンコーダーレイヤーのforward呼び出しを、エンコーダーレイヤーの[`torch.nn.TransformerEncoderLayer`](https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html)の相当するものにディスパッチします。これにより、エンコーダーレイヤーの高速実装が実行されます。 `torch.nn.TransformerEncoderLayer`の高速実装はトレーニングをサポートしていないため、代わりに`torch.nn.functional.scaled_dot_product_attention`にディスパッチされます。これにより、ネストされたテンソルを活用しないFlash AttentionまたはMemory-Efficient Attentionの融合カーネルを使用できます。 BetterTransformerのパフォーマンスの詳細については、この[ブログ投稿](https://medium.com/pytorch/bettertransformer-out-of-the-box-performance-for-huggingface-transformers-3fbe27d50ab2)をご覧いただけます。また、エンコーダーモデル用のBetterTransformerについては、この[ブログ](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/)で詳しく学ぶことができます。 ## Advanced usage: mixing FP4 (or Int8) and BetterTransformer モデルの最良のパフォーマンスを得るために、上記で説明した異なる方法を組み合わせることができます。例えば、FP4ミックスプレシジョン推論+Flash Attentionを使用したBetterTransformerを組み合わせることができます。 ```py import torch from torch.nn.attention import SDPBackend, sdpa_kernel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16 ) tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", quantization_config=quantization_config) input_text = "Hello my dog is cute and" inputs = tokenizer(input_text, return_tensors="pt").to("cuda") with sdpa_kernel(SDPBackend.FLASH_ATTENTION): outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ```

transformers/docs/source/ja/perf_infer_gpu_many.md/0

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# Checks on a Pull Request 🤗 Transformersリポジトリでプルリクエストを開くと、追加しているパッチが既存のものを壊していないことを確認するために、かなりの数のチェックが実行されます。これらのチェックには、次の4つのタイプがあります： - 通常のテスト - ドキュメンテーションのビルド - コードとドキュメンテーションのスタイル - リポジトリ全体の一貫性このドキュメントでは、これらのさまざまなチェックとその背後にある理由、そしてそれらのいずれかがあなたのプルリクエストで失敗した場合のローカルでのデバッグ方法について説明します。なお、理想的には、開発者用のインストールが必要です： ```bash pip install transformers[dev] ``` または編集可能なインストールの場合： ```bash pip install -e .[dev] ``` トランスフォーマーズのリポジトリ内で作業しています。トランスフォーマーズのオプションの依存関係の数が増えたため、すべてを取得できない可能性があります。開発用インストールが失敗した場合、作業しているディープラーニングフレームワーク（PyTorch、TensorFlow、および/またはFlax）をインストールし、次の手順を実行してください。 ```bash pip install transformers[quality] ``` または編集可能なインストールの場合： ```bash pip install -e .[quality] ``` ## Tests `ci/circleci: run_tests_` で始まるすべてのジョブは、Transformersのテストスイートの一部を実行します。これらのジョブは、特定の環境でライブラリの一部に焦点を当てて実行されます。たとえば、`ci/circleci: run_tests_pipelines_tf` は、TensorFlowのみがインストールされた環境でパイプラインのテストを実行します。テストスイートの一部のみが実行されるように注意してください。テストスイートは、変更前と変更後のPRのライブラリの違いを決定し、その違いに影響を受けるテストを選択するためのユーティリティが実行されます。このユーティリティは、ローカルで以下のコマンドを実行して実行できます： ```bash python utils/tests_fetcher.py ``` 1. リポジトリのルートからスクリプトを実行します。これは次のステップを実行します： 1. 差分内の各ファイルをチェックし、変更がコード内にあるか、コメントやドキュメンテーション文字列のみにあるかを確認します。実際のコード変更があるファイルのみを保持します。 2. 内部のマップを構築します。このマップは、ライブラリのソースコードの各ファイルが再帰的に影響を与えるすべてのファイルを提供します。モジュールAがモジュールBに影響を与えるとは、モジュールBがモジュールAをインポートする場合を指します。再帰的な影響を得るには、モジュールAからモジュールBへのモジュールのチェーンが必要で、各モジュールは前のモジュールをインポートする必要があります。 3. このマップをステップ1で収集したファイルに適用します。これにより、PRに影響を受けるモデルファイルのリストが得られます。 4. これらのファイルをそれに対応するテストファイルにマップし、実行するテストのリストを取得します。 2. スクリプトをローカルで実行する場合、ステップ1、3、および4の結果が表示され、実行するテストがわかります。スクリプトはまた、`test_list.txt` という名前のファイルを作成します。このファイルには実行するテストのリストが含まれており、次のコマンドでローカルで実行できます： ```bash python -m pytest -n 8 --dist=loadfile -rA -s $(cat test_list.txt) ``` ## Documentation build `build_pr_documentation` ジョブは、ドキュメンテーションのビルドを行い、あなたのPRがマージされた後にすべてが正常に表示されることを確認します。ボットがプレビューのドキュメンテーションへのリンクをPRに追加します。PRに対する変更は、プレビューに自動的に反映されます。ドキュメンテーションのビルドに失敗した場合、失敗したジョブの隣にある「詳細」をクリックして、何が問題になっているかを確認できます。多くの場合、問題は`toctree`内のファイルが不足しているなど、単純なものです。ドキュメンテーションをローカルでビルドまたはプレビューしたい場合は、[docsフォルダ内の`README.md`](https://github.com/huggingface/transformers/tree/main/docs)をご覧ください。 ## Code and documentation style すべてのソースファイル、例、テストには、`black`と`ruff`を使用してコードのフォーマットが適用されます。また、ドックストリングと`rst`ファイルのフォーマット、Transformersの`__init__.py`ファイルで実行される遅延インポートの順序についてもカスタムツールが存在します（`utils/style_doc.py`と`utils/custom_init_isort.py`）。これらすべては、以下を実行することで起動できます。 ```bash make style ``` CIは、`ci/circleci: check_code_quality` チェック内でこれらのチェックが適用されていることを確認します。また、`ruff` を実行し、未定義の変数や使用されていない変数がある場合にエラーを報告します。このチェックをローカルで実行するには、以下のコマンドを使用してください。 ```bash make quality ``` 時間がかかることがあります。したがって、現在のブランチで変更したファイルのみで同じことを実行するには、次のコマンドを実行します。 ```bash make fixup ``` この最後のコマンドは、リポジトリの整合性のためのすべての追加のチェックも実行します。それらを詳しく見てみましょう。 ## Repository consistency これには、あなたのPRがリポジトリを適切な状態に保ったままであることを確認するためのすべてのテストが含まれており、ci/`circleci: check_repository_consistency` チェックによって実行されます。ローカルでこのチェックを実行するには、以下を実行します。 ```bash make repo-consistency ``` これを確認します： - `utils/check_repo.py` によって実行される、init に追加されたすべてのオブジェクトが文書化されています。 - `utils/check_inits.py` によって実行される、すべての `__init__.py` ファイルがその2つのセクションで同じ内容を持っています。 - `utils/check_copies.py` によって実行される、他のモジュールからのコピーとして識別されたすべてのコードが元のコードと一致しています。 - `utils/check_config_docstrings.py` によって実行される、すべての設定クラスには少なくとも1つの有効なチェックポイントがドキュメント文字列に記載されています。 - `utils/check_config_attributes.py` によって実行される、すべての設定クラスには、対応するモデリングファイルで使用されている属性のみが含まれています。 - `utils/check_copies.py` によって実行される、README とドキュメントのインデックスの翻訳が、メインのREADME と同じモデルリストを持っています。 - `utils/check_table.py` によって実行される、ドキュメンテーションの自動生成テーブルが最新であることを確認します。 - `utils/check_dummies.py` によって実行される、すべてのオブジェクトが利用可能であり、オプションの依存関係がすべてインストールされていなくても問題ありません。このチェックが失敗する場合、最初の2つの項目は手動で修正する必要があり、最後の4つはコマンドを実行して自動的に修正できます。 ```bash make fix-copies ``` 追加のチェックポイントは、新しいモデルを追加するPull Request（PR）に関連しています。主に次の点を確認します： - すべての追加されたモデルは、Auto-mapping（`utils/check_repo.py`で実行）に含まれています。  - すべてのモデルが適切にテストされています（`utils/check_repo.py`で実行）。  ### Check copies Transformersライブラリは、モデルコードに関して非常に意見があるため、各モデルは他のモデルに依存せずに完全に1つのファイルに実装する必要があります。したがって、特定のモデルのコードのコピーが元のコードと一貫しているかどうかを確認する仕組みを追加しました。これにより、バグ修正がある場合、他の影響を受けるモデルをすべて確認し、変更を伝達するかコピーを破棄するかを選択できます。 <Tip> ファイルが別のファイルの完全なコピーである場合、それを`utils/check_copies.py`の`FULL_COPIES`定数に登録する必要があります。 </Tip> この仕組みは、`# Copied from xxx`という形式のコメントに依存しています。`xxx`は、コピーされているクラスまたは関数の完全なパスを含む必要があります。例えば、`RobertaSelfOutput`は`BertSelfOutput`クラスの直接のコピーですので、[こちら](https://github.com/huggingface/transformers/blob/2bd7a27a671fd1d98059124024f580f8f5c0f3b5/src/transformers/models/roberta/modeling_roberta.py#L289)にコメントがあります。 ```py # Copied from transformers.models.bert.modeling_bert.BertSelfOutput ``` 注意点として、これをクラス全体に適用する代わりに、コピー元の関連メソッドに適用できます。たとえば、[こちら](https://github.com/huggingface/transformers/blob/2bd7a27a671fd1d98059124024f580f8f5c0f3b5/src/transformers/models/roberta/modeling_roberta.py#L598)では、`RobertaPreTrainedModel._init_weights` が `BertPreTrainedModel` からコピーされており、以下のコメントがあります： ```py # Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta ``` 注：矢印の周りにはスペースが含まれていてはいけません（もちろん、そのスペースが置換パターンの一部である場合を除きます）。カンマで区切られた複数のパターンを追加できます。例えば、ここでは `CamemberForMaskedLM` は `RobertaForMaskedLM` の直接のコピーで、2つの置換があります： `Roberta` から `Camembert` へ、そして `ROBERTA` から `CAMEMBERT` へと置換されます。[こちら](https://github.com/huggingface/transformers/blob/15082a9dc6950ecae63a0d3e5060b2fc7f15050a/src/transformers/models/camembert/modeling_camembert.py#L929)で、この作業はコメント付きで行われています。 ```py # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT ``` もし順序が重要な場合（以前の置換と競合する可能性があるため）、置換は左から右に実行されます。 <Tip> もし置換がフォーマットを変更する場合（たとえば、短い名前を非常に長い名前に置き換える場合など）、自動フォーマッタを適用した後にコピーが確認されます。 </Tip> パターンが同じ置換の異なるケース（大文字と小文字のバリアントがある）の場合、オプションとして `all-casing` を追加するだけの別の方法もあります。[こちら](https://github.com/huggingface/transformers/blob/15082a9dc6950ecae63a0d3e5060b2fc7f15050a/src/transformers/models/mobilebert/modeling_mobilebert.py#L1237)は、`MobileBertForSequenceClassification` 内の例で、コメントがついています。 ```py # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing ``` この場合、コードは「BertForSequenceClassification」からコピーされ、次のように置換されます： - `Bert` を `MobileBert` に置き換える（例：`init`で `MobileBertModel` を使用する場合） - `bert` を `mobilebert` に置き換える（例：`self.mobilebert` を定義する場合） - `BERT` を `MOBILEBERT` に置き換える（定数 `MOBILEBERT_INPUTS_DOCSTRING` 内で）

transformers/docs/source/ja/pr_checks.md/0

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# Monocular depth estimation 単眼奥行き推定は、シーンの奥行き情報を画像から予測することを含むコンピュータービジョンタスクです。単一の画像。言い換えれば、シーン内のオブジェクトの距離を距離から推定するプロセスです。単一カメラの視点。単眼奥行き推定には、3D 再構築、拡張現実、自動運転、そしてロボット工学。モデルがオブジェクト間の複雑な関係を理解する必要があるため、これは困難な作業です。シーンとそれに対応する深度情報（照明条件などの要因の影響を受ける可能性があります）オクルージョンとテクスチャ。 <Tip> このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/depth-estimation) を確認することをお勧めします。 </Tip> このガイドでは、次の方法を学びます。 * 深度推定パイプラインを作成する * 手動で深度推定推論を実行します始める前に、必要なライブラリがすべてインストールされていることを確認してください。 ```bash pip install -q transformers ``` ## Depth estimation pipeline 深度推定をサポートするモデルで推論を試す最も簡単な方法は、対応する [`pipeline`] を使用することです。 [Hugging Face Hub のチェックポイント](https://huggingface.co/models?pipeline_tag=Depth-estimation&sort=downloads) からパイプラインをインスタンス化します。 ```py >>> from transformers import pipeline >>> checkpoint = "vinvino02/glpn-nyu" >>> depth_estimator = pipeline("depth-estimation", model=checkpoint) ``` 次に、分析する画像を選択します。 ```py >>> from PIL import Image >>> import requests >>> url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-estimation-example.jpg" alt="Photo of a busy street"/> </div> 画像をパイプラインに渡します。 ```py >>> predictions = depth_estimator(image) ``` パイプラインは 2 つのエントリを含む辞書を返します。最初のものは`predicted_ Depth`と呼ばれ、次の値を持つテンソルです。深さは各ピクセルのメートル単位で表されます。 2 番目の`depth`は、深度推定結果を視覚化する PIL 画像です。視覚化された結果を見てみましょう。 ```py >>> predictions["depth"] ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/> </div> ## Depth estimation inference by hand 深度推定パイプラインの使用方法を理解したので、同じ結果を手動で複製する方法を見てみましょう。まず、[Hugging Face Hub のチェックポイント](https://huggingface.co/models?pipeline_tag=Depth-estimation&sort=downloads) からモデルと関連プロセッサをロードします。ここでは、前と同じチェックポイントを使用します。 ```py >>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation >>> checkpoint = "vinvino02/glpn-nyu" >>> image_processor = AutoImageProcessor.from_pretrained(checkpoint) >>> model = AutoModelForDepthEstimation.from_pretrained(checkpoint) ``` 必要な画像変換を処理する`image_processor`を使用して、モデルの画像入力を準備します。サイズ変更や正規化など: ```py >>> pixel_values = image_processor(image, return_tensors="pt").pixel_values ``` 準備された入力をモデルに渡します。 ```py >>> import torch >>> with torch.no_grad(): ... outputs = model(pixel_values) ... predicted_depth = outputs.predicted_depth ``` 結果を視覚化します。 ```py >>> import numpy as np >>> # interpolate to original size >>> prediction = torch.nn.functional.interpolate( ... predicted_depth.unsqueeze(1), ... size=image.size[::-1], ... mode="bicubic", ... align_corners=False, ... ).squeeze() >>> output = prediction.numpy() >>> formatted = (output * 255 / np.max(output)).astype("uint8") >>> depth = Image.fromarray(formatted) >>> depth ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/> </div>

transformers/docs/source/ja/tasks/monocular_depth_estimation.md/0

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# Testing 🤗 Transformersモデルがどのようにテストされ、新しいテストを書いて既存のテストを改善できるかを見てみましょう。このリポジトリには2つのテストスイートがあります： 1. `tests` -- 一般的なAPI用のテスト 2. `examples` -- APIの一部ではないさまざまなアプリケーション用のテスト ## How transformers are tested 1. PRが提出されると、9つのCircleCiジョブでテストされます。PRへの新しいコミットごとに再テストされます。これらのジョブは、[この設定ファイル](https://github.com/huggingface/transformers/tree/main/.circleci/config.yml)で定義されており、必要な場合は同じ環境を自分のマシンで再現できます。これらのCIジョブは `@slow` テストを実行しません。 2. [GitHub Actions](https://github.com/huggingface/transformers/actions)によって実行される3つのジョブがあります： - [torch hub integration](https://github.com/huggingface/transformers/tree/main/.github/workflows/github-torch-hub.yml): torch hubの統合が動作するかどうかを確認します。 - [self-hosted (push)](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-push.yml): `main` にコミットが行われた場合に、GPUで高速テストを実行します。このジョブは、`main` でのコミットが以下のフォルダーのコードを更新した場合にのみ実行されます：`src`、`tests`、`.github`（追加されたモデルカード、ノートブックなどの実行を防ぐため）。 - [self-hosted runner](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-scheduled.yml): GPUで `tests` と `examples` の通常のテストと遅いテストを実行します。 ```bash RUN_SLOW=1 pytest tests/ RUN_SLOW=1 pytest examples/ ``` 結果は[here](https://github.com/huggingface/transformers/actions)で観察できます。 ## Running tests ### Choosing which tests to run このドキュメントは、テストを実行する方法の多くの詳細について説明しています。すべてを読んだ後でも、さらに詳細が必要な場合は、[こちら](https://docs.pytest.org/en/latest/usage.html)で見つけることができます。以下は、テストを実行するためのいくつかの最も便利な方法です。すべて実行します: ```console pytest ``` または： ```bash make test ``` 後者は次のように定義されることに注意してください。 ```bash python -m pytest -n auto --dist=loadfile -s -v ./tests/ ``` 以下は、pytestに渡す設定情報です。 - テストプロセスをCPUコアの数と同じだけ実行するように指示します。ただし、RAMが十分でない場合は注意が必要です。 - 同じファイルからのすべてのテストは、同じテストプロセスで実行されるようにします。 - 出力のキャプチャを行いません。 - 冗長モードで実行します。 ### Getting the list of all tests テストスイートのすべてのテスト： ```bash pytest --collect-only -q ``` 指定されたテストファイルのすべてのテスト: ```bash pytest tests/test_optimization.py --collect-only -q ``` ### Run a specific test module 個別のテストモジュールを実行するには: ```bash pytest tests/utils/test_logging.py ``` ### Run specific tests ほとんどのテストでunittestが使用されているため、特定のサブテストを実行するには、それらのテストを含むunittestクラスの名前を知っている必要があります。例えば、それは次のようになるかもしれません： ```bash pytest tests/test_optimization.py::OptimizationTest::test_adam_w ``` テストの実行方法: テストファイル: `tests/test_optimization.py` クラス名: `OptimizationTest` テスト関数の名前: `test_adam_w` ファイルに複数のクラスが含まれている場合は、特定のクラスのテストのみを実行することを選択できます。例えば： ```bash pytest tests/test_optimization.py::OptimizationTest ``` テストクラス内のすべてのテストを実行します。前述の通り、`OptimizationTest` クラスに含まれるテストを実行するには、次のコマンドを実行できます： ```bash pytest tests/test_optimization.py::OptimizationTest --collect-only -q ``` キーワード式を使用してテストを実行できます。名前に `adam` が含まれるテストのみを実行するには： ```bash pytest -k adam tests/test_optimization.py ``` `and`および`or`は、すべてのキーワードが一致するか、いずれかを示すために使用できます。`not`は否定するために使用できます。 `adam`という名前を含むテストを除いてすべてのテストを実行するには： ```bash pytest -k "not adam" tests/test_optimization.py ``` 以下は、提供されたテキストの日本語訳です。 ```bash pytest -k "ada and not adam" tests/test_optimization.py ``` たとえば、`test_adafactor`と`test_adam_w`の両方を実行するには、以下のコマンドを使用できます: ```bash pytest -k "test_adam_w or test_adam_w" tests/test_optimization.py ``` 注意: ここでは、`or` を使用しています。キーワードのいずれか一つが一致すれば、両方を含めるためです。両方のパターンを含むテストのみを含めたい場合は、`and` を使用してください。 ```bash pytest -k "test and ada" tests/test_optimization.py ``` ### Run `accelerate` tests 時々、モデルに対して `accelerate` テストを実行する必要があります。たとえば、`OPT` 実行に対してこれらのテストを実行したい場合、コマンドに `-m accelerate_tests` を追加するだけで済みます： ```bash RUN_SLOW=1 pytest -m accelerate_tests tests/models/opt/test_modeling_opt.py ``` ### Run documentation tests ドキュメンテーションの例が正しいかどうかをテストするには、`doctests` が合格しているかを確認する必要があります。例として、[`WhisperModel.forward` のドックストリング](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py#L1017-L1035)を使用しましょう。 ```python r""" Returns: Example: ```python >>> import torch >>> from transformers import WhisperModel, WhisperFeatureExtractor >>> from datasets import load_dataset >>> model = WhisperModel.from_pretrained("openai/whisper-base") >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_features = inputs.input_features >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state >>> list(last_hidden_state.shape) [1, 2, 512] ```""" ``` 指定したファイル内のすべてのドックストリング例を自動的にテストするために、以下の行を実行してください： ```bash pytest --doctest-modules <path_to_file_or_dir> ``` ファイルにマークダウン拡張子がある場合は、`--doctest-glob="*.md"`引数を追加する必要があります。 ### Run only modified tests [pytest-picked](https://github.com/anapaulagomes/pytest-picked)を使用すると、未ステージングのファイルまたは現在のブランチ（Gitに従って）に関連するテストを実行できます。これは、変更内容に関連するテストのみ実行されるため、変更が何も壊れていないことを迅速に確認する素晴らしい方法です。変更されていないファイルに関連するテストは実行されません。 ```bash pip install pytest-picked ``` ```bash pytest --picked ``` すべてのテストは、変更されたがまだコミットされていないファイルとフォルダから実行されます。 ### Automatically rerun failed tests on source modification [pytest-xdist](https://github.com/pytest-dev/pytest-xdist)は、非常に便利な機能を提供しており、すべての失敗したテストを検出し、ファイルを修正する間にそれらの失敗したテストを連続して再実行することができます。そのため、修正を行った後にpytestを再起動する必要がありません。すべてのテストが合格するまで繰り返され、その後再度フルランが実行されます。 ```bash pip install pytest-xdist ``` モードに入るには： `pytest -f`または`pytest --looponfail` ファイルの変更は、`looponfailroots`ルートディレクトリとその内容全体（再帰的に）を見て検出されます。この値のデフォルトが機能しない場合、`setup.cfg`で設定オプションを変更してプロジェクト内で変更できます。 ```ini [tool:pytest] looponfailroots = transformers tests ``` または `pytest.ini`/`tox.ini` ファイル: ```ini [pytest] looponfailroots = transformers tests ``` ファイルの変更を探すことは、iniファイルのディレクトリを基準にして指定されたディレクトリ内でのみ行われます。 [pytest-watch](https://github.com/joeyespo/pytest-watch) は、この機能の代替実装です。 ### Skip a test module 特定のテストモジュールを除外してすべてのテストモジュールを実行したい場合、実行するテストの明示的なリストを指定することができます。例えば、`test_modeling_*.py` テストを除外してすべてを実行するには次のようにします： ```bash pytest *ls -1 tests/*py | grep -v test_modeling* ``` ### Clearing state CIビルドおよび速度に対する隔離が重要な場合（キャッシュに対して）、キャッシュをクリアする必要があります： ```bash pytest --cache-clear tests ``` ### Running tests in parallel 前述のように、`make test` は `pytest-xdist` プラグインを介してテストを並列実行します（`-n X` 引数、例: `-n 2` で2つの並列ジョブを実行）。 `pytest-xdist` の `--dist=` オプションを使用すると、テストがどのようにグループ化されるかを制御できます。`--dist=loadfile` は同じファイルにあるテストを同じプロセスに配置します。テストの実行順序が異なり予測不可能であるため、`pytest-xdist` を使用してテストスイートを実行すると失敗が発生する場合（つまり、いくつかの未検出の連動テストがある場合）、[pytest-replay](https://github.com/ESSS/pytest-replay) を使用してテストを同じ順序で再生し、その後、失敗するシーケンスを最小限にするのに役立ちます。 ### Test order and repetition 潜在的な相互依存性や状態に関連するバグ（ティアダウン）を検出するために、テストを複数回、連続して、ランダムに、またはセットで繰り返すことは有用です。そして、単純な複数回の繰り返しは、DLのランダム性によって明らかになるいくつかの問題を検出するのに役立ちます。 #### Repeat tests - [pytest-flakefinder](https://github.com/dropbox/pytest-flakefinder): ```bash pip install pytest-flakefinder ``` そして、すべてのテストを複数回実行します (デフォルトでは 50 回)。 ```bash pytest --flake-finder --flake-runs=5 tests/test_failing_test.py ``` <Tip> このプラグインは、`pytest-xdist` の `-n` フラグでは動作しません。 </Tip> <Tip> 別のプラグイン `pytest-repeat` もありますが、これは `unittest` では動作しません。 </Tip> #### Run tests in a random order ```bash pip install pytest-random-order ``` 重要: `pytest-random-order` が存在すると、テストは自動的にランダム化されます。設定の変更や変更は必要ありません。コマンドラインオプションは必須です。前に説明したように、これにより、結合されたテスト (1 つのテストの状態が別のテストの状態に影響を与える) の検出が可能になります。いつ `pytest-random-order` がインストールされていると、そのセッションに使用されたランダムシードが出力されます。例: ```bash pytest tests [...] Using --random-order-bucket=module Using --random-order-seed=573663 ``` そのため、指定された特定のシーケンスが失敗した場合、その正確なシードを追加することでそれを再現できます。例: ```bash pytest --random-order-seed=573663 [...] Using --random-order-bucket=module Using --random-order-seed=573663 ``` 特定のテストのリストを使用しない場合、またはまったくリストを使用しない場合、同じテストの正確な順序を再現します。テストのリストを手動で絞り込み始めると、シードに依存せず、テストが失敗した正確な順序で手動でリストを指定する必要があります。これには、`--random-order-bucket=none` を使用してランダム化を無効にするようpytestに指示する必要があります。例えば、次のようにします： ```bash pytest --random-order-bucket=none tests/test_a.py tests/test_c.py tests/test_b.py ``` すべてのテストのシャッフルを無効にするには: ```bash pytest --random-order-bucket=none ``` デフォルトでは、`--random-order-bucket=module` が暗黙的に適用され、モジュールレベルでファイルをシャッフルします。また、`class`、`package`、`global`、および`none` レベルでシャッフルすることもできます。詳細については、その[ドキュメンテーション](https://github.com/jbasko/pytest-random-order)を参照してください。別のランダム化の代替手段は、[`pytest-randomly`](https://github.com/pytest-dev/pytest-randomly) です。このモジュールは非常に似た機能/インターフェースを持っていますが、`pytest-random-order` で利用可能なバケットモードを持っていません。インストール後に自動的に有効になるという同じ問題があります。 ### Look and feel variations #### pytest-sugar [pytest-sugar](https://github.com/Frozenball/pytest-sugar) は、外観と操作性を向上させ、プログレスバーを追加し、即座に失敗したテストとアサーションを表示するプラグインです。インストール後に自動的にアクティブ化されます。 ```bash pip install pytest-sugar ``` これを使用せずにテストを実行するには、次を実行します。 ```bash pytest -p no:sugar ``` またはアンインストールします。 #### Report each sub-test name and its progress `pytest` による単一またはグループのテストの場合 (`pip install pytest-pspec` の後): ```bash pytest --pspec tests/test_optimization.py ``` #### Instantly shows failed tests [pytest-instafail](https://github.com/pytest-dev/pytest-instafail) では、失敗とエラーが即座に表示されます。テストセッションが終了するまで待機します。 ```bash pip install pytest-instafail ``` ```bash pytest --instafail ``` ### To GPU or not to GPU GPU が有効な設定で、CPU のみモードでテストするには、`CUDA_VISIBLE_DEVICES=""`を追加します。 ```bash CUDA_VISIBLE_DEVICES="" pytest tests/utils/test_logging.py ``` または、複数の GPU がある場合は、`pytest` でどれを使用するかを指定できます。たとえば、 2 番目の GPU GPU `0` と `1` がある場合は、次を実行できます。 ```bash CUDA_VISIBLE_DEVICES="1" pytest tests/utils/test_logging.py ``` これは、異なるGPUで異なるタスクを実行したい場合に便利です。一部のテストはCPUのみで実行する必要があり、他のテストはCPU、GPU、またはTPUで実行する必要があり、また別のテストは複数のGPUで実行する必要があります。次のスキップデコレーターは、テストのCPU/GPU/TPUに関する要件を設定するために使用されます： - `require_torch` - このテストはtorchの下でのみ実行されます。 - `require_torch_gpu` - `require_torch` に加えて、少なくとも1つのGPUが必要です。 - `require_torch_multi_gpu` - `require_torch` に加えて、少なくとも2つのGPUが必要です。 - `require_torch_non_multi_gpu` - `require_torch` に加えて、0または1つのGPUが必要です。 - `require_torch_up_to_2_gpus` - `require_torch` に加えて、0、1、または2つのGPUが必要です。 - `require_torch_xla` - `require_torch` に加えて、少なくとも1つのTPUが必要です。以下の表にGPUの要件を示します： | n gpus | decorator | |--------+--------------------------------| | `>= 0` | `@require_torch` | | `>= 1` | `@require_torch_gpu` | | `>= 2` | `@require_torch_multi_gpu` | | `< 2` | `@require_torch_non_multi_gpu` | | `< 3` | `@require_torch_up_to_2_gpus` | たとえば、使用可能な GPU が 2 つ以上あり、pytorch がインストールされている場合にのみ実行する必要があるテストを次に示します。 ```python no-style @require_torch_multi_gpu def test_example_with_multi_gpu(): ``` テストに `tensorflow` が必要な場合は、`require_tf` デコレータを使用します。例えば： ```python no-style @require_tf def test_tf_thing_with_tensorflow(): ``` これらのデコレータは積み重ねることができます。たとえば、テストが遅く、pytorch で少なくとも 1 つの GPU が必要な場合は、次のようになります。設定方法: ```python no-style @require_torch_gpu @slow def test_example_slow_on_gpu(): ``` `@parametrized` のような一部のデコレータはテスト名を書き換えるため、`@require_*` スキップデコレータをリストする必要があります。最後にそれらが正しく動作するようにします。正しい使用例は次のとおりです ```python no-style @parameterized.expand(...) @require_torch_multi_gpu def test_integration_foo(): ``` この順序の問題は `@pytest.mark.parametrize` には存在しません。最初または最後に配置しても、それでも問題は解決されます。仕事。ただし、それは非単体テストでのみ機能します。内部テスト: - 利用可能な GPU の数: ```python from transformers.testing_utils import get_gpu_count n_gpu = get_gpu_count() # works with torch and tf ``` ### Testing with a specific PyTorch backend or device 特定のtorchデバイスでテストスイートを実行するには、`TRANSFORMERS_TEST_DEVICE="$device"` を追加します。ここで `$device` は対象のバックエンドです。例えば、CPUでテストするには以下のようにします： ```bash TRANSFORMERS_TEST_DEVICE="cpu" pytest tests/utils/test_logging.py ``` この変数は、`mps`などのカスタムまたはあまり一般的ではない PyTorch バックエンドをテストするのに役立ちます。また、特定の GPU をターゲットにしたり、CPU 専用モードでテストしたりすることで、`CUDA_VISIBLE_DEVICES`と同じ効果を達成するために使用することもできます。特定のデバイスでは、初めて「torch」をインポートした後、追加のインポートが必要になります。これは、環境変数 `TRANSFORMERS_TEST_BACKEND` を使用して指定できます。 ```bash TRANSFORMERS_TEST_BACKEND="torch_npu" pytest tests/utils/test_logging.py ``` ### Distributed training `pytest` は直接的に分散トレーニングを処理することはできません。試みると、サブプロセスは正しい処理を行わず、自分自身が `pytest` であると思い込んでテストスイートをループで実行し続けます。ただし、通常のプロセスを生成し、それから複数のワーカーを生成し、IOパイプを管理するプロセスを生成すれば機能します。これを使用するいくつかのテストがあります： - [test_trainer_distributed.py](https://github.com/huggingface/transformers/tree/main/tests/trainer/test_trainer_distributed.py) - [test_deepspeed.py](https://github.com/huggingface/transformers/tree/main/tests/deepspeed/test_deepspeed.py) 実行ポイントにすぐに移動するには、これらのテスト内で `execute_subprocess_async` 呼び出しを検索してください。これらのテストを実行するには、少なくとも2つのGPUが必要です： ```bash CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/test_trainer_distributed.py ``` ### Output capture テストの実行中に、`stdout` および `stderr` に送信された出力はキャプチャされます。テストまたはセットアップメソッドが失敗した場合、通常、それに対応するキャプチャされた出力が失敗のトレースバックと共に表示されます。出力のキャプチャを無効にし、`stdout` と `stderr` を通常通りに取得するには、`-s` または `--capture=no` を使用してください：これらのテストを実行するには少なくとも2つのGPUが必要です： ```bash pytest -s tests/utils/test_logging.py ``` テスト結果を JUnit 形式の出力に送信するには: ```bash py.test tests --junitxml=result.xml ``` ### Color control 色を持たないようにする（例：黄色のテキストを白い背景に表示すると読みにくいです）： ```bash pytest --color=no tests/utils/test_logging.py ``` ### Sending test report to online pastebin service テスト失敗ごとに URL を作成します。 ```bash pytest --pastebin=failed tests/utils/test_logging.py ``` これにより、テスト実行情報がリモートのPasteサービスに送信され、各エラーに対してURLが提供されます。通常通りテストを選択するか、たとえば特定のエラーのみを送信したい場合は `-x` を追加で指定できます。テストセッション全体のログに対するURLを作成する方法： ```bash pytest --pastebin=all tests/utils/test_logging.py ``` ## Writing tests 🤗 transformersのテストは `unittest` を基にしていますが、 `pytest` で実行されるため、ほとんどの場合、両方のシステムの機能を使用できます。 [こちら](https://docs.pytest.org/en/stable/unittest.html)でサポートされている機能を読むことができますが、重要なことは、ほとんどの `pytest` のフィクスチャが動作しないことです。パラメータ化も同様ですが、似たような方法で動作する `parameterized` モジュールを使用しています。 ### Parametrization 同じテストを異なる引数で複数回実行する必要があることがよくあります。これはテスト内部から行うこともできますが、その場合、そのテストを単一の引数セットで実行する方法はありません。 ```python # test_this1.py import unittest from parameterized import parameterized class TestMathUnitTest(unittest.TestCase): @parameterized.expand( [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ] ) def test_floor(self, name, input, expected): assert_equal(math.floor(input), expected) ``` デフォルトでは、このテストは3回実行され、それぞれの実行で `test_floor` の最後の3つの引数がパラメータリストの対応する引数に割り当てられます。そして、`negative` と `integer` パラメータのセットのみを実行することもできます: ```bash pytest -k "negative and integer" tests/test_mytest.py ``` または、`Negative`のサブテストを除くすべての場合、次のようになります。 ```bash pytest -k "not negative" tests/test_mytest.py ``` `-k` フィルターを使用することに加えて、各サブテストの正確な名前を調べ、その正確な名前を使用して任意のサブテストまたはすべてのサブテストを実行することができます。 ```bash pytest test_this1.py --collect-only -q ``` すると次のものがリストされます: ```bash test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer test_this1.py::TestMathUnitTest::test_floor_2_large_fraction ``` したがって、2 つの特定のサブテストのみを実行できるようになりました。 ```bash pytest test_this1.py::TestMathUnitTest::test_floor_0_negative test_this1.py::TestMathUnitTest::test_floor_1_integer ``` `transformers`の開発者依存関係にすでに含まれているモジュール[parameterized](https://pypi.org/project/parameterized/) は、`unittests` と `pytest` テストの両方で機能します。ただし、テストが `unittest` でない場合、`pytest.mark.parametrize` を使用することができます（または既存のテストのいくつかで、主に `examples` の下で使用されているのを見ることができます）。次に、同じ例を示しますが、今度は `pytest` の `parametrize` マーカーを使用しています： ```python # test_this2.py import pytest @pytest.mark.parametrize( "name, input, expected", [ ("negative", -1.5, -2.0), ("integer", 1, 1.0), ("large fraction", 1.6, 1), ], ) def test_floor(name, input, expected): assert_equal(math.floor(input), expected) ``` `parameterized` と同様に、`pytest.mark.parametrize` を使用すると、`-k` フィルタが役立たない場合でも、サブテストの実行を細かく制御できます。ただし、このパラメータ化関数はサブテストの名前をわずかに異なるものにします。以下にその例を示します： ```bash pytest test_this2.py --collect-only -q ``` すると次のものがリストされます: ```bash test_this2.py::test_floor[integer-1-1.0] test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[large fraction-1.6-1] ``` これで、特定のテストのみを実行できるようになりました。 ```bash pytest test_this2.py::test_floor[negative--1.5--2.0] test_this2.py::test_floor[integer-1-1.0] ``` 前の例と同様に。 ### Files and directories テストの中で、現在のテストファイルからの相対位置を知る必要があることがよくあります。しかし、これは簡単なことではありません。なぜなら、テストは複数のディレクトリから呼び出されるか、異なる深さのサブディレクトリに存在することがあるからです。`transformers.test_utils.TestCasePlus` というヘルパークラスは、すべての基本パスを整理し、簡単にアクセスできるようにすることで、この問題を解決します。 - `pathlib` オブジェクト（すべて完全に解決されたもの）： - `test_file_path` - 現在のテストファイルのパス、つまり `__file__` - `test_file_dir` - 現在のテストファイルを含むディレクトリ - `tests_dir` - `tests` テストスイートのディレクトリ - `examples_dir` - `examples` テストスイートのディレクトリ - `repo_root_dir` - リポジトリのディレクトリ - `src_dir` - `transformers` サブディレクトリが存在する場所 - パスの文字列表現――上記と同じですが、これらは `pathlib` オブジェクトではなく文字列としてパスを返します： - `test_file_path_str` - `test_file_dir_str` - `tests_dir_str` - `examples_dir_str` - `repo_root_dir_str` - `src_dir_str` これらを使用し始めるには、テストが `transformers.test_utils.TestCasePlus` のサブクラスに存在することを確認するだけです。例： ```python from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_local_locations(self): data_dir = self.tests_dir / "fixtures/tests_samples/wmt_en_ro" ``` もし、`pathlib` を介してパスを操作する必要がない場合、または単に文字列としてパスが必要な場合は、`pathlib` オブジェクトに `str()` を呼び出すか、`_str` で終わるアクセサを使用できます。例： ```python from transformers.testing_utils import TestCasePlus class PathExampleTest(TestCasePlus): def test_something_involving_stringified_locations(self): examples_dir = self.examples_dir_str ``` ### Temporary files and directories 一意の一時ファイルとディレクトリの使用は、並列テストの実行には欠かせません。これにより、テストがお互いのデータを上書きしないようにします。また、これらを作成した各テストの終了時に一時ファイルとディレクトリが削除されることを望みます。そのため、これらのニーズを満たすパッケージである `tempfile` のようなパッケージの使用は重要です。しかし、テストのデバッグ時には、一時ファイルやディレクトリに何が格納されているかを確認できる必要があり、テストを再実行するたびにランダムに変更されないその正確なパスを知りたいと思います。 `transformers.test_utils.TestCasePlus` というヘルパークラスは、このような目的に最適です。これは `unittest.TestCase` のサブクラスであるため、テストモジュールで簡単に継承することができます。以下はその使用例です： ```python from transformers.testing_utils import TestCasePlus class ExamplesTests(TestCasePlus): def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() ``` このコードはユニークな一時ディレクトリを作成し、`tmp_dir` をその場所に設定します。 - ユニークな一時ディレクトリを作成します： ```python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir() ``` `tmp_dir` には、作成された一時ディレクトリへのパスが含まれます。期間終了後は自動的に削除されますテスト。 - 任意の一時ディレクトリを作成し、テストの開始前にそれが空であることを確認し、テスト後には空にしないでください。 ```python def test_whatever(self): tmp_dir = self.get_auto_remove_tmp_dir("./xxx") ``` これは、特定のディレクトリを監視し、前のテストがそこにデータを残さないことを確認したい場合に、デバッグに役立ちます。 - `before` と `after` 引数を直接オーバーライドすることで、デフォルトの動作をオーバーライドできます。以下のいずれかの動作に導きます： - `before=True`：テストの開始時に常に一時ディレクトリがクリアされます。 - `before=False`：一時ディレクトリが既に存在する場合、既存のファイルはそのままになります。 - `after=True`：テストの終了時に常に一時ディレクトリが削除されます。 - `after=False`：テストの終了時に常に一時ディレクトリはそのままになります。 <Tip> `rm -r`の相当を安全に実行するために、明示的な `tmp_dir` が使用される場合、プロジェクトリポジトリのチェックアウトのサブディレクトリのみが許可されます。誤って `/tmp` などのファイルシステムの重要な部分が削除されないように、常に `./` から始まるパスを渡してください。 </Tip> <Tip> 各テストは複数の一時ディレクトリを登録でき、要求がない限りすべて自動で削除されます。 </Tip> ### Temporary sys.path override 別のテストからインポートするために一時的に `sys.path` をオーバーライドする必要がある場合、`ExtendSysPath` コンテキストマネージャを使用できます。例： ```python import os from transformers.testing_utils import ExtendSysPath bindir = os.path.abspath(os.path.dirname(__file__)) with ExtendSysPath(f"{bindir}/.."): from test_trainer import TrainerIntegrationCommon # noqa ``` ### Skipping tests これは、バグが見つかり、新しいテストが作成された場合であっても、バグがまだ修正されていない場合に役立ちます。メインリポジトリにコミットできるようにするには、`make test` の実行中にそれをスキップする必要があります。メソッド： - **skip** は、テストが特定の条件が満たされた場合にのみパスすることを期待しており、それ以外の場合は pytest がテストの実行をスキップします。一般的な例は、Windows専用のテストを非Windowsプラットフォームでスキップする場合、または現在利用できない外部リソースに依存するテストをスキップする場合です（例: データベースが利用できない場合）。 - **xfail** は、何らかの理由でテストが失敗することを期待しています。一般的な例は、まだ実装されていない機能のテストや、まだ修正されていないバグのテストです。テストが予想される失敗にもかかわらずパスした場合（pytest.mark.xfailでマークされたテスト）、それはxpassとしてテストサマリーに報告されます。これらの2つの間の重要な違いの1つは、`skip` はテストを実行しない点であり、`xfail` は実行します。したがって、バグのあるコードが他のテストに影響を与える場合は、`xfail` を使用しないでください。 #### Implementation - テスト全体を無条件にスキップする方法は次のとおりです： ```python no-style @unittest.skip(reason="this bug needs to be fixed") def test_feature_x(): ``` または pytest 経由: ```python no-style @pytest.mark.skip(reason="this bug needs to be fixed") ``` または `xfail` の方法: ```python no-style @pytest.mark.xfail def test_feature_x(): ``` - テスト内の内部チェックに基づいてテストをスキップする方法は次のとおりです。 ```python def test_feature_x(): if not has_something(): pytest.skip("unsupported configuration") ``` またはモジュール全体: ```python import pytest if not pytest.config.getoption("--custom-flag"): pytest.skip("--custom-flag is missing, skipping tests", allow_module_level=True) ``` または `xfail` の方法: ```python def test_feature_x(): pytest.xfail("expected to fail until bug XYZ is fixed") ``` - 一部のインポートが欠落している場合にモジュール内のすべてのテストをスキップする方法は次のとおりです。 ```python docutils = pytest.importorskip("docutils", minversion="0.3") ``` - 条件に基づいてテストをスキップします。 ```python no-style @pytest.mark.skipif(sys.version_info < (3,6), reason="requires python3.6 or higher") def test_feature_x(): ``` または： ```python no-style @unittest.skipIf(torch_device == "cpu", "Can't do half precision") def test_feature_x(): ``` またはモジュール全体をスキップします。 ```python no-style @pytest.mark.skipif(sys.platform == 'win32', reason="does not run on windows") class TestClass(): def test_feature_x(self): ``` 詳細、例、および方法についての詳細は[こちら](https://docs.pytest.org/en/latest/skipping.html)を参照してください。 ### Slow tests テストライブラリは着実に成長しており、テストの一部は数分かかります。そのため、CIでテストスイートの完了を待つのは1時間待つ余裕がないことがあります。したがって、いくつかの例外を除いて、遅いテストは以下の例のようにマークすべきです： ```python no-style from transformers.testing_utils import slow @slow def test_integration_foo(): ``` テストが`@slow`としてマークされたら、そのようなテストを実行するには、環境変数 `RUN_SLOW=1`を設定します。例: ```bash RUN_SLOW=1 pytest tests ``` `@parameterized` のようなデコレータはテスト名を書き換えるため、`@slow` および他のスキップデコレータ `@require_*` は正しく動作するためには、最後にリストアップする必要があります。以下は正しい使用例の一例です： ```python no-style @parameterized.expand(...) @slow def test_integration_foo(): ``` このドキュメントの冒頭で説明したように、遅いテストは定期的なスケジュールに従って実行され、PRのCIチェックでは実行されません。そのため、一部の問題がPRの提出時に見落とされ、マージされる可能性があります。そのような問題は次回のスケジュールされたCIジョブで検出されます。しかし、それはまた、PRを提出する前に自分のマシンで遅いテストを実行する重要性を意味しています。どのテストを遅いテストとしてマークすべきかを選択するための、おおまかな意思決定メカニズムが次に示されています： - テストがライブラリの内部コンポーネントの1つに焦点を当てている場合（例: モデリングファイル、トークン化ファイル、パイプライン）、そのテストは遅いテストスイートで実行する必要があります。それがライブラリの他の側面、たとえばドキュメンテーションや例に焦点を当てている場合、それらのテストは遅いテストスイートで実行する必要があります。そして、このアプローチを洗練させるために例外を設ける必要があります。 - 重いウェイトセットや約50MB以上のデータセットをダウンロードする必要があるすべてのテスト（例: モデル統合テスト、トークナイザ統合テスト、パイプライン統合テスト）は遅いテストとして設定する必要があります。新しいモデルを追加する場合、統合テスト用にランダムなウェイトを持つ小さなバージョンを作成し、ハブにアップロードする必要があります。これについては以下の段落で詳しく説明します。 - 特に高速化されていないトレーニングを行う必要があるすべてのテストは遅いテストとして設定する必要があります。 - 一部の「遅い」であるべきでないテストが非常に遅い場合、およびそれらを `@slow` として設定する必要がある場合には例外を導入できます。大容量のファイルをディスクに保存および読み込みする自動モデリングテストは、`@slow` としてマークされたテストの良い例です。 - CIで1秒未満でテストが完了する場合（ダウンロードを含む）、それは通常のテストであるべきです。すべての非遅いテストは、さまざまな内部要素を完全にカバーする必要がありますが、高速である必要があります。たとえば、特別に作成された小さなモデル（レイヤー数が最小限で、語彙サイズが小さいなど）を使用して、かなりのカバレッジを実現できます。その後、`@slow` テストでは大規模な遅いモデルを使用して質的なテストを実行できます。これらを使用するには、以下のように *tiny* モデルを探してください： ```bash grep tiny tests examples ``` [スクリプトの例](https://github.com/huggingface/transformers/tree/main/scripts/fsmt/fsmt-make-tiny-model.py)があり、これにより tiny-wmt19-en-de のような小さなモデルが作成されます。特定のモデルのアーキテクチャに簡単に調整できます。実行時間を誤って測定することが簡単です。たとえば、巨大なモデルのダウンロードに関するオーバーヘッドがある場合、ローカルでテストするとダウンロードされたファイルがキャッシュされ、ダウンロード時間が計測されなくなります。したがって、CIログの実行速度レポート（`pytest --durations=0 tests` の出力）を確認してください。このレポートは、遅いテストとしてマークされていない遅い外れ値や、高速に書き直す必要があるテストを見つけるのにも役立ちます。テストスイートがCIで遅くなり始めた場合、このレポートのトップリストには最も遅いテストが表示されます。 ### Testing the stdout/stderr output `stdout` および/または `stderr` に書き込む関数をテストするために、テストは `pytest` の [capsys システム](https://docs.pytest.org/en/latest/capture.html) を使用してこれらのストリームにアクセスできます。以下はその方法です： ```python import sys def print_to_stdout(s): print(s) def print_to_stderr(s): sys.stderr.write(s) def test_result_and_stdout(capsys): msg = "Hello" print_to_stdout(msg) print_to_stderr(msg) out, err = capsys.readouterr() # consume the captured output streams # optional: if you want to replay the consumed streams: sys.stdout.write(out) sys.stderr.write(err) # test: assert msg in out assert msg in err ``` そしてもちろん、ほとんどの場合、`stderr`は例外の一部として提供されるため、そのような場合には try/excel を使用する必要があります。ケース： ```python def raise_exception(msg): raise ValueError(msg) def test_something_exception(): msg = "Not a good value" error = "" try: raise_exception(msg) except Exception as e: error = str(e) assert msg in error, f"{msg} is in the exception:\n{error}" ``` stdout をキャプチャするもう 1 つのアプローチは、`contextlib.redirect_stdout`を使用することです。 ```python from io import StringIO from contextlib import redirect_stdout def print_to_stdout(s): print(s) def test_result_and_stdout(): msg = "Hello" buffer = StringIO() with redirect_stdout(buffer): print_to_stdout(msg) out = buffer.getvalue() # optional: if you want to replay the consumed streams: sys.stdout.write(out) # test: assert msg in out ``` stdout をキャプチャする際の重要な潜在的な問題は、通常の `print` でこれまでに出力された内容をリセットする可能性がある `\r` 文字が含まれている可能性があることです。`pytest` 自体には問題はありませんが、`pytest -s` ではこれらの文字がバッファに含まれるため、`-s` ありとなしでテストを実行できるようにするには、`re.sub(r'~.*\r', '', buf, 0, re.M)` を使用してキャプチャされた出力に対して追加のクリーンアップを行う必要があります。しかし、その後、`\r` が含まれているかどうかにかかわらず、すべての操作を自動的に処理するヘルパーコンテキストマネージャラッパーがあります。したがって、次のように簡単に行えます： ```python from transformers.testing_utils import CaptureStdout with CaptureStdout() as cs: function_that_writes_to_stdout() print(cs.out) ``` 完全なテスト例は次のとおりです。 ```python from transformers.testing_utils import CaptureStdout msg = "Secret message\r" final = "Hello World" with CaptureStdout() as cs: print(msg + final) assert cs.out == final + "\n", f"captured: {cs.out}, expecting {final}" ``` `stderr` をキャプチャしたい場合は、代わりに `CaptureStderr` クラスを使用してください。 ```python from transformers.testing_utils import CaptureStderr with CaptureStderr() as cs: function_that_writes_to_stderr() print(cs.err) ``` 両方のストリームを一度にキャプチャする必要がある場合は、親の `CaptureStd` クラスを使用します。 ```python from transformers.testing_utils import CaptureStd with CaptureStd() as cs: function_that_writes_to_stdout_and_stderr() print(cs.err, cs.out) ``` また、テストの問題のデバッグを支援するために、デフォルトで、これらのコンテキストマネージャーは終了時にキャプチャされたストリームを自動的に再生します。文脈から。 ### Capturing logger stream ロガーの出力を検証する必要がある場合は、`CaptureLogger`を使用できます。 ```python from transformers import logging from transformers.testing_utils import CaptureLogger msg = "Testing 1, 2, 3" logging.set_verbosity_info() logger = logging.get_logger("transformers.models.bart.tokenization_bart") with CaptureLogger(logger) as cl: logger.info(msg) assert cl.out, msg + "\n" ``` ### Testing with environment variables 特定のテストで環境変数の影響をテストしたい場合は、ヘルパーデコレータを使用できます。 `transformers.testing_utils.mockenv` ```python from transformers.testing_utils import mockenv class HfArgumentParserTest(unittest.TestCase): @mockenv(TRANSFORMERS_VERBOSITY="error") def test_env_override(self): env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None) ``` 場合によっては、外部プログラムを呼び出す必要があるため、`os.environ` に`PYTHONPATH`を設定してインクルードする必要があります。複数のローカルパス。ヘルパークラス `transformers.test_utils.TestCasePlus` が役に立ちます。 ```python from transformers.testing_utils import TestCasePlus class EnvExampleTest(TestCasePlus): def test_external_prog(self): env = self.get_env() # now call the external program, passing `env` to it ``` テストファイルが `tests` テストスイートまたは `examples` のどちらにあるかに応じて `env[PYTHONPATH]` を使用して、これら 2 つのディレクトリのいずれかを含めます。また、テストが確実に行われるようにするための `src` ディレクトリも含めます。現在のリポジトリに対して実行され、最後に、テストが実行される前にすでに設定されていた `env[PYTHONPATH]` を使用して実行されます。何かあれば呼ばれます。このヘルパーメソッドは `os.environ` オブジェクトのコピーを作成するため、元のオブジェクトはそのまま残ります。 ### Getting reproducible results 状況によっては、テストのランダム性を削除したい場合があります。同一の再現可能な結果セットを取得するには、シードを修正する必要があります: ```python seed = 42 # python RNG import random random.seed(seed) # pytorch RNGs import torch torch.manual_seed(seed) torch.backends.cudnn.deterministic = True if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) # numpy RNG import numpy as np np.random.seed(seed) # tf RNG tf.random.set_seed(seed) ``` ### Debugging tests 警告が発生した時点でデバッガーを開始するには、次の手順を実行します。 ```bash pytest tests/utils/test_logging.py -W error::UserWarning --pdb ``` ## Working with github actions workflows セルフプッシュのワークフローCIジョブをトリガーするには、以下の手順を実行する必要があります： 1. `transformers` のリモートリポジトリで新しいブランチを作成します（フォークではなく、元のリポジトリで行います）。 2. ブランチの名前は `ci_` または `ci-` で始まる必要があります（`main` もトリガーしますが、`main` ではPRを作成できません）。また、特定のパスでのみトリガーされます - このドキュメントが書かれた後に変更された場合に備えて、最新の定義は[こちら](https://github.com/huggingface/transformers/blob/main/.github/workflows/self-push.yml)の *push:* にあります。 3. このブランチからPRを作成します。 4. その後、このジョブが[ここ](https://github.com/huggingface/transformers/actions/workflows/self-push.yml)に表示されます。ジョブはバックログがある場合、すぐに実行されないことがあります。 ## Testing Experimental CI Features CI機能のテストは通常のCIの正常な動作に干渉する可能性があるため、新しいCI機能を追加する場合、以下の手順に従う必要があります。 1. テストが必要なものをテストするための新しい専用のジョブを作成します。 2. 新しいジョブは常に成功する必要があるため、常にグリーン ✓（詳細は以下参照）を表示する必要があります。 3. さまざまな種類のPR（ユーザーフォークブランチ、非フォークブランチ、github.com UIから直接ファイルを編集するブランチ、さまざまな強制プッシュなど）が実行されるまでいくつかの日間実行し、実験的なジョブのログを監視します（意図的に常にグリーンになるようになっている全体のジョブの緑ではなく）。 4. すべてが安定していることが明確になったら、新しい変更を既存のジョブに統合します。このように、CI機能自体の実験が通常のワークフローに干渉しないようにできます。では、新しいCI機能が開発中である間、ジョブを常に成功させるにはどうすればいいでしょうか？ TravisCIのような一部のCIは `ignore-step-failure` をサポートし、全体のジョブを成功として報告しますが、この文書が作成された時点ではCircleCIとGithub Actionsはそれをサポートしていません。したがって、以下のワークアラウンドを使用できます： 1. bashスクリプト内で潜在的な失敗を抑制するために実行コマンドの冒頭に `set +euo pipefail` を記述します。 2. 最後のコマンドは成功する必要があります。たとえば `echo "done"` または単に `true` を使用できます。以下は例です： ```yaml - run: name: run CI experiment command: | set +euo pipefail echo "setting run-all-despite-any-errors-mode" this_command_will_fail echo "but bash continues to run" # emulate another failure false # but the last command must be a success echo "during experiment do not remove: reporting success to CI, even if there were failures" ``` 単純なコマンドの場合は、次のようにすることもできます。 ```bash cmd_that_may_fail || true ``` もちろん、結果に満足したら、実験的なステップやジョブを通常のジョブと統合し、`set +euo pipefail` などの追加した要素を削除して、実験的なジョブが通常のCIの動作に干渉しないようにします。このプロセス全体は、実験的なステップに対して `allow-failure` のようなものを設定し、PRの全体のステータスに影響を与えずに失敗させることができれば、はるかに簡単になったでしょう。しかし、前述の通り、現在はCircleCIとGithub Actionsはこの機能をサポートしていません。この機能に関しての投票や、CIに特有のスレッドでその進捗状況を確認できます： - [Github Actions:](https://github.com/actions/toolkit/issues/399) - [CircleCI:](https://ideas.circleci.com/ideas/CCI-I-344)

transformers/docs/source/ja/testing.md/0

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# 큰 모델 인스턴스화 [[instantiating-a-big-model]] 매우 큰 사전훈련된 모델을 사용하려면, RAM 사용을 최소화해야 하는 과제가 있습니다. 일반적인 PyTorch 워크플로우는 다음과 같습니다: 1. 무작위 가중치로 모델을 생성합니다. 2. 사전훈련된 가중치를 불러옵니다. 3. 사전훈련된 가중치를 무작위 모델에 적용합니다. 1단계와 2단계 모두 모델의 전체 버전을 메모리에 적재해야 하며, 대부분 문제가 없지만 모델이 기가바이트급의 용량을 차지하기 시작하면 복사본 2개가 RAM을 초과하여 메모리 부족 이슈를 야기할 수 있습니다. 더 심각한 문제는 분산 학습을 위해 `torch.distributed`를 사용하는 경우, 프로세스마다 사전훈련된 모델을 로드하고 복사본을 2개씩 RAM에 저장한다는 것입니다. <Tip> 무작위로 생성된 모델은 "비어 있는" (즉 그때 메모리에 있던 것으로 이뤄진) 텐서로 초기화되며 메모리 공간을 차지합니다. 초기화된 모델/파라미터의 종류에 적합한 분포(예: 정규 분포)에 따른 무작위 초기화는 가능한 한 빠르게 하기 위해 초기화되지 않은 가중치에 대해 3단계 이후에만 수행됩니다! </Tip> 이 안내서에서는 Transformers가 이 문제를 해결하기 위해 제공하는 솔루션을 살펴봅니다. 주의할 점은 아직 활발히 개발 중인 분야이므로 여기서 설명하는 API가 앞으로 약간 변경될 수 있다는 것입니다. ## 샤딩된 체크포인트 [[sharded-checkpoints]] 4.18.0 버전 이후, 10GB 이상의 공간을 차지하는 모델 체크포인트는 자동으로 작은 조각들로 샤딩됩니다. `model.save_pretrained(save_dir)`를 실행할 때 하나의 단일 체크포인트를 가지게 될 대신, 여러 부분 체크포인트(각각의 크기는 10GB 미만)와 매개변수 이름을 해당 파일에 매핑하는 인덱스가 생성됩니다. `max_shard_size` 매개변수로 샤딩 전 최대 크기를 제어할 수 있으므로, 이 예제를 위해 샤드 크기가 작은 일반 크기의 모델을 사용하겠습니다: 전통적인 BERT 모델을 사용해 봅시다. ```py from transformers import AutoModel model = AutoModel.from_pretrained("google-bert/bert-base-cased") ``` [`~PreTrainedModel.save_pretrained`]을 사용하여 모델을 저장하면, 모델의 구성과 가중치가 들어있는 두 개의 파일이 있는 새 폴더가 생성됩니다: ```py >>> import os >>> import tempfile >>> with tempfile.TemporaryDirectory() as tmp_dir: ... model.save_pretrained(tmp_dir) ... print(sorted(os.listdir(tmp_dir))) ['config.json', 'pytorch_model.bin'] ``` 이제 최대 샤드 크기를 200MB로 사용해 봅시다: ```py >>> with tempfile.TemporaryDirectory() as tmp_dir: ... model.save_pretrained(tmp_dir, max_shard_size="200MB") ... print(sorted(os.listdir(tmp_dir))) ['config.json', 'pytorch_model-00001-of-00003.bin', 'pytorch_model-00002-of-00003.bin', 'pytorch_model-00003-of-00003.bin', 'pytorch_model.bin.index.json'] ``` 모델의 구성에 더해, 세 개의 다른 가중치 파일과 파라미터 이름과 해당 파일의 매핑이 포함된 `index.json` 파일을 볼 수 있습니다. 이러한 체크포인트는 [`~PreTrainedModel.from_pretrained`] 메서드를 사용하여 완전히 다시 로드할 수 있습니다: ```py >>> with tempfile.TemporaryDirectory() as tmp_dir: ... model.save_pretrained(tmp_dir, max_shard_size="200MB") ... new_model = AutoModel.from_pretrained(tmp_dir) ``` 큰 모델의 경우 이러한 방식으로 처리하는 주된 장점은 위에서 보여준 흐름의 2단계에서, 각 샤드가 이전 샤드 다음에 로드되므로 메모리 사용량이 모델 크기와 가장 큰 샤드의 크기를 초과하지 않는다는 점입니다. 이 인덱스 파일은 키가 체크포인트에 있는지, 그리고 해당 가중치가 어디에 저장되어 있는지를 결정하는 데 사용됩니다. 이 인덱스를 json과 같이 로드하고 딕셔너리를 얻을 수 있습니다: ```py >>> import json >>> with tempfile.TemporaryDirectory() as tmp_dir: ... model.save_pretrained(tmp_dir, max_shard_size="200MB") ... with open(os.path.join(tmp_dir, "pytorch_model.bin.index.json"), "r") as f: ... index = json.load(f) >>> print(index.keys()) dict_keys(['metadata', 'weight_map']) ``` 메타데이터는 현재 모델의 총 크기만 포함됩니다. 앞으로 다른 정보를 추가할 계획입니다: ```py >>> index["metadata"] {'total_size': 433245184} ``` 가중치 맵은 이 인덱스의 주요 부분으로, 각 매개변수 이름(PyTorch 모델 `state_dict`에서 보통 찾을 수 있는)을 해당 파일에 매핑합니다: ```py >>> index["weight_map"] {'embeddings.LayerNorm.bias': 'pytorch_model-00001-of-00003.bin', 'embeddings.LayerNorm.weight': 'pytorch_model-00001-of-00003.bin', ... ``` 만약 [`~PreTrainedModel.from_pretrained`]를 사용하지 않고 모델 내에서 이러한 샤딩된 체크포인트를 직접 가져오려면 (전체 체크포인트를 위해 `model.load_state_dict()`를 수행하는 것처럼), [`~modeling_utils.load_sharded_checkpoint`]를 사용해야 합니다. ```py >>> from transformers.modeling_utils import load_sharded_checkpoint >>> with tempfile.TemporaryDirectory() as tmp_dir: ... model.save_pretrained(tmp_dir, max_shard_size="200MB") ... load_sharded_checkpoint(model, tmp_dir) ``` ## 저(低)메모리 로딩 [[low-memory-loading]] 샤딩된 체크포인트는 위에서 언급한 작업 흐름의 2단계에서 메모리 사용량을 줄이지만, 저(低)메모리 설정에서 모델을 사용하기 위해 우리의 Accelerate 라이브러리를 기반으로 한 도구를 활용하는 것이 좋습니다. 자세한 사항은 다음 가이드를 참조해주세요: [Accelerate로 대규모 모델 가져오기 (영문)](../en/main_classes/model#large-model-loading)

transformers/docs/source/ko/big_models.md/0

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# 설치방법[[installation]] 🤗 Transformers를 사용 중인 딥러닝 라이브러리에 맞춰 설치하고, 캐시를 구성하거나 선택적으로 오프라인에서도 실행할 수 있도록 🤗 Transformers를 설정하는 방법을 배우겠습니다. 🤗 Transformers는 Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+ 및 Flax에서 테스트되었습니다. 딥러닝 라이브러리를 설치하려면 아래 링크된 저마다의 공식 사이트를 참고해주세요. * [PyTorch](https://pytorch.org/get-started/locally/) 설치하기 * [TensorFlow 2.0](https://www.tensorflow.org/install/pip) 설치하기 * [Flax](https://flax.readthedocs.io/en/latest/) 설치하기 ## pip으로 설치하기[[install-with-pip]] 🤗 Transformers를 [가상 환경](https://docs.python.org/3/library/venv.html)에 설치하는 것을 추천드립니다. Python 가상 환경에 익숙하지 않다면, 이 [가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 참고하세요. 가상 환경을 사용하면 서로 다른 프로젝트들을 보다 쉽게 관리할 수 있고, 의존성 간의 호환성 문제를 방지할 수 있습니다. 먼저 프로젝트 디렉토리에서 가상 환경을 만들어 줍니다. ```bash python -m venv .env ``` 가상 환경을 활성화해주세요. Linux나 MacOS의 경우: ```bash source .env/bin/activate ``` Windows의 경우: ```bash .env/Scripts/activate ``` 이제 🤗 Transformers를 설치할 준비가 되었습니다. 다음 명령을 입력해주세요. ```bash pip install transformers ``` CPU만 써도 된다면, 🤗 Transformers와 딥러닝 라이브러리를 단 1줄로 설치할 수 있습니다. 예를 들어 🤗 Transformers와 PyTorch의 경우: ```bash pip install transformers[torch] ``` 🤗 Transformers와 TensorFlow 2.0의 경우: ```bash pip install transformers[tf-cpu] ``` 🤗 Transformers와 Flax의 경우: ```bash pip install transformers[flax] ``` 마지막으로 🤗 Transformers가 제대로 설치되었는지 확인할 차례입니다. 사전훈련된 모델을 다운로드하는 코드입니다. ```bash python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))" ``` 라벨과 점수가 출력되면 잘 설치된 것입니다. ```bash [{'label': 'POSITIVE', 'score': 0.9998704791069031}] ``` ## 소스에서 설치하기[[install-from-source]] 🤗 Transformers를 소스에서 설치하려면 아래 명령을 실행하세요. ```bash pip install git+https://github.com/huggingface/transformers ``` 위 명령은 최신이지만 (안정적인) `stable` 버전이 아닌 실험성이 짙은 `main` 버전을 설치합니다. `main` 버전은 개발 현황과 발맞추는데 유용합니다. 예시로 마지막 공식 릴리스 이후 발견된 버그가 패치되었지만, 새 릴리스로 아직 롤아웃되지는 않은 경우를 들 수 있습니다. 바꿔 말하면 `main` 버전이 안정성과는 거리가 있다는 뜻이기도 합니다. 저희는 `main` 버전을 사용하는데 문제가 없도록 노력하고 있으며, 대부분의 문제는 대개 몇 시간이나 하루 안에 해결됩니다. 만약 문제가 발생하면 [이슈](https://github.com/huggingface/transformers/issues)를 열어주시면 더 빨리 해결할 수 있습니다! 전과 마찬가지로 🤗 Transformers가 제대로 설치되었는지 확인할 차례입니다. ```bash python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))" ``` ## 수정 가능한 설치[[editable-install]] 수정 가능한 설치가 필요한 경우는 다음과 같습니다. * `main` 버전의 소스 코드를 사용하기 위해 * 🤗 Transformers에 기여하고 싶어서 코드의 변경 사항을 테스트하기 위해 리포지터리를 복제하고 🤗 Transformers를 설치하려면 다음 명령을 입력해주세요. ```bash git clone https://github.com/huggingface/transformers.git cd transformers pip install -e . ``` 위 명령은 리포지터리를 복제한 위치의 폴더와 Python 라이브러리의 경로를 연결시킵니다. Python이 일반 라이브러리 경로 외에 복제한 폴더 내부를 확인할 것입니다. 예를 들어 Python 패키지가 일반적으로 `~/anaconda3/envs/main/lib/python3.7/site-packages/`에 설치되어 있는데, 명령을 받은 Python이 이제 복제한 폴더인 `~/transformers/`도 검색하게 됩니다. <Tip warning={true}> 라이브러리를 계속 사용하려면 `transformers` 폴더를 꼭 유지해야 합니다. </Tip> 복제본은 최신 버전의 🤗 Transformers로 쉽게 업데이트할 수 있습니다. ```bash cd ~/transformers/ git pull ``` Python 환경을 다시 실행하면 업데이트된 🤗 Transformers의 `main` 버전을 찾아낼 것입니다. ## conda로 설치하기[[install-with-conda]] `conda-forge` conda 채널에서 설치할 수 있습니다. ```bash conda install conda-forge::transformers ``` ## 캐시 구성하기[[cache-setup]] 사전훈련된 모델은 다운로드된 후 로컬 경로 `~/.cache/huggingface/hub`에 캐시됩니다. 셸 환경 변수 `TRANSFORMERS_CACHE`의 기본 디렉터리입니다. Windows의 경우 기본 디렉터리는 `C:\Users\username\.cache\huggingface\hub`입니다. 아래의 셸 환경 변수를 (우선 순위) 순서대로 변경하여 다른 캐시 디렉토리를 지정할 수 있습니다. 1. 셸 환경 변수 (기본): `HF_HUB_CACHE` 또는 `TRANSFORMERS_CACHE` 2. 셸 환경 변수: `HF_HOME` 3. 셸 환경 변수: `XDG_CACHE_HOME` + `/huggingface` <Tip> 과거 🤗 Transformers에서 쓰였던 셸 환경 변수 `PYTORCH_TRANSFORMERS_CACHE` 또는 `PYTORCH_PRETRAINED_BERT_CACHE`이 설정되있다면, 셸 환경 변수 `TRANSFORMERS_CACHE`을 지정하지 않는 한 우선 사용됩니다. </Tip> ## 오프라인 모드[[offline-mode]] 🤗 Transformers를 로컬 파일만 사용하도록 해서 방화벽 또는 오프라인 환경에서 실행할 수 있습니다. 활성화하려면 `HF_HUB_OFFLINE=1` 환경 변수를 설정하세요. <Tip> `HF_DATASETS_OFFLINE=1` 환경 변수를 설정하여 오프라인 훈련 과정에 [🤗 Datasets](https://huggingface.co/docs/datasets/)을 추가할 수 있습니다. </Tip> 예를 들어 외부 기기 사이에 방화벽을 둔 일반 네트워크에서 평소처럼 프로그램을 다음과 같이 실행할 수 있습니다. ```bash python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ... ``` 오프라인 기기에서 동일한 프로그램을 다음과 같이 실행할 수 있습니다. ```bash HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \ python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ... ``` 이제 스크립트는 로컬 파일에 한해서만 검색할 것이므로, 스크립트가 중단되거나 시간이 초과될 때까지 멈춰있지 않고 잘 실행될 것입니다. ### 오프라인용 모델 및 토크나이저 만들어두기[[fetch-models-and-tokenizers-to-use-offline]] Another option for using 🤗 Transformers offline is to download the files ahead of time, and then point to their local path when you need to use them offline. There are three ways to do this: 🤗 Transformers를 오프라인으로 사용하는 또 다른 방법은 파일을 미리 다운로드한 다음, 오프라인일 때 사용할 로컬 경로를 지정해두는 것입니다. 3가지 중 편한 방법을 고르세요. * [Model Hub](https://huggingface.co/models)의 UI를 통해 파일을 다운로드하려면 ↓ 아이콘을 클릭하세요. ![download-icon](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/download-icon.png) * [`PreTrainedModel.from_pretrained`]와 [`PreTrainedModel.save_pretrained`] 워크플로를 활용하세요. 1. 미리 [`PreTrainedModel.from_pretrained`]로 파일을 다운로드해두세요. ```py >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> tokenizer = AutoTokenizer.from_pretrained("bigscience/T0_3B") >>> model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0_3B") ``` 2. [`PreTrainedModel.save_pretrained`]로 지정된 경로에 파일을 저장해두세요. ```py >>> tokenizer.save_pretrained("./your/path/bigscience_t0") >>> model.save_pretrained("./your/path/bigscience_t0") ``` 3. 이제 오프라인일 때 [`PreTrainedModel.from_pretrained`]로 저장해뒀던 파일을 지정된 경로에서 다시 불러오세요. ```py >>> tokenizer = AutoTokenizer.from_pretrained("./your/path/bigscience_t0") >>> model = AutoModel.from_pretrained("./your/path/bigscience_t0") ``` * [huggingface_hub](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub) 라이브러리를 활용해서 파일을 다운로드하세요. 1. 가상환경에 `huggingface_hub` 라이브러리를 설치하세요. ```bash python -m pip install huggingface_hub ``` 2. [`hf_hub_download`](https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub) 함수로 파일을 특정 위치에 다운로드할 수 있습니다. 예를 들어 아래 명령은 [T0](https://huggingface.co/bigscience/T0_3B) 모델의 `config.json` 파일을 지정된 경로에 다운로드합니다. ```py >>> from huggingface_hub import hf_hub_download >>> hf_hub_download(repo_id="bigscience/T0_3B", filename="config.json", cache_dir="./your/path/bigscience_t0") ``` 파일을 다운로드하고 로컬에 캐시 해놓고 나면, 나중에 불러와 사용할 수 있도록 로컬 경로를 지정해두세요. ```py >>> from transformers import AutoConfig >>> config = AutoConfig.from_pretrained("./your/path/bigscience_t0/config.json") ``` <Tip> Hub에 저장된 파일을 다운로드하는 방법을 더 자세히 알아보려면 [Hub에서 파일 다운로드하기](https://huggingface.co/docs/hub/how-to-downstream) 섹션을 참고해주세요. </Tip>

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# 데이터 콜레이터(Data Collator)[[data-collator]] 데이터 콜레이터는 데이터셋 요소들의 리스트를 입력으로 사용하여 배치를 형성하는 객체입니다. 이러한 요소들은 `train_dataset` 또는 `eval_dataset의` 요소들과 동일한 타입 입니다. 배치를 구성하기 위해, 데이터 콜레이터는 (패딩과 같은) 일부 처리를 적용할 수 있습니다. [`DataCollatorForLanguageModeling`]과 같은 일부 콜레이터는 형성된 배치에 (무작위 마스킹과 같은) 일부 무작위 데이터 증강도 적용합니다. 사용 예시는 [예제 스크립트](../examples)나 [예제 노트북](../notebooks)에서 찾을 수 있습니다. ## 기본 데이터 콜레이터[[transformers.default_data_collator]] [[autodoc]] data.data_collator.default_data_collator ## DefaultDataCollator[[transformers.DefaultDataCollator]] [[autodoc]] data.data_collator.DefaultDataCollator ## DataCollatorWithPadding[[transformers.DataCollatorWithPadding]] [[autodoc]] data.data_collator.DataCollatorWithPadding ## DataCollatorForTokenClassification[[transformers.DataCollatorForTokenClassification]] [[autodoc]] data.data_collator.DataCollatorForTokenClassification ## DataCollatorForSeq2Seq[[transformers.DataCollatorForSeq2Seq]] [[autodoc]] data.data_collator.DataCollatorForSeq2Seq ## DataCollatorForLanguageModeling[[transformers.DataCollatorForLanguageModeling]] [[autodoc]] data.data_collator.DataCollatorForLanguageModeling - numpy_mask_tokens - tf_mask_tokens - torch_mask_tokens ## DataCollatorForWholeWordMask[[transformers.DataCollatorForWholeWordMask]] [[autodoc]] data.data_collator.DataCollatorForWholeWordMask - numpy_mask_tokens - tf_mask_tokens - torch_mask_tokens ## DataCollatorForPermutationLanguageModeling[[transformers.DataCollatorForPermutationLanguageModeling]] [[autodoc]] data.data_collator.DataCollatorForPermutationLanguageModeling - numpy_mask_tokens - tf_mask_tokens - torch_mask_tokens ## DataCollatorWithFlatteningtransformers.DataCollatorWithFlattening [[autodoc]] data.data_collator.DataCollatorWithFlattening

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# Gemma [[gemma]] ## 개요 [[overview]] Gemma 모델은 Google의 Gemma 팀이 작성한 [Gemma: Open Models Based on Gemini Technology and Research](https://blog.google/technology/developers/gemma-open-models/)에서 제안되었습니다. Gemma 모델은 6조 토큰으로 학습되었으며, 2b와 7b의 두 가지 버전으로 출시되었습니다. 논문의 초록은 다음과 같습니다: *이 연구는 언어 이해, 추론 및 안전성에 대한 학술 벤치마크에서 뛰어난 성능을 보이는 새로운 오픈 언어 모델 계열인 Gemma를 소개합니다. 우리는 두 가지 크기(20억 및 70억 매개변수)의 모델을 출시하며, 사전 학습된 체크포인트와 미세 조정된 체크포인트를 모두 제공합니다. Gemma는 18개의 텍스트 기반 작업 중 11개에서 유사한 크기의 오픈 모델을 능가하며, 우리는 모델 개발에 대한 상세한 설명과 함께 안전성과 책임 측면에 대한 종합적인 평가를 제공합니다. 우리는 LLM의 책임감 있는 공개가 최첨단 모델의 안전성을 향상시키고 다음 세대의 LLM 혁신을 가능하게 하는 데 중요하다고 믿습니다.* 팁: - 원본 체크포인트는 변환 스크립트 `src/transformers/models/gemma/convert_gemma_weights_to_hf.py`를 사용하여 변환할 수 있습니다. 이 모델은 [Arthur Zucker](https://huggingface.co/ArthurZ), [Younes Belkada](https://huggingface.co/ybelkada), [Sanchit Gandhi](https://huggingface.co/sanchit-gandhi), [Pedro Cuenca](https://huggingface.co/pcuenq)가 기여했습니다. ## GemmaConfig [[transformers.GemmaConfig]] [[autodoc]] GemmaConfig ## GemmaTokenizer [[transformers.GemmaTokenizer]] [[autodoc]] GemmaTokenizer ## GemmaTokenizerFast [[transformers.GemmaTokenizerFast]] [[autodoc]] GemmaTokenizerFast ## GemmaModel [[transformers.GemmaModel]] [[autodoc]] GemmaModel - forward ## GemmaForCausalLM [[transformers.GemmaForCausalLM]] [[autodoc]] GemmaForCausalLM - forward ## GemmaForSequenceClassification [[transformers.GemmaForSequenceClassification]] [[autodoc]] GemmaForSequenceClassification - forward ## GemmaForTokenClassification [[transformers.GemmaForTokenClassification]] [[autodoc]] GemmaForTokenClassification - forward ## FlaxGemmaModel [[transformers.FlaxGemmaModel]] [[autodoc]] FlaxGemmaModel - __call__ ## FlaxGemmaForCausalLM [[transformers.FlaxGemmaForCausalLM]] [[autodoc]] FlaxGemmaForCausalLM - __call__

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# RAG(검색 증강 생성) [[rag]] <div class="flex flex-wrap space-x-1"> <a href="https://huggingface.co/models?filter=rag"> <img alt="Models" src="https://img.shields.io/badge/All_model_pages-rag-blueviolet"> </a> </div> ## 개요 [[overview]] 검색 증강 생성(Retrieval-augmented generation, "RAG") 모델은 사전 훈련된 밀집 검색(DPR)과 시퀀스-투-시퀀스 모델의 장점을 결합합니다. RAG 모델은 문서를 검색하고, 이를 시퀀스-투-시퀀스 모델에 전달한 다음, 주변화(marginalization)를 통해 출력을 생성합니다. 검색기와 시퀀스-투-시퀀스 모듈은 사전 훈련된 모델로 초기화되며, 함께 미세 조정되어 검색과 생성 모두 다운스트림 작업(모델을 특정 태스크에 적용하는 것)에 적응할 수 있게 합니다. 이 모델은 Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela의 논문 [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)를 기반으로 합니다. 논문의 초록은 다음과 같습니다. *대규모 사전 훈련 언어 모델들은 그들의 매개변수에 사실적 지식을 저장하고 있으며, 다운스트림 NLP 작업에 대해 미세 조정될 때 최첨단 결과를 달성합니다. 그러나 지식에 접근하고 정확하게 조작하는 능력은 여전히 제한적이며, 따라서 지식 집약적 작업에서 그들의 성능은 작업별 아키텍처에 비해 뒤떨어집니다. 또한, 그들의 결정에 대한 근거를 제공하고 세계 지식을 업데이트하는 것은 여전히 열린 연구 문제로 남아 있습니다. 명시적 비매개변수 메모리에 대한 미분 가능한 접근 메커니즘을 가진 사전 훈련 모델은 이 문제를 극복할 수 있지만, 지금까지는 추출적 다운스트림 작업에 대해서만 연구되었습니다. 우리는 언어 생성을 위해 사전 훈련된 매개변수 및 비매개변수 메모리를 결합하는 모델인 검색 증강 생성(RAG)에 대한 일반적인 목적의 미세 조정 방법을 탐구합니다. 우리는 매개변수 메모리가 사전 훈련된 시퀀스-투-시퀀스 모델이고 비매개변수 메모리가 사전 훈련된 신경 검색기로 접근되는 위키피디아의 밀집 벡터 인덱스인 RAG 모델을 소개합니다. 우리는 생성된 전체 시퀀스에 걸쳐 동일한 검색된 구절을 조건으로 하는 RAG 공식과 토큰별로 다른 구절을 사용할 수 있는 RAG 공식을 비교합니다. 우리는 광범위한 지식 집약적 NLP 작업에 대해 모델을 미세 조정하고 평가하며, 매개변수 시퀀스-투-시퀀스 모델과 작업별 검색-추출 아키텍처를 능가하여 세 가지 개방형 도메인 QA 작업에서 최첨단 성능을 달성합니다. 언어 생성 작업의 경우, RAG 모델이 최첨단 매개변수 전용 시퀀스-투-시퀀스 기준선보다 더 구체적이고, 다양하며, 사실적인 언어를 생성한다는 것을 발견했습니다.* 이 모델은 [ola13](https://huggingface.co/ola13)에 의해 기여되었습니다. ## 사용 팁 [[usage-tips]] 검색 증강 생성(Retrieval-augmented generation, "RAG") 모델은 사전 훈련된 밀집 검색(DPR)과 시퀀스-투-시퀀스 모델의 강점을 결합합니다. RAG 모델은 문서를 검색하고, 이를 시퀀스-투-시퀀스 모델에 전달한 다음, 주변화(marginalization)를 통해 출력을 생성합니다. 검색기와 시퀀스-투-시퀀스 모듈은 사전 훈련된 모델로 초기화되며, 함께 미세 조정됩니다. 이를 통해 검색과 생성 모두 다운스트림 작업에 적응할 수 있게 됩니다. ## RagConfig [[transformers.RagConfig]] [[autodoc]] RagConfig ## RagTokenizer [[transformers.RagTokenizer]] [[autodoc]] RagTokenizer ## Rag specific outputs [[transformers.models.rag.modeling_rag.RetrievAugLMMarginOutput]] [[autodoc]] models.rag.modeling_rag.RetrievAugLMMarginOutput [[autodoc]] models.rag.modeling_rag.RetrievAugLMOutput ## RagRetriever [[transformers.RagRetriever]] [[autodoc]] RagRetriever <frameworkcontent> <pt> ## RagModel [[transformers.RagModel]] [[autodoc]] RagModel - forward ## RagSequenceForGeneration [[transformers.RagSequenceForGeneration]] [[autodoc]] RagSequenceForGeneration - forward - generate ## RagTokenForGeneration [[transformers.RagTokenForGeneration]] [[autodoc]] RagTokenForGeneration - forward - generate </pt> <tf> ## TFRagModel [[transformers.TFRagModel]] [[autodoc]] TFRagModel - call ## TFRagSequenceForGeneration [[transformers.TFRagSequenceForGeneration]] [[autodoc]] TFRagSequenceForGeneration - call - generate ## TFRagTokenForGeneration [[transformers.TFRagTokenForGeneration]] [[autodoc]] TFRagTokenForGeneration - call - generate </tf> </frameworkcontent>

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# 🤗 PEFT로 어댑터 가져오기 [[load-adapters-with-peft]] [[open-in-colab]] [Parameter-Efficient Fine Tuning (PEFT)](https://huggingface.co/blog/peft) 방법은 사전훈련된 모델의 매개변수를 미세 조정 중 고정시키고, 그 위에 훈련할 수 있는 매우 적은 수의 매개변수(어댑터)를 추가합니다. 어댑터는 작업별 정보를 학습하도록 훈련됩니다. 이 접근 방식은 완전히 미세 조정된 모델에 필적하는 결과를 생성하면서, 메모리 효율적이고 비교적 적은 컴퓨팅 리소스를 사용합니다. 또한 PEFT로 훈련된 어댑터는 일반적으로 전체 모델보다 훨씬 작기 때문에 공유, 저장 및 가져오기가 편리합니다. <div class="flex flex-col justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png"/> <figcaption class="text-center">Hub에 저장된 OPTForCausalLM 모델의 어댑터 가중치는 최대 700MB에 달하는 모델 가중치의 전체 크기에 비해 약 6MB에 불과합니다.</figcaption> </div> 🤗 PEFT 라이브러리에 대해 자세히 알아보려면 [문서](https://huggingface.co/docs/peft/index)를 확인하세요. ## 설정 [[setup]] 🤗 PEFT를 설치하여 시작하세요: ```bash pip install peft ``` 새로운 기능을 사용해보고 싶다면, 다음 소스에서 라이브러리를 설치하는 것이 좋습니다: ```bash pip install git+https://github.com/huggingface/peft.git ``` ## 지원되는 PEFT 모델 [[supported-peft-models]] 🤗 Transformers는 기본적으로 일부 PEFT 방법을 지원하며, 로컬이나 Hub에 저장된 어댑터 가중치를 가져오고 몇 줄의 코드만으로 쉽게 실행하거나 훈련할 수 있습니다. 다음 방법을 지원합니다: - [Low Rank Adapters](https://huggingface.co/docs/peft/conceptual_guides/lora) - [IA3](https://huggingface.co/docs/peft/conceptual_guides/ia3) - [AdaLoRA](https://arxiv.org/abs/2303.10512) 🤗 PEFT와 관련된 다른 방법(예: 프롬프트 훈련 또는 프롬프트 튜닝) 또는 일반적인 🤗 PEFT 라이브러리에 대해 자세히 알아보려면 [문서](https://huggingface.co/docs/peft/index)를 참조하세요. ## PEFT 어댑터 가져오기 [[load-a-peft-adapter]] 🤗 Transformers에서 PEFT 어댑터 모델을 가져오고 사용하려면 Hub 저장소나 로컬 디렉터리에 `adapter_config.json` 파일과 어댑터 가중치가 포함되어 있는지 확인하십시오. 그런 다음 `AutoModelFor` 클래스를 사용하여 PEFT 어댑터 모델을 가져올 수 있습니다. 예를 들어 인과 관계 언어 모델용 PEFT 어댑터 모델을 가져오려면 다음 단계를 따르십시오: 1. PEFT 모델 ID를 지정하십시오. 2. [`AutoModelForCausalLM`] 클래스에 전달하십시오. ```py from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "ybelkada/opt-350m-lora" model = AutoModelForCausalLM.from_pretrained(peft_model_id) ``` <Tip> `AutoModelFor` 클래스나 기본 모델 클래스(예: `OPTForCausalLM` 또는 `LlamaForCausalLM`) 중 하나를 사용하여 PEFT 어댑터를 가져올 수 있습니다. </Tip> `load_adapter` 메소드를 호출하여 PEFT 어댑터를 가져올 수도 있습니다. ```py from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "facebook/opt-350m" peft_model_id = "ybelkada/opt-350m-lora" model = AutoModelForCausalLM.from_pretrained(model_id) model.load_adapter(peft_model_id) ``` ## 8비트 또는 4비트로 가져오기 [[load-in-8bit-or-4bit]] `bitsandbytes` 통합은 8비트와 4비트 정밀도 데이터 유형을 지원하므로 큰 모델을 가져올 때 유용하면서 메모리도 절약합니다. 모델을 하드웨어에 효과적으로 분배하려면 [`~PreTrainedModel.from_pretrained`]에 `load_in_8bit` 또는 `load_in_4bit` 매개변수를 추가하고 `device_map="auto"`를 설정하세요: ```py from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig peft_model_id = "ybelkada/opt-350m-lora" model = AutoModelForCausalLM.from_pretrained(peft_model_id, quantization_config=BitsAndBytesConfig(load_in_8bit=True)) ``` ## 새 어댑터 추가 [[add-a-new-adapter]] 새 어댑터가 현재 어댑터와 동일한 유형인 경우에 한해 기존 어댑터가 있는 모델에 새 어댑터를 추가하려면 [`~peft.PeftModel.add_adapter`]를 사용할 수 있습니다. 예를 들어 모델에 기존 LoRA 어댑터가 연결되어 있는 경우: ```py from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer from peft import PeftConfig model_id = "facebook/opt-350m" model = AutoModelForCausalLM.from_pretrained(model_id) lora_config = LoraConfig( target_modules=["q_proj", "k_proj"], init_lora_weights=False ) model.add_adapter(lora_config, adapter_name="adapter_1") ``` 새 어댑터를 추가하려면: ```py # attach new adapter with same config model.add_adapter(lora_config, adapter_name="adapter_2") ``` 이제 [`~peft.PeftModel.set_adapter`]를 사용하여 어댑터를 사용할 어댑터로 설정할 수 있습니다: ```py # use adapter_1 model.set_adapter("adapter_1") output = model.generate(**inputs) print(tokenizer.decode(output_disabled[0], skip_special_tokens=True)) # use adapter_2 model.set_adapter("adapter_2") output_enabled = model.generate(**inputs) print(tokenizer.decode(output_enabled[0], skip_special_tokens=True)) ``` ## 어댑터 활성화 및 비활성화 [[enable-and-disable-adapters]] 모델에 어댑터를 추가한 후 어댑터 모듈을 활성화 또는 비활성화할 수 있습니다. 어댑터 모듈을 활성화하려면: ```py from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer from peft import PeftConfig model_id = "facebook/opt-350m" adapter_model_id = "ybelkada/opt-350m-lora" tokenizer = AutoTokenizer.from_pretrained(model_id) text = "Hello" inputs = tokenizer(text, return_tensors="pt") model = AutoModelForCausalLM.from_pretrained(model_id) peft_config = PeftConfig.from_pretrained(adapter_model_id) # to initiate with random weights peft_config.init_lora_weights = False model.add_adapter(peft_config) model.enable_adapters() output = model.generate(**inputs) ``` 어댑터 모듈을 비활성화하려면: ```py model.disable_adapters() output = model.generate(**inputs) ``` ## PEFT 어댑터 훈련 [[train-a-peft-adapter]] PEFT 어댑터는 [`Trainer`] 클래스에서 지원되므로 특정 사용 사례에 맞게 어댑터를 훈련할 수 있습니다. 몇 줄의 코드를 추가하기만 하면 됩니다. 예를 들어 LoRA 어댑터를 훈련하려면: <Tip> [`Trainer`]를 사용하여 모델을 미세 조정하는 것이 익숙하지 않다면 [사전훈련된 모델을 미세 조정하기](training) 튜토리얼을 확인하세요. </Tip> 1. 작업 유형 및 하이퍼파라미터를 지정하여 어댑터 구성을 정의합니다. 하이퍼파라미터에 대한 자세한 내용은 [`~peft.LoraConfig`]를 참조하세요. ```py from peft import LoraConfig peft_config = LoraConfig( lora_alpha=16, lora_dropout=0.1, r=64, bias="none", task_type="CAUSAL_LM", ) ``` 2. 모델에 어댑터를 추가합니다. ```py model.add_adapter(peft_config) ``` 3. 이제 모델을 [`Trainer`]에 전달할 수 있습니다! ```py trainer = Trainer(model=model, ...) trainer.train() ``` 훈련한 어댑터를 저장하고 다시 가져오려면: ```py model.save_pretrained(save_dir) model = AutoModelForCausalLM.from_pretrained(save_dir) ```

transformers/docs/source/ko/peft.md/0

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# AWQ [[awq]] <Tip> 이 [노트북](https://colab.research.google.com/drive/1HzZH89yAXJaZgwJDhQj9LqSBux932BvY) 으로 AWQ 양자화를 실습해보세요 ! </Tip> [Activation-aware Weight Quantization (AWQ)](https://hf.co/papers/2306.00978)은 모델의 모든 가중치를 양자화하지 않고, LLM 성능에 중요한 가중치를 유지합니다. 이로써 4비트 정밀도로 모델을 실행해도 성능 저하 없이 양자화 손실을 크게 줄일 수 있습니다. AWQ 알고리즘을 사용하여 모델을 양자화할 수 있는 여러 라이브러리가 있습니다. 예를 들어 [llm-awq](https://github.com/mit-han-lab/llm-awq), [autoawq](https://github.com/casper-hansen/AutoAWQ) , [optimum-intel](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc) 등이 있습니다. Transformers는 llm-awq, autoawq 라이브러리를 이용해 양자화된 모델을 가져올 수 있도록 지원합니다. 이 가이드에서는 autoawq로 양자화된 모델을 가져오는 방법을 보여드리나, llm-awq로 양자화된 모델의 경우도 유사한 절차를 따릅니다. autoawq가 설치되어 있는지 확인하세요: ```bash pip install autoawq ``` AWQ 양자화된 모델은 해당 모델의 [config.json](https://huggingface.co/TheBloke/zephyr-7B-alpha-AWQ/blob/main/config.json) 파일의 `quantization_config` 속성을 통해 식별할 수 있습니다.: ```json { "_name_or_path": "/workspace/process/huggingfaceh4_zephyr-7b-alpha/source", "architectures": [ "MistralForCausalLM" ], ... ... ... "quantization_config": { "quant_method": "awq", "zero_point": true, "group_size": 128, "bits": 4, "version": "gemm" } } ``` 양자화된 모델은 [`~PreTrainedModel.from_pretrained`] 메서드를 사용하여 가져옵니다. 모델을 CPU에 가져왔다면, 먼저 모델을 GPU 장치로 옮겨야 합니다. `device_map` 파라미터를 사용하여 모델을 배치할 위치를 지정하세요: ```py from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "TheBloke/zephyr-7B-alpha-AWQ" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0") ``` AWQ 양자화 모델을 가져오면 자동으로 성능상의 이유로 인해 가중치들의 기본값이 fp16으로 설정됩니다. 가중치를 다른 형식으로 가져오려면, `torch_dtype` 파라미터를 사용하세요: ```py from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "TheBloke/zephyr-7B-alpha-AWQ" model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32) ``` 추론을 더욱 가속화하기 위해 AWQ 양자화와 [FlashAttention-2](../perf_infer_gpu_one#flashattention-2) 를 결합 할 수 있습니다: ```py from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-alpha-AWQ", attn_implementation="flash_attention_2", device_map="cuda:0") ``` ## 퓨즈된 모듈 [[fused-modules]] 퓨즈된 모듈은 정확도와 성능을 개선합니다. 퓨즈된 모듈은 [Llama](https://huggingface.co/meta-llama) 아키텍처와 [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) 아키텍처의 AWQ모듈에 기본적으로 지원됩니다. 그러나 지원되지 않는 아키텍처에 대해서도 AWQ 모듈을 퓨즈할 수 있습니다. <Tip warning={true}> 퓨즈된 모듈은 FlashAttention-2와 같은 다른 최적화 기술과 결합할 수 없습니다. </Tip> <hfoptions id="fuse"> <hfoption id="supported architectures"> 지원되는 아키텍처에서 퓨즈된 모듈을 활성화하려면, [`AwqConfig`] 를 생성하고 매개변수 `fuse_max_seq_len` 과 `do_fuse=True`를 설정해야 합니다. `fuse_max_seq_len` 매개변수는 전체 시퀀스 길이로, 컨텍스트 길이와 예상 생성 길이를 포함해야 합니다. 안전하게 사용하기 위해 더 큰 값으로 설정할 수 있습니다. 예를 들어, [TheBloke/Mistral-7B-OpenOrca-AWQ](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ) 모델의 AWQ 모듈을 퓨즈해보겠습니다. ```python import torch from transformers import AwqConfig, AutoModelForCausalLM model_id = "TheBloke/Mistral-7B-OpenOrca-AWQ" quantization_config = AwqConfig( bits=4, fuse_max_seq_len=512, do_fuse=True, ) model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config).to(0) ``` [TheBloke/Mistral-7B-OpenOrca-AWQ](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ) 모델은 퓨즈된 모듈이 있는 경우와 없는 경우 모두 `batch_size=1` 로 성능 평가되었습니다. <figcaption class="text-center text-gray-500 text-lg">퓨즈되지 않은 모듈</figcaption> | 배치 크기 | 프리필 길이 | 디코드 길이 | 프리필 토큰/초 | 디코드 토큰/초 | 메모리 (VRAM) | |-------------:|-----------------:|----------------:|-------------------:|------------------:|:----------------| | 1 | 32 | 32 | 60.0984 | 38.4537 | 4.50 GB (5.68%) | | 1 | 64 | 64 | 1333.67 | 31.6604 | 4.50 GB (5.68%) | | 1 | 128 | 128 | 2434.06 | 31.6272 | 4.50 GB (5.68%) | | 1 | 256 | 256 | 3072.26 | 38.1731 | 4.50 GB (5.68%) | | 1 | 512 | 512 | 3184.74 | 31.6819 | 4.59 GB (5.80%) | | 1 | 1024 | 1024 | 3148.18 | 36.8031 | 4.81 GB (6.07%) | | 1 | 2048 | 2048 | 2927.33 | 35.2676 | 5.73 GB (7.23%) | <figcaption class="text-center text-gray-500 text-lg">퓨즈된 모듈</figcaption> | 배치 크기 | 프리필 길이 | 디코드 길이 | 프리필 토큰/초 | 디코드 토큰/초 | 메모리 (VRAM) | |-------------:|-----------------:|----------------:|-------------------:|------------------:|:----------------| | 1 | 32 | 32 | 81.4899 | 80.2569 | 4.00 GB (5.05%) | | 1 | 64 | 64 | 1756.1 | 106.26 | 4.00 GB (5.05%) | | 1 | 128 | 128 | 2479.32 | 105.631 | 4.00 GB (5.06%) | | 1 | 256 | 256 | 1813.6 | 85.7485 | 4.01 GB (5.06%) | | 1 | 512 | 512 | 2848.9 | 97.701 | 4.11 GB (5.19%) | | 1 | 1024 | 1024 | 3044.35 | 87.7323 | 4.41 GB (5.57%) | | 1 | 2048 | 2048 | 2715.11 | 89.4709 | 5.57 GB (7.04%) | 퓨즈된 모듈 및 퓨즈되지 않은 모듈의 속도와 처리량은 [optimum-benchmark](https://github.com/huggingface/optimum-benchmark)라이브러리를 사용하여 테스트 되었습니다. <div class="flex gap-4"> <div> <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/fused_forward_memory_plot.png" alt="generate throughput per batch size" /> <figcaption class="mt-2 text-center text-sm text-gray-500">포워드 피크 메모리 (forward peak memory)/배치 크기</figcaption> </div> <div> <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/fused_generate_throughput_plot.png" alt="forward latency per batch size" /> <figcaption class="mt-2 text-center text-sm text-gray-500"> 생성 처리량/배치크기</figcaption> </div> </div> </hfoption> <hfoption id="unsupported architectures"> 퓨즈된 모듈을 지원하지 않는 아키텍처의 경우, `modules_to_fuse` 매개변수를 사용해 직접 퓨즈 매핑을 만들어 어떤 모듈을 퓨즈할지 정의해야합니다. 예로, [TheBloke/Yi-34B-AWQ](https://huggingface.co/TheBloke/Yi-34B-AWQ) 모델의 AWQ 모듈을 퓨즈하는 방법입니다. ```python import torch from transformers import AwqConfig, AutoModelForCausalLM model_id = "TheBloke/Yi-34B-AWQ" quantization_config = AwqConfig( bits=4, fuse_max_seq_len=512, modules_to_fuse={ "attention": ["q_proj", "k_proj", "v_proj", "o_proj"], "layernorm": ["ln1", "ln2", "norm"], "mlp": ["gate_proj", "up_proj", "down_proj"], "use_alibi": False, "num_attention_heads": 56, "num_key_value_heads": 8, "hidden_size": 7168 } ) model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config).to(0) ``` `modules_to_fuse` 매개변수는 다음을 포함해야 합니다: - `"attention"`: 어텐션 레이어는 다음 순서로 퓨즈하세요 : 쿼리 (query), 키 (key), 값 (value) , 출력 프로젝션 계층 (output projection layer). 해당 레이어를 퓨즈하지 않으려면 빈 리스트를 전달하세요. - `"layernorm"`: 사용자 정의 퓨즈 레이어 정규화로 교할 레이어 정규화 레이어명. 해당 레이어를 퓨즈하지 않으려면 빈 리스트를 전달하세요. - `"mlp"`: 단일 MLP 레이어로 퓨즈할 MLP 레이어 순서 : (게이트 (gate) (덴스(dense), 레이어(layer), 포스트 어텐션(post-attention)) / 위 / 아래 레이어). - `"use_alibi"`: 모델이 ALiBi positional embedding을 사용할 경우 설정합니다. - `"num_attention_heads"`: 어텐션 헤드 (attention heads)의 수를 설정합니다. - `"num_key_value_heads"`: 그룹화 쿼리 어텐션 (GQA)을 구현하는데 사용되는 키 값 헤드의 수를 설정합니다. `num_key_value_heads=num_attention_heads`로 설정할 경우, 모델은 다중 헤드 어텐션 (MHA)가 사용되며, `num_key_value_heads=1` 는 다중 쿼리 어텐션 (MQA)가, 나머지는 GQA가 사용됩니다. - `"hidden_size"`: 숨겨진 표현(hidden representations)의 차원을 설정합니다. </hfoption> </hfoptions> ## ExLlama-v2 서포트 [[exllama-v2-support]] 최신 버전 `autoawq`는 빠른 프리필과 디코딩을 위해 ExLlama-v2 커널을 지원합니다. 시작하기 위해 먼저 최신 버전 `autoawq` 를 설치하세요 : ```bash pip install git+https://github.com/casper-hansen/AutoAWQ.git ``` 매개변수를 `version="exllama"`로 설정해 `AwqConfig()`를 생성하고 모델에 넘겨주세요. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig quantization_config = AwqConfig(version="exllama") model = AutoModelForCausalLM.from_pretrained( "TheBloke/Mistral-7B-Instruct-v0.1-AWQ", quantization_config=quantization_config, device_map="auto", ) input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device="cuda") output = model(input_ids) print(output.logits) tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-AWQ") input_ids = tokenizer.encode("How to make a cake", return_tensors="pt").to(model.device) output = model.generate(input_ids, do_sample=True, max_length=50, pad_token_id=50256) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` <Tip warning={true}> 이 기능은 AMD GPUs에서 지원됩니다. </Tip>

transformers/docs/source/ko/quantization/awq.md/0

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# 이미지 특징 추출[[image-feature-extraction]] [[open-in-colab]] 이미지 특징 추출은 주어진 이미지에서 의미론적으로 의미 있는 특징을 추출하는 작업입니다. 이는 이미지 유사성 및 이미지 검색 등 다양한 사용 사례가 있습니다. 게다가 대부분의 컴퓨터 비전 모델은 이미지 특징 추출에 사용할 수 있으며, 여기서 작업 특화 헤드(이미지 분류, 물체 감지 등)를 제거하고 특징을 얻을 수 있습니다. 이러한 특징은 가장자리 감지, 모서리 감지 등 고차원 수준에서 매우 유용합니다. 또한 모델의 깊이에 따라 실제 세계에 대한 정보(예: 고양이가 어떻게 생겼는지)를 포함할 수도 있습니다. 따라서 이러한 출력은 특정 데이터 세트에 대한 새로운 분류기를 훈련하는 데 사용할 수 있습니다. 이 가이드에서는: - `image-feature-extraction` 파이프라인을 활용하여 간단한 이미지 유사성 시스템을 구축하는 방법을 배웁니다. - 기본 모델 추론으로 동일한 작업을 수행합니다. ## `image-feature-extraction` 파이프라인을 이용한 이미지 유사성[[image-similarity-using-image-feature-extraction-pipeline]] 물고기 그물 위에 앉아 있는 두 장의 고양이 사진이 있습니다. 이 중 하나는 생성된 이미지입니다. ```python from PIL import Image import requests img_urls = ["https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.jpeg"] image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB") image_gen = Image.open(requests.get(img_urls[1], stream=True).raw).convert("RGB") ``` 파이프라인을 실행해 봅시다. 먼저 파이프라인을 초기화하세요. 모델을 지정하지 않으면, 파이프라인은 자동으로 [google/vit-base-patch16-224](google/vit-base-patch16-224) 모델로 초기화됩니다. 유사도를 계산하려면 `pool`을 True로 설정하세요. ```python import torch from transformers import pipeline DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-384", device=DEVICE, pool=True) ``` `pipe`를 사용하여 추론하려면 두 이미지를 모두 전달하세요. ```python outputs = pipe([image_real, image_gen]) ``` 출력에는 두 이미지의 풀링된(pooled) 임베딩이 포함되어 있습니다. ```python # 단일 출력의 길이 구하기 print(len(outputs[0][0])) # 출력 결과 표시하기 print(outputs) # 768 # [[[-0.03909236937761307, 0.43381670117378235, -0.06913255900144577, ``` 유사도 점수를 얻으려면, 이들을 유사도 함수에 전달해야 합니다. ```python from torch.nn.functional import cosine_similarity similarity_score = cosine_similarity(torch.Tensor(outputs[0]), torch.Tensor(outputs[1]), dim=1) print(similarity_score) # tensor([0.6043]) ``` 풀링 이전의 마지막 은닉 상태를 얻고 싶다면, `pool` 매개변수에 아무 값도 전달하지 마세요. 또한, 기본값은 `False`로 설정되어 있습니다. 이 은닉 상태는 모델의 특징을 기반으로 새로운 분류기나 모델을 훈련시키는 데 유용합니다. ```python pipe = pipeline(task="image-feature-extraction", model_name="google/vit-base-patch16-224", device=DEVICE) output = pipe(image_real) ``` 아직 출력이 풀링되지 않았기 때문에, 첫 번째 차원은 배치 크기이고 마지막 두 차원은 임베딩 형태인 마지막 은닉 상태를 얻을 수 있습니다. ```python import numpy as np print(np.array(outputs).shape) # (1, 197, 768) ``` ## `AutoModel`을 사용하여 특징과 유사성 얻기[[getting-features-and-similarities-using-automodel]] transformers의 `AutoModel` 클래스를 사용하여 특징을 얻을 수도 있습니다. `AutoModel`은 작업 특화 헤드 없이 모든 transformers 모델을 로드할 수 있으며, 이를 통해 특징을 추출할 수 있습니다. ```python from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") model = AutoModel.from_pretrained("google/vit-base-patch16-224").to(DEVICE) ``` 추론을 위한 간단한 함수를 작성해 보겠습니다. 먼저 입력값을 `processor`에 전달한 다음, 그 출력값을 `model`에 전달할 것입니다. ```python def infer(image): inputs = processor(image, return_tensors="pt").to(DEVICE) outputs = model(**inputs) return outputs.pooler_output ``` 이 함수에 이미지를 직접 전달하여 임베딩을 얻을 수 있습니다. ```python embed_real = infer(image_real) embed_gen = infer(image_gen) ``` 그리고 이 임베딩을 사용하여 다시 유사도를 계산할 수 있습니다. ```python from torch.nn.functional import cosine_similarity similarity_score = cosine_similarity(embed_real, embed_gen, dim=1) print(similarity_score) # tensor([0.6061], device='cuda:0', grad_fn=<SumBackward1>) ```

transformers/docs/source/ko/tasks/image_feature_extraction.md/0

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# 영상 분류 [[video-classification]] [[open-in-colab]] 영상 분류는 영상 전체에 레이블 또는 클래스를 지정하는 작업입니다. 각 영상에는 하나의 클래스가 있을 것으로 예상됩니다. 영상 분류 모델은 영상을 입력으로 받아 어느 클래스에 속하는지에 대한 예측을 반환합니다. 이러한 모델은 영상이 어떤 내용인지 분류하는 데 사용될 수 있습니다. 영상 분류의 실제 응용 예는 피트니스 앱에서 유용한 동작 / 운동 인식 서비스가 있습니다. 이는 또한 시각 장애인이 이동할 때 보조하는데 사용될 수 있습니다 이 가이드에서는 다음을 수행하는 방법을 보여줍니다: 1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하기. 2. 미세 조정한 모델을 추론에 사용하기. <Tip> 이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/video-classification)를 확인하는 것이 좋습니다. </Tip> 시작하기 전에 필요한 모든 라이브러리가 설치되었는지 확인하세요: ```bash pip install -q pytorchvideo transformers evaluate ``` 영상을 처리하고 준비하기 위해 [PyTorchVideo](https://pytorchvideo.org/)(이하 `pytorchvideo`)를 사용합니다. 커뮤니티에 모델을 업로드하고 공유할 수 있도록 Hugging Face 계정에 로그인하는 것을 권장합니다. 프롬프트가 나타나면 토큰을 입력하여 로그인하세요: ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## UCF101 데이터셋 불러오기 [[load-ufc101-dataset]] [UCF-101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합(subset)을 불러오는 것으로 시작할 수 있습니다. 전체 데이터 세트를 학습하는데 더 많은 시간을 할애하기 전에 데이터의 하위 집합을 불러와 모든 것이 잘 작동하는지 실험하고 확인할 수 있습니다. ```py >>> from huggingface_hub import hf_hub_download >>> hf_dataset_identifier = "sayakpaul/ucf101-subset" >>> filename = "UCF101_subset.tar.gz" >>> file_path = hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type="dataset") ``` 데이터 세트의 하위 집합이 다운로드 되면, 압축된 파일의 압축을 해제해야 합니다: ```py >>> import tarfile >>> with tarfile.open(file_path) as t: ... t.extractall(".") ``` 전체 데이터 세트는 다음과 같이 구성되어 있습니다. ```bash UCF101_subset/ train/ BandMarching/ video_1.mp4 video_2.mp4 ... Archery video_1.mp4 video_2.mp4 ... ... val/ BandMarching/ video_1.mp4 video_2.mp4 ... Archery video_1.mp4 video_2.mp4 ... ... test/ BandMarching/ video_1.mp4 video_2.mp4 ... Archery video_1.mp4 video_2.mp4 ... ... ``` 정렬된 영상의 경로는 다음과 같습니다: ```bash ... 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c04.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c06.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c01.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c02.avi', 'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c06.avi' ... ``` 동일한 그룹/장면에 속하는 영상 클립은 파일 경로에서 `g`로 표시되어 있습니다. 예를 들면, `v_ApplyEyeMakeup_g07_c04.avi`와 `v_ApplyEyeMakeup_g07_c06.avi` 이 있습니다. 이 둘은 같은 그룹입니다. 검증 및 평가 데이터 분할을 할 때, [데이터 누출(data leakage)](https://www.kaggle.com/code/alexisbcook/data-leakage)을 방지하기 위해 동일한 그룹 / 장면의 영상 클립을 사용하지 않아야 합니다. 이 튜토리얼에서 사용하는 하위 집합은 이러한 정보를 고려하고 있습니다. 그 다음으로, 데이터 세트에 존재하는 라벨을 추출합니다. 또한, 모델을 초기화할 때 도움이 될 딕셔너리(dictionary data type)를 생성합니다. * `label2id`: 클래스 이름을 정수에 매핑합니다. * `id2label`: 정수를 클래스 이름에 매핑합니다. ```py >>> class_labels = sorted({str(path).split("/")[2] for path in all_video_file_paths}) >>> label2id = {label: i for i, label in enumerate(class_labels)} >>> id2label = {i: label for label, i in label2id.items()} >>> print(f"Unique classes: {list(label2id.keys())}.") # Unique classes: ['ApplyEyeMakeup', 'ApplyLipstick', 'Archery', 'BabyCrawling', 'BalanceBeam', 'BandMarching', 'BaseballPitch', 'Basketball', 'BasketballDunk', 'BenchPress']. ``` 이 데이터 세트에는 총 10개의 고유한 클래스가 있습니다. 각 클래스마다 30개의 영상이 훈련 세트에 있습니다 ## 미세 조정하기 위해 모델 가져오기 [[load-a-model-to-fine-tune]] 사전 훈련된 체크포인트와 체크포인트에 연관된 이미지 프로세서를 사용하여 영상 분류 모델을 인스턴스화합니다. 모델의 인코더에는 미리 학습된 매개변수가 제공되며, 분류 헤드(데이터를 분류하는 마지막 레이어)는 무작위로 초기화됩니다. 데이터 세트의 전처리 파이프라인을 작성할 때는 이미지 프로세서가 유용합니다. ```py >>> from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification >>> model_ckpt = "MCG-NJU/videomae-base" >>> image_processor = VideoMAEImageProcessor.from_pretrained(model_ckpt) >>> model = VideoMAEForVideoClassification.from_pretrained( ... model_ckpt, ... label2id=label2id, ... id2label=id2label, ... ignore_mismatched_sizes=True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint ... ) ``` 모델을 가져오는 동안, 다음과 같은 경고를 마주칠 수 있습니다: ```bash Some weights of the model checkpoint at MCG-NJU/videomae-base were not used when initializing VideoMAEForVideoClassification: [..., 'decoder.decoder_layers.1.attention.output.dense.bias', 'decoder.decoder_layers.2.attention.attention.key.weight'] - This IS expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` 위 경고는 우리가 일부 가중치(예: `classifier` 층의 가중치와 편향)를 버리고 새로운 `classifier` 층의 가중치와 편향을 무작위로 초기화하고 있다는 것을 알려줍니다. 이 경우에는 미리 학습된 가중치가 없는 새로운 헤드를 추가하고 있으므로, 라이브러리가 모델을 추론에 사용하기 전에 미세 조정하라고 경고를 보내는 것은 당연합니다. 그리고 이제 우리는 이 모델을 미세 조정할 예정입니다. **참고** 이 [체크포인트](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics)는 도메인이 많이 중첩된 유사한 다운스트림 작업에 대해 미세 조정하여 얻은 체크포인트이므로 이 작업에서 더 나은 성능을 보일 수 있습니다. `MCG-NJU/videomae-base-finetuned-kinetics` 데이터 세트를 미세 조정하여 얻은 [체크포인트](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset)도 있습니다. ## 훈련을 위한 데이터 세트 준비하기[[prepare-the-datasets-for-training]] 영상 전처리를 위해 [PyTorchVideo 라이브러리](https://pytorchvideo.org/)를 활용할 것입니다. 필요한 종속성을 가져오는 것으로 시작하세요. ```py >>> import pytorchvideo.data >>> from pytorchvideo.transforms import ( ... ApplyTransformToKey, ... Normalize, ... RandomShortSideScale, ... RemoveKey, ... ShortSideScale, ... UniformTemporalSubsample, ... ) >>> from torchvision.transforms import ( ... Compose, ... Lambda, ... RandomCrop, ... RandomHorizontalFlip, ... Resize, ... ) ``` 학습 데이터 세트 변환에는 '균일한 시간 샘플링(uniform temporal subsampling)', '픽셀 정규화(pixel normalization)', '랜덤 잘라내기(random cropping)' 및 '랜덤 수평 뒤집기(random horizontal flipping)'의 조합을 사용합니다. 검증 및 평가 데이터 세트 변환에는 '랜덤 잘라내기'와 '랜덤 뒤집기'를 제외한 동일한 변환 체인을 유지합니다. 이러한 변환에 대해 자세히 알아보려면 [PyTorchVideo 공식 문서](https://pytorchvideo.org)를 확인하세요. 사전 훈련된 모델과 관련된 이미지 프로세서를 사용하여 다음 정보를 얻을 수 있습니다: * 영상 프레임 픽셀을 정규화하는 데 사용되는 이미지 평균과 표준 편차 * 영상 프레임이 조정될 공간 해상도 먼저, 몇 가지 상수를 정의합니다. ```py >>> mean = image_processor.image_mean >>> std = image_processor.image_std >>> if "shortest_edge" in image_processor.size: ... height = width = image_processor.size["shortest_edge"] >>> else: ... height = image_processor.size["height"] ... width = image_processor.size["width"] >>> resize_to = (height, width) >>> num_frames_to_sample = model.config.num_frames >>> sample_rate = 4 >>> fps = 30 >>> clip_duration = num_frames_to_sample * sample_rate / fps ``` 이제 데이터 세트에 특화된 전처리(transform)과 데이터 세트 자체를 정의합니다. 먼저 훈련 데이터 세트로 시작합니다: ```py >>> train_transform = Compose( ... [ ... ApplyTransformToKey( ... key="video", ... transform=Compose( ... [ ... UniformTemporalSubsample(num_frames_to_sample), ... Lambda(lambda x: x / 255.0), ... Normalize(mean, std), ... RandomShortSideScale(min_size=256, max_size=320), ... RandomCrop(resize_to), ... RandomHorizontalFlip(p=0.5), ... ] ... ), ... ), ... ] ... ) >>> train_dataset = pytorchvideo.data.Ucf101( ... data_path=os.path.join(dataset_root_path, "train"), ... clip_sampler=pytorchvideo.data.make_clip_sampler("random", clip_duration), ... decode_audio=False, ... transform=train_transform, ... ) ``` 같은 방식의 작업 흐름을 검증과 평가 세트에도 적용할 수 있습니다. ```py >>> val_transform = Compose( ... [ ... ApplyTransformToKey( ... key="video", ... transform=Compose( ... [ ... UniformTemporalSubsample(num_frames_to_sample), ... Lambda(lambda x: x / 255.0), ... Normalize(mean, std), ... Resize(resize_to), ... ] ... ), ... ), ... ] ... ) >>> val_dataset = pytorchvideo.data.Ucf101( ... data_path=os.path.join(dataset_root_path, "val"), ... clip_sampler=pytorchvideo.data.make_clip_sampler("uniform", clip_duration), ... decode_audio=False, ... transform=val_transform, ... ) >>> test_dataset = pytorchvideo.data.Ucf101( ... data_path=os.path.join(dataset_root_path, "test"), ... clip_sampler=pytorchvideo.data.make_clip_sampler("uniform", clip_duration), ... decode_audio=False, ... transform=val_transform, ... ) ``` **참고**: 위의 데이터 세트의 파이프라인은 [공식 파이토치 예제](https://pytorchvideo.org/docs/tutorial_classification#dataset)에서 가져온 것입니다. 우리는 UCF-101 데이터셋에 맞게 [`pytorchvideo.data.Ucf101()`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.Ucf101) 함수를 사용하고 있습니다. 내부적으로 이 함수는 [`pytorchvideo.data.labeled_video_dataset.LabeledVideoDataset`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.LabeledVideoDataset) 객체를 반환합니다. `LabeledVideoDataset` 클래스는 PyTorchVideo 데이터셋에서 모든 영상 관련 작업의 기본 클래스입니다. 따라서 PyTorchVideo에서 미리 제공하지 않는 사용자 지정 데이터 세트를 사용하려면, 이 클래스를 적절하게 확장하면 됩니다. 더 자세한 사항이 알고 싶다면 `data` API [문서](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html) 를 참고하세요. 또한 위의 예시와 유사한 구조를 갖는 데이터 세트를 사용하고 있다면, `pytorchvideo.data.Ucf101()` 함수를 사용하는 데 문제가 없을 것입니다. 데이터 세트에 영상의 개수를 알기 위해 `num_videos` 인수에 접근할 수 있습니다. ```py >>> print(train_dataset.num_videos, val_dataset.num_videos, test_dataset.num_videos) # (300, 30, 75) ``` ## 더 나은 디버깅을 위해 전처리 영상 시각화하기[[visualize-the-preprocessed-video-for-better-debugging]] ```py >>> import imageio >>> import numpy as np >>> from IPython.display import Image >>> def unnormalize_img(img): ... """Un-normalizes the image pixels.""" ... img = (img * std) + mean ... img = (img * 255).astype("uint8") ... return img.clip(0, 255) >>> def create_gif(video_tensor, filename="sample.gif"): ... """Prepares a GIF from a video tensor. ... ... The video tensor is expected to have the following shape: ... (num_frames, num_channels, height, width). ... """ ... frames = [] ... for video_frame in video_tensor: ... frame_unnormalized = unnormalize_img(video_frame.permute(1, 2, 0).numpy()) ... frames.append(frame_unnormalized) ... kargs = {"duration": 0.25} ... imageio.mimsave(filename, frames, "GIF", **kargs) ... return filename >>> def display_gif(video_tensor, gif_name="sample.gif"): ... """Prepares and displays a GIF from a video tensor.""" ... video_tensor = video_tensor.permute(1, 0, 2, 3) ... gif_filename = create_gif(video_tensor, gif_name) ... return Image(filename=gif_filename) >>> sample_video = next(iter(train_dataset)) >>> video_tensor = sample_video["video"] >>> display_gif(video_tensor) ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/sample_gif.gif" alt="Person playing basketball"/> </div> ## 모델 훈련하기[[train-the-model]] 🤗 Transformers의 [`Trainer`](https://huggingface.co/docs/transformers/main_classes/trainer)를 사용하여 모델을 훈련시켜보세요. `Trainer`를 인스턴스화하려면 훈련 설정과 평가 지표를 정의해야 합니다. 가장 중요한 것은 [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments)입니다. 이 클래스는 훈련을 구성하는 모든 속성을 포함하며, 훈련 중 체크포인트를 저장할 출력 폴더 이름을 필요로 합니다. 또한 🤗 Hub의 모델 저장소의 모든 정보를 동기화하는 데 도움이 됩니다. 대부분의 훈련 인수는 따로 설명할 필요는 없습니다. 하지만 여기에서 중요한 인수는 `remove_unused_columns=False` 입니다. 이 인자는 모델의 호출 함수에서 사용되지 않는 모든 속성 열(columns)을 삭제합니다. 기본값은 일반적으로 True입니다. 이는 사용되지 않는 기능 열을 삭제하는 것이 이상적이며, 입력을 모델의 호출 함수로 풀기(unpack)가 쉬워지기 때문입니다. 하지만 이 경우에는 `pixel_values`(모델의 입력으로 필수적인 키)를 생성하기 위해 사용되지 않는 기능('video'가 특히 그렇습니다)이 필요합니다. 따라서 remove_unused_columns을 False로 설정해야 합니다. ```py >>> from transformers import TrainingArguments, Trainer >>> model_name = model_ckpt.split("/")[-1] >>> new_model_name = f"{model_name}-finetuned-ucf101-subset" >>> num_epochs = 4 >>> args = TrainingArguments( ... new_model_name, ... remove_unused_columns=False, ... eval_strategy="epoch", ... save_strategy="epoch", ... learning_rate=5e-5, ... per_device_train_batch_size=batch_size, ... per_device_eval_batch_size=batch_size, ... warmup_ratio=0.1, ... logging_steps=10, ... load_best_model_at_end=True, ... metric_for_best_model="accuracy", ... push_to_hub=True, ... max_steps=(train_dataset.num_videos // batch_size) * num_epochs, ... ) ``` `pytorchvideo.data.Ucf101()` 함수로 반환되는 데이터 세트는 `__len__` 메소드가 이식되어 있지 않습니다. 따라서, `TrainingArguments`를 인스턴스화할 때 `max_steps`를 정의해야 합니다. 다음으로, 평가지표를 불러오고, 예측값에서 평가지표를 계산할 함수를 정의합니다. 필요한 전처리 작업은 예측된 로짓(logits)에 argmax 값을 취하는 것뿐입니다: ```py import evaluate metric = evaluate.load("accuracy") def compute_metrics(eval_pred): predictions = np.argmax(eval_pred.predictions, axis=1) return metric.compute(predictions=predictions, references=eval_pred.label_ids) ``` **평가에 대한 참고사항**: [VideoMAE 논문](https://arxiv.org/abs/2203.12602)에서 저자는 다음과 같은 평가 전략을 사용합니다. 테스트 영상에서 여러 클립을 선택하고 그 클립에 다양한 크롭을 적용하여 집계 점수를 보고합니다. 그러나 이번 튜토리얼에서는 간단함과 간결함을 위해 해당 전략을 고려하지 않습니다. 또한, 예제를 묶어서 배치를 형성하는 `collate_fn`을 정의해야합니다. 각 배치는 `pixel_values`와 `labels`라는 2개의 키로 구성됩니다. ```py >>> def collate_fn(examples): ... # permute to (num_frames, num_channels, height, width) ... pixel_values = torch.stack( ... [example["video"].permute(1, 0, 2, 3) for example in examples] ... ) ... labels = torch.tensor([example["label"] for example in examples]) ... return {"pixel_values": pixel_values, "labels": labels} ``` 그런 다음 이 모든 것을 데이터 세트와 함께 `Trainer`에 전달하기만 하면 됩니다: ```py >>> trainer = Trainer( ... model, ... args, ... train_dataset=train_dataset, ... eval_dataset=val_dataset, ... processing_class=image_processor, ... compute_metrics=compute_metrics, ... data_collator=collate_fn, ... ) ``` 데이터를 이미 처리했는데도 불구하고 `image_processor`를 토크나이저 인수로 넣은 이유는 JSON으로 저장되는 이미지 프로세서 구성 파일이 Hub의 저장소에 업로드되도록 하기 위함입니다. `train` 메소드를 호출하여 모델을 미세 조정하세요: ```py >>> train_results = trainer.train() ``` 학습이 완료되면, 모델을 [`~transformers.Trainer.push_to_hub`] 메소드를 사용하여 허브에 공유하여 누구나 모델을 사용할 수 있도록 합니다: ```py >>> trainer.push_to_hub() ``` ## 추론하기[[inference]] 좋습니다. 이제 미세 조정된 모델을 추론하는 데 사용할 수 있습니다. 추론에 사용할 영상을 불러오세요: ```py >>> sample_test_video = next(iter(test_dataset)) ``` <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/sample_gif_two.gif" alt="Teams playing basketball"/> </div> 미세 조정된 모델을 추론에 사용하는 가장 간단한 방법은 [`pipeline`](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.VideoClassificationPipeline)에서 모델을 사용하는 것입니다. 모델로 영상 분류를 하기 위해 `pipeline`을 인스턴스화하고 영상을 전달하세요: ```py >>> from transformers import pipeline >>> video_cls = pipeline(model="my_awesome_video_cls_model") >>> video_cls("https://huggingface.co/datasets/sayakpaul/ucf101-subset/resolve/main/v_BasketballDunk_g14_c06.avi") [{'score': 0.9272987842559814, 'label': 'BasketballDunk'}, {'score': 0.017777055501937866, 'label': 'BabyCrawling'}, {'score': 0.01663011871278286, 'label': 'BalanceBeam'}, {'score': 0.009560945443809032, 'label': 'BandMarching'}, {'score': 0.0068979403004050255, 'label': 'BaseballPitch'}] ``` 만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다: ```py >>> def run_inference(model, video): ... # (num_frames, num_channels, height, width) ... perumuted_sample_test_video = video.permute(1, 0, 2, 3) ... inputs = { ... "pixel_values": perumuted_sample_test_video.unsqueeze(0), ... "labels": torch.tensor( ... [sample_test_video["label"]] ... ), # this can be skipped if you don't have labels available. ... } ... device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ... inputs = {k: v.to(device) for k, v in inputs.items()} ... model = model.to(device) ... # forward pass ... with torch.no_grad(): ... outputs = model(**inputs) ... logits = outputs.logits ... return logits ``` 모델에 입력값을 넣고 `logits`을 반환받으세요: ```py >>> logits = run_inference(trained_model, sample_test_video["video"]) ``` `logits`을 디코딩하면, 우리는 다음 결과를 얻을 수 있습니다: ```py >>> predicted_class_idx = logits.argmax(-1).item() >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) # Predicted class: BasketballDunk ```

transformers/docs/source/ko/tasks/video_classification.md/0

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# 性能与可扩展性训练大型transformer模型并将其部署到生产环境会面临各种挑战。在训练过程中，模型可能需要比可用的GPU内存更多的资源，或者表现出较慢的训练速度。在部署阶段，模型可能在生产环境中难以处理所需的吞吐量。本文档旨在帮助您克服这些挑战，并找到适合您使用场景的最佳设置。教程分为训练和推理部分，因为每个部分都有不同的挑战和解决方案。在每个部分中，您将找到针对不同硬件配置的单独指南，例如单GPU与多GPU用于训练或CPU与GPU用于推理。将此文档作为您的起点，进一步导航到与您的情况匹配的方法。 ## 训练高效训练大型transformer模型需要使用加速器硬件，如GPU或TPU。最常见的情况是您只有一个GPU。您应用于单个GPU上提高训练效率的方法可以扩展到其他设置，如多个GPU。然而，也有一些特定于多GPU或CPU训练的技术。我们在单独的部分中介绍它们。 * [在单个GPU上进行高效训练的方法和工具](perf_train_gpu_one)：从这里开始学习常见的方法，可以帮助优化GPU内存利用率、加快训练速度或两者兼备。 * [多GPU训练部分](perf_train_gpu_many)：探索此部分以了解适用于多GPU设置的进一步优化方法，例如数据并行、张量并行和流水线并行。 * [CPU训练部分](perf_train_cpu)：了解在CPU上的混合精度训练。 * [在多个CPU上进行高效训练](perf_train_cpu_many)：了解分布式CPU训练。 * [使用TensorFlow在TPU上进行训练](perf_train_tpu_tf)：如果您对TPU还不熟悉，请参考此部分，了解有关在TPU上进行训练和使用XLA的建议性介绍。 * [自定义硬件进行训练](perf_hardware)：在构建自己的深度学习机器时查找技巧和窍门。 * [使用Trainer API进行超参数搜索](hpo_train) ## 推理在生产环境中对大型模型进行高效推理可能与训练它们一样具有挑战性。在接下来的部分中，我们将详细介绍如何在CPU和单/多GPU设置上进行推理的步骤。 * [在单个CPU上进行推理](perf_infer_cpu) * [在单个GPU上进行推理](perf_infer_gpu_one) * [多GPU推理](perf_infer_gpu_one) * [TensorFlow模型的XLA集成](tf_xla) ## 训练和推理在这里，您将找到适用于训练模型或使用它进行推理的技巧、窍门和技巧。 * [实例化大型模型](big_models) * [解决性能问题](debugging) ## 贡献这份文档还远远没有完成，还有很多需要添加的内容，所以如果你有补充或更正的内容，请毫不犹豫地提交一个PR（Pull Request），或者如果你不确定，可以创建一个Issue，我们可以在那里讨论细节。在做出贡献时，如果A比B更好，请尽量包含可重复的基准测试和(或)该信息来源的链接（除非它直接来自您）。

transformers/docs/source/zh/performance.md/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2021 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. """ Fine-tuning the library models for question answering. """ # You can also adapt this script on your own question answering task. Pointers for this are left as comments. import json import logging import math import os import random import sys import time from dataclasses import asdict, dataclass, field from enum import Enum from pathlib import Path from typing import Any, Callable, Dict, Optional, Tuple import datasets import evaluate import jax import jax.numpy as jnp import numpy as np import optax from datasets import load_dataset from flax import struct, traverse_util from flax.jax_utils import pad_shard_unpad, replicate, unreplicate from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from huggingface_hub import HfApi from tqdm import tqdm from utils_qa import postprocess_qa_predictions import transformers from transformers import ( AutoConfig, AutoTokenizer, EvalPrediction, FlaxAutoModelForQuestionAnswering, HfArgumentParser, PreTrainedTokenizerFast, is_tensorboard_available, ) from transformers.utils import check_min_version, send_example_telemetry logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.49.0.dev0") Array = Any Dataset = datasets.arrow_dataset.Dataset PRNGKey = Any # region Arguments @dataclass class TrainingArguments: 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."}) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} ) 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."}) adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) hub_model_id: str = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) def __post_init__(self): if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) 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. """ d = asdict(self) 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 @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": "Path to directory 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`)." ) }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether to trust the execution of code from datasets/models defined on the Hub." " 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." ) }, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ 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 text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) test_file: Optional[str] = field( default=None, metadata={"help": "An optional input test data file to evaluate the perplexity on (a text 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_seq_length: int = field( default=384, 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: 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 (which can be faster on GPU but will be slower on 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." ) }, ) version_2_with_negative: bool = field( default=False, metadata={"help": "If true, some of the examples do not have an answer."} ) null_score_diff_threshold: float = field( default=0.0, metadata={ "help": ( "The threshold used to select the null answer: if the best answer has a score that is less than " "the score of the null answer minus this threshold, the null answer is selected for this example. " "Only useful when `version_2_with_negative=True`." ) }, ) doc_stride: int = field( default=128, metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}, ) n_best_size: int = field( default=20, metadata={"help": "The total number of n-best predictions to generate when looking for an answer."}, ) max_answer_length: int = field( default=30, metadata={ "help": ( "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another." ) }, ) def __post_init__(self): if ( self.dataset_name is None and self.train_file is None and self.validation_file is None and self.test_file is None ): raise ValueError("Need either a dataset name or a training/validation file/test_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." if self.test_file is not None: extension = self.test_file.split(".")[-1] assert extension in ["csv", "json"], "`test_file` should be a csv or a json file." # endregion # region Create a train state def create_train_state( model: FlaxAutoModelForQuestionAnswering, learning_rate_fn: Callable[[int], float], num_labels: int, training_args: TrainingArguments, ) -> train_state.TrainState: """Create initial training state.""" class TrainState(train_state.TrainState): """Train state with an Optax optimizer. The two functions below differ depending on whether the task is classification or regression. Args: logits_fn: Applied to last layer to obtain the logits. loss_fn: Function to compute the loss. """ logits_fn: Callable = struct.field(pytree_node=False) loss_fn: Callable = struct.field(pytree_node=False) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) # find out all LayerNorm parameters layer_norm_candidates = ["layernorm", "layer_norm", "ln"] layer_norm_named_params = { layer[-2:] for layer_norm_name in layer_norm_candidates for layer in flat_params.keys() if layer_norm_name in "".join(layer).lower() } flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) tx = optax.adamw( learning_rate=learning_rate_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) def cross_entropy_loss(logits, labels): start_loss = optax.softmax_cross_entropy(logits[0], onehot(labels[0], num_classes=num_labels)) end_loss = optax.softmax_cross_entropy(logits[1], onehot(labels[1], num_classes=num_labels)) xentropy = (start_loss + end_loss) / 2.0 return jnp.mean(xentropy) return TrainState.create( apply_fn=model.__call__, params=model.params, tx=tx, logits_fn=lambda logits: logits, loss_fn=cross_entropy_loss, ) # endregion # region Create learning rate function def create_learning_rate_fn( train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float ) -> Callable[[int], jnp.ndarray]: """Returns a linear warmup, linear_decay learning rate function.""" steps_per_epoch = train_ds_size // train_batch_size num_train_steps = steps_per_epoch * num_train_epochs warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps) decay_fn = optax.linear_schedule( init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps ) schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps]) return schedule_fn # endregion # region train data iterator def train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int): """Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices.""" steps_per_epoch = len(dataset) // batch_size perms = jax.random.permutation(rng, len(dataset)) perms = perms[: steps_per_epoch * batch_size] # Skip incomplete batch. perms = perms.reshape((steps_per_epoch, batch_size)) for perm in perms: batch = dataset[perm] batch = {k: np.array(v) for k, v in batch.items()} batch = shard(batch) yield batch # endregion # region eval data iterator def eval_data_collator(dataset: Dataset, batch_size: int): """Returns batches of size `batch_size` from `eval dataset`. Sharding handled by `pad_shard_unpad` in the eval loop.""" batch_idx = np.arange(len(dataset)) steps_per_epoch = math.ceil(len(dataset) / batch_size) batch_idx = np.array_split(batch_idx, steps_per_epoch) for idx in batch_idx: batch = dataset[idx] # Ignore `offset_mapping` to avoid numpy/JAX array conversion issue. batch = {k: np.array(v) for k, v in batch.items() if k != "offset_mapping"} yield batch # endregion def main(): # region Argument parsing # 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() # 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_qa", model_args, data_args, framework="flax") # endregion # region Logging # 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, ) # Setup logging, we only want one process per machine to log things on the screen. logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR) if jax.process_index() == 0: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # endregion # Handle the repository creation if training_args.push_to_hub: # Retrieve of infer repo_name repo_name = training_args.hub_model_id if repo_name is None: repo_name = Path(training_args.output_dir).absolute().name # Create repo and retrieve repo_id api = HfApi() repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id # region Load Data # 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 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 'text' or the first column if no column called # 'text' 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, cache_dir=model_args.cache_dir, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) else: # Loading the dataset from local csv or json file. 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] if data_args.test_file is not None: data_files["test"] = data_args.test_file extension = data_args.test_file.split(".")[-1] raw_datasets = load_dataset( extension, data_files=data_files, field="data", cache_dir=model_args.cache_dir, 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. # endregion # region Load pretrained model and tokenizer # # Load pretrained model and tokenizer config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) tokenizer = AutoTokenizer.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=True, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) # endregion # region Tokenizer check: this script requires a fast tokenizer. if not isinstance(tokenizer, PreTrainedTokenizerFast): raise ValueError( "This example script only works for models that have a fast tokenizer. Checkout the big table of models at" " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet" " this requirement" ) # endregion # region Preprocessing the datasets # Preprocessing is slightly different for training and evaluation. if training_args.do_train: column_names = raw_datasets["train"].column_names elif training_args.do_eval: column_names = raw_datasets["validation"].column_names else: column_names = raw_datasets["test"].column_names question_column_name = "question" if "question" in column_names else column_names[0] context_column_name = "context" if "context" in column_names else column_names[1] answer_column_name = "answers" if "answers" in column_names else column_names[2] # Padding side determines if we do (question|context) or (context|question). pad_on_right = tokenizer.padding_side == "right" 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) # Training preprocessing def prepare_train_features(examples): # Some of the questions have lots of whitespace on the left, which is not useful and will make the # truncation of the context fail (the tokenized question will take a lots of space). So we remove that # left whitespace examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples[question_column_name if pad_on_right else context_column_name], examples[context_column_name if pad_on_right else question_column_name], truncation="only_second" if pad_on_right else "only_first", max_length=max_seq_length, stride=data_args.doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # The offset mappings will give us a map from token to character position in the original context. This will # help us compute the start_positions and end_positions. offset_mapping = tokenized_examples.pop("offset_mapping") # Let's label those examples! tokenized_examples["start_positions"] = [] tokenized_examples["end_positions"] = [] for i, offsets in enumerate(offset_mapping): # We will label impossible answers with the index of the CLS token. input_ids = tokenized_examples["input_ids"][i] cls_index = input_ids.index(tokenizer.cls_token_id) # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] answers = examples[answer_column_name][sample_index] # If no answers are given, set the cls_index as answer. if len(answers["answer_start"]) == 0: tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Start/end character index of the answer in the text. start_char = answers["answer_start"][0] end_char = start_char + len(answers["text"][0]) # Start token index of the current span in the text. token_start_index = 0 while sequence_ids[token_start_index] != (1 if pad_on_right else 0): token_start_index += 1 # End token index of the current span in the text. token_end_index = len(input_ids) - 1 while sequence_ids[token_end_index] != (1 if pad_on_right else 0): token_end_index -= 1 # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index). if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char): tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Otherwise move the token_start_index and token_end_index to the two ends of the answer. # Note: we could go after the last offset if the answer is the last word (edge case). while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char: token_start_index += 1 tokenized_examples["start_positions"].append(token_start_index - 1) while offsets[token_end_index][1] >= end_char: token_end_index -= 1 tokenized_examples["end_positions"].append(token_end_index + 1) return tokenized_examples processed_raw_datasets = {} 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: # We will select sample from whole data if argument is specified max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) # Create train feature from dataset train_dataset = train_dataset.map( prepare_train_features, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, ) if data_args.max_train_samples is not None: # Number of samples might increase during Feature Creation, We select only specified max samples max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) processed_raw_datasets["train"] = train_dataset # Validation preprocessing def prepare_validation_features(examples): # Some of the questions have lots of whitespace on the left, which is not useful and will make the # truncation of the context fail (the tokenized question will take a lots of space). So we remove that # left whitespace examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples[question_column_name if pad_on_right else context_column_name], examples[context_column_name if pad_on_right else question_column_name], truncation="only_second" if pad_on_right else "only_first", max_length=max_seq_length, stride=data_args.doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the # corresponding example_id and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples if training_args.do_eval: if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_examples = raw_datasets["validation"] if data_args.max_eval_samples is not None: # We will select sample from whole data max_eval_samples = min(len(eval_examples), data_args.max_eval_samples) eval_examples = eval_examples.select(range(max_eval_samples)) # Validation Feature Creation eval_dataset = eval_examples.map( prepare_validation_features, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, ) if data_args.max_eval_samples is not None: # During Feature creation dataset samples might increase, we will select required samples again max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) processed_raw_datasets["validation"] = eval_dataset if training_args.do_predict: if "test" not in raw_datasets: raise ValueError("--do_predict requires a test dataset") predict_examples = raw_datasets["test"] if data_args.max_predict_samples is not None: # We will select sample from whole data predict_examples = predict_examples.select(range(data_args.max_predict_samples)) # Predict Feature Creation predict_dataset = predict_examples.map( prepare_validation_features, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, ) if data_args.max_predict_samples is not None: # During Feature creation dataset samples might increase, we will select required samples again max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) predict_dataset = predict_dataset.select(range(max_predict_samples)) processed_raw_datasets["test"] = predict_dataset # endregion # region Metrics and Post-processing: def post_processing_function(examples, features, predictions, stage="eval"): # Post-processing: we match the start logits and end logits to answers in the original context. predictions = postprocess_qa_predictions( examples=examples, features=features, predictions=predictions, version_2_with_negative=data_args.version_2_with_negative, n_best_size=data_args.n_best_size, max_answer_length=data_args.max_answer_length, null_score_diff_threshold=data_args.null_score_diff_threshold, output_dir=training_args.output_dir, prefix=stage, ) # Format the result to the format the metric expects. if data_args.version_2_with_negative: formatted_predictions = [ {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items() ] else: formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()] references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples] return EvalPrediction(predictions=formatted_predictions, label_ids=references) metric = evaluate.load( "squad_v2" if data_args.version_2_with_negative else "squad", cache_dir=model_args.cache_dir ) def compute_metrics(p: EvalPrediction): return metric.compute(predictions=p.predictions, references=p.label_ids) # Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor def create_and_fill_np_array(start_or_end_logits, dataset, max_len): """ Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor Args: start_or_end_logits(:obj:`tensor`): This is the output predictions of the model. We can only enter either start or end logits. eval_dataset: Evaluation dataset max_len(:obj:`int`): The maximum length of the output tensor. ( See the model.eval() part for more details ) """ step = 0 # create a numpy array and fill it with -100. logits_concat = np.full((len(dataset), max_len), -100, dtype=np.float64) # Now since we have create an array now we will populate it with the outputs of the model. for i, output_logit in enumerate(start_or_end_logits): # populate columns # We have to fill it such that we have to take the whole tensor and replace it on the newly created array # And after every iteration we have to change the step batch_size = output_logit.shape[0] cols = output_logit.shape[1] if step + batch_size < len(dataset): logits_concat[step : step + batch_size, :cols] = output_logit else: logits_concat[step:, :cols] = output_logit[: len(dataset) - step] step += batch_size return logits_concat # endregion # region Training steps and logging init train_dataset = processed_raw_datasets["train"] eval_dataset = processed_raw_datasets["validation"] # 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]}.") # Define a summary writer has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter summary_writer = SummaryWriter(training_args.output_dir) summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)}) except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) else: logger.warning( "Unable to display metrics through TensorBoard because the package is not installed: " "Please run pip install tensorboard to enable." ) def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) num_epochs = int(training_args.num_train_epochs) rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) train_batch_size = int(training_args.per_device_train_batch_size) * jax.local_device_count() per_device_eval_batch_size = int(training_args.per_device_eval_batch_size) eval_batch_size = per_device_eval_batch_size * jax.local_device_count() # endregion # region Load model model = FlaxAutoModelForQuestionAnswering.from_pretrained( model_args.model_name_or_path, config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), ) learning_rate_fn = create_learning_rate_fn( len(train_dataset), train_batch_size, training_args.num_train_epochs, training_args.warmup_steps, training_args.learning_rate, ) state = create_train_state(model, learning_rate_fn, num_labels=max_seq_length, training_args=training_args) # endregion # region Define train step functions def train_step( state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey ) -> Tuple[train_state.TrainState, float]: """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`.""" dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) start_positions = batch.pop("start_positions") end_positions = batch.pop("end_positions") targets = (start_positions, end_positions) def loss_fn(params): logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True) loss = state.loss_fn(logits, targets) return loss grad_fn = jax.value_and_grad(loss_fn) loss, grad = grad_fn(state.params) grad = jax.lax.pmean(grad, "batch") new_state = state.apply_gradients(grads=grad) metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch") return new_state, metrics, new_dropout_rng p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,)) # endregion # region Define eval step functions def eval_step(state, batch): logits = state.apply_fn(**batch, params=state.params, train=False) return state.logits_fn(logits) p_eval_step = jax.pmap(eval_step, axis_name="batch") # endregion # region Define train and eval loop logger.info(f"===== Starting training ({num_epochs} epochs) =====") train_time = 0 # make sure weights are replicated on each device state = replicate(state) train_time = 0 step_per_epoch = len(train_dataset) // train_batch_size total_steps = step_per_epoch * num_epochs epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0) for epoch in epochs: train_start = time.time() train_metrics = [] # Create sampling rng rng, input_rng = jax.random.split(rng) # train for step, batch in enumerate( tqdm( train_data_collator(input_rng, train_dataset, train_batch_size), total=step_per_epoch, desc="Training...", position=1, ), 1, ): state, train_metric, dropout_rngs = p_train_step(state, batch, dropout_rngs) train_metrics.append(train_metric) cur_step = epoch * step_per_epoch + step if cur_step % training_args.logging_steps == 0 and cur_step > 0: # Save metrics train_metric = unreplicate(train_metric) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, cur_step) epochs.write( f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate:" f" {train_metric['learning_rate']})" ) train_metrics = [] if ( training_args.do_eval and (cur_step % training_args.eval_steps == 0 or cur_step % step_per_epoch == 0) and cur_step > 0 ): eval_metrics = {} all_start_logits = [] all_end_logits = [] # evaluate for batch in tqdm( eval_data_collator(eval_dataset, eval_batch_size), total=math.ceil(len(eval_dataset) / eval_batch_size), desc="Evaluating ...", position=2, ): _ = batch.pop("example_id") predictions = pad_shard_unpad(p_eval_step)( state, batch, min_device_batch=per_device_eval_batch_size ) start_logits = np.array(predictions[0]) end_logits = np.array(predictions[1]) all_start_logits.append(start_logits) all_end_logits.append(end_logits) max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor # concatenate the numpy array start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len) end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len) # delete the list of numpy arrays del all_start_logits del all_end_logits outputs_numpy = (start_logits_concat, end_logits_concat) prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy) eval_metrics = compute_metrics(prediction) logger.info(f"Step... ({cur_step}/{total_steps} | Evaluation metrics: {eval_metrics})") if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, cur_step) if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps): # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(unreplicate(state.params)) model.save_pretrained(training_args.output_dir, params=params) tokenizer.save_pretrained(training_args.output_dir) if training_args.push_to_hub: api.upload_folder( commit_message=f"Saving weights and logs of step {cur_step}", folder_path=training_args.output_dir, repo_id=repo_id, repo_type="model", token=training_args.hub_token, ) epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}" # endregion # Eval after training if training_args.do_eval: eval_metrics = {} all_start_logits = [] all_end_logits = [] eval_loader = eval_data_collator(eval_dataset, eval_batch_size) for batch in tqdm( eval_loader, total=math.ceil(len(eval_dataset) / eval_batch_size), desc="Evaluating ...", position=2 ): _ = batch.pop("example_id") predictions = pad_shard_unpad(p_eval_step)(state, batch, min_device_batch=per_device_eval_batch_size) start_logits = np.array(predictions[0]) end_logits = np.array(predictions[1]) all_start_logits.append(start_logits) all_end_logits.append(end_logits) max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor # concatenate the numpy array start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len) end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len) # delete the list of numpy arrays del all_start_logits del all_end_logits outputs_numpy = (start_logits_concat, end_logits_concat) prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy) eval_metrics = compute_metrics(prediction) if jax.process_index() == 0: eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()} path = os.path.join(training_args.output_dir, "eval_results.json") with open(path, "w") as f: json.dump(eval_metrics, f, indent=4, sort_keys=True) if __name__ == "__main__": main()

transformers/examples/flax/question-answering/run_qa.py/0

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#### Fine-tuning BERT on SQuAD1.0 with relative position embeddings The following examples show how to fine-tune BERT models with different relative position embeddings. The BERT model `google-bert/bert-base-uncased` was pretrained with default absolute position embeddings. We provide the following pretrained models which were pre-trained on the same training data (BooksCorpus and English Wikipedia) as in the BERT model training, but with different relative position embeddings. * `zhiheng-huang/bert-base-uncased-embedding-relative-key`, trained from scratch with relative embedding proposed by Shaw et al., [Self-Attention with Relative Position Representations](https://arxiv.org/abs/1803.02155) * `zhiheng-huang/bert-base-uncased-embedding-relative-key-query`, trained from scratch with relative embedding method 4 in Huang et al. [Improve Transformer Models with Better Relative Position Embeddings](https://arxiv.org/abs/2009.13658) * `zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query`, fine-tuned from model `google-bert/bert-large-uncased-whole-word-masking` with 3 additional epochs with relative embedding method 4 in Huang et al. [Improve Transformer Models with Better Relative Position Embeddings](https://arxiv.org/abs/2009.13658) ##### Base models fine-tuning ```bash export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 ./examples/question-answering/run_squad.py \ --model_name_or_path zhiheng-huang/bert-base-uncased-embedding-relative-key-query \ --dataset_name squad \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 512 \ --doc_stride 128 \ --output_dir relative_squad \ --per_device_eval_batch_size=60 \ --per_device_train_batch_size=6 ``` Training with the above command leads to the following results. It boosts the BERT default from f1 score of 88.52 to 90.54. ```bash 'exact': 83.6802270577105, 'f1': 90.54772098174814 ``` The change of `max_seq_length` from 512 to 384 in the above command leads to the f1 score of 90.34. Replacing the above model `zhiheng-huang/bert-base-uncased-embedding-relative-key-query` with `zhiheng-huang/bert-base-uncased-embedding-relative-key` leads to the f1 score of 89.51. The changing of 8 gpus to one gpu training leads to the f1 score of 90.71. ##### Large models fine-tuning ```bash export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 ./examples/question-answering/run_squad.py \ --model_name_or_path zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query \ --dataset_name squad \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 512 \ --doc_stride 128 \ --output_dir relative_squad \ --per_gpu_eval_batch_size=6 \ --per_gpu_train_batch_size=2 \ --gradient_accumulation_steps 3 ``` Training with the above command leads to the f1 score of 93.52, which is slightly better than the f1 score of 93.15 for `google-bert/bert-large-uncased-whole-word-masking`. #### Distributed training Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1: ```bash torchrun --nproc_per_node=8 ./examples/question-answering/run_squad.py \ --model_name_or_path google-bert/bert-large-uncased-whole-word-masking \ --dataset_name squad \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \ --per_device_eval_batch_size=3 \ --per_device_train_batch_size=3 \ ``` Training with the previously defined hyper-parameters yields the following results: ```bash f1 = 93.15 exact_match = 86.91 ``` This fine-tuned model is available as a checkpoint under the reference [`google-bert/bert-large-uncased-whole-word-masking-finetuned-squad`](https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking-finetuned-squad). ## Results Larger batch size may improve the performance while costing more memory. ##### Results for SQuAD1.0 with the previously defined hyper-parameters: ```python { "exact": 85.45884578997162, "f1": 92.5974600601065, "total": 10570, "HasAns_exact": 85.45884578997162, "HasAns_f1": 92.59746006010651, "HasAns_total": 10570 } ``` ##### Results for SQuAD2.0 with the previously defined hyper-parameters: ```python { "exact": 80.4177545691906, "f1": 84.07154997729623, "total": 11873, "HasAns_exact": 76.73751686909581, "HasAns_f1": 84.05558584352873, "HasAns_total": 5928, "NoAns_exact": 84.0874684608915, "NoAns_f1": 84.0874684608915, "NoAns_total": 5945 } ```

transformers/examples/legacy/question-answering/README.md/0

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#!/usr/bin/env python # 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 pathlib import Path import fire def minify(src_dir: str, dest_dir: str, n: int): """Write first n lines of each file f in src_dir to dest_dir/f""" src_dir = Path(src_dir) dest_dir = Path(dest_dir) dest_dir.mkdir(exist_ok=True) for path in src_dir.iterdir(): new = [x.rstrip() for x in list(path.open().readlines())][:n] dest_path = dest_dir.joinpath(path.name) print(dest_path) dest_path.open("w").write("\n".join(new)) if __name__ == "__main__": fire.Fire(minify)

transformers/examples/legacy/seq2seq/minify_dataset.py/0

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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 re from filelock import FileLock try: import nltk NLTK_AVAILABLE = True except (ImportError, ModuleNotFoundError): NLTK_AVAILABLE = False if NLTK_AVAILABLE: with FileLock(".lock") as lock: nltk.download("punkt", quiet=True) def add_newline_to_end_of_each_sentence(x: str) -> str: """This was added to get rougeLsum scores matching published rougeL scores for BART and PEGASUS.""" re.sub("<n>", "", x) # remove pegasus newline char assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)" return "\n".join(nltk.sent_tokenize(x))

transformers/examples/legacy/seq2seq/sentence_splitter.py/0

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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. python finetune_trainer.py \ --model_name_or_path=facebook/mbart-large-cc25 \ --data_dir $ENRO_DIR \ --output_dir mbart_cc25_enro --overwrite_output_dir \ --learning_rate=3e-5 \ --warmup_steps 500 \ --fp16 \ --label_smoothing 0.1 \ --adam_eps 1e-06 \ --src_lang en_XX --tgt_lang ro_RO \ --freeze_embeds \ --per_device_train_batch_size=4 --per_device_eval_batch_size=4 \ --max_source_length 128 --max_target_length 128 --val_max_target_length 128 --test_max_target_length 128\ --sortish_sampler \ --num_train_epochs 6 \ --save_steps 25000 --eval_steps 25000 --logging_steps 1000 \ --do_train --do_eval --do_predict \ --eval_strategy steps \ --predict_with_generate --logging_first_step \ --task translation \ "$@"

transformers/examples/legacy/seq2seq/train_mbart_cc25_enro.sh/0

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from typing import List, Optional, Tuple, Union import torch from transformers.models.bert.modeling_bert import BertModel from ...modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions class DummyBertModel(BertModel): def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: 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]: return super().forward(input_ids)

transformers/examples/modular-transformers/modular_dummy_bert.py/0

{ "file_path": "transformers/examples/modular-transformers/modular_dummy_bert.py", "repo_id": "transformers", "token_count": 432 }

#!/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. import logging import os import sys import warnings from dataclasses import dataclass, field from random import randint from typing import Optional import datasets import evaluate import numpy as np from datasets import DatasetDict, load_dataset import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForAudioClassification, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.49.0.dev0") require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt") def random_subsample(wav: np.ndarray, max_length: float, sample_rate: int = 16000): """Randomly sample chunks of `max_length` seconds from the input audio""" sample_length = int(round(sample_rate * max_length)) if len(wav) <= sample_length: return wav random_offset = randint(0, len(wav) - sample_length - 1) return wav[random_offset : random_offset + sample_length] @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=None, metadata={"help": "Name of a dataset from the datasets package"}) 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": "A file containing the training audio paths and labels."} ) eval_file: Optional[str] = field( default=None, metadata={"help": "A file containing the validation audio paths and labels."} ) train_split_name: str = field( default="train", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) eval_split_name: str = field( default="validation", metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to 'validation'" ) }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) label_column_name: str = field( default="label", metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} ) 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_length_seconds: float = field( default=20, metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}, ) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( default="facebook/wav2vec2-base", 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"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) feature_extractor_name: Optional[str] = field( default=None, metadata={"help": "Name or path of preprocessor config."} ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) attention_mask: bool = field( default=True, metadata={"help": "Whether to generate an attention mask in the feature extractor."} ) 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`)." ) }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether to trust the execution of code from datasets/models defined on the Hub." " 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." ) }, ) freeze_feature_extractor: Optional[bool] = field( default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."} ) ignore_mismatched_sizes: bool = field( default=False, metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, ) def __post_init__(self): if not self.freeze_feature_extractor and self.freeze_feature_encoder: warnings.warn( "The argument `--freeze_feature_extractor` is deprecated and " "will be removed in a future version. Use `--freeze_feature_encoder` " "instead. Setting `freeze_feature_encoder==True`.", FutureWarning, ) if self.freeze_feature_extractor and not self.freeze_feature_encoder: raise ValueError( "The argument `--freeze_feature_extractor` is deprecated and " "should not be used in combination with `--freeze_feature_encoder`. " "Only make use of `--freeze_feature_encoder`." ) 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() # 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_audio_classification", model_args, data_args) # 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)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() log_level = training_args.get_process_log_level() logger.setLevel(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: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # 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 train from scratch." ) 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." ) # Initialize our dataset and prepare it for the audio classification task. raw_datasets = DatasetDict() raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) raw_datasets["eval"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) if data_args.audio_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--audio_column_name` to the correct audio column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) if data_args.label_column_name not in raw_datasets["train"].column_names: raise ValueError( f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--label_column_name` to the correct text column - one of " f"{', '.join(raw_datasets['train'].column_names)}." ) # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over # transformer outputs in the classifier, but it doesn't always lead to better accuracy feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name or model_args.model_name_or_path, return_attention_mask=model_args.attention_mask, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) # `datasets` takes care of automatically loading and resampling the audio, # so we just need to set the correct target sampling rate. raw_datasets = raw_datasets.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) model_input_name = feature_extractor.model_input_names[0] def train_transforms(batch): """Apply train_transforms across a batch.""" subsampled_wavs = [] for audio in batch[data_args.audio_column_name]: wav = random_subsample( audio["array"], max_length=data_args.max_length_seconds, sample_rate=feature_extractor.sampling_rate ) subsampled_wavs.append(wav) inputs = feature_extractor(subsampled_wavs, sampling_rate=feature_extractor.sampling_rate) output_batch = {model_input_name: inputs.get(model_input_name)} output_batch["labels"] = list(batch[data_args.label_column_name]) return output_batch def val_transforms(batch): """Apply val_transforms across a batch.""" wavs = [audio["array"] for audio in batch[data_args.audio_column_name]] inputs = feature_extractor(wavs, sampling_rate=feature_extractor.sampling_rate) output_batch = {model_input_name: inputs.get(model_input_name)} output_batch["labels"] = list(batch[data_args.label_column_name]) return output_batch # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. labels = raw_datasets["train"].features[data_args.label_column_name].names label2id, id2label = {}, {} for i, label in enumerate(labels): label2id[label] = str(i) id2label[str(i)] = label # Load the accuracy metric from the datasets package metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir) # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with # `predictions` and `label_ids` fields) and has to return a dictionary string to float. def compute_metrics(eval_pred): """Computes accuracy on a batch of predictions""" predictions = np.argmax(eval_pred.predictions, axis=1) return metric.compute(predictions=predictions, references=eval_pred.label_ids) config = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path, num_labels=len(labels), label2id=label2id, id2label=id2label, finetuning_task="audio-classification", cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) model = AutoModelForAudioClassification.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=model_args.token, trust_remote_code=model_args.trust_remote_code, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, ) # freeze the convolutional waveform encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() if training_args.do_train: if data_args.max_train_samples is not None: raw_datasets["train"] = ( raw_datasets["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples)) ) # Set the training transforms raw_datasets["train"].set_transform(train_transforms, output_all_columns=False) if training_args.do_eval: if data_args.max_eval_samples is not None: raw_datasets["eval"] = ( raw_datasets["eval"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples)) ) # Set the validation transforms raw_datasets["eval"].set_transform(val_transforms, output_all_columns=False) # Initialize our trainer trainer = Trainer( model=model, args=training_args, train_dataset=raw_datasets["train"] if training_args.do_train else None, eval_dataset=raw_datasets["eval"] if training_args.do_eval else None, compute_metrics=compute_metrics, processing_class=feature_extractor, ) # 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) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # Evaluation if training_args.do_eval: metrics = trainer.evaluate() trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "audio-classification", "dataset": data_args.dataset_name, "tags": ["audio-classification"], } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

transformers/examples/pytorch/audio-classification/run_audio_classification.py/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2024 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 """Finetuning 🤗 Transformers model for instance segmentation leveraging the Trainer API.""" import logging import os import sys from dataclasses import dataclass, field from functools import partial from typing import Any, Dict, List, Mapping, Optional import albumentations as A import numpy as np import torch from datasets import load_dataset from torchmetrics.detection.mean_ap import MeanAveragePrecision import transformers from transformers import ( AutoImageProcessor, AutoModelForUniversalSegmentation, HfArgumentParser, Trainer, TrainingArguments, ) from transformers.image_processing_utils import BatchFeature from transformers.trainer import EvalPrediction from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.49.0.dev0") require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/instance-segmentation/requirements.txt") @dataclass class Arguments: """ 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. """ model_name_or_path: str = field( default="facebook/mask2former-swin-tiny-coco-instance", metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, ) dataset_name: str = field( default="qubvel-hf/ade20k-mini", metadata={ "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)." }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether to trust the execution of code from datasets/models defined on the Hub." " 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." ) }, ) image_height: Optional[int] = field(default=512, metadata={"help": "Image height after resizing."}) image_width: Optional[int] = field(default=512, metadata={"help": "Image width after resizing."}) 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`)." ) }, ) do_reduce_labels: bool = field( default=False, metadata={ "help": ( "If background class is labeled as 0 and you want to remove it from the labels, set this flag to True." ) }, ) def augment_and_transform_batch( examples: Mapping[str, Any], transform: A.Compose, image_processor: AutoImageProcessor ) -> BatchFeature: batch = { "pixel_values": [], "mask_labels": [], "class_labels": [], } for pil_image, pil_annotation in zip(examples["image"], examples["annotation"]): image = np.array(pil_image) semantic_and_instance_masks = np.array(pil_annotation)[..., :2] # Apply augmentations output = transform(image=image, mask=semantic_and_instance_masks) aug_image = output["image"] aug_semantic_and_instance_masks = output["mask"] aug_instance_mask = aug_semantic_and_instance_masks[..., 1] # Create mapping from instance id to semantic id unique_semantic_id_instance_id_pairs = np.unique(aug_semantic_and_instance_masks.reshape(-1, 2), axis=0) instance_id_to_semantic_id = { instance_id: semantic_id for semantic_id, instance_id in unique_semantic_id_instance_id_pairs } # Apply the image processor transformations: resizing, rescaling, normalization model_inputs = image_processor( images=[aug_image], segmentation_maps=[aug_instance_mask], instance_id_to_semantic_id=instance_id_to_semantic_id, return_tensors="pt", ) batch["pixel_values"].append(model_inputs.pixel_values[0]) batch["mask_labels"].append(model_inputs.mask_labels[0]) batch["class_labels"].append(model_inputs.class_labels[0]) return batch def collate_fn(examples): batch = {} batch["pixel_values"] = torch.stack([example["pixel_values"] for example in examples]) batch["class_labels"] = [example["class_labels"] for example in examples] batch["mask_labels"] = [example["mask_labels"] for example in examples] if "pixel_mask" in examples[0]: batch["pixel_mask"] = torch.stack([example["pixel_mask"] for example in examples]) return batch @dataclass class ModelOutput: class_queries_logits: torch.Tensor masks_queries_logits: torch.Tensor def nested_cpu(tensors): if isinstance(tensors, (list, tuple)): return type(tensors)(nested_cpu(t) for t in tensors) elif isinstance(tensors, Mapping): return type(tensors)({k: nested_cpu(t) for k, t in tensors.items()}) elif isinstance(tensors, torch.Tensor): return tensors.cpu().detach() else: return tensors class Evaluator: """ Compute metrics for the instance segmentation task. """ def __init__( self, image_processor: AutoImageProcessor, id2label: Mapping[int, str], threshold: float = 0.0, ): """ Initialize evaluator with image processor, id2label mapping and threshold for filtering predictions. Args: image_processor (AutoImageProcessor): Image processor for `post_process_instance_segmentation` method. id2label (Mapping[int, str]): Mapping from class id to class name. threshold (float): Threshold to filter predicted boxes by confidence. Defaults to 0.0. """ self.image_processor = image_processor self.id2label = id2label self.threshold = threshold self.metric = self.get_metric() def get_metric(self): metric = MeanAveragePrecision(iou_type="segm", class_metrics=True) return metric def reset_metric(self): self.metric.reset() def postprocess_target_batch(self, target_batch) -> List[Dict[str, torch.Tensor]]: """Collect targets in a form of list of dictionaries with keys "masks", "labels".""" batch_masks = target_batch[0] batch_labels = target_batch[1] post_processed_targets = [] for masks, labels in zip(batch_masks, batch_labels): post_processed_targets.append( { "masks": masks.to(dtype=torch.bool), "labels": labels, } ) return post_processed_targets def get_target_sizes(self, post_processed_targets) -> List[List[int]]: target_sizes = [] for target in post_processed_targets: target_sizes.append(target["masks"].shape[-2:]) return target_sizes def postprocess_prediction_batch(self, prediction_batch, target_sizes) -> List[Dict[str, torch.Tensor]]: """Collect predictions in a form of list of dictionaries with keys "masks", "labels", "scores".""" model_output = ModelOutput(class_queries_logits=prediction_batch[0], masks_queries_logits=prediction_batch[1]) post_processed_output = self.image_processor.post_process_instance_segmentation( model_output, threshold=self.threshold, target_sizes=target_sizes, return_binary_maps=True, ) post_processed_predictions = [] for image_predictions, target_size in zip(post_processed_output, target_sizes): if image_predictions["segments_info"]: post_processed_image_prediction = { "masks": image_predictions["segmentation"].to(dtype=torch.bool), "labels": torch.tensor([x["label_id"] for x in image_predictions["segments_info"]]), "scores": torch.tensor([x["score"] for x in image_predictions["segments_info"]]), } else: # for void predictions, we need to provide empty tensors post_processed_image_prediction = { "masks": torch.zeros([0, *target_size], dtype=torch.bool), "labels": torch.tensor([]), "scores": torch.tensor([]), } post_processed_predictions.append(post_processed_image_prediction) return post_processed_predictions @torch.no_grad() def __call__(self, evaluation_results: EvalPrediction, compute_result: bool = False) -> Mapping[str, float]: """ Update metrics with current evaluation results and return metrics if `compute_result` is True. Args: evaluation_results (EvalPrediction): Predictions and targets from evaluation. compute_result (bool): Whether to compute and return metrics. Returns: Mapping[str, float]: Metrics in a form of dictionary {<metric_name>: <metric_value>} """ prediction_batch = nested_cpu(evaluation_results.predictions) target_batch = nested_cpu(evaluation_results.label_ids) # For metric computation we need to provide: # - targets in a form of list of dictionaries with keys "masks", "labels" # - predictions in a form of list of dictionaries with keys "masks", "labels", "scores" post_processed_targets = self.postprocess_target_batch(target_batch) target_sizes = self.get_target_sizes(post_processed_targets) post_processed_predictions = self.postprocess_prediction_batch(prediction_batch, target_sizes) # Compute metrics self.metric.update(post_processed_predictions, post_processed_targets) if not compute_result: return metrics = self.metric.compute() # Replace list of per class metrics with separate metric for each class classes = metrics.pop("classes") map_per_class = metrics.pop("map_per_class") mar_100_per_class = metrics.pop("mar_100_per_class") for class_id, class_map, class_mar in zip(classes, map_per_class, mar_100_per_class): class_name = self.id2label[class_id.item()] if self.id2label is not None else class_id.item() metrics[f"map_{class_name}"] = class_map metrics[f"mar_100_{class_name}"] = class_mar metrics = {k: round(v.item(), 4) for k, v in metrics.items()} # Reset metric for next evaluation self.reset_metric() return metrics def setup_logging(training_args: TrainingArguments) -> None: """Setup logging according to `training_args`.""" logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() log_level = training_args.get_process_log_level() logger.setLevel(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() def find_last_checkpoint(training_args: TrainingArguments) -> Optional[str]: """Find the last checkpoint in the output directory according to parameters specified in `training_args`.""" checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif os.path.isdir(training_args.output_dir) and not training_args.overwrite_output_dir: checkpoint = get_last_checkpoint(training_args.output_dir) if 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 checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) return checkpoint def main(): # See all possible arguments in https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments # or by passing the --help flag to this script. parser = HfArgumentParser([Arguments, 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. args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: args, training_args = parser.parse_args_into_dataclasses() # Set default training arguments for instance segmentation training_args.eval_do_concat_batches = False training_args.batch_eval_metrics = True training_args.remove_unused_columns = False # # 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_instance_segmentation", args) # Setup logging and log on each process the small summary: setup_logging(training_args) logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, " + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Load last checkpoint from output_dir if it exists (and we are not overwriting it) checkpoint = find_last_checkpoint(training_args) # ------------------------------------------------------------------------------------------------ # Load dataset, prepare splits # ------------------------------------------------------------------------------------------------ dataset = load_dataset(args.dataset_name, trust_remote_code=args.trust_remote_code) # We need to specify the label2id mapping for the model # it is a mapping from semantic class name to class index. # In case your dataset does not provide it, you can create it manually: # label2id = {"background": 0, "cat": 1, "dog": 2} label2id = dataset["train"][0]["semantic_class_to_id"] if args.do_reduce_labels: label2id = {name: idx for name, idx in label2id.items() if idx != 0} # remove background class label2id = {name: idx - 1 for name, idx in label2id.items()} # shift class indices by -1 id2label = {v: k for k, v in label2id.items()} # ------------------------------------------------------------------------------------------------ # Load pretrained config, model and image processor # ------------------------------------------------------------------------------------------------ model = AutoModelForUniversalSegmentation.from_pretrained( args.model_name_or_path, label2id=label2id, id2label=id2label, ignore_mismatched_sizes=True, token=args.token, ) image_processor = AutoImageProcessor.from_pretrained( args.model_name_or_path, do_resize=True, size={"height": args.image_height, "width": args.image_width}, do_reduce_labels=args.do_reduce_labels, reduce_labels=args.do_reduce_labels, # TODO: remove when mask2former support `do_reduce_labels` token=args.token, ) # ------------------------------------------------------------------------------------------------ # Define image augmentations and dataset transforms # ------------------------------------------------------------------------------------------------ train_augment_and_transform = A.Compose( [ A.HorizontalFlip(p=0.5), A.RandomBrightnessContrast(p=0.5), A.HueSaturationValue(p=0.1), ], ) validation_transform = A.Compose( [A.NoOp()], ) # Make transform functions for batch and apply for dataset splits train_transform_batch = partial( augment_and_transform_batch, transform=train_augment_and_transform, image_processor=image_processor ) validation_transform_batch = partial( augment_and_transform_batch, transform=validation_transform, image_processor=image_processor ) dataset["train"] = dataset["train"].with_transform(train_transform_batch) dataset["validation"] = dataset["validation"].with_transform(validation_transform_batch) # ------------------------------------------------------------------------------------------------ # Model training and evaluation with Trainer API # ------------------------------------------------------------------------------------------------ compute_metrics = Evaluator(image_processor=image_processor, id2label=id2label, threshold=0.0) trainer = Trainer( model=model, args=training_args, train_dataset=dataset["train"] if training_args.do_train else None, eval_dataset=dataset["validation"] if training_args.do_eval else None, processing_class=image_processor, data_collator=collate_fn, compute_metrics=compute_metrics, ) # Training if training_args.do_train: train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() trainer.log_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics) trainer.save_state() # Final evaluation if training_args.do_eval: metrics = trainer.evaluate(eval_dataset=dataset["validation"], metric_key_prefix="test") trainer.log_metrics("test", metrics) trainer.save_metrics("test", metrics) # Write model card and (optionally) push to hub kwargs = { "finetuned_from": args.model_name_or_path, "dataset": args.dataset_name, "tags": ["image-segmentation", "instance-segmentation", "vision"], } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) if __name__ == "__main__": main()

transformers/examples/pytorch/instance-segmentation/run_instance_segmentation.py/0

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# Object detection examples This directory contains 2 scripts that showcase how to fine-tune any model supported by the [`AutoModelForObjectDetection` API](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForObjectDetection) (such as [DETR](https://huggingface.co/docs/transformers/main/en/model_doc/detr), [DETA](https://huggingface.co/docs/transformers/main/en/model_doc/deta), [Deformable DETR](https://huggingface.co/docs/transformers/main/en/model_doc/deformable_detr)) using PyTorch. Content: * [PyTorch version, Trainer](#pytorch-version-trainer) * [PyTorch version, no Trainer](#pytorch-version-no-trainer) * [Reload and perform inference](#reload-and-perform-inference) * [Note on custom data](#note-on-custom-data) ## PyTorch version, Trainer Based on the script [`run_object_detection.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/object-detection/run_object_detection.py). The script leverages the [🤗 Trainer API](https://huggingface.co/docs/transformers/main_classes/trainer) to automatically take care of the training for you, running on distributed environments right away. Here we show how to fine-tune a [DETR](https://huggingface.co/facebook/detr-resnet-50) model on the [CPPE-5](https://huggingface.co/datasets/cppe-5) dataset: ```bash python run_object_detection.py \ --model_name_or_path facebook/detr-resnet-50 \ --dataset_name cppe-5 \ --do_train true \ --do_eval true \ --output_dir detr-finetuned-cppe-5-10k-steps \ --num_train_epochs 100 \ --image_square_size 600 \ --fp16 true \ --learning_rate 5e-5 \ --weight_decay 1e-4 \ --dataloader_num_workers 4 \ --dataloader_prefetch_factor 2 \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 1 \ --remove_unused_columns false \ --eval_do_concat_batches false \ --ignore_mismatched_sizes true \ --metric_for_best_model eval_map \ --greater_is_better true \ --load_best_model_at_end true \ --logging_strategy epoch \ --evaluation_strategy epoch \ --save_strategy epoch \ --save_total_limit 2 \ --push_to_hub true \ --push_to_hub_model_id detr-finetuned-cppe-5-10k-steps \ --hub_strategy end \ --seed 1337 ``` > Note: `--eval_do_concat_batches false` is required for correct evaluation of detection models; `--ignore_mismatched_sizes true` is required to load detection model for finetuning with different number of classes. The resulting model can be seen here: https://huggingface.co/qubvel-hf/qubvel-hf/detr-resnet-50-finetuned-10k-cppe5. The corresponding Weights and Biases report [here](https://api.wandb.ai/links/qubvel-hf-co/bnm0r5ex). Note that it's always advised to check the original paper to know the details regarding training hyperparameters. Hyperparameters for current example were not tuned. To improve model quality you could try: - changing image size parameters (`--shortest_edge`/`--longest_edge`) - changing training parameters, such as learning rate, batch size, warmup, optimizer and many more (see [TrainingArguments](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments)) - adding more image augmentations (we created a helpful [HF Space](https://huggingface.co/spaces/qubvel-hf/albumentations-demo) to choose some) Note that you can replace the model and dataset by simply setting the `model_name_or_path` and `dataset_name` arguments respectively, with model or dataset from the [hub](https://huggingface.co/). For dataset, make sure it provides labels in the same format as [CPPE-5](https://huggingface.co/datasets/cppe-5) dataset and boxes are provided in [COCO format](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco). ![W&B report](https://i.imgur.com/ASNjamQ.png) ## PyTorch version, no Trainer Based on the script [`run_object_detection_no_trainer.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/object-detection/run_object_detection.py). The script leverages [🤗 `Accelerate`](https://github.com/huggingface/accelerate), which allows to write your own training loop in PyTorch, but have it run instantly on any (distributed) environment, including CPU, multi-CPU, GPU, multi-GPU and TPU. It also supports mixed precision. First, run: ```bash accelerate config ``` and reply to the questions asked regarding the environment on which you'd like to train. Then ```bash accelerate test ``` that will check everything is ready for training. Finally, you can launch training with ```bash accelerate launch run_object_detection_no_trainer.py \ --model_name_or_path "facebook/detr-resnet-50" \ --dataset_name cppe-5 \ --output_dir "detr-resnet-50-finetuned" \ --num_train_epochs 100 \ --image_square_size 600 \ --per_device_train_batch_size 8 \ --per_device_eval_batch_size 8 \ --checkpointing_steps epoch \ --learning_rate 5e-5 \ --ignore_mismatched_sizes \ --with_tracking \ --push_to_hub ``` and boom, you're training, possibly on multiple GPUs, logging everything to all trackers found in your environment (like Weights and Biases, Tensorboard) and regularly pushing your model to the hub (with the repo name being equal to `args.output_dir` at your HF username) 🤗 With the default settings, the script fine-tunes a [DETR](https://huggingface.co/facebook/detr-resnet-50) model on the [CPPE-5](https://huggingface.co/datasets/cppe-5) dataset. The resulting model can be seen here: https://huggingface.co/qubvel-hf/detr-resnet-50-finetuned-10k-cppe5-no-trainer. ## Reload and perform inference This means that after training, you can easily load your trained model and perform inference as follows:: ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, AutoModelForObjectDetection # Name of repo on the hub or path to a local folder model_name = "qubvel-hf/detr-resnet-50-finetuned-10k-cppe5" image_processor = AutoImageProcessor.from_pretrained(model_name) model = AutoModelForObjectDetection.from_pretrained(model_name) # Load image for inference url = "https://images.pexels.com/photos/8413299/pexels-photo-8413299.jpeg?auto=compress&cs=tinysrgb&w=630&h=375&dpr=2" image = Image.open(requests.get(url, stream=True).raw) # Prepare image for the model inputs = image_processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) # Post process model predictions # this include conversion to Pascal VOC format and filtering non confident boxes width, height = image.size target_sizes = torch.tensor([height, width]).unsqueeze(0) # add batch dim results = image_processor.post_process_object_detection(outputs, threshold=0.5, 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}" ) ``` And visualize with the following code: ```python from PIL import ImageDraw draw = ImageDraw.Draw(image) for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] x, y, x2, y2 = tuple(box) draw.rectangle((x, y, x2, y2), outline="red", width=1) draw.text((x, y), model.config.id2label[label.item()], fill="white") image ``` ## Note on custom data In case you'd like to use the script with custom data, you could prepare your data with the following way: ```bash custom_dataset/ └── train ├── 0001.jpg ├── 0002.jpg ├── ... └── metadata.jsonl └── validation └── ... └── test └── ... ``` Where `metadata.jsonl` is a file with the following structure: ```json {"file_name": "0001.jpg", "objects": {"bbox": [[302.0, 109.0, 73.0, 52.0]], "categories": [0], "id": [1], "area": [50.0]}} {"file_name": "0002.jpg", "objects": {"bbox": [[810.0, 100.0, 57.0, 28.0]], "categories": [1], "id": [2], "area": [40.0]}} ... ``` Trining script support bounding boxes in COCO format (x_min, y_min, width, height). Then, you cat load the dataset with just a few lines of code: ```python from datasets import load_dataset # Load dataset dataset = load_dataset("imagefolder", data_dir="custom_dataset/") # >>> DatasetDict({ # ... train: Dataset({ # ... features: ['image', 'objects'], # ... num_rows: 2 # ... }) # ... }) # Push to hub (assumes you have ran the huggingface-cli login command in a terminal/notebook) dataset.push_to_hub("name of repo on the hub") # optionally, you can push to a private repo on the hub # dataset.push_to_hub("name of repo on the hub", private=True) ``` And the final step, for training you should provide id2label mapping in the following way: ```python id2label = {0: "Car", 1: "Bird", ...} ``` Just find it in code and replace for simplicity, or save `json` locally and with the dataset on the hub! See also: [Dataset Creation Guide](https://huggingface.co/docs/datasets/image_dataset#create-an-image-dataset)

transformers/examples/pytorch/object-detection/README.md/0

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# coding=utf-8 # Copyright 2018 HuggingFace 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. import json import logging import os import sys from unittest.mock import patch from transformers import ViTMAEForPreTraining, Wav2Vec2ForPreTraining from transformers.testing_utils import ( CaptureLogger, TestCasePlus, backend_device_count, is_torch_fp16_available_on_device, slow, torch_device, ) SRC_DIRS = [ os.path.join(os.path.dirname(__file__), dirname) for dirname in [ "text-generation", "text-classification", "token-classification", "language-modeling", "multiple-choice", "question-answering", "summarization", "translation", "image-classification", "speech-recognition", "audio-classification", "speech-pretraining", "image-pretraining", "semantic-segmentation", "object-detection", "instance-segmentation", ] ] sys.path.extend(SRC_DIRS) if SRC_DIRS is not None: import run_audio_classification import run_clm import run_generation import run_glue import run_image_classification import run_instance_segmentation import run_mae import run_mlm import run_ner import run_object_detection import run_qa as run_squad import run_semantic_segmentation import run_seq2seq_qa as run_squad_seq2seq import run_speech_recognition_ctc import run_speech_recognition_ctc_adapter import run_speech_recognition_seq2seq import run_summarization import run_swag import run_translation import run_wav2vec2_pretraining_no_trainer logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() def get_results(output_dir): results = {} path = os.path.join(output_dir, "all_results.json") if os.path.exists(path): with open(path, "r") as f: results = json.load(f) else: raise ValueError(f"can't find {path}") return results stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) class ExamplesTests(TestCasePlus): def test_run_glue(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_glue.py --model_name_or_path distilbert/distilbert-base-uncased --output_dir {tmp_dir} --overwrite_output_dir --train_file ./tests/fixtures/tests_samples/MRPC/train.csv --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv --do_train --do_eval --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --learning_rate=1e-4 --max_steps=10 --warmup_steps=2 --seed=42 --max_seq_length=128 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_glue.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) def test_run_clm(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_clm.py --model_name_or_path distilbert/distilgpt2 --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --do_train --do_eval --block_size 128 --per_device_train_batch_size 5 --per_device_eval_batch_size 5 --num_train_epochs 2 --output_dir {tmp_dir} --overwrite_output_dir """.split() if backend_device_count(torch_device) > 1: # Skipping because there are not enough batches to train the model + would need a drop_last to work. return if torch_device == "cpu": testargs.append("--use_cpu") with patch.object(sys, "argv", testargs): run_clm.main() result = get_results(tmp_dir) self.assertLess(result["perplexity"], 100) def test_run_clm_config_overrides(self): # test that config_overrides works, despite the misleading dumps of default un-updated # config via tokenizer tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_clm.py --model_type gpt2 --tokenizer_name openai-community/gpt2 --train_file ./tests/fixtures/sample_text.txt --output_dir {tmp_dir} --config_overrides n_embd=10,n_head=2 """.split() if torch_device == "cpu": testargs.append("--use_cpu") logger = run_clm.logger with patch.object(sys, "argv", testargs): with CaptureLogger(logger) as cl: run_clm.main() self.assertIn('"n_embd": 10', cl.out) self.assertIn('"n_head": 2', cl.out) def test_run_mlm(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_mlm.py --model_name_or_path distilbert/distilroberta-base --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --output_dir {tmp_dir} --overwrite_output_dir --do_train --do_eval --prediction_loss_only --num_train_epochs=1 """.split() if torch_device == "cpu": testargs.append("--use_cpu") with patch.object(sys, "argv", testargs): run_mlm.main() result = get_results(tmp_dir) self.assertLess(result["perplexity"], 42) def test_run_ner(self): # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu epochs = 7 if backend_device_count(torch_device) > 1 else 2 tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_ner.py --model_name_or_path google-bert/bert-base-uncased --train_file tests/fixtures/tests_samples/conll/sample.json --validation_file tests/fixtures/tests_samples/conll/sample.json --output_dir {tmp_dir} --overwrite_output_dir --do_train --do_eval --warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=2 --num_train_epochs={epochs} --seed 7 """.split() if torch_device == "cpu": testargs.append("--use_cpu") with patch.object(sys, "argv", testargs): run_ner.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) self.assertLess(result["eval_loss"], 0.5) def test_run_squad(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_qa.py --model_name_or_path google-bert/bert-base-uncased --version_2_with_negative --train_file tests/fixtures/tests_samples/SQUAD/sample.json --validation_file tests/fixtures/tests_samples/SQUAD/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=10 --warmup_steps=2 --do_train --do_eval --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 """.split() with patch.object(sys, "argv", testargs): run_squad.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_f1"], 30) self.assertGreaterEqual(result["eval_exact"], 30) def test_run_squad_seq2seq(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_seq2seq_qa.py --model_name_or_path google-t5/t5-small --context_column context --question_column question --answer_column answers --version_2_with_negative --train_file tests/fixtures/tests_samples/SQUAD/sample.json --validation_file tests/fixtures/tests_samples/SQUAD/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=10 --warmup_steps=2 --do_train --do_eval --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --predict_with_generate """.split() with patch.object(sys, "argv", testargs): run_squad_seq2seq.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_f1"], 30) self.assertGreaterEqual(result["eval_exact"], 30) def test_run_swag(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_swag.py --model_name_or_path google-bert/bert-base-uncased --train_file tests/fixtures/tests_samples/swag/sample.json --validation_file tests/fixtures/tests_samples/swag/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=20 --warmup_steps=2 --do_train --do_eval --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 """.split() with patch.object(sys, "argv", testargs): run_swag.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.8) def test_generation(self): testargs = ["run_generation.py", "--prompt=Hello", "--length=10", "--seed=42"] if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") model_type, model_name = ( "--model_type=gpt2", "--model_name_or_path=sshleifer/tiny-gpt2", ) with patch.object(sys, "argv", testargs + [model_type, model_name]): result = run_generation.main() self.assertGreaterEqual(len(result[0]), 10) @slow def test_run_summarization(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_summarization.py --model_name_or_path google-t5/t5-small --train_file tests/fixtures/tests_samples/xsum/sample.json --validation_file tests/fixtures/tests_samples/xsum/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=50 --warmup_steps=8 --do_train --do_eval --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --predict_with_generate """.split() with patch.object(sys, "argv", testargs): run_summarization.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_rouge1"], 10) self.assertGreaterEqual(result["eval_rouge2"], 2) self.assertGreaterEqual(result["eval_rougeL"], 7) self.assertGreaterEqual(result["eval_rougeLsum"], 7) @slow def test_run_translation(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_translation.py --model_name_or_path sshleifer/student_marian_en_ro_6_1 --source_lang en --target_lang ro --train_file tests/fixtures/tests_samples/wmt16/sample.json --validation_file tests/fixtures/tests_samples/wmt16/sample.json --output_dir {tmp_dir} --overwrite_output_dir --max_steps=50 --warmup_steps=8 --do_train --do_eval --learning_rate=3e-3 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --predict_with_generate --source_lang en_XX --target_lang ro_RO --max_source_length 512 """.split() with patch.object(sys, "argv", testargs): run_translation.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_bleu"], 30) def test_run_image_classification(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_image_classification.py --output_dir {tmp_dir} --model_name_or_path google/vit-base-patch16-224-in21k --dataset_name hf-internal-testing/cats_vs_dogs_sample --trust_remote_code --do_train --do_eval --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --remove_unused_columns False --overwrite_output_dir True --dataloader_num_workers 16 --metric_for_best_model accuracy --max_steps 10 --train_val_split 0.1 --seed 42 --label_column_name labels """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_image_classification.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.8) def test_run_speech_recognition_ctc(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_speech_recognition_ctc.py --output_dir {tmp_dir} --model_name_or_path hf-internal-testing/tiny-random-wav2vec2 --dataset_name hf-internal-testing/librispeech_asr_dummy --dataset_config_name clean --train_split_name validation --eval_split_name validation --trust_remote_code --do_train --do_eval --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --remove_unused_columns False --overwrite_output_dir True --preprocessing_num_workers 16 --max_steps 10 --seed 42 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_speech_recognition_ctc.main() result = get_results(tmp_dir) self.assertLess(result["eval_loss"], result["train_loss"]) def test_run_speech_recognition_ctc_adapter(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_speech_recognition_ctc_adapter.py --output_dir {tmp_dir} --model_name_or_path hf-internal-testing/tiny-random-wav2vec2 --dataset_name hf-internal-testing/librispeech_asr_dummy --dataset_config_name clean --train_split_name validation --eval_split_name validation --trust_remote_code --do_train --do_eval --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --remove_unused_columns False --overwrite_output_dir True --preprocessing_num_workers 16 --max_steps 10 --target_language tur --seed 42 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_speech_recognition_ctc_adapter.main() result = get_results(tmp_dir) self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "./adapter.tur.safetensors"))) self.assertLess(result["eval_loss"], result["train_loss"]) def test_run_speech_recognition_seq2seq(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_speech_recognition_seq2seq.py --output_dir {tmp_dir} --model_name_or_path hf-internal-testing/tiny-random-speech-encoder-decoder --dataset_name hf-internal-testing/librispeech_asr_dummy --dataset_config_name clean --train_split_name validation --eval_split_name validation --trust_remote_code --do_train --do_eval --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 4 --remove_unused_columns False --overwrite_output_dir True --preprocessing_num_workers 16 --max_steps 10 --seed 42 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_speech_recognition_seq2seq.main() result = get_results(tmp_dir) self.assertLess(result["eval_loss"], result["train_loss"]) def test_run_audio_classification(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_audio_classification.py --output_dir {tmp_dir} --model_name_or_path hf-internal-testing/tiny-random-wav2vec2 --dataset_name anton-l/superb_demo --trust_remote_code --dataset_config_name ks --train_split_name test --eval_split_name test --audio_column_name audio --label_column_name label --do_train --do_eval --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --remove_unused_columns False --overwrite_output_dir True --num_train_epochs 10 --max_steps 50 --seed 42 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_audio_classification.main() result = get_results(tmp_dir) self.assertLess(result["eval_loss"], result["train_loss"]) def test_run_wav2vec2_pretraining(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_wav2vec2_pretraining_no_trainer.py --output_dir {tmp_dir} --model_name_or_path hf-internal-testing/tiny-random-wav2vec2 --dataset_name hf-internal-testing/librispeech_asr_dummy --dataset_config_names clean --dataset_split_names validation --trust_remote_code --learning_rate 1e-4 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --preprocessing_num_workers 16 --max_train_steps 2 --validation_split_percentage 5 --seed 42 """.split() with patch.object(sys, "argv", testargs): run_wav2vec2_pretraining_no_trainer.main() model = Wav2Vec2ForPreTraining.from_pretrained(tmp_dir) self.assertIsNotNone(model) def test_run_vit_mae_pretraining(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_mae.py --output_dir {tmp_dir} --dataset_name hf-internal-testing/cats_vs_dogs_sample --trust_remote_code --do_train --do_eval --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --remove_unused_columns False --overwrite_output_dir True --dataloader_num_workers 16 --metric_for_best_model accuracy --max_steps 10 --train_val_split 0.1 --seed 42 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_mae.main() model = ViTMAEForPreTraining.from_pretrained(tmp_dir) self.assertIsNotNone(model) def test_run_semantic_segmentation(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_semantic_segmentation.py --output_dir {tmp_dir} --dataset_name huggingface/semantic-segmentation-test-sample --do_train --do_eval --remove_unused_columns False --overwrite_output_dir True --max_steps 10 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --seed 32 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_semantic_segmentation.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_overall_accuracy"], 0.1) @patch.dict(os.environ, {"WANDB_DISABLED": "true"}) def test_run_object_detection(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_object_detection.py --model_name_or_path qubvel-hf/detr-resnet-50-finetuned-10k-cppe5 --output_dir {tmp_dir} --dataset_name qubvel-hf/cppe-5-sample --do_train --do_eval --remove_unused_columns False --overwrite_output_dir True --eval_do_concat_batches False --max_steps 10 --learning_rate=1e-6 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --seed 32 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_object_detection.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["test_map"], 0.1) @patch.dict(os.environ, {"WANDB_DISABLED": "true"}) def test_run_instance_segmentation(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" run_instance_segmentation.py --model_name_or_path qubvel-hf/finetune-instance-segmentation-ade20k-mini-mask2former --output_dir {tmp_dir} --dataset_name qubvel-hf/ade20k-nano --do_reduce_labels --image_height 256 --image_width 256 --do_train --num_train_epochs 1 --learning_rate 1e-5 --lr_scheduler_type constant --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --do_eval --evaluation_strategy epoch --seed 32 """.split() if is_torch_fp16_available_on_device(torch_device): testargs.append("--fp16") with patch.object(sys, "argv", testargs): run_instance_segmentation.main() result = get_results(tmp_dir) self.assertGreaterEqual(result["test_map"], 0.1)

transformers/examples/pytorch/test_pytorch_examples.py/0

{ "file_path": "transformers/examples/pytorch/test_pytorch_examples.py", "repo_id": "transformers", "token_count": 12107 }

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation. # 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. """Training and inference using the library models for sequence classification on GLUE (Bert, Albert) with PABEE.""" import argparse import glob import json import logging import os import random import numpy as np import torch from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import transformers from transformers import ( WEIGHTS_NAME, AdamW, AlbertConfig, AlbertTokenizer, BertConfig, BertTokenizer, get_linear_schedule_with_warmup, ) from transformers import glue_compute_metrics as compute_metrics from transformers import glue_convert_examples_to_features as convert_examples_to_features from transformers import glue_output_modes as output_modes from transformers import glue_processors as processors from transformers.trainer_utils import is_main_process try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForSequenceClassificationWithPabee, BertTokenizer), "albert": (AlbertConfig, AlbertForSequenceClassificationWithPabee, AlbertTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def train(args, train_dataset, model, tokenizer): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) 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, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 0 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to global_step of last saved checkpoint from model path global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info( " Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch, ) tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0], ) set_seed(args) # Added here for reproducibility for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) inputs = { "input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3], } inputs["token_type_ids"] = batch[2] outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: logs = {} if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): eval_key = "eval_{}".format(key) logs[eval_key] = value loss_scalar = (tr_loss - logging_loss) / args.logging_steps learning_rate_scalar = scheduler.get_lr()[0] logs["learning_rate"] = learning_rate_scalar logs["loss"] = loss_scalar logging_loss = tr_loss for key, value in logs.items(): tb_writer.add_scalar(key, value, global_step) print(json.dumps({**logs, **{"step": global_step}})) if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix="", patience=0): if args.model_type == "albert": model.albert.set_regression_threshold(args.regression_threshold) model.albert.set_patience(patience) model.albert.reset_stats() elif args.model_type == "bert": model.bert.set_regression_threshold(args.regression_threshold) model.bert.set_patience(patience) model.bert.reset_stats() else: raise NotImplementedError() # Loop to handle MNLI double evaluation (matched, mis-matched) eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,) results = {} for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: os.makedirs(eval_output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3], } inputs["token_type_ids"] = batch[2] outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps if args.output_mode == "classification": preds = np.argmax(preds, axis=1) elif args.output_mode == "regression": preds = np.squeeze(preds) result = compute_metrics(eval_task, preds, out_label_ids) results.update(result) output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results {} *****".format(prefix)) for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) print(" %s = %s" % (key, str(result[key]))) writer.write("%s = %s\n" % (key, str(result[key]))) if args.eval_all_checkpoints and patience != 0: if args.model_type == "albert": model.albert.log_stats() elif args.model_type == "bert": model.bert.log_stats() else: raise NotImplementedError() return results def load_and_cache_examples(args, task, tokenizer, evaluate=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache processor = processors[task]() output_mode = output_modes[task] # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), str(task), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) label_list = processor.get_labels() if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]: # HACK(label indices are swapped in RoBERTa pretrained model) label_list[1], label_list[2] = label_list[2], label_list[1] examples = ( processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) ) features = convert_examples_to_features( examples, tokenizer, label_list=label_list, max_length=args.max_seq_length, output_mode=output_mode, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) if output_mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif output_mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name.", ) parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(processors.keys()), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--patience", default="0", type=str, required=False, ) parser.add_argument( "--regression_threshold", default=0, type=float, required=False, ) # Other parameters parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--max_seq_length", default=128, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.", ) parser.add_argument( "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--per_gpu_eval_batch_size", default=1, type=int, help="Batch size per GPU/CPU for evaluation.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument( "--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.", ) 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("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets", ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. " "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument( "--local_rank", type=int, default=-1, help="For distributed training: local_rank", ) parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = torch.cuda.device_count() else: # Initializes the distributed backend which will take care of synchronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set seed set_seed(args) # Prepare GLUE task args.task_name = args.task_name.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] label_list = processor.get_labels() num_labels = len(label_list) if args.patience != "0" and args.per_gpu_eval_batch_size != 1: raise ValueError("The eval batch size must be 1 with PABEE inference on.") # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) print("Total Model Parameters:", sum(param.numel() for param in model.parameters())) output_layers_param_num = sum(param.numel() for param in model.classifiers.parameters()) print("Output Layers Parameters:", output_layers_param_num) single_output_layer_param_num = sum(param.numel() for param in model.classifiers[0].parameters()) print( "Added Output Layers Parameters:", output_layers_param_num - single_output_layer_param_num, ) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir) model.to(args.device) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: patience_list = [int(x) for x in args.patience.split(",")] tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = [ os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ] logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) print(f"Evaluation for checkpoint {prefix}") for patience in patience_list: result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience) result = {k + "_{}".format(global_step): v for k, v in result.items()} results.update(result) return results if __name__ == "__main__": main()

transformers/examples/research_projects/bert-loses-patience/run_glue_with_pabee.py/0

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# DeeBERT: Early Exiting for *BERT This is the code base for the paper [DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://www.aclweb.org/anthology/2020.acl-main.204/), modified from its [original code base](https://github.com/castorini/deebert). The original code base also has information for downloading sample models that we have trained in advance. ## Usage There are three scripts in the folder which can be run directly. In each script, there are several things to modify before running: * `PATH_TO_DATA`: path to the GLUE dataset. * `--output_dir`: path for saving fine-tuned models. Default: `./saved_models`. * `--plot_data_dir`: path for saving evaluation results. Default: `./results`. Results are printed to stdout and also saved to `npy` files in this directory to facilitate plotting figures and further analyses. * `MODEL_TYPE`: bert or roberta * `MODEL_SIZE`: base or large * `DATASET`: SST-2, MRPC, RTE, QNLI, QQP, or MNLI #### train_deebert.sh This is for fine-tuning DeeBERT models. #### eval_deebert.sh This is for evaluating each exit layer for fine-tuned DeeBERT models. #### entropy_eval.sh This is for evaluating fine-tuned DeeBERT models, given a number of different early exit entropy thresholds. ## Citation Please cite our paper if you find the resource useful: ```bibtex @inproceedings{xin-etal-2020-deebert, title = "{D}ee{BERT}: Dynamic Early Exiting for Accelerating {BERT} Inference", author = "Xin, Ji and Tang, Raphael and Lee, Jaejun and Yu, Yaoliang and Lin, Jimmy", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.204", pages = "2246--2251", } ```

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

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# coding=utf-8 # Copyright 2019-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. """ Preprocessing script before distillation. """ import argparse import logging import pickle import random import time import numpy as np from transformers import BertTokenizer, GPT2Tokenizer, RobertaTokenizer logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO ) logger = logging.getLogger(__name__) def main(): parser = argparse.ArgumentParser( description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." ) parser.add_argument("--file_path", type=str, default="data/dump.txt", help="The path to the data.") parser.add_argument("--tokenizer_type", type=str, default="bert", choices=["bert", "roberta", "gpt2"]) parser.add_argument("--tokenizer_name", type=str, default="bert-base-uncased", help="The tokenizer to use.") parser.add_argument("--dump_file", type=str, default="data/dump", help="The dump file prefix.") args = parser.parse_args() logger.info(f"Loading Tokenizer ({args.tokenizer_name})") if args.tokenizer_type == "bert": tokenizer = BertTokenizer.from_pretrained(args.tokenizer_name) bos = tokenizer.special_tokens_map["cls_token"] # `[CLS]` sep = tokenizer.special_tokens_map["sep_token"] # `[SEP]` elif args.tokenizer_type == "roberta": tokenizer = RobertaTokenizer.from_pretrained(args.tokenizer_name) bos = tokenizer.special_tokens_map["cls_token"] # `<s>` sep = tokenizer.special_tokens_map["sep_token"] # `</s>` elif args.tokenizer_type == "gpt2": tokenizer = GPT2Tokenizer.from_pretrained(args.tokenizer_name) bos = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>` sep = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>` logger.info(f"Loading text from {args.file_path}") with open(args.file_path, "r", encoding="utf8") as fp: data = fp.readlines() logger.info("Start encoding") logger.info(f"{len(data)} examples to process.") rslt = [] iter = 0 interval = 10000 start = time.time() for text in data: text = f"{bos} {text.strip()} {sep}" token_ids = tokenizer.encode(text, add_special_tokens=False) rslt.append(token_ids) iter += 1 if iter % interval == 0: end = time.time() logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl") start = time.time() logger.info("Finished binarization") logger.info(f"{len(data)} examples processed.") dp_file = f"{args.dump_file}.{args.tokenizer_name}.pickle" vocab_size = tokenizer.vocab_size if vocab_size < (1 << 16): rslt_ = [np.uint16(d) for d in rslt] else: rslt_ = [np.int32(d) for d in rslt] random.shuffle(rslt_) logger.info(f"Dump to {dp_file}") with open(dp_file, "wb") as handle: pickle.dump(rslt_, handle, protocol=pickle.HIGHEST_PROTOCOL) if __name__ == "__main__": main()

transformers/examples/research_projects/distillation/scripts/binarized_data.py/0

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import torch from transformers import AutoModel class FSNERModel(torch.nn.Module): """ The FSNER model implements a few-shot named entity recognition method from the paper `Example-Based Named Entity Recognition <https://arxiv.org/abs/2008.10570>`__ by Morteza Ziyadi, Yuting Sun, Abhishek Goswami, Jade Huang, Weizhu Chen. To identify entity spans in a new domain, it uses a train-free few-shot learning approach inspired by question-answering. """ def __init__(self, pretrained_model_name_or_path="sayef/fsner-bert-base-uncased"): super(FSNERModel, self).__init__() self.bert = AutoModel.from_pretrained(pretrained_model_name_or_path, return_dict=True) self.cos = torch.nn.CosineSimilarity(3, 1e-08) self.softmax = torch.nn.Softmax(dim=1) def BERT(self, **inputs): return self.bert(**inputs).last_hidden_state def VectorSum(self, token_embeddings): return token_embeddings.sum(2, keepdim=True) def Atten(self, q_rep, S_rep, T=1): return self.softmax(T * self.cos(q_rep, S_rep)) def forward(self, W_query, W_supports): """ Find scores of each token being start and end token for an entity. Args: W_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of query sequence tokens in the vocabulary. W_supports (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of support sequence tokens in the vocabulary. Returns: p_start (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Scores of each token as being start token of an entity p_end (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Scores of each token as being end token of an entity """ support_sizes = W_supports["sizes"].tolist() start_token_id = W_supports["start_token_id"].item() end_token_id = W_supports["end_token_id"].item() del W_supports["sizes"] del W_supports["start_token_id"] del W_supports["end_token_id"] q = self.BERT(**W_query) S = self.BERT(**W_supports) p_starts = None p_ends = None start_token_masks = W_supports["input_ids"] == start_token_id end_token_masks = W_supports["input_ids"] == end_token_id for i, size in enumerate(support_sizes): if i == 0: s = 0 else: s = support_sizes[i - 1] s_start = S[s : s + size][start_token_masks[s : s + size]] s_end = S[s : s + size][end_token_masks[s : s + size]] p_start = torch.matmul(q[i], s_start.T).sum(1).softmax(0) p_end = torch.matmul(q[i], s_end.T).sum(1).softmax(0) if p_starts is not None: p_starts = torch.vstack((p_starts, p_start)) p_ends = torch.vstack((p_ends, p_end)) else: p_starts = p_start p_ends = p_end return p_starts, p_ends

transformers/examples/research_projects/fsner/src/fsner/model.py/0

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import os from dataclasses import replace import jax import wandb from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step from datasets import load_dataset from flax import jax_utils from transformers import BigBirdTokenizerFast if __name__ == "__main__": print("#################### AVAILABLE DEVICES ####################") print(jax.devices()) print("###########################################################") # setup for wandb sweep args = Args() logger = wandb.init(project="bigbird-natural-questions", config=args.__dict__) wandb_args = dict(logger.config) del wandb_args["batch_size"] args = replace(args, **wandb_args) base_dir = args.base_dir + "-" + wandb.run.id args = replace(args, base_dir=base_dir) print(args) tr_dataset = load_dataset("json", data_files=args.tr_data_path)["train"] val_dataset = load_dataset("json", data_files=args.val_data_path)["train"] # drop extra batch for now indices = range(len(tr_dataset) - len(tr_dataset) % args.batch_size) tr_dataset = tr_dataset.shuffle().select(indices) indices = range(len(val_dataset) - len(val_dataset) % args.batch_size) val_dataset = val_dataset.shuffle().select(indices) if os.environ.get("TRAIN_ON_SMALL", "false") == "true": tr_dataset = tr_dataset.shuffle().select(range(80000)) val_dataset = val_dataset.shuffle().select(range(8000)) print(tr_dataset) print(val_dataset) model = FlaxBigBirdForNaturalQuestions.from_pretrained( args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks ) tokenizer = BigBirdTokenizerFast.from_pretrained(args.model_id) data_collator = DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096) tx_args = { "lr": args.lr, "init_lr": args.init_lr, "warmup_steps": args.warmup_steps, "num_train_steps": args.max_epochs * (len(tr_dataset) // args.batch_size), "weight_decay": args.weight_decay, } tx, lr = build_tx(**tx_args) trainer = Trainer( args=args, data_collator=data_collator, model_save_fn=model.save_pretrained, train_step_fn=train_step, val_step_fn=val_step, logger=logger, scheduler_fn=lr, ) ckpt_dir = None state = trainer.create_state(model, tx, num_train_steps=tx_args["num_train_steps"], ckpt_dir=ckpt_dir) try: trainer.train(state, tr_dataset, val_dataset) except KeyboardInterrupt: print("Oooops; TRAINING STOPPED UNFORTUNATELY") print("SAVING WEIGHTS IN `final-weights`") params = jax_utils.unreplicate(state.params) model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)

transformers/examples/research_projects/jax-projects/big_bird/train.py/0

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# 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. """Fine-pruning Masked BERT on sequence classification on GLUE.""" import argparse import glob import json import logging import os import random import numpy as np import torch from emmental import MaskedBertConfig, MaskedBertForSequenceClassification from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange from transformers import ( WEIGHTS_NAME, AdamW, BertConfig, BertForSequenceClassification, BertTokenizer, get_linear_schedule_with_warmup, ) from transformers import glue_compute_metrics as compute_metrics from transformers import glue_convert_examples_to_features as convert_examples_to_features from transformers import glue_output_modes as output_modes from transformers import glue_processors as processors try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForSequenceClassification, BertTokenizer), "masked_bert": (MaskedBertConfig, MaskedBertForSequenceClassification, BertTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def schedule_threshold( step: int, total_step: int, warmup_steps: int, initial_threshold: float, final_threshold: float, initial_warmup: int, final_warmup: int, final_lambda: float, ): if step <= initial_warmup * warmup_steps: threshold = initial_threshold elif step > (total_step - final_warmup * warmup_steps): threshold = final_threshold else: spars_warmup_steps = initial_warmup * warmup_steps spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps) threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff**3) regu_lambda = final_lambda * threshold / final_threshold return threshold, regu_lambda def regularization(model: nn.Module, mode: str): regu, counter = 0, 0 for name, param in model.named_parameters(): if "mask_scores" in name: if mode == "l1": regu += torch.norm(torch.sigmoid(param), p=1) / param.numel() elif mode == "l0": regu += torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1)).sum() / param.numel() else: raise ValueError("Don't know this mode.") counter += 1 return regu / counter def train(args, train_dataset, model, tokenizer, teacher=None): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter(log_dir=args.output_dir) args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if "mask_score" in n and p.requires_grad], "lr": args.mask_scores_learning_rate, }, { "params": [ p for n, p in model.named_parameters() if "mask_score" not in n and p.requires_grad and not any(nd in n for nd in no_decay) ], "lr": args.learning_rate, "weight_decay": args.weight_decay, }, { "params": [ p for n, p in model.named_parameters() if "mask_score" not in n and p.requires_grad and any(nd in n for nd in no_decay) ], "lr": args.learning_rate, "weight_decay": 0.0, }, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) # Distillation if teacher is not None: logger.info(" Training with distillation") global_step = 0 # Global TopK if args.global_topk: threshold_mem = None epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to global_step of last saved checkpoint from model path try: global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) except ValueError: global_step = 0 epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0], ) set_seed(args) # Added here for reproducibility for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) threshold, regu_lambda = schedule_threshold( step=global_step, total_step=t_total, warmup_steps=args.warmup_steps, final_threshold=args.final_threshold, initial_threshold=args.initial_threshold, final_warmup=args.final_warmup, initial_warmup=args.initial_warmup, final_lambda=args.final_lambda, ) # Global TopK if args.global_topk: if threshold == 1.0: threshold = -1e2 # Or an indefinitely low quantity else: if (threshold_mem is None) or (global_step % args.global_topk_frequency_compute == 0): # Sort all the values to get the global topK concat = torch.cat( [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name] ) n = concat.numel() kth = max(n - (int(n * threshold) + 1), 1) threshold_mem = concat.kthvalue(kth).values.item() threshold = threshold_mem else: threshold = threshold_mem inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids if "masked" in args.model_type: inputs["threshold"] = threshold outputs = model(**inputs) loss, logits_stu = outputs # model outputs are always tuple in transformers (see doc) # Distillation loss if teacher is not None: if "token_type_ids" not in inputs: inputs["token_type_ids"] = None if args.teacher_type == "xlm" else batch[2] with torch.no_grad(): (logits_tea,) = teacher( input_ids=inputs["input_ids"], token_type_ids=inputs["token_type_ids"], attention_mask=inputs["attention_mask"], ) loss_logits = nn.functional.kl_div( input=nn.functional.log_softmax(logits_stu / args.temperature, dim=-1), target=nn.functional.softmax(logits_tea / args.temperature, dim=-1), reduction="batchmean", ) * (args.temperature**2) loss = args.alpha_distil * loss_logits + args.alpha_ce * loss # Regularization if args.regularization is not None: regu_ = regularization(model=model, mode=args.regularization) loss = loss + regu_lambda * regu_ if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0 or ( # last step in epoch but step is always smaller than gradient_accumulation_steps len(epoch_iterator) <= args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator) ): if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: tb_writer.add_scalar("threshold", threshold, global_step) for name, param in model.named_parameters(): if not param.requires_grad: continue tb_writer.add_scalar("parameter_mean/" + name, param.data.mean(), global_step) tb_writer.add_scalar("parameter_std/" + name, param.data.std(), global_step) tb_writer.add_scalar("parameter_min/" + name, param.data.min(), global_step) tb_writer.add_scalar("parameter_max/" + name, param.data.max(), global_step) tb_writer.add_scalar("grad_mean/" + name, param.grad.data.mean(), global_step) tb_writer.add_scalar("grad_std/" + name, param.grad.data.std(), global_step) if args.regularization is not None and "mask_scores" in name: if args.regularization == "l1": perc = (torch.sigmoid(param) > threshold).sum().item() / param.numel() elif args.regularization == "l0": perc = (torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1))).sum().item() / param.numel() tb_writer.add_scalar("retained_weights_perc/" + name, perc, global_step) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: logs = {} if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): eval_key = "eval_{}".format(key) logs[eval_key] = value loss_scalar = (tr_loss - logging_loss) / args.logging_steps learning_rate_scalar = scheduler.get_lr() logs["learning_rate"] = learning_rate_scalar[0] if len(learning_rate_scalar) > 1: for idx, lr in enumerate(learning_rate_scalar[1:]): logs[f"learning_rate/{idx+1}"] = lr logs["loss"] = loss_scalar if teacher is not None: logs["loss/distil"] = loss_logits.item() if args.regularization is not None: logs["loss/regularization"] = regu_.item() if (teacher is not None) or (args.regularization is not None): if (teacher is not None) and (args.regularization is not None): logs["loss/instant_ce"] = ( loss.item() - regu_lambda * logs["loss/regularization"] - args.alpha_distil * logs["loss/distil"] ) / args.alpha_ce elif teacher is not None: logs["loss/instant_ce"] = ( loss.item() - args.alpha_distil * logs["loss/distil"] ) / args.alpha_ce else: logs["loss/instant_ce"] = loss.item() - regu_lambda * logs["loss/regularization"] logging_loss = tr_loss for key, value in logs.items(): tb_writer.add_scalar(key, value, global_step) print(json.dumps({**logs, **{"step": global_step}})) if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix=""): # Loop to handle MNLI double evaluation (matched, mis-matched) eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) eval_outputs_dirs = (args.output_dir, args.output_dir + "/MM") if args.task_name == "mnli" else (args.output_dir,) results = {} for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: os.makedirs(eval_output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None # Global TopK if args.global_topk: threshold_mem = None for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None ) # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids if "masked" in args.model_type: inputs["threshold"] = args.final_threshold if args.global_topk: if threshold_mem is None: concat = torch.cat( [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name] ) n = concat.numel() kth = max(n - (int(n * args.final_threshold) + 1), 1) threshold_mem = concat.kthvalue(kth).values.item() inputs["threshold"] = threshold_mem outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps if args.output_mode == "classification": from scipy.special import softmax probs = softmax(preds, axis=-1) entropy = np.exp((-probs * np.log(probs)).sum(axis=-1).mean()) preds = np.argmax(preds, axis=1) elif args.output_mode == "regression": preds = np.squeeze(preds) result = compute_metrics(eval_task, preds, out_label_ids) results.update(result) if entropy is not None: result["eval_avg_entropy"] = entropy output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results {} *****".format(prefix)) for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) return results def load_and_cache_examples(args, task, tokenizer, evaluate=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache processor = processors[task]() output_mode = output_modes[task] # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), str(task), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) label_list = processor.get_labels() if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]: # HACK(label indices are swapped in RoBERTa pretrained model) label_list[1], label_list[2] = label_list[2], label_list[1] examples = ( processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) ) features = convert_examples_to_features( examples, tokenizer, max_length=args.max_seq_length, label_list=label_list, output_mode=output_mode, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) if output_mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif output_mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pretrained model or model identifier from huggingface.co/models", ) parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(processors.keys()), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) # Other parameters parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--max_seq_length", default=128, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.", ) parser.add_argument( "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.", ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") # Pruning parameters parser.add_argument( "--mask_scores_learning_rate", default=1e-2, type=float, help="The Adam initial learning rate of the mask scores.", ) parser.add_argument( "--initial_threshold", default=1.0, type=float, help="Initial value of the threshold (for scheduling)." ) parser.add_argument( "--final_threshold", default=0.7, type=float, help="Final value of the threshold (for scheduling)." ) parser.add_argument( "--initial_warmup", default=1, type=int, help=( "Run `initial_warmup` * `warmup_steps` steps of threshold warmup during which threshold stays " "at its `initial_threshold` value (sparsity schedule)." ), ) parser.add_argument( "--final_warmup", default=2, type=int, help=( "Run `final_warmup` * `warmup_steps` steps of threshold cool-down during which threshold stays " "at its final_threshold value (sparsity schedule)." ), ) parser.add_argument( "--pruning_method", default="topK", type=str, help=( "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning," " sigmoied_threshold = Soft movement pruning)." ), ) parser.add_argument( "--mask_init", default="constant", type=str, help="Initialization method for the mask scores. Choices: constant, uniform, kaiming.", ) parser.add_argument( "--mask_scale", default=0.0, type=float, help="Initialization parameter for the chosen initialization method." ) parser.add_argument("--regularization", default=None, help="Add L0 or L1 regularization to the mask scores.") parser.add_argument( "--final_lambda", default=0.0, type=float, help="Regularization intensity (used in conjunction with `regularization`.", ) parser.add_argument("--global_topk", action="store_true", help="Global TopK on the Scores.") parser.add_argument( "--global_topk_frequency_compute", default=25, type=int, help="Frequency at which we compute the TopK global threshold.", ) # Distillation parameters (optional) parser.add_argument( "--teacher_type", default=None, type=str, help=( "Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for" " distillation." ), ) parser.add_argument( "--teacher_name_or_path", default=None, type=str, help="Path to the already fine-tuned teacher model. Only for distillation.", ) parser.add_argument( "--alpha_ce", default=0.5, type=float, help="Cross entropy loss linear weight. Only for distillation." ) parser.add_argument( "--alpha_distil", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation." ) parser.add_argument( "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation." ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.") 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("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets", ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. " "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") args = parser.parse_args() # Regularization if args.regularization == "null": args.regularization = None if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to" " overcome." ) # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of synchronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set seed set_seed(args) # Prepare GLUE task args.task_name = args.task_name.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] label_list = processor.get_labels() num_labels = len(label_list) # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name, cache_dir=args.cache_dir if args.cache_dir else None, pruning_method=args.pruning_method, mask_init=args.mask_init, mask_scale=args.mask_scale, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None, do_lower_case=args.do_lower_case, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.teacher_type is not None: assert args.teacher_name_or_path is not None assert args.alpha_distil > 0.0 assert args.alpha_distil + args.alpha_ce > 0.0 teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type] teacher_config = teacher_config_class.from_pretrained(args.teacher_name_or_path) teacher = teacher_model_class.from_pretrained( args.teacher_name_or_path, from_tf=False, config=teacher_config, cache_dir=args.cache_dir if args.cache_dir else None, ) teacher.to(args.device) else: teacher = None if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) model.to(args.device) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = [ os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ] logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) result = evaluate(args, model, tokenizer, prefix=prefix) result = {k + "_{}".format(global_step): v for k, v in result.items()} results.update(result) return results if __name__ == "__main__": main()

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

{ "file_path": "transformers/examples/research_projects/movement-pruning/masked_run_glue.py", "repo_id": "transformers", "token_count": 18298 }

import json import os import shutil import sys import tempfile import unittest from unittest import TestCase from unittest.mock import patch import faiss import numpy as np from datasets import Dataset from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available from transformers.integrations import is_ray_available from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES from transformers.testing_utils import require_ray sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # noqa: E402 # isort:skip if is_torch_available(): from distributed_pytorch_retriever import RagPyTorchDistributedRetriever # noqa: E402 # isort:skip else: RagPyTorchDistributedRetriever = None if is_ray_available(): import ray # noqa: E402 # isort:skip from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever # noqa: E402 # isort:skip else: ray = None RagRayDistributedRetriever = None RayRetriever = None def require_distributed_retrieval(test_case): """ Decorator marking a test that requires a set of dependencies necessary for pefrorm retrieval with :class:`~transformers.RagRetriever`. These tests are skipped when respective libraries are not installed. """ if not (is_datasets_available() and is_faiss_available() and is_psutil_available()): test_case = unittest.skip("test requires Datasets, Faiss, psutil")(test_case) return test_case @require_distributed_retrieval class RagRetrieverTest(TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() self.retrieval_vector_size = 8 # DPR tok vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] dpr_tokenizer_path = os.path.join(self.tmpdirname, "dpr_tokenizer") os.makedirs(dpr_tokenizer_path, exist_ok=True) self.vocab_file = os.path.join(dpr_tokenizer_path, DPR_VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) # BART tok vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": "<unk>"} bart_tokenizer_path = os.path.join(self.tmpdirname, "bart_tokenizer") os.makedirs(bart_tokenizer_path, exist_ok=True) self.vocab_file = os.path.join(bart_tokenizer_path, BART_VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(bart_tokenizer_path, BART_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_dpr_tokenizer(self) -> DPRQuestionEncoderTokenizer: return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, "dpr_tokenizer")) def get_bart_tokenizer(self) -> BartTokenizer: return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname, "bart_tokenizer")) def tearDown(self): shutil.rmtree(self.tmpdirname) def get_dummy_dataset(self): dataset = Dataset.from_dict( { "id": ["0", "1"], "text": ["foo", "bar"], "title": ["Foo", "Bar"], "embeddings": [np.ones(self.retrieval_vector_size), 2 * np.ones(self.retrieval_vector_size)], } ) dataset.add_faiss_index("embeddings", string_factory="Flat", metric_type=faiss.METRIC_INNER_PRODUCT) return dataset def get_dummy_pytorch_distributed_retriever( self, init_retrieval: bool, port=12345 ) -> RagPyTorchDistributedRetriever: dataset = self.get_dummy_dataset() config = RagConfig( retrieval_vector_size=self.retrieval_vector_size, question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict(), ) with patch("transformers.models.rag.retrieval_rag.load_dataset") as mock_load_dataset: mock_load_dataset.return_value = dataset retriever = RagPyTorchDistributedRetriever( config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), ) if init_retrieval: retriever.init_retrieval(port) return retriever def get_dummy_ray_distributed_retriever(self, init_retrieval: bool) -> RagRayDistributedRetriever: # Have to run in local mode because sys.path modifications at top of # file are not propogated to remote workers. # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder ray.init(local_mode=True) config = RagConfig( retrieval_vector_size=self.retrieval_vector_size, question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict(), ) remote_cls = ray.remote(RayRetriever) workers = [remote_cls.remote() for _ in range(1)] with patch("transformers.models.rag.retrieval_rag.load_dataset") as mock_load_dataset: mock_load_dataset.return_value = self.get_dummy_dataset() retriever = RagRayDistributedRetriever( config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), retrieval_workers=workers, ) if init_retrieval: retriever.init_retrieval() return retriever def get_dummy_custom_hf_index_pytorch_retriever(self, init_retrieval: bool, from_disk: bool, port=12345): dataset = self.get_dummy_dataset() config = RagConfig( retrieval_vector_size=self.retrieval_vector_size, question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict(), index_name="custom", ) if from_disk: config.passages_path = os.path.join(self.tmpdirname, "dataset") config.index_path = os.path.join(self.tmpdirname, "index.faiss") dataset.get_index("embeddings").save(os.path.join(self.tmpdirname, "index.faiss")) dataset.drop_index("embeddings") dataset.save_to_disk(os.path.join(self.tmpdirname, "dataset")) del dataset retriever = RagPyTorchDistributedRetriever( config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), ) else: retriever = RagPyTorchDistributedRetriever( config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), index=CustomHFIndex(config.retrieval_vector_size, dataset), ) if init_retrieval: retriever.init_retrieval(port) return retriever def get_dummy_custom_hf_index_ray_retriever(self, init_retrieval: bool, from_disk: bool): # Have to run in local mode because sys.path modifications at top of # file are not propogated to remote workers. # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder ray.init(local_mode=True) dataset = self.get_dummy_dataset() config = RagConfig( retrieval_vector_size=self.retrieval_vector_size, question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict(), index_name="custom", ) remote_cls = ray.remote(RayRetriever) workers = [remote_cls.remote() for _ in range(1)] if from_disk: config.passages_path = os.path.join(self.tmpdirname, "dataset") config.index_path = os.path.join(self.tmpdirname, "index.faiss") dataset.get_index("embeddings").save(os.path.join(self.tmpdirname, "index.faiss")) dataset.drop_index("embeddings") dataset.save_to_disk(os.path.join(self.tmpdirname, "dataset")) del dataset retriever = RagRayDistributedRetriever( config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), retrieval_workers=workers, index=CustomHFIndex.load_from_disk( vector_size=config.retrieval_vector_size, dataset_path=config.passages_path, index_path=config.index_path, ), ) else: retriever = RagRayDistributedRetriever( config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer(), retrieval_workers=workers, index=CustomHFIndex(config.retrieval_vector_size, dataset), ) if init_retrieval: retriever.init_retrieval() return retriever def distributed_retriever_check(self, retriever: RagRetriever, hidden_states: np.array, n_docs: int) -> None: retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs) self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size)) self.assertEqual(len(doc_dicts), 2) self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"]) self.assertEqual(len(doc_dicts[0]["id"]), n_docs) self.assertEqual(doc_dicts[0]["id"][0], "1") # max inner product is reached with second doc self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc self.assertListEqual(doc_ids.tolist(), [[1], [0]]) def test_pytorch_distributed_retriever_retrieve(self): n_docs = 1 hidden_states = np.array( [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32 ) self.distributed_retriever_check( self.get_dummy_pytorch_distributed_retriever(init_retrieval=True), hidden_states, n_docs ) def test_custom_hf_index_pytorch_retriever_retrieve(self): n_docs = 1 hidden_states = np.array( [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32 ) self.distributed_retriever_check( self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=True, from_disk=False), hidden_states, n_docs, ) def test_custom_pytorch_distributed_retriever_retrieve_from_disk(self): n_docs = 1 hidden_states = np.array( [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32 ) self.distributed_retriever_check( self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=True, from_disk=True), hidden_states, n_docs, ) @require_ray def test_ray_distributed_retriever_retrieve(self): n_docs = 1 hidden_states = np.array( [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32 ) self.distributed_retriever_check( self.get_dummy_ray_distributed_retriever(init_retrieval=True), hidden_states, n_docs ) ray.shutdown() @require_ray def test_custom_hf_index_ray_retriever_retrieve(self): n_docs = 1 hidden_states = np.array( [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32 ) with self.assertRaises(ValueError): self.distributed_retriever_check( self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=True, from_disk=False), hidden_states, n_docs, ) ray.shutdown() @require_ray def test_custom_ray_distributed_retriever_retrieve_from_disk(self): n_docs = 1 hidden_states = np.array( [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32 ) self.distributed_retriever_check( self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=True, from_disk=True), hidden_states, n_docs ) ray.shutdown()

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

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# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module. import os import sys from pathlib import Path import torch from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu from utils import load_json CUDA_AVAILABLE = torch.cuda.is_available() ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."] SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"] CHEAP_ARGS = { "max_tokens_per_batch": None, "supervise_forward": True, "normalize_hidden": True, "label_smoothing": 0.2, "eval_max_gen_length": None, "eval_beams": 1, "val_metric": "loss", "save_top_k": 1, "adafactor": True, "early_stopping_patience": 2, "logger_name": "default", "length_penalty": 0.5, "cache_dir": "", "task": "summarization", "num_workers": 2, "alpha_hid": 0, "freeze_embeds": True, "enc_only": False, "tgt_suffix": "", "resume_from_checkpoint": None, "sortish_sampler": True, "student_decoder_layers": 1, "val_check_interval": 1.0, "output_dir": "", "fp16": False, # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp "no_teacher": False, "fp16_opt_level": "O1", "gpus": 1 if CUDA_AVAILABLE else 0, "n_tpu_cores": 0, "max_grad_norm": 1.0, "do_train": True, "do_predict": True, "accumulate_grad_batches": 1, "server_ip": "", "server_port": "", "seed": 42, "model_name_or_path": "sshleifer/bart-tiny-random", "config_name": "", "tokenizer_name": "facebook/bart-large", "do_lower_case": False, "learning_rate": 0.3, "lr_scheduler": "linear", "weight_decay": 0.0, "adam_epsilon": 1e-08, "warmup_steps": 0, "max_epochs": 1, "train_batch_size": 2, "eval_batch_size": 2, "max_source_length": 12, "max_target_length": 12, "val_max_target_length": 12, "test_max_target_length": 12, "fast_dev_run": False, "no_cache": False, "n_train": -1, "n_val": -1, "n_test": -1, "student_encoder_layers": 1, "freeze_encoder": False, "auto_scale_batch_size": False, "overwrite_output_dir": False, "student": None, } def _dump_articles(path: Path, articles: list): content = "\n".join(articles) Path(path).open("w").writelines(content) def make_test_data_dir(tmp_dir): for split in ["train", "val", "test"]: _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES) _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES) return tmp_dir class TestSummarizationDistillerMultiGPU(TestCasePlus): @classmethod def setUpClass(cls): return cls @require_torch_multi_gpu def test_multi_gpu(self): updates = { "no_teacher": True, "freeze_encoder": True, "gpus": 2, "overwrite_output_dir": True, "sortish_sampler": True, } self._test_distiller_cli_fork(updates, check_contents=False) def _test_distiller_cli_fork(self, updates, check_contents=True): default_updates = { "label_smoothing": 0.0, "early_stopping_patience": -1, "train_batch_size": 1, "eval_batch_size": 2, "max_epochs": 2, "alpha_mlm": 0.2, "alpha_ce": 0.8, "do_predict": True, "model_name_or_path": "sshleifer/tinier_bart", "teacher": CHEAP_ARGS["model_name_or_path"], "val_check_interval": 0.5, } default_updates.update(updates) args_d: dict = CHEAP_ARGS.copy() tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) output_dir = self.get_auto_remove_tmp_dir() args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates) def convert(k, v): if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]: return "" if v is False or v is None: return "" if v is True: # or len(str(v))==0: return f"--{k}" return f"--{k}={v}" cli_args = [x for x in (convert(k, v) for k, v in args_d.items()) if len(x)] cmd = [sys.executable, f"{self.test_file_dir}/distillation.py"] + cli_args execute_subprocess_async(cmd, env=self.get_env()) contents = os.listdir(output_dir) contents = {os.path.basename(p) for p in contents} ckpt_files = [p for p in contents if p.endswith("ckpt")] assert len(ckpt_files) > 0 self.assertIn("test_generations.txt", contents) self.assertIn("test_results.txt", contents) # get the following from the module, (we don't have access to `model` here) metrics_save_path = os.path.join(output_dir, "metrics.json") val_metric = "rouge2" metrics = load_json(metrics_save_path) # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]} print(metrics) last_step_stats = metrics["val"][-1] self.assertGreaterEqual(last_step_stats["val_avg_gen_time"], 0.01) self.assertIsInstance(last_step_stats[f"val_avg_{val_metric}"], float) self.assertEqual(len(metrics["test"]), 1) desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1) self.assertEqual(len(metrics["val"]), desired_n_evals)

transformers/examples/research_projects/seq2seq-distillation/_test_seq2seq_examples_multi_gpu.py/0

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<a name="readme-top"></a>  ## What is token healing? Token healing rectifies the token boundary bias in greedy tokenization. It does this by trimming and regrowing the prompt to better align with the model's tokenizer, thus enhancing generation quality. The improvement is clearest with completion models. Example: given a completion prompt with a partial url ending with `:`, the model might have seen the expected completion `://` as a _single_ token in training. However, the prompt's tail token `:` tells it that the next token is not `//`, and so it looks for wrong completions. Such errors compound in auto-regressive language models. Debiasing token boundaries also addresses output sensitivity to prompts ending with whitespace. A more thorough explanation can be found on [The Art of Prompt Design: Prompt Boundaries and Token Healing | by Scott Lundberg](https://towardsdatascience.com/the-art-of-prompt-design-prompt-boundaries-and-token-healing-3b2448b0be38). ## Usage ```py prompt = 'The link is <a href="http:' raw_output = generate(prompt, completion_model, tokenizer, token_healing=False) # The link is <a href="http://www/dailymail&# # The model saw '://' as a single token in training. Seeing a prompt ending with `:` tells it that the # next token is likely not `//`, because otherwise it would've seen `://`. # Thus, it completes with a token other than `//`, in this case, `&`. healed_output = generate(prompt, completion_model, tokenizer, token_healing=True) # The link is <a href="http://www.365doki.com/post/3699 # You can also use token healing in isolation # This can be useful if you have other work to do before the generation # Or if you want to delegate generation to another process input_ids = tokenizer(test_prompts, return_tensors='pt', padding=True).input_ids.cuda() healed_ids = model.heal_tokens(input_ids) healed_prompts = tokenizer.batch_decode(healed_ids, skip_special_tokens=True) # outputs the healed prompts without further completion/generation ``` See `run_token_healing.py` for the full example. <p align="right">(<a href="#readme-top">back to top</a>)</p>

transformers/examples/research_projects/token-healing/README.md/0

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# Fine-Tuning week of XLSR-Wav2Vec2 on 60 languages 🌍 Welcome to the fine-tuning week! The goal of this week is to have state-of-the-art automatic speech recognition (ASR) models in as many languages as possible. The fine-tuning week ends on Friday, the 26th March at midnight PST time. Participants are encouraged to fine-tune the pretrained [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) checkpoint on one or more of the 60 languages of [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets). Furthermore, it is very much appreciated if participants fine-tune XLSR-Wav2Vec2 on a language that is not included in the Common Voice dataset. All fine-tuned models uploaded until Friday, the 26th March midnight PST, will be taken into account for competition, and the best model per language will be awarded a prize if the best model performs reasonably well. The testing data to evaluate the models will be the official [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets) *`test data`* of version 6.1. Again, participants are very much encouraged to fine-tune XLSR-Wav2Vec2 on languages that are not found in the Common Voice dataset since those languages are even more likely to be underrepresented in the speech community. Each model fine-tuned on a language not found in Common Voice, will be evaluated by the Hugging Face team after Friday, the 26th March at midnight PST, and if the model performs reasonably well, the model receives a prize as well. For more information on which data can be used for training, how the models are evaluated exactly, and what type of data preprocessing can be used, please see ["Training and Evaluation Rules"](#training-and-evaluation-rules). **Please keep in mind:** The spirit of the fine-tuning week is to provide state-of-the-art speech recognition in as many languages as possible to the community! So while we encourage healthy competition between people/groups of the same language so that better results are obtained, it is extremely important that we help each other and share our insights with the whole team/community. What matters in the end is what has been achieved by the team as a whole during the fine-tuning week. That being said, we strongly encourage people to share tips & tricks on the forum or Slack, help each other when team members encounter bugs, and work in groups. To make it easier to share and help, forum threads have been created under the name {language} ASR: Fine-Tuning Wav2Vec2, e.g. here. It is very much possible that prizes will be given to groups of people instead of individuals. Also, don't hesitate to ask questions, propose improvements to the organization, to the material given to participants, etc...🤗 ## Table of Contents - [Organization of the fine tuning week](#organization-of-the-fine-tuning-week) - [How to fine tune XLSR Wav2Vec2](#how-to-fine-tune-xlsr-wav2vec2) - [Google colab setup](#google-colab-setup) - [Local machine](#local-machine) - [How to upload my trained checkpoint](#how-to-upload-my-trained-checkpoint) - [How to create the README](#how-to-create-the-readme) - [How to evaluate my trained checkpoint](#how-to-evaluate-my-trained-checkpoint) - [Rules of training and evaluation](#rules-of-training-and-evaluation) - [Tips and tricks](#tips-and-tricks) - [How to combine multiple datasests into one](#how-to-combine-multiple-datasets-into-one) - [How to effectively preprocess the data](#how-to-effectively-preprocess-the-data) - [How to efficiently preproces the data](#how-to-do-efficiently-load-datasets-with-limited-ram-and-hard-drive-space) - [How to do hyperparameter tuning](#how-to-do-hyperparameter-tuning) - [How to preprocess and evaluate character based languages](#how-to-preprocess-and-evaluate-character-based-languages) - [Further reading material](#further-reading-material) - [FAQ](#faq) ## Organization of the fine tuning week The week officially starts on 22.03.2021 and ends on 29.03.2021, but you are more than welcome to start fine-tuning models before the start date. General questions you might have, general problems you encounter, and general tips can be shared directly on the Slack channel (see [this post](https://discuss.huggingface.co/t/open-to-the-community-xlsr-wav2vec2-fine-tuning-week-for-low-resource-languages/4467) on how to be added to Slack). More language-specific questions or specific bugs should be posted on the [forum](https://discuss.huggingface.co/) (feel free to use already existing language-specific threads, *e.g.* [this one](https://discuss.huggingface.co/t/arabic-asr-fine-tuning-wav2vec2/4608) or open a new one if there is no thread for your language yet) or directly on [github](https://github.com/huggingface/transformers) if you think some code or document needs correction/improvement. Starting on Monday, the 22.03.2021, the Hugging Face team will try to provide an overview of currently trained models along with their evaluation results. All the necessary information on: - How to fine-tune the XLSR model - How to upload the model - How to share your evaluation results & training/eval script - What are the training/evaluation rules can be found in the sections below. If something is still unclear, feel free to drop a message in the Slack channel. ## How to fine tune XLSR Wav2Vec2 This chapter gives an in-detail explanation of how to fine-tune [Facebook's multi-lingual Wav2vec2](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on any language of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets). Two possible setups can be used to fine-tune Wav2Vec2. The easiest setup is to simply use [google colab](https://colab.research.google.com/). It is possible to train the full model in a *free* google colab, but it is recommended to use google colab pro since it is more stable. The other option is to run a script locally. While this can be more difficult to set up, it also means that you have more control over the training run and probably access to better GPUs than you would have in a google colab. For small datasets, it is usually totally sufficient to train your model in a google colab. For larger and thus more memory-intensive datasets, it is probably better to fine-tune the model locally. For each option, we explain in detail how to fine-tune XLSR-Wav2Vec2 in the following. ### Google colab setup **Note**: Instead of reading the following section, you can simply watch [this](https://www.youtube.com/watch?v=UynYn2C3tI0&ab_channel=PatrickvonPlaten) video, where Patrick explains how to adapt the google colab for your specific language. **1.**: If you plan on training XLSR-Wav2Vec2 in a google colab, you should first make sure to have a valid gmail account. You can sign up for a gmail account [here](https://accounts.google.com/signup/v2/webcreateaccount?hl=en&flowName=GlifWebSignIn&flowEntry=SignUp). Having successfully signed up for gmail, you can now sign in to your account to make sure you are logged in when opening new tabs in your browser. **2.**: Next, head over to the official [Fine-Tune XLSR-Wav2Vec2 with 🤗 Transformes](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb) google colab. The first thing you should do is to make a copy of it - click `->File->Save a copy in Drive`. This should save a copy of the google colab in your google drive. **3.**: Now it is highly recommended to carefully read the google colab without running the cells yet. You should get an understanding of the model is trained and what you will have to change when training the model in a different language. Having done so, you can again head over to [Common Voice](https://commonvoice.mozilla.org/en/datasets) and pick a language you want to fine-tune [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on. Make sure you remember the language code (For each language, you can find it under the field "*Version*". It corresponds to **all characters before the first underscore**. *E.g.* for Greek it is *el*, while for Irish it is *ga-IE*. **4.**: Now you should replace the language code used for the demo of this colab, being *tr* for Turkish with the language code corresponding to the language you just chose in the **second** cell of the google colab. This will load the correct data for your language. **5.**: It is time to start running the google colab! Make sure that you have selected "GPU" as your runtime environment and you can start running the cells one-by-one. Make sure you attentively read the text between the cells to understand what is happening and to eventually correct the cells to improve the fine-tuning script for your language. Things you might want to improve/change: - Data loading. It is very much recommended to use more than just the official training data of the Common Voice dataset. If you find more data on the internet, feel free to use it! Check out the section ["How to combined multiple datasets into one"](#how-to-combine-multiple-datasets-into-one) - Data Processing. You should adapt the data processing to your specific language. In data processing, you should make the data more uniform so that it will be easier for the model to learn how to classify speech in your data. Here it can be really helpful to be proficient in the language to know what can be done to simplify the language without changing the meaning. Data processing methods include, but are not limited to: - Normalizing your data. Make sure all characters are lower-cased. - Remove typographical symbols and punctuation marks. See a list [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks). Be careful to not remove punctuation marks that can change the meaning of the sentence. *E.g.* you should not remove the single quotation mark `'` in English, as it would change the words `"it's"` to `"its"` which is a different word and has thus a different meaning. For more tips on data processing see ["How to effectively preprocess the data"](#how-to-effectively-preprocess-the-data") - Hyperparameter Tuning. Depending on the size of the data you should probably change the hyperparameters of the google colab. You can change any parameter you like. For more tips and tricks see ["How to do hyperparameter tuning for my language"](#how-to-do-hyperparameter-tuning-for-my-language) When running the google colab make sure that you uncomment the cell corresponding to mounting your google drive to the colab. This cell looks as follows: ```python # from google.colab import drive # drive.mount('/content/gdrive/') ``` Uncomment it, run it, and follow the instructions to mount your google drive. This way you can be sure that the model parameters and created tokenizer & feature extractor files are saved in **your** google drive. Also, make sure that you uncomment the cells corresponding to save the preprocessing files and trained model weights to your drive. Otherwise, you might lose a trained model if you google crashes. You should change the name of your model from `wav2vec2-large-xlsr-turkish-demo` to `wav2vec2-large-xlsr-{your_favorite_name}`. Those cells correspond to: ```python # processor.save_pretrained("/content/gdrive/MyDrive/wav2vec2-large-xlsr-turkish-demo") ``` and the line: ```python output_dir="/content/gdrive/MyDrive/wav2vec2-large-xlsr-turkish-demo", ``` further below (which should already be uncommented). Having finished the training you should find the following files/folders under the folder `wav2vec2-large-xlsr-{your_favorite_name}` in your google drive: - `preprocessor_config.json` - the parameters of the feature extractor - `special_tokens_map.json` - the special token map of the tokenizer - `tokenizer_config.json` - the parameters of the tokenizer - `vocab.json` - the vocabulary of the tokenizer - `checkpoint-{...}/` - the saved checkpoints saved during training. Each checkpoint should contain the files: `config.json`, `optimizer.pt`, `pytorch_model.bin`, `scheduler.pt`, `training_args.bin`. The files `config.json` and `pytorch_model.bin` define your model. If you are happy with your training results it is time to upload your model! Download the following files to your local computer: **`preprocessor_config.json`, `special_tokens_map.json`, `tokenizer_config.json`, `vocab.json`, `config.json`, `pytorch_model.bin`**. Those files fully define a XLSR-Wav2Vec2 model checkpoint. Awesome you have successfully trained a XLSR-Wav2Vec2 model 😎. Now you can jump to the section ["How to upload my trained checkpoint"](#how-to-upload-my-trained-checkpoint) ### Local machine We have provided `run_common_voice.py` script to run fine-tuning on local machine. The script is similar to the colab but allows you to launch training using command line, save and continue training from previous checkpoints and launch training on multiple GPUs. For bigger datasets, we recommend to train Wav2Vec2 locally instead of in a google colab. 1. To begin with, we should clone transformers localy and install all the required packages. First, you need to clone the `transformers` repo with: ```bash $ git clone https://github.com/huggingface/transformers.git ``` Second, head over to the `examples/research_projects/wav2vec2` directory, where the `run_common_voice.py` script is located. ```bash $ cd transformers/examples/research_projects/wav2vec2 ``` Third, install the required packages. The packages are listed in the `requirements.txt` file and can be installed with ```bash $ pip install -r requirements.txt ``` **Note**: Installing the latest version of `torchaudio` will also upgrade `torch` to it's latest stable version. If you are using specific version of `torch` then make sure to use the correct `torchaudio` version compatible with your version of `torch`. By default the `requirements.txt` will install the latest version of `torchaudio`. 2. Next, take a look at the `run_common_voice.py` script to get an understanding of how it works. In short the script does the following: - Load the given common voice dataset - Create vocab for the language - Load the model with given hyperparameters - Pre-process the dataset to input into the model - Run training - Run evaluation 3. The following examples show how you can launch fine-tuning for the common voice dataset. Here we will run the script on the *Turkish* Common Voice dataset for demonstration purposes. **To lanuch fine-tuninig on a single GPU:** ```bash python run_common_voice.py \ --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \ --dataset_config_name="tr" \ # use this argument to specify the language code --output_dir=./wav2vec2-large-xlsr-turkish-demo \ --overwrite_output_dir \ --num_train_epochs="5" \ --per_device_train_batch_size="16" \ --learning_rate="3e-4" \ --warmup_steps="500" \ --eval_strategy="steps" \ --save_steps="400" \ --eval_steps="400" \ --logging_steps="400" \ --save_total_limit="3" \ --freeze_feature_extractor \ --feat_proj_dropout="0.0" \ --layerdrop="0.1" \ --gradient_checkpointing \ --fp16 \ --group_by_length \ --do_train --do_eval ``` **To lanuch fine-tuninig on multiple GPUs:** ```bash python -m torch.distributed.launch \ --nproc_per_node 4 run_common_voice.py \ --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \ --dataset_config_name="tr" \ # use this argument to specify the language code --output_dir=./wav2vec2-large-xlsr-turkish-demo \ --overwrite_output_dir \ --num_train_epochs="5" \ --per_device_train_batch_size="16" \ --learning_rate="3e-4" \ --warmup_steps="500" \ --eval_strategy="steps" \ --save_steps="400" \ --eval_steps="400" \ --logging_steps="400" \ --save_total_limit="3" \ --freeze_feature_extractor \ --feat_proj_dropout="0.0" \ --layerdrop="0.1" \ --gradient_checkpointing \ --fp16 \ --group_by_length \ --do_train --do_eval ``` The above command will launch the training on 4 GPUs. Use the `--nproc_per_node` option to specify the number of GPUs. Once the training is finished, the model and checkpoints will be saved under the directory specified by the `--output_dir` argument. 4. The script also allows you to resume training from the last saved checkpoint. To resume training from last saved checkpoint remove the `--overwrite_output_dir` option and run the same command again. And to continue training from a specific checkpoint, keep the `--overwrite_output_dir` option and pass the path of the checkpoint as `--model_name_or_path`. As the script is based on the `Trainer` API, refer to the [Trainer docs](https://huggingface.co/transformers/main_classes/trainer.html) for more information about ``Trainer`` and ``TrainingArguments``. [OVH cloud](https://www.ovh.com/world/) has generously offered free compute for this sprint. Please refer to [this video](https://www.youtube.com/watch?v=2hlkWAESMk8&ab_channel=Databuzzword) to get started with OVH. ## How to upload my trained checkpoint To upload your trained checkpoint, you have to create a new model repository on the 🤗 model hub, from this page: https://huggingface.co/new > You can also follow the more in-depth instructions [here](https://huggingface.co/transformers/model_sharing.html) if needed. Having created your model repository on the hub, you should clone it locally: ```bash git lfs install git clone https://huggingface.co/username/your-model-name ``` Then and add the following files that fully define a XLSR-Wav2Vec2 checkpoint into the repository. You should have added the following files. - `preprocessor_config.json` - `special_tokens_map.json` - `tokenizer_config.json` - `vocab.json` - `config.json` - `pytorch_model.bin` Having added the above files, you should run the following to push files to your model repository. ```bash git add . && git commit -m "Add model files" && git push ``` The next **very important** step is to create the model card. For people to use your fine-tuned model it is important to understand: - What kind of model is it? - What is your model useful for? - What data was your model trained on? - How well does your model perform? All these questions should be answered in a model card which is the first thing people see when visiting your model on the hub under `https://huggingface.co/{your_username}/{your_modelname}`. **Note**: It is extremely important that you add this model card or else we cannot find your model and thus cannot take the model into account for the final evaluation. ### How to create the readme The model card is written in markdown (`.md`) and should be added by simply clicking on the "Add model card" button which is found on the top right corner. You are encouraged to copy-paste the following template into your model card. **Make sure that** instead of copying the output of the markdown file you copy the **raw** version of the following part. To get the raw version of this file, simply click on the "`raw`" button on the top right corner of this file next to "`blame`" and copy everything below the marker. Make sure that you read and consequently remove all #TODO: statements from the model card. <======================Copy **raw** version from here========================= --- language: {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. datasets: - common_voice #TODO: remove if you did not use the common voice dataset - TODO: add more datasets if you have used additional datasets. Make sure to use the exact same dataset name as the one found [here](https://huggingface.co/datasets). If the dataset can not be found in the official datasets, just give it a new name metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: {human_readable_name} #TODO: replace {human_readable_name} with a name of your model as it should appear on the leaderboard. It could be something like `Elgeish XLSR Wav2Vec2 Large 53` results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. type: common_voice args: {lang_id} #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. metrics: - name: Test WER type: wer value: {wer_result_on_test} #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). **Please** remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value --- # Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, *e.g.* French Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voice When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "{lang_id}", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ``` ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "{lang_id}", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # TODO: adapt this list to include all special characters you removed from the data resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: XX.XX % # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags. ## Training The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO: adapt to state all the datasets that were used for training. The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here. =======================To here===============================> Your model in then available under *huggingface.co/{your_username}/{your_chosen_xlsr-large_model_name}* for everybody to use 🎉. ## How to evaluate my trained checkpoint Having uploaded your model, you should now evaluate your model in a final step. This should be as simple as copying the evaluation code of your model card into a python script and running it. Make sure to note the final result on the model card **both** under the YAML tags at the very top **and** below your evaluation code under "Test Results". ## Rules of training and evaluation In this section, we will quickly go over what data is allowed to be used as training data, what kind of data preprocessing is allowed be used, and how the model should be evaluated. To make it very simple regarding the first point: **All data except the official common voice `test` data set can be used as training data**. For models trained in a language that is not included in Common Voice, the author of the model is responsible to leave a reasonable amount of data for evaluation. Second, the rules regarding the preprocessing are not that as straight-forward. It is allowed (and recommended) to normalize the data to only have lower-case characters. It is also allowed (and recommended) to remove typographical symbols and punctuation marks. A list of such symbols can *e.g.* be fonud [here](https://en.wikipedia.org/wiki/List_of_typographical_symbols_and_punctuation_marks) - however here we already must be careful. We should **not** remove a symbol that would change the meaning of the words, *e.g.* in English, we should not remove the single quotation mark `'` since it would change the meaning of the word `"it's"` to `"its"` which would then be incorrect. So the golden rule here is to not remove any characters that could change the meaning of a word into another word. This is not always obvious and should be given some consideration. As another example, it is fine to remove the "Hypen-minus" sign "`-`" since it doesn't change the meaninng of a word to another one. *E.g.* "`fine-tuning`" would be changed to "`finetuning`" which has still the same meaning. Since those choices are not always obvious when in doubt feel free to ask on Slack or even better post on the forum, as was done, *e.g.* [here](https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586). ## Tips and tricks This section summarizes a couple of tips and tricks across various topics. It will continously be updated during the week. ### How to combine multiple datasets into one Check out [this](https://discuss.huggingface.co/t/how-to-combine-local-data-files-with-an-official-dataset/4685) post. ### How to effectively preprocess the data ### How to do efficiently load datasets with limited ram and hard drive space Check out [this](https://discuss.huggingface.co/t/german-asr-fine-tuning-wav2vec2/4558/8?u=patrickvonplaten) post. ### How to do hyperparameter tuning ### How to preprocess and evaluate character based languages ## Further reading material It is recommended that take some time to read up on how Wav2vec2 works in theory. Getting a better understanding of the theory and the inner mechanisms of the model often helps when fine-tuning the model. **However**, if you don't like reading blog posts/papers, don't worry - it is by no means necessary to go through the theory to fine-tune Wav2Vec2 on your language of choice. If you are interested in learning more about the model though, here are a couple of resources that are important to better understand Wav2Vec2: - [Facebook's Wav2Vec2 blog post](https://ai.facebook.com/blog/wav2vec-state-of-the-art-speech-recognition-through-self-supervision/) - [Official Wav2Vec2 paper](https://arxiv.org/abs/2006.11477) - [Official XLSR Wav2vec2 paper](https://arxiv.org/pdf/2006.13979.pdf) - [Hugging Face Blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) - [How does CTC (Connectionist Temporal Classification) work](https://distill.pub/2017/ctc/) It helps to have a good understanding of the following points: - How was XLSR-Wav2Vec2 pretrained? -> Feature vectors were masked and had to be predicted by the model; very similar in spirit to masked language model of BERT. - What parts of XLSR-Wav2Vec2 are responsible for what? What is the feature extractor part used for? -> extract feature vectors from the 1D raw audio waveform; What is the transformer part doing? -> mapping feature vectors to contextualized feature vectors; ... - What part of the model needs to be fine-tuned? -> The pretrained model **does not** include a language head to classify the contextualized features to letters. This is randomly initialized when loading the pretrained checkpoint and has to be fine-tuned. Also, note that the authors recommend to **not** further fine-tune the feature extractor. - What data was used to XLSR-Wav2Vec2? The checkpoint we will use for further fine-tuning was pretrained on **53** languages. - What languages are considered to be similar by XLSR-Wav2Vec2? In the official [XLSR Wav2Vec2 paper](https://arxiv.org/pdf/2006.13979.pdf), the authors show nicely which languages share a common contextualized latent space. It might be useful for you to extend your training data with data of other languages that are considered to be very similar by the model (or you). ## FAQ - Can a participant fine-tune models for more than one language? Yes! A participant can fine-tune models in as many languages she/he likes - Can a participant use extra data (apart from the common voice data)? Yes! All data except the official common voice `test data` can be used for training. If a participant wants to train a model on a language that is not part of Common Voice (which is very much encouraged!), the participant should make sure that some test data is held out to make sure the model is not overfitting. - Can we fine-tune for high-resource languages? Yes! While we do not really recommend people to fine-tune models in English since there are already so many fine-tuned speech recognition models in English. However, it is very much appreciated if participants want to fine-tune models in other "high-resource" languages, such as French, Spanish, or German. For such cases, one probably needs to train locally and apply might have to apply tricks such as lazy data loading (check the ["Lazy data loading"](#how-to-do-lazy-data-loading) section for more details).

transformers/examples/research_projects/wav2vec2/FINE_TUNE_XLSR_WAV2VEC2.md/0

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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. # XXX: we want transformers master here - in the absense of conftest manipulating sys.path: # hack it in for now: import sys from pathlib import Path git_repo_path = Path(__file__).resolve().parents[3] / "src" sys.path.insert(1, str(git_repo_path)) import dataclasses # noqa import io # noqa import itertools # noqa import json # noqa import os # noqa import unittest # noqa from copy import deepcopy # noqa from parameterized import parameterized # noqa from transformers import TrainingArguments, is_torch_available # noqa from transformers.integrations.deepspeed import is_deepspeed_available # noqa from transformers.file_utils import WEIGHTS_NAME # noqa from transformers.testing_utils import ( # noqa CaptureLogger, ExtendSysPath, TestCasePlus, execute_subprocess_async, get_gpu_count, mockenv_context, require_deepspeed, require_torch_gpu, require_torch_multi_gpu, slow, ) from transformers.trainer_utils import set_seed # noqa set_seed(42) models = {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"} ZERO2 = "zero2" ZERO3 = "zero3" stages = [ZERO2, ZERO3] def custom_name_func(func, param_num, param): # customize the test name generator function as we want both params to appear in the sub-test # name, as by default it shows only the first param param_based_name = parameterized.to_safe_name("_".join(str(x) for x in param.args)) return f"{func.__name__}_{param_based_name}" # Cartesian-product of zero stages with models to test params = list(itertools.product(stages, models.keys())) @slow @require_deepspeed @require_torch_gpu class TestDeepSpeedWav2Vec2(TestCasePlus): @parameterized.expand(params, name_func=custom_name_func) def test_fp32_non_distributed(self, stage, model): self.run_and_check( stage=stage, model=model, distributed=False, fp16=False, ) @require_torch_multi_gpu @parameterized.expand(params, name_func=custom_name_func) def test_fp32_distributed(self, stage, model): self.run_and_check( stage=stage, model=model, distributed=True, fp16=False, ) @parameterized.expand(params, name_func=custom_name_func) def test_fp16_non_distributed(self, stage, model): self.run_and_check( stage=stage, model=model, distributed=False, fp16=True, ) @require_torch_multi_gpu @parameterized.expand(params, name_func=custom_name_func) def test_fp16_distributed(self, stage, model): self.run_and_check( stage=stage, model=model, distributed=True, fp16=True, ) def do_checks(self, output_dir): # XXX: run_asr is premature and doesn't save any results # so all we check for now is that the process didn't fail pass # XXX: need to do better validation beyond just that the run was successful def run_and_check( self, stage: str, model: str, eval_steps: int = 10, distributed: bool = True, quality_checks: bool = True, fp16: bool = True, ): model_name = models[model] output_dir = self.run_trainer( stage=stage, model_name=model_name, eval_steps=eval_steps, num_train_epochs=1, distributed=distributed, fp16=fp16, ) self.do_checks(output_dir) return output_dir def run_trainer( self, stage: str, model_name: str, eval_steps: int = 10, num_train_epochs: int = 1, distributed: bool = True, fp16: bool = True, ): output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False) args = f""" --model_name_or_path {model_name} --dataset_name hf-internal-testing/librispeech_asr_dummy --dataset_config_name clean --train_split_name validation --validation_split_name validation --output_dir {output_dir} --num_train_epochs {str(num_train_epochs)} --per_device_train_batch_size 2 --per_device_eval_batch_size 2 --eval_strategy steps --learning_rate 5e-4 --warmup_steps 8 --orthography timit --preprocessing_num_workers 1 --group_by_length --freeze_feature_extractor --report_to none --save_steps 0 --eval_steps {eval_steps} --report_to none """.split() if fp16: args.extend(["--fp16"]) # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true, # hence the separate config files ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split() script = [f"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"] launcher = self.get_launcher(distributed) cmd = launcher + script + args + ds_args # keep for quick debug # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die execute_subprocess_async(cmd, env=self.get_env()) return output_dir def get_launcher(self, distributed=False): # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup # - it won't be able to handle that # 2. for now testing with just 2 gpus max (since some quality tests may give different # results with mode gpus because we use very little data) num_gpus = min(2, get_gpu_count()) if distributed else 1 return f"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()

transformers/examples/research_projects/wav2vec2/test_wav2vec2_deepspeed.py/0

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# Training a masked language model end-to-end from scratch on TPUs In this example, we're going to demonstrate how to train a TensorFlow model from 🤗 Transformers from scratch. If you're interested in some background theory on training Hugging Face models with TensorFlow on TPU, please check out our [tutorial doc](https://huggingface.co/docs/transformers/main/perf_train_tpu_tf) on this topic! If you're interested in smaller-scale TPU training from a pre-trained checkpoint, you can also check out the [TPU fine-tuning example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb). This example will demonstrate pre-training language models at the 100M-1B parameter scale, similar to BERT or GPT-2. More concretely, we will show how to train a [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) (base model) from scratch on the [WikiText dataset (v1)](https://huggingface.co/datasets/wikitext). We've tried to ensure that all the practices we show you here are scalable, though - with relatively few changes, the code could be scaled up to much larger models. Google's gargantuan [PaLM model](https://arxiv.org/abs/2204.02311), with over 500B parameters, is a good example of how far you can go with pure TPU training, though gathering the dataset and the budget to train at that scale is not an easy task! ### Table of contents - [Setting up a TPU-VM](#setting-up-a-tpu-vm) - [Training a tokenizer](#training-a-tokenizer) - [Preparing the dataset](#preparing-the-dataset) - [Training the model](#training-the-model) - [Inference](#inference) ## Setting up a TPU-VM Since this example focuses on using TPUs, the first step is to set up access to TPU hardware. For this example, we chose to use a TPU v3-8 VM. Follow [this guide](https://cloud.google.com/tpu/docs/run-calculation-tensorflow) to quickly create a TPU VM with TensorFlow pre-installed. > 💡 **Note**: You don't need a TPU-enabled hardware for tokenizer training and TFRecord shard preparation. ## Training a tokenizer To train a language model from scratch, the first step is to tokenize text. In most Hugging Face examples, we begin from a pre-trained model and use its tokenizer. However, in this example, we're going to train a tokenizer from scratch as well. The script for this is `train_unigram.py`. An example command is: ```bash python train_unigram.py --batch_size 1000 --vocab_size 25000 --export_to_hub ``` The script will automatically load the `train` split of the WikiText dataset and train a [Unigram tokenizer](https://huggingface.co/course/chapter6/7?fw=pt) on it. > 💡 **Note**: In order for `export_to_hub` to work, you must authenticate yourself with the `huggingface-cli`. Run `huggingface-cli login` and follow the on-screen instructions. ## Preparing the dataset The next step is to prepare the dataset. This consists of loading a text dataset from the Hugging Face Hub, tokenizing it and grouping it into chunks of a fixed length ready for training. The script for this is `prepare_tfrecord_shards.py`. The reason we create TFRecord output files from this step is that these files work well with [`tf.data` pipelines](https://www.tensorflow.org/guide/data_performance). This makes them very suitable for scalable TPU training - the dataset can easily be sharded and read in parallel just by tweaking a few parameters in the pipeline. An example command is: ```bash python prepare_tfrecord_shards.py \ --tokenizer_name_or_path tf-tpu/unigram-tokenizer-wikitext \ --shard_size 5000 \ --split test --max_length 128 \ --output_dir gs://tf-tpu-training-resources ``` **Notes**: * While running the above script, you need to specify the `split` accordingly. The example command above will only filter the `test` split of the dataset. * If you append `gs://` in your `output_dir` the TFRecord shards will be directly serialized to a Google Cloud Storage (GCS) bucket. Ensure that you have already [created the GCS bucket](https://cloud.google.com/storage/docs). * If you're using a TPU node, you must stream data from a GCS bucket. Otherwise, if you're using a TPU VM,you can store the data locally. You may need to [attach](https://cloud.google.com/tpu/docs/setup-persistent-disk) a persistent storage to the VM. * Additional CLI arguments are also supported. We encourage you to run `python prepare_tfrecord_shards.py -h` to know more about them. ## Training the model Once that's done, the model is ready for training. By default, training takes place on TPU, but you can use the `--no_tpu` flag to train on CPU for testing purposes. An example command is: ```bash python3 run_mlm.py \ --train_dataset gs://tf-tpu-training-resources/train/ \ --eval_dataset gs://tf-tpu-training-resources/validation/ \ --tokenizer tf-tpu/unigram-tokenizer-wikitext \ --output_dir trained_model ``` If you had specified a `hub_model_id` while launching training, then your model will be pushed to a model repository on the Hugging Face Hub. You can find such an example repository here: [tf-tpu/roberta-base-epochs-500-no-wd](https://huggingface.co/tf-tpu/roberta-base-epochs-500-no-wd). ## Inference Once the model is trained, you can use 🤗 Pipelines to perform inference: ```python from transformers import pipeline model_id = "tf-tpu/roberta-base-epochs-500-no-wd" unmasker = pipeline("fill-mask", model=model_id, framework="tf") unmasker("Goal of my life is to [MASK].") [{'score': 0.1003185287117958, 'token': 52, 'token_str': 'be', 'sequence': 'Goal of my life is to be.'}, {'score': 0.032648514956235886, 'token': 5, 'token_str': '', 'sequence': 'Goal of my life is to .'}, {'score': 0.02152673341333866, 'token': 138, 'token_str': 'work', 'sequence': 'Goal of my life is to work.'}, {'score': 0.019547373056411743, 'token': 984, 'token_str': 'act', 'sequence': 'Goal of my life is to act.'}, {'score': 0.01939118467271328, 'token': 73, 'token_str': 'have', 'sequence': 'Goal of my life is to have.'}] ``` You can also try out inference using the [Inference Widget](https://huggingface.co/tf-tpu/roberta-base-epochs-500-no-wd?text=Goal+of+my+life+is+to+%5BMASK%5D.) from the model page.

transformers/examples/tensorflow/language-modeling-tpu/README.md/0

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# Summarization example This script shows an example of training a *summarization* model with the 🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. ### Multi-GPU and TPU usage By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs can also be used by passing the name of the TPU resource with the `--tpu` argument. ### Example command ``` python run_summarization.py \ --model_name_or_path facebook/bart-base \ --dataset_name cnn_dailymail \ --dataset_config "3.0.0" \ --output_dir /tmp/tst-summarization \ --per_device_train_batch_size 8 \ --per_device_eval_batch_size 16 \ --num_train_epochs 3 \ --do_train \ --do_eval ```

transformers/examples/tensorflow/summarization/README.md/0

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#!/usr/bin/env python # HF Trainer benchmarking tool # # This tool can be used to run and compare multiple dimensions of the HF Trainers args. # # It then prints a report once in github format with all the information that needs to be shared # with others and second time in a console-friendly format, so it's easier to use for tuning things up. # # The main idea is: # # ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \ # --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \ # --target-metric-key train_samples_per_second # # The variations can be any command line argument that you want to compare and not just dtype as in # the example. # # --variations allows you to compare variations in multiple dimensions. # # as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6 # times adding one of: # # 1. --tf32 0 --fp16 0 # 2. --tf32 0 --fp16 1 # 3. --tf32 0 --bf16 1 # 4. --tf32 1 --fp16 0 # 5. --tf32 1 --fp16 1 # 6. --tf32 1 --bf16 1 # # and print the results. This is just a cartesian product - and more than 2 dimensions can be used. # # If you want to rely on defaults, this: # --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' # is identical to this: # --variations '--tf32 0|--tf32 1' '|--fp16|--bf16' # # the leading empty variation in the 2nd dimension is a valid variation. # # So here we get the following 6 variations: # # 1. --tf32 0 # 2. --tf32 0 --fp16 # 3. --tf32 0 --bf16 # 4. --tf32 1 # 5. --tf32 1 --fp16 # 6. --tf32 1 --bf16 # # In this particular case we don't know what the default tf32 setting is as it's normally # pytorch-version dependent). That's why it's best to do an explicit setting of each variation: # `--tf32 0|--tf32 1` # # Here is a full example of a train: # # CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \ # --base-cmd \ # ' examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small \ # --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \ # --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \ # --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \ # --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \ # --source_prefix "translate English to Romanian: " --warmup_steps 50 \ # --max_train_samples 20000 --dataloader_num_workers 2 ' \ # --target-metric-key train_samples_per_second --repeat-times 1 --variations \ # '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \ # --repeat-times 1 --base-variation '--tf32 0' # # and here is a possible output: # # # | Variation | Train | Diff | Train | # | | samples | % | loss | # | | per | | | # | | second | | | # |:----------------|----------:|-------:|--------:| # | --tf32 0 | 285.11 | 0 | 2.51 | # | --tf32 1 | 342.09 | 20 | 2.51 | # | --fp16 --tf32 0 | 423.49 | 49 | 2.51 | # | --fp16 --tf32 1 | 423.13 | 48 | 2.51 | # | --bf16 --tf32 0 | 416.80 | 46 | 2.52 | # | --bf16 --tf32 1 | 415.87 | 46 | 2.52 | # # # So you can quickly compare the different outcomes. # # Typically running each experiment once is enough, but if the environment is unstable you can # re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results. # # By default it'll use the lowest result as the base line to use as 100% and then compare the rest to # it as can be seen from the table above, but you can also specify which combination is the one to use as # the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0' # # --target-metric-key is there to tell the program which metrics to compare - the different metric keys are # inside output_dir/all_results.json. e.g., to measure eval performance instead of train use: # --target-metric-key eval_samples_per_second # but of course you will need to adjust the --base-cmd value in the example to perform evaluation as # well (as currently it doesn't) # import argparse import datetime import io import itertools import json import math import os import platform import re import shlex import subprocess import sys from pathlib import Path from statistics import fmean import pandas as pd import torch from tqdm import tqdm import transformers nan = float("nan") class Tee: """ A helper class to tee print's output into a file. Usage: sys.stdout = Tee(filename) """ def __init__(self, filename): self.stdout = sys.stdout self.file = open(filename, "a") def __getattr__(self, attr): return getattr(self.stdout, attr) def write(self, msg): self.stdout.write(msg) # strip tqdm codes self.file.write(re.sub(r"^.*\r", "", msg, 0, re.M)) def get_original_command(max_width=80, full_python_path=False): """ Return the original command line string that can be replayed nicely and wrapped for 80 char width. Args: max_width (`int`, *optional*, defaults to 80): The width to wrap for. full_python_path (`bool`, `optional`, defaults to `False`): Whether to replicate the full path or just the last segment (i.e. `python`). """ cmd = [] # deal with critical env vars env_keys = ["CUDA_VISIBLE_DEVICES"] for key in env_keys: val = os.environ.get(key, None) if val is not None: cmd.append(f"{key}={val}") # python executable (not always needed if the script is executable) python = sys.executable if full_python_path else sys.executable.split("/")[-1] cmd.append(python) # now the normal args cmd += list(map(shlex.quote, sys.argv)) # split up into up to MAX_WIDTH lines with shell multi-line escapes lines = [] current_line = "" while len(cmd) > 0: current_line += f"{cmd.pop(0)} " if len(cmd) == 0 or len(current_line) + len(cmd[0]) + 1 > max_width - 1: lines.append(current_line) current_line = "" return "\\\n".join(lines) def get_base_command(args, output_dir): # unwrap multi-line input args.base_cmd = re.sub(r"[\\\n]+", " ", args.base_cmd) # remove --output_dir if any and set our own args.base_cmd = re.sub("--output_dir\s+[^\s]+", "", args.base_cmd) args.base_cmd += f" --output_dir {output_dir}" # ensure we have --overwrite_output_dir args.base_cmd = re.sub("--overwrite_output_dir\s+", "", args.base_cmd) args.base_cmd += " --overwrite_output_dir" return [sys.executable] + shlex.split(args.base_cmd) def process_run_single(id, cmd, variation, output_dir, target_metric_key, metric_keys, verbose): # Enable to debug everything but the run itself, to do it fast and see the progress. # This is useful for debugging the output formatting quickly - we can remove it later once # everybody is happy with the output if 0: import random from time import sleep sleep(0) return dict( {k: random.uniform(0, 100) for k in metric_keys}, **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222])}, ) result = subprocess.run(cmd, capture_output=True, text=True) if verbose: print("STDOUT", result.stdout) print("STDERR", result.stderr) # save the streams prefix = variation.replace(" ", "-") with open(Path(output_dir) / f"log.{prefix}.stdout.txt", "w") as f: f.write(result.stdout) with open(Path(output_dir) / f"log.{prefix}.stderr.txt", "w") as f: f.write(result.stderr) if result.returncode != 0: if verbose: print("failed") return {target_metric_key: nan} with io.open(f"{output_dir}/all_results.json", "r", encoding="utf-8") as f: metrics = json.load(f) # filter out just the keys we want return {k: v for k, v in metrics.items() if k in metric_keys} def process_run( id, cmd, variation_key, variation, longest_variation_len, target_metric_key, report_metric_keys, repeat_times, output_dir, verbose, ): results = [] metrics = [] preamble = f"{id}: {variation:<{longest_variation_len}}" outcome = f"{preamble}: " metric_keys = set(report_metric_keys + [target_metric_key]) for i in tqdm(range(repeat_times), desc=preamble, leave=False): single_run_metrics = process_run_single( id, cmd, variation, output_dir, target_metric_key, metric_keys, verbose ) result = single_run_metrics[target_metric_key] if not math.isnan(result): metrics.append(single_run_metrics) results.append(result) outcome += "✓" else: outcome += "✘" outcome = f"\33[2K\r{outcome}" if len(metrics) > 0: mean_metrics = {k: fmean([x[k] for x in metrics]) for k in metrics[0].keys()} mean_target = round(mean_metrics[target_metric_key], 2) results_str = f"{outcome} {mean_target}" if len(metrics) > 1: results_str += f" {tuple(round(x, 2) for x in results)}" print(results_str) mean_metrics[variation_key] = variation return mean_metrics else: print(outcome) return {variation_key: variation, target_metric_key: nan} def get_versions(): properties = torch.cuda.get_device_properties(torch.device("cuda")) return f""" Datetime : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Software: transformers: {transformers.__version__} torch : {torch.__version__} cuda : {torch.version.cuda} python : {platform.python_version()} Hardware: {torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB """ def process_results(results, target_metric_key, report_metric_keys, base_variation, output_dir): df = pd.DataFrame(results) variation_key = "variation" diff_key = "diff_%" sentinel_value = nan if base_variation is not None and len(df[df[variation_key] == base_variation]): # this may still return nan sentinel_value = df.loc[df[variation_key] == base_variation][target_metric_key].item() if math.isnan(sentinel_value): # as a fallback, use the minimal value as the sentinel sentinel_value = df.loc[df[target_metric_key] != nan][target_metric_key].min() # create diff column if possible if not math.isnan(sentinel_value): df[diff_key] = df.apply( lambda r: round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value) if not math.isnan(r[target_metric_key]) else 0, axis="columns", ) # re-order columns cols = [variation_key, target_metric_key, diff_key, *report_metric_keys] df = df.reindex(cols, axis="columns") # reorder cols # capitalize df = df.rename(str.capitalize, axis="columns") # make the cols as narrow as possible df_github = df.rename(lambda c: c.replace("_", "<br>"), axis="columns") df_console = df.rename(lambda c: c.replace("_", "\n"), axis="columns") report = ["", "Copy between the cut-here-lines and paste as is to github or a forum"] report += ["----------8<-----------------8<--------"] report += ["*** Results:", df_github.to_markdown(index=False, floatfmt=".2f")] report += ["```"] report += ["*** Setup:", get_versions()] report += ["*** The benchmark command line was:", get_original_command()] report += ["```"] report += ["----------8<-----------------8<--------"] report += ["*** Results (console):", df_console.to_markdown(index=False, floatfmt=".2f")] print("\n\n".join(report)) def main(): parser = argparse.ArgumentParser() parser.add_argument( "--base-cmd", default=None, type=str, required=True, help="Base cmd", ) parser.add_argument( "--variations", default=None, type=str, nargs="+", required=True, help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'", ) parser.add_argument( "--base-variation", default=None, type=str, help="Baseline variation to compare to. if None the minimal target value will be used to compare against", ) parser.add_argument( "--target-metric-key", default=None, type=str, required=True, help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second", ) parser.add_argument( "--report-metric-keys", default="", type=str, help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples", ) parser.add_argument( "--repeat-times", default=1, type=int, help="How many times to re-run each variation - an average will be reported", ) parser.add_argument( "--output_dir", default="output_benchmark", type=str, help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked", ) parser.add_argument( "--verbose", default=False, action="store_true", help="Whether to show the outputs of each run or just the benchmark progress", ) args = parser.parse_args() output_dir = args.output_dir Path(output_dir).mkdir(exist_ok=True) base_cmd = get_base_command(args, output_dir) # split each dimension into its --foo variations dims = [list(map(str.strip, re.split(r"\|", x))) for x in args.variations] # build a cartesian product of dimensions and convert those back into cmd-line arg strings, # while stripping white space for inputs that were empty variations = list(map(str.strip, map(" ".join, itertools.product(*dims)))) longest_variation_len = max(len(x) for x in variations) # split wanted keys report_metric_keys = args.report_metric_keys.split() # capture prints into a log file for convenience report_fn = f"benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.txt" print(f"\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt") print(f"and this script's output is also piped into {report_fn}") sys.stdout = Tee(report_fn) print(f"\n*** Running {len(variations)} benchmarks:") print(f"Base command: {' '.join(base_cmd)}") variation_key = "variation" results = [] for id, variation in enumerate(tqdm(variations, desc="Total completion: ", leave=False)): cmd = base_cmd + variation.split() results.append( process_run( id + 1, cmd, variation_key, variation, longest_variation_len, args.target_metric_key, report_metric_keys, args.repeat_times, output_dir, args.verbose, ) ) process_results(results, args.target_metric_key, report_metric_keys, args.base_variation, output_dir) if __name__ == "__main__": main()

transformers/scripts/benchmark/trainer-benchmark.py/0

{ "file_path": "transformers/scripts/benchmark/trainer-benchmark.py", "repo_id": "transformers", "token_count": 6341 }

#!/usr/bin/env python # coding=utf-8 # Copyright 2024 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 copy import deepcopy from enum import Enum from typing import Dict, List, Optional from huggingface_hub import InferenceClient from .. import AutoTokenizer from ..pipelines.base import Pipeline from ..utils import logging logger = logging.get_logger(__name__) class MessageRole(str, Enum): USER = "user" ASSISTANT = "assistant" SYSTEM = "system" TOOL_CALL = "tool-call" TOOL_RESPONSE = "tool-response" @classmethod def roles(cls): return [r.value for r in cls] def get_clean_message_list(message_list: List[Dict[str, str]], role_conversions: Dict[str, str] = {}): """ Subsequent messages with the same role will be concatenated to a single message. Args: message_list (`List[Dict[str, str]]`): List of chat messages. """ final_message_list = [] message_list = deepcopy(message_list) # Avoid modifying the original list for message in message_list: if not set(message.keys()) == {"role", "content"}: raise ValueError("Message should contain only 'role' and 'content' keys!") role = message["role"] if role not in MessageRole.roles(): raise ValueError(f"Incorrect role {role}, only {MessageRole.roles()} are supported for now.") if role in role_conversions: message["role"] = role_conversions[role] if len(final_message_list) > 0 and message["role"] == final_message_list[-1]["role"]: final_message_list[-1]["content"] += "\n=======\n" + message["content"] else: final_message_list.append(message) return final_message_list llama_role_conversions = { MessageRole.TOOL_RESPONSE: MessageRole.USER, } class HfEngine: def __init__(self, model_id: Optional[str] = None): self.last_input_token_count = None self.last_output_token_count = None if model_id is None: model_id = "HuggingFaceTB/SmolLM2-1.7B-Instruct" logger.warning(f"Using default model for token counting: '{model_id}'") try: self.tokenizer = AutoTokenizer.from_pretrained(model_id) except Exception as e: logger.warning(f"Failed to load tokenizer for model {model_id}: {e}. Loading default tokenizer instead.") self.tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct") def get_token_counts(self): return { "input_token_count": self.last_input_token_count, "output_token_count": self.last_output_token_count, } def generate( self, messages: List[Dict[str, str]], stop_sequences: Optional[List[str]] = None, grammar: Optional[str] = None ): raise NotImplementedError def __call__( self, messages: List[Dict[str, str]], stop_sequences: Optional[List[str]] = None, grammar: Optional[str] = None ) -> str: """Process the input messages and return the model's response. This method sends a list of messages to the Hugging Face Inference API, optionally with stop sequences and grammar customization. Parameters: messages (`List[Dict[str, str]]`): A list of message dictionaries to be processed. Each dictionary should have the structure `{"role": "user/system", "content": "message content"}`. stop_sequences (`List[str]`, *optional*): A list of strings that will stop the generation if encountered in the model's output. grammar (`str`, *optional*): The grammar or formatting structure to use in the model's response. Returns: `str`: The text content of the model's response. Example: ```python >>> engine = HfApiEngine( ... model="meta-llama/Meta-Llama-3.1-8B-Instruct", ... token="your_hf_token_here", ... max_tokens=2000 ... ) >>> messages = [{"role": "user", "content": "Explain quantum mechanics in simple terms."}] >>> response = engine(messages, stop_sequences=["END"]) >>> print(response) "Quantum mechanics is the branch of physics that studies..." ``` """ if not isinstance(messages, List): raise ValueError("Messages should be a list of dictionaries with 'role' and 'content' keys.") if stop_sequences is None: stop_sequences = [] response = self.generate(messages, stop_sequences, grammar) self.last_input_token_count = len(self.tokenizer.apply_chat_template(messages, tokenize=True)) self.last_output_token_count = len(self.tokenizer.encode(response)) # Remove stop sequences from LLM output for stop_seq in stop_sequences: if response[-len(stop_seq) :] == stop_seq: response = response[: -len(stop_seq)] return response class HfApiEngine(HfEngine): """A class to interact with Hugging Face's Inference API for language model interaction. This engine allows you to communicate with Hugging Face's models using the Inference API. It can be used in both serverless mode or with a dedicated endpoint, supporting features like stop sequences and grammar customization. Parameters: model (`str`, *optional*, defaults to `"meta-llama/Meta-Llama-3.1-8B-Instruct"`): The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub. token (`str`, *optional*): Token used by the Hugging Face API for authentication. If not provided, the class will use the token stored in the Hugging Face CLI configuration. max_tokens (`int`, *optional*, defaults to 1500): The maximum number of tokens allowed in the output. timeout (`int`, *optional*, defaults to 120): Timeout for the API request, in seconds. Raises: ValueError: If the model name is not provided. """ def __init__( self, model: str = "meta-llama/Meta-Llama-3.1-8B-Instruct", token: Optional[str] = None, max_tokens: Optional[int] = 1500, timeout: Optional[int] = 120, ): super().__init__(model_id=model) self.model = model self.client = InferenceClient(self.model, token=token, timeout=timeout) self.max_tokens = max_tokens def generate( self, messages: List[Dict[str, str]], stop_sequences: Optional[List[str]] = None, grammar: Optional[str] = None ) -> str: # Get clean message list messages = get_clean_message_list(messages, role_conversions=llama_role_conversions) # Send messages to the Hugging Face Inference API if grammar is not None: response = self.client.chat_completion( messages, stop=stop_sequences, max_tokens=self.max_tokens, response_format=grammar ) else: response = self.client.chat_completion(messages, stop=stop_sequences, max_tokens=self.max_tokens) response = response.choices[0].message.content return response class TransformersEngine(HfEngine): """This engine uses a pre-initialized local text-generation pipeline.""" def __init__(self, pipeline: Pipeline, model_id: Optional[str] = None): super().__init__(model_id) self.pipeline = pipeline def generate( self, messages: List[Dict[str, str]], stop_sequences: Optional[List[str]] = None, grammar: Optional[str] = None, max_length: int = 1500, ) -> str: # Get clean message list messages = get_clean_message_list(messages, role_conversions=llama_role_conversions) # Get LLM output if stop_sequences is not None and len(stop_sequences) > 0: stop_strings = stop_sequences else: stop_strings = None output = self.pipeline( messages, stop_strings=stop_strings, max_length=max_length, tokenizer=self.pipeline.tokenizer, ) response = output[0]["generated_text"][-1]["content"] return response DEFAULT_JSONAGENT_REGEX_GRAMMAR = { "type": "regex", "value": 'Thought: .+?\\nAction:\\n\\{\\n\\s{4}"action":\\s"[^"\\n]+",\\n\\s{4}"action_input":\\s"[^"\\n]+"\\n\\}\\n<end_action>', } DEFAULT_CODEAGENT_REGEX_GRAMMAR = { "type": "regex", "value": "Thought: .+?\\nCode:\\n```(?:py|python)?\\n(?:.|\\s)+?\\n```<end_action>", }

transformers/src/transformers/agents/llm_engine.py/0

{ "file_path": "transformers/src/transformers/agents/llm_engine.py", "repo_id": "transformers", "token_count": 3688 }

# 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 argparse import ArgumentParser from . import BaseTransformersCLICommand def download_command_factory(args): return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code) class DownloadCommand(BaseTransformersCLICommand): @staticmethod def register_subcommand(parser: ArgumentParser): download_parser = parser.add_parser("download") download_parser.add_argument( "--cache-dir", type=str, default=None, help="Path to location to store the models" ) download_parser.add_argument( "--force", action="store_true", help="Force the model to be download even if already in cache-dir" ) download_parser.add_argument( "--trust-remote-code", action="store_true", help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine", ) download_parser.add_argument("model", type=str, help="Name of the model to download") download_parser.set_defaults(func=download_command_factory) def __init__(self, model: str, cache: str, force: bool, trust_remote_code: bool): self._model = model self._cache = cache self._force = force self._trust_remote_code = trust_remote_code def run(self): from ..models.auto import AutoModel, AutoTokenizer AutoModel.from_pretrained( self._model, cache_dir=self._cache, force_download=self._force, trust_remote_code=self._trust_remote_code ) AutoTokenizer.from_pretrained( self._model, cache_dir=self._cache, force_download=self._force, trust_remote_code=self._trust_remote_code )

transformers/src/transformers/commands/download.py/0

{ "file_path": "transformers/src/transformers/commands/download.py", "repo_id": "transformers", "token_count": 828 }

# 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. """ Feature extraction saving/loading class for common feature extractors. """ import copy import json import os import warnings from collections import UserDict from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union import numpy as np from .dynamic_module_utils import custom_object_save from .utils import ( FEATURE_EXTRACTOR_NAME, PushToHubMixin, TensorType, add_model_info_to_auto_map, add_model_info_to_custom_pipelines, cached_file, copy_func, download_url, is_flax_available, is_jax_tensor, is_numpy_array, is_offline_mode, is_remote_url, is_tf_available, is_torch_available, is_torch_device, is_torch_dtype, logging, requires_backends, ) if TYPE_CHECKING: if is_torch_available(): import torch # noqa logger = logging.get_logger(__name__) PreTrainedFeatureExtractor = Union["SequenceFeatureExtractor"] # noqa: F821 class BatchFeature(UserDict): r""" Holds the output of the [`~SequenceFeatureExtractor.pad`] and feature extractor specific `__call__` methods. This class is derived from a python dictionary and can be used as a dictionary. Args: data (`dict`, *optional*): Dictionary of lists/arrays/tensors returned by the __call__/pad methods ('input_values', 'attention_mask', etc.). tensor_type (`Union[None, str, TensorType]`, *optional*): You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization. """ def __init__(self, data: Optional[Dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None): super().__init__(data) self.convert_to_tensors(tensor_type=tensor_type) def __getitem__(self, item: str) -> Union[Any]: """ If the key is a string, returns the value of the dict associated to `key` ('input_values', 'attention_mask', etc.). """ if isinstance(item, str): return self.data[item] else: raise KeyError("Indexing with integers is not available when using Python based feature extractors") def __getattr__(self, item: str): try: return self.data[item] except KeyError: raise AttributeError def __getstate__(self): return {"data": self.data} def __setstate__(self, state): if "data" in state: self.data = state["data"] # Copied from transformers.tokenization_utils_base.BatchEncoding.keys def keys(self): return self.data.keys() # Copied from transformers.tokenization_utils_base.BatchEncoding.values def values(self): return self.data.values() # Copied from transformers.tokenization_utils_base.BatchEncoding.items def items(self): return self.data.items() def _get_is_as_tensor_fns(self, tensor_type: Optional[Union[str, TensorType]] = None): if tensor_type is None: return None, None # Convert to TensorType if not isinstance(tensor_type, TensorType): tensor_type = TensorType(tensor_type) # Get a function reference for the correct framework if tensor_type == TensorType.TENSORFLOW: if not is_tf_available(): raise ImportError( "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed." ) import tensorflow as tf as_tensor = tf.constant is_tensor = tf.is_tensor elif tensor_type == TensorType.PYTORCH: if not is_torch_available(): raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.") import torch # noqa def as_tensor(value): if isinstance(value, (list, tuple)) and len(value) > 0: if isinstance(value[0], np.ndarray): value = np.array(value) elif ( isinstance(value[0], (list, tuple)) and len(value[0]) > 0 and isinstance(value[0][0], np.ndarray) ): value = np.array(value) if isinstance(value, np.ndarray): return torch.from_numpy(value) else: return torch.tensor(value) is_tensor = torch.is_tensor elif tensor_type == TensorType.JAX: if not is_flax_available(): raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed.") import jax.numpy as jnp # noqa: F811 as_tensor = jnp.array is_tensor = is_jax_tensor else: def as_tensor(value, dtype=None): if isinstance(value, (list, tuple)) and isinstance(value[0], (list, tuple, np.ndarray)): value_lens = [len(val) for val in value] if len(set(value_lens)) > 1 and dtype is None: # we have a ragged list so handle explicitly value = as_tensor([np.asarray(val) for val in value], dtype=object) return np.asarray(value, dtype=dtype) is_tensor = is_numpy_array return is_tensor, as_tensor def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None): """ Convert the inner content to tensors. Args: tensor_type (`str` or [`~utils.TensorType`], *optional*): The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If `None`, no modification is done. """ if tensor_type is None: return self is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type) # Do the tensor conversion in batch for key, value in self.items(): try: if not is_tensor(value): tensor = as_tensor(value) self[key] = tensor except: # noqa E722 if key == "overflowing_values": raise ValueError("Unable to create tensor returning overflowing values of different lengths. ") raise ValueError( "Unable to create tensor, you should probably activate padding " "with 'padding=True' to have batched tensors with the same length." ) return self def to(self, *args, **kwargs) -> "BatchFeature": """ Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in different `dtypes` and sending the `BatchFeature` to a different `device`. Args: args (`Tuple`): Will be passed to the `to(...)` function of the tensors. kwargs (`Dict`, *optional*): Will be passed to the `to(...)` function of the tensors. To enable asynchronous data transfer, set the `non_blocking` flag in `kwargs` (defaults to `False`). Returns: [`BatchFeature`]: The same instance after modification. """ requires_backends(self, ["torch"]) import torch # noqa new_data = {} device = kwargs.get("device") non_blocking = kwargs.get("non_blocking", False) # Check if the args are a device or a dtype if device is None and len(args) > 0: # device should be always the first argument arg = args[0] if is_torch_dtype(arg): # The first argument is a dtype pass elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int): device = arg else: # it's something else raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.") # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor` for k, v in self.items(): # check if v is a floating point if isinstance(v, torch.Tensor) and torch.is_floating_point(v): # cast and send to device new_data[k] = v.to(*args, **kwargs) elif isinstance(v, torch.Tensor) and device is not None: new_data[k] = v.to(device=device, non_blocking=non_blocking) else: new_data[k] = v self.data = new_data return self class FeatureExtractionMixin(PushToHubMixin): """ This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature extractors. """ _auto_class = None def __init__(self, **kwargs): """Set elements of `kwargs` as attributes.""" # Pop "processor_class" as it should be saved as private attribute self._processor_class = kwargs.pop("processor_class", None) # Additional attributes without default values for key, value in kwargs.items(): try: setattr(self, key, value) except AttributeError as err: logger.error(f"Can't set {key} with value {value} for {self}") raise err def _set_processor_class(self, processor_class: str): """Sets processor class as an attribute.""" self._processor_class = processor_class @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, local_files_only: bool = False, token: Optional[Union[str, bool]] = None, revision: str = "main", **kwargs, ): r""" Instantiate a type of [`~feature_extraction_utils.FeatureExtractionMixin`] from a feature extractor, *e.g.* a derived class of [`SequenceFeatureExtractor`]. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on huggingface.co. - a path to a *directory* containing a feature extractor file saved using the [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] method, e.g., `./my_model_directory/`. - a path or url to a saved feature extractor JSON *file*, e.g., `./my_model_directory/preprocessor_config.json`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model feature extractor should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the feature extractor files and override the cached versions if they exist. resume_download: Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. <Tip> To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`. </Tip> return_unused_kwargs (`bool`, *optional*, defaults to `False`): If `False`, then this function returns just the final feature extractor object. If `True`, then this functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored. kwargs (`Dict[str, Any]`, *optional*): The values in kwargs of any keys which are feature extractor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is controlled by the `return_unused_kwargs` keyword parameter. Returns: A feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`]. Examples: ```python # We can't instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let's show the examples on a # derived class: *Wav2Vec2FeatureExtractor* feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base-960h" ) # Download feature_extraction_config from huggingface.co and cache. feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "./test/saved_model/" ) # E.g. feature_extractor (or model) was saved using *save_pretrained('./test/saved_model/')* feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("./test/saved_model/preprocessor_config.json") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False ) assert feature_extractor.return_attention_mask is False feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base-960h", return_attention_mask=False, foo=False, return_unused_kwargs=True ) assert feature_extractor.return_attention_mask is False assert unused_kwargs == {"foo": False} ```""" kwargs["cache_dir"] = cache_dir kwargs["force_download"] = force_download kwargs["local_files_only"] = local_files_only kwargs["revision"] = revision use_auth_token = kwargs.pop("use_auth_token", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.", FutureWarning, ) if token is not None: raise ValueError( "`token` and `use_auth_token` are both specified. Please set only the argument `token`." ) token = use_auth_token if token is not None: kwargs["token"] = token feature_extractor_dict, kwargs = cls.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs) return cls.from_dict(feature_extractor_dict, **kwargs) def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs): """ Save a feature_extractor object to the directory `save_directory`, so that it can be re-loaded using the [`~feature_extraction_utils.FeatureExtractionMixin.from_pretrained`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the feature extractor JSON file will be saved (will be created if it does not exist). push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). kwargs (`Dict[str, Any]`, *optional*): Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. """ use_auth_token = kwargs.pop("use_auth_token", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.", FutureWarning, ) if kwargs.get("token", None) is not None: raise ValueError( "`token` and `use_auth_token` are both specified. Please set only the argument `token`." ) kwargs["token"] = use_auth_token if os.path.isfile(save_directory): raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file") os.makedirs(save_directory, exist_ok=True) if push_to_hub: commit_message = kwargs.pop("commit_message", None) repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1]) repo_id = self._create_repo(repo_id, **kwargs) files_timestamps = self._get_files_timestamps(save_directory) # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be # loaded from the Hub. if self._auto_class is not None: custom_object_save(self, save_directory, config=self) # If we save using the predefined names, we can load using `from_pretrained` output_feature_extractor_file = os.path.join(save_directory, FEATURE_EXTRACTOR_NAME) self.to_json_file(output_feature_extractor_file) logger.info(f"Feature extractor saved in {output_feature_extractor_file}") if push_to_hub: self._upload_modified_files( save_directory, repo_id, files_timestamps, commit_message=commit_message, token=kwargs.get("token"), ) return [output_feature_extractor_file] @classmethod def get_feature_extractor_dict( cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs ) -> Tuple[Dict[str, Any], Dict[str, Any]]: """ From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`] using `from_dict`. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`): The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. Returns: `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the feature extractor object. """ cache_dir = kwargs.pop("cache_dir", None) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", None) proxies = kwargs.pop("proxies", None) subfolder = kwargs.pop("subfolder", None) token = kwargs.pop("token", None) use_auth_token = kwargs.pop("use_auth_token", None) local_files_only = kwargs.pop("local_files_only", False) revision = kwargs.pop("revision", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.", FutureWarning, ) if token is not None: raise ValueError( "`token` and `use_auth_token` are both specified. Please set only the argument `token`." ) token = use_auth_token from_pipeline = kwargs.pop("_from_pipeline", None) from_auto_class = kwargs.pop("_from_auto", False) user_agent = {"file_type": "feature extractor", "from_auto_class": from_auto_class} if from_pipeline is not None: user_agent["using_pipeline"] = from_pipeline if is_offline_mode() and not local_files_only: logger.info("Offline mode: forcing local_files_only=True") local_files_only = True pretrained_model_name_or_path = str(pretrained_model_name_or_path) is_local = os.path.isdir(pretrained_model_name_or_path) if os.path.isdir(pretrained_model_name_or_path): feature_extractor_file = os.path.join(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME) if os.path.isfile(pretrained_model_name_or_path): resolved_feature_extractor_file = pretrained_model_name_or_path is_local = True elif is_remote_url(pretrained_model_name_or_path): feature_extractor_file = pretrained_model_name_or_path resolved_feature_extractor_file = download_url(pretrained_model_name_or_path) else: feature_extractor_file = FEATURE_EXTRACTOR_NAME try: # Load from local folder or from cache or download from model Hub and cache resolved_feature_extractor_file = cached_file( pretrained_model_name_or_path, feature_extractor_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, subfolder=subfolder, token=token, user_agent=user_agent, revision=revision, ) except EnvironmentError: # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to # the original exception. raise except Exception: # For any other exception, we throw a generic error. raise EnvironmentError( f"Can't load feature extractor for '{pretrained_model_name_or_path}'. If you were trying to load" " it from 'https://huggingface.co/models', make sure you don't have a local directory with the" f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a" f" directory containing a {FEATURE_EXTRACTOR_NAME} file" ) try: # Load feature_extractor dict with open(resolved_feature_extractor_file, "r", encoding="utf-8") as reader: text = reader.read() feature_extractor_dict = json.loads(text) except json.JSONDecodeError: raise EnvironmentError( f"It looks like the config file at '{resolved_feature_extractor_file}' is not a valid JSON file." ) if is_local: logger.info(f"loading configuration file {resolved_feature_extractor_file}") else: logger.info( f"loading configuration file {feature_extractor_file} from cache at {resolved_feature_extractor_file}" ) if not is_local: if "auto_map" in feature_extractor_dict: feature_extractor_dict["auto_map"] = add_model_info_to_auto_map( feature_extractor_dict["auto_map"], pretrained_model_name_or_path ) if "custom_pipelines" in feature_extractor_dict: feature_extractor_dict["custom_pipelines"] = add_model_info_to_custom_pipelines( feature_extractor_dict["custom_pipelines"], pretrained_model_name_or_path ) return feature_extractor_dict, kwargs @classmethod def from_dict(cls, feature_extractor_dict: Dict[str, Any], **kwargs) -> PreTrainedFeatureExtractor: """ Instantiates a type of [`~feature_extraction_utils.FeatureExtractionMixin`] from a Python dictionary of parameters. Args: feature_extractor_dict (`Dict[str, Any]`): Dictionary that will be used to instantiate the feature extractor object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the [`~feature_extraction_utils.FeatureExtractionMixin.to_dict`] method. kwargs (`Dict[str, Any]`): Additional parameters from which to initialize the feature extractor object. Returns: [`~feature_extraction_utils.FeatureExtractionMixin`]: The feature extractor object instantiated from those parameters. """ return_unused_kwargs = kwargs.pop("return_unused_kwargs", False) # Update feature_extractor with kwargs if needed to_remove = [] for key, value in kwargs.items(): if key in feature_extractor_dict: feature_extractor_dict[key] = value to_remove.append(key) for key in to_remove: kwargs.pop(key, None) feature_extractor = cls(**feature_extractor_dict) logger.info(f"Feature extractor {feature_extractor}") if return_unused_kwargs: return feature_extractor, kwargs else: return feature_extractor def to_dict(self) -> Dict[str, Any]: """ Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance. """ output = copy.deepcopy(self.__dict__) output["feature_extractor_type"] = self.__class__.__name__ if "mel_filters" in output: del output["mel_filters"] if "window" in output: del output["window"] return output @classmethod def from_json_file(cls, json_file: Union[str, os.PathLike]) -> PreTrainedFeatureExtractor: """ Instantiates a feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`] from the path to a JSON file of parameters. Args: json_file (`str` or `os.PathLike`): Path to the JSON file containing the parameters. Returns: A feature extractor of type [`~feature_extraction_utils.FeatureExtractionMixin`]: The feature_extractor object instantiated from that JSON file. """ with open(json_file, "r", encoding="utf-8") as reader: text = reader.read() feature_extractor_dict = json.loads(text) return cls(**feature_extractor_dict) def to_json_string(self) -> str: """ Serializes this instance to a JSON string. Returns: `str`: String containing all the attributes that make up this feature_extractor instance in JSON format. """ dictionary = self.to_dict() for key, value in dictionary.items(): if isinstance(value, np.ndarray): dictionary[key] = value.tolist() # make sure private name "_processor_class" is correctly # saved as "processor_class" _processor_class = dictionary.pop("_processor_class", None) if _processor_class is not None: dictionary["processor_class"] = _processor_class return json.dumps(dictionary, indent=2, sort_keys=True) + "\n" def to_json_file(self, json_file_path: Union[str, os.PathLike]): """ Save this instance to a JSON file. Args: json_file_path (`str` or `os.PathLike`): Path to the JSON file in which this feature_extractor instance's parameters will be saved. """ with open(json_file_path, "w", encoding="utf-8") as writer: writer.write(self.to_json_string()) def __repr__(self): return f"{self.__class__.__name__} {self.to_json_string()}" @classmethod def register_for_auto_class(cls, auto_class="AutoFeatureExtractor"): """ Register this class with a given auto class. This should only be used for custom feature extractors as the ones in the library are already mapped with `AutoFeatureExtractor`. <Tip warning={true}> This API is experimental and may have some slight breaking changes in the next releases. </Tip> Args: auto_class (`str` or `type`, *optional*, defaults to `"AutoFeatureExtractor"`): The auto class to register this new feature extractor with. """ if not isinstance(auto_class, str): auto_class = auto_class.__name__ import transformers.models.auto as auto_module if not hasattr(auto_module, auto_class): raise ValueError(f"{auto_class} is not a valid auto class.") cls._auto_class = auto_class FeatureExtractionMixin.push_to_hub = copy_func(FeatureExtractionMixin.push_to_hub) if FeatureExtractionMixin.push_to_hub.__doc__ is not None: FeatureExtractionMixin.push_to_hub.__doc__ = FeatureExtractionMixin.push_to_hub.__doc__.format( object="feature extractor", object_class="AutoFeatureExtractor", object_files="feature extractor file" )

transformers/src/transformers/feature_extraction_utils.py/0

{ "file_path": "transformers/src/transformers/feature_extraction_utils.py", "repo_id": "transformers", "token_count": 13316 }

# 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 dataclasses import json import os import sys import types from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError from copy import copy from enum import Enum from inspect import isclass from pathlib import Path from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints import yaml DataClass = NewType("DataClass", Any) DataClassType = NewType("DataClassType", Any) # From https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse def string_to_bool(v): if isinstance(v, bool): return v if v.lower() in ("yes", "true", "t", "y", "1"): return True elif v.lower() in ("no", "false", "f", "n", "0"): return False else: raise ArgumentTypeError( f"Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)." ) def make_choice_type_function(choices: list) -> Callable[[str], Any]: """ Creates a mapping function from each choices string representation to the actual value. Used to support multiple value types for a single argument. Args: choices (list): List of choices. Returns: Callable[[str], Any]: Mapping function from string representation to actual value for each choice. """ str_to_choice = {str(choice): choice for choice in choices} return lambda arg: str_to_choice.get(arg, arg) def HfArg( *, aliases: Union[str, List[str]] = None, help: str = None, default: Any = dataclasses.MISSING, default_factory: Callable[[], Any] = dataclasses.MISSING, metadata: dict = None, **kwargs, ) -> dataclasses.Field: """Argument helper enabling a concise syntax to create dataclass fields for parsing with `HfArgumentParser`. Example comparing the use of `HfArg` and `dataclasses.field`: ``` @dataclass class Args: regular_arg: str = dataclasses.field(default="Huggingface", metadata={"aliases": ["--example", "-e"], "help": "This syntax could be better!"}) hf_arg: str = HfArg(default="Huggingface", aliases=["--example", "-e"], help="What a nice syntax!") ``` Args: aliases (Union[str, List[str]], optional): Single string or list of strings of aliases to pass on to argparse, e.g. `aliases=["--example", "-e"]`. Defaults to None. help (str, optional): Help string to pass on to argparse that can be displayed with --help. Defaults to None. default (Any, optional): Default value for the argument. If not default or default_factory is specified, the argument is required. Defaults to dataclasses.MISSING. default_factory (Callable[[], Any], optional): The default_factory is a 0-argument function called to initialize a field's value. It is useful to provide default values for mutable types, e.g. lists: `default_factory=list`. Mutually exclusive with `default=`. Defaults to dataclasses.MISSING. metadata (dict, optional): Further metadata to pass on to `dataclasses.field`. Defaults to None. Returns: Field: A `dataclasses.Field` with the desired properties. """ if metadata is None: # Important, don't use as default param in function signature because dict is mutable and shared across function calls metadata = {} if aliases is not None: metadata["aliases"] = aliases if help is not None: metadata["help"] = help return dataclasses.field(metadata=metadata, default=default, default_factory=default_factory, **kwargs) class HfArgumentParser(ArgumentParser): """ This subclass of `argparse.ArgumentParser` uses type hints on dataclasses to generate arguments. The class is designed to play well with the native argparse. In particular, you can add more (non-dataclass backed) arguments to the parser after initialization and you'll get the output back after parsing as an additional namespace. Optional: To create sub argument groups use the `_argument_group_name` attribute in the dataclass. Args: dataclass_types (`DataClassType` or `Iterable[DataClassType]`, *optional*): Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args. kwargs (`Dict[str, Any]`, *optional*): Passed to `argparse.ArgumentParser()` in the regular way. """ dataclass_types: Iterable[DataClassType] def __init__(self, dataclass_types: Optional[Union[DataClassType, Iterable[DataClassType]]] = None, **kwargs): # Make sure dataclass_types is an iterable if dataclass_types is None: dataclass_types = [] elif not isinstance(dataclass_types, Iterable): dataclass_types = [dataclass_types] # To make the default appear when using --help if "formatter_class" not in kwargs: kwargs["formatter_class"] = ArgumentDefaultsHelpFormatter super().__init__(**kwargs) if dataclasses.is_dataclass(dataclass_types): dataclass_types = [dataclass_types] self.dataclass_types = list(dataclass_types) for dtype in self.dataclass_types: self._add_dataclass_arguments(dtype) @staticmethod def _parse_dataclass_field(parser: ArgumentParser, field: dataclasses.Field): # Long-option strings are conventionlly separated by hyphens rather # than underscores, e.g., "--long-format" rather than "--long_format". # Argparse converts hyphens to underscores so that the destination # string is a valid attribute name. Hf_argparser should do the same. long_options = [f"--{field.name}"] if "_" in field.name: long_options.append(f"--{field.name.replace('_', '-')}") kwargs = field.metadata.copy() # field.metadata is not used at all by Data Classes, # it is provided as a third-party extension mechanism. if isinstance(field.type, str): raise RuntimeError( "Unresolved type detected, which should have been done with the help of " "`typing.get_type_hints` method by default" ) aliases = kwargs.pop("aliases", []) if isinstance(aliases, str): aliases = [aliases] origin_type = getattr(field.type, "__origin__", field.type) if origin_type is Union or (hasattr(types, "UnionType") and isinstance(origin_type, types.UnionType)): if str not in field.type.__args__ and ( len(field.type.__args__) != 2 or type(None) not in field.type.__args__ ): raise ValueError( "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because" " the argument parser only supports one type per argument." f" Problem encountered in field '{field.name}'." ) if type(None) not in field.type.__args__: # filter `str` in Union field.type = field.type.__args__[0] if field.type.__args__[1] is str else field.type.__args__[1] origin_type = getattr(field.type, "__origin__", field.type) elif bool not in field.type.__args__: # filter `NoneType` in Union (except for `Union[bool, NoneType]`) field.type = ( field.type.__args__[0] if isinstance(None, field.type.__args__[1]) else field.type.__args__[1] ) origin_type = getattr(field.type, "__origin__", field.type) # A variable to store kwargs for a boolean field, if needed # so that we can init a `no_*` complement argument (see below) bool_kwargs = {} if origin_type is Literal or (isinstance(field.type, type) and issubclass(field.type, Enum)): if origin_type is Literal: kwargs["choices"] = field.type.__args__ else: kwargs["choices"] = [x.value for x in field.type] kwargs["type"] = make_choice_type_function(kwargs["choices"]) if field.default is not dataclasses.MISSING: kwargs["default"] = field.default else: kwargs["required"] = True elif field.type is bool or field.type == Optional[bool]: # Copy the currect kwargs to use to instantiate a `no_*` complement argument below. # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument bool_kwargs = copy(kwargs) # Hack because type=bool in argparse does not behave as we want. kwargs["type"] = string_to_bool if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING): # Default value is False if we have no default when of type bool. default = False if field.default is dataclasses.MISSING else field.default # This is the value that will get picked if we don't include --{field.name} in any way kwargs["default"] = default # This tells argparse we accept 0 or 1 value after --{field.name} kwargs["nargs"] = "?" # This is the value that will get picked if we do --{field.name} (without value) kwargs["const"] = True elif isclass(origin_type) and issubclass(origin_type, list): kwargs["type"] = field.type.__args__[0] kwargs["nargs"] = "+" if field.default_factory is not dataclasses.MISSING: kwargs["default"] = field.default_factory() elif field.default is dataclasses.MISSING: kwargs["required"] = True else: kwargs["type"] = field.type if field.default is not dataclasses.MISSING: kwargs["default"] = field.default elif field.default_factory is not dataclasses.MISSING: kwargs["default"] = field.default_factory() else: kwargs["required"] = True parser.add_argument(*long_options, *aliases, **kwargs) # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added. # Order is important for arguments with the same destination! # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down # here and we do not need those changes/additional keys. if field.default is True and (field.type is bool or field.type == Optional[bool]): bool_kwargs["default"] = False parser.add_argument( f"--no_{field.name}", f"--no-{field.name.replace('_', '-')}", action="store_false", dest=field.name, **bool_kwargs, ) def _add_dataclass_arguments(self, dtype: DataClassType): if hasattr(dtype, "_argument_group_name"): parser = self.add_argument_group(dtype._argument_group_name) else: parser = self try: type_hints: Dict[str, type] = get_type_hints(dtype) except NameError: raise RuntimeError( f"Type resolution failed for {dtype}. Try declaring the class in global scope or " "removing line of `from __future__ import annotations` which opts in Postponed " "Evaluation of Annotations (PEP 563)" ) except TypeError as ex: # Remove this block when we drop Python 3.9 support if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(ex): python_version = ".".join(map(str, sys.version_info[:3])) raise RuntimeError( f"Type resolution failed for {dtype} on Python {python_version}. Try removing " "line of `from __future__ import annotations` which opts in union types as " "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To " "support Python versions that lower than 3.10, you need to use " "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of " "`X | None`." ) from ex raise for field in dataclasses.fields(dtype): if not field.init: continue field.type = type_hints[field.name] self._parse_dataclass_field(parser, field) def parse_args_into_dataclasses( self, args=None, return_remaining_strings=False, look_for_args_file=True, args_filename=None, args_file_flag=None, ) -> Tuple[DataClass, ...]: """ Parse command-line args into instances of the specified dataclass types. This relies on argparse's `ArgumentParser.parse_known_args`. See the doc at: docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.parse_args Args: args: List of strings to parse. The default is taken from sys.argv. (same as argparse.ArgumentParser) return_remaining_strings: If true, also return a list of remaining argument strings. look_for_args_file: If true, will look for a ".args" file with the same base name as the entry point script for this process, and will append its potential content to the command line args. args_filename: If not None, will uses this file instead of the ".args" file specified in the previous argument. args_file_flag: If not None, will look for a file in the command-line args specified with this flag. The flag can be specified multiple times and precedence is determined by the order (last one wins). Returns: Tuple consisting of: - the dataclass instances in the same order as they were passed to the initializer.abspath - if applicable, an additional namespace for more (non-dataclass backed) arguments added to the parser after initialization. - The potential list of remaining argument strings. (same as argparse.ArgumentParser.parse_known_args) """ if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)): args_files = [] if args_filename: args_files.append(Path(args_filename)) elif look_for_args_file and len(sys.argv): args_files.append(Path(sys.argv[0]).with_suffix(".args")) # args files specified via command line flag should overwrite default args files so we add them last if args_file_flag: # Create special parser just to extract the args_file_flag values args_file_parser = ArgumentParser() args_file_parser.add_argument(args_file_flag, type=str, action="append") # Use only remaining args for further parsing (remove the args_file_flag) cfg, args = args_file_parser.parse_known_args(args=args) cmd_args_file_paths = vars(cfg).get(args_file_flag.lstrip("-"), None) if cmd_args_file_paths: args_files.extend([Path(p) for p in cmd_args_file_paths]) file_args = [] for args_file in args_files: if args_file.exists(): file_args += args_file.read_text().split() # in case of duplicate arguments the last one has precedence # args specified via the command line should overwrite args from files, so we add them last args = file_args + args if args is not None else file_args + sys.argv[1:] namespace, remaining_args = self.parse_known_args(args=args) outputs = [] for dtype in self.dataclass_types: keys = {f.name for f in dataclasses.fields(dtype) if f.init} inputs = {k: v for k, v in vars(namespace).items() if k in keys} for k in keys: delattr(namespace, k) obj = dtype(**inputs) outputs.append(obj) if len(namespace.__dict__) > 0: # additional namespace. outputs.append(namespace) if return_remaining_strings: return (*outputs, remaining_args) else: if remaining_args: raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}") return (*outputs,) def parse_dict(self, args: Dict[str, Any], allow_extra_keys: bool = False) -> Tuple[DataClass, ...]: """ Alternative helper method that does not use `argparse` at all, instead uses a dict and populating the dataclass types. Args: args (`dict`): dict containing config values allow_extra_keys (`bool`, *optional*, defaults to `False`): Defaults to False. If False, will raise an exception if the dict contains keys that are not parsed. Returns: Tuple consisting of: - the dataclass instances in the same order as they were passed to the initializer. """ unused_keys = set(args.keys()) outputs = [] for dtype in self.dataclass_types: keys = {f.name for f in dataclasses.fields(dtype) if f.init} inputs = {k: v for k, v in args.items() if k in keys} unused_keys.difference_update(inputs.keys()) obj = dtype(**inputs) outputs.append(obj) if not allow_extra_keys and unused_keys: raise ValueError(f"Some keys are not used by the HfArgumentParser: {sorted(unused_keys)}") return tuple(outputs) def parse_json_file( self, json_file: Union[str, os.PathLike], allow_extra_keys: bool = False ) -> Tuple[DataClass, ...]: """ Alternative helper method that does not use `argparse` at all, instead loading a json file and populating the dataclass types. Args: json_file (`str` or `os.PathLike`): File name of the json file to parse allow_extra_keys (`bool`, *optional*, defaults to `False`): Defaults to False. If False, will raise an exception if the json file contains keys that are not parsed. Returns: Tuple consisting of: - the dataclass instances in the same order as they were passed to the initializer. """ with open(Path(json_file), encoding="utf-8") as open_json_file: data = json.loads(open_json_file.read()) outputs = self.parse_dict(data, allow_extra_keys=allow_extra_keys) return tuple(outputs) def parse_yaml_file( self, yaml_file: Union[str, os.PathLike], allow_extra_keys: bool = False ) -> Tuple[DataClass, ...]: """ Alternative helper method that does not use `argparse` at all, instead loading a yaml file and populating the dataclass types. Args: yaml_file (`str` or `os.PathLike`): File name of the yaml file to parse allow_extra_keys (`bool`, *optional*, defaults to `False`): Defaults to False. If False, will raise an exception if the json file contains keys that are not parsed. Returns: Tuple consisting of: - the dataclass instances in the same order as they were passed to the initializer. """ outputs = self.parse_dict(yaml.safe_load(Path(yaml_file).read_text()), allow_extra_keys=allow_extra_keys) return tuple(outputs)

transformers/src/transformers/hf_argparser.py/0

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from typing import Optional, Tuple import torch from ..modeling_flash_attention_utils import _flash_attention_forward from ..utils import is_flash_attn_greater_or_equal_2_10 _use_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def flash_attention_forward( module: torch.nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], dropout: float = 0.0, scaling: Optional[float] = None, sliding_window: Optional[int] = None, softcap: Optional[float] = None, **kwargs, ) -> Tuple[torch.Tensor, None]: # This is before the transpose seq_len = query.shape[2] # FA2 uses non-transposed inputs query = query.transpose(1, 2) key = key.transpose(1, 2) value = value.transpose(1, 2) # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (usually our RMSNorm modules handle it correctly) target_dtype = None if query.dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(module.config, "_pre_quantization_dtype"): target_dtype = module.config._pre_quantization_dtype else: target_dtype = next(layer for layer in module.modules() if isinstance(layer, torch.nn.Linear)).weight.dtype # FA2 always relies on the value set in the module, so remove it if present in kwargs to avoid passing it twice kwargs.pop("is_causal", None) attn_output = _flash_attention_forward( query, key, value, attention_mask, query_length=seq_len, is_causal=module.is_causal, dropout=dropout, softmax_scale=scaling, sliding_window=sliding_window, softcap=softcap, use_top_left_mask=_use_top_left_mask, target_dtype=target_dtype, **kwargs, ) return attn_output, None

transformers/src/transformers/integrations/flash_attention.py/0

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__global__ void fast_hash_ver1_cuda_kernel( int *mask, // [batch_size, num_vector] float *vector, // [batch_size, num_vector, vector_dim] int *Dmat, // [3, num_part, vector_dim] int *hash_code, // [batch_size, num_vector, num_hash_f] int batch_size, int num_vector, int vector_dim, int num_part, int num_hash_f, int hash_code_len ); __global__ void lsh_cumulation_ver1_step1_cuda_kernel( int *key_mask, // [batch_size, num_key] int *key_hash_code, // [batch_size, num_key, num_hash_f] float *value, // [batch_size, num_key, value_dim] float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, value_dim] int batch_size, int num_hash_f, int hashtable_capacity, int num_key, int value_dim, int offset_warp ); __global__ void lsh_cumulation_ver1_step2_cuda_kernel( int *query_mask, // [batch_size, num_query] int *query_hash_code, // [batch_size, num_query, num_hash_f] float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, value_dim] float *cumulation_value, // [batch_size, num_query, value_dim] int batch_size, int num_hash_f, int hashtable_capacity, int num_query, int value_dim, int offset_warp ); __global__ void lsh_weighted_cumulation_ver1_step1_cuda_kernel( int *key_mask, // [batch_size, num_key] int *key_hash_code, // [batch_size, num_key, num_hash_f] float *key_weight, // [batch_size, num_key, weight_dim] float *value, // [batch_size, num_key, value_dim] float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] int batch_size, int num_hash_f, int hashtable_capacity, int num_key, int value_dim, int weight_dim, int offset_warp, int weight_idx ); __global__ void lsh_weighted_cumulation_ver1_step2_cuda_kernel( int *query_mask, // [batch_size, num_query] int *query_hash_code, // [batch_size, num_query, num_hash_f] float *query_weight, // [batch_size, num_query, weight_dim] float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] float *cumulation_value, // [batch_size, num_query, value_dim] int batch_size, int num_hash_f, int hashtable_capacity, int num_query, int value_dim, int weight_dim, int offset_warp, int weight_idx ); __global__ void count_sort_step1_cuda_kernel( int *key_mask, // [batch_size, num_key] int *key_hash_code, // [batch_size, num_key, num_hash_f] int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] int batch_size, int num_hash_f, int hashtable_capacity, int num_key ); __global__ void count_sort_step2_cuda_kernel( int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] int batch_size, int num_hash_f, int hashtable_capacity ); __global__ void count_sort_step3_cuda_kernel( int *key_mask, // [batch_size, num_key] int *key_hash_code, // [batch_size, num_key, num_hash_f] int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] int *key_sorted_idxes, // [batch_size, num_hash_f, num_key] int batch_size, int num_hash_f, int hashtable_capacity, int num_key ); __global__ void extract_query_info_cuda_kernel( int *query_mask, // [batch_size, num_query] int *query_hash_code, // [batch_size, num_query, num_hash_f] int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] int *query_info, // [batch_size, num_query, 2, num_hash_f] int batch_size, int num_hash_f, int hashtable_capacity, int num_query ); __global__ void lsh_weighted_cumulation_ver2_step2_cuda_kernel( int *query_mask, // [batch_size, num_query] int *query_info, // [batch_size, num_query, 2, num_hash_f] int *key_sorted_idxes, // [batch_size, num_hash_f, num_key] float *query_weight, // [batch_size, num_query, weight_dim] float *key_weight, // [batch_size, num_key, weight_dim] float *value, // [batch_size, num_key, value_dim] float *cumulation_value, // [batch_size, num_query, value_dim] int batch_size, int num_hash_f, int num_query, int num_key, int value_dim, int weight_dim ); __global__ void lsh_weighted_cumulation_ver3_step2_cuda_kernel( int *query_sorted_idxes, // [batch_size, num_hash_f, num_query] int *key_mask, // [batch_size, num_key] int *key_info, // [batch_size, num_key, 2, num_hash_f] float *query_weight, // [batch_size, num_query, weight_dim] float *key_weight, // [batch_size, num_key, weight_dim] float *value, // [batch_size, num_key, value_dim] float *cumulation_value, // [batch_size, num_query, value_dim] int batch_size, int num_hash_f, int num_query, int num_key, int value_dim, int weight_dim ); __global__ void lsh_weighted_cumulation_ver4_step2_cuda_kernel( int *query_sorted_idxes, // [batch_size, num_hash_f, num_query] int *key_mask, // [batch_size, num_key] int *key_info, // [batch_size, num_key, 2, num_hash_f] float *query_weight, // [batch_size, num_query, weight_dim] float *key_weight, // [batch_size, num_key, weight_dim] float *value, // [batch_size, num_key, value_dim] float *cumulation_value, // [batch_size, num_query, value_dim] int batch_size, int num_hash_f, int num_query, int num_key, int value_dim, int weight_dim );

transformers/src/transformers/kernels/yoso/fast_lsh_cumulation_cuda.h/0

{ "file_path": "transformers/src/transformers/kernels/yoso/fast_lsh_cumulation_cuda.h", "repo_id": "transformers", "token_count": 2369 }

# 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 __future__ import annotations import warnings from dataclasses import dataclass from typing import List, Optional, Tuple import tensorflow as tf from .utils import ModelOutput @dataclass class TFBaseModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`tf.Tensor` 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(tf.FloatTensor)`, *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. """ last_hidden_state: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFBaseModelOutputWithNoAttention(ModelOutput): """ Base class for model's outputs, with potential hidden states. Args: last_hidden_state (`tf.Tensor` shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. 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, if the model has an embedding layer, + 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 optional initial embedding outputs. """ last_hidden_state: tf.Tensor = None hidden_states: Optional[Tuple[tf.Tensor, ...]] = None @dataclass class TFBaseModelOutputWithPooling(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. This output is usually *not* a good summary of the semantic content of the input, you're often better with averaging or pooling the sequence of hidden-states for the whole input sequence. 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. """ last_hidden_state: tf.Tensor = None pooler_output: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFBaseModelOutputWithPoolingAndNoAttention(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state after a pooling operation on the spatial dimensions. 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, if the model has an embedding layer, + 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 optional initial embedding outputs. """ last_hidden_state: tf.Tensor = None pooler_output: tf.Tensor = None hidden_states: Optional[Tuple[tf.Tensor, ...]] = None @dataclass class TFBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. This output is usually *not* a good summary of the semantic content of the input, you're often better with averaging or pooling the sequence of hidden-states for the whole input sequence. past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. 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. cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: tf.Tensor = None pooler_output: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFBaseModelOutputWithPast(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. 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. """ last_hidden_state: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFBaseModelOutputWithCrossAttentions(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`tf.Tensor` 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(tf.FloatTensor)`, *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. cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFBaseModelOutputWithPastAndCrossAttentions(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(tf.FloatTensor)`, *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. cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSeq2SeqModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`tf.Tensor` 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. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_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 decoder at the output of each layer plus the initial embedding outputs. decoder_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 of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_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 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 (`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 of the model. encoder_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 encoder at the output of each layer plus the initial embedding outputs. encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None decoder_hidden_states: Tuple[tf.Tensor] | None = None decoder_attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None encoder_last_hidden_state: tf.Tensor | None = None encoder_hidden_states: Tuple[tf.Tensor] | None = None encoder_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFCausalLMOutput(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary 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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFCausalLMOutputWithPast(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. 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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFCausalLMOutputWithCrossAttentions(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary 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. cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ loss: tf.Tensor | None = None logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFMaskedLMOutput(ModelOutput): """ Base class for masked language models outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided): Masked language modeling (MLM) loss. logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary 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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSeq2SeqLMOutput(ModelOutput): """ Base class for sequence-to-sequence language models outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided): Language modeling loss. logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_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 decoder at the output of each layer plus the initial embedding outputs. decoder_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 of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_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 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 (`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 of the model. encoder_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 encoder at the output of each layer plus the initial embedding outputs. encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None decoder_hidden_states: Tuple[tf.Tensor] | None = None decoder_attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None encoder_last_hidden_state: tf.Tensor | None = None encoder_hidden_states: Tuple[tf.Tensor] | None = None encoder_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFNextSentencePredictorOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `next_sentence_label` is provided): Next sentence prediction loss. logits (`tf.Tensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation 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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSeq2SeqSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence sentence classification models. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `label` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_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 decoder at the output of each layer plus the initial embedding outputs. decoder_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 of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_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)` encoder_last_hidden_state (`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 of the model. encoder_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 encoder at the output of each layer plus the initial embedding outputs. encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None decoder_hidden_states: Tuple[tf.Tensor] | None = None decoder_attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None encoder_last_hidden_state: tf.Tensor | None = None encoder_hidden_states: Tuple[tf.Tensor] | None = None encoder_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSemanticSegmenterOutput(ModelOutput): """ Base class for outputs of semantic segmentation models. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`): Classification scores for each pixel. <Tip warning={true}> The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the original image size as post-processing. You should always check your logits shape and resize as needed. </Tip> 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, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional 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, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSemanticSegmenterOutputWithNoAttention(ModelOutput): """ Base class for outputs of semantic segmentation models that do not output attention scores. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`): Classification scores for each pixel. <Tip warning={true}> The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the original image size as post-processing. You should always check your logits shape and resize as needed. </Tip> 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, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None @dataclass class TFImageClassifierOutput(ModelOutput): """ Base class for outputs of image classification models. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (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, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage. 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, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFMultipleChoiceModelOutput(ModelOutput): """ Base class for outputs of multiple choice models. Args: loss (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labels` is provided): Classification loss. logits (`tf.Tensor` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFTokenClassifierOutput(ModelOutput): """ Base class for outputs of token classification models. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of unmasked labels, returned when `labels` is provided) : Classification loss. logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of question answering models. Args: loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `start_positions` and `end_positions` are provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Span-end scores (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. """ loss: tf.Tensor | None = None start_logits: tf.Tensor = None end_logits: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence question answering models. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_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 decoder at the output of each layer plus the initial embedding outputs. decoder_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 of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. encoder_last_hidden_state (`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 of the model. encoder_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 encoder at the output of each layer plus the initial embedding outputs. encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None start_logits: tf.Tensor = None end_logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None decoder_hidden_states: Tuple[tf.Tensor] | None = None decoder_attentions: Tuple[tf.Tensor] | None = None encoder_last_hidden_state: tf.Tensor | None = None encoder_hidden_states: Tuple[tf.Tensor] | None = None encoder_attentions: Tuple[tf.Tensor] | None = None @dataclass class TFSequenceClassifierOutputWithPast(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. 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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @dataclass class TFImageClassifierOutputWithNoAttention(ModelOutput): """ Base class for outputs of image classification models. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (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, if the model has an embedding layer, + 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. """ loss: tf.Tensor | None = None logits: tf.Tensor = None hidden_states: Optional[Tuple[tf.Tensor, ...]] = None @dataclass class TFMaskedImageModelingOutput(ModelOutput): """ Base class for outputs of masked image completion / in-painting models. Args: loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided): Reconstruction loss. reconstruction (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Reconstructed / completed images. 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, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage. 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, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: tf.Tensor | None = None reconstruction: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None @property def logits(self): warnings.warn( "logits attribute is deprecated and will be removed in version 5 of Transformers." " Please use the reconstruction attribute to retrieve the final output instead.", FutureWarning, ) return self.reconstruction

transformers/src/transformers/modeling_tf_outputs.py/0

{ "file_path": "transformers/src/transformers/modeling_tf_outputs.py", "repo_id": "transformers", "token_count": 20580 }

# coding=utf-8 # Copyright 2023 The Google Research Team Authors 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 ALIGN model.""" import math from dataclasses import dataclass from typing import Any, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithNoAttention, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, BaseModelOutputWithPoolingAndNoAttention, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "kakaobrain/align-base" _CONFIG_FOR_DOC = "AlignConfig" ALIGN_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 ([`AlignConfig`]): 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. """ ALIGN_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) 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) 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. """ ALIGN_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 [`EfficientNetImageProcessor.__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. """ ALIGN_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) 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) 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. 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 [`EfficientNetImageProcessor.__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. """ @dataclass class AlignVisionModelOutput(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. """ image_embeds: Optional[torch.FloatTensor] = None last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class AlignTextModelOutput(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 class AlignOutput(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 [`AlignTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The output of [`AlignVisionModel`]. text_model_output(`BaseModelOutputWithPoolingAndCrossAttentions`): The output of the [`AlignTextModel`]. vision_model_output(`BaseModelOutputWithPoolingAndNoAttention`): The output of the [`AlignVisionModel`]. """ 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: BaseModelOutputWithPoolingAndCrossAttentions = None vision_model_output: BaseModelOutputWithPoolingAndNoAttention = 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() ) # contrastive loss function, adapted from # https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device), label_smoothing=0.1) def align_loss(similarity: torch.Tensor) -> torch.Tensor: caption_loss = contrastive_loss(similarity) image_loss = contrastive_loss(similarity.t()) return (caption_loss + image_loss) / 2.0 # Copied from transformers.models.efficientnet.modeling_efficientnet.round_filters with EfficientNet->AlignVision def round_filters(config: AlignVisionConfig, num_channels: int): r""" Round number of filters based on depth multiplier. """ divisor = config.depth_divisor num_channels *= config.width_coefficient new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_dim < 0.9 * num_channels: new_dim += divisor return int(new_dim) # Copied from transformers.models.efficientnet.modeling_efficientnet.correct_pad def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True): r""" Utility function to get the tuple padding value for the depthwise convolution. Args: kernel_size (`int` or `tuple`): Kernel size of the convolution layers. adjust (`bool`, *optional*, defaults to `True`): Adjusts padding value to apply to right and bottom sides of the input. """ if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size) correct = (kernel_size[0] // 2, kernel_size[1] // 2) if adjust: return (correct[1] - 1, correct[1], correct[0] - 1, correct[0]) else: return (correct[1], correct[1], correct[0], correct[0]) # Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetEmbeddings with EfficientNet->AlignVision class AlignVisionEmbeddings(nn.Module): r""" A module that corresponds to the stem module of the original work. """ def __init__(self, config: AlignVisionConfig): super().__init__() self.out_dim = round_filters(config, 32) self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1)) self.convolution = nn.Conv2d( config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False ) self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum) self.activation = ACT2FN[config.hidden_act] def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: features = self.padding(pixel_values) features = self.convolution(features) features = self.batchnorm(features) features = self.activation(features) return features # Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseConv2d with EfficientNet->AlignVision class AlignVisionDepthwiseConv2d(nn.Conv2d): def __init__( self, in_channels, depth_multiplier=1, kernel_size=3, stride=1, padding=0, dilation=1, bias=True, padding_mode="zeros", ): out_channels = in_channels * depth_multiplier super().__init__( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_channels, bias=bias, padding_mode=padding_mode, ) # Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetExpansionLayer with EfficientNet->AlignVision class AlignVisionExpansionLayer(nn.Module): r""" This corresponds to the expansion phase of each block in the original implementation. """ def __init__(self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int): super().__init__() self.expand_conv = nn.Conv2d( in_channels=in_dim, out_channels=out_dim, kernel_size=1, padding="same", bias=False, ) self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps) self.expand_act = ACT2FN[config.hidden_act] def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: # Expand phase hidden_states = self.expand_conv(hidden_states) hidden_states = self.expand_bn(hidden_states) hidden_states = self.expand_act(hidden_states) return hidden_states # Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseLayer with EfficientNet->AlignVision class AlignVisionDepthwiseLayer(nn.Module): r""" This corresponds to the depthwise convolution phase of each block in the original implementation. """ def __init__( self, config: AlignVisionConfig, in_dim: int, stride: int, kernel_size: int, adjust_padding: bool, ): super().__init__() self.stride = stride conv_pad = "valid" if self.stride == 2 else "same" padding = correct_pad(kernel_size, adjust=adjust_padding) self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding) self.depthwise_conv = AlignVisionDepthwiseConv2d( in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False ) self.depthwise_norm = nn.BatchNorm2d( num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum ) self.depthwise_act = ACT2FN[config.hidden_act] def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: # Depthwise convolution if self.stride == 2: hidden_states = self.depthwise_conv_pad(hidden_states) hidden_states = self.depthwise_conv(hidden_states) hidden_states = self.depthwise_norm(hidden_states) hidden_states = self.depthwise_act(hidden_states) return hidden_states # Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetSqueezeExciteLayer with EfficientNet->AlignVision class AlignVisionSqueezeExciteLayer(nn.Module): r""" This corresponds to the Squeeze and Excitement phase of each block in the original implementation. """ def __init__(self, config: AlignVisionConfig, in_dim: int, expand_dim: int, expand: bool = False): super().__init__() self.dim = expand_dim if expand else in_dim self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio)) self.squeeze = nn.AdaptiveAvgPool2d(output_size=1) self.reduce = nn.Conv2d( in_channels=self.dim, out_channels=self.dim_se, kernel_size=1, padding="same", ) self.expand = nn.Conv2d( in_channels=self.dim_se, out_channels=self.dim, kernel_size=1, padding="same", ) self.act_reduce = ACT2FN[config.hidden_act] self.act_expand = nn.Sigmoid() def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: inputs = hidden_states hidden_states = self.squeeze(hidden_states) hidden_states = self.reduce(hidden_states) hidden_states = self.act_reduce(hidden_states) hidden_states = self.expand(hidden_states) hidden_states = self.act_expand(hidden_states) hidden_states = torch.mul(inputs, hidden_states) return hidden_states class AlignVisionFinalBlockLayer(nn.Module): r""" This corresponds to the final phase of each block in the original implementation. """ def __init__( self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool ): super().__init__() self.apply_dropout = stride == 1 and not id_skip self.project_conv = nn.Conv2d( in_channels=in_dim, out_channels=out_dim, kernel_size=1, padding="same", bias=False, ) self.project_bn = nn.BatchNorm2d( num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum ) self.dropout = nn.Dropout(p=drop_rate) def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor: hidden_states = self.project_conv(hidden_states) hidden_states = self.project_bn(hidden_states) if self.apply_dropout: hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + embeddings return hidden_states class AlignVisionBlock(nn.Module): r""" This corresponds to the block module of original the EfficientNet vision encoder implementation. Args: config ([`AlignVisionConfig`]): Model configuration class. in_dim (`int`): Number of input channels. out_dim (`int`): Number of output channels. stride (`int`): Stride size to be used in convolution layers. expand_ratio (`int`): Expand ratio to set the output dimensions for the expansion and squeeze-excite layers. kernel_size (`int`): Kernel size for the depthwise convolution layer. drop_rate (`float`): Dropout rate to be used in the final phase of each block. id_skip (`bool`): Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase of each block. Set to `True` for the first block of each stage. adjust_padding (`bool`): Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution operation, set to `True` for inputs with odd input sizes. """ def __init__( self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int, expand_ratio: int, kernel_size: int, drop_rate: float, id_skip: bool, adjust_padding: bool, ): super().__init__() self.expand_ratio = expand_ratio self.expand = True if self.expand_ratio != 1 else False expand_in_dim = in_dim * expand_ratio if self.expand: self.expansion = AlignVisionExpansionLayer( config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride ) self.depthwise_conv = AlignVisionDepthwiseLayer( config=config, in_dim=expand_in_dim if self.expand else in_dim, stride=stride, kernel_size=kernel_size, adjust_padding=adjust_padding, ) self.squeeze_excite = AlignVisionSqueezeExciteLayer( config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand ) self.projection = AlignVisionFinalBlockLayer( config=config, in_dim=expand_in_dim if self.expand else in_dim, out_dim=out_dim, stride=stride, drop_rate=drop_rate, id_skip=id_skip, ) def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: embeddings = hidden_states # Expansion and depthwise convolution phase if self.expand_ratio != 1: hidden_states = self.expansion(hidden_states) hidden_states = self.depthwise_conv(hidden_states) # Squeeze and excite phase hidden_states = self.squeeze_excite(hidden_states) hidden_states = self.projection(embeddings, hidden_states) return hidden_states class AlignVisionEncoder(nn.Module): r""" Forward propogates the embeddings through each vision encoder (EfficientNet) block. Args: config ([`AlignVisionConfig`]): Model configuration class. """ def __init__(self, config: AlignVisionConfig): super().__init__() self.depth_coefficient = config.depth_coefficient def round_repeats(repeats): # Round number of block repeats based on depth multiplier. return int(math.ceil(self.depth_coefficient * repeats)) num_base_blocks = len(config.in_channels) num_blocks = sum(round_repeats(n) for n in config.num_block_repeats) curr_block_num = 0 blocks = [] for i in range(num_base_blocks): in_dim = round_filters(config, config.in_channels[i]) out_dim = round_filters(config, config.out_channels[i]) stride = config.strides[i] kernel_size = config.kernel_sizes[i] expand_ratio = config.expand_ratios[i] for j in range(round_repeats(config.num_block_repeats[i])): id_skip = True if j == 0 else False stride = 1 if j > 0 else stride in_dim = out_dim if j > 0 else in_dim adjust_padding = False if curr_block_num in config.depthwise_padding else True drop_rate = config.drop_connect_rate * curr_block_num / num_blocks block = AlignVisionBlock( config=config, in_dim=in_dim, out_dim=out_dim, stride=stride, kernel_size=kernel_size, expand_ratio=expand_ratio, drop_rate=drop_rate, id_skip=id_skip, adjust_padding=adjust_padding, ) blocks.append(block) curr_block_num += 1 self.blocks = nn.ModuleList(blocks) def forward( self, hidden_states: torch.FloatTensor, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> BaseModelOutputWithPoolingAndNoAttention: all_hidden_states = (hidden_states,) if output_hidden_states else None for block in self.blocks: hidden_states = block(hidden_states) if output_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, ) # Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->AlignText class AlignTextEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values_length: int = 0, ) -> torch.Tensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings # Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->AlignText class AlignTextSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=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) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder 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: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(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(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: 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) use_cache = past_key_value is not None 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_layer, 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)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in AlignTextModel forward() function) attention_scores = attention_scores + attention_mask # 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,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->AlignText class AlignTextSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) 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 = self.LayerNorm(hidden_states + input_tensor) return hidden_states ALIGN_TEXT_SELF_ATTENTION_CLASSES = { "eager": AlignTextSelfAttention, } # Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->AlignText,BERT->ALIGN_TEXT class AlignTextAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = ALIGN_TEXT_SELF_ATTENTION_CLASSES[config._attn_implementation]( config, position_embedding_type=position_embedding_type ) self.output = AlignTextSelfOutput(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: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, 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 # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->AlignText class AlignTextIntermediate(nn.Module): def __init__(self, config): 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 # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->AlignText class AlignTextOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) 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 = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->AlignText class AlignTextLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = AlignTextAttention(config) 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 = AlignTextAttention(config, position_embedding_type="absolute") self.intermediate = AlignTextIntermediate(config) self.output = AlignTextOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.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( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) 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( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) 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 layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->AlignText class AlignTextEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([AlignTextLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () 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 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 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,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) 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, ) # Copied from transformers.models.bert.modeling_bert.BertPooler with Bert -> AlignText class AlignTextPooler(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 AlignPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = AlignConfig base_model_prefix = "align" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): 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, AlignModel): nn.init.xavier_uniform_(module.text_projection.weight) module.text_projection.bias.data.zero_() module.text_projection._is_hf_initialized = True 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_() if isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) @add_start_docstrings( """The text model from ALIGN without any head or projection on top.""", ALIGN_START_DOCSTRING, ) class AlignTextModel(AlignPreTrainedModel): config_class = AlignTextConfig _no_split_modules = ["AlignTextEmbeddings"] def __init__(self, config: AlignTextConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = AlignTextEmbeddings(config) self.encoder = AlignTextEncoder(config) self.pooler = AlignTextPooler(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 @add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=AlignTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]: r""" Returns: Examples: ```python >>> from transformers import AutoTokenizer, AlignTextModel >>> model = AlignTextModel.from_pretrained("kakaobrain/align-base") >>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled (EOS token) states ```""" 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: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: 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 = 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, 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, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings( """The vision model from ALIGN without any head or projection on top.""", ALIGN_START_DOCSTRING, ) class AlignVisionModel(AlignPreTrainedModel): config_class = AlignVisionConfig main_input_name = "pixel_values" supports_gradient_checkpointing = False def __init__(self, config: AlignVisionConfig): super().__init__(config) self.config = config self.embeddings = AlignVisionEmbeddings(config) self.encoder = AlignVisionEncoder(config) # Final pooling layer if config.pooling_type == "mean": self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True) elif config.pooling_type == "max": self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True) else: raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}") # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.convolution @add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=AlignVisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AlignVisionModel >>> model = AlignVisionModel.from_pretrained("kakaobrain/align-base") >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base") >>> 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 ```""" 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, ) # Apply pooling last_hidden_state = encoder_outputs[0] pooled_output = self.pooler(last_hidden_state) # Reshape (batch_size, projection_dim, 1 , 1) -> (batch_size, projection_dim) pooled_output = pooled_output.reshape(pooled_output.shape[:2]) 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(ALIGN_START_DOCSTRING) class AlignModel(AlignPreTrainedModel): config_class = AlignConfig def __init__(self, config: AlignConfig): super().__init__(config) if not isinstance(config.text_config, AlignTextConfig): raise TypeError( "config.text_config is expected to be of type AlignTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.vision_config, AlignVisionConfig): raise TypeError( "config.vision_config is expected to be of type AlignVisionConfig 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.text_model = AlignTextModel(text_config) self.vision_model = AlignVisionModel(vision_config) self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim) self.temperature = nn.Parameter(torch.tensor(self.config.temperature_init_value)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: 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 [`AlignTextModel`]. Examples: ```python >>> from transformers import AutoTokenizer, AlignModel >>> model = AlignModel.from_pretrained("kakaobrain/align-base") >>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> text_features = model.get_text_features(**inputs) ```""" # Use ALIGN 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, 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, ) last_hidden_state = text_outputs[0][:, 0, :] text_features = self.text_projection(last_hidden_state) return text_features @add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = 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 [`AlignVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AlignModel >>> model = AlignModel.from_pretrained("kakaobrain/align-base") >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base") >>> 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 ALIGN model's config for some fields (if specified) instead of those of vision & text components. 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_hidden_states=output_hidden_states, return_dict=return_dict, ) image_features = vision_outputs[1] # pooled_output return image_features @add_start_docstrings_to_model_forward(ALIGN_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=AlignOutput, config_class=AlignConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, AlignOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AlignModel >>> model = AlignModel.from_pretrained("kakaobrain/align-base") >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... images=image, text=["a photo of a cat", "a photo of a dog"], return_tensors="pt", padding=True ... ) >>> 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 ALIGN 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_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, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] text_embeds = text_outputs[0][:, 0, :] text_embeds = self.text_projection(text_embeds) # 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.temperature logits_per_image = logits_per_text.t() loss = None if return_loss: loss = align_loss(logits_per_text) 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 AlignOutput( 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, ) __all__ = ["AlignPreTrainedModel", "AlignTextModel", "AlignVisionModel", "AlignModel"]

transformers/src/transformers/models/align/modeling_align.py/0

{ "file_path": "transformers/src/transformers/models/align/modeling_align.py", "repo_id": "transformers", "token_count": 30152 }

# 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. """ Feature extractor class for Audio Spectrogram Transformer. """ from typing import List, Optional, Union import numpy as np from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import TensorType, is_speech_available, is_torch_available, logging if is_speech_available(): import torchaudio.compliance.kaldi as ta_kaldi if is_torch_available(): import torch logger = logging.get_logger(__name__) class ASTFeatureExtractor(SequenceFeatureExtractor): r""" Constructs a Audio Spectrogram Transformer (AST) feature extractor. This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. This class extracts mel-filter bank features from raw speech using TorchAudio if installed or using numpy otherwise, pads/truncates them to a fixed length and normalizes them using a mean and standard deviation. Args: feature_size (`int`, *optional*, defaults to 1): The feature dimension of the extracted features. sampling_rate (`int`, *optional*, defaults to 16000): The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). num_mel_bins (`int`, *optional*, defaults to 128): Number of Mel-frequency bins. max_length (`int`, *optional*, defaults to 1024): Maximum length to which to pad/truncate the extracted features. do_normalize (`bool`, *optional*, defaults to `True`): Whether or not to normalize the log-Mel features using `mean` and `std`. mean (`float`, *optional*, defaults to -4.2677393): The mean value used to normalize the log-Mel features. Uses the AudioSet mean by default. std (`float`, *optional*, defaults to 4.5689974): The standard deviation value used to normalize the log-Mel features. Uses the AudioSet standard deviation by default. return_attention_mask (`bool`, *optional*, defaults to `False`): Whether or not [`~ASTFeatureExtractor.__call__`] should return `attention_mask`. """ model_input_names = ["input_values", "attention_mask"] def __init__( self, feature_size=1, sampling_rate=16000, num_mel_bins=128, max_length=1024, padding_value=0.0, do_normalize=True, mean=-4.2677393, std=4.5689974, return_attention_mask=False, **kwargs, ): super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) self.num_mel_bins = num_mel_bins self.max_length = max_length self.do_normalize = do_normalize self.mean = mean self.std = std self.return_attention_mask = return_attention_mask if not is_speech_available(): mel_filters = mel_filter_bank( num_frequency_bins=256, num_mel_filters=self.num_mel_bins, min_frequency=20, max_frequency=sampling_rate // 2, sampling_rate=sampling_rate, norm=None, mel_scale="kaldi", triangularize_in_mel_space=True, ) self.mel_filters = np.pad(mel_filters, ((0, 1), (0, 0))) self.window = window_function(400, "hann", periodic=False) def _extract_fbank_features( self, waveform: np.ndarray, max_length: int, ) -> np.ndarray: """ Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs and hence the waveform should not be normalized before feature extraction. """ # waveform = waveform * (2**15) # Kaldi compliance: 16-bit signed integers if is_speech_available(): waveform = torch.from_numpy(waveform).unsqueeze(0) fbank = ta_kaldi.fbank( waveform, sample_frequency=self.sampling_rate, window_type="hanning", num_mel_bins=self.num_mel_bins, ) else: waveform = np.squeeze(waveform) fbank = spectrogram( waveform, self.window, frame_length=400, hop_length=160, fft_length=512, power=2.0, center=False, preemphasis=0.97, mel_filters=self.mel_filters, log_mel="log", mel_floor=1.192092955078125e-07, remove_dc_offset=True, ).T fbank = torch.from_numpy(fbank) n_frames = fbank.shape[0] difference = max_length - n_frames # pad or truncate, depending on difference if difference > 0: pad_module = torch.nn.ZeroPad2d((0, 0, 0, difference)) fbank = pad_module(fbank) elif difference < 0: fbank = fbank[0:max_length, :] fbank = fbank.numpy() return fbank def normalize(self, input_values: np.ndarray) -> np.ndarray: return (input_values - (self.mean)) / (self.std * 2) def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], sampling_rate: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs, ) -> BatchFeature: """ Main method to featurize and prepare for the model one or several sequence(s). Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep. sampling_rate (`int`, *optional*): The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. """ if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of" f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with" f" {self.sampling_rate} and not {sampling_rate}." ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 if is_batched_numpy and len(raw_speech.shape) > 2: raise ValueError(f"Only mono-channel audio is supported for input to {self}") is_batched = is_batched_numpy or ( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list))) ) if is_batched: raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech] elif not is_batched and not isinstance(raw_speech, np.ndarray): raw_speech = np.asarray(raw_speech, dtype=np.float32) elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64): raw_speech = raw_speech.astype(np.float32) # always return batch if not is_batched: raw_speech = [raw_speech] # extract fbank features and pad/truncate to max_length features = [self._extract_fbank_features(waveform, max_length=self.max_length) for waveform in raw_speech] # convert into BatchFeature padded_inputs = BatchFeature({"input_values": features}) # make sure list is in array format input_values = padded_inputs.get("input_values") if isinstance(input_values[0], list): padded_inputs["input_values"] = [np.asarray(feature, dtype=np.float32) for feature in input_values] # normalization if self.do_normalize: padded_inputs["input_values"] = [self.normalize(feature) for feature in input_values] if return_tensors is not None: padded_inputs = padded_inputs.convert_to_tensors(return_tensors) return padded_inputs __all__ = ["ASTFeatureExtractor"]

transformers/src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py/0

{ "file_path": "transformers/src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py", "repo_id": "transformers", "token_count": 4219 }

# coding=utf-8 # Copyright 2024 IBM 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. """Bamba model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class BambaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`BambaModel`]. It is used to instantiate a BambaModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with defaults taken from [ibm-fms/Bamba-9.8b-2.2T-hf](https://huggingface.co/ibm-fms/Bamba-9.8b-2.2T-hf). The BambaModel is a hybrid [mamba2](https://github.com/state-spaces/mamba) architecture with SwiGLU. The checkpoints are jointly trained by IBM, Princeton, and UIUC. 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 128000): Vocabulary size of the Bamba model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BambaModel`] tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the model has a output word embedding layer. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 14336): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 8): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. num_logits_to_keep (`int` or `None`, *optional*, defaults to 1): Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the logits of the last prompt token are needed for generation. For long sequences, the logits for the entire sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint significantly. pad_token_id (`int`, *optional*, defaults to 0): The id of the padding token. bos_token_id (`int`, *optional*, defaults to 1): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 2): The id of the "end-of-sequence" token. max_position_embeddings (`int`, *optional*, defaults to 262144): Max cached sequence length for the model attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. attn_layer_indices (`list`, *optional*): Specifies the layer indices that will have full attention. Must contain values at most num_hidden_layers. mamba_n_heads (`int`, *optional*, defaults to 128): The number of mamba heads used in the v2 implementation. mamba_d_head (`int`, *optional*, defaults to `"auto"`): Head embeddding dimension size mamba_n_groups (`int`, *optional*, defaults to 1): The number of the mamba groups used in the v2 implementation. mamba_d_state (`int`, *optional*, defaults to 256): The dimension the mamba state space latents mamba_d_conv (`int`, *optional*, defaults to 4): The size of the mamba convolution kernel mamba_expand (`int`, *optional*, defaults to 2): Expanding factor (relative to hidden_size) used to determine the mamba intermediate size mamba_chunk_size (`int`, *optional*, defaults to 256): The chunks in which to break the sequence when doing prefill/training mamba_conv_bias (`bool`, *optional*, defaults to `True`): Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block. mamba_proj_bias (`bool`, *optional*, defaults to `False`): Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block """ model_type = "bamba" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=128000, tie_word_embeddings=False, hidden_size=4096, intermediate_size=14336, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=8, hidden_act="silu", initializer_range=0.02, rms_norm_eps=1e-5, use_cache=True, num_logits_to_keep=1, pad_token_id=0, bos_token_id=1, eos_token_id=2, max_position_embeddings=262144, attention_dropout=0.0, attn_layer_indices=None, mamba_n_heads=128, mamba_d_head="auto", mamba_n_groups=1, mamba_d_state=256, mamba_d_conv=4, mamba_expand=2, mamba_chunk_size=256, mamba_conv_bias=True, mamba_proj_bias=False, **kwargs, ): self.vocab_size = vocab_size self.tie_word_embeddings = tie_word_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_position_embeddings = max_position_embeddings self.attention_dropout = attention_dropout self.attention_bias = False self.mlp_bias = False # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.num_logits_to_keep = num_logits_to_keep self.attn_layer_indices = attn_layer_indices self.rope_theta = 10000.0 self.rope_scaling = None self.partial_rotary_factor = 0.5 mamba_intermediate = mamba_expand * hidden_size if mamba_intermediate % mamba_n_heads != 0: raise ValueError("mamba_n_heads must divide mamba_expand * hidden_size") # for the mamba_v2, must satisfy the following if mamba_d_head == "auto": mamba_d_head = mamba_intermediate // mamba_n_heads if mamba_d_head * mamba_n_heads != mamba_intermediate: raise ValueError("The dimensions for the Mamba head state do not match the model intermediate_size") self.mamba_n_heads = mamba_n_heads self.mamba_d_head = mamba_d_head self.mamba_n_groups = mamba_n_groups self.mamba_d_state = mamba_d_state self.mamba_d_conv = mamba_d_conv self.mamba_expand = mamba_expand self.mamba_chunk_size = mamba_chunk_size self.mamba_conv_bias = mamba_conv_bias self.mamba_proj_bias = mamba_proj_bias super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) @property def layers_block_type(self): return [ "attention" if (self.attn_layer_indices and i in self.attn_layer_indices) else "mamba" for i in range(self.num_hidden_layers) ] __all__ = ["BambaConfig"]

transformers/src/transformers/models/bamba/configuration_bamba.py/0

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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. """ This script can be used to convert a head-less TF2.x Bert model to PyTorch, as published on the official (now deprecated) GitHub: https://github.com/tensorflow/models/tree/v2.3.0/official/nlp/bert TF2.x uses different variable names from the original BERT (TF 1.4) implementation. The script re-maps the TF2.x Bert weight names to the original names, so the model can be imported with Huggingface/transformer. You may adapt this script to include classification/MLM/NSP/etc. heads. Note: This script is only working with an older version of the TensorFlow models repository (<= v2.3.0). Models trained with never versions are not compatible with this script. """ import argparse import os import re import tensorflow as tf import torch from transformers import BertConfig, BertModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def load_tf2_weights_in_bert(model, tf_checkpoint_path, config): tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] layer_depth = [] for full_name, shape in init_vars: # logger.info(f"Loading TF weight {name} with shape {shape}") name = full_name.split("/") if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]: logger.info(f"Skipping non-model layer {full_name}") continue if "optimizer" in full_name: logger.info(f"Skipping optimization layer {full_name}") continue if name[0] == "model": # ignore initial 'model' name = name[1:] # figure out how many levels deep the name is depth = 0 for _name in name: if _name.startswith("layer_with_weights"): depth += 1 else: break layer_depth.append(depth) # read data array = tf.train.load_variable(tf_path, full_name) names.append("/".join(name)) arrays.append(array) logger.info(f"Read a total of {len(arrays):,} layers") # Sanity check if len(set(layer_depth)) != 1: raise ValueError(f"Found layer names with different depths (layer depth {list(set(layer_depth))})") layer_depth = list(set(layer_depth))[0] if layer_depth != 1: raise ValueError( "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP" " heads." ) # convert layers logger.info("Converting weights...") for full_name, array in zip(names, arrays): name = full_name.split("/") pointer = model trace = [] for i, m_name in enumerate(name): if m_name == ".ATTRIBUTES": # variable names end with .ATTRIBUTES/VARIABLE_VALUE break if m_name.startswith("layer_with_weights"): layer_num = int(m_name.split("-")[-1]) if layer_num <= 2: # embedding layers # layer_num 0: word_embeddings # layer_num 1: position_embeddings # layer_num 2: token_type_embeddings continue elif layer_num == 3: # embedding LayerNorm trace.extend(["embeddings", "LayerNorm"]) pointer = getattr(pointer, "embeddings") pointer = getattr(pointer, "LayerNorm") elif layer_num > 3 and layer_num < config.num_hidden_layers + 4: # encoder layers trace.extend(["encoder", "layer", str(layer_num - 4)]) pointer = getattr(pointer, "encoder") pointer = getattr(pointer, "layer") pointer = pointer[layer_num - 4] elif layer_num == config.num_hidden_layers + 4: # pooler layer trace.extend(["pooler", "dense"]) pointer = getattr(pointer, "pooler") pointer = getattr(pointer, "dense") elif m_name == "embeddings": trace.append("embeddings") pointer = getattr(pointer, "embeddings") if layer_num == 0: trace.append("word_embeddings") pointer = getattr(pointer, "word_embeddings") elif layer_num == 1: trace.append("position_embeddings") pointer = getattr(pointer, "position_embeddings") elif layer_num == 2: trace.append("token_type_embeddings") pointer = getattr(pointer, "token_type_embeddings") else: raise ValueError(f"Unknown embedding layer with name {full_name}") trace.append("weight") pointer = getattr(pointer, "weight") elif m_name == "_attention_layer": # self-attention layer trace.extend(["attention", "self"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "self") elif m_name == "_attention_layer_norm": # output attention norm trace.extend(["attention", "output", "LayerNorm"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_attention_output_dense": # output attention dense trace.extend(["attention", "output", "dense"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_dense": # output dense trace.extend(["output", "dense"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output dense trace.extend(["output", "LayerNorm"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_key_dense": # attention key trace.append("key") pointer = getattr(pointer, "key") elif m_name == "_query_dense": # attention query trace.append("query") pointer = getattr(pointer, "query") elif m_name == "_value_dense": # attention value trace.append("value") pointer = getattr(pointer, "value") elif m_name == "_intermediate_dense": # attention intermediate dense trace.extend(["intermediate", "dense"]) pointer = getattr(pointer, "intermediate") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output layer norm trace.append("output") pointer = getattr(pointer, "output") # weights & biases elif m_name in ["bias", "beta"]: trace.append("bias") pointer = getattr(pointer, "bias") elif m_name in ["kernel", "gamma"]: trace.append("weight") pointer = getattr(pointer, "weight") else: logger.warning(f"Ignored {m_name}") # for certain layers reshape is necessary trace = ".".join(trace) if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", trace) or re.match( r"(\S+)\.attention\.output\.dense\.weight", trace ): array = array.reshape(pointer.data.shape) if "kernel" in full_name: array = array.transpose() if pointer.shape == array.shape: pointer.data = torch.from_numpy(array) else: raise ValueError( f"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:" f" {array.shape}" ) logger.info(f"Successfully set variable {full_name} to PyTorch layer {trace}") return model def convert_tf2_checkpoint_to_pytorch(tf_checkpoint_path, config_path, pytorch_dump_path): # Instantiate model logger.info(f"Loading model based on config from {config_path}...") config = BertConfig.from_json_file(config_path) model = BertModel(config) # Load weights from checkpoint logger.info(f"Loading weights from checkpoint {tf_checkpoint_path}...") load_tf2_weights_in_bert(model, tf_checkpoint_path, config) # Save pytorch-model logger.info(f"Saving PyTorch model to {pytorch_dump_path}...") torch.save(model.state_dict(), pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path." ) parser.add_argument( "--bert_config_file", type=str, required=True, help="The config json file corresponding to the BERT model. This specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", type=str, required=True, help="Path to the output PyTorch model (must include filename).", ) args = parser.parse_args() convert_tf2_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

transformers/src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 4808 }

# 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. """PyTorch BioGPT 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 ACT2FN from ...generation import GenerationMixin from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_biogpt import BioGptConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "microsoft/biogpt" _CONFIG_FOR_DOC = "BioGptConfig" # copied from transformers.models.opt.modeling_opt.OPTLearnedPositionalEmbedding with OPT->BioGpt # TODO @ArthurZucker bring copied from back class BioGptLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # BioGpt is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): """`input_ids_shape` is expected to be [bsz x seqlen].""" attention_mask = attention_mask.long() # create positions depending on attention_mask positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 # cut positions if `past_key_values_length` is > 0 positions = positions[:, past_key_values_length:] return super().forward(positions + self.offset) # Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->BioGpt class BioGptScaledWordEmbedding(nn.Embedding): """ This module overrides nn.Embeddings' forward by multiplying with embeddings scale. """ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0): super().__init__(num_embeddings, embedding_dim, padding_idx) self.embed_scale = embed_scale def forward(self, input_ids: torch.Tensor): return super().forward(input_ids) * self.embed_scale # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BioGpt class BioGptAttention(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[BioGptConfig] = 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.BartSdpaAttention with Bart->BioGpt class BioGptSdpaAttention(BioGptAttention): 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 output_attentions or layer_head_mask is not None: # TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented. logger.warning_once( "BioGptModel is using BioGptSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention" ' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( hidden_states, key_value_states=key_value_states, past_key_value=past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) # 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) # 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) query_states = self._shape(query_states, tgt_len, bsz) # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. # The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1. is_causal = True if self.is_causal and attention_mask is None and tgt_len > 1 else False # NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask, # but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577 attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=self.dropout if self.training else 0.0, is_causal=is_causal, ) 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.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, None, past_key_value BIOGPT_ATTENTION_CLASSES = { "eager": BioGptAttention, "sdpa": BioGptSdpaAttention, } class BioGptDecoderLayer(nn.Module): def __init__(self, config: BioGptConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = BIOGPT_ATTENTION_CLASSES[config._attn_implementation]( embed_dim=self.embed_dim, num_heads=config.num_attention_heads, dropout=config.attention_probs_dropout_prob, is_decoder=True, is_causal=True, ) self.dropout = config.hidden_dropout_prob self.activation_fn = ACT2FN[config.hidden_act] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, 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, 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. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_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. 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`). """ residual = hidden_states hidden_states = self.self_attn_layer_norm(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 # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(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,) if use_cache: outputs += (present_key_value,) return outputs class BioGptPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = BioGptConfig base_model_prefix = "biogpt" supports_gradient_checkpointing = True _supports_sdpa = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, 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, 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) BIOGPT_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 ([`~BioGptConfig`]): 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. """ BIOGPT_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) 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. 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. 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 BioGPT Model transformer outputting raw hidden-states without any specific head on top.", BIOGPT_START_DOCSTRING, ) class BioGptModel(BioGptPreTrainedModel): def __init__(self, config: BioGptConfig): super().__init__(config) self.config = config self.layerdrop = config.layerdrop self.dropout = config.hidden_dropout_prob self.embed_dim = config.hidden_size self.padding_idx = config.pad_token_id embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 self.embed_tokens = BioGptScaledWordEmbedding( config.vocab_size, self.embed_dim, self.padding_idx, embed_scale=embed_scale ) self.embed_positions = BioGptLearnedPositionalEmbedding(config.max_position_embeddings, self.embed_dim) self.layers = nn.ModuleList([BioGptDecoderLayer(config) for _ in range(config.num_hidden_layers)]) self.layer_norm = nn.LayerNorm(self.embed_dim) self.gradient_checkpointing = False self._use_sdpa = config._attn_implementation == "sdpa" # 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 @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, 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, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, # NOOP kwargs, for now ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: 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 input_ids and inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_shape = input.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either input_ids or 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) if attention_mask is None: attention_mask = torch.ones( (inputs_embeds.shape[0], inputs_embeds.shape[1] + past_key_values_length), dtype=torch.bool, device=inputs_embeds.device, ) elif attention_mask.shape[1] != past_key_values_length + input_shape[1]: raise ValueError( f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be " f"{past_key_values_length + input_shape[1]} (sum of the lengths of current and past inputs)" ) # embed positions positions = self.embed_positions(attention_mask, past_key_values_length) if self._use_sdpa and not output_attentions and head_mask is None: # output_attentions=True & head_mask can not be supported when using SDPA, fall back to # the manual implementation that requires a 4D causal mask in all cases. attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( attention_mask, input_shape, inputs_embeds, past_key_values_length ) else: attention_mask = _prepare_4d_causal_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) 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 all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = None next_decoder_cache = () if use_cache 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 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, head_mask[idx] if head_mask is not None else None, None, output_attentions, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, layer_head_mask=(head_mask[idx] if 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[2 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) hidden_states = self.layer_norm(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( """BioGPT Model with a `language modeling` head on top for CLM fine-tuning.""", BIOGPT_START_DOCSTRING ) class BioGptForCausalLM(BioGptPreTrainedModel, GenerationMixin): _tied_weights_keys = ["output_projection.weight"] def __init__(self, config): super().__init__(config) self.biogpt = BioGptModel(config) self.output_projection = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.output_projection def set_output_embeddings(self, new_embeddings): self.output_projection = new_embeddings @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, 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, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, **kwargs, ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: 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]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.biogpt( input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, 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] prediction_scores = self.output_projection(sequence_output) lm_loss = None if labels is not None: lm_loss = self.loss_function( prediction_scores, labels, vocab_size=self.config.vocab_size, **kwargs, ) if not return_dict: output = (prediction_scores,) + outputs[1:] 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, ) @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 @add_start_docstrings( """ BioGPT 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. """, BIOGPT_START_DOCSTRING, ) class BioGptForTokenClassification(BioGptPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.biogpt = BioGptModel(config) if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout else: classifier_dropout = config.hidden_dropout_prob self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.post_init() @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING) @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, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, TokenClassifierOutput]: 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 transformer_outputs = self.biogpt( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, 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] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels) active_labels = torch.where( active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels) ) loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The BioGpt Model transformer with a sequence classification head on top (linear layer). [`BioGptForSequenceClassification`] 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 is required 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). """, BIOGPT_START_DOCSTRING, ) class BioGptForSequenceClassification(BioGptPreTrainedModel): def __init__(self, config: BioGptConfig): super().__init__(config) self.num_labels = config.num_labels self.biogpt = BioGptModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutputWithPast, 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, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, SequenceClassifierOutputWithPast]: 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 transformer_outputs = self.biogpt( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, 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.score(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: sequence_length = -1 else: if input_ids is not None: sequence_length = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) else: sequence_length = -1 logger.warning_once( 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[torch.arange(batch_size, device=logits.device), sequence_length] 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[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def get_input_embeddings(self): return self.biogpt.embed_tokens def set_input_embeddings(self, value): self.biogpt.embed_tokens = value __all__ = [ "BioGptForCausalLM", "BioGptForTokenClassification", "BioGptForSequenceClassification", "BioGptModel", "BioGptPreTrainedModel", ]

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

{ "file_path": "transformers/src/transformers/models/biogpt/modeling_biogpt.py", "repo_id": "transformers", "token_count": 20514 }

# 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. """Convert CANINE checkpoint.""" import argparse from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine from transformers.utils import logging logging.set_verbosity_info() def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, pytorch_dump_path): # Initialize PyTorch model config = CanineConfig() model = CanineModel(config) model.eval() print(f"Building PyTorch model from configuration: {config}") # Load weights from tf checkpoint load_tf_weights_in_canine(model, config, tf_checkpoint_path) # Save pytorch-model (weights and configuration) print(f"Save PyTorch model to {pytorch_dump_path}") model.save_pretrained(pytorch_dump_path) # Save tokenizer files tokenizer = CanineTokenizer() print(f"Save tokenizer files to {pytorch_dump_path}") tokenizer.save_pretrained(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. Should end with model.ckpt", ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to a folder where the PyTorch model will be placed.", ) args = parser.parse_args() convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)

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

{ "file_path": "transformers/src/transformers/models/canine/convert_canine_original_tf_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 742 }

# coding=utf-8 # Copyright 2021 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 CLIP.""" import json import os import unicodedata from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) 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 def whitespace_clean(text): text = re.sub(r"\s+", " ", text) text = text.strip() return text # Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize def whitespace_tokenize(text): """Runs basic whitespace cleaning and splitting on a piece of text.""" text = text.strip() if not text: return [] tokens = text.split() return tokens # Copied from transformers.models.bert.tokenization_bert.BasicTokenizer class BasicTokenizer: """ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. never_split (`Iterable`, *optional*): Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True` tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). do_split_on_punc (`bool`, *optional*, defaults to `True`): In some instances we want to skip the basic punctuation splitting so that later tokenization can capture the full context of the words, such as contractions. """ def __init__( self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None, do_split_on_punc=True, ): if never_split is None: never_split = [] self.do_lower_case = do_lower_case self.never_split = set(never_split) self.tokenize_chinese_chars = tokenize_chinese_chars self.strip_accents = strip_accents self.do_split_on_punc = do_split_on_punc def tokenize(self, text, never_split=None): """ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. Args: never_split (`List[str]`, *optional*) Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of token not to split. """ # union() returns a new set by concatenating the two sets. never_split = self.never_split.union(set(never_split)) if never_split else self.never_split text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since the English models were not trained on any Chinese data # and generally don't have any Chinese data in them (there are Chinese # characters in the vocabulary because Wikipedia does have some Chinese # words in the English Wikipedia.). if self.tokenize_chinese_chars: text = self._tokenize_chinese_chars(text) # prevents treating the same character with different unicode codepoints as different characters unicode_normalized_text = unicodedata.normalize("NFC", text) orig_tokens = whitespace_tokenize(unicode_normalized_text) split_tokens = [] for token in orig_tokens: if token not in never_split: if self.do_lower_case: token = token.lower() if self.strip_accents is not False: token = self._run_strip_accents(token) elif self.strip_accents: token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token, never_split)) output_tokens = whitespace_tokenize(" ".join(split_tokens)) return output_tokens def _run_strip_accents(self, text): """Strips accents from a piece of text.""" text = unicodedata.normalize("NFD", text) output = [] for char in text: cat = unicodedata.category(char) if cat == "Mn": continue output.append(char) return "".join(output) def _run_split_on_punc(self, text, never_split=None): """Splits punctuation on a piece of text.""" if not self.do_split_on_punc or (never_split is not None and text in never_split): return [text] chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if _is_punctuation(char): output.append([char]) start_new_word = True else: if start_new_word: output.append([]) start_new_word = False output[-1].append(char) i += 1 return ["".join(x) for x in output] def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] for char in text: cp = ord(char) if self._is_chinese_char(cp): output.append(" ") output.append(char) output.append(" ") else: output.append(char) return "".join(output) def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x20000 and cp <= 0x2A6DF) # or (cp >= 0x2A700 and cp <= 0x2B73F) # or (cp >= 0x2B740 and cp <= 0x2B81F) # or (cp >= 0x2B820 and cp <= 0x2CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2F800 and cp <= 0x2FA1F) # ): # return True return False def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp == 0 or cp == 0xFFFD or _is_control(char): continue if _is_whitespace(char): output.append(" ") else: output.append(char) return "".join(output) class CLIPTokenizer(PreTrainedTokenizer): """ Construct a CLIP tokenizer. Based on byte-level 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. 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. unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`): 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 `"<|startoftext|>"`): The beginning of sequence token. eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): The end of sequence token. pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`): The token used for padding, for example when batching sequences of different lengths. """ vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, merges_file, errors="replace", unk_token="<|endoftext|>", bos_token="<|startoftext|>", eos_token="<|endoftext|>", pad_token="<|endoftext|>", # hack to enable padding **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 unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token try: import ftfy self.fix_text = ftfy.fix_text except ImportError: logger.info("ftfy or spacy is not installed using custom BasicTokenizer instead of ftfy.") self.nlp = BasicTokenizer(strip_accents=False, do_split_on_punc=False) self.fix_text = None 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()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().strip().split("\n")[1 : 49152 - 256 - 2 + 1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"} self.pat = re.compile( r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE, ) super().__init__( errors=errors, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, **kwargs, ) @property def vocab_size(self): return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) 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 CLIP sequence has the following format: - single sequence: `<|startoftext|> X <|endoftext|>` Pairs of sequences are not the expected use case, but they will be handled without a separator. 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. """ bos_token = [self.bos_token_id] eos_token = [self.eos_token_id] if token_ids_1 is None: return bos_token + token_ids_0 + eos_token return bos_token + token_ids_0 + eos_token + eos_token + token_ids_1 + eos_token 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. CLIP 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. """ bos_token = [self.bos_token_id] eos_token = [self.eos_token_id] if token_ids_1 is None: return len(bos_token + token_ids_0 + eos_token) * [0] return len(bos_token + token_ids_0 + eos_token + eos_token + token_ids_1 + eos_token) * [0] def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token[:-1]) + (token[-1] + "</w>",) 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) self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] if self.fix_text is None: text = " ".join(self.nlp.tokenize(text)) else: text = whitespace_clean(self.fix_text(text)).lower() for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_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) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" text = "".join(tokens) byte_array = bytearray([self.byte_decoder[c] for c in text]) text = byte_array.decode("utf-8", errors=self.errors).replace("</w>", " ").strip() return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error("Vocabulary path ({}) should be a directory".format(save_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: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( "Saving vocabulary to {}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!".format(merge_file) ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file __all__ = ["CLIPTokenizer"]

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

{ "file_path": "transformers/src/transformers/models/clip/tokenization_clip.py", "repo_id": "transformers", "token_count": 9223 }

# coding=utf-8 # Copyright 2023 MetaAI 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 Code LLaMA.""" import os from shutil import copyfile from typing import 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, requires_backends logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} 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 CodeLlamaTokenizer(PreTrainedTokenizer): """ Construct a CodeLlama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is no padding token in the original model. The default configuration match that of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf/blob/main/tokenizer_config.json) which supports prompt infilling. Args: vocab_file (`str`): Path to the vocabulary 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. 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> prefix_token (`str`, *optional*, defaults to `"▁<PRE>"`): Prefix token used for infilling. middle_token (`str`, *optional*, defaults to `"▁<MID>"`): Middle token used for infilling. suffix_token (`str`, *optional*, defaults to `"▁<SUF>"`): Suffix token used for infilling. eot_token (`str`, *optional*, defaults to `"▁<EOT>"`): End of text token used for infilling. fill_token (`str`, *optional*, defaults to `"<FILL_ME>"`): The token used to split the input between the prefix and suffix. suffix_first (`bool`, *optional*, defaults to `False`): Whether the input prompt and suffix should be formatted with the suffix first. 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. add_bos_token (`bool`, *optional*, defaults to `True`): Whether to add a beginning of sequence token at the start of sequences. add_eos_token (`bool`, *optional*, defaults to `False`): Whether to add an end of sequence token at the end of sequences. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the tokenization spaces. additional_special_tokens (`List[str]`, *optional*): Additional special tokens used by the tokenizer. use_default_system_prompt (`bool`, *optional*, defaults to `False`): Whether or not the default system prompt for Llama should be used. """ vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, unk_token="<unk>", bos_token="<s>", eos_token="</s>", prefix_token="▁<PRE>", middle_token="▁<MID>", suffix_token="▁<SUF>", eot_token="▁<EOT>", fill_token="<FILL_ME>", suffix_first=False, sp_model_kwargs: Optional[Dict[str, Any]] = None, add_bos_token=True, add_eos_token=False, clean_up_tokenization_spaces=False, additional_special_tokens=None, use_default_system_prompt=False, **kwargs, ): requires_backends(self, "protobuf") 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 self.use_default_system_prompt = use_default_system_prompt # mark tokens special to skip them additional_special_tokens = additional_special_tokens or [] for token in [prefix_token, middle_token, suffix_token, eot_token]: additional_special_tokens += [token] if token is not None else [] self.vocab_file = vocab_file self.add_bos_token = add_bos_token self.add_eos_token = add_eos_token self._prefix_token = prefix_token self._middle_token = middle_token self._suffix_token = suffix_token self._eot_token = eot_token self.fill_token = fill_token self.suffix_first = suffix_first self.sp_model = self.get_spm_processor() super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, add_bos_token=add_bos_token, add_eos_token=add_eos_token, prefix_token=prefix_token, middle_token=middle_token, suffix_token=suffix_token, eot_token=eot_token, fill_token=fill_token, sp_model_kwargs=self.sp_model_kwargs, suffix_first=suffix_first, clean_up_tokenization_spaces=clean_up_tokenization_spaces, additional_special_tokens=additional_special_tokens, use_default_system_prompt=use_default_system_prompt, **kwargs, ) @property def unk_token_length(self): return len(self.sp_model.encode(str(self.unk_token))) def get_spm_processor(self): tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs) with open(self.vocab_file, "rb") as f: sp_model = f.read() model_pb2 = import_protobuf() 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 @property def prefix_token(self): return self._prefix_token @property def prefix_id(self): if self._prefix_token is None: return None return self.convert_tokens_to_ids(self.prefix_token) @property def middle_token(self): return self._middle_token @property def middle_id(self): if self._middle_token is None: return None return self.convert_tokens_to_ids(self.middle_token) @property def suffix_token(self): return self._suffix_token @property def suffix_id(self): if self._suffix_token is None: return None return self.convert_tokens_to_ids(self.suffix_token) @property def eot_token(self): return self._eot_token @property def eot_id(self): if self._eot_token is None: return None return self.convert_tokens_to_ids(self.eot_token) @property def vocab_size(self): """Returns vocab size""" return self.sp_model.get_piece_size() # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.get_vocab 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 def tokenize(self, prefix, suffix=None, suffix_first=False, **kwargs) -> List[int]: # add a prefix space to `prefix` if self.fill_token is not None and self.fill_token in prefix and suffix is None: prefix, suffix = prefix.split(self.fill_token) if len(prefix) > 0: prefix = SPIECE_UNDERLINE + prefix.replace(SPIECE_UNDERLINE, " ") if suffix is None or len(suffix) < 1: tokens = super().tokenize(prefix, **kwargs) if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: tokens = tokens[1:] return tokens prefix_tokens = self._tokenize(prefix) # prefix has an extra `SPIECE_UNDERLINE` if None in (self.prefix_id, self.middle_id, self.suffix_id): raise ValueError( "The input either includes a `prefix` and a `suffix` used for the infilling task," f" or can be split on the {self.fill_token} token, creating a suffix and prefix," " but the model does not support `infilling`." ) suffix_tokens = self._tokenize(suffix) # make sure CodeLlama sp model does not mess up suffix_first = suffix_first if suffix_first is not None else self.suffix_first if suffix_first: # format as " <PRE> <SUF>{suf} <MID> {pre}" return [self.prefix_token, self.suffix_token] + suffix_tokens + [self.middle_token] + prefix_tokens else: # format as " <PRE> {pre} <SUF>{suf} <MID>" return [self.prefix_token] + prefix_tokens + [self.suffix_token] + suffix_tokens + [self.middle_token] 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 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 # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer._convert_token_to_id 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) # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer._convert_id_to_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 = "" for _, token in enumerate(tokens): # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode(current_sub_tokens) + token current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode(current_sub_tokens) return out_string # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.save_vocabulary 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,) # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.build_inputs_with_special_tokens 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 # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.get_special_tokens_mask 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 ) # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.create_token_type_ids_from_sequences 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 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) __all__ = ["CodeLlamaTokenizer"]

transformers/src/transformers/models/code_llama/tokenization_code_llama.py/0

{ "file_path": "transformers/src/transformers/models/code_llama/tokenization_code_llama.py", "repo_id": "transformers", "token_count": 8250 }

# 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. """TF 2.0 ConvBERT model.""" from __future__ import annotations 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 ( TFBaseModelOutput, 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 ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_convbert import ConvBertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "YituTech/conv-bert-base" _CONFIG_FOR_DOC = "ConvBertConfig" # Copied from transformers.models.albert.modeling_tf_albert.TFAlbertEmbeddings with Albert->ConvBert class TFConvBertEmbeddings(keras.layers.Layer): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config: ConvBertConfig, **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 TFConvBertSelfAttention(keras.layers.Layer): def __init__(self, config, **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 of attention " f"heads ({config.num_attention_heads})" ) new_num_attention_heads = int(config.num_attention_heads / config.head_ratio) if new_num_attention_heads < 1: self.head_ratio = config.num_attention_heads num_attention_heads = 1 else: num_attention_heads = new_num_attention_heads self.head_ratio = config.head_ratio self.num_attention_heads = num_attention_heads self.conv_kernel_size = config.conv_kernel_size if config.hidden_size % self.num_attention_heads != 0: raise ValueError("hidden_size should be divisible by num_attention_heads") self.attention_head_size = config.hidden_size // config.num_attention_heads self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.key_conv_attn_layer = keras.layers.SeparableConv1D( self.all_head_size, self.conv_kernel_size, padding="same", activation=None, depthwise_initializer=get_initializer(1 / self.conv_kernel_size), pointwise_initializer=get_initializer(config.initializer_range), name="key_conv_attn_layer", ) self.conv_kernel_layer = keras.layers.Dense( self.num_attention_heads * self.conv_kernel_size, activation=None, name="conv_kernel_layer", kernel_initializer=get_initializer(config.initializer_range), ) self.conv_out_layer = keras.layers.Dense( self.all_head_size, activation=None, name="conv_out_layer", kernel_initializer=get_initializer(config.initializer_range), ) self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob) self.config = config def transpose_for_scores(self, x, batch_size): # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size)) return tf.transpose(x, perm=[0, 2, 1, 3]) def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False): batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) mixed_key_conv_attn_layer = self.key_conv_attn_layer(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) conv_attn_layer = tf.multiply(mixed_key_conv_attn_layer, mixed_query_layer) conv_kernel_layer = self.conv_kernel_layer(conv_attn_layer) conv_kernel_layer = tf.reshape(conv_kernel_layer, [-1, self.conv_kernel_size, 1]) conv_kernel_layer = stable_softmax(conv_kernel_layer, axis=1) paddings = tf.constant( [ [ 0, 0, ], [int((self.conv_kernel_size - 1) / 2), int((self.conv_kernel_size - 1) / 2)], [0, 0], ] ) conv_out_layer = self.conv_out_layer(hidden_states) conv_out_layer = tf.reshape(conv_out_layer, [batch_size, -1, self.all_head_size]) conv_out_layer = tf.pad(conv_out_layer, paddings, "CONSTANT") unfold_conv_out_layer = tf.stack( [ tf.slice(conv_out_layer, [0, i, 0], [batch_size, shape_list(mixed_query_layer)[1], self.all_head_size]) for i in range(self.conv_kernel_size) ], axis=-1, ) conv_out_layer = tf.reshape(unfold_conv_out_layer, [-1, self.attention_head_size, self.conv_kernel_size]) conv_out_layer = tf.matmul(conv_out_layer, conv_kernel_layer) conv_out_layer = tf.reshape(conv_out_layer, [-1, self.all_head_size]) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = tf.matmul( query_layer, key_layer, transpose_b=True ) # (batch size, num_heads, seq_len_q, seq_len_k) dk = tf.cast(shape_list(key_layer)[-1], attention_scores.dtype) # scale attention_scores attention_scores = attention_scores / tf.math.sqrt(dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFBertModel call() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = stable_softmax(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(attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask value_layer = tf.reshape( mixed_value_layer, [batch_size, -1, self.num_attention_heads, self.attention_head_size] ) value_layer = tf.transpose(value_layer, [0, 2, 1, 3]) context_layer = tf.matmul(attention_probs, value_layer) context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) conv_out = tf.reshape(conv_out_layer, [batch_size, -1, self.num_attention_heads, self.attention_head_size]) context_layer = tf.concat([context_layer, conv_out], 2) context_layer = tf.reshape( context_layer, (batch_size, -1, self.head_ratio * self.all_head_size) ) # (batch_size, seq_len_q, all_head_size) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) 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]) if getattr(self, "key_conv_attn_layer", None) is not None: with tf.name_scope(self.key_conv_attn_layer.name): self.key_conv_attn_layer.build([None, None, self.config.hidden_size]) if getattr(self, "conv_kernel_layer", None) is not None: with tf.name_scope(self.conv_kernel_layer.name): self.conv_kernel_layer.build([None, None, self.all_head_size]) if getattr(self, "conv_out_layer", None) is not None: with tf.name_scope(self.conv_out_layer.name): self.conv_out_layer.build([None, None, self.config.hidden_size]) class TFConvBertSelfOutput(keras.layers.Layer): 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" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.config = config def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.LayerNorm(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]) class TFConvBertAttention(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.self_attention = TFConvBertSelfAttention(config, name="self") self.dense_output = TFConvBertSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call(self, input_tensor, attention_mask, head_mask, output_attentions, training=False): self_outputs = self.self_attention( input_tensor, attention_mask, head_mask, output_attentions, training=training ) attention_output = self.dense_output(self_outputs[0], input_tensor, training=training) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them 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) class GroupedLinearLayer(keras.layers.Layer): def __init__(self, input_size, output_size, num_groups, kernel_initializer, **kwargs): super().__init__(**kwargs) self.input_size = input_size self.output_size = output_size self.num_groups = num_groups self.kernel_initializer = kernel_initializer self.group_in_dim = self.input_size // self.num_groups self.group_out_dim = self.output_size // self.num_groups def build(self, input_shape=None): self.kernel = self.add_weight( "kernel", shape=[self.group_out_dim, self.group_in_dim, self.num_groups], initializer=self.kernel_initializer, trainable=True, ) self.bias = self.add_weight( "bias", shape=[self.output_size], initializer=self.kernel_initializer, dtype=self.dtype, trainable=True ) super().build(input_shape) def call(self, hidden_states): batch_size = shape_list(hidden_states)[0] x = tf.transpose(tf.reshape(hidden_states, [-1, self.num_groups, self.group_in_dim]), [1, 0, 2]) x = tf.matmul(x, tf.transpose(self.kernel, [2, 1, 0])) x = tf.transpose(x, [1, 0, 2]) x = tf.reshape(x, [batch_size, -1, self.output_size]) x = tf.nn.bias_add(value=x, bias=self.bias) return x class TFConvBertIntermediate(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) if config.num_groups == 1: self.dense = keras.layers.Dense( config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) else: self.dense = GroupedLinearLayer( config.hidden_size, config.intermediate_size, num_groups=config.num_groups, 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): hidden_states = self.dense(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]) class TFConvBertOutput(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) if config.num_groups == 1: self.dense = keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) else: self.dense = GroupedLinearLayer( config.intermediate_size, config.hidden_size, num_groups=config.num_groups, 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(config.hidden_dropout_prob) self.config = config def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.LayerNorm(hidden_states + input_tensor) 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.hidden_size]) if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) class TFConvBertLayer(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.attention = TFConvBertAttention(config, name="attention") self.intermediate = TFConvBertIntermediate(config, name="intermediate") self.bert_output = TFConvBertOutput(config, name="output") def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False): attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions, training=training ) attention_output = attention_outputs[0] intermediate_output = self.intermediate(attention_output) layer_output = self.bert_output(intermediate_output, attention_output, training=training) outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them 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) class TFConvBertEncoder(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.layer = [TFConvBertLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states, attention_mask, head_mask, output_attentions, output_hidden_states, return_dict, training=False, ): all_hidden_states = () if output_hidden_states else None all_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_outputs = layer_module( hidden_states, attention_mask, head_mask[i], output_attentions, training=training ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) # 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] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_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) class TFConvBertPredictionHeadTransform(keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.transform_act_fn = get_tf_activation(config.hidden_act) else: self.transform_act_fn = config.hidden_act self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.config = config def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(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, "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]) @keras_serializable class TFConvBertMainLayer(keras.layers.Layer): config_class = ConvBertConfig def __init__(self, config, **kwargs): super().__init__(**kwargs) self.embeddings = TFConvBertEmbeddings(config, name="embeddings") if config.embedding_size != config.hidden_size: self.embeddings_project = keras.layers.Dense(config.hidden_size, name="embeddings_project") self.encoder = TFConvBertEncoder(config, name="encoder") self.config = config def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings.weight = value self.embeddings.vocab_size = value.shape[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): if attention_mask is None: attention_mask = tf.fill(input_shape, 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. extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_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) extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 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=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=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") if attention_mask is None: attention_mask = tf.fill(input_shape, 1) if token_type_ids is None: token_type_ids = tf.fill(input_shape, 0) hidden_states = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training) extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, hidden_states.dtype) 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, extended_attention_mask, head_mask, output_attentions, output_hidden_states, 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]) class TFConvBertPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ConvBertConfig base_model_prefix = "convbert" CONVBERT_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> Args: config ([`ConvBertConfig`]): 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. """ CONVBERT_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 ConvBERT Model transformer outputting raw hidden-states without any specific head on top.", CONVBERT_START_DOCSTRING, ) class TFConvBertModel(TFConvBertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.convbert = TFConvBertMainLayer(config, name="convbert") @unpack_inputs @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, attention_mask: Optional[Union[np.array, tf.Tensor]] = None, token_type_ids: Optional[Union[np.array, tf.Tensor]] = None, position_ids: Optional[Union[np.array, tf.Tensor]] = None, head_mask: Optional[Union[np.array, tf.Tensor]] = None, inputs_embeds: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: outputs = self.convbert( 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, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "convbert", None) is not None: with tf.name_scope(self.convbert.name): self.convbert.build(None) class TFConvBertMaskedLMHead(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 class TFConvBertGeneratorPredictions(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]) @add_start_docstrings("""ConvBERT Model with a `language modeling` head on top.""", CONVBERT_START_DOCSTRING) class TFConvBertForMaskedLM(TFConvBertPreTrainedModel, TFMaskedLanguageModelingLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, **kwargs) self.config = config self.convbert = TFConvBertMainLayer(config, name="convbert") self.generator_predictions = TFConvBertGeneratorPredictions(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 = TFConvBertMaskedLMHead(config, self.convbert.embeddings, name="generator_lm_head") def get_lm_head(self): return self.generator_lm_head def get_prefix_bias_name(self): return self.name + "/" + self.generator_lm_head.name @unpack_inputs @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMaskedLMOutput, 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: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[Tuple, TFMaskedLMOutput]: 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.convbert( 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, "convbert", None) is not None: with tf.name_scope(self.convbert.name): self.convbert.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 TFConvBertClassificationHead(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.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, hidden_states, **kwargs): x = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = get_tf_activation(self.config.hidden_act)(x) 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( """ ConvBERT Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks. """, CONVBERT_START_DOCSTRING, ) class TFConvBertForSequenceClassification(TFConvBertPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convbert = TFConvBertMainLayer(config, name="convbert") self.classifier = TFConvBertClassificationHead(config, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSequenceClassifierOutput, 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: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[Tuple, TFSequenceClassifierOutput]: 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.convbert( 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], training=training) 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, "convbert", None) is not None: with tf.name_scope(self.convbert.name): self.convbert.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ ConvBERT 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. """, CONVBERT_START_DOCSTRING, ) class TFConvBertForMultipleChoice(TFConvBertPreTrainedModel, TFMultipleChoiceLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.convbert = TFConvBertMainLayer(config, name="convbert") 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( CONVBERT_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: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[Tuple, TFMultipleChoiceModelOutput]: 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.convbert( flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, flat_inputs_embeds, output_attentions, output_hidden_states, return_dict=return_dict, training=training, ) logits = self.sequence_summary(outputs[0], training=training) 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, "convbert", None) is not None: with tf.name_scope(self.convbert.name): self.convbert.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( """ ConvBERT 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. """, CONVBERT_START_DOCSTRING, ) class TFConvBertForTokenClassification(TFConvBertPreTrainedModel, TFTokenClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convbert = TFConvBertMainLayer(config, name="convbert") 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(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFTokenClassifierOutput, 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: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[Tuple, TFTokenClassifierOutput]: 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.convbert( 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[1:] 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, "convbert", None) is not None: with tf.name_scope(self.convbert.name): self.convbert.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( """ ConvBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CONVBERT_START_DOCSTRING, ) class TFConvBertForQuestionAnswering(TFConvBertPreTrainedModel, TFQuestionAnsweringLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convbert = TFConvBertMainLayer(config, name="convbert") 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(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFQuestionAnsweringModelOutput, 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: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: tf.Tensor | None = None, end_positions: tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[Tuple, TFQuestionAnsweringModelOutput]: 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.convbert( 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[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=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "convbert", None) is not None: with tf.name_scope(self.convbert.name): self.convbert.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]) __all__ = [ "TFConvBertForMaskedLM", "TFConvBertForMultipleChoice", "TFConvBertForQuestionAnswering", "TFConvBertForSequenceClassification", "TFConvBertForTokenClassification", "TFConvBertLayer", "TFConvBertModel", "TFConvBertPreTrainedModel", ]

transformers/src/transformers/models/convbert/modeling_tf_convbert.py/0

{ "file_path": "transformers/src/transformers/models/convbert/modeling_tf_convbert.py", "repo_id": "transformers", "token_count": 26638 }

# 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. """TF 2.0 Cvt model.""" from __future__ import annotations import collections.abc from dataclasses import dataclass from typing import Optional, Tuple, Union import tensorflow as tf from ...modeling_tf_outputs import TFImageClassifierOutputWithNoAttention from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_cvt import CvtConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "CvtConfig" @dataclass class TFBaseModelOutputWithCLSToken(ModelOutput): """ Base class for model's outputs. Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. cls_token_value (`tf.Tensor` of shape `(batch_size, 1, hidden_size)`): Classification token at the output of the last layer of the model. 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. """ last_hidden_state: tf.Tensor = None cls_token_value: tf.Tensor = None hidden_states: Tuple[tf.Tensor, ...] | None = None class TFCvtDropPath(keras.layers.Layer): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). References: (1) github.com:rwightman/pytorch-image-models """ def __init__(self, drop_prob: float, **kwargs): super().__init__(**kwargs) self.drop_prob = drop_prob def call(self, x: tf.Tensor, training=None): if self.drop_prob == 0.0 or not training: return x keep_prob = 1 - self.drop_prob shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1) random_tensor = keep_prob + tf.random.uniform(shape, 0, 1, dtype=self.compute_dtype) random_tensor = tf.floor(random_tensor) return (x / keep_prob) * random_tensor class TFCvtEmbeddings(keras.layers.Layer): """Construct the Convolutional Token Embeddings.""" def __init__( self, config: CvtConfig, patch_size: int, num_channels: int, embed_dim: int, stride: int, padding: int, dropout_rate: float, **kwargs, ): super().__init__(**kwargs) self.convolution_embeddings = TFCvtConvEmbeddings( config, patch_size=patch_size, num_channels=num_channels, embed_dim=embed_dim, stride=stride, padding=padding, name="convolution_embeddings", ) self.dropout = keras.layers.Dropout(dropout_rate) def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_state = self.convolution_embeddings(pixel_values) hidden_state = self.dropout(hidden_state, training=training) return hidden_state def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "convolution_embeddings", None) is not None: with tf.name_scope(self.convolution_embeddings.name): self.convolution_embeddings.build(None) class TFCvtConvEmbeddings(keras.layers.Layer): """Image to Convolution Embeddings. This convolutional operation aims to model local spatial contexts.""" def __init__( self, config: CvtConfig, patch_size: int, num_channels: int, embed_dim: int, stride: int, padding: int, **kwargs, ): super().__init__(**kwargs) self.padding = keras.layers.ZeroPadding2D(padding=padding) self.patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) self.projection = keras.layers.Conv2D( filters=embed_dim, kernel_size=patch_size, strides=stride, padding="valid", data_format="channels_last", kernel_initializer=get_initializer(config.initializer_range), name="projection", ) # Using the same default epsilon as PyTorch self.normalization = keras.layers.LayerNormalization(epsilon=1e-5, name="normalization") self.num_channels = num_channels self.embed_dim = embed_dim def call(self, pixel_values: tf.Tensor) -> tf.Tensor: if isinstance(pixel_values, dict): pixel_values = pixel_values["pixel_values"] pixel_values = self.projection(self.padding(pixel_values)) # "batch_size, height, width, num_channels -> batch_size, (height*width), num_channels" batch_size, height, width, num_channels = shape_list(pixel_values) hidden_size = height * width pixel_values = tf.reshape(pixel_values, shape=(batch_size, hidden_size, num_channels)) pixel_values = self.normalization(pixel_values) # "batch_size, (height*width), num_channels -> batch_size, height, width, num_channels" pixel_values = tf.reshape(pixel_values, shape=(batch_size, height, width, num_channels)) return pixel_values def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "projection", None) is not None: with tf.name_scope(self.projection.name): self.projection.build([None, None, None, self.num_channels]) if getattr(self, "normalization", None) is not None: with tf.name_scope(self.normalization.name): self.normalization.build([None, None, self.embed_dim]) class TFCvtSelfAttentionConvProjection(keras.layers.Layer): """Convolutional projection layer.""" def __init__(self, config: CvtConfig, embed_dim: int, kernel_size: int, stride: int, padding: int, **kwargs): super().__init__(**kwargs) self.padding = keras.layers.ZeroPadding2D(padding=padding) self.convolution = keras.layers.Conv2D( filters=embed_dim, kernel_size=kernel_size, kernel_initializer=get_initializer(config.initializer_range), padding="valid", strides=stride, use_bias=False, name="convolution", groups=embed_dim, ) # Using the same default epsilon as PyTorch, TF uses (1 - pytorch momentum) self.normalization = keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization") self.embed_dim = embed_dim def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_state = self.convolution(self.padding(hidden_state)) hidden_state = self.normalization(hidden_state, training=training) return hidden_state def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "convolution", None) is not None: with tf.name_scope(self.convolution.name): self.convolution.build([None, None, None, self.embed_dim]) if getattr(self, "normalization", None) is not None: with tf.name_scope(self.normalization.name): self.normalization.build([None, None, None, self.embed_dim]) class TFCvtSelfAttentionLinearProjection(keras.layers.Layer): """Linear projection layer used to flatten tokens into 1D.""" def call(self, hidden_state: tf.Tensor) -> tf.Tensor: # "batch_size, height, width, num_channels -> batch_size, (height*width), num_channels" batch_size, height, width, num_channels = shape_list(hidden_state) hidden_size = height * width hidden_state = tf.reshape(hidden_state, shape=(batch_size, hidden_size, num_channels)) return hidden_state class TFCvtSelfAttentionProjection(keras.layers.Layer): """Convolutional Projection for Attention.""" def __init__( self, config: CvtConfig, embed_dim: int, kernel_size: int, stride: int, padding: int, projection_method: str = "dw_bn", **kwargs, ): super().__init__(**kwargs) if projection_method == "dw_bn": self.convolution_projection = TFCvtSelfAttentionConvProjection( config, embed_dim, kernel_size, stride, padding, name="convolution_projection" ) self.linear_projection = TFCvtSelfAttentionLinearProjection() def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_state = self.convolution_projection(hidden_state, training=training) hidden_state = self.linear_projection(hidden_state) return hidden_state def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "convolution_projection", None) is not None: with tf.name_scope(self.convolution_projection.name): self.convolution_projection.build(None) class TFCvtSelfAttention(keras.layers.Layer): """ Self-attention layer. A depth-wise separable convolution operation (Convolutional Projection), is applied for query, key, and value embeddings. """ def __init__( self, config: CvtConfig, num_heads: int, embed_dim: int, kernel_size: int, stride_q: int, stride_kv: int, padding_q: int, padding_kv: int, qkv_projection_method: str, qkv_bias: bool, attention_drop_rate: float, with_cls_token: bool = True, **kwargs, ): super().__init__(**kwargs) self.scale = embed_dim**-0.5 self.with_cls_token = with_cls_token self.embed_dim = embed_dim self.num_heads = num_heads self.convolution_projection_query = TFCvtSelfAttentionProjection( config, embed_dim, kernel_size, stride_q, padding_q, projection_method="linear" if qkv_projection_method == "avg" else qkv_projection_method, name="convolution_projection_query", ) self.convolution_projection_key = TFCvtSelfAttentionProjection( config, embed_dim, kernel_size, stride_kv, padding_kv, projection_method=qkv_projection_method, name="convolution_projection_key", ) self.convolution_projection_value = TFCvtSelfAttentionProjection( config, embed_dim, kernel_size, stride_kv, padding_kv, projection_method=qkv_projection_method, name="convolution_projection_value", ) self.projection_query = keras.layers.Dense( units=embed_dim, kernel_initializer=get_initializer(config.initializer_range), use_bias=qkv_bias, bias_initializer="zeros", name="projection_query", ) self.projection_key = keras.layers.Dense( units=embed_dim, kernel_initializer=get_initializer(config.initializer_range), use_bias=qkv_bias, bias_initializer="zeros", name="projection_key", ) self.projection_value = keras.layers.Dense( units=embed_dim, kernel_initializer=get_initializer(config.initializer_range), use_bias=qkv_bias, bias_initializer="zeros", name="projection_value", ) self.dropout = keras.layers.Dropout(attention_drop_rate) def rearrange_for_multi_head_attention(self, hidden_state: tf.Tensor) -> tf.Tensor: batch_size, hidden_size, _ = shape_list(hidden_state) head_dim = self.embed_dim // self.num_heads hidden_state = tf.reshape(hidden_state, shape=(batch_size, hidden_size, self.num_heads, head_dim)) hidden_state = tf.transpose(hidden_state, perm=(0, 2, 1, 3)) return hidden_state def call(self, hidden_state: tf.Tensor, height: int, width: int, training: bool = False) -> tf.Tensor: if self.with_cls_token: cls_token, hidden_state = tf.split(hidden_state, [1, height * width], 1) # "batch_size, (height*width), num_channels -> batch_size, height, width, num_channels" batch_size, hidden_size, num_channels = shape_list(hidden_state) hidden_state = tf.reshape(hidden_state, shape=(batch_size, height, width, num_channels)) key = self.convolution_projection_key(hidden_state, training=training) query = self.convolution_projection_query(hidden_state, training=training) value = self.convolution_projection_value(hidden_state, training=training) if self.with_cls_token: query = tf.concat((cls_token, query), axis=1) key = tf.concat((cls_token, key), axis=1) value = tf.concat((cls_token, value), axis=1) head_dim = self.embed_dim // self.num_heads query = self.rearrange_for_multi_head_attention(self.projection_query(query)) key = self.rearrange_for_multi_head_attention(self.projection_key(key)) value = self.rearrange_for_multi_head_attention(self.projection_value(value)) attention_score = tf.matmul(query, key, transpose_b=True) * self.scale attention_probs = stable_softmax(logits=attention_score, axis=-1) attention_probs = self.dropout(attention_probs, training=training) context = tf.matmul(attention_probs, value) # "batch_size, num_heads, hidden_size, head_dim -> batch_size, hidden_size, (num_heads*head_dim)" _, _, hidden_size, _ = shape_list(context) context = tf.transpose(context, perm=(0, 2, 1, 3)) context = tf.reshape(context, (batch_size, hidden_size, self.num_heads * head_dim)) return context def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "convolution_projection_query", None) is not None: with tf.name_scope(self.convolution_projection_query.name): self.convolution_projection_query.build(None) if getattr(self, "convolution_projection_key", None) is not None: with tf.name_scope(self.convolution_projection_key.name): self.convolution_projection_key.build(None) if getattr(self, "convolution_projection_value", None) is not None: with tf.name_scope(self.convolution_projection_value.name): self.convolution_projection_value.build(None) if getattr(self, "projection_query", None) is not None: with tf.name_scope(self.projection_query.name): self.projection_query.build([None, None, self.embed_dim]) if getattr(self, "projection_key", None) is not None: with tf.name_scope(self.projection_key.name): self.projection_key.build([None, None, self.embed_dim]) if getattr(self, "projection_value", None) is not None: with tf.name_scope(self.projection_value.name): self.projection_value.build([None, None, self.embed_dim]) class TFCvtSelfOutput(keras.layers.Layer): """Output of the Attention layer .""" def __init__(self, config: CvtConfig, embed_dim: int, drop_rate: float, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(drop_rate) self.embed_dim = embed_dim def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_state = self.dense(inputs=hidden_state) hidden_state = self.dropout(inputs=hidden_state, training=training) return hidden_state 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.embed_dim]) class TFCvtAttention(keras.layers.Layer): """Attention layer. First chunk of the convolutional transformer block.""" def __init__( self, config: CvtConfig, num_heads: int, embed_dim: int, kernel_size: int, stride_q: int, stride_kv: int, padding_q: int, padding_kv: int, qkv_projection_method: str, qkv_bias: bool, attention_drop_rate: float, drop_rate: float, with_cls_token: bool = True, **kwargs, ): super().__init__(**kwargs) self.attention = TFCvtSelfAttention( config, num_heads, embed_dim, kernel_size, stride_q, stride_kv, padding_q, padding_kv, qkv_projection_method, qkv_bias, attention_drop_rate, with_cls_token, name="attention", ) self.dense_output = TFCvtSelfOutput(config, embed_dim, drop_rate, name="output") def prune_heads(self, heads): raise NotImplementedError def call(self, hidden_state: tf.Tensor, height: int, width: int, training: bool = False): self_output = self.attention(hidden_state, height, width, training=training) attention_output = self.dense_output(self_output, training=training) return attention_output 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, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) class TFCvtIntermediate(keras.layers.Layer): """Intermediate dense layer. Second chunk of the convolutional transformer block.""" def __init__(self, config: CvtConfig, embed_dim: int, mlp_ratio: int, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=int(embed_dim * mlp_ratio), kernel_initializer=get_initializer(config.initializer_range), activation="gelu", name="dense", ) self.embed_dim = embed_dim def call(self, hidden_state: tf.Tensor) -> tf.Tensor: hidden_state = self.dense(hidden_state) return hidden_state 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.embed_dim]) class TFCvtOutput(keras.layers.Layer): """ Output of the Convolutional Transformer Block (last chunk). It consists of a MLP and a residual connection. """ def __init__(self, config: CvtConfig, embed_dim: int, mlp_ratio: int, drop_rate: int, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(drop_rate) self.embed_dim = embed_dim self.mlp_ratio = mlp_ratio def call(self, hidden_state: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_state = self.dense(inputs=hidden_state) hidden_state = self.dropout(inputs=hidden_state, training=training) hidden_state = hidden_state + input_tensor return hidden_state 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, int(self.embed_dim * self.mlp_ratio)]) class TFCvtLayer(keras.layers.Layer): """ Convolutional Transformer Block composed by attention layers, normalization and multi-layer perceptrons (mlps). It consists of 3 chunks : an attention layer, an intermediate dense layer and an output layer. This corresponds to the `Block` class in the original implementation. """ def __init__( self, config: CvtConfig, num_heads: int, embed_dim: int, kernel_size: int, stride_q: int, stride_kv: int, padding_q: int, padding_kv: int, qkv_projection_method: str, qkv_bias: bool, attention_drop_rate: float, drop_rate: float, mlp_ratio: float, drop_path_rate: float, with_cls_token: bool = True, **kwargs, ): super().__init__(**kwargs) self.attention = TFCvtAttention( config, num_heads, embed_dim, kernel_size, stride_q, stride_kv, padding_q, padding_kv, qkv_projection_method, qkv_bias, attention_drop_rate, drop_rate, with_cls_token, name="attention", ) self.intermediate = TFCvtIntermediate(config, embed_dim, mlp_ratio, name="intermediate") self.dense_output = TFCvtOutput(config, embed_dim, mlp_ratio, drop_rate, name="output") # Using `layers.Activation` instead of `tf.identity` to better control `training` behaviour. self.drop_path = ( TFCvtDropPath(drop_path_rate, name="drop_path") if drop_path_rate > 0.0 else keras.layers.Activation("linear", name="drop_path") ) # Using the same default epsilon as PyTorch self.layernorm_before = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_before") self.layernorm_after = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_after") self.embed_dim = embed_dim def call(self, hidden_state: tf.Tensor, height: int, width: int, training: bool = False) -> tf.Tensor: # in Cvt, layernorm is applied before self-attention attention_output = self.attention(self.layernorm_before(hidden_state), height, width, training=training) attention_output = self.drop_path(attention_output, training=training) # first residual connection hidden_state = attention_output + hidden_state # in Cvt, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_state) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.dense_output(layer_output, hidden_state) layer_output = self.drop_path(layer_output, training=training) return layer_output 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, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) if getattr(self, "drop_path", None) is not None: with tf.name_scope(self.drop_path.name): self.drop_path.build(None) if getattr(self, "layernorm_before", None) is not None: with tf.name_scope(self.layernorm_before.name): self.layernorm_before.build([None, None, self.embed_dim]) if getattr(self, "layernorm_after", None) is not None: with tf.name_scope(self.layernorm_after.name): self.layernorm_after.build([None, None, self.embed_dim]) class TFCvtStage(keras.layers.Layer): """ Cvt stage (encoder block). Each stage has 2 parts : - (1) A Convolutional Token Embedding layer - (2) A Convolutional Transformer Block (layer). The classification token is added only in the last stage. Args: config ([`CvtConfig`]): Model configuration class. stage (`int`): Stage number. """ def __init__(self, config: CvtConfig, stage: int, **kwargs): super().__init__(**kwargs) self.config = config self.stage = stage if self.config.cls_token[self.stage]: self.cls_token = self.add_weight( shape=(1, 1, self.config.embed_dim[-1]), initializer=get_initializer(self.config.initializer_range), trainable=True, name="cvt.encoder.stages.2.cls_token", ) self.embedding = TFCvtEmbeddings( self.config, patch_size=config.patch_sizes[self.stage], num_channels=config.num_channels if self.stage == 0 else config.embed_dim[self.stage - 1], stride=config.patch_stride[self.stage], embed_dim=config.embed_dim[self.stage], padding=config.patch_padding[self.stage], dropout_rate=config.drop_rate[self.stage], name="embedding", ) drop_path_rates = tf.linspace(0.0, config.drop_path_rate[self.stage], config.depth[stage]) drop_path_rates = [x.numpy().item() for x in drop_path_rates] self.layers = [ TFCvtLayer( config, num_heads=config.num_heads[self.stage], embed_dim=config.embed_dim[self.stage], kernel_size=config.kernel_qkv[self.stage], stride_q=config.stride_q[self.stage], stride_kv=config.stride_kv[self.stage], padding_q=config.padding_q[self.stage], padding_kv=config.padding_kv[self.stage], qkv_projection_method=config.qkv_projection_method[self.stage], qkv_bias=config.qkv_bias[self.stage], attention_drop_rate=config.attention_drop_rate[self.stage], drop_rate=config.drop_rate[self.stage], mlp_ratio=config.mlp_ratio[self.stage], drop_path_rate=drop_path_rates[self.stage], with_cls_token=config.cls_token[self.stage], name=f"layers.{j}", ) for j in range(config.depth[self.stage]) ] def call(self, hidden_state: tf.Tensor, training: bool = False): cls_token = None hidden_state = self.embedding(hidden_state, training) # "batch_size, height, width, num_channels -> batch_size, (height*width), num_channels" batch_size, height, width, num_channels = shape_list(hidden_state) hidden_size = height * width hidden_state = tf.reshape(hidden_state, shape=(batch_size, hidden_size, num_channels)) if self.config.cls_token[self.stage]: cls_token = tf.repeat(self.cls_token, repeats=batch_size, axis=0) hidden_state = tf.concat((cls_token, hidden_state), axis=1) for layer in self.layers: layer_outputs = layer(hidden_state, height, width, training=training) hidden_state = layer_outputs if self.config.cls_token[self.stage]: cls_token, hidden_state = tf.split(hidden_state, [1, height * width], 1) # "batch_size, (height*width), num_channels -> batch_size, height, width, num_channels" hidden_state = tf.reshape(hidden_state, shape=(batch_size, height, width, num_channels)) return hidden_state, cls_token def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embedding", None) is not None: with tf.name_scope(self.embedding.name): self.embedding.build(None) if getattr(self, "layers", None) is not None: for layer in self.layers: with tf.name_scope(layer.name): layer.build(None) class TFCvtEncoder(keras.layers.Layer): """ Convolutional Vision Transformer encoder. CVT has 3 stages of encoder blocks with their respective number of layers (depth) being 1, 2 and 10. Args: config ([`CvtConfig`]): Model configuration class. """ config_class = CvtConfig def __init__(self, config: CvtConfig, **kwargs): super().__init__(**kwargs) self.config = config self.stages = [ TFCvtStage(config, stage_idx, name=f"stages.{stage_idx}") for stage_idx in range(len(config.depth)) ] def call( self, pixel_values: TFModelInputType, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithCLSToken, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None hidden_state = pixel_values # When running on CPU, `keras.layers.Conv2D` doesn't support (batch_size, num_channels, height, width) # as input format. So change the input format to (batch_size, height, width, num_channels). hidden_state = tf.transpose(hidden_state, perm=(0, 2, 3, 1)) cls_token = None for _, (stage_module) in enumerate(self.stages): hidden_state, cls_token = stage_module(hidden_state, training=training) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_state,) # Change back to (batch_size, num_channels, height, width) format to have uniformity in the modules hidden_state = tf.transpose(hidden_state, perm=(0, 3, 1, 2)) if output_hidden_states: all_hidden_states = tuple([tf.transpose(hs, perm=(0, 3, 1, 2)) for hs in all_hidden_states]) if not return_dict: return tuple(v for v in [hidden_state, cls_token, all_hidden_states] if v is not None) return TFBaseModelOutputWithCLSToken( last_hidden_state=hidden_state, cls_token_value=cls_token, hidden_states=all_hidden_states, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "stages", None) is not None: for layer in self.stages: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFCvtMainLayer(keras.layers.Layer): """Construct the Cvt model.""" config_class = CvtConfig def __init__(self, config: CvtConfig, **kwargs): super().__init__(**kwargs) self.config = config self.encoder = TFCvtEncoder(config, name="encoder") @unpack_inputs def call( self, pixel_values: TFModelInputType | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithCLSToken, Tuple[tf.Tensor]]: if pixel_values is None: raise ValueError("You have to specify pixel_values") encoder_outputs = self.encoder( pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] if not return_dict: return (sequence_output,) + encoder_outputs[1:] return TFBaseModelOutputWithCLSToken( last_hidden_state=sequence_output, cls_token_value=encoder_outputs.cls_token_value, hidden_states=encoder_outputs.hidden_states, ) 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) class TFCvtPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CvtConfig base_model_prefix = "cvt" main_input_name = "pixel_values" TFCVT_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> TF 2.0 models accepts two formats as inputs: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional arguments. This second option is useful when using [`keras.Model.fit`] method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`. </Tip> Args: config ([`CvtConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ TFCVT_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CvtImageProcessor.__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. 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 Cvt Model transformer outputting raw hidden-states without any specific head on top.", TFCVT_START_DOCSTRING, ) class TFCvtModel(TFCvtPreTrainedModel): def __init__(self, config: CvtConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.cvt = TFCvtMainLayer(config, name="cvt") @unpack_inputs @add_start_docstrings_to_model_forward(TFCVT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBaseModelOutputWithCLSToken, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: tf.Tensor | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithCLSToken, Tuple[tf.Tensor]]: r""" Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFCvtModel >>> 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("microsoft/cvt-13") >>> model = TFCvtModel.from_pretrained("microsoft/cvt-13") >>> inputs = image_processor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state ```""" if pixel_values is None: raise ValueError("You have to specify pixel_values") outputs = self.cvt( pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) if not return_dict: return (outputs[0],) + outputs[1:] return TFBaseModelOutputWithCLSToken( last_hidden_state=outputs.last_hidden_state, cls_token_value=outputs.cls_token_value, hidden_states=outputs.hidden_states, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "cvt", None) is not None: with tf.name_scope(self.cvt.name): self.cvt.build(None) @add_start_docstrings( """ Cvt 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. """, TFCVT_START_DOCSTRING, ) class TFCvtForImageClassification(TFCvtPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: CvtConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.cvt = TFCvtMainLayer(config, name="cvt") # Using same default epsilon as in the original implementation. self.layernorm = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm") # Classifier head self.classifier = keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), use_bias=True, bias_initializer="zeros", name="classifier", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(TFCVT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: tf.Tensor | None = None, labels: tf.Tensor | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFImageClassifierOutputWithNoAttention, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` or `np.ndarray` 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 >>> from transformers import AutoImageProcessor, TFCvtForImageClassification >>> import tensorflow as tf >>> 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("microsoft/cvt-13") >>> model = TFCvtForImageClassification.from_pretrained("microsoft/cvt-13") >>> inputs = image_processor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0] >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)]) ```""" outputs = self.cvt( pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] cls_token = outputs[1] if self.config.cls_token[-1]: sequence_output = self.layernorm(cls_token) else: # rearrange "batch_size, num_channels, height, width -> batch_size, (height*width), num_channels" batch_size, num_channels, height, width = shape_list(sequence_output) sequence_output = tf.reshape(sequence_output, shape=(batch_size, num_channels, height * width)) sequence_output = tf.transpose(sequence_output, perm=(0, 2, 1)) sequence_output = self.layernorm(sequence_output) sequence_output_mean = tf.reduce_mean(sequence_output, axis=1) logits = self.classifier(sequence_output_mean) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "cvt", None) is not None: with tf.name_scope(self.cvt.name): self.cvt.build(None) if getattr(self, "layernorm", None) is not None: with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, self.config.embed_dim[-1]]) if getattr(self, "classifier", None) is not None: if hasattr(self.classifier, "name"): with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.embed_dim[-1]]) __all__ = ["TFCvtForImageClassification", "TFCvtModel", "TFCvtPreTrainedModel"]

transformers/src/transformers/models/cvt/modeling_tf_cvt.py/0

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#!/usr/bin/env python3 import argparse import json import torch from huggingface_hub import hf_hub_download from PIL import Image from timm.models import create_model from transformers import ( BeitImageProcessor, Data2VecVisionConfig, Data2VecVisionForImageClassification, Data2VecVisionModel, ) def create_rename_keys(config, has_lm_head=False, is_semantic=False, hf_prefix="data2vec."): prefix = "backbone." if is_semantic else "" 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"{prefix}blocks.{i}.norm1.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_before.weight") ) rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")) rename_keys.append( (f"{prefix}blocks.{i}.attn.proj.weight", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight") ) rename_keys.append( (f"{prefix}blocks.{i}.attn.proj.bias", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias") ) rename_keys.append( (f"{prefix}blocks.{i}.norm2.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_after.weight") ) rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")) rename_keys.append( (f"{prefix}blocks.{i}.mlp.fc1.weight", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight") ) rename_keys.append( (f"{prefix}blocks.{i}.mlp.fc1.bias", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias") ) rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"{hf_prefix}encoder.layer.{i}.output.dense.weight")) rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"{hf_prefix}encoder.layer.{i}.output.dense.bias")) # projection layer + position embeddings rename_keys.extend( [ (f"{prefix}cls_token", f"{hf_prefix}embeddings.cls_token"), (f"{prefix}patch_embed.proj.weight", f"{hf_prefix}embeddings.patch_embeddings.projection.weight"), (f"{prefix}patch_embed.proj.bias", f"{hf_prefix}embeddings.patch_embeddings.projection.bias"), ] ) if has_lm_head: # mask token + shared relative position bias + layernorm rename_keys.extend( [ ("mask_token", f"{hf_prefix}embeddings.mask_token"), ( "rel_pos_bias.relative_position_bias_table", f"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table", ), ( "rel_pos_bias.relative_position_index", f"{hf_prefix}encoder.relative_position_bias.relative_position_index", ), ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias"), ] ) elif is_semantic: # semantic segmentation classification heads rename_keys.extend( [ ("decode_head.conv_seg.weight", "decode_head.classifier.weight"), ("decode_head.conv_seg.bias", "decode_head.classifier.bias"), ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"), ] ) else: # layernorm + classification head rename_keys.extend( [ ("fc_norm.weight", f"{hf_prefix}pooler.layernorm.weight"), ("fc_norm.bias", f"{hf_prefix}pooler.layernorm.bias"), ("head.weight", "classifier.weight"), ("head.bias", "classifier.bias"), ] ) return rename_keys def read_in_q_k_v(state_dict, config, has_lm_head=False, is_semantic=False, hf_prefix="data2vec_vision."): for i in range(config.num_hidden_layers): prefix = "backbone." if is_semantic else "" # queries, keys and values in_proj_weight = state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight") q_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias") v_bias = state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias") state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ : config.hidden_size, : ] state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.query.bias"] = q_bias state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ -config.hidden_size :, : ] state_dict[f"{hf_prefix}encoder.layer.{i}.attention.attention.value.bias"] = v_bias # gamma_1 and gamma_2 # we call them lambda because otherwise they are renamed when using .from_pretrained gamma_1 = state_dict.pop(f"{prefix}blocks.{i}.gamma_1") gamma_2 = state_dict.pop(f"{prefix}blocks.{i}.gamma_2") state_dict[f"{hf_prefix}encoder.layer.{i}.lambda_1"] = gamma_1 state_dict[f"{hf_prefix}encoder.layer.{i}.lambda_2"] = gamma_2 # relative_position bias table + index if not has_lm_head: # each layer has its own relative position bias table = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table") index = state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index") state_dict[ f"{hf_prefix}encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_bias_table" ] = table state_dict[ f"{hf_prefix}encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_index" ] = index def get_args(): parser = argparse.ArgumentParser( "Convert Data2VecVision to HF for image classification and pretraining", add_help=False ) parser.add_argument("--hf_checkpoint_name", type=str) parser.add_argument("--input_size", default=224, type=int, help="images input size") parser.add_argument("--beit_checkpoint", default="", help="beit checkpoint") return parser.parse_args() def load_beit_model(args, is_finetuned, is_large): def load_state_dict(model, state_dict, prefix="", ignore_missing="relative_position_index"): missing_keys = [] unexpected_keys = [] error_msgs = [] # copy state_dict so _load_from_state_dict can modify it metadata = getattr(state_dict, "_metadata", None) state_dict = state_dict.copy() if metadata is not None: state_dict._metadata = metadata def load(module, prefix=""): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs ) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + ".") load(model, prefix=prefix) warn_missing_keys = [] ignore_missing_keys = [] for key in missing_keys: keep_flag = True for ignore_key in ignore_missing.split("|"): if ignore_key in key: keep_flag = False break if keep_flag: warn_missing_keys.append(key) else: ignore_missing_keys.append(key) missing_keys = warn_missing_keys if len(missing_keys) > 0: print( "Weights of {} not initialized from pretrained model: {}".format( model.__class__.__name__, missing_keys ) ) if len(unexpected_keys) > 0: print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__, unexpected_keys)) if len(ignore_missing_keys) > 0: print( "Ignored weights of {} not initialized from pretrained model: {}".format( model.__class__.__name__, ignore_missing_keys ) ) if len(error_msgs) > 0: print("\n".join(error_msgs)) model_kwargs = { "pretrained": False, "use_shared_rel_pos_bias": True, "use_abs_pos_emb": False, "init_values": 0.1, } if is_finetuned: model_kwargs.update( { "num_classes": 1000, "use_mean_pooling": True, "init_scale": 0.001, "use_rel_pos_bias": True, } ) model = create_model( "beit_large_patch16_224" if is_large else "beit_base_patch16_224", **model_kwargs, ) patch_size = model.patch_embed.patch_size args.window_size = (args.input_size // patch_size[0], args.input_size // patch_size[1]) checkpoint = torch.load(args.beit_checkpoint, map_location="cpu") print(f"Load ckpt from {args.beit_checkpoint}") checkpoint_model = None for model_key in ("model", "module"): if model_key in checkpoint: checkpoint_model = checkpoint[model_key] print(f"Load state_dict by model_key = {model_key}") break all_keys = list(checkpoint_model.keys()) for key in all_keys: if "relative_position_index" in key: checkpoint_model.pop(key) if "relative_position_bias_table" in key: rel_pos_bias = checkpoint_model[key] src_num_pos, num_attn_heads = rel_pos_bias.size() dst_num_pos, _ = model.state_dict()[key].size() dst_patch_shape = model.patch_embed.patch_shape if dst_patch_shape[0] != dst_patch_shape[1]: raise NotImplementedError() load_state_dict(model, checkpoint_model, prefix="") return model def main(): args = get_args() is_finetuned = "ft1k" in args.hf_checkpoint_name is_large = "large" in args.hf_checkpoint_name if is_finetuned: # To convert Beit's data2vec_vision to HF you need to copy # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py # into this folder. import modeling_finetune # noqa: F401 else: # To convert Beit's data2vec_vision to HF you need to copy # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py # into this folder # IMPORTANT: Note that for now we've only converted the down-stream # model and not the full pretrained model. This means for the integration # test you need to add a `return x` after the following line: # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197 # to make the integration test pass. import modeling_cyclical # noqa: F401 # 1. Create model config config = Data2VecVisionConfig() if is_finetuned: config.use_relative_position_bias = True config.use_shared_relative_position_bias = False config.use_mean_pooling = True config.num_labels = 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()} else: config.use_relative_position_bias = False config.use_shared_relative_position_bias = True config.use_mean_pooling = False if is_large: config.hidden_size = 1024 config.intermediate_size = 4096 config.num_hidden_layers = 24 config.num_attention_heads = 16 # 2. Load Beit model orig_model = load_beit_model(args, is_finetuned, is_large) orig_model.eval() # 3. Forward Beit model image_processor = BeitImageProcessor(size=config.image_size, do_center_crop=False) image = Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png") encoding = image_processor(images=image, return_tensors="pt") pixel_values = encoding["pixel_values"] orig_args = (pixel_values,) if is_finetuned else (pixel_values, None) with torch.no_grad(): orig_model_output = orig_model(*orig_args) # 4. Load HF Data2VecVision model if is_finetuned: hf_model = Data2VecVisionForImageClassification(config) hf_model.eval() has_lm_head = False hf_prefix = "data2vec_vision." else: hf_model = Data2VecVisionModel(config) hf_model.eval() has_lm_head = True hf_prefix = "" rename_keys = create_rename_keys(config, hf_prefix=hf_prefix, has_lm_head=has_lm_head) state_dict = orig_model.state_dict() for src, dest in rename_keys: val = state_dict.pop(src) state_dict[dest] = val read_in_q_k_v(state_dict, config, hf_prefix=hf_prefix, has_lm_head=has_lm_head) missing_keys, unexpected_keys = hf_model.load_state_dict(state_dict, strict=False) print("HF missing", missing_keys) print("HF unexpected_keys", unexpected_keys) # 5. Forward HF Data2VecVision model with torch.no_grad(): hf_model_output = hf_model(pixel_values) hf_output = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state # 6. Compare max_absolute_diff = torch.max(torch.abs(hf_output - orig_model_output)).item() print(f"max_absolute_diff = {max_absolute_diff}") success = torch.allclose(hf_output, orig_model_output, atol=1e-3) print("Do both models output the same tensors?", "🔥" if success else "💩") if not success: raise Exception("Something went wRoNg") # 7. Save print(f"Saving to {args.hf_checkpoint_name}") hf_model.save_pretrained(args.hf_checkpoint_name) image_processor.save_pretrained(args.hf_checkpoint_name) if __name__ == "__main__": main() # Run the following to convert checkpoints # python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \ # --beit_checkpoint ./pretrained_base.pt \ # --hf_checkpoint_name "./data2vec-vision-base" # python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \ # --beit_checkpoint ./finetuned_base.pt \ # --hf_checkpoint_name "./data2vec-vision-base-ft1k" # python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \ # --beit_checkpoint ./pretrained_large.pt \ # --hf_checkpoint_name "./data2vec-vision-large" # python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \ # --beit_checkpoint ./finetuned_large.pt \ # --hf_checkpoint_name "./data2vec-vision-large-ft1k"

transformers/src/transformers/models/data2vec/convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py/0

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# coding=utf-8 # Copyright 2022 SenseTime 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 DETA model.""" import copy import math import os import warnings from dataclasses import dataclass from pathlib import Path from typing import Dict, List, Optional, Tuple, Union import torch import torch.nn.functional as F from torch import Tensor, nn from torch.autograd import Function from torch.autograd.function import once_differentiable from ....activations import ACT2FN from ....file_utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_scipy_available, is_torch_cuda_available, is_vision_available, replace_return_docstrings, ) from ....modeling_attn_mask_utils import _prepare_4d_attention_mask from ....modeling_outputs import BaseModelOutput from ....modeling_utils import PreTrainedModel from ....pytorch_utils import meshgrid from ....utils import is_accelerate_available, is_ninja_available, is_torchvision_available, logging, requires_backends from ....utils.backbone_utils import load_backbone from .configuration_deta import DetaConfig logger = logging.get_logger(__name__) MultiScaleDeformableAttention = None def load_cuda_kernels(): from torch.utils.cpp_extension import load global MultiScaleDeformableAttention root = Path(__file__).resolve().parent.parent.parent / "kernels" / "deta" src_files = [ root / filename for filename in [ "vision.cpp", os.path.join("cpu", "ms_deform_attn_cpu.cpp"), os.path.join("cuda", "ms_deform_attn_cuda.cu"), ] ] load( "MultiScaleDeformableAttention", src_files, with_cuda=True, extra_include_paths=[str(root)], extra_cflags=["-DWITH_CUDA=1"], extra_cuda_cflags=[ "-DCUDA_HAS_FP16=1", "-D__CUDA_NO_HALF_OPERATORS__", "-D__CUDA_NO_HALF_CONVERSIONS__", "-D__CUDA_NO_HALF2_OPERATORS__", ], ) class MultiScaleDeformableAttentionFunction(Function): @staticmethod def forward( context, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step, ): context.im2col_step = im2col_step output = MultiScaleDeformableAttention.ms_deform_attn_forward( value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, context.im2col_step, ) context.save_for_backward( value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights ) return output @staticmethod @once_differentiable def backward(context, grad_output): ( value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ) = context.saved_tensors grad_value, grad_sampling_loc, grad_attn_weight = MultiScaleDeformableAttention.ms_deform_attn_backward( value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, context.im2col_step, ) return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None if is_accelerate_available(): from accelerate import PartialState from accelerate.utils import reduce if is_vision_available(): from transformers.image_transforms import center_to_corners_format if is_torchvision_available(): from torchvision.ops.boxes import batched_nms if is_scipy_available(): from scipy.optimize import linear_sum_assignment logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DetaConfig" _CHECKPOINT_FOR_DOC = "jozhang97/deta-swin-large-o365" @dataclass class DetaDecoderOutput(ModelOutput): """ Base class for outputs of the DetaDecoder. This class adds two attributes to BaseModelOutputWithCrossAttentions, namely: - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) - a stacked tensor of intermediate reference points. 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. intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): Stacked intermediate hidden states (output of each layer of the decoder). intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): Stacked intermediate reference points (reference points of each layer of the 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 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. """ last_hidden_state: torch.FloatTensor = None intermediate_hidden_states: torch.FloatTensor = None intermediate_reference_points: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class DetaModelOutput(ModelOutput): """ Base class for outputs of the Deformable DETR encoder-decoder model. Args: init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Initial reference points sent through the Transformer decoder. last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): Stacked intermediate hidden states (output of each layer of the decoder). intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): Stacked intermediate reference points (reference points of each layer 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 layer) of shape `(batch_size, num_queries, 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, num_queries, num_queries)`. 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_queries, num_heads, 4, 4)`. 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_queries, num_heads, 4, 4)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background). enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Logits of predicted bounding boxes coordinates in the first stage. output_proposals (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`): Logits of proposal bounding boxes coordinates in the gen_encoder_output_proposals. """ init_reference_points: torch.FloatTensor = None last_hidden_state: torch.FloatTensor = None intermediate_hidden_states: torch.FloatTensor = None intermediate_reference_points: 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 enc_outputs_class: Optional[torch.FloatTensor] = None enc_outputs_coord_logits: Optional[torch.FloatTensor] = None output_proposals: Optional[torch.FloatTensor] = None @dataclass class DetaObjectDetectionOutput(ModelOutput): """ Output type of [`DetaForObjectDetection`]. 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 [`~DetaProcessor.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, num_queries, 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, num_queries, 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, num_queries, num_queries)`. 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_queries, num_heads, 4, 4)`. 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, sequence_length, num_heads, 4, 4)`. 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 `(batch_size, config.decoder_layers, num_queries, hidden_size)`): Stacked intermediate hidden states (output of each layer of the decoder). intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): Stacked intermediate reference points (reference points of each layer of the decoder). init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): Initial reference points sent through the Transformer decoder. enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background). enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): Logits of predicted bounding boxes coordinates in the first stage. output_proposals (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`): Logits of proposal bounding boxes coordinates in the gen_encoder_output_proposals. """ 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 init_reference_points: Optional[torch.FloatTensor] = None last_hidden_state: Optional[torch.FloatTensor] = None intermediate_hidden_states: Optional[torch.FloatTensor] = None intermediate_reference_points: 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 enc_outputs_class: Optional = None enc_outputs_coord_logits: Optional = None output_proposals: Optional[torch.FloatTensor] = None def _get_clones(module, N): return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) def inverse_sigmoid(x, eps=1e-5): x = x.clamp(min=0, max=1) x1 = x.clamp(min=eps) x2 = (1 - x).clamp(min=eps) return torch.log(x1 / x2) class DetaFrozenBatchNorm2d(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 def replace_batch_norm(model): r""" Recursively replace all `torch.nn.BatchNorm2d` with `DetaFrozenBatchNorm2d`. Args: model (torch.nn.Module): input model """ for name, module in model.named_children(): if isinstance(module, nn.BatchNorm2d): new_module = DetaFrozenBatchNorm2d(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) class DetaBackboneWithPositionalEncodings(nn.Module): """ Backbone model with positional embeddings. nn.BatchNorm2d layers are replaced by DetaFrozenBatchNorm2d as defined above. """ def __init__(self, config): super().__init__() backbone = load_backbone(config) with torch.no_grad(): replace_batch_norm(backbone) self.model = backbone self.intermediate_channel_sizes = self.model.channels # TODO fix this if config.backbone_config.model_type == "resnet": for name, parameter in self.model.named_parameters(): if "stages.1" not in name and "stages.2" not in name and "stages.3" not in name: parameter.requires_grad_(False) self.position_embedding = build_position_encoding(config) def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): """ Outputs feature maps of latter stages C_3 through C_5 in ResNet if `config.num_feature_levels > 1`, otherwise outputs feature maps of C_5. """ # first, send pixel_values through the backbone to get list of feature maps features = self.model(pixel_values).feature_maps # next, create position embeddings out = [] pos = [] 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] position_embeddings = self.position_embedding(feature_map, mask).to(feature_map.dtype) out.append((feature_map, mask)) pos.append(position_embeddings) return out, pos class DetaSinePositionEmbedding(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: eps = 1e-6 y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * 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 class DetaLearnedPositionEmbedding(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 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 = DetaSinePositionEmbedding(n_steps, normalize=True) elif config.position_embedding_type == "learned": position_embedding = DetaLearnedPositionEmbedding(n_steps) else: raise ValueError(f"Not supported {config.position_embedding_type}") return position_embedding def multi_scale_deformable_attention( value: Tensor, value_spatial_shapes: Tensor, sampling_locations: Tensor, attention_weights: Tensor ) -> Tensor: batch_size, _, num_heads, hidden_dim = value.shape _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape value_list = value.split([height.item() * width.item() for height, width in value_spatial_shapes], dim=1) sampling_grids = 2 * sampling_locations - 1 sampling_value_list = [] for level_id, (height, width) in enumerate(value_spatial_shapes): # batch_size, height*width, num_heads, hidden_dim # -> batch_size, height*width, num_heads*hidden_dim # -> batch_size, num_heads*hidden_dim, height*width # -> batch_size*num_heads, hidden_dim, height, width value_l_ = ( value_list[level_id].flatten(2).transpose(1, 2).reshape(batch_size * num_heads, hidden_dim, height, width) ) # batch_size, num_queries, num_heads, num_points, 2 # -> batch_size, num_heads, num_queries, num_points, 2 # -> batch_size*num_heads, num_queries, num_points, 2 sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) # batch_size*num_heads, hidden_dim, num_queries, num_points sampling_value_l_ = nn.functional.grid_sample( value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False ) sampling_value_list.append(sampling_value_l_) # (batch_size, num_queries, num_heads, num_levels, num_points) # -> (batch_size, num_heads, num_queries, num_levels, num_points) # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) attention_weights = attention_weights.transpose(1, 2).reshape( batch_size * num_heads, 1, num_queries, num_levels * num_points ) output = ( (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) .sum(-1) .view(batch_size, num_heads * hidden_dim, num_queries) ) return output.transpose(1, 2).contiguous() class DetaMultiscaleDeformableAttention(nn.Module): """ Multiscale deformable attention as proposed in Deformable DETR. """ def __init__(self, config: DetaConfig, num_heads: int, n_points: int): super().__init__() kernel_loaded = MultiScaleDeformableAttention is not None if is_torch_cuda_available() and is_ninja_available() and not kernel_loaded: try: load_cuda_kernels() except Exception as e: logger.warning(f"Could not load the custom kernel for multi-scale deformable attention: {e}") if config.d_model % num_heads != 0: raise ValueError( f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" ) dim_per_head = config.d_model // num_heads # check if dim_per_head is power of 2 if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): warnings.warn( "You'd better set embed_dim (d_model) in DetaMultiscaleDeformableAttention to make the" " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" " implementation." ) self.im2col_step = 64 self.d_model = config.d_model self.n_levels = config.num_feature_levels self.n_heads = num_heads self.n_points = n_points self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) self.value_proj = nn.Linear(config.d_model, config.d_model) self.output_proj = nn.Linear(config.d_model, config.d_model) self.disable_custom_kernels = config.disable_custom_kernels self._reset_parameters() def _reset_parameters(self): nn.init.constant_(self.sampling_offsets.weight.data, 0.0) default_dtype = torch.get_default_dtype() thetas = torch.arange(self.n_heads, dtype=torch.int64).to(default_dtype) * (2.0 * math.pi / self.n_heads) grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) grid_init = ( (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) .view(self.n_heads, 1, 1, 2) .repeat(1, self.n_levels, self.n_points, 1) ) for i in range(self.n_points): grid_init[:, :, i, :] *= i + 1 with torch.no_grad(): self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) nn.init.constant_(self.attention_weights.weight.data, 0.0) nn.init.constant_(self.attention_weights.bias.data, 0.0) nn.init.xavier_uniform_(self.value_proj.weight.data) nn.init.constant_(self.value_proj.bias.data, 0.0) nn.init.xavier_uniform_(self.output_proj.weight.data) nn.init.constant_(self.output_proj.bias.data, 0.0) def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]): return tensor if position_embeddings is None else tensor + position_embeddings def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states=None, encoder_attention_mask=None, position_embeddings: Optional[torch.Tensor] = None, reference_points=None, spatial_shapes=None, level_start_index=None, output_attentions: bool = False, ): # add position embeddings to the hidden states before projecting to queries and keys if position_embeddings is not None: hidden_states = self.with_pos_embed(hidden_states, position_embeddings) batch_size, num_queries, _ = hidden_states.shape batch_size, sequence_length, _ = encoder_hidden_states.shape if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length: raise ValueError( "Make sure to align the spatial shapes with the sequence length of the encoder hidden states" ) value = self.value_proj(encoder_hidden_states) if attention_mask is not None: # we invert the attention_mask value = value.masked_fill(~attention_mask[..., None], float(0)) value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) sampling_offsets = self.sampling_offsets(hidden_states).view( batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 ) attention_weights = self.attention_weights(hidden_states).view( batch_size, num_queries, self.n_heads, self.n_levels * self.n_points ) attention_weights = F.softmax(attention_weights, -1).view( batch_size, num_queries, self.n_heads, self.n_levels, self.n_points ) # batch_size, num_queries, n_heads, n_levels, n_points, 2 num_coordinates = reference_points.shape[-1] if num_coordinates == 2: offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) sampling_locations = ( reference_points[:, :, None, :, None, :] + sampling_offsets / offset_normalizer[None, None, None, :, None, :] ) elif num_coordinates == 4: sampling_locations = ( reference_points[:, :, None, :, None, :2] + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 ) else: raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") if self.disable_custom_kernels: # PyTorch implementation output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights) else: try: # custom kernel output = MultiScaleDeformableAttentionFunction.apply( value, spatial_shapes, level_start_index, sampling_locations, attention_weights, self.im2col_step, ) except Exception: # PyTorch implementation output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights) output = self.output_proj(output) return output, attention_weights class DetaMultiheadAttention(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 Deformable 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, position_embeddings: Optional[Tensor]): return tensor if position_embeddings is None else tensor + position_embeddings def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_embeddings: 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""" batch_size, target_len, embed_dim = hidden_states.size() # add position embeddings to the hidden states before projecting to queries and keys if position_embeddings is not None: hidden_states_original = hidden_states hidden_states = self.with_pos_embed(hidden_states, position_embeddings) # get queries, keys and values query_states = self.q_proj(hidden_states) * self.scaling 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()}" ) # 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, hidden_states.dtype) 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 DetaEncoderLayer(nn.Module): def __init__(self, config: DetaConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = DetaMultiscaleDeformableAttention( config, num_heads=config.encoder_attention_heads, n_points=config.encoder_n_points, ) 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, position_embeddings: torch.Tensor = None, reference_points=None, spatial_shapes=None, level_start_index=None, output_attentions: bool = False, ): """ Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Input to the layer. attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Attention mask. position_embeddings (`torch.FloatTensor`, *optional*): Position embeddings, to be added to `hidden_states`. reference_points (`torch.FloatTensor`, *optional*): Reference points. spatial_shapes (`torch.LongTensor`, *optional*): Spatial shapes of the backbone feature maps. level_start_index (`torch.LongTensor`, *optional*): Level start index. 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 # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps. hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, position_embeddings=position_embeddings, reference_points=reference_points, spatial_shapes=spatial_shapes, level_start_index=level_start_index, 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 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 DetaDecoderLayer(nn.Module): def __init__(self, config: DetaConfig): super().__init__() self.embed_dim = config.d_model # self-attention self.self_attn = DetaMultiheadAttention( 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) # cross-attention self.encoder_attn = DetaMultiscaleDeformableAttention( config, num_heads=config.decoder_attention_heads, n_points=config.decoder_n_points, ) self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) # feedforward neural networks 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, position_embeddings: Optional[torch.Tensor] = None, reference_points=None, spatial_shapes=None, level_start_index=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)`. position_embeddings (`torch.FloatTensor`, *optional*): Position embeddings that are added to the queries and keys in the self-attention layer. reference_points (`torch.FloatTensor`, *optional*): Reference points. spatial_shapes (`torch.LongTensor`, *optional*): Spatial shapes. level_start_index (`torch.LongTensor`, *optional*): Level start index. 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 # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, position_embeddings=position_embeddings, 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) second_residual = hidden_states # Cross-Attention cross_attn_weights = None hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, attention_mask=encoder_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, position_embeddings=position_embeddings, reference_points=reference_points, spatial_shapes=spatial_shapes, level_start_index=level_start_index, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = second_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 DetaPreTrainedModel(PreTrainedModel): config_class = DetaConfig base_model_prefix = "model" main_input_name = "pixel_values" _no_split_modules = [r"DetaBackboneWithPositionalEncodings", r"DetaEncoderLayer", r"DetaDecoderLayer"] supports_gradient_checkpointing = True def _init_weights(self, module): std = self.config.init_std if isinstance(module, DetaLearnedPositionEmbedding): nn.init.uniform_(module.row_embeddings.weight) nn.init.uniform_(module.column_embeddings.weight) elif isinstance(module, DetaMultiscaleDeformableAttention): module._reset_parameters() elif 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_() if hasattr(module, "reference_points") and not self.config.two_stage: nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0) nn.init.constant_(module.reference_points.bias.data, 0.0) if hasattr(module, "level_embed"): nn.init.normal_(module.level_embed) DETA_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 ([`DetaConfig`]): 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. """ DETA_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 [`AutoImageProcessor.__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) 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 [`~file_utils.ModelOutput`] instead of a plain tuple. """ class DetaEncoder(DetaPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a [`DetaEncoderLayer`]. The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers. Args: config: DetaConfig """ def __init__(self, config: DetaConfig): super().__init__(config) self.dropout = config.dropout self.layers = nn.ModuleList([DetaEncoderLayer(config) for _ in range(config.encoder_layers)]) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() @staticmethod def get_reference_points(spatial_shapes, valid_ratios, device): """ Get reference points for each feature map. Used in decoder. Args: spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): Spatial shapes of each feature map. valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): Valid ratios of each feature map. device (`torch.device`): Device on which to create the tensors. Returns: `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)` """ reference_points_list = [] for level, (height, width) in enumerate(spatial_shapes): ref_y, ref_x = meshgrid( torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device), torch.linspace(0.5, width - 0.5, width, dtype=torch.float32, device=device), indexing="ij", ) # TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36 ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height) ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width) ref = torch.stack((ref_x, ref_y), -1) reference_points_list.append(ref) reference_points = torch.cat(reference_points_list, 1) reference_points = reference_points[:, :, None] * valid_ratios[:, None] return reference_points def forward( self, inputs_embeds=None, attention_mask=None, position_embeddings=None, spatial_shapes=None, level_start_index=None, valid_ratios=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) position_embeddings (`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. spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): Spatial shapes of each feature map. level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`): Starting index of each feature map. valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): Ratio of valid area in each feature level. 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 [`~file_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) reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=inputs_embeds.device) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) layer_outputs = encoder_layer( hidden_states, attention_mask, position_embeddings=position_embeddings, reference_points=reference_points, spatial_shapes=spatial_shapes, level_start_index=level_start_index, 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 DetaDecoder(DetaPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetaDecoderLayer`]. The decoder updates the query embeddings through multiple self-attention and cross-attention layers. Some tweaks for Deformable DETR: - `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass. - it also returns a stack of intermediate outputs and reference points from all decoding layers. Args: config: DetaConfig """ def __init__(self, config: DetaConfig): super().__init__(config) self.dropout = config.dropout self.layers = nn.ModuleList([DetaDecoderLayer(config) for _ in range(config.decoder_layers)]) self.gradient_checkpointing = False # hack implementation for iterative bounding box refinement and two-stage Deformable DETR self.bbox_embed = None self.class_embed = None # Initialize weights and apply final processing self.post_init() def forward( self, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, position_embeddings=None, reference_points=None, spatial_shapes=None, level_start_index=None, valid_ratios=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): The query embeddings that are passed into the decoder. 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 of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, 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**). 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. reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): Spatial shapes of the feature maps. level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): Indexes for the start of each feature level. In range `[0, sequence_length]`. valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*): Ratio of valid area in each feature level. 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 [`~file_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 if inputs_embeds is not None: hidden_states = inputs_embeds # 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 intermediate = () intermediate_reference_points = () for idx, decoder_layer in enumerate(self.layers): if reference_points.shape[-1] == 4: reference_points_input = ( reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None] ) else: if reference_points.shape[-1] != 2: raise ValueError("Reference points' last dimension must be of size 2") reference_points_input = reference_points[:, :, None] * valid_ratios[:, None] if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, position_embeddings, reference_points_input, spatial_shapes, level_start_index, encoder_hidden_states, encoder_attention_mask, output_attentions, ) else: layer_outputs = decoder_layer( hidden_states, position_embeddings=position_embeddings, encoder_hidden_states=encoder_hidden_states, reference_points=reference_points_input, spatial_shapes=spatial_shapes, level_start_index=level_start_index, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] # hack implementation for iterative bounding box refinement if self.bbox_embed is not None: tmp = self.bbox_embed[idx](hidden_states) if reference_points.shape[-1] == 4: new_reference_points = tmp + inverse_sigmoid(reference_points) new_reference_points = new_reference_points.sigmoid() else: if reference_points.shape[-1] != 2: raise ValueError( f"Reference points' last dimension must be of size 2, but is {reference_points.shape[-1]}" ) new_reference_points = tmp new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points) new_reference_points = new_reference_points.sigmoid() reference_points = new_reference_points.detach() intermediate += (hidden_states,) intermediate_reference_points += (reference_points,) if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # Keep batch_size as first dimension intermediate = torch.stack(intermediate, dim=1) intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, intermediate, intermediate_reference_points, all_hidden_states, all_self_attns, all_cross_attentions, ] if v is not None ) return DetaDecoderOutput( last_hidden_state=hidden_states, intermediate_hidden_states=intermediate, intermediate_reference_points=intermediate_reference_points, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( """ The bare DETA Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without any specific head on top. """, DETA_START_DOCSTRING, ) class DetaModel(DetaPreTrainedModel): def __init__(self, config: DetaConfig): super().__init__(config) if config.two_stage: requires_backends(self, ["torchvision"]) # Create backbone with positional encoding self.backbone = DetaBackboneWithPositionalEncodings(config) intermediate_channel_sizes = self.backbone.intermediate_channel_sizes # Create input projection layers if config.num_feature_levels > 1: num_backbone_outs = len(intermediate_channel_sizes) input_proj_list = [] for _ in range(num_backbone_outs): in_channels = intermediate_channel_sizes[_] input_proj_list.append( nn.Sequential( nn.Conv2d(in_channels, config.d_model, kernel_size=1), nn.GroupNorm(32, config.d_model), ) ) for _ in range(config.num_feature_levels - num_backbone_outs): input_proj_list.append( nn.Sequential( nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1), nn.GroupNorm(32, config.d_model), ) ) in_channels = config.d_model self.input_proj = nn.ModuleList(input_proj_list) else: self.input_proj = nn.ModuleList( [ nn.Sequential( nn.Conv2d(intermediate_channel_sizes[-1], config.d_model, kernel_size=1), nn.GroupNorm(32, config.d_model), ) ] ) if not config.two_stage: self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model * 2) self.encoder = DetaEncoder(config) self.decoder = DetaDecoder(config) self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model)) if config.two_stage: self.enc_output = nn.Linear(config.d_model, config.d_model) self.enc_output_norm = nn.LayerNorm(config.d_model) self.pos_trans = nn.Linear(config.d_model * 2, config.d_model * 2) self.pos_trans_norm = nn.LayerNorm(config.d_model * 2) self.pix_trans = nn.Linear(config.d_model, config.d_model) self.pix_trans_norm = nn.LayerNorm(config.d_model) else: self.reference_points = nn.Linear(config.d_model, 2) self.assign_first_stage = config.assign_first_stage self.two_stage_num_proposals = config.two_stage_num_proposals 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.model.named_parameters(): param.requires_grad_(False) def unfreeze_backbone(self): for name, param in self.backbone.model.named_parameters(): param.requires_grad_(True) def get_valid_ratio(self, mask, dtype=torch.float32): """Get the valid ratio of all feature maps.""" _, height, width = mask.shape valid_height = torch.sum(mask[:, :, 0], 1) valid_width = torch.sum(mask[:, 0, :], 1) valid_ratio_height = valid_height.to(dtype) / height valid_ratio_width = valid_width.to(dtype) / width valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1) return valid_ratio def get_proposal_pos_embed(self, proposals): """Get the position embedding of the proposals.""" num_pos_feats = self.config.d_model // 2 temperature = 10000 scale = 2 * math.pi dim_t = torch.arange(num_pos_feats, dtype=torch.int64, device=proposals.device).float() dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats) # batch_size, num_queries, 4 proposals = proposals.sigmoid() * scale # batch_size, num_queries, 4, 128 pos = proposals[:, :, :, None] / dim_t # batch_size, num_queries, 4, 64, 2 -> batch_size, num_queries, 512 pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2) return pos def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes): """Generate the encoder output proposals from encoded enc_output. Args: enc_output (Tensor[batch_size, sequence_length, hidden_size]): Output of the encoder. padding_mask (Tensor[batch_size, sequence_length]): Padding mask for `enc_output`. spatial_shapes (Tensor[num_feature_levels, 2]): Spatial shapes of the feature maps. Returns: `tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction. - object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to directly predict a bounding box. (without the need of a decoder) - output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse sigmoid. """ batch_size = enc_output.shape[0] proposals = [] _cur = 0 level_ids = [] for level, (height, width) in enumerate(spatial_shapes): mask_flatten_ = padding_mask[:, _cur : (_cur + height * width)].view(batch_size, height, width, 1) valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1) valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1) grid_y, grid_x = meshgrid( torch.linspace(0, height - 1, height, dtype=torch.float32, device=enc_output.device), torch.linspace(0, width - 1, width, dtype=torch.float32, device=enc_output.device), indexing="ij", ) grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2) grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale width_heigth = torch.ones_like(grid) * 0.05 * (2.0**level) proposal = torch.cat((grid, width_heigth), -1).view(batch_size, -1, 4) proposals.append(proposal) _cur += height * width level_ids.append(grid.new_ones(height * width, dtype=torch.long) * level) output_proposals = torch.cat(proposals, 1) output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True) output_proposals = torch.log(output_proposals / (1 - output_proposals)) # inverse sigmoid output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf")) output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf")) # assign each pixel as an object query object_query = enc_output object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0)) object_query = object_query.masked_fill(~output_proposals_valid, float(0)) object_query = self.enc_output_norm(self.enc_output(object_query)) level_ids = torch.cat(level_ids) return object_query, output_proposals, level_ids @add_start_docstrings_to_model_forward(DETA_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetaModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, pixel_mask: Optional[torch.LongTensor] = 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], DetaModelOutput]: r""" Returns: Examples: ```python >>> from transformers import AutoImageProcessor, DetaModel >>> 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("jozhang97/deta-swin-large-o365") >>> model = DetaModel.from_pretrained("jozhang97/deta-swin-large-o365", two_stage=False) >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 900, 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)), dtype=torch.long, device=device) # Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper) # First, sent pixel_values + pixel_mask through Backbone to obtain the features # which is a list of tuples features, position_embeddings_list = self.backbone(pixel_values, pixel_mask) # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) sources = [] masks = [] for level, (source, mask) in enumerate(features): sources.append(self.input_proj[level](source)) masks.append(mask) if mask is None: raise ValueError("No attention mask was provided") # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage if self.config.num_feature_levels > len(sources): _len_sources = len(sources) for level in range(_len_sources, self.config.num_feature_levels): if level == _len_sources: source = self.input_proj[level](features[-1][0]) else: source = self.input_proj[level](sources[-1]) mask = nn.functional.interpolate(pixel_mask[None].float(), size=source.shape[-2:]).to(torch.bool)[0] pos_l = self.backbone.position_embedding(source, mask).to(source.dtype) sources.append(source) masks.append(mask) position_embeddings_list.append(pos_l) # Create queries query_embeds = None if not self.config.two_stage: query_embeds = self.query_position_embeddings.weight # Prepare encoder inputs (by flattening) spatial_shapes = [(source.shape[2:]) for source in sources] source_flatten = [source.flatten(2).transpose(1, 2) for source in sources] mask_flatten = [mask.flatten(1) for mask in masks] lvl_pos_embed_flatten = [] for level, pos_embed in enumerate(position_embeddings_list): pos_embed = pos_embed.flatten(2).transpose(1, 2) lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1) lvl_pos_embed_flatten.append(lvl_pos_embed) source_flatten = torch.cat(source_flatten, 1) mask_flatten = torch.cat(mask_flatten, 1) lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=source_flatten.device) level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) valid_ratios = valid_ratios.float() # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder # Also provide spatial_shapes, level_start_index and valid_ratios if encoder_outputs is None: encoder_outputs = self.encoder( inputs_embeds=source_flatten, attention_mask=mask_flatten, position_embeddings=lvl_pos_embed_flatten, spatial_shapes=spatial_shapes, level_start_index=level_start_index, valid_ratios=valid_ratios, 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, prepare decoder inputs batch_size, _, num_channels = encoder_outputs[0].shape enc_outputs_class = None enc_outputs_coord_logits = None output_proposals = None if self.config.two_stage: object_query_embedding, output_proposals, level_ids = self.gen_encoder_output_proposals( encoder_outputs[0], ~mask_flatten, spatial_shapes ) # hack implementation for two-stage DETA # apply a detection head to each pixel (A.4 in paper) # linear projection for bounding box binary classification (i.e. foreground and background) enc_outputs_class = self.decoder.class_embed[-1](object_query_embedding) # 3-layer FFN to predict bounding boxes coordinates (bbox regression branch) delta_bbox = self.decoder.bbox_embed[-1](object_query_embedding) enc_outputs_coord_logits = delta_bbox + output_proposals # only keep top scoring `config.two_stage_num_proposals` proposals topk = self.two_stage_num_proposals proposal_logit = enc_outputs_class[..., 0] if self.assign_first_stage: proposal_boxes = center_to_corners_format(enc_outputs_coord_logits.sigmoid().float()).clamp(0, 1) topk_proposals = [] for b in range(batch_size): prop_boxes_b = proposal_boxes[b] prop_logits_b = proposal_logit[b] # pre-nms per-level topk pre_nms_topk = 1000 pre_nms_inds = [] for lvl in range(len(spatial_shapes)): lvl_mask = level_ids == lvl pre_nms_inds.append(torch.topk(prop_logits_b.sigmoid() * lvl_mask, pre_nms_topk)[1]) pre_nms_inds = torch.cat(pre_nms_inds) # nms on topk indices post_nms_inds = batched_nms( prop_boxes_b[pre_nms_inds], prop_logits_b[pre_nms_inds], level_ids[pre_nms_inds], 0.9 ) keep_inds = pre_nms_inds[post_nms_inds] if len(keep_inds) < self.two_stage_num_proposals: print( f"[WARNING] nms proposals ({len(keep_inds)}) < {self.two_stage_num_proposals}, running" " naive topk" ) keep_inds = torch.topk(proposal_logit[b], topk)[1] # keep top Q/L indices for L levels q_per_l = topk // len(spatial_shapes) is_level_ordered = ( level_ids[keep_inds][None] == torch.arange(len(spatial_shapes), device=level_ids.device)[:, None] ) keep_inds_mask = is_level_ordered & (is_level_ordered.cumsum(1) <= q_per_l) # LS keep_inds_mask = keep_inds_mask.any(0) # S # pad to Q indices (might let ones filtered from pre-nms sneak by... unlikely because we pick high conf anyways) if keep_inds_mask.sum() < topk: num_to_add = topk - keep_inds_mask.sum() pad_inds = (~keep_inds_mask).nonzero()[:num_to_add] keep_inds_mask[pad_inds] = True keep_inds_topk = keep_inds[keep_inds_mask] topk_proposals.append(keep_inds_topk) topk_proposals = torch.stack(topk_proposals) else: topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1] topk_coords_logits = torch.gather( enc_outputs_coord_logits, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4) ) topk_coords_logits = topk_coords_logits.detach() reference_points = topk_coords_logits.sigmoid() init_reference_points = reference_points pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_logits))) query_embed, target = torch.split(pos_trans_out, num_channels, dim=2) topk_feats = torch.stack( [object_query_embedding[b][topk_proposals[b]] for b in range(batch_size)] ).detach() target = target + self.pix_trans_norm(self.pix_trans(topk_feats)) else: query_embed, target = torch.split(query_embeds, num_channels, dim=1) query_embed = query_embed.unsqueeze(0).expand(batch_size, -1, -1) target = target.unsqueeze(0).expand(batch_size, -1, -1) reference_points = self.reference_points(query_embed).sigmoid() init_reference_points = reference_points decoder_outputs = self.decoder( inputs_embeds=target, position_embeddings=query_embed, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=mask_flatten, reference_points=reference_points, spatial_shapes=spatial_shapes, level_start_index=level_start_index, valid_ratios=valid_ratios, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: enc_outputs = tuple(value for value in [enc_outputs_class, enc_outputs_coord_logits] if value is not None) tuple_outputs = (init_reference_points,) + decoder_outputs + encoder_outputs + enc_outputs return tuple_outputs return DetaModelOutput( init_reference_points=init_reference_points, last_hidden_state=decoder_outputs.last_hidden_state, intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, intermediate_reference_points=decoder_outputs.intermediate_reference_points, 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, enc_outputs_class=enc_outputs_class, enc_outputs_coord_logits=enc_outputs_coord_logits, output_proposals=output_proposals, ) @add_start_docstrings( """ DETA Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection. """, DETA_START_DOCSTRING, ) class DetaForObjectDetection(DetaPreTrainedModel): # When using clones, all layers > 0 will be clones, but layer 0 *is* required _tied_weights_keys = [r"bbox_embed\.\d+", r"class_embed\.\d+"] # We can't initialize the model on meta device as some weights are modified during the initialization _no_split_modules = None def __init__(self, config: DetaConfig): super().__init__(config) # Deformable DETR encoder-decoder model self.model = DetaModel(config) # Detection heads on top self.class_embed = nn.Linear(config.d_model, config.num_labels) self.bbox_embed = DetaMLPPredictionHead( input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3 ) prior_prob = 0.01 bias_value = -math.log((1 - prior_prob) / prior_prob) self.class_embed.bias.data = torch.ones(config.num_labels) * bias_value nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0) nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0) # if two-stage, the last class_embed and bbox_embed is for region proposal generation num_pred = (config.decoder_layers + 1) if config.two_stage else config.decoder_layers if config.with_box_refine: self.class_embed = _get_clones(self.class_embed, num_pred) self.bbox_embed = _get_clones(self.bbox_embed, num_pred) nn.init.constant_(self.bbox_embed[0].layers[-1].bias.data[2:], -2.0) # hack implementation for iterative bounding box refinement self.model.decoder.bbox_embed = self.bbox_embed else: nn.init.constant_(self.bbox_embed.layers[-1].bias.data[2:], -2.0) self.class_embed = nn.ModuleList([self.class_embed for _ in range(num_pred)]) self.bbox_embed = nn.ModuleList([self.bbox_embed for _ in range(num_pred)]) self.model.decoder.bbox_embed = None if config.two_stage: # hack implementation for two-stage self.model.decoder.class_embed = self.class_embed for box_embed in self.bbox_embed: nn.init.constant_(box_embed.layers[-1].bias.data[2:], 0.0) # Initialize weights and apply final processing self.post_init() @torch.jit.unused 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. aux_loss = [ {"logits": logits, "pred_boxes": pred_boxes} for logits, pred_boxes in zip(outputs_class.transpose(0, 1)[:-1], outputs_coord.transpose(0, 1)[:-1]) ] return aux_loss @add_start_docstrings_to_model_forward(DETA_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=DetaObjectDetectionOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, pixel_mask: Optional[torch.LongTensor] = 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], DetaObjectDetectionOutput]: 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 transformers import AutoImageProcessor, DetaForObjectDetection >>> 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("jozhang97/deta-swin-large") >>> model = DetaForObjectDetection.from_pretrained("jozhang97/deta-swin-large") >>> 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=0.5, 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 cat with confidence 0.802 at location [9.87, 54.36, 316.93, 473.44] Detected cat with confidence 0.795 at location [346.62, 24.35, 639.62, 373.2] Detected remote with confidence 0.725 at location [40.41, 73.36, 175.77, 117.29] Detected remote with confidence 0.638 at location [333.34, 76.81, 370.22, 187.94] Detected couch with confidence 0.584 at location [0.03, 0.99, 640.02, 474.93] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict # First, sent images through DETR 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, ) hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2] init_reference = outputs.init_reference_points if return_dict else outputs[0] inter_references = outputs.intermediate_reference_points if return_dict else outputs[3] # class logits + predicted bounding boxes outputs_classes = [] outputs_coords = [] for level in range(hidden_states.shape[1]): if level == 0: reference = init_reference else: reference = inter_references[:, level - 1] reference = inverse_sigmoid(reference) outputs_class = self.class_embed[level](hidden_states[:, level]) delta_bbox = self.bbox_embed[level](hidden_states[:, level]) if reference.shape[-1] == 4: outputs_coord_logits = delta_bbox + reference elif reference.shape[-1] == 2: delta_bbox[..., :2] += reference outputs_coord_logits = delta_bbox else: raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}") outputs_coord = outputs_coord_logits.sigmoid() outputs_classes.append(outputs_class) outputs_coords.append(outputs_coord) # Keep batch_size as first dimension outputs_class = torch.stack(outputs_classes, dim=1) outputs_coord = torch.stack(outputs_coords, dim=1) logits = outputs_class[:, -1] pred_boxes = outputs_coord[:, -1] loss, loss_dict, auxiliary_outputs = None, None, None if labels is not None: # First: create the matcher matcher = DetaHungarianMatcher( 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 = DetaLoss( matcher=matcher, num_classes=self.config.num_labels, focal_alpha=self.config.focal_alpha, losses=losses, num_queries=self.config.num_queries, assign_first_stage=self.config.assign_first_stage, assign_second_stage=self.config.assign_second_stage, ) criterion.to(logits.device) # Third: compute the losses, based on outputs and labels outputs_loss = {} outputs_loss["logits"] = logits outputs_loss["pred_boxes"] = pred_boxes outputs_loss["init_reference"] = init_reference if self.config.auxiliary_loss: auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) outputs_loss["auxiliary_outputs"] = auxiliary_outputs if self.config.two_stage: enc_outputs_coord = outputs.enc_outputs_coord_logits.sigmoid() outputs_loss["enc_outputs"] = { "logits": outputs.enc_outputs_class, "pred_boxes": enc_outputs_coord, "anchors": outputs.output_proposals.sigmoid(), } 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()}) aux_weight_dict.update({k + "_enc": 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 tuple_outputs = ((loss, loss_dict) + output) if loss is not None else output return tuple_outputs dict_outputs = DetaObjectDetectionOutput( 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, intermediate_hidden_states=outputs.intermediate_hidden_states, intermediate_reference_points=outputs.intermediate_reference_points, init_reference_points=outputs.init_reference_points, enc_outputs_class=outputs.enc_outputs_class, enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, output_proposals=outputs.output_proposals, ) return dict_outputs 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 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 class DetaLoss(nn.Module): """ This class computes the losses for `DetaForObjectDetection`. 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 (supervised class and box). Args: matcher (`DetaHungarianMatcher`): Module able to compute a matching between targets and proposals. num_classes (`int`): Number of object categories, omitting the special no-object category. focal_alpha (`float`): Alpha parameter in focal loss. 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, focal_alpha, losses, num_queries, assign_first_stage=False, assign_second_stage=False, ): super().__init__() self.matcher = matcher self.num_classes = num_classes self.focal_alpha = focal_alpha self.losses = losses self.assign_first_stage = assign_first_stage self.assign_second_stage = assign_second_stage if self.assign_first_stage: self.stg1_assigner = DetaStage1Assigner() if self.assign_second_stage: self.stg2_assigner = DetaStage2Assigner(num_queries) def loss_labels(self, outputs, targets, indices, num_boxes): """ Classification loss (Binary focal loss) 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 target_classes_onehot = torch.zeros( [source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1], dtype=source_logits.dtype, layout=source_logits.layout, device=source_logits.device, ) target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) target_classes_onehot = target_classes_onehot[:, :, :-1] loss_ce = ( sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2) * source_logits.shape[1] ) 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 _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, } 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 not in ("auxiliary_outputs", "enc_outputs")} # Retrieve the matching between the outputs of the last layer and the targets if self.assign_second_stage: indices = self.stg2_assigner(outputs_without_aux, targets) else: indices = self.matcher(outputs_without_aux, targets) # Compute the average number of target boxes accross 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) # Check that we have initialized the distributed state world_size = 1 if is_accelerate_available(): if PartialState._shared_state != {}: num_boxes = reduce(num_boxes) world_size = PartialState().num_processes num_boxes = torch.clamp(num_boxes / world_size, 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"]): if not self.assign_second_stage: indices = self.matcher(auxiliary_outputs, targets) for loss in self.losses: 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) if "enc_outputs" in outputs: enc_outputs = outputs["enc_outputs"] bin_targets = copy.deepcopy(targets) for bt in bin_targets: bt["class_labels"] = torch.zeros_like(bt["class_labels"]) if self.assign_first_stage: indices = self.stg1_assigner(enc_outputs, bin_targets) else: indices = self.matcher(enc_outputs, bin_targets) for loss in self.losses: l_dict = self.get_loss(loss, enc_outputs, bin_targets, indices, num_boxes) l_dict = {k + "_enc": v for k, v in l_dict.items()} losses.update(l_dict) return losses class DetaMLPPredictionHead(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/detr/blob/master/models/detr.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 class DetaHungarianMatcher(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).sigmoid() # [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. alpha = 0.25 gamma = 2.0 neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log()) pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log()) class_cost = pos_cost_class[:, target_ids] - neg_cost_class[:, 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] 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() 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]) 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 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 # from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/layers/wrappers.py#L100 def nonzero_tuple(x): """ A 'as_tuple=True' version of torch.nonzero to support torchscript. because of https://github.com/pytorch/pytorch/issues/38718 """ if torch.jit.is_scripting(): if x.dim() == 0: return x.unsqueeze(0).nonzero().unbind(1) return x.nonzero().unbind(1) else: return x.nonzero(as_tuple=True) # from https://github.com/facebookresearch/detectron2/blob/9921a2caa585d4fa66c4b534b6fab6e74d89b582/detectron2/modeling/matcher.py#L9 class DetaMatcher: """ This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements. The matching is determined by the MxN match_quality_matrix, that characterizes how well each (ground-truth, prediction)-pair match each other. For example, if the elements are boxes, this matrix may contain box intersection-over-union overlap values. The matcher returns (a) a vector of length N containing the index of the ground-truth element m in [0, M) that matches to prediction n in [0, N). (b) a vector of length N containing the labels for each prediction. """ def __init__(self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False): """ Args: thresholds (`list[float]`): A list of thresholds used to stratify predictions into levels. labels (`list[int`): A list of values to label predictions belonging at each level. A label can be one of {-1, 0, 1} signifying {ignore, negative class, positive class}, respectively. allow_low_quality_matches (`bool`, *optional*, defaults to `False`): If `True`, produce additional matches for predictions with maximum match quality lower than high_threshold. See `set_low_quality_matches_` for more details. For example, thresholds = [0.3, 0.5] labels = [0, -1, 1] All predictions with iou < 0.3 will be marked with 0 and thus will be considered as false positives while training. All predictions with 0.3 <= iou < 0.5 will be marked with -1 and thus will be ignored. All predictions with 0.5 <= iou will be marked with 1 and thus will be considered as true positives. """ # Add -inf and +inf to first and last position in thresholds thresholds = thresholds[:] if thresholds[0] < 0: raise ValueError("Thresholds should be positive") thresholds.insert(0, -float("inf")) thresholds.append(float("inf")) # Currently torchscript does not support all + generator if not all(low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])): raise ValueError("Thresholds should be sorted.") if not all(l in [-1, 0, 1] for l in labels): raise ValueError("All labels should be either -1, 0 or 1") if len(labels) != len(thresholds) - 1: raise ValueError("Number of labels should be equal to number of thresholds - 1") self.thresholds = thresholds self.labels = labels self.allow_low_quality_matches = allow_low_quality_matches def __call__(self, match_quality_matrix): """ Args: match_quality_matrix (Tensor[float]): an MxN tensor, containing the pairwise quality between M ground-truth elements and N predicted elements. All elements must be >= 0 (due to the us of `torch.nonzero` for selecting indices in `set_low_quality_matches_`). Returns: matches (Tensor[int64]): a vector of length N, where matches[i] is a matched ground-truth index in [0, M) match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates whether a prediction is a true or false positive or ignored """ assert match_quality_matrix.dim() == 2 if match_quality_matrix.numel() == 0: default_matches = match_quality_matrix.new_full((match_quality_matrix.size(1),), 0, dtype=torch.int64) # When no gt boxes exist, we define IOU = 0 and therefore set labels # to `self.labels[0]`, which usually defaults to background class 0 # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds default_match_labels = match_quality_matrix.new_full( (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8 ) return default_matches, default_match_labels assert torch.all(match_quality_matrix >= 0) # match_quality_matrix is M (gt) x N (predicted) # Max over gt elements (dim 0) to find best gt candidate for each prediction matched_vals, matches = match_quality_matrix.max(dim=0) match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) for l, low, high in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): low_high = (matched_vals >= low) & (matched_vals < high) match_labels[low_high] = l if self.allow_low_quality_matches: self.set_low_quality_matches_(match_labels, match_quality_matrix) return matches, match_labels def set_low_quality_matches_(self, match_labels, match_quality_matrix): """ Produce additional matches for predictions that have only low-quality matches. Specifically, for each ground-truth G find the set of predictions that have maximum overlap with it (including ties); for each prediction in that set, if it is unmatched, then match it to the ground-truth G. This function implements the RPN assignment case (i) in Sec. 3.1.2 of :paper:`Faster R-CNN`. """ # For each gt, find the prediction with which it has highest quality highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) # Find the highest quality match available, even if it is low, including ties. # Note that the matches qualities must be positive due to the use of # `torch.nonzero`. _, pred_inds_with_highest_quality = nonzero_tuple(match_quality_matrix == highest_quality_foreach_gt[:, None]) # If an anchor was labeled positive only due to a low-quality match # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B. # This follows the implementation in Detectron, and is found to have no significant impact. match_labels[pred_inds_with_highest_quality] = 1 # from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/modeling/sampling.py#L9 def subsample_labels(labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int): """ Return `num_samples` (or fewer, if not enough found) random samples from `labels` which is a mixture of positives & negatives. It will try to return as many positives as possible without exceeding `positive_fraction * num_samples`, and then try to fill the remaining slots with negatives. Args: labels (Tensor): (N, ) label vector with values: * -1: ignore * bg_label: background ("negative") class * otherwise: one or more foreground ("positive") classes num_samples (int): The total number of labels with value >= 0 to return. Values that are not sampled will be filled with -1 (ignore). positive_fraction (float): The number of subsampled labels with values > 0 is `min(num_positives, int(positive_fraction * num_samples))`. The number of negatives sampled is `min(num_negatives, num_samples - num_positives_sampled)`. In order words, if there are not enough positives, the sample is filled with negatives. If there are also not enough negatives, then as many elements are sampled as is possible. bg_label (int): label index of background ("negative") class. Returns: pos_idx, neg_idx (Tensor): 1D vector of indices. The total length of both is `num_samples` or fewer. """ positive = nonzero_tuple((labels != -1) & (labels != bg_label))[0] negative = nonzero_tuple(labels == bg_label)[0] num_pos = int(num_samples * positive_fraction) # protect against not enough positive examples num_pos = min(positive.numel(), num_pos) num_neg = num_samples - num_pos # protect against not enough negative examples num_neg = min(negative.numel(), num_neg) # randomly select positive and negative examples perm1 = torch.randperm(positive.numel(), device=positive.device)[:num_pos] perm2 = torch.randperm(negative.numel(), device=negative.device)[:num_neg] pos_idx = positive[perm1] neg_idx = negative[perm2] return pos_idx, neg_idx def sample_topk_per_gt(pr_inds, gt_inds, iou, k): if len(gt_inds) == 0: return pr_inds, gt_inds # find topk matches for each gt gt_inds2, counts = gt_inds.unique(return_counts=True) scores, pr_inds2 = iou[gt_inds2].topk(k, dim=1) gt_inds2 = gt_inds2[:, None].repeat(1, k) # filter to as many matches that gt has pr_inds3 = torch.cat([pr[:c] for c, pr in zip(counts, pr_inds2)]) gt_inds3 = torch.cat([gt[:c] for c, gt in zip(counts, gt_inds2)]) return pr_inds3, gt_inds3 # modified from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/modeling/roi_heads/roi_heads.py#L123 class DetaStage2Assigner(nn.Module): def __init__(self, num_queries, max_k=4): super().__init__() self.positive_fraction = 0.25 self.bg_label = 400 # number > 91 to filter out later self.batch_size_per_image = num_queries self.proposal_matcher = DetaMatcher(thresholds=[0.6], labels=[0, 1], allow_low_quality_matches=True) self.k = max_k def _sample_proposals(self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor): """ Based on the matching between N proposals and M groundtruth, sample the proposals and set their classification labels. Args: matched_idxs (Tensor): a vector of length N, each is the best-matched gt index in [0, M) for each proposal. matched_labels (Tensor): a vector of length N, the matcher's label (one of cfg.MODEL.ROI_HEADS.IOU_LABELS) for each proposal. gt_classes (Tensor): a vector of length M. Returns: Tensor: a vector of indices of sampled proposals. Each is in [0, N). Tensor: a vector of the same length, the classification label for each sampled proposal. Each sample is labeled as either a category in [0, num_classes) or the background (num_classes). """ has_gt = gt_classes.numel() > 0 # Get the corresponding GT for each proposal if has_gt: gt_classes = gt_classes[matched_idxs] # Label unmatched proposals (0 label from matcher) as background (label=num_classes) gt_classes[matched_labels == 0] = self.bg_label # Label ignore proposals (-1 label) gt_classes[matched_labels == -1] = -1 else: gt_classes = torch.zeros_like(matched_idxs) + self.bg_label sampled_fg_idxs, sampled_bg_idxs = subsample_labels( gt_classes, self.batch_size_per_image, self.positive_fraction, self.bg_label ) sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0) return sampled_idxs, gt_classes[sampled_idxs] def forward(self, outputs, targets, return_cost_matrix=False): # COCO categories are from 1 to 90. They set num_classes=91 and apply sigmoid. bs = len(targets) indices = [] ious = [] for b in range(bs): iou, _ = box_iou( center_to_corners_format(targets[b]["boxes"]), center_to_corners_format(outputs["init_reference"][b].detach()), ) matched_idxs, matched_labels = self.proposal_matcher( iou ) # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.6, 0 ow] ( sampled_idxs, sampled_gt_classes, ) = self._sample_proposals( # list of sampled proposal_ids, sampled_id -> [0, num_classes)+[bg_label] matched_idxs, matched_labels, targets[b]["class_labels"] ) pos_pr_inds = sampled_idxs[sampled_gt_classes != self.bg_label] pos_gt_inds = matched_idxs[pos_pr_inds] pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou) indices.append((pos_pr_inds, pos_gt_inds)) ious.append(iou) if return_cost_matrix: return indices, ious return indices def postprocess_indices(self, pr_inds, gt_inds, iou): return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k) # modified from https://github.com/facebookresearch/detectron2/blob/cbbc1ce26473cb2a5cc8f58e8ada9ae14cb41052/detectron2/modeling/proposal_generator/rpn.py#L181 class DetaStage1Assigner(nn.Module): def __init__(self, t_low=0.3, t_high=0.7, max_k=4): super().__init__() self.positive_fraction = 0.5 self.batch_size_per_image = 256 self.k = max_k self.t_low = t_low self.t_high = t_high self.anchor_matcher = DetaMatcher( thresholds=[t_low, t_high], labels=[0, -1, 1], allow_low_quality_matches=True ) def _subsample_labels(self, label): """ Randomly sample a subset of positive and negative examples, and overwrite the label vector to the ignore value (-1) for all elements that are not included in the sample. Args: labels (Tensor): a vector of -1, 0, 1. Will be modified in-place and returned. """ pos_idx, neg_idx = subsample_labels(label, self.batch_size_per_image, self.positive_fraction, 0) # Fill with the ignore label (-1), then set positive and negative labels label.fill_(-1) label.scatter_(0, pos_idx, 1) label.scatter_(0, neg_idx, 0) return label def forward(self, outputs, targets): bs = len(targets) indices = [] for b in range(bs): anchors = outputs["anchors"][b] if len(targets[b]["boxes"]) == 0: indices.append( ( torch.tensor([], dtype=torch.long, device=anchors.device), torch.tensor([], dtype=torch.long, device=anchors.device), ) ) continue iou, _ = box_iou( center_to_corners_format(targets[b]["boxes"]), center_to_corners_format(anchors), ) matched_idxs, matched_labels = self.anchor_matcher( iou ) # proposal_id -> highest_iou_gt_id, proposal_id -> [1 if iou > 0.7, 0 if iou < 0.3, -1 ow] matched_labels = self._subsample_labels(matched_labels) all_pr_inds = torch.arange(len(anchors), device=matched_labels.device) pos_pr_inds = all_pr_inds[matched_labels == 1] pos_gt_inds = matched_idxs[pos_pr_inds] pos_pr_inds, pos_gt_inds = self.postprocess_indices(pos_pr_inds, pos_gt_inds, iou) pos_pr_inds, pos_gt_inds = pos_pr_inds.to(anchors.device), pos_gt_inds.to(anchors.device) indices.append((pos_pr_inds, pos_gt_inds)) return indices def postprocess_indices(self, pr_inds, gt_inds, iou): return sample_topk_per_gt(pr_inds, gt_inds, iou, self.k)

transformers/src/transformers/models/deprecated/deta/modeling_deta.py/0

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# 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. """MEGA 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__) class MegaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MegaModel`]. It is used to instantiate a Mega 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 Mega [mnaylor/mega-base-wikitext](https://huggingface.co/mnaylor/mega-base-wikitext) 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 Mega model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MegaModel`]. hidden_size (`int`, *optional*, defaults to 128): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 4): Number of hidden layers in the Mega encoder. intermediate_size (`int`, *optional*, defaults to 256): Dimensionality of the hidden size (self-attention value projection) within the Mega encoder ema_projection_size (`int`, *optional*, defaults to 16): Dimensionality of the MegaMultiDimensionDampedEma bidirectional (`bool`, *optional*, defaults to `True`): Whether the MegaMultiDimensionDampedEma used in Mega's self-attention should work bidirectionally (`True`) or unidirectionally (`False`). Bidirectional EMA is incompatible with causal decoding, so this should be False if you intend to use the model as a decoder. shared_representation_size (`int`, *optional*, defaults to 64): Dimensionality of the linear projection for shared representation of self-attention queries and keys use_chunking (`bool`, *optional*, defaults to `False`): Whether to chunk inputs for linear self-attention complexity (described as Mega-chunk in the paper) chunk_size (`int`, *optional*, defaults to -1): If `use_chunking` is set to `True`, determines the size of the chunks to apply to the input sequence. If chunking is used, input sequences must be padded to a multiple of `chunk_size` truncation (`int`, *optional*): If specified, the sequence length for which to truncate MegaMultiDimensionDampedEma normalize_before_mega (`bool`, *optional*, defaults to `True`): Whether to normalize before (`True`) or after (`False`) passing through Mega encoder blocks normalization_type (`str`, *optional*, defaults to `"scalenorm"`): Type of normalization to use in Mega encoder blocks. Choose one of `"scalenorm"`, `"layernorm"`, `"rmsnorm"`, `"batchnorm"`, or `"syncbatchnorm"` (GPU required for syncbatchnorm) norm_affine (`bool`, *optional*, defaults to `True`): If `True`, applies a parameterized affine transformation to inputs during normalization activation (`str`, *optional*, defaults to `"silu"`): Activation function to apply within Mega encoder blocks. Choose one of `"silu"`, `"relu"`, `"linear"`, `"gelu"`, or `"gelu_accurate"` attention_activation (`str`, *optional*, defaults to `"softmax"`): Activation function to apply for single-headed self-attention (a la Transformer). Choose one of `"softmax"`, `"laplace"`, or `"relu2"` dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for EMA self-attention 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. use_feature_dropout (`bool`, *optional*, defaults to `False`): Whether to use feature-based (`True`) or standard dropout (`False`) use_normalized_ffn (`bool`, *optional*, defaults to `True`): Whether to use the normalized feed-forward sub-layer in Mega blocks (`True`) or pass Mega encoder output as-is (`False`) nffn_hidden_size (`int`, *optional*, defaults to 256): If using the normalized feed-forward network (NFFN) layer within Mega (`use_normalized_ffn = True`), this is the hidden size of the NFFN normalize_before_ffn (`bool`, *optional*, defaults to `True`): Whether to normalize before (`True`) or after (`False`) the feed-forward portion of NFFN nffn_activation_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the NFFN component. max_positions (`int`, *optional*, defaults to 2048): The maximum sequence length to use for positional representations. For `"simple"` relative positional bias, this is a hard limit on input length; `"rotary"` relative positional bias will extrapolate to longer sequences add_token_type_embeddings (`bool`, *optional*, defaults to `True`): Whether to account for token types in embeddings. Left as optional to maintain compatibility with original implementation while adding support for token types. type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`MegaModel`]. Only used if `add_token_type_embeddings = True` initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. ema_delta_alpha_range (`float`, *optional*, defaults to 0.2): The standard deviation for initializing the delta (damping factor) and alpha (decay factor) parameters in MegaMultiDimensionDampedEma. ema_beta_range (`float`, *optional*, defaults to 0.02): The standard deviation for initializing the beta parameter (expansion matrix) in MegaMultiDimensionDampedEma. ema_gamma_omega_range (`float`, *optional*, defaults to 1.0): The standard deviation for initializing the gamma (projection matrix) and omega (residual weight) parameters in MultiDimensionEMA. relative_positional_bias (`str`, *optional*, defaults to `"rotary"`): Type of relative positional encoding. Choose one of `"rotary"` or `"simple"`. If `"simple"` is selected, `max_positions` is used as a limit on input size, while `"rotary"` extrapolates beyond `max_positions`. is_decoder (`bool`, *optional*, defaults to `False`): Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. classifier_dropout (`float`, *optional*): The dropout ratio for the classification head. add_lm_hidden_dense_layer (`bool`, *optional*, defaults to `True`): Whether to include a hidden layer for projection between encoder outputs and LM heads (`True`) or pass hidden states directly to LM head (`False`). Remains optional for compatibility with original implementation Examples: ```python >>> from transformers import MegaConfig, MegaModel >>> # Initializing a Mega configuration >>> configuration = MegaConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = MegaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "mega" def __init__( self, vocab_size=30522, hidden_size=128, num_hidden_layers=4, intermediate_size=256, ema_projection_size=16, bidirectional=True, shared_representation_size=64, use_chunking=False, chunk_size=-1, truncation=None, normalize_before_mega=True, normalization_type="scalenorm", norm_affine=True, activation="silu", attention_activation="softmax", dropout_prob=0.1, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, use_feature_dropout=False, use_normalized_ffn=True, nffn_hidden_size=256, normalize_before_ffn=True, nffn_activation_dropout_prob=0.1, max_positions=2048, add_token_type_embeddings=False, type_vocab_size=2, initializer_range=0.02, ema_delta_alpha_range=0.2, ema_beta_range=0.02, ema_gamma_omega_range=1.0, pad_token_id=1, bos_token_id=0, eos_token_id=2, relative_positional_bias="rotary", classifier_dropout=None, use_cache=True, add_lm_hidden_dense_layer=True, **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.num_hidden_layers = num_hidden_layers self.activation = activation self.attention_activation = attention_activation self.intermediate_size = intermediate_size self.ema_projection_size = ema_projection_size self.bidirectional = bidirectional self.shared_representation_size = shared_representation_size self.use_chunking = use_chunking self.chunk_size = chunk_size self.truncation = truncation self.normalize_before_mega = normalize_before_mega self.normalization_type = normalization_type self.norm_affine = norm_affine self.dropout_prob = dropout_prob self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.use_feature_dropout = use_feature_dropout self.use_normalized_ffn = use_normalized_ffn self.nffn_hidden_size = nffn_hidden_size self.normalize_before_ffn = normalize_before_ffn self.nffn_activation_dropout_prob = nffn_activation_dropout_prob self.max_positions = max_positions self.add_token_type_embeddings = add_token_type_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.ema_delta_alpha_range = ema_delta_alpha_range self.ema_beta_range = ema_beta_range self.ema_gamma_omega_range = ema_gamma_omega_range self.relative_positional_bias = relative_positional_bias self.use_cache = use_cache self.classifier_dropout = classifier_dropout self.add_lm_hidden_dense_layer = add_lm_hidden_dense_layer self.num_attention_heads = 1 # not used but required by Hugging Face class MegaOnnxConfig(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/deprecated/mega/configuration_mega.py/0

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# 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. """ Speech processor class for Speech2Text2 """ import warnings from contextlib import contextmanager from ....processing_utils import ProcessorMixin class Speech2Text2Processor(ProcessorMixin): r""" Constructs a Speech2Text2 processor which wraps a Speech2Text2 feature extractor and a Speech2Text2 tokenizer into a single processor. [`Speech2Text2Processor`] offers all the functionalities of [`AutoFeatureExtractor`] and [`Speech2Text2Tokenizer`]. See the [`~Speech2Text2Processor.__call__`] and [`~Speech2Text2Processor.decode`] for more information. Args: feature_extractor (`AutoFeatureExtractor`): An instance of [`AutoFeatureExtractor`]. The feature extractor is a required input. tokenizer (`Speech2Text2Tokenizer`): An instance of [`Speech2Text2Tokenizer`]. The tokenizer is a required input. """ feature_extractor_class = "AutoFeatureExtractor" tokenizer_class = "Speech2Text2Tokenizer" def __init__(self, feature_extractor, tokenizer): super().__init__(feature_extractor, tokenizer) self.current_processor = self.feature_extractor self._in_target_context_manager = False def __call__(self, *args, **kwargs): """ When used in normal mode, this method forwards all its arguments to AutoFeatureExtractor's [`~AutoFeatureExtractor.__call__`] and returns its output. If used in the context [`~Speech2Text2Processor.as_target_processor`] this method forwards all its arguments to Speech2Text2Tokenizer's [`~Speech2Text2Tokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information. """ # For backward compatibility if self._in_target_context_manager: return self.current_processor(*args, **kwargs) if "raw_speech" in kwargs: warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.") audio = kwargs.pop("raw_speech") else: audio = kwargs.pop("audio", None) sampling_rate = kwargs.pop("sampling_rate", None) text = kwargs.pop("text", None) if len(args) > 0: audio = args[0] args = args[1:] if audio is None and text is None: raise ValueError("You need to specify either an `audio` or `text` input to process.") if audio is not None: inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs) if text is not None: encodings = self.tokenizer(text, **kwargs) if text is None: return inputs elif audio is None: return encodings else: inputs["labels"] = encodings["input_ids"] return inputs def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to Speech2Text2Tokenizer'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 Speech2Text2Tokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @contextmanager def as_target_processor(self): """ Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning Speech2Text2. """ warnings.warn( "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your " "labels by using the argument `text` of the regular `__call__` method (either in the same call as " "your audio inputs, or in a separate call." ) self._in_target_context_manager = True self.current_processor = self.tokenizer yield self.current_processor = self.feature_extractor self._in_target_context_manager = False

transformers/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py/0

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# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # 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. from typing import TYPE_CHECKING from ....utils import ( OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available, ) _import_structure = { "configuration_tvlt": ["TvltConfig"], "feature_extraction_tvlt": ["TvltFeatureExtractor"], "processing_tvlt": ["TvltProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tvlt"] = [ "TvltModel", "TvltForPreTraining", "TvltForAudioVisualClassification", "TvltPreTrainedModel", ] try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["image_processing_tvlt"] = ["TvltImageProcessor"] if TYPE_CHECKING: from .configuration_tvlt import TvltConfig from .processing_tvlt import TvltProcessor from .feature_extraction_tvlt import TvltFeatureExtractor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tvlt import ( TvltForAudioVisualClassification, TvltForPreTraining, TvltModel, TvltPreTrainedModel, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_tvlt import TvltImageProcessor else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/deprecated/tvlt/__init__.py/0

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# coding=utf-8 # Copyright 2020 The Microsoft 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. """XLM-ProphetNet model configuration""" from typing import Callable, Optional, Union from ....configuration_utils import PretrainedConfig from ....utils import logging logger = logging.get_logger(__name__) class XLMProphetNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`XLMProphetNetModel`]. It is used to instantiate a XLMProphetNet 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 XLMProphetNet [microsoft/xprophetnet-large-wiki100-cased](https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: activation_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for activations inside the fully connected layer. 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. vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the ProphetNET model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XLMProphetNetModel`]. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. num_encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. num_encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the `intermediate` (often named feed-forward) layer in decoder. num_decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. num_decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. 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). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. add_cross_attention (`bool`, *optional*, defaults to `True`): Whether cross-attention layers should be added to the model. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether this is an encoder/decoder model. pad_token_id (`int`, *optional*, defaults to 1) Padding token id. bos_token_id (`int`, *optional*, defaults to 0) Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2) End of stream token id. ngram (`int`, *optional*, defaults to 2) Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first token. num_buckets (`int`, *optional*, defaults to 32) The number of buckets to use for each attention layer. This is for relative position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details. relative_max_distance (`int`, *optional*, defaults to 128) Relative distances greater than this number will be put into the last same bucket. This is for relative position calculation. See the [T5 paper](see https://arxiv.org/abs/1910.10683) for more details. disable_ngram_loss (`bool`, *optional*, defaults to `False`): Whether be trained predicting only the next first token. eps (`float`, *optional*, defaults to 0.0): Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label smoothing is performed. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ model_type = "xlm-prophetnet" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "num_encoder_attention_heads", } def __init__( self, activation_dropout: Optional[float] = 0.1, activation_function: Optional[Union[str, Callable]] = "gelu", vocab_size: Optional[int] = 30522, hidden_size: Optional[int] = 1024, encoder_ffn_dim: Optional[int] = 4096, num_encoder_layers: Optional[int] = 12, num_encoder_attention_heads: Optional[int] = 16, decoder_ffn_dim: Optional[int] = 4096, num_decoder_layers: Optional[int] = 12, num_decoder_attention_heads: Optional[int] = 16, attention_dropout: Optional[float] = 0.1, dropout: Optional[float] = 0.1, max_position_embeddings: Optional[int] = 512, init_std: Optional[float] = 0.02, is_encoder_decoder: Optional[bool] = True, add_cross_attention: Optional[bool] = True, decoder_start_token_id: Optional[int] = 0, ngram: Optional[int] = 2, num_buckets: Optional[int] = 32, relative_max_distance: Optional[int] = 128, disable_ngram_loss: Optional[bool] = False, eps: Optional[float] = 0.0, use_cache: Optional[bool] = True, pad_token_id: Optional[int] = 0, bos_token_id: Optional[int] = 1, eos_token_id: Optional[int] = 2, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.encoder_ffn_dim = encoder_ffn_dim self.num_encoder_layers = num_encoder_layers self.num_encoder_attention_heads = num_encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.num_decoder_layers = num_decoder_layers self.num_decoder_attention_heads = num_decoder_attention_heads self.max_position_embeddings = max_position_embeddings self.init_std = init_std # Normal(0, this parameter) self.activation_function = activation_function # parameters for xlmprophetnet self.ngram = ngram self.num_buckets = num_buckets self.relative_max_distance = relative_max_distance self.disable_ngram_loss = disable_ngram_loss self.eps = eps # 3 Types of Dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.dropout = dropout self.use_cache = use_cache super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, add_cross_attention=add_cross_attention, decoder_start_token_id=decoder_start_token_id, **kwargs, ) @property def num_hidden_layers(self) -> int: return self.num_encoder_layers + self.num_decoder_layers @num_hidden_layers.setter def num_hidden_layers(self, value): raise NotImplementedError( "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and" " `num_decoder_layers`." )

transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py/0

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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 argparse import os import torch from transformers.utils import WEIGHTS_NAME DIALOGPT_MODELS = ["small", "medium", "large"] OLD_KEY = "lm_head.decoder.weight" NEW_KEY = "lm_head.weight" def convert_dialogpt_checkpoint(checkpoint_path: str, pytorch_dump_folder_path: str): d = torch.load(checkpoint_path) d[NEW_KEY] = d.pop(OLD_KEY) os.makedirs(pytorch_dump_folder_path, exist_ok=True) torch.save(d, os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--dialogpt_path", default=".", type=str) args = parser.parse_args() for MODEL in DIALOGPT_MODELS: checkpoint_path = os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl") pytorch_dump_folder_path = f"./DialoGPT-{MODEL}" convert_dialogpt_checkpoint( checkpoint_path, pytorch_dump_folder_path, )

transformers/src/transformers/models/dialogpt/convert_dialogpt_original_pytorch_checkpoint_to_pytorch.py/0

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# 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. """PyTorch Donut Swin Transformer model. This implementation is identical to a regular Swin Transformer, without final layer norm on top of the final hidden states.""" import collections.abc import math from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, meshgrid, prune_linear_layer from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, torch_int, ) from .configuration_donut_swin import DonutSwinConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "DonutSwinConfig" # Base docstring _CHECKPOINT_FOR_DOC = "https://huggingface.co/naver-clova-ix/donut-base" _EXPECTED_OUTPUT_SHAPE = [1, 49, 768] @dataclass # Copied from transformers.models.swin.modeling_swin.SwinEncoderOutput with Swin->DonutSwin class DonutSwinEncoderOutput(ModelOutput): """ DonutSwin encoder's outputs, with potential hidden states and attentions. 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 stage) 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 stage) 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. reshaped_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, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass # Copied from transformers.models.swin.modeling_swin.SwinModelOutput with Swin->DonutSwin class DonutSwinModelOutput(ModelOutput): """ DonutSwin model's outputs that also contains a pooling of the last hidden states. 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. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed): Average pooling of the last layer hidden-state. 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 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 stage) 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. reshaped_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, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. """ last_hidden_state: torch.FloatTensor = None pooler_output: Optional[torch.FloatTensor] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None # Copied from transformers.models.swin.modeling_swin.window_partition def window_partition(input_feature, window_size): """ Partitions the given input into windows. """ batch_size, height, width, num_channels = input_feature.shape input_feature = input_feature.view( batch_size, height // window_size, window_size, width // window_size, window_size, num_channels ) windows = input_feature.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels) return windows # Copied from transformers.models.swin.modeling_swin.window_reverse def window_reverse(windows, window_size, height, width): """ Merges windows to produce higher resolution features. """ num_channels = windows.shape[-1] windows = windows.view(-1, height // window_size, width // window_size, window_size, window_size, num_channels) windows = windows.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, height, width, num_channels) return windows # Copied from transformers.models.swin.modeling_swin.SwinEmbeddings with Swin->DonutSwin class DonutSwinEmbeddings(nn.Module): """ Construct the patch and position embeddings. Optionally, also the mask token. """ def __init__(self, config, use_mask_token=False): super().__init__() self.patch_embeddings = DonutSwinPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.patch_grid = self.patch_embeddings.grid_size self.mask_token = nn.Parameter(torch.zeros(1, 1, config.embed_dim)) if use_mask_token else None if config.use_absolute_embeddings: self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.embed_dim)) else: self.position_embeddings = None self.norm = nn.LayerNorm(config.embed_dim) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.patch_size = config.patch_size self.config = config # Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding 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. This method is also adapted to support torch.jit tracing. Adapted from: - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 """ num_patches = embeddings.shape[1] - 1 num_positions = self.position_embeddings.shape[1] - 1 # always interpolate when tracing to ensure the exported model works for dynamic input shapes if not torch.jit.is_tracing() and num_patches == num_positions and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, :1] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] new_height = height // self.patch_size new_width = width // self.patch_size sqrt_num_positions = torch_int(num_positions**0.5) patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, size=(new_height, new_width), mode="bicubic", align_corners=False, ) patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed, patch_pos_embed), dim=1) def forward( self, pixel_values: Optional[torch.FloatTensor], bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> Tuple[torch.Tensor]: _, num_channels, height, width = pixel_values.shape embeddings, output_dimensions = self.patch_embeddings(pixel_values) embeddings = self.norm(embeddings) batch_size, seq_len, _ = embeddings.size() if bool_masked_pos is not None: mask_tokens = self.mask_token.expand(batch_size, seq_len, -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 if self.position_embeddings is not None: 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, output_dimensions # Copied from transformers.models.swin.modeling_swin.SwinPatchEmbeddings with Swin->DonutSwin class DonutSwinPatchEmbeddings(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.embed_dim 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.grid_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1]) self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) def maybe_pad(self, pixel_values, height, width): if width % self.patch_size[1] != 0: pad_values = (0, self.patch_size[1] - width % self.patch_size[1]) pixel_values = nn.functional.pad(pixel_values, pad_values) if height % self.patch_size[0] != 0: pad_values = (0, 0, 0, self.patch_size[0] - height % self.patch_size[0]) pixel_values = nn.functional.pad(pixel_values, pad_values) return pixel_values def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]: _, num_channels, height, width = pixel_values.shape # pad the input to be divisible by self.patch_size, if needed pixel_values = self.maybe_pad(pixel_values, height, width) embeddings = self.projection(pixel_values) _, _, height, width = embeddings.shape output_dimensions = (height, width) embeddings = embeddings.flatten(2).transpose(1, 2) return embeddings, output_dimensions # Copied from transformers.models.swin.modeling_swin.SwinPatchMerging class DonutSwinPatchMerging(nn.Module): """ Patch Merging Layer. Args: input_resolution (`Tuple[int]`): Resolution of input feature. dim (`int`): Number of input channels. norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`): Normalization layer class. """ def __init__(self, input_resolution: Tuple[int], dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None: super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(4 * dim) def maybe_pad(self, input_feature, height, width): should_pad = (height % 2 == 1) or (width % 2 == 1) if should_pad: pad_values = (0, 0, 0, width % 2, 0, height % 2) input_feature = nn.functional.pad(input_feature, pad_values) return input_feature def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor: height, width = input_dimensions # `dim` is height * width batch_size, dim, num_channels = input_feature.shape input_feature = input_feature.view(batch_size, height, width, num_channels) # pad input to be disible by width and height, if needed input_feature = self.maybe_pad(input_feature, height, width) # [batch_size, height/2, width/2, num_channels] input_feature_0 = input_feature[:, 0::2, 0::2, :] # [batch_size, height/2, width/2, num_channels] input_feature_1 = input_feature[:, 1::2, 0::2, :] # [batch_size, height/2, width/2, num_channels] input_feature_2 = input_feature[:, 0::2, 1::2, :] # [batch_size, height/2, width/2, num_channels] input_feature_3 = input_feature[:, 1::2, 1::2, :] # batch_size height/2 width/2 4*num_channels input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1) input_feature = input_feature.view(batch_size, -1, 4 * num_channels) # batch_size height/2*width/2 4*C input_feature = self.norm(input_feature) input_feature = self.reduction(input_feature) return input_feature # 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.swin.modeling_swin.SwinDropPath class DonutSwinDropPath(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) # Copied from transformers.models.swin.modeling_swin.SwinSelfAttention with Swin->DonutSwin class DonutSwinSelfAttention(nn.Module): def __init__(self, config, dim, num_heads, window_size): super().__init__() if dim % num_heads != 0: raise ValueError( f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})" ) self.num_attention_heads = num_heads self.attention_head_size = int(dim / num_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.window_size = ( window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size) ) self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), num_heads) ) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.window_size[0]) coords_w = torch.arange(self.window_size[1]) coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij")) coords_flatten = torch.flatten(coords, 1) relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] relative_coords = relative_coords.permute(1, 2, 0).contiguous() relative_coords[:, :, 0] += self.window_size[0] - 1 relative_coords[:, :, 1] += self.window_size[1] - 1 relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 relative_position_index = relative_coords.sum(-1) self.register_buffer("relative_position_index", relative_position_index) self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) 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: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: batch_size, dim, num_channels = hidden_states.shape 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) relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)] relative_position_bias = relative_position_bias.view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 ) relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() attention_scores = attention_scores + relative_position_bias.unsqueeze(0) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in DonutSwinModel forward() function) mask_shape = attention_mask.shape[0] attention_scores = attention_scores.view( batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim ) attention_scores = attention_scores + attention_mask.unsqueeze(1).unsqueeze(0) attention_scores = attention_scores.view(-1, self.num_attention_heads, dim, dim) # 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.swin.modeling_swin.SwinSelfOutput class DonutSwinSelfOutput(nn.Module): def __init__(self, config, dim): super().__init__() self.dense = nn.Linear(dim, dim) self.dropout = nn.Dropout(config.attention_probs_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.swin.modeling_swin.SwinAttention with Swin->DonutSwin class DonutSwinAttention(nn.Module): def __init__(self, config, dim, num_heads, window_size): super().__init__() self.self = DonutSwinSelfAttention(config, dim, num_heads, window_size) self.output = DonutSwinSelfOutput(config, dim) 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: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self(hidden_states, attention_mask, 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 # Copied from transformers.models.swin.modeling_swin.SwinIntermediate class DonutSwinIntermediate(nn.Module): def __init__(self, config, dim): super().__init__() self.dense = nn.Linear(dim, int(config.mlp_ratio * dim)) 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 # Copied from transformers.models.swin.modeling_swin.SwinOutput class DonutSwinOutput(nn.Module): def __init__(self, config, dim): super().__init__() self.dense = nn.Linear(int(config.mlp_ratio * dim), dim) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.swin.modeling_swin.SwinLayer with Swin->DonutSwin class DonutSwinLayer(nn.Module): def __init__(self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.shift_size = shift_size self.window_size = config.window_size self.input_resolution = input_resolution self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps) self.attention = DonutSwinAttention(config, dim, num_heads, window_size=self.window_size) self.drop_path = DonutSwinDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity() self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps) self.intermediate = DonutSwinIntermediate(config, dim) self.output = DonutSwinOutput(config, dim) def set_shift_and_window_size(self, input_resolution): if min(input_resolution) <= self.window_size: # if window size is larger than input resolution, we don't partition windows self.shift_size = torch_int(0) self.window_size = ( torch.min(torch.tensor(input_resolution)) if torch.jit.is_tracing() else min(input_resolution) ) def get_attn_mask(self, height, width, dtype, device): if self.shift_size > 0: # calculate attention mask for SW-MSA img_mask = torch.zeros((1, height, width, 1), dtype=dtype, device=device) height_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) width_slices = ( slice(0, -self.window_size), slice(-self.window_size, -self.shift_size), slice(-self.shift_size, None), ) count = 0 for height_slice in height_slices: for width_slice in width_slices: img_mask[:, height_slice, width_slice, :] = count count += 1 mask_windows = window_partition(img_mask, self.window_size) mask_windows = mask_windows.view(-1, self.window_size * self.window_size) attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) else: attn_mask = None return attn_mask def maybe_pad(self, hidden_states, height, width): pad_right = (self.window_size - width % self.window_size) % self.window_size pad_bottom = (self.window_size - height % self.window_size) % self.window_size pad_values = (0, 0, 0, pad_right, 0, pad_bottom) hidden_states = nn.functional.pad(hidden_states, pad_values) return hidden_states, pad_values def forward( self, hidden_states: torch.Tensor, input_dimensions: Tuple[int, int], head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, always_partition: Optional[bool] = False, ) -> Tuple[torch.Tensor, torch.Tensor]: if not always_partition: self.set_shift_and_window_size(input_dimensions) else: pass height, width = input_dimensions batch_size, _, channels = hidden_states.size() shortcut = hidden_states hidden_states = self.layernorm_before(hidden_states) hidden_states = hidden_states.view(batch_size, height, width, channels) # pad hidden_states to multiples of window size hidden_states, pad_values = self.maybe_pad(hidden_states, height, width) _, height_pad, width_pad, _ = hidden_states.shape # cyclic shift if self.shift_size > 0: shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) else: shifted_hidden_states = hidden_states # partition windows hidden_states_windows = window_partition(shifted_hidden_states, self.window_size) hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels) attn_mask = self.get_attn_mask( height_pad, width_pad, dtype=hidden_states.dtype, device=hidden_states_windows.device ) attention_outputs = self.attention( hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions ) attention_output = attention_outputs[0] attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels) shifted_windows = window_reverse(attention_windows, self.window_size, height_pad, width_pad) # reverse cyclic shift if self.shift_size > 0: attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) else: attention_windows = shifted_windows was_padded = pad_values[3] > 0 or pad_values[5] > 0 if was_padded: attention_windows = attention_windows[:, :height, :width, :].contiguous() attention_windows = attention_windows.view(batch_size, height * width, channels) hidden_states = shortcut + self.drop_path(attention_windows) layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) layer_output = hidden_states + self.output(layer_output) layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,) return layer_outputs # Copied from transformers.models.swin.modeling_swin.SwinStage with Swin->DonutSwin class DonutSwinStage(nn.Module): def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample): super().__init__() self.config = config self.dim = dim self.blocks = nn.ModuleList( [ DonutSwinLayer( config=config, dim=dim, input_resolution=input_resolution, num_heads=num_heads, drop_path_rate=drop_path[i], shift_size=0 if (i % 2 == 0) else config.window_size // 2, ) for i in range(depth) ] ) # patch merging layer if downsample is not None: self.downsample = downsample(input_resolution, dim=dim, norm_layer=nn.LayerNorm) else: self.downsample = None self.pointing = False def forward( self, hidden_states: torch.Tensor, input_dimensions: Tuple[int, int], head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, always_partition: Optional[bool] = False, ) -> Tuple[torch.Tensor]: height, width = input_dimensions for i, layer_module in enumerate(self.blocks): layer_head_mask = head_mask[i] if head_mask is not None else None layer_outputs = layer_module( hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition ) hidden_states = layer_outputs[0] hidden_states_before_downsampling = hidden_states if self.downsample is not None: height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2 output_dimensions = (height, width, height_downsampled, width_downsampled) hidden_states = self.downsample(hidden_states_before_downsampling, input_dimensions) else: output_dimensions = (height, width, height, width) stage_outputs = (hidden_states, hidden_states_before_downsampling, output_dimensions) if output_attentions: stage_outputs += layer_outputs[1:] return stage_outputs # Copied from transformers.models.swin.modeling_swin.SwinEncoder with Swin->DonutSwin class DonutSwinEncoder(nn.Module): def __init__(self, config, grid_size): super().__init__() self.num_layers = len(config.depths) self.config = config dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))] self.layers = nn.ModuleList( [ DonutSwinStage( config=config, dim=int(config.embed_dim * 2**i_layer), input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)), depth=config.depths[i_layer], num_heads=config.num_heads[i_layer], drop_path=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])], downsample=DonutSwinPatchMerging if (i_layer < self.num_layers - 1) else None, ) for i_layer in range(self.num_layers) ] ) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, input_dimensions: Tuple[int, int], head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, output_hidden_states_before_downsampling: Optional[bool] = False, always_partition: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, DonutSwinEncoderOutput]: all_hidden_states = () if output_hidden_states else None all_reshaped_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None if output_hidden_states: batch_size, _, hidden_size = hidden_states.shape # rearrange b (h w) c -> b c h w reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size) reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2) all_hidden_states += (hidden_states,) all_reshaped_hidden_states += (reshaped_hidden_state,) for i, layer_module in enumerate(self.layers): 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, input_dimensions, layer_head_mask, output_attentions, always_partition, ) else: layer_outputs = layer_module( hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition ) hidden_states = layer_outputs[0] hidden_states_before_downsampling = layer_outputs[1] output_dimensions = layer_outputs[2] input_dimensions = (output_dimensions[-2], output_dimensions[-1]) if output_hidden_states and output_hidden_states_before_downsampling: batch_size, _, hidden_size = hidden_states_before_downsampling.shape # rearrange b (h w) c -> b c h w # here we use the original (not downsampled) height and width reshaped_hidden_state = hidden_states_before_downsampling.view( batch_size, *(output_dimensions[0], output_dimensions[1]), hidden_size ) reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2) all_hidden_states += (hidden_states_before_downsampling,) all_reshaped_hidden_states += (reshaped_hidden_state,) elif output_hidden_states and not output_hidden_states_before_downsampling: batch_size, _, hidden_size = hidden_states.shape # rearrange b (h w) c -> b c h w reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size) reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2) all_hidden_states += (hidden_states,) all_reshaped_hidden_states += (reshaped_hidden_state,) if output_attentions: all_self_attentions += layer_outputs[3:] if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return DonutSwinEncoderOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, reshaped_hidden_states=all_reshaped_hidden_states, ) # Copied from transformers.models.swin.modeling_swin.SwinPreTrainedModel with Swin->DonutSwin class DonutSwinPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DonutSwinConfig base_model_prefix = "swin" main_input_name = "pixel_values" supports_gradient_checkpointing = True _no_split_modules = ["DonutSwinStage"] 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) SWIN_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 ([`DonutSwinConfig`]): 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. """ SWIN_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 [`DonutImageProcessor.__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*, defaults to `False`): 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 Donut Swin Model transformer outputting raw hidden-states without any specific head on top.", SWIN_START_DOCSTRING, ) class DonutSwinModel(DonutSwinPreTrainedModel): def __init__(self, config, add_pooling_layer=True, use_mask_token=False): super().__init__(config) self.config = config self.num_layers = len(config.depths) self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1)) self.embeddings = DonutSwinEmbeddings(config, use_mask_token=use_mask_token) self.encoder = DonutSwinEncoder(config, self.embeddings.patch_grid) self.pooler = nn.AdaptiveAvgPool1d(1) 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. 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(SWIN_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=DonutSwinModelOutput, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: bool = False, return_dict: Optional[bool] = None, ) -> Union[Tuple, DonutSwinModelOutput]: 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). """ 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, len(self.config.depths)) embedding_output, input_dimensions = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, input_dimensions, 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 = None if self.pooler is not None: pooled_output = self.pooler(sequence_output.transpose(1, 2)) pooled_output = torch.flatten(pooled_output, 1) if not return_dict: output = (sequence_output, pooled_output) + encoder_outputs[1:] return output return DonutSwinModelOutput( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, reshaped_hidden_states=encoder_outputs.reshaped_hidden_states, ) __all__ = ["DonutSwinModel", "DonutSwinPreTrainedModel"]

transformers/src/transformers/models/donut/modeling_donut_swin.py/0

{ "file_path": "transformers/src/transformers/models/donut/modeling_donut_swin.py", "repo_id": "transformers", "token_count": 19137 }

# 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 TypeError("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 }

# coding=utf-8 # Copyright 2023 The Espnet authors, IMS Toucan 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 FastSpeech2Conformer model.""" import math from dataclasses import dataclass from typing import Optional, Tuple, Union import torch from torch import nn from ...modeling_outputs import BaseModelOutput from ...modeling_utils import PreTrainedModel from ...utils import ModelOutput, add_start_docstrings, logging, replace_return_docstrings from .configuration_fastspeech2_conformer import ( FastSpeech2ConformerConfig, FastSpeech2ConformerHifiGanConfig, FastSpeech2ConformerWithHifiGanConfig, ) logger = logging.get_logger(__name__) @dataclass class FastSpeech2ConformerModelOutput(ModelOutput): """ Output type of [`FastSpeech2ConformerModel`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Spectrogram generation loss. spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`): The predicted spectrogram. 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, 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 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. 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, 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 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. duration_outputs (`torch.LongTensor` of shape `(batch_size, max_text_length + 1)`, *optional*): Outputs of the duration predictor. pitch_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): Outputs of the pitch predictor. energy_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): Outputs of the energy predictor. """ loss: Optional[torch.FloatTensor] = None spectrogram: 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 decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None duration_outputs: torch.LongTensor = None pitch_outputs: torch.FloatTensor = None energy_outputs: torch.FloatTensor = None @dataclass class FastSpeech2ConformerWithHifiGanOutput(FastSpeech2ConformerModelOutput): """ Output type of [`FastSpeech2ConformerWithHifiGan`]. Args: waveform (`torch.FloatTensor` of shape `(batch_size, audio_length)`): Speech output as a result of passing the predicted mel spectrogram through the vocoder. loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Spectrogram generation loss. spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`): The predicted spectrogram. 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, 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 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. 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, 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 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. duration_outputs (`torch.LongTensor` of shape `(batch_size, max_text_length + 1)`, *optional*): Outputs of the duration predictor. pitch_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): Outputs of the pitch predictor. energy_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): Outputs of the energy predictor. """ waveform: torch.FloatTensor = None _CONFIG_FOR_DOC = "FastSpeech2ConformerConfig" FASTSPEECH2_CONFORMER_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 ([`FastSpeech2ConformerConfig`]): 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. """ HIFIGAN_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 ([`FastSpeech2ConformerConfig`]): 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. """ FASTSPEECH2_CONFORMER_WITH_HIFIGAN_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 ([`FastSpeech2ConformerWithHifiGanConfig`]): 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. """ def length_regulator(encoded_embeddings, duration_labels, speaking_speed=1.0): """ Length regulator for feed-forward Transformer. This is the length regulator module described in `FastSpeech: Fast, Robust and Controllable Text to Speech` https://arxiv.org/pdf/1905.09263.pdf. The length regulator expands char or phoneme-level embedding features to frame-level by repeating each feature based on the corresponding predicted durations. Args: encoded_embeddings (`torch.Tensor` of shape `(batch_size, max_text_length, embedding_dim)`): Batch of sequences of char or phoneme embeddings. duration_labels (`torch.LongTensor` of shape `(batch_size, time)`): Batch of durations of each frame. speaking_speed (`float`, *optional*, defaults to 1.0): Value to control speed of speech. Returns: `torch.Tensor`: Replicated input tensor based on durations (batch_size, time*, embedding_dim). """ if speaking_speed <= 0: raise ValueError("`speaking_speed` must be greater than 0.") elif speaking_speed != 1.0: duration_labels = torch.round(duration_labels.float() * speaking_speed).long() if duration_labels.sum() == 0: duration_labels[duration_labels.sum(dim=1).eq(0)] = 1 # Calculate the maximum length needed max_len = torch.sum(duration_labels, dim=1).max() # Create a padded tensor to hold the results hidden_states = torch.zeros( (encoded_embeddings.size(0), max_len, encoded_embeddings.size(2)), dtype=torch.float, device=encoded_embeddings.device, ) # Loop through the batch and fill in the data for i, (encoded_embedding, target_duration) in enumerate(zip(encoded_embeddings, duration_labels)): repeated = torch.repeat_interleave(encoded_embedding, target_duration, dim=0) hidden_states[i, : repeated.size(0)] = repeated return hidden_states class FastSpeech2ConformerDurationPredictor(nn.Module): """ Duration predictor module. This is a module of duration predictor described in the paper 'FastSpeech: Fast, Robust and Controllable Text to Speech' https://arxiv.org/pdf/1905.09263.pdf The duration predictor predicts a duration of each frame in log domain from the hidden embeddings of encoder. Note: The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`, the outputs are calculated in log domain but in `inference`, those are calculated in linear domain. """ def __init__(self, config: FastSpeech2ConformerConfig): super().__init__() self.conv_layers = nn.ModuleList() self.log_domain_offset = 1.0 for layer_idx in range(config.duration_predictor_layers): num_chans = config.duration_predictor_channels input_channels = config.hidden_size if layer_idx == 0 else num_chans layer = FastSpeech2ConformerPredictorLayer( input_channels, num_chans, config.duration_predictor_kernel_size, config.duration_predictor_dropout_rate, ) self.conv_layers.append(layer) self.linear = nn.Linear(config.duration_predictor_channels, 1) def forward(self, encoder_hidden_states): """ Args: hidden_states (`torch.Tensor` of shape `(batch_size, max_text_length, input_dim)`): Batch of input sequences. padding_masks (`torch.ByteTensor` of shape `(batch_size, max_text_length)`, *optional*): Batch of masks indicating padded part. Returns: `torch.Tensor`: Batch of predicted durations in log domain `(batch_size, max_text_length)`. """ # (batch_size, input_dim, max_text_length) hidden_states = encoder_hidden_states.transpose(1, -1) for layer in self.conv_layers: hidden_states = layer(hidden_states) # NOTE: calculate in log domain, (batch_size, max_text_length) hidden_states = self.linear(hidden_states.transpose(1, -1)).squeeze(-1) if not self.training: # NOTE: calculate in linear domain hidden_states = torch.clamp(torch.round(hidden_states.exp() - self.log_domain_offset), min=0).long() return hidden_states # Copied from transformers.models.speecht5.modeling_speecht5.SpeechT5BatchNormConvLayer class FastSpeech2ConformerBatchNormConvLayer(nn.Module): def __init__(self, config, layer_id=0): super().__init__() if layer_id == 0: in_conv_dim = config.num_mel_bins else: in_conv_dim = config.speech_decoder_postnet_units if layer_id == config.speech_decoder_postnet_layers - 1: out_conv_dim = config.num_mel_bins else: out_conv_dim = config.speech_decoder_postnet_units self.conv = nn.Conv1d( in_conv_dim, out_conv_dim, kernel_size=config.speech_decoder_postnet_kernel, stride=1, padding=(config.speech_decoder_postnet_kernel - 1) // 2, bias=False, ) self.batch_norm = nn.BatchNorm1d(out_conv_dim) if layer_id < config.speech_decoder_postnet_layers - 1: self.activation = nn.Tanh() else: self.activation = None self.dropout = nn.Dropout(config.speech_decoder_postnet_dropout) def forward(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = self.batch_norm(hidden_states) if self.activation is not None: hidden_states = self.activation(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class FastSpeech2ConformerSpeechDecoderPostnet(nn.Module): def __init__(self, config): super().__init__() self.config = config self.feat_out = nn.Linear(config.hidden_size, config.num_mel_bins * config.reduction_factor) self.layers = nn.ModuleList( [FastSpeech2ConformerBatchNormConvLayer(config, i) for i in range(config.speech_decoder_postnet_layers)] ) def forward(self, hidden_states: torch.Tensor): outputs_before_postnet = self.feat_out(hidden_states).view(hidden_states.size(0), -1, self.config.num_mel_bins) layer_output = outputs_before_postnet.transpose(1, 2) for layer in self.layers: layer_output = layer(layer_output) outputs_after_postnet = outputs_before_postnet + layer_output.transpose(1, 2) return outputs_before_postnet, outputs_after_postnet class FastSpeech2ConformerPredictorLayer(nn.Module): def __init__(self, input_channels, num_chans, kernel_size, dropout_rate): super().__init__() self.conv = nn.Conv1d( input_channels, num_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, ) self.activation = nn.ReLU() self.layer_norm = nn.LayerNorm(num_chans) self.dropout = nn.Dropout(dropout_rate) def forward(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = self.activation(hidden_states) # Perform layer norm on dimension 1 hidden_states = hidden_states.transpose(1, -1) hidden_states = self.layer_norm(hidden_states) hidden_states = hidden_states.transpose(1, -1) hidden_states = self.dropout(hidden_states) return hidden_states class FastSpeech2ConformerVariancePredictor(nn.Module): def __init__( self, config: FastSpeech2ConformerConfig, num_layers=2, num_chans=384, kernel_size=3, dropout_rate=0.5, ): """ Initilize variance predictor module. Args: input_dim (`int`): Input dimension. num_layers (`int`, *optional*, defaults to 2): Number of convolutional layers. num_chans (`int`, *optional*, defaults to 384): Number of channels of convolutional layers. kernel_size (`int`, *optional*, defaults to 3): Kernel size of convolutional layers. dropout_rate (`float`, *optional*, defaults to 0.5): Dropout rate. """ super().__init__() self.conv_layers = nn.ModuleList() for idx in range(num_layers): input_channels = config.hidden_size if idx == 0 else num_chans layer = FastSpeech2ConformerPredictorLayer(input_channels, num_chans, kernel_size, dropout_rate) self.conv_layers.append(layer) self.linear = nn.Linear(num_chans, 1) def forward(self, encoder_hidden_states, padding_masks=None): """ Calculate forward propagation. Args: encoder_hidden_states (`torch.Tensor` of shape `(batch_size, max_text_length, input_dim)`): Batch of input sequences. padding_masks (`torch.ByteTensor` of shape `(batch_size, max_text_length)`, *optional*): Batch of masks indicating padded part. Returns: Tensor: Batch of predicted sequences `(batch_size, max_text_length, 1)`. """ # (batch_size, input_dim, max_text_length) hidden_states = encoder_hidden_states.transpose(1, -1) for layer in self.conv_layers: hidden_states = layer(hidden_states) hidden_states = self.linear(hidden_states.transpose(1, 2)) if padding_masks is not None: hidden_states = hidden_states.masked_fill(padding_masks, 0.0) return hidden_states class FastSpeech2ConformerVarianceEmbedding(nn.Module): def __init__( self, in_channels=1, out_channels=384, kernel_size=1, padding=0, dropout_rate=0.0, ): super().__init__() self.conv = nn.Conv1d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, padding=padding, ) self.dropout = nn.Dropout(dropout_rate) def forward(self, hidden_states): hidden_states = hidden_states.transpose(1, 2) hidden_states = self.conv(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states.transpose(1, 2) return hidden_states class FastSpeech2ConformerAttention(nn.Module): """ Multi-Head attention layer with relative position encoding. Details can be found in https://github.com/espnet/espnet/pull/2816. Paper: https://arxiv.org/abs/1901.02860. """ def __init__(self, config: FastSpeech2ConformerConfig, module_config): """Construct an FastSpeech2ConformerAttention object.""" super().__init__() # We assume d_v always equals dim_key self.num_heads = module_config["num_attention_heads"] self.hidden_size = config.hidden_size self.dim_key = self.hidden_size // self.num_heads self.head_dim = self.hidden_size // self.num_heads self.linear_q = nn.Linear(self.hidden_size, self.hidden_size) self.linear_k = nn.Linear(self.hidden_size, self.hidden_size) self.linear_v = nn.Linear(self.hidden_size, self.hidden_size) self.linear_out = nn.Linear(self.hidden_size, self.hidden_size) self.dropout = nn.Dropout(p=module_config["attention_dropout_rate"]) # linear transformation for positional encoding self.linear_pos = nn.Linear(self.hidden_size, self.hidden_size, bias=False) # these two learnable bias are used in matrix c and matrix d # as described in https://arxiv.org/abs/1901.02860 Section 3.3 self.pos_bias_u = nn.Parameter(torch.Tensor(self.num_heads, self.head_dim)) self.pos_bias_v = nn.Parameter(torch.Tensor(self.num_heads, self.head_dim)) def shift_relative_position_tensor(self, pos_tensor): """ Args: pos_tensor (torch.Tensor of shape (batch_size, head, time1, 2*time1-1)): Input tensor. """ zero_pad = torch.zeros((*pos_tensor.size()[:3], 1), device=pos_tensor.device, dtype=pos_tensor.dtype) pos_tensor_padded = torch.cat([zero_pad, pos_tensor], dim=-1) pos_tensor_padded = pos_tensor_padded.view(*pos_tensor.size()[:2], pos_tensor.size(3) + 1, pos_tensor.size(2)) # only keep the positions from 0 to time2 pos_tensor = pos_tensor_padded[:, :, 1:].view_as(pos_tensor)[:, :, :, : pos_tensor.size(-1) // 2 + 1] return pos_tensor def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, pos_emb: Optional[torch.Tensor] = None, output_attentions: Optional[torch.Tensor] = False, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: hidden_states (`torch.Tensor` of shape `(batch, time2, size)`): Values of the hidden states attention_mask (`torch.Tensor` of shape `(batch, time1, time2)`): Mask tensor. pos_emb (`torch.Tensor` of shape `(batch, 2*time1-1, size)`): Positional embedding tensor. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. Returns: `torch.Tensor`: Output tensor of shape `(batch, time1, d_model)`. """ bsz, q_len, _ = hidden_states.size() query_states = self.linear_q(hidden_states).view(bsz, -1, self.num_heads, self.head_dim) key_states = self.linear_k(hidden_states).view(bsz, -1, self.num_heads, self.head_dim) value_states = self.linear_v(hidden_states).view(bsz, -1, self.num_heads, self.head_dim) bsz_pos = pos_emb.size(0) pos_encoding = self.linear_pos(pos_emb).view(bsz_pos, -1, self.num_heads, self.head_dim) # (batch_size, head, time1, dim_key) query_with_bias_u = (query_states + self.pos_bias_u).transpose(1, 2) # (batch_size, head, time1, dim_key) query_with_bias_v = (query_states + self.pos_bias_v).transpose(1, 2) # compute attention score # first compute matrix a and matrix c # as described in https://arxiv.org/abs/1901.02860 Section 3.3 # (batch_size, head, time1, time2) matrix_ac = torch.matmul(query_with_bias_u, key_states.permute(0, 2, 3, 1)) # compute matrix b and matrix d # (batch_size, head, time1, 2*time1-1) matrix_bd = torch.matmul(query_with_bias_v, pos_encoding.permute(0, 2, 3, 1)) matrix_bd = self.shift_relative_position_tensor(matrix_bd) # (batch_size, head, time1, time2) scores = (matrix_ac + matrix_bd) / math.sqrt(self.dim_key) # Forward attention if attention_mask is not None: expected_size = (bsz, 1, q_len) if attention_mask.size() != expected_size: raise ValueError(f"Attention mask should be of size {expected_size}, but is {attention_mask.size()}") attention_mask = attention_mask.unsqueeze(1).eq(0) min_value = float(torch.finfo(scores.dtype).min) scores = scores.masked_fill(attention_mask, min_value) attn_weights = torch.softmax(scores, dim=-1).masked_fill(attention_mask, 0.0) else: attn_weights = torch.softmax(scores, dim=-1) attn_weights = self.dropout(attn_weights) attn_output = torch.matmul(attn_weights, value_states.transpose(1, 2)) attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, -1) attn_output = self.linear_out(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights class FastSpeech2ConformerConvolutionModule(nn.Module): def __init__(self, config: FastSpeech2ConformerConfig, module_config): super().__init__() # kernel_size should be an odd number for 'SAME' padding channels = config.hidden_size kernel_size = module_config["kernel_size"] self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=True) self.depthwise_conv = nn.Conv1d( channels, channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias=True ) self.norm = nn.BatchNorm1d(channels) self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1, stride=1, padding=0, bias=True) def forward(self, hidden_states): """ Compute convolution module. Args: hidden_states (`torch.Tensor` of shape `(batch, time, channels)`): Input tensor. Returns: `torch.Tensor`: Output tensor of shape `(batch, time, channels)`. """ # exchange the temporal dimension and the feature dimension hidden_states = hidden_states.transpose(1, 2) # GLU mechanism, (batch_size, 2*channel, dim) hidden_states = self.pointwise_conv1(hidden_states) # (batch_size, channel, dim) hidden_states = nn.functional.glu(hidden_states, dim=1) # 1D Depthwise Conv hidden_states = self.depthwise_conv(hidden_states) hidden_states = self.norm(hidden_states) hidden_states = hidden_states * torch.sigmoid(hidden_states) hidden_states = self.pointwise_conv2(hidden_states) return hidden_states.transpose(1, 2) class FastSpeech2ConformerEncoderLayer(nn.Module): def __init__(self, config: FastSpeech2ConformerConfig, module_config): super().__init__() # self-attention module definition self.self_attn = FastSpeech2ConformerAttention(config, module_config) # feed-forward module definition self.feed_forward = FastSpeech2ConformerMultiLayeredConv1d(config, module_config) self.macaron_style = config.use_macaron_style_in_conformer if self.macaron_style: self.feed_forward_macaron = FastSpeech2ConformerMultiLayeredConv1d(config, module_config) self.ff_macaron_layer_norm = nn.LayerNorm(config.hidden_size) self.ff_scale = 0.5 else: self.ff_scale = 1.0 # convolution module definition self.use_cnn_module = config.use_cnn_in_conformer if self.use_cnn_module: self.conv_module = FastSpeech2ConformerConvolutionModule(config, module_config) self.conv_layer_norm = nn.LayerNorm(config.hidden_size) self.final_layer_norm = nn.LayerNorm(config.hidden_size) self.ff_layer_norm = nn.LayerNorm(config.hidden_size) self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size) self.dropout = nn.Dropout(module_config["dropout_rate"]) self.size = config.hidden_size self.normalize_before = module_config["normalize_before"] self.concat_after = module_config["concat_after"] if self.concat_after: self.concat_linear = nn.Linear(config.hidden_size + config.hidden_size, config.hidden_size) def forward( self, hidden_states: torch.Tensor, pos_emb: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[torch.Tensor] = False, ): """ Compute encoded features. Args: hidden_states (`torch.Tensor` of shape `(batch, time, size)`): Input tensor. pos_emb (`torch.Tensor` of shape `(1, time, size)`): Positional embeddings tensor. attention_mask (`torch.Tensor` of shape `(batch, time)`): Attention mask tensor for the input. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. Returns: `torch.Tensor`: Output tensor of shape `(batch, time, size)`. """ # whether to use macaron style if self.macaron_style: residual = hidden_states if self.normalize_before: hidden_states = self.ff_macaron_layer_norm(hidden_states) hidden_states = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(hidden_states)) if not self.normalize_before: hidden_states = self.ff_macaron_layer_norm(hidden_states) # multi-headed self-attention module residual = hidden_states if self.normalize_before: hidden_states = self.self_attn_layer_norm(hidden_states) attention_output, attention_scores = self.self_attn( hidden_states, attention_mask=attention_mask, pos_emb=pos_emb, output_attentions=output_attentions ) if self.concat_after: x_concat = torch.cat((hidden_states, attention_output), dim=-1) hidden_states = self.concat_linear(x_concat) hidden_states = residual + hidden_states else: hidden_states = self.dropout(attention_output) hidden_states = residual + hidden_states if not self.normalize_before: hidden_states = self.self_attn_layer_norm(hidden_states) # convolution module if self.use_cnn_module: residual = hidden_states if self.normalize_before: hidden_states = self.conv_layer_norm(hidden_states) hidden_states = self.conv_module(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = residual + hidden_states if not self.normalize_before: hidden_states = self.conv_layer_norm(hidden_states) # feed forward module residual = hidden_states if self.normalize_before: hidden_states = self.ff_layer_norm(hidden_states) hidden_states = self.feed_forward(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = residual + self.ff_scale * hidden_states if not self.normalize_before: hidden_states = self.ff_layer_norm(hidden_states) if self.conv_module is not None: hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (attention_scores,) return outputs class FastSpeech2ConformerMultiLayeredConv1d(nn.Module): """ Multi-layered conv1d for Transformer block. This is a module of multi-layered conv1d designed to replace positionwise feed-forward network in Transformer block, which is introduced in 'FastSpeech: Fast, Robust and Controllable Text to Speech' https://arxiv.org/pdf/1905.09263.pdf """ def __init__(self, config: FastSpeech2ConformerConfig, module_config): """ Initialize FastSpeech2ConformerMultiLayeredConv1d module. Args: input_channels (`int`): Number of input channels. hidden_channels (`int`): Number of hidden channels. kernel_size (`int`): Kernel size of conv1d. dropout_rate (`float`): Dropout rate. """ super().__init__() input_channels = config.hidden_size hidden_channels = module_config["linear_units"] kernel_size = config.positionwise_conv_kernel_size self.conv1 = nn.Conv1d(input_channels, hidden_channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2) self.conv2 = nn.Conv1d(hidden_channels, input_channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2) self.dropout = nn.Dropout(module_config["dropout_rate"]) def forward(self, hidden_states): """ Calculate forward propagation. Args: hidden_states (torch.Tensor): Batch of input tensors (batch_size, time, input_channels). Returns: torch.Tensor: Batch of output tensors (batch_size, time, hidden_channels). """ hidden_states = hidden_states.transpose(-1, 1) hidden_states = self.conv1(hidden_states) hidden_states = torch.relu(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) hidden_states = hidden_states.transpose(-1, 1) return hidden_states class FastSpeech2ConformerRelPositionalEncoding(nn.Module): """ Args: Relative positional encoding module (new implementation). Details can be found in https://github.com/espnet/espnet/pull/2816. See : Appendix Batch in https://arxiv.org/abs/1901.02860 config (`FastSpeech2ConformerConfig`): FastSpeech2ConformerConfig instance. module_config (`dict`): Dictionary containing the encoder or decoder module configuration from the `FastSpeech2ConformerConfig`. """ def __init__(self, config: FastSpeech2ConformerConfig, module_config): """ Construct an PositionalEncoding object. """ super().__init__() self.embed_dim = config.hidden_size self.input_scale = math.sqrt(self.embed_dim) self.dropout = nn.Dropout(p=module_config["positional_dropout_rate"]) self.pos_enc = None self.max_len = 5000 self.extend_pos_enc(torch.tensor(0.0).expand(1, self.max_len)) def extend_pos_enc(self, x): """Reset the positional encodings.""" if self.pos_enc is not None: # self.pos_enc contains both positive and negative parts # the length of self.pos_enc is 2 * input_len - 1 if self.pos_enc.size(1) >= x.size(1) * 2 - 1: if self.pos_enc.dtype != x.dtype or self.pos_enc.device != x.device: self.pos_enc = self.pos_enc.to(dtype=x.dtype, device=x.device) return # Suppose `i` means to the position of query vector and `j` means the # position of key vector. We use position relative positions when keys # are to the left (i>j) and negative relative positions otherwise (i<j). pos_enc_positive = torch.zeros(x.size(1), self.embed_dim) pos_enc_negative = torch.zeros(x.size(1), self.embed_dim) position = torch.arange(0, x.size(1), dtype=torch.int64).float().unsqueeze(1) div_term = torch.exp( torch.arange(0, self.embed_dim, 2, dtype=torch.int64).float() * -(math.log(10000.0) / self.embed_dim) ) pos_enc_positive[:, 0::2] = torch.sin(position * div_term) pos_enc_positive[:, 1::2] = torch.cos(position * div_term) pos_enc_negative[:, 0::2] = torch.sin(-1 * position * div_term) pos_enc_negative[:, 1::2] = torch.cos(-1 * position * div_term) # Reserve the order of positive indices and concat both positive and # negative indices. This is used to support the shifting trick # as in https://arxiv.org/abs/1901.02860 pos_enc_positive = torch.flip(pos_enc_positive, [0]).unsqueeze(0) pos_enc_negative = pos_enc_negative[1:].unsqueeze(0) pos_enc = torch.cat([pos_enc_positive, pos_enc_negative], dim=1) self.pos_enc = pos_enc.to(device=x.device, dtype=x.dtype) def forward(self, feature_representation): """ Args: feature_representation (`torch.Tensor` of shape (batch_size, time, `*`)): Input tensor. Returns: `torch.Tensor`: Encoded tensor (batch_size, time, `*`). """ self.extend_pos_enc(feature_representation) hidden_states = feature_representation * self.input_scale center_idx = self.pos_enc.size(1) // 2 pos_emb = self.pos_enc[:, center_idx - hidden_states.size(1) + 1 : center_idx + hidden_states.size(1)] return self.dropout(hidden_states), self.dropout(pos_emb) class FastSpeech2ConformerEncoder(nn.Module): """ FastSpeech2ConformerEncoder encoder module. Args: config (`FastSpeech2ConformerConfig`): FastSpeech2ConformerConfig instance. module_config (`dict`): Dictionary containing the encoder or decoder module configuration from the `FastSpeech2ConformerConfig`. use_encoder_input_layer (`bool`, *optional*, defaults to `False`): Input layer type. """ def __init__( self, config: FastSpeech2ConformerConfig, module_config, use_encoder_input_layer=False, ): super().__init__() self.embed = None if use_encoder_input_layer: self.embed = nn.Embedding( num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, padding_idx=0 ) self.pos_enc = FastSpeech2ConformerRelPositionalEncoding(config, module_config) self.conformer_layers = nn.ModuleList( [FastSpeech2ConformerEncoderLayer(config, module_config) for _ in range(module_config["layers"])] ) def forward( self, input_tensor: torch.LongTensor, attention_mask: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = False, return_dict: Optional[bool] = None, ): """ 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) 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 all attention layers. See `attentions` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: `torch.Tensor`: Output tensor of shape `(batch, time, attention_dim)`. """ feature_representation = input_tensor if self.embed is not None: feature_representation = self.embed(feature_representation) hidden_states, pos_emb = self.pos_enc(feature_representation) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for conformer_layer in self.conformer_layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = conformer_layer(hidden_states, pos_emb, attention_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) # 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_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions ) class FastSpeech2ConformerLoss(nn.Module): def __init__(self, config: FastSpeech2ConformerConfig): super().__init__() use_masking = config.use_masking use_weighted_masking = config.use_weighted_masking if use_masking and use_weighted_masking: raise ValueError("Either use_masking or use_weighted_masking can be True, but not both.") self.use_masking = use_masking self.use_weighted_masking = use_weighted_masking # define criterions reduction = "none" if self.use_weighted_masking else "mean" self.l1_criterion = nn.L1Loss(reduction=reduction) self.mse_criterion = nn.MSELoss(reduction=reduction) self.duration_criterion = nn.MSELoss(reduction=reduction) self.log_domain_offset = 1.0 def forward( self, outputs_after_postnet, outputs_before_postnet, duration_outputs, pitch_outputs, energy_outputs, spectrogram_labels, duration_labels, pitch_labels, energy_labels, duration_mask, spectrogram_mask, ): """ Args: outputs_after_postnet (`torch.Tensor` of shape `(batch_size, max_spectrogram_length, num_mel_bins)`): Batch of outputs after postnet. outputs_before_postnet (`torch.Tensor` of shape `(batch_size, max_spectrogram_length, num_mel_bins)`): Batch of outputs before postnet. duration_outputs (`torch.LongTensor` of shape `(batch_size, max_text_length)`): Batch of outputs of duration predictor. pitch_outputs (`torch.Tensor` of shape `(batch_size, max_text_length, 1)`): Batch of outputs of pitch predictor. energy_outputs (`torch.Tensor` of shape `(batch_size, max_text_length, 1)`): Batch of outputs of energy predictor. spectrogram_labels (`torch.Tensor` of shape `(batch_size, max_spectrogram_length, num_mel_bins)`): Batch of target features. duration_labels (`torch.LongTensor` of shape `(batch_size, max_text_length)`): Batch of durations. pitch_labels (`torch.Tensor` of shape `(batch_size, max_text_length, 1)`): Batch of target token-averaged pitch. energy_labels (`torch.Tensor` of shape `(batch_size, max_text_length, 1)`): Batch of target token-averaged energy. duration_mask (`torch.LongTensor`): Mask used to discern which values the duration loss should be calculated for. spectrogram_mask (`torch.LongTensor`): Mask used to discern which values the spectrogam loss should be calculated for. Returns: `tuple(torch.FloatTensor)`: Tuple of tensors containing, in order, the L1 loss value, duration predictor loss value, pitch predictor loss value, and energy predictor loss value. """ pitch_and_energy_masks = duration_mask.unsqueeze(-1) # apply mask to remove padded part if self.use_masking: outputs_before_postnet = outputs_before_postnet.masked_select(spectrogram_mask) if outputs_after_postnet is not None: outputs_after_postnet = outputs_after_postnet.masked_select(spectrogram_mask) spectrogram_labels = spectrogram_labels.masked_select(spectrogram_mask) duration_outputs = duration_outputs.masked_select(duration_mask) duration_labels = duration_labels.masked_select(duration_mask) pitch_outputs = pitch_outputs.masked_select(pitch_and_energy_masks) energy_outputs = energy_outputs.masked_select(pitch_and_energy_masks) pitch_labels = pitch_labels.masked_select(pitch_and_energy_masks) energy_labels = energy_labels.masked_select(pitch_and_energy_masks) # calculate loss l1_loss = self.l1_criterion(outputs_before_postnet, spectrogram_labels) if outputs_after_postnet is not None: l1_loss = l1_loss + self.l1_criterion(outputs_after_postnet, spectrogram_labels) duration_labels = torch.log(duration_labels.float() + self.log_domain_offset) duration_loss = self.duration_criterion(duration_outputs, duration_labels) pitch_loss = self.mse_criterion(pitch_outputs, pitch_labels) energy_loss = self.mse_criterion(energy_outputs, energy_labels) # make weighted mask and apply it if self.use_weighted_masking: spectrogram_mask = nn.functional.pad( spectrogram_mask.transpose(1, 2), [0, spectrogram_labels.size(1) - spectrogram_mask.size(1), 0, 0, 0, 0], value=False, ).transpose(1, 2) out_weights = spectrogram_mask.float() / spectrogram_mask.sum(dim=1, keepdim=True).float() out_weights /= spectrogram_labels.size(0) * spectrogram_labels.size(2) duration_weights = duration_mask.float() / duration_mask.sum(dim=1, keepdim=True).float() duration_weights /= duration_labels.size(0) # apply weight l1_loss = l1_loss.mul(out_weights).masked_select(spectrogram_mask).sum() duration_loss = duration_loss.mul(duration_weights).masked_select(duration_mask).sum() pitch_weights = duration_weights.unsqueeze(-1) pitch_loss = pitch_loss.mul(pitch_weights).masked_select(pitch_and_energy_masks).sum() energy_loss = energy_loss.mul(pitch_weights).masked_select(pitch_and_energy_masks).sum() return l1_loss + duration_loss + pitch_loss + energy_loss class FastSpeech2ConformerPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FastSpeech2ConformerConfig base_model_prefix = "fastspeech2_conformer" main_input_name = "input_ids" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.LayerNorm)): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, nn.Conv1d): nn.init.kaiming_normal_(module.weight) if module.bias is not None: key = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0])) nn.init.uniform_(module.bias, a=-key, b=key) elif isinstance(module, nn.Embedding): module.weight.data.normal_() if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, FastSpeech2ConformerAttention): nn.init.xavier_uniform_(module.pos_bias_u) nn.init.xavier_uniform_(module.pos_bias_v) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, FastSpeech2ConformerEncoder): module.gradient_checkpointing = value @add_start_docstrings( """FastSpeech2Conformer Model.""", FASTSPEECH2_CONFORMER_START_DOCSTRING, ) class FastSpeech2ConformerModel(FastSpeech2ConformerPreTrainedModel): """ FastSpeech 2 module. This is a module of FastSpeech 2 described in 'FastSpeech 2: Fast and High-Quality End-to-End Text to Speech' https://arxiv.org/abs/2006.04558. Instead of quantized pitch and energy, we use token-averaged value introduced in FastPitch: Parallel Text-to-speech with Pitch Prediction. The encoder and decoder are Conformers instead of regular Transformers. """ def __init__(self, config: FastSpeech2ConformerConfig): super().__init__(config) self.config = config # store hyperparameters self.vocab_size = config.vocab_size self.num_mel_bins = config.num_mel_bins self.hidden_size = config.hidden_size self.reduction_factor = config.reduction_factor self.stop_gradient_from_pitch_predictor = config.stop_gradient_from_pitch_predictor self.stop_gradient_from_energy_predictor = config.stop_gradient_from_energy_predictor self.multilingual_model = config.num_languages is not None and config.num_languages > 1 if self.multilingual_model: self.language_id_embedding = torch.nn.Embedding(config.num_languages, self.hidden_size) self.multispeaker_model = config.num_speakers is not None and config.num_speakers > 1 if self.multispeaker_model: self.speaker_id_embedding = torch.nn.Embedding(config.num_speakers, config.hidden_size) self.speaker_embed_dim = config.speaker_embed_dim if self.speaker_embed_dim: self.projection = nn.Linear(config.hidden_size + self.speaker_embed_dim, config.hidden_size) self.encoder = FastSpeech2ConformerEncoder(config, config.encoder_config, use_encoder_input_layer=True) self.duration_predictor = FastSpeech2ConformerDurationPredictor(config) self.pitch_predictor = FastSpeech2ConformerVariancePredictor( config, num_layers=config.pitch_predictor_layers, num_chans=config.pitch_predictor_channels, kernel_size=config.pitch_predictor_kernel_size, dropout_rate=config.pitch_predictor_dropout, ) # continuous pitch + FastPitch style avg self.pitch_embed = FastSpeech2ConformerVarianceEmbedding( out_channels=self.hidden_size, kernel_size=config.pitch_embed_kernel_size, padding=(config.pitch_embed_kernel_size - 1) // 2, dropout_rate=config.pitch_embed_dropout, ) self.energy_predictor = FastSpeech2ConformerVariancePredictor( config, num_layers=config.energy_predictor_layers, num_chans=config.energy_predictor_channels, kernel_size=config.energy_predictor_kernel_size, dropout_rate=config.energy_predictor_dropout, ) # continuous energy + FastPitch style avg self.energy_embed = FastSpeech2ConformerVarianceEmbedding( out_channels=self.hidden_size, kernel_size=config.energy_embed_kernel_size, padding=(config.energy_embed_kernel_size - 1) // 2, dropout_rate=config.energy_embed_dropout, ) # The decoder is an encoder self.decoder = FastSpeech2ConformerEncoder(config, config.decoder_config, use_encoder_input_layer=False) self.speech_decoder_postnet = FastSpeech2ConformerSpeechDecoderPostnet(config) self.criterion = FastSpeech2ConformerLoss(config) self.post_init() @replace_return_docstrings(output_type=FastSpeech2ConformerModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor, attention_mask: Optional[torch.LongTensor] = None, spectrogram_labels: Optional[torch.FloatTensor] = None, duration_labels: Optional[torch.LongTensor] = None, pitch_labels: Optional[torch.FloatTensor] = None, energy_labels: Optional[torch.FloatTensor] = None, speaker_ids: Optional[torch.LongTensor] = None, lang_ids: Optional[torch.LongTensor] = None, speaker_embedding: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> Union[Tuple, FastSpeech2ConformerModelOutput]: """ Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Input sequence of text vectors. attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to `None`): Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, 1]`: 0 for tokens that are **masked**, 1 for tokens that are **not masked**. spectrogram_labels (`torch.FloatTensor` of shape `(batch_size, max_spectrogram_length, num_mel_bins)`, *optional*, defaults to `None`): Batch of padded target features. duration_labels (`torch.LongTensor` of shape `(batch_size, sequence_length + 1)`, *optional*, defaults to `None`): Batch of padded durations. pitch_labels (`torch.FloatTensor` of shape `(batch_size, sequence_length + 1, 1)`, *optional*, defaults to `None`): Batch of padded token-averaged pitch. energy_labels (`torch.FloatTensor` of shape `(batch_size, sequence_length + 1, 1)`, *optional*, defaults to `None`): Batch of padded token-averaged energy. speaker_ids (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*, defaults to `None`): Speaker ids used to condition features of speech output by the model. lang_ids (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*, defaults to `None`): Language ids used to condition features of speech output by the model. speaker_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`, *optional*, defaults to `None`): Embedding containing conditioning signals for the features of the speech. return_dict (`bool`, *optional*, defaults to `None`): Whether or not to return a [`FastSpeech2ConformerModelOutput`] instead of a plain tuple. output_attentions (`bool`, *optional*, defaults to `None`): 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*, defaults to `None`): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. Returns: Example: ```python >>> from transformers import ( ... FastSpeech2ConformerTokenizer, ... FastSpeech2ConformerModel, ... FastSpeech2ConformerHifiGan, ... ) >>> tokenizer = FastSpeech2ConformerTokenizer.from_pretrained("espnet/fastspeech2_conformer") >>> inputs = tokenizer("some text to convert to speech", return_tensors="pt") >>> input_ids = inputs["input_ids"] >>> model = FastSpeech2ConformerModel.from_pretrained("espnet/fastspeech2_conformer") >>> output_dict = model(input_ids, return_dict=True) >>> spectrogram = output_dict["spectrogram"] >>> vocoder = FastSpeech2ConformerHifiGan.from_pretrained("espnet/fastspeech2_conformer_hifigan") >>> waveform = vocoder(spectrogram) >>> print(waveform.shape) torch.Size([1, 49664]) ``` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict 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 ) if attention_mask is None: attention_mask = torch.ones(input_ids.shape, device=input_ids.device) has_missing_labels = ( spectrogram_labels is None or duration_labels is None or pitch_labels is None or energy_labels is None ) if self.training and has_missing_labels: raise ValueError("All labels must be provided to run in training mode.") # forward encoder text_masks = attention_mask.unsqueeze(-2) encoder_outputs = self.encoder( input_ids, text_masks, output_hidden_states=output_hidden_states, output_attentions=output_attentions, return_dict=return_dict, ) hidden_states = encoder_outputs[0] # Integrate with language id, speaker id, and speaker embedding if self.multispeaker_model and speaker_ids is not None: speaker_id_embeddings = self.speaker_id_embedding(speaker_ids.view(-1)) hidden_states = hidden_states + speaker_id_embeddings.unsqueeze(1) if self.multilingual_model and lang_ids is not None: language_id_embbedings = self.language_id_embedding(lang_ids.view(-1)) hidden_states = hidden_states + language_id_embbedings.unsqueeze(1) if self.speaker_embed_dim is not None and speaker_embedding is not None: embeddings_expanded = ( nn.functional.normalize(speaker_embedding).unsqueeze(1).expand(-1, hidden_states.size(1), -1) ) hidden_states = self.projection(torch.cat([hidden_states, embeddings_expanded], dim=-1)) # forward duration predictor and variance predictors duration_mask = ~attention_mask.bool() if self.stop_gradient_from_pitch_predictor: pitch_predictions = self.pitch_predictor(hidden_states.detach(), duration_mask.unsqueeze(-1)) else: pitch_predictions = self.pitch_predictor(hidden_states, duration_mask.unsqueeze(-1)) if self.stop_gradient_from_energy_predictor: energy_predictions = self.energy_predictor(hidden_states.detach(), duration_mask.unsqueeze(-1)) else: energy_predictions = self.energy_predictor(hidden_states, duration_mask.unsqueeze(-1)) duration_predictions = self.duration_predictor(hidden_states) duration_predictions = duration_predictions.masked_fill(duration_mask, 0.0) if not self.training: # use prediction in inference embedded_pitch_curve = self.pitch_embed(pitch_predictions) embedded_energy_curve = self.energy_embed(energy_predictions) hidden_states = hidden_states + embedded_energy_curve + embedded_pitch_curve hidden_states = length_regulator(hidden_states, duration_predictions, self.config.speaking_speed) else: # use groundtruth in training embedded_pitch_curve = self.pitch_embed(pitch_labels) embedded_energy_curve = self.energy_embed(energy_labels) hidden_states = hidden_states + embedded_energy_curve + embedded_pitch_curve hidden_states = length_regulator(hidden_states, duration_labels) # forward decoder if not self.training: hidden_mask = None else: spectrogram_mask = (spectrogram_labels != -100).any(dim=-1) spectrogram_mask = spectrogram_mask.int() if self.reduction_factor > 1: length_dim = spectrogram_mask.shape[1] - spectrogram_mask.shape[1] % self.reduction_factor spectrogram_mask = spectrogram_mask[:, :, :length_dim] hidden_mask = spectrogram_mask.unsqueeze(-2) decoder_outputs = self.decoder( hidden_states, hidden_mask, output_hidden_states=output_hidden_states, output_attentions=output_attentions, return_dict=return_dict, ) outputs_before_postnet, outputs_after_postnet = self.speech_decoder_postnet(decoder_outputs[0]) loss = None if self.training: # calculate loss loss_duration_mask = ~duration_mask loss_spectrogram_mask = spectrogram_mask.unsqueeze(-1).bool() loss = self.criterion( outputs_after_postnet=outputs_after_postnet, outputs_before_postnet=outputs_before_postnet, duration_outputs=duration_predictions, pitch_outputs=pitch_predictions, energy_outputs=energy_predictions, spectrogram_labels=spectrogram_labels, duration_labels=duration_labels, pitch_labels=pitch_labels, energy_labels=energy_labels, duration_mask=loss_duration_mask, spectrogram_mask=loss_spectrogram_mask, ) if not return_dict: postnet_outputs = (outputs_after_postnet,) audio_feature_predictions = ( duration_predictions, pitch_predictions, energy_predictions, ) outputs = postnet_outputs + encoder_outputs + decoder_outputs[1:] + audio_feature_predictions return ((loss,) + outputs) if loss is not None else outputs return FastSpeech2ConformerModelOutput( loss=loss, spectrogram=outputs_after_postnet, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, duration_outputs=duration_predictions, pitch_outputs=pitch_predictions, energy_outputs=energy_predictions, ) # Copied from transformers.models.speecht5.modeling_speecht5.HifiGanResidualBlock class HifiGanResidualBlock(nn.Module): def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1): super().__init__() self.leaky_relu_slope = leaky_relu_slope self.convs1 = nn.ModuleList( [ nn.Conv1d( channels, channels, kernel_size, stride=1, dilation=dilation[i], padding=self.get_padding(kernel_size, dilation[i]), ) for i in range(len(dilation)) ] ) self.convs2 = nn.ModuleList( [ nn.Conv1d( channels, channels, kernel_size, stride=1, dilation=1, padding=self.get_padding(kernel_size, 1), ) for _ in range(len(dilation)) ] ) def get_padding(self, kernel_size, dilation=1): return (kernel_size * dilation - dilation) // 2 def apply_weight_norm(self): weight_norm = nn.utils.weight_norm if hasattr(nn.utils.parametrizations, "weight_norm"): weight_norm = nn.utils.parametrizations.weight_norm for layer in self.convs1: weight_norm(layer) for layer in self.convs2: weight_norm(layer) def remove_weight_norm(self): for layer in self.convs1: nn.utils.remove_weight_norm(layer) for layer in self.convs2: nn.utils.remove_weight_norm(layer) def forward(self, hidden_states): for conv1, conv2 in zip(self.convs1, self.convs2): residual = hidden_states hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) hidden_states = conv1(hidden_states) hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) hidden_states = conv2(hidden_states) hidden_states = hidden_states + residual return hidden_states @add_start_docstrings( """HiFi-GAN vocoder.""", HIFIGAN_START_DOCSTRING, ) # Copied from transformers.models.speecht5.modeling_speecht5.SpeechT5HifiGan with SpeechT5->FastSpeech2Conformer class FastSpeech2ConformerHifiGan(PreTrainedModel): config_class = FastSpeech2ConformerHifiGanConfig main_input_name = "spectrogram" def __init__(self, config: FastSpeech2ConformerHifiGanConfig): super().__init__(config) self.num_kernels = len(config.resblock_kernel_sizes) self.num_upsamples = len(config.upsample_rates) self.conv_pre = nn.Conv1d( config.model_in_dim, config.upsample_initial_channel, kernel_size=7, stride=1, padding=3, ) self.upsampler = nn.ModuleList() for i, (upsample_rate, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)): self.upsampler.append( nn.ConvTranspose1d( config.upsample_initial_channel // (2**i), config.upsample_initial_channel // (2 ** (i + 1)), kernel_size=kernel_size, stride=upsample_rate, padding=(kernel_size - upsample_rate) // 2, ) ) self.resblocks = nn.ModuleList() for i in range(len(self.upsampler)): channels = config.upsample_initial_channel // (2 ** (i + 1)) for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes): self.resblocks.append(HifiGanResidualBlock(channels, kernel_size, dilation, config.leaky_relu_slope)) self.conv_post = nn.Conv1d(channels, 1, kernel_size=7, stride=1, padding=3) self.register_buffer("mean", torch.zeros(config.model_in_dim)) self.register_buffer("scale", torch.ones(config.model_in_dim)) # Initialize weights and apply final processing self.post_init() def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear, nn.Conv1d)): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() def apply_weight_norm(self): weight_norm = nn.utils.weight_norm if hasattr(nn.utils.parametrizations, "weight_norm"): weight_norm = nn.utils.parametrizations.weight_norm weight_norm(self.conv_pre) for layer in self.upsampler: weight_norm(layer) for layer in self.resblocks: layer.apply_weight_norm() weight_norm(self.conv_post) def remove_weight_norm(self): nn.utils.remove_weight_norm(self.conv_pre) for layer in self.upsampler: nn.utils.remove_weight_norm(layer) for layer in self.resblocks: layer.remove_weight_norm() nn.utils.remove_weight_norm(self.conv_post) def forward(self, spectrogram: torch.FloatTensor) -> torch.FloatTensor: r""" Converts a log-mel spectrogram into a speech waveform. Passing a batch of log-mel spectrograms returns a batch of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech waveform. Args: spectrogram (`torch.FloatTensor`): Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length, config.model_in_dim)`, or un-batched and of shape `(sequence_length, config.model_in_dim)`. Returns: `torch.FloatTensor`: Tensor containing the speech waveform. If the input spectrogram is batched, will be of shape `(batch_size, num_frames,)`. If un-batched, will be of shape `(num_frames,)`. """ if self.config.normalize_before: spectrogram = (spectrogram - self.mean) / self.scale is_batched = spectrogram.dim() == 3 if not is_batched: spectrogram = spectrogram.unsqueeze(0) hidden_states = spectrogram.transpose(2, 1) hidden_states = self.conv_pre(hidden_states) for i in range(self.num_upsamples): hidden_states = nn.functional.leaky_relu(hidden_states, self.config.leaky_relu_slope) hidden_states = self.upsampler[i](hidden_states) res_state = self.resblocks[i * self.num_kernels](hidden_states) for j in range(1, self.num_kernels): res_state += self.resblocks[i * self.num_kernels + j](hidden_states) hidden_states = res_state / self.num_kernels hidden_states = nn.functional.leaky_relu(hidden_states) hidden_states = self.conv_post(hidden_states) hidden_states = torch.tanh(hidden_states) if not is_batched: # remove batch dim and collapse tensor to 1-d audio waveform waveform = hidden_states.squeeze(0).transpose(1, 0).view(-1) else: # remove seq-len dim since this collapses to 1 waveform = hidden_states.squeeze(1) return waveform @add_start_docstrings( "The FastSpeech2ConformerModel with a FastSpeech2ConformerHifiGan vocoder head that performs text-to-speech (waveform).", FASTSPEECH2_CONFORMER_WITH_HIFIGAN_START_DOCSTRING, ) class FastSpeech2ConformerWithHifiGan(PreTrainedModel): config_class = FastSpeech2ConformerWithHifiGanConfig def __init__(self, config: FastSpeech2ConformerWithHifiGanConfig): super().__init__(config) self.model = FastSpeech2ConformerModel(config.model_config) self.vocoder = FastSpeech2ConformerHifiGan(config.vocoder_config) self.config = config @replace_return_docstrings( output_type=FastSpeech2ConformerWithHifiGanOutput, config_class=FastSpeech2ConformerWithHifiGanConfig ) def forward( self, input_ids: torch.LongTensor, attention_mask: Optional[torch.LongTensor] = None, spectrogram_labels: Optional[torch.FloatTensor] = None, duration_labels: Optional[torch.LongTensor] = None, pitch_labels: Optional[torch.FloatTensor] = None, energy_labels: Optional[torch.FloatTensor] = None, speaker_ids: Optional[torch.LongTensor] = None, lang_ids: Optional[torch.LongTensor] = None, speaker_embedding: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> Union[Tuple, FastSpeech2ConformerModelOutput]: """ Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Input sequence of text vectors. attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*, defaults to `None`): Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, 1]`: 0 for tokens that are **masked**, 1 for tokens that are **not masked**. spectrogram_labels (`torch.FloatTensor` of shape `(batch_size, max_spectrogram_length, num_mel_bins)`, *optional*, defaults to `None`): Batch of padded target features. duration_labels (`torch.LongTensor` of shape `(batch_size, sequence_length + 1)`, *optional*, defaults to `None`): Batch of padded durations. pitch_labels (`torch.FloatTensor` of shape `(batch_size, sequence_length + 1, 1)`, *optional*, defaults to `None`): Batch of padded token-averaged pitch. energy_labels (`torch.FloatTensor` of shape `(batch_size, sequence_length + 1, 1)`, *optional*, defaults to `None`): Batch of padded token-averaged energy. speaker_ids (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*, defaults to `None`): Speaker ids used to condition features of speech output by the model. lang_ids (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*, defaults to `None`): Language ids used to condition features of speech output by the model. speaker_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`, *optional*, defaults to `None`): Embedding containing conditioning signals for the features of the speech. return_dict (`bool`, *optional*, defaults to `None`): Whether or not to return a [`FastSpeech2ConformerModelOutput`] instead of a plain tuple. output_attentions (`bool`, *optional*, defaults to `None`): 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*, defaults to `None`): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. Returns: Example: ```python >>> from transformers import ( ... FastSpeech2ConformerTokenizer, ... FastSpeech2ConformerWithHifiGan, ... ) >>> tokenizer = FastSpeech2ConformerTokenizer.from_pretrained("espnet/fastspeech2_conformer") >>> inputs = tokenizer("some text to convert to speech", return_tensors="pt") >>> input_ids = inputs["input_ids"] >>> model = FastSpeech2ConformerWithHifiGan.from_pretrained("espnet/fastspeech2_conformer_with_hifigan") >>> output_dict = model(input_ids, return_dict=True) >>> waveform = output_dict["waveform"] >>> print(waveform.shape) torch.Size([1, 49664]) ``` """ return_dict = return_dict if return_dict is not None else self.config.model_config.use_return_dict output_attentions = ( output_attentions if output_attentions is not None else self.config.model_config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.model_config.output_hidden_states ) model_outputs = self.model( input_ids, attention_mask, spectrogram_labels=spectrogram_labels, duration_labels=duration_labels, pitch_labels=pitch_labels, energy_labels=energy_labels, speaker_ids=speaker_ids, lang_ids=lang_ids, speaker_embedding=speaker_embedding, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) if not return_dict: has_missing_labels = ( spectrogram_labels is None or duration_labels is None or pitch_labels is None or energy_labels is None ) if has_missing_labels: spectrogram = model_outputs[0] else: spectrogram = model_outputs[1] else: spectrogram = model_outputs["spectrogram"] waveform = self.vocoder(spectrogram) if not return_dict: return model_outputs + (waveform,) return FastSpeech2ConformerWithHifiGanOutput(waveform=waveform, **model_outputs) __all__ = [ "FastSpeech2ConformerWithHifiGan", "FastSpeech2ConformerHifiGan", "FastSpeech2ConformerModel", "FastSpeech2ConformerPreTrainedModel", ]

transformers/src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py/0

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# coding=utf-8 # Copyright 2021 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. """FNet model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class FNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet 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 FNet [google/fnet-base](https://huggingface.co/google/fnet-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 32000): Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. 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. 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_new"`): 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. 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 4): The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. 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. use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`): Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms. Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used. tpu_short_seq_length (`int`, *optional*, defaults to 512): The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT matrix only when *use_tpu_fourier_optimizations* is set to `True` and the input sequence is shorter than or equal to 4096 tokens. Example: ```python >>> from transformers import FNetConfig, FNetModel >>> # Initializing a FNet fnet-base style configuration >>> configuration = FNetConfig() >>> # Initializing a model (with random weights) from the fnet-base style configuration >>> model = FNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "fnet" def __init__( self, vocab_size=32000, hidden_size=768, num_hidden_layers=12, intermediate_size=3072, hidden_act="gelu_new", hidden_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=4, initializer_range=0.02, layer_norm_eps=1e-12, use_tpu_fourier_optimizations=False, tpu_short_seq_length=512, pad_token_id=3, bos_token_id=1, eos_token_id=2, **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.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.initializer_range = initializer_range self.type_vocab_size = type_vocab_size self.layer_norm_eps = layer_norm_eps self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations self.tpu_short_seq_length = tpu_short_seq_length __all__ = ["FNetConfig"]

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

{ "file_path": "transformers/src/transformers/models/fnet/configuration_fnet.py", "repo_id": "transformers", "token_count": 2053 }

# coding=utf-8 # Copyright 2024 Google Inc. 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, Any, Dict, List, Optional, Tuple, Union import sentencepiece as spm import torch import torch.utils.checkpoint from torch import nn from ...cache_utils import Cache, DynamicCache from ...configuration_utils import PretrainedConfig from ...modeling_outputs import BaseModelOutputWithPast from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging from ..llama.modeling_llama import ( LlamaForCausalLM, LlamaForSequenceClassification, LlamaForTokenClassification, LlamaMLP, LlamaModel, ) from ..llama.tokenization_llama import LlamaTokenizer if TYPE_CHECKING: from ...tokenization_utils_base import TextInput VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} SPIECE_UNDERLINE = "▁" _CHECKPOINT_FOR_DOC = "google/gemma-7b" logger = logging.get_logger(__name__) class GemmaConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma 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 Gemma-7B. e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b) 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 256000): Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`GemmaModel`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The legacy activation function. It is overwritten by the `hidden_activation`. hidden_activation (`str` or `function`, *optional*): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import GemmaModel, GemmaConfig >>> # Initializing a Gemma gemma-7b style configuration >>> configuration = GemmaConfig() >>> # Initializing a model from the gemma-7b style configuration >>> model = GemmaModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "gemma" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } def __init__( self, vocab_size=256000, hidden_size=3072, intermediate_size=24576, num_hidden_layers=28, num_attention_heads=16, num_key_value_heads=16, head_dim=256, hidden_act="gelu_pytorch_tanh", hidden_activation=None, max_position_embeddings=8192, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=0, eos_token_id=1, bos_token_id=2, tie_word_embeddings=True, rope_theta=10000.0, attention_bias=False, attention_dropout=0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.head_dim = head_dim self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.hidden_activation = hidden_activation self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_bias = attention_bias self.attention_dropout = attention_dropout super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) class GemmaTokenizer(LlamaTokenizer, PreTrainedTokenizer): """ Construct a Gemma 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 `"<bos>"`): 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 `"<eos>"`): The end of sequence token. pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"`): 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 Gemma should be used. spaces_between_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to add spaces between special tokens. """ def __init__( self, vocab_file, unk_token="<unk>", bos_token="<bos>", eos_token="<eos>", pad_token="<pad>", 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, **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 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 = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) PreTrainedTokenizer.__init__( self, 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=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, **kwargs, ) def get_spm_processor(self): raise AttributeError("Not needed for Gemma") def unk_token_length(self): raise AttributeError("Not needed for Gemma") def tokenize(self, text: "TextInput", **kwargs) -> List[str]: """ Args: text: TextInput Simply calls PreTrainedTokenizer's method """ return PreTrainedTokenizer.tokenize(self, text, **kwargs) def _tokenize(self, text, **kwargs): """ Args: text: TextInput Returns a tokenized string. The Gemma tokenizer never adds a prefix space. """ return self.sp_model.encode(text, out_type=str) def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, spaces_between_special_tokens: bool = False, **kwargs, ) -> str: sub_texts = [] current_sub_text = [] for ids in token_ids: if skip_special_tokens and ids in self.all_special_ids: continue if ids in self._added_tokens_decoder: if current_sub_text: sub_texts.append(self.sp_model.decode(current_sub_text)) sub_texts.append(self._added_tokens_decoder[ids].content) current_sub_text = [] else: current_sub_text.append(ids) if current_sub_text: sub_texts.append(self.sp_model.decode(current_sub_text)) if spaces_between_special_tokens: sub_texts = " ".join(sub_texts) else: sub_texts = "".join(sub_texts) return sub_texts.replace(SPIECE_UNDERLINE, " ") def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) 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._added_tokens_encoder: out_string += self.sp_model.decode(current_sub_tokens) + token current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode(current_sub_tokens) return out_string class GemmaRMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.zeros(dim)) def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): output = self._norm(x.float()) # Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16) # See https://github.com/huggingface/transformers/pull/29402 output = output * (1.0 + self.weight.float()) return output.type_as(x) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.eps}" class GemmaMLP(LlamaMLP): def __init__(self, config): super().__init__() self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) class GemmaModel(LlamaModel): def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, List[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, cache_position: Optional[torch.LongTensor] = None, **kwargs, # NOOP kwarg for now ) -> Union[Tuple, BaseModelOutputWithPast]: 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 (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache() if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) # embed positions hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # normalized # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5 # See https://github.com/huggingface/transformers/pull/29402 normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype) hidden_states = hidden_states * normalizer # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers[: self.config.num_hidden_layers]: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) output = BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) return output if return_dict else output.to_tuple() class GemmaForCausalLM(LlamaForCausalLM): def forward(**super_kwargs): r""" Args: 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]`. logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns: Example: ```python >>> from transformers import AutoTokenizer, GemmaForCausalLM >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b") >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b") >>> prompt = "What is your favorite condiment?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "What is your favorite condiment?" ```""" return super().forward(**super_kwargs) class GemmaForSequenceClassification(LlamaForSequenceClassification): pass class GemmaForTokenClassification(LlamaForTokenClassification): pass __all__ = [ "GemmaConfig", "GemmaTokenizer", "GemmaModel", "GemmaForCausalLM", "GemmaForSequenceClassification", "GemmaForTokenClassification", "GemmaPreTrainedModel", # noqa: F822 ]

transformers/src/transformers/models/gemma/modular_gemma.py/0

{ "file_path": "transformers/src/transformers/models/gemma/modular_gemma.py", "repo_id": "transformers", "token_count": 9796 }

# coding=utf-8 # Copyright 2021 The Eleuther AI 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. """PyTorch GPT Neo model.""" import os 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 ...cache_utils import Cache, DynamicCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask from ...modeling_outputs import ( BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, is_torch_fx_available, logging, ) from .configuration_gpt_neo import GPTNeoConfig if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. # It means that the function will not be traced through and simply appear as a node in the graph. if is_torch_fx_available(): _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "GPTNeoConfig" _CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neo-1.3B" def load_tf_weights_in_gpt_neo(model, config, gpt_neo_checkpoint_path): """Load tf checkpoints in a pytorch model""" try: import re import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(gpt_neo_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: if "global_step" not in name and "adam" not in name: array = tf.train.load_variable(tf_path, name) array = tf.dtypes.cast(array.squeeze(), tf.float32).numpy() name = name.replace("attn/q", "attn/attention/q_proj/w") name = name.replace("attn/k", "attn/attention/k_proj/w") name = name.replace("attn/v", "attn/attention/v_proj/w") name = name.replace("attn/o", "attn/attention/out_proj/w") name = name.replace("norm_1", "ln_1") name = name.replace("norm_2", "ln_2") name = name.replace("attn/compute_output_bias/o_b", "attn/attention/out_proj/b") name = name.replace("conv1d_main/c_fc/kernel", "c_fc/w") name = name.replace("conv1d_main/c_fc/bias", "c_fc/b") name = name.replace("conv1d_main/c_proj/kernel", "c_proj/w") name = name.replace("conv1d_main/c_proj/bias", "c_proj/b") names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name[5:] # skip "gpt2/" name = name.split("/") pointer = model.transformer for m_name in name: if re.fullmatch(r"[A-Za-z]+\d+", m_name): scope_names = re.split(r"(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "w" or scope_names[0] == "g": pointer = getattr(pointer, "weight") elif scope_names[0] == "b": pointer = getattr(pointer, "bias") elif scope_names[0] == "wpe" or scope_names[0] == "wte": pointer = getattr(pointer, scope_names[0]) pointer = getattr(pointer, "weight") else: pointer = getattr(pointer, scope_names[0]) if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if name[-1] == "w" and name[-2] in ["out_proj", "k_proj", "q_proj", "v_proj", "c_proj", "c_fc"]: array = array.transpose() if name == ["wte"]: # if vocab is padded, then trim off the padding embeddings array = array[: config.vocab_size] if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched {name}") print(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) # init the final linear layer using word embeddings embs = model.transformer.wte.weight lin = nn.Linear(embs.size()[1], embs.size()[0], bias=False) lin.weight = embs model.set_output_embeddings(lin) return model class GPTNeoSelfAttention(nn.Module): def __init__(self, config, attention_type, layer_id=None): super().__init__() self.config = config max_positions = config.max_position_embeddings bias = torch.tril(torch.ones((max_positions, max_positions), dtype=bool)).view( 1, 1, max_positions, max_positions ) # local causal self attention is a sliding window where each token can only attend to the previous # window_size tokens. This is implemented by updating the causal mask such that for each token # all other tokens are masked except the previous window_size tokens. if attention_type == "local": bias = torch.bitwise_xor(bias, torch.tril(bias, -config.window_size)) self.register_buffer("bias", bias, persistent=False) self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False) self.attn_dropout = nn.Dropout(float(config.attention_dropout)) self.resid_dropout = nn.Dropout(float(config.resid_dropout)) self.is_causal = True self.layer_id = layer_id self.embed_dim = config.hidden_size self.num_heads = config.num_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.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) def _split_heads(self, tensor, num_heads, attn_head_size): """ Splits hidden_size dim into attn_head_size and num_heads """ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) tensor = tensor.view(new_shape) return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) def _merge_heads(self, tensor, num_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden_size """ tensor = tensor.permute(0, 2, 1, 3).contiguous() new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) return tensor.view(new_shape) def _attn(self, query, key, value, attention_mask=None, head_mask=None): # Keep the attention weights computation in fp32 to avoid overflow issues query = query.to(torch.float32) key = key.to(torch.float32) attn_weights = torch.matmul(query, key.transpose(-1, -2)) # Apply sliding window masking for local attention layers query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights, mask_value) if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) attn_weights = attn_weights.to(value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def forward( self, hidden_states, attention_mask=None, layer_past=None, head_mask=None, use_cache=False, output_attentions=False, cache_position=None, ): query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: cache_kwargs = {"cache_position": cache_position} key, value = layer_past.update(key, value, self.layer_id, cache_kwargs) attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) return outputs # a, past_kv, (attentions) class GPTNeoFlashAttention2(GPTNeoSelfAttention): """ GPTNeo flash attention module. This module inherits from `GPTNeoSelfAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states, attention_mask=None, layer_past=None, head_mask=None, use_cache=False, output_attentions=False, cache_position=None, ): bsz, _, _ = hidden_states.size() query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: cache_kwargs = {"cache_position": cache_position} key, value = layer_past.update(key, value, self.layer_id, cache_kwargs) query_length = query.shape[2] tgt_len = key.shape[2] # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim query = query.transpose(1, 2).view(bsz, query_length, self.num_heads, self.head_dim) key = key.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim) value = value.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim) attn_dropout = self.config.attention_dropout if self.training else 0.0 if attention_mask is not None: # no matter the length, we just slice it attention_mask = attention_mask[:, :, :, : key.shape[-2]] # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) if query.dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query = query.to(target_dtype) key = key.to(target_dtype) value = value.to(target_dtype) attn_output = _flash_attention_forward( query, key, value, attention_mask, query_length, dropout=attn_dropout, softmax_scale=1.0, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) attn_weights_reshaped = attn_output.reshape(bsz, query_length, self.num_heads * self.head_dim) attn_output = self.out_proj(attn_weights_reshaped) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights_reshaped,) return outputs GPT_NEO_ATTENTION_CLASSES = { "eager": GPTNeoSelfAttention, "flash_attention_2": GPTNeoFlashAttention2, } class GPTNeoAttention(nn.Module): def __init__(self, config, layer_id=0): super().__init__() self.layer_id = layer_id self.attention_layers = config.attention_layers self.attention_type = self.attention_layers[layer_id] if self.attention_type in ["global", "local"]: self.attention = GPT_NEO_ATTENTION_CLASSES[config._attn_implementation]( config, self.attention_type, layer_id ) else: raise NotImplementedError( "Only attn layer types 'global' and 'local' exist, but got `config.attention_layers`: " f"{config.attention_layers}. Select attn layer types from ['global', 'local'] only." ) def forward( self, hidden_states, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False, cache_position=None, ): return self.attention( hidden_states, attention_mask=attention_mask, layer_past=layer_past, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) class GPTNeoMLP(nn.Module): def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * hidden_size super().__init__() embed_dim = config.hidden_size self.c_fc = nn.Linear(embed_dim, intermediate_size) self.c_proj = nn.Linear(intermediate_size, embed_dim) self.act = ACT2FN[config.activation_function] self.dropout = nn.Dropout(float(config.resid_dropout)) def forward(self, hidden_states): hidden_states = self.c_fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.c_proj(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class GPTNeoBlock(nn.Module): def __init__(self, config, layer_id=None): super().__init__() hidden_size = config.hidden_size inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.attn = GPTNeoAttention(config, layer_id) self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.mlp = GPTNeoMLP(inner_dim, config) def forward( self, hidden_states, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False, cache_position=None, ): residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs = self.attn( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] # residual connection hidden_states = attn_output + residual residual = hidden_states hidden_states = self.ln_2(hidden_states) feed_forward_hidden_states = self.mlp(hidden_states) # residual connection hidden_states = residual + feed_forward_hidden_states if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs # hidden_states, past_kv, attentions class GPTNeoPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTNeoConfig load_tf_weights = load_tf_weights_in_gpt_neo base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["GPTNeoBlock"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = False # TODO: needs a HybridCache def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (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, 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) GPT_NEO_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 ([`GPTNeoConfig`]): 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. """ GPT_NEO_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][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.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - 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)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. 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, input_ids_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. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see `past_key_values`). 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. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ @add_start_docstrings( "The bare GPT Neo Model transformer outputting raw hidden-states without any specific head on top.", GPT_NEO_START_DOCSTRING, ) class GPTNeoModel(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.embed_dim = config.hidden_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) self.drop = nn.Dropout(float(config.embed_dropout)) self.h = nn.ModuleList([GPTNeoBlock(config, layer_id=i) for i in range(config.num_layers)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Union[Cache, Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: 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 (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") 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 if inputs_embeds is None: inputs_embeds = self.wte(input_ids) # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() else: past_key_values = DynamicCache.from_legacy_cache(past_key_values) logger.warning_once( "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) seq_length = inputs_embeds.shape[1] if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x num_heads x N x N # head_mask has shape n_layer x batch x num_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.num_layers) position_embeds = self.wpe(position_ids) hidden_states = inputs_embeds + position_embeds if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, seq_length) token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) output_shape = (-1, seq_length, hidden_states.size(-1)) next_decoder_cache = None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, block in enumerate(self.h): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func( block.__call__, hidden_states, None, causal_mask, head_mask[i], use_cache, output_attentions, cache_position, ) else: outputs = block( hidden_states, layer_past=past_key_values, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states = outputs[0] if use_cache: next_decoder_cache = outputs[1] if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @add_start_docstrings( """ The GPT Neo Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForCausalLM(GPTNeoPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = GPTNeoModel(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_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(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Union[Cache, Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: 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]` """ 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, cache_position=cache_position, ) hidden_states = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # Compute loss in fp32 to match with mesh-tf version # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179 lm_logits = lm_logits.to(torch.float32) # Flatten the tokens loss = self.loss_function( lm_logits, labels, vocab_size=self.config.vocab_size, **kwargs, ) lm_logits = lm_logits.to(hidden_states.dtype) loss = loss.to(hidden_states.dtype) 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 GPTNeo Model transformer with a sequence classification head on top (linear layer). [`GPTNeoForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) 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). """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForSequenceClassification(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTNeoModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Union[Cache, Tuple[torch.FloatTensor]]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = 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], SequenceClassifierOutputWithPast]: 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 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.score(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: last_non_pad_token = -1 elif input_ids is not None: # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32) token_indices = torch.arange(input_ids.shape[-1], device=logits.device) last_non_pad_token = (token_indices * non_pad_mask).argmax(-1) else: last_non_pad_token = -1 logger.warning_once( 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[torch.arange(batch_size, device=logits.device), last_non_pad_token] 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[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ GPT Neo 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. """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForTokenClassification(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTNeoModel(config) self.dropout = nn.Dropout(config.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(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint="EleutherAI/gpt-neo-125m", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_loss=0.25, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = 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, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *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 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] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: labels = labels.to(logits.device) loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The GPT-Neo Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForQuestionAnswering(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTNeoModel(config) self.qa_outputs = nn.Linear(config.hidden_size, 2) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, real_checkpoint=_CHECKPOINT_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[Tuple, 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.transformer( 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, ) __all__ = [ "GPTNeoForCausalLM", "GPTNeoForQuestionAnswering", "GPTNeoForSequenceClassification", "GPTNeoForTokenClassification", "GPTNeoModel", "GPTNeoPreTrainedModel", "load_tf_weights_in_gpt_neo", ]

transformers/src/transformers/models/gpt_neo/modeling_gpt_neo.py/0

{ "file_path": "transformers/src/transformers/models/gpt_neo/modeling_gpt_neo.py", "repo_id": "transformers", "token_count": 25763 }

# coding=utf-8 # Copyright 2021 The EleutherAI and HuggingFace Teams. 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 GPT-J model.""" import warnings from typing import Optional, Tuple, Union import torch import torch.fx import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, is_torch_fx_proxy, logging, ) from ...utils.model_parallel_utils import assert_device_map, get_device_map from .configuration_gptj import GPTJConfig if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "hf-internal-testing/tiny-random-gptj" _REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B" _CONFIG_FOR_DOC = "GPTJConfig" def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor: inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim)) sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.int64).float(), inv_freq).float() return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1) @torch.fx.wrap def get_embed_positions(embed_positions, position_ids): return embed_positions.to(position_ids.device).repeat(position_ids.shape[0], 1, 1) def rotate_every_two(x: torch.Tensor) -> torch.Tensor: x1 = x[:, :, :, ::2] x2 = x[:, :, :, 1::2] x = torch.stack((-x2, x1), dim=-1) return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)') def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor: sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3) cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3) return (tensor * cos) + (rotate_every_two(tensor) * sin) class GPTJAttention(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() self.config = config max_positions = config.max_position_embeddings self.attn_dropout = nn.Dropout(config.attn_pdrop) self.resid_dropout = nn.Dropout(config.resid_pdrop) self.is_causal = True self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.embed_dim = config.hidden_size self.num_attention_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_attention_heads if self.head_dim * self.num_attention_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and" f" `num_attention_heads`: {self.num_attention_heads})." ) self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()) self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.rotary_dim = config.rotary_dim pos_embd_dim = self.rotary_dim or self.embed_dim self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim) def _split_heads(self, tensor, num_attention_heads, attn_head_size, rotary): """ Splits hidden dim into attn_head_size and num_attention_heads """ new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size) tensor = tensor.view(new_shape) if rotary: return tensor if len(tensor.shape) == 5: return tensor.permute(0, 1, 3, 2, 4) # (batch, blocks, head, block_length, head_features) elif len(tensor.shape) == 4: return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) else: raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}") def _merge_heads(self, tensor, num_attention_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden dim """ if len(tensor.shape) == 5: tensor = tensor.permute(0, 1, 3, 2, 4).contiguous() elif len(tensor.shape) == 4: tensor = tensor.permute(0, 2, 1, 3).contiguous() else: raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}") new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,) return tensor.view(new_shape) def _attn( self, query, key, value, attention_mask=None, head_mask=None, ): # Keep the attention weights computation in fp32 to avoid overflow issues query = query.to(torch.float32) key = key.to(torch.float32) attn_weights = torch.matmul(query, key.transpose(-1, -2)) attn_weights = attn_weights / self.scale_attn if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) attn_weights = attn_weights.to(value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def _get_embed_positions(self, position_ids): embed_positions = self.embed_positions if embed_positions.device != position_ids.device: embed_positions = embed_positions.to(position_ids.device) self.embed_positions = embed_positions return embed_positions.repeat(position_ids.shape[0], 1, 1) def forward( self, hidden_states: torch.FloatTensor, layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> Union[ Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]], ]: query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, self.num_attention_heads, self.head_dim, True) key = self._split_heads(key, self.num_attention_heads, self.head_dim, True) value = self._split_heads(value, self.num_attention_heads, self.head_dim, False) if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing(): # The logic to conditionally copy to GPU could not be traced, so we do this # every time in the torch.fx case embed_positions = get_embed_positions(self.embed_positions, position_ids) else: embed_positions = self._get_embed_positions(position_ids) repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1]) sincos = torch.gather(embed_positions, 1, repeated_position_ids) sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1) if self.rotary_dim is not None: k_rot = key[:, :, :, : self.rotary_dim] k_pass = key[:, :, :, self.rotary_dim :] q_rot = query[:, :, :, : self.rotary_dim] q_pass = query[:, :, :, self.rotary_dim :] k_rot = apply_rotary_pos_emb(k_rot, sin, cos) q_rot = apply_rotary_pos_emb(q_rot, sin, cos) key = torch.cat([k_rot, k_pass], dim=-1) query = torch.cat([q_rot, q_pass], dim=-1) else: key = apply_rotary_pos_emb(key, sin, cos) query = apply_rotary_pos_emb(query, sin, cos) key = key.permute(0, 2, 1, 3) query = query.permute(0, 2, 1, 3) if layer_past is not None: cache_kwargs = { "sin": sin, "cos": cos, "partial_rotation_size": self.rotary_dim, "cache_position": cache_position, } key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) # compute self-attention: V x Softmax(QK^T) attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim) attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) return outputs # a, present, (attentions) class GPTJFlashAttention2(GPTJAttention): """ GPTJ flash attention module. This module inherits from `GPTJAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.FloatTensor, layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> Union[ Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]], ]: query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, self.num_attention_heads, self.head_dim, True) key = self._split_heads(key, self.num_attention_heads, self.head_dim, True) value = self._split_heads(value, self.num_attention_heads, self.head_dim, False) if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing(): # The logic to conditionally copy to GPU could not be traced, so we do this # every time in the torch.fx case embed_positions = get_embed_positions(self.embed_positions, position_ids) else: embed_positions = self._get_embed_positions(position_ids) repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1]) sincos = torch.gather(embed_positions, 1, repeated_position_ids) sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1) if self.rotary_dim is not None: k_rot = key[:, :, :, : self.rotary_dim] k_pass = key[:, :, :, self.rotary_dim :] q_rot = query[:, :, :, : self.rotary_dim] q_pass = query[:, :, :, self.rotary_dim :] k_rot = apply_rotary_pos_emb(k_rot, sin, cos) q_rot = apply_rotary_pos_emb(q_rot, sin, cos) key = torch.cat([k_rot, k_pass], dim=-1) query = torch.cat([q_rot, q_pass], dim=-1) else: key = apply_rotary_pos_emb(key, sin, cos) query = apply_rotary_pos_emb(query, sin, cos) # tanspose to have the desired shape # before transpose: batch_size x seq_length x num_attention_heads x head_dim # after transpose: batch_size x num_attention_heads x seq_length x head_dim key = key.permute(0, 2, 1, 3) query = query.permute(0, 2, 1, 3) # value: batch_size x num_attention_heads x seq_length x head_dim if layer_past is not None: cache_kwargs = { "sin": sin, "cos": cos, "partial_rotation_size": self.rotary_dim, "cache_position": cache_position, } key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) # The Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we need to keep the original shape for query and key, and reshape value # to have the correct shape. key = key.permute(0, 2, 1, 3).contiguous() query = query.permute(0, 2, 1, 3).contiguous() value = value.permute(0, 2, 1, 3).contiguous() # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) input_dtype = query.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query = query.to(target_dtype) key = key.to(target_dtype) value = value.to(target_dtype) attention_dropout = self.config.attn_pdrop if self.training else 0.0 # attn_pdrop in gptj query_length = query.shape[1] # Compute attention attn_weights = _flash_attention_forward( query, key, value, attention_mask, query_length, dropout=attention_dropout, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) # Reshape outputs attn_output = attn_weights.reshape( attn_weights.shape[0], attn_weights.shape[1], attn_weights.shape[2] * attn_weights.shape[3] ) attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) return outputs GPTJ_ATTENTION_CLASSES = { "eager": GPTJAttention, "flash_attention_2": GPTJFlashAttention2, } class GPTJMLP(nn.Module): def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim super().__init__() embed_dim = config.n_embd self.fc_in = nn.Linear(embed_dim, intermediate_size) self.fc_out = nn.Linear(intermediate_size, embed_dim) self.act = ACT2FN[config.activation_function] self.dropout = nn.Dropout(config.resid_pdrop) def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.fc_in(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.fc_out(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class GPTJBlock(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.attn = GPTJ_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) self.mlp = GPTJMLP(inner_dim, config) def forward( self, hidden_states: Optional[torch.FloatTensor], layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]: residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs = self.attn( hidden_states=hidden_states, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] feed_forward_hidden_states = self.mlp(hidden_states) hidden_states = attn_output + feed_forward_hidden_states + residual if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs # hidden_states, present, (attentions) class GPTJPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTJConfig base_model_prefix = "transformer" is_parallelizable = True supports_gradient_checkpointing = True _no_split_modules = ["GPTJBlock"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True _supports_param_buffer_assignment = False def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear,)): # Slightly different from Mesh Transformer JAX 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) GPTJ_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 ([`GPTJConfig`]): 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. """ GPTJ_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.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_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_dim)`, *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. past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - 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)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_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. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ PARALLELIZE_DOCSTRING = r""" This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks across all devices. Args: device_map (`Dict[int, list]`, *optional*): A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always automatically mapped to the first device (for esoteric reasons). That means that the first device should have fewer attention modules mapped to it than other devices. For reference, the GPT-J models have the following number of attention modules: - gpt-j-6B: 28 Example: ```python # Here is an example of a device map on a machine with 4 GPUs using gpt-j-6B, which has a total of 28 attention modules: model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B") device_map = { 0: [0, 1, 2, 3, 4, 5, 6], 1: [7, 8, 9, 10, 11, 12, 13], 2: [14, 15, 16, 17, 18, 19, 20], 3: [21, 22, 23, 24, 25, 26, 27], } model.parallelize(device_map) ``` """ DEPARALLELIZE_DOCSTRING = r""" Moves the model to CPU from a model parallel state. Example: ```python # On a 4 GPU machine with gpt-j-6B: model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B") device_map = { 0: [0, 1, 2, 3, 4, 5, 6], 1: [7, 8, 9, 10, 11, 12, 13], 2: [14, 15, 16, 17, 18, 19, 20], 3: [21, 22, 23, 24, 25, 26, 27], } model.parallelize(device_map) # Splits the model across several devices model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache() ``` """ @add_start_docstrings( "The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.", GPTJ_START_DOCSTRING, ) class GPTJModel(GPTJPreTrainedModel): def __init__(self, config): super().__init__(config) self.embed_dim = config.n_embd self.vocab_size = config.vocab_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.drop = nn.Dropout(config.embd_pdrop) self.h = nn.ModuleList([GPTJBlock(config, layer_idx=i) for i in range(config.n_layer)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) # Model parallel self.model_parallel = False self.device_map = None self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): warnings.warn( "`GPTJModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your" " model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own" " `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1," " ...}", FutureWarning, ) # Check validity of device_map self.device_map = ( get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.h)) self.model_parallel = True self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys())) self.last_device = "cuda:" + str(max(self.device_map.keys())) self.wte = self.wte.to(self.first_device) # Load onto devices for k, v in self.device_map.items(): for block in v: cuda_device = "cuda:" + str(k) self.h[block] = self.h[block].to(cuda_device) # ln_f to last self.ln_f = self.ln_f.to(self.last_device) @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.model_parallel = False self.device_map = None self.first_device = "cpu" self.last_device = "cpu" self.wte = self.wte.to("cpu") for index in range(len(self.h)): self.h[index] = self.h[index].to("cpu") self.ln_f = self.ln_f.to("cpu") torch.cuda.empty_cache() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = 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, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: 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 (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") 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 if inputs_embeds is None: inputs_embeds = self.wte(input_ids) # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() else: past_key_values = DynamicCache.from_legacy_cache(past_key_values) logger.warning_once( "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) seq_length = inputs_embeds.shape[1] if cache_position is None: past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x num_attention_heads x N x N # head_mask has shape n_layer x batch x num_attention_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.n_layer) hidden_states = inputs_embeds if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, seq_length) token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) output_shape = (-1, seq_length, hidden_states.size(-1)) next_decoder_cache = None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, block in enumerate(self.h): # Model parallel if self.model_parallel: torch.cuda.set_device(hidden_states.device) # Ensure layer_past is on same device as hidden_states (might not be correct) if past_key_values is not None: past_key_values.key_cache = past_key_values.key_cache.to(hidden_states.device) past_key_values.value_cache = past_key_values.value_cache.to(hidden_states.device) # Ensure that attention_mask is always on the same device as hidden_states if causal_mask is not None: causal_mask = causal_mask.to(hidden_states.device) if isinstance(head_mask, torch.Tensor): head_mask = head_mask.to(hidden_states.device) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func( block.__call__, hidden_states, None, causal_mask, position_ids, head_mask[i], use_cache, output_attentions, cache_position, ) else: outputs = block( hidden_states=hidden_states, layer_past=past_key_values, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states = outputs[0] if use_cache is True: next_decoder_cache = outputs[1] if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) # Model Parallel: If it's the last layer for that device, put things on the next device if self.model_parallel: for k, v in self.device_map.items(): if i == v[-1] and "cuda:" + str(k) != self.last_device: hidden_states = hidden_states.to("cuda:" + str(k + 1)) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @add_start_docstrings( """ The GPT-J Model transformer with a language modeling head on top. """, GPTJ_START_DOCSTRING, ) class GPTJForCausalLM(GPTJPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = GPTJModel(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings(PARALLELIZE_DOCSTRING) def parallelize(self, device_map=None): warnings.warn( "`GPTJForCausalLM.parallelize` is deprecated and will be removed in v5 of Transformers, you should load" " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own" " `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':" " 0, 'transformer.h.1': 1, ...}", FutureWarning, ) self.device_map = ( get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) if device_map is None else device_map ) assert_device_map(self.device_map, len(self.transformer.h)) self.transformer.parallelize(self.device_map) self.lm_head = self.lm_head.to(self.transformer.first_device) self.model_parallel = True @add_start_docstrings(DEPARALLELIZE_DOCSTRING) def deparallelize(self): warnings.warn( "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.", FutureWarning, ) self.transformer.deparallelize() self.transformer = self.transformer.to("cpu") self.lm_head = self.lm_head.to("cpu") self.model_parallel = False torch.cuda.empty_cache() 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(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = 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, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Union[Tuple, 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]` """ 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, cache_position=cache_position, ) hidden_states = transformer_outputs[0] # Set device for model parallelism if self.model_parallel: torch.cuda.set_device(self.transformer.first_device) hidden_states = hidden_states.to(self.lm_head.weight.device) # make sure sampling in fp16 works correctly and # compute loss in fp32 to match with mesh-tf version # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179 lm_logits = self.lm_head(hidden_states).to(torch.float32) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # Flatten the tokens loss = self.loss_function( lm_logits, labels, vocab_size=self.config.vocab_size, **kwargs, ) loss = loss.to(hidden_states.dtype) 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 GPT-J Model transformer with a sequence classification head on top (linear layer). [`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT, GPT-2, GPT-Neo) 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). """, GPTJ_START_DOCSTRING, ) class GPTJForSequenceClassification(GPTJPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTJModel(config) self.score = nn.Linear(config.n_embd, self.num_labels, bias=False) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="ydshieh/tiny-random-gptj-for-sequence-classification", output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, SequenceClassifierOutputWithPast]: 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 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.score(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] 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: last_non_pad_token = -1 elif input_ids is not None: # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32) token_indices = torch.arange(input_ids.shape[-1], device=logits.device) last_non_pad_token = (token_indices * non_pad_mask).argmax(-1) else: last_non_pad_token = -1 logger.warning_once( 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[torch.arange(batch_size, device=logits.device), last_non_pad_token] loss = None if labels is not None: labels = labels.to(pooled_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(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[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The GPT-J Model transformer 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`). """, GPTJ_START_DOCSTRING, ) class GPTJForQuestionAnswering(GPTJPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTJModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_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[Tuple, 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.transformer( 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).to(start_logits.device) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1).to(end_logits.device) # 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, ) __all__ = [ "GPTJForCausalLM", "GPTJForQuestionAnswering", "GPTJForSequenceClassification", "GPTJModel", "GPTJPreTrainedModel", ]

transformers/src/transformers/models/gptj/modeling_gptj.py/0

{ "file_path": "transformers/src/transformers/models/gptj/modeling_gptj.py", "repo_id": "transformers", "token_count": 27393 }

# 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""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional 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__) 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" base_config_key = "text_config" 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 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" base_config_key = "vision_config" 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 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): Dimensionality of text and vision projection layers. projection_intermediate_dim (`int`, *optional*, defaults to 4096): Dimensionality of intermediate layer of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The initial value of the *logit_scale* parameter. Default is used as per the original GroupViT implementation. kwargs (*optional*): Dictionary of keyword arguments. """ model_type = "groupvit" sub_configs = {"text_config": GroupViTTextConfig, "vision_config": GroupViTVisionConfig} 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 overridden.' ) 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 overridden.' ) 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 __all__ = ["GroupViTConfig", "GroupViTOnnxConfig", "GroupViTTextConfig", "GroupViTVisionConfig"]

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

{ "file_path": "transformers/src/transformers/models/groupvit/configuration_groupvit.py", "repo_id": "transformers", "token_count": 7778 }

# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License. # # MIT License # # Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """ Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore. References: - DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model - Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch """ from typing import Optional, Tuple import tensorflow as tf from ...modeling_tf_utils import shape_list from .configuration_idefics import IdeficsConfig class TFIdeficsPerceiverResampler(tf.keras.layers.Layer): def __init__( self, config: IdeficsConfig, embed_dim: int, depth: int, n_heads: int, head_dim: int, n_latents: int, **kwargs ) -> None: """ Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler. Could be e.g., VIT embed_dim, ResNet pool dim, and so on. Args: config (`IdeficsConfig`): config object embed_dim (`int`): The size of each embedding vector depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3). n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention). head_dim (`int`): Dimensionality of each head projection in the Transformer block. n_latents (`int`): Number of latent embeddings to resample ("compress") the input sequence to (usually < 128). """ super().__init__(**kwargs) self.embed_dim, self.n_heads, self.head_dim, self.n_latents = embed_dim, n_heads, head_dim, n_latents self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver self.intermediate_dim = ( self.embed_dim * 4 if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim * 4 ) # Create Transformer Blocks self.blocks = [] for i in range(depth): self.blocks.append( [ TFIdeficsPerceiverAttention( self.embed_dim, self.n_heads, self.head_dim, self.qk_layer_norms, name=f"blocks.{i}.0" ), TFIdeficsMLP(self.intermediate_dim, config, name=f"blocks.{i}.1"), ] ) self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm") def build(self, input_shape): # Create Latents for Perceiver self.latents = self.add_weight( shape=(self.n_latents, self.embed_dim), initializer="random_normal", trainable=True, name="latents" ) super().build(input_shape) def call(self, context: tf.Tensor) -> tf.Tensor: """Resample arbitrary length context & *compress* down to self.n_latents latent embeddings""" # tf.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0]) latents = tf.expand_dims(self.latents, axis=0) latents = tf.tile(latents, [tf.shape(context)[0], 1, 1]) # Feed through Perceiver Attention blocks... for attn, ff in self.blocks: latents = attn(context, latents) + latents latents = ff(latents) + latents return self.layer_norm(latents) class TFIdeficsPerceiverAttention(tf.keras.layers.Layer): def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool, **kwargs) -> None: """Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`""" super().__init__(**kwargs) self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim self.qk_layer_norms = qk_layer_norms # Normalization & Scaling self.context_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="context_layer_norm") self.latents_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="latents_layer_norm") if self.qk_layer_norms: self.q_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="q_layer_norm") self.k_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="k_layer_norm") self.qk_scale = self.head_dim**-0.5 # Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers). self.q_proj = tf.keras.layers.Dense(self.n_heads * self.head_dim, use_bias=False, name="q_proj") self.k_proj = tf.keras.layers.Dense(self.n_heads * self.head_dim, use_bias=False, name="k_proj") self.v_proj = tf.keras.layers.Dense(self.n_heads * self.head_dim, use_bias=False, name="v_proj") self.output_proj = tf.keras.layers.Dense(embed_dim, use_bias=False, name="output_proj") def call(self, context: tf.Tensor, latents: tf.Tensor) -> tf.Tensor: """ Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension! Args: context (`tf.Tensor`): Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample. latents (`tf.Tensor`): Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to. Returns: `tf.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross from context. """ context = self.context_layer_norm(context) latents = self.latents_layer_norm(latents) batch_size, seq_length, embed_dim = shape_list(context) # Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn! # Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents` q = self.q_proj(latents) k = self.k_proj(tf.concat([context, latents], axis=-2)) v = self.v_proj(tf.concat([context, latents], axis=-2)) # Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call) # =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)] q, k, v = [ tf.transpose(tf.reshape(x, (batch_size, x.shape[1], self.n_heads, self.head_dim)), perm=[0, 2, 1, 3]) for x in (q, k, v) ] if self.qk_layer_norms: q = self.q_layer_norm(q) k = self.k_layer_norm(k) scores = tf.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k) stabilized_scores = scores - tf.reduce_max(scores, axis=-1, keepdims=True) attn = tf.nn.softmax(stabilized_scores, axis=-1) # Attend & project back to output... resampled = tf.einsum("... i j, ... j d -> ... i d", attn, v) return self.output_proj( tf.reshape(tf.transpose(resampled, perm=[0, 2, 1, 3]), (batch_size, -1, self.n_heads * self.head_dim)) ) class TFIdeficsMLP(tf.keras.layers.Layer): def __init__(self, intermediate_size, config: IdeficsConfig, **kwargs): """Simple MLP block with intermediate_size and embedding size""" super().__init__(**kwargs) self.embed_dim = config.vision_config.embed_dim self.ln = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="ln") self.fc = tf.keras.layers.Dense(intermediate_size, use_bias=False, name="fc") self.act = tf.keras.layers.ReLU(name="act") self.c_proj = tf.keras.layers.Dense(self.embed_dim, use_bias=False, name="c_proj") def call(self, hidden_states: Optional[Tuple[tf.Tensor]]) -> tf.Tensor: hidden_states = self.ln(hidden_states) hidden_states = self.fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.c_proj(hidden_states) return hidden_states

transformers/src/transformers/models/idefics/perceiver_tf.py/0

{ "file_path": "transformers/src/transformers/models/idefics/perceiver_tf.py", "repo_id": "transformers", "token_count": 4053 }

# coding=utf-8 # Copyright 2024 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 Llava-Onevision model.""" import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...generation import GenerationMixin from ...image_processing_utils import select_best_resolution from ...modeling_outputs import ModelOutput from ...modeling_utils import PreTrainedModel from ...utils import ( add_start_docstrings, logging, ) from ...utils.deprecation import deprecate_kwarg from ..auto import AutoModel, AutoModelForCausalLM from .configuration_llava_onevision import LlavaOnevisionConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "LlavaNextConfig" # Copied from transformers.models.llava_next.modeling_llava_next.get_anyres_image_grid_shape def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): """ Calculate the shape of the image patch grid after the preprocessing for images of any resolution. Args: image_size (`tuple`): The size of the input image in the format (width, height). grid_pinpoints (`List`): A list containing possible resolutions. Each item in the list should be a tuple or list of the form `(height, width)`. patch_size (`int`): The size of each image patch. Returns: tuple: The shape of the image patch grid in the format (width, height). """ if not isinstance(grid_pinpoints, list): raise TypeError("grid_pinpoints should be a list of tuples or lists") # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate if not isinstance(image_size, (list, tuple)): if not isinstance(image_size, (torch.Tensor, np.ndarray)): raise TypeError( f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor" ) image_size = image_size.tolist() height, width = select_best_resolution(image_size, grid_pinpoints) return height // patch_size, width // patch_size # Copied from transformers.models.llava_next.modeling_llava_next.image_size_to_num_patches def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int): """ Calculate the number of patches after the preprocessing for images of any resolution. Args: image_size (`torch.LongTensor` or `np.ndarray` or `Tuple[int, int]`): The size of the input image in the format (height, width). ? grid_pinpoints (`List`): A list containing possible resolutions. Each item in the list should be a tuple or list of the form `(height, width)`. patch_size (`int`): The size of each image patch. Returns: int: the number of patches """ if not isinstance(grid_pinpoints, list): raise TypeError("grid_pinpoints should be a list of tuples or lists") # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate if not isinstance(image_size, (list, tuple)): if not isinstance(image_size, (torch.Tensor, np.ndarray)): raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}") image_size = image_size.tolist() best_resolution = select_best_resolution(image_size, grid_pinpoints) height, width = best_resolution num_patches = 0 # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1 for i in range(0, height, patch_size): for j in range(0, width, patch_size): num_patches += 1 # add the base patch num_patches += 1 return num_patches # Copied from transformers.models.llava_next.modeling_llava_next.unpad_image def unpad_image(tensor, original_size): """ Unpads a PyTorch tensor of a padded and resized image. Args: tensor (`torch.Tensor`): The image tensor, assumed to be of shape (num_channels, height, width). original_size (`tuple`): The original size of the image (height, width). Returns: `torch.Tensor`: The unpadded image tensor. """ if not isinstance(original_size, (list, tuple)): if not isinstance(original_size, (torch.Tensor, np.ndarray)): raise TypeError( f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor" ) original_size = original_size.tolist() original_height, original_width = original_size current_height, current_width = tensor.shape[1:] original_aspect_ratio = original_width / original_height current_aspect_ratio = current_width / current_height if original_aspect_ratio > current_aspect_ratio: scale_factor = current_width / original_width new_height = int(round(original_height * scale_factor, 7)) padding = (current_height - new_height) // 2 unpadded_tensor = tensor[:, padding : current_height - padding, :] else: scale_factor = current_height / original_height new_width = int(round(original_width * scale_factor, 7)) padding = (current_width - new_width) // 2 unpadded_tensor = tensor[:, :, padding : current_width - padding] return unpadded_tensor @dataclass # Copied from transformers.models.llava_next_video.modeling_llava_next_video.LlavaNextVideoCausalLMOutputWithPast with LlavaNextVideo->LlavaOnevision class LlavaOnevisionCausalLMOutputWithPast(ModelOutput): """ Base class for LlavaOnevision causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). 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)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. 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_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. video_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`. video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None image_hidden_states: Optional[torch.FloatTensor] = None video_hidden_states: Optional[torch.FloatTensor] = None # Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->LlavaOnevision class LlavaOnevisionMultiModalProjector(nn.Module): def __init__(self, config: LlavaOnevisionConfig): super().__init__() # We have hidden_size * the number of vision feature layers num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer) self.linear_1 = nn.Linear( config.vision_config.hidden_size * num_feature_layers, config.text_config.hidden_size, bias=config.multimodal_projector_bias, ) self.act = ACT2FN[config.projector_hidden_act] self.linear_2 = nn.Linear( config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias ) def forward(self, image_features): hidden_states = self.linear_1(image_features) hidden_states = self.act(hidden_states) hidden_states = self.linear_2(hidden_states) return hidden_states LLAVA_ONEVISION_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 ([`LlavaNextConfig`] or [`LlavaNextVisionConfig`]): 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( "The bare LLaVA-Onevision Model outputting raw hidden-states without any specific head on top.", LLAVA_ONEVISION_START_DOCSTRING, ) class LlavaOnevisionPreTrainedModel(PreTrainedModel): config_class = LlavaOnevisionConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["LlavaOnevisionVisionAttention"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_cache_class = True _supports_static_cache = False # Qwen2 doesn't but llava has no reasons to not support _supports_quantized_cache = True _supports_sdpa = True # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextPreTrainedModel._init_weights def _init_weights(self, module): # important: this ported version of LlavaNext isn't meant for training from scratch - only # inference and fine-tuning - so the proper init weights code has been removed - the original codebase # https://github.com/haotian-liu/LLaVA/tree/main/llava_next should serve for that purpose std = ( self.config.initializer_range if hasattr(self.config, "initializer_range") else self.config.text_config.initializer_range ) if hasattr(module, "class_embedding"): module.class_embedding.data.normal_(mean=0.0, std=std) if isinstance(module, (nn.Linear, nn.Conv2d)): 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_() LLAVA_ONEVISION_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) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)): The tensors corresponding to the input images. Pixel values can be obtained using [`AutoImageProcessor`]. See [`LlavaNextImageProcessor.__call__`] for details. [`LlavaProcessor`] uses [`LlavaNextImageProcessor`] for processing images. image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*): The sizes of the images in the batch, being (height, width) for each image. pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, frames, num_channels, image_size, image_size)): The tensors corresponding to the input videos. Pixel values can be obtained using [`LlavaNextVideoProcessor`]. See [`LlavaNextVideoProcessor.__call__`] for details. [`LlavaProcessor`] uses [`LlavaNextVideoProcessor`] for processing videos. image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*): The sizes of the videos in the batch, being (height, width) for each frame in the video. 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) 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 (`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.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) 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. vision_feature_layer (`Union[int, List[int]], *optional*, defaults to -2`): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features. If `"full"`, the full vision features are used. vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`): Aspect ratio used when processong image features. The default value is "anyres_max_9". 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. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ @add_start_docstrings( """The LLaVA-Onevision model which consists of a vision backbone and a language model.""", LLAVA_ONEVISION_START_DOCSTRING, ) class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, GenerationMixin): def __init__(self, config: LlavaOnevisionConfig): super().__init__(config) self.vision_tower = AutoModel.from_config(config.vision_config) self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config) embed_std = 1 / math.sqrt(config.text_config.hidden_size) self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std) self.vocab_size = config.text_config.vocab_size self.language_model = AutoModelForCausalLM.from_config(config.text_config) if self.language_model._tied_weights_keys is not None: self._tied_weights_keys = [f"language_model.{k}" for k in self.language_model._tied_weights_keys] self.post_init() # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_input_embeddings def get_input_embeddings(self): return self.language_model.get_input_embeddings() # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_input_embeddings def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value) # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_output_embeddings def get_output_embeddings(self): return self.language_model.get_output_embeddings() # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_output_embeddings def set_output_embeddings(self, new_embeddings): self.language_model.set_output_embeddings(new_embeddings) # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_decoder def set_decoder(self, decoder): self.language_model.set_decoder(decoder) # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_decoder def get_decoder(self): return self.language_model.get_decoder() def pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres_max_9"): """ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors. Args: image_features (`List[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`) List of image feature tensor, each contains all the visual feature of all patches. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). image_newline (`torch.Tensor` of shape `(embed_dim)`) New line embedding vector. vision_aspect_ratio (`str`, *optional*, "anyres_max_9"): Aspect ratio used when processong image features. The default value is "anyres_max_9". Returns: image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`) feature_lens (`List[int]`) token length of each image in image_features """ new_image_features = [] feature_lens = [] for image_idx, image_feature in enumerate(image_features): if image_feature.shape[0] > 1: base_image_feature = image_feature[0] image_feature = image_feature[1:] height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size if height * width != base_image_feature.shape[0]: raise ValueError("The number of patches is not consistent with the image size.") num_patch_height, num_patch_width = get_anyres_image_grid_shape( image_sizes[image_idx], self.config.image_grid_pinpoints, self.config.vision_config.image_size, ) image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() image_feature = image_feature.flatten(1, 2).flatten(2, 3) image_feature = unpad_image(image_feature, image_sizes[image_idx]) max_num_patches = int(vision_aspect_ratio.strip("anyres_max_")) channels, curr_height, curr_width = image_feature.shape ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2)) if ratio > 1.1: image_feature = image_feature[None] image_feature = nn.functional.interpolate( image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear" )[0] if image_newline is not None: image_feature = torch.cat( ( image_feature, image_newline[:, None, None] .expand(*image_feature.shape[:-1], 1) .to(image_feature.device, image_feature.dtype), ), dim=-1, ) image_feature = image_feature.flatten(1, 2).transpose(0, 1) image_feature = torch.cat((base_image_feature, image_feature), dim=0) else: image_feature = image_feature[0] if image_newline is not None: image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0) new_image_features.append(image_feature) feature_lens.append(image_feature.size(0)) image_features = torch.cat(new_image_features, dim=0) feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features.device) return image_features, feature_lens def apply_pooling(self, image_features): height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size batch_frames, seq_len, dim = image_features.shape image_features = image_features.view(batch_frames, height, width, -1) image_features = image_features.permute(0, 3, 1, 2).contiguous() height, width = image_features.shape[2:] scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)] image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear") image_features = image_features.permute(0, 2, 3, 1) image_features = image_features.view(batch_frames, -1, dim) return image_features def get_image_features( self, pixel_values: torch.FloatTensor, image_sizes: torch.Tensor, vision_feature_layer: Union[int, List[int]], vision_feature_select_strategy: str, ): """ Obtains image last hidden states from the vision tower and apply multimodal projection. Args: pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`) The tensors corresponding to the input images. image_sizes (`torch.Tensor` of shape `(num_images, 2)`) Actual image size of each images (H, W). vision_feature_layer (`Union[int, List[int]], *optional*, defaults to -2`): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` Returns: image_features (List[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches and are of shape `(num_patches, image_length, embed_dim)`). """ # ! infer image_num_patches from image_sizes image_num_patches = [ image_size_to_num_patches( image_size=imsize, grid_pinpoints=self.config.image_grid_pinpoints, patch_size=self.config.vision_config.image_size, ) for imsize in image_sizes ] if pixel_values.dim() == 5: # stacked if input is (batch_size, num_patches, num_channels, height, width) _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)] pixel_values = torch.cat(_pixel_values_list, dim=0) elif pixel_values.dim() != 4: # otherwise has to be stacked from list of (num_patches, num_channels, height, width) raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions") image_features = self.vision_tower(pixel_values, output_hidden_states=True) # If we have one vision feature layer, return the corresponding hidden states, # otherwise, select the hidden states of each feature layer and concatenate them if isinstance(vision_feature_layer, int): selected_image_feature = image_features.hidden_states[vision_feature_layer] else: hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] selected_image_feature = torch.cat(hs_pool, dim=-1) if vision_feature_select_strategy == "default": selected_image_feature = selected_image_feature[:, 1:] elif vision_feature_select_strategy == "full": selected_image_feature = selected_image_feature image_features = self.multi_modal_projector(selected_image_feature) image_features = torch.split(image_features, image_num_patches, dim=0) return image_features def get_video_features( self, pixel_values: torch.FloatTensor, vision_feature_layer: Union[int, List[int]], vision_feature_select_strategy: str, ): """ Obtains video last hidden states from the vision tower, apply multimodal projection and pooling. Args: pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`) The tensors corresponding to the input video. vision_feature_layer (`Union[int, List[int]], *optional*, defaults to -2`): The index of the layer to select the vision feature. If multiple indices are provided, the vision feature of the corresponding indices will be concatenated to form the vision features. vision_feature_select_strategy (`str`): The feature selection strategy used to select the vision feature from the vision backbone. Can be one of `"default"` or `"full"` Returns: video_features (List[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches and are of shape `(num_videos, video_length, embed_dim)`). """ batch_size, frames, channels, height, width = pixel_values.shape pixel_values = pixel_values.view(batch_size * frames, channels, height, width) video_features = self.vision_tower(pixel_values, output_hidden_states=True) # If we have one vision feature layer, return the corresponding hidden states, # otherwise, select the hidden states of each feature layer and concatenate them if isinstance(vision_feature_layer, int): selected_video_feature = video_features.hidden_states[vision_feature_layer] else: hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer] selected_video_feature = torch.cat(hs_pool, dim=-1) if vision_feature_select_strategy == "default": selected_video_feature = selected_video_feature[:, 1:] elif vision_feature_select_strategy == "full": selected_video_feature = selected_video_feature video_features = self.multi_modal_projector(selected_video_feature) video_features = self.apply_pooling(video_features) video_features = video_features.reshape(batch_size, frames * video_features.shape[1], -1) return video_features @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") @add_start_docstrings(LLAVA_ONEVISION_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, pixel_values: torch.FloatTensor = None, image_sizes: Optional[torch.LongTensor] = None, pixel_values_videos: torch.FloatTensor = None, image_sizes_videos: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, vision_feature_layer: Optional[Union[int, List[int]]] = None, vision_feature_select_strategy: Optional[str] = None, vision_aspect_ratio: Optional[str] = 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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **lm_kwargs, ) -> Union[Tuple, LlavaOnevisionCausalLMOutputWithPast]: r""" Args: 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]`. logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns: [`~LlavaOnevisionCausalLMOutputWithPast`] (if `return_dict=True`) or a `tuple`. Example: ```python >>> from PIL import Image >>> import requests >>> import torch >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype="float16", device_map="cuda:0") >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf") >>> conversation = [ ... { ... "role": "user", ... "content": [ ... {"type": "text", "text": "What is shown in this image?"}, ... {"type": "image"}, ... ], ... }, ... ] >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) >>> image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> raw_image = Image.open(requests.get(image_file, stream=True).raw) >>> inputs = processor(text=prompt, images=raw_image, return_tensors='pt').to(0, torch.float16) >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False) >>> processor.batch_decode(output, skip_special_tokens=True)[0] "user\n\nWhat is shown in this image?\nassistant\ncat" ```""" 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_feature_layer = ( vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer ) vision_feature_select_strategy = ( vision_feature_select_strategy if vision_feature_select_strategy is not None else self.config.vision_feature_select_strategy ) vision_aspect_ratio = ( vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio ) if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None: raise ValueError( "You cannot specify both `pixel_values`/`pixel_values_videos` and `inputs_embeds` at the same time, " "and must specify either one" ) if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(input_ids) # Images are processed with Anyres if pixel_values is not None: image_features = self.get_image_features( pixel_values, image_sizes, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, ) image_features, feature_lens = self.pack_image_features( image_features, image_sizes, image_newline=self.image_newline, vision_aspect_ratio=vision_aspect_ratio, ) n_image_tokens = (input_ids == self.config.image_token_index).sum().item() n_image_features = image_features.shape[0] if n_image_tokens != n_image_features: raise ValueError( f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" ) special_image_mask = ( (input_ids == self.config.image_token_index) .unsqueeze(-1) .expand_as(inputs_embeds) .to(inputs_embeds.device) ) image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) # Video are simply embedded and further pooled to decrease seq len if pixel_values_videos is not None: video_features = self.get_video_features( pixel_values_videos, vision_feature_layer=vision_feature_layer, vision_feature_select_strategy=vision_feature_select_strategy, ) image_newline = ( self.image_newline[None, None, :].repeat(video_features.shape[0], 1, 1).to(video_features.device) ) video_features = torch.cat((video_features, image_newline), dim=1) video_features = video_features.flatten(0, 1) n_video_tokens = (input_ids == self.config.video_token_index).sum().item() n_video_features = video_features.shape[0] if n_video_tokens != n_video_features: raise ValueError( f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}" ) special_video_mask = ( (input_ids == self.config.video_token_index) .unsqueeze(-1) .expand_as(inputs_embeds) .to(inputs_embeds.device) ) video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype) inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features) outputs = self.language_model( attention_mask=attention_mask, position_ids=position_ids, 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, cache_position=cache_position, logits_to_keep=logits_to_keep, **lm_kwargs, ) logits = outputs[0] loss = None if labels is not None: # Shift so that tokens < n predict n if attention_mask is not None: # we use the input attention mask to shift the logits and labels, because it is 2D. # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device) shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous() shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous() else: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() loss = loss_fct( shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device) ) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return LlavaOnevisionCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=image_features if pixel_values is not None else None, video_hidden_states=video_features if pixel_values_videos is not None else None, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, image_sizes=None, pixel_values_videos=None, image_sizes_videos=None, attention_mask=None, cache_position=None, logits_to_keep=None, **kwargs, ): # Overwritten -- in specific circumstances we don't want to forward image inputs to the model model_inputs = self.language_model.prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, logits_to_keep=logits_to_keep, **kwargs, ) if cache_position[0] == 0: # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore # Otherwise we need pixel values to be passed to model model_inputs["pixel_values"] = pixel_values model_inputs["image_sizes"] = image_sizes model_inputs["pixel_values_videos"] = pixel_values_videos model_inputs["image_sizes_videos"] = image_sizes_videos return model_inputs __all__ = ["LlavaOnevisionForConditionalGeneration", "LlavaOnevisionPreTrainedModel"]

transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py/0

{ "file_path": "transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py", "repo_id": "transformers", "token_count": 17952 }

# Copyright 2021 The Fairseq 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. """Tokenization classes for M2M100.""" 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 BatchEncoding, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "spm_file": "sentencepiece.bpe.model", "tokenizer_config_file": "tokenizer_config.json", } # fmt: off FAIRSEQ_LANGUAGE_CODES = { "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"], "wmt21": ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de'] } # fmt: on class M2M100Tokenizer(PreTrainedTokenizer): """ Construct an M2M100 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`): Path to the vocabulary file. spm_file (`str`): Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary. src_lang (`str`, *optional*): A string representing the source language. tgt_lang (`str`, *optional*): A string representing the target language. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. 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. 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. language_codes (`str`, *optional*, defaults to `"m2m100"`): What language codes to use. Should be one of `"m2m100"` or `"wmt21"`. 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. Examples: ```python >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro") >>> src_text = " UN Chief Says There Is No Military Solution in Syria" >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria" >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt") >>> outputs = model(**model_inputs) # should work ```""" vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file, spm_file, src_lang=None, tgt_lang=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", pad_token="<pad>", unk_token="<unk>", language_codes="m2m100", sp_model_kwargs: Optional[Dict[str, Any]] = None, num_madeup_words=8, **kwargs, ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.language_codes = language_codes fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes] self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code} additional_special_tokens = kwargs.pop("additional_special_tokens", []) for lang_code in fairseq_language_code: token = self.get_lang_token(lang_code) if token not in additional_special_tokens and lang_code not in str(token) not in self.added_tokens_encoder: additional_special_tokens.append(token) self.vocab_file = vocab_file 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) self.encoder_size = len(self.encoder) self.lang_token_to_id = { self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code) } self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)} self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()} self._src_lang = src_lang if src_lang is not None else "en" self.tgt_lang = tgt_lang self.cur_lang_id = self.get_lang_id(self._src_lang) self.num_madeup_words = num_madeup_words super().__init__( src_lang=src_lang, tgt_lang=tgt_lang, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, unk_token=unk_token, pad_token=pad_token, language_codes=language_codes, sp_model_kwargs=self.sp_model_kwargs, additional_special_tokens=additional_special_tokens, num_madeup_words=num_madeup_words, **kwargs, ) self.set_src_lang_special_tokens(self._src_lang) @property def vocab_size(self) -> int: return len(self.encoder) def get_vocab(self) -> Dict: vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab @property def src_lang(self) -> str: return self._src_lang @src_lang.setter def src_lang(self, new_src_lang: str) -> None: self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): if token in self.lang_token_to_id: return self.lang_token_to_id[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.""" if index in self.id_to_lang_token: return self.id_to_lang_token[index] 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.""" 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: out_string += self.sp_model.decode(current_sub_tokens) + token current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() 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] * len(self.suffix_tokens) 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 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 MBART sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `X [eos, src_lang_code]` - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. 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.prefix_tokens + token_ids_0 + self.suffix_tokens # 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.suffix_tokens 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) if not save_dir.is_dir(): raise OSError(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 prepare_seq2seq_batch( self, src_texts: List[str], src_lang: str = "en", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "ro", **kwargs, ) -> BatchEncoding: self.src_lang = src_lang self.tgt_lang = tgt_lang self.set_src_lang_special_tokens(self.src_lang) return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs): """Used by translation pipeline, to prepare inputs for the generate function""" if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs) tgt_lang_id = self.get_lang_id(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs def _switch_to_input_mode(self): self.set_src_lang_special_tokens(self.src_lang) def _switch_to_target_mode(self): self.set_tgt_lang_special_tokens(self.tgt_lang) def set_src_lang_special_tokens(self, src_lang: str) -> None: """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].""" lang_token = self.get_lang_token(src_lang) self.cur_lang_id = self.lang_token_to_id[lang_token] self.prefix_tokens = [self.cur_lang_id] self.suffix_tokens = [self.eos_token_id] def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].""" lang_token = self.get_lang_token(tgt_lang) self.cur_lang_id = self.lang_token_to_id[lang_token] self.prefix_tokens = [self.cur_lang_id] self.suffix_tokens = [self.eos_token_id] def get_lang_token(self, lang: str) -> str: return self.lang_code_to_token[lang] def get_lang_id(self, lang: str) -> int: lang_token = self.get_lang_token(lang) return self.lang_token_to_id[lang_token] 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) __all__ = ["M2M100Tokenizer"]

transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py/0

{ "file_path": "transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py", "repo_id": "transformers", "token_count": 7124 }

# 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 re import warnings 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__) VOCAB_FILES_NAMES = { "source_spm": "source.spm", "target_spm": "target.spm", "vocab": "vocab.json", "target_vocab_file": "target_vocab.json", "tokenizer_config_file": "tokenizer_config.json", } SPIECE_UNDERLINE = "▁" # Example URL https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json class MarianTokenizer(PreTrainedTokenizer): r""" Construct a Marian 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: source_spm (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary for the source language. target_spm (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary for the target language. source_lang (`str`, *optional*): A string representing the source language. target_lang (`str`, *optional*): A string representing the target language. 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 `"</s>"`): The end of sequence token. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. model_max_length (`int`, *optional*, defaults to 512): The maximum sentence length the model accepts. 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. Examples: ```python >>> from transformers import MarianForCausalLM, MarianTokenizer >>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de") >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de") >>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."] >>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional >>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True) >>> outputs = model(**inputs) # should work ```""" vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] language_code_re = re.compile(">>.+<<") # type: re.Pattern def __init__( self, source_spm, target_spm, vocab, target_vocab_file=None, source_lang=None, target_lang=None, unk_token="<unk>", eos_token="</s>", pad_token="<pad>", model_max_length=512, sp_model_kwargs: Optional[Dict[str, Any]] = None, separate_vocabs=False, **kwargs, ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs assert Path(source_spm).exists(), f"cannot find spm source {source_spm}" self.separate_vocabs = separate_vocabs self.encoder = load_json(vocab) if str(unk_token) not in self.encoder: raise KeyError("<unk> token must be in the vocab") assert str(pad_token) in self.encoder if separate_vocabs: self.target_encoder = load_json(target_vocab_file) self.decoder = {v: k for k, v in self.target_encoder.items()} self.supported_language_codes = [] else: self.decoder = {v: k for k, v in self.encoder.items()} self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")] self.source_lang = source_lang self.target_lang = target_lang self.spm_files = [source_spm, target_spm] # load SentencePiece model for pre-processing self.spm_source = load_spm(source_spm, self.sp_model_kwargs) self.spm_target = load_spm(target_spm, self.sp_model_kwargs) self.current_spm = self.spm_source self.current_encoder = self.encoder # Multilingual target side: default to using first supported language code. self._setup_normalizer() super().__init__( # bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id source_lang=source_lang, target_lang=target_lang, unk_token=unk_token, eos_token=eos_token, pad_token=pad_token, model_max_length=model_max_length, sp_model_kwargs=self.sp_model_kwargs, target_vocab_file=target_vocab_file, separate_vocabs=separate_vocabs, **kwargs, ) def _setup_normalizer(self): try: from sacremoses import MosesPunctNormalizer self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize except (ImportError, FileNotFoundError): warnings.warn("Recommended: pip install sacremoses.") self.punc_normalizer = lambda x: x def normalize(self, x: str) -> str: """Cover moses empty string edge case. They return empty list for '' input!""" return self.punc_normalizer(x) if x else "" def _convert_token_to_id(self, token): return self.current_encoder.get(token, self.current_encoder[self.unk_token]) def remove_language_code(self, text: str): """Remove language codes like >>fr<< before sentencepiece""" match = self.language_code_re.match(text) code: list = [match.group(0)] if match else [] return code, self.language_code_re.sub("", text) def _tokenize(self, text: str) -> List[str]: code, text = self.remove_language_code(text) pieces = self.current_spm.encode(text, out_type=str) return code + pieces 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 batch_decode(self, sequences, **kwargs): """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). use_source_tokenizer (`bool`, *optional*, defaults to `False`): Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence problems). kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]`: The list of decoded sentences. """ return super().batch_decode(sequences, **kwargs) def decode(self, token_ids, **kwargs): """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). use_source_tokenizer (`bool`, *optional*, defaults to `False`): Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence problems). kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ return super().decode(token_ids, **kwargs) def convert_tokens_to_string(self, tokens: List[str]) -> str: """Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise""" sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target 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: out_string += sp_model.decode_pieces(current_sub_tokens) + token + " " current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += sp_model.decode_pieces(current_sub_tokens) out_string = out_string.replace(SPIECE_UNDERLINE, " ") 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 token_ids_0 + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return token_ids_0 + token_ids_1 + [self.eos_token_id] def _switch_to_input_mode(self): self.current_spm = self.spm_source self.current_encoder = self.encoder def _switch_to_target_mode(self): self.current_spm = self.spm_target if self.separate_vocabs: self.current_encoder = self.target_encoder @property def vocab_size(self) -> int: return len(self.encoder) 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 saved_files = [] if self.separate_vocabs: out_src_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"], ) out_tgt_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"], ) save_json(self.encoder, out_src_vocab_file) save_json(self.target_encoder, out_tgt_vocab_file) saved_files.append(out_src_vocab_file) saved_files.append(out_tgt_vocab_file) else: out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] ) save_json(self.encoder, out_vocab_file) saved_files.append(out_vocab_file) for spm_save_filename, spm_orig_path, spm_model in zip( [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]], self.spm_files, [self.spm_source, self.spm_target], ): spm_save_path = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + spm_save_filename ) if os.path.abspath(spm_orig_path) != os.path.abspath(spm_save_path) and os.path.isfile(spm_orig_path): copyfile(spm_orig_path, spm_save_path) saved_files.append(spm_save_path) elif not os.path.isfile(spm_orig_path): with open(spm_save_path, "wb") as fi: content_spiece_model = spm_model.serialized_model_proto() fi.write(content_spiece_model) saved_files.append(spm_save_path) return tuple(saved_files) def get_vocab(self) -> Dict: return self.get_src_vocab() def get_src_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def get_tgt_vocab(self): return dict(self.target_encoder, **self.added_tokens_decoder) def __getstate__(self) -> Dict: state = self.__dict__.copy() state.update( {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]} ) 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.spm_source, self.spm_target = (load_spm(f, self.sp_model_kwargs) for f in self.spm_files) self.current_spm = self.spm_source self._setup_normalizer() def num_special_tokens_to_add(self, *args, **kwargs): """Just EOS""" return 1 def _special_token_mask(self, seq): all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special return [1 if x in all_special_ids else 0 for x in seq] def get_special_tokens_mask( self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False ) -> List[int]: """Get list where entries are [1] if a token is [eos] or [pad] else 0.""" if already_has_special_tokens: return self._special_token_mask(token_ids_0) elif token_ids_1 is None: return self._special_token_mask(token_ids_0) + [1] else: return self._special_token_mask(token_ids_0 + token_ids_1) + [1] def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor: spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs) spm.Load(path) return spm def save_json(data, path: str) -> None: with open(path, "w") as f: json.dump(data, f, indent=2) def load_json(path: str) -> Union[Dict, List]: with open(path, "r") as f: return json.load(f) __all__ = ["MarianTokenizer"]

transformers/src/transformers/models/marian/tokenization_marian.py/0

{ "file_path": "transformers/src/transformers/models/marian/tokenization_marian.py", "repo_id": "transformers", "token_count": 7228 }

# 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. import sys from argparse import ArgumentParser from dataclasses import dataclass from pathlib import Path from pprint import pformat from typing import Any, Dict, Iterator, List, Set, Tuple import requests import torch import torchvision.transforms as T from detectron2.checkpoint import DetectionCheckpointer from detectron2.config import get_cfg from detectron2.data import MetadataCatalog from detectron2.projects.deeplab import add_deeplab_config from PIL import Image from torch import Tensor, nn from transformers.models.maskformer.feature_extraction_maskformer import MaskFormerImageProcessor from transformers.models.maskformer.modeling_maskformer import ( MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerForInstanceSegmentationOutput, MaskFormerModel, MaskFormerModelOutput, ) from transformers.utils import logging StateDict = Dict[str, Tensor] logging.set_verbosity_info() logger = logging.get_logger() torch.manual_seed(0) class TrackedStateDict: def __init__(self, to_track: Dict): """This class "tracks" a python dictionary by keeping track of which item is accessed. Args: to_track (Dict): The dictionary we wish to track """ self.to_track = to_track self._seen: Set[str] = set() def __getitem__(self, key: str) -> Any: return self.to_track[key] def __setitem__(self, key: str, item: Any): self._seen.add(key) self.to_track[key] = item def diff(self) -> List[str]: """This method returns a set difference between the keys in the tracked state dict and the one we have access so far. This is an effective method to check if we have update all the keys Returns: List[str]: List of keys not yet updated """ return set(self.to_track.keys()) - self._seen def copy(self) -> Dict: # proxy the call to the internal dictionary return self.to_track.copy() # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" img_data = requests.get(url, stream=True).raw im = Image.open(img_data) return im @dataclass class Args: """Fake command line arguments needed by maskformer/detectron implementation""" config_file: str def setup_cfg(args: Args): # load config from file and command-line arguments cfg = get_cfg() add_deeplab_config(cfg) add_mask_former_config(cfg) cfg.merge_from_file(args.config_file) cfg.freeze() return cfg class OriginalMaskFormerConfigToOursConverter: def __call__(self, original_config: object) -> MaskFormerConfig: model = original_config.MODEL mask_former = model.MASK_FORMER swin = model.SWIN dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0]) id2label = dict(enumerate(dataset_catalog.stuff_classes)) label2id = {label: idx for idx, label in id2label.items()} config: MaskFormerConfig = MaskFormerConfig( fpn_feature_size=model.SEM_SEG_HEAD.CONVS_DIM, mask_feature_size=model.SEM_SEG_HEAD.MASK_DIM, num_labels=model.SEM_SEG_HEAD.NUM_CLASSES, no_object_weight=mask_former.NO_OBJECT_WEIGHT, num_queries=mask_former.NUM_OBJECT_QUERIES, backbone_config={ "pretrain_img_size": swin.PRETRAIN_IMG_SIZE, "image_size": swin.PRETRAIN_IMG_SIZE, "in_channels": 3, "patch_size": swin.PATCH_SIZE, "embed_dim": swin.EMBED_DIM, "depths": swin.DEPTHS, "num_heads": swin.NUM_HEADS, "window_size": swin.WINDOW_SIZE, "drop_path_rate": swin.DROP_PATH_RATE, "model_type": "swin", }, dice_weight=mask_former.DICE_WEIGHT, ce_weight=1.0, mask_weight=mask_former.MASK_WEIGHT, decoder_config={ "model_type": "detr", "max_position_embeddings": 1024, "encoder_layers": 6, "encoder_ffn_dim": 2048, "encoder_attention_heads": 8, "decoder_layers": mask_former.DEC_LAYERS, "decoder_ffn_dim": mask_former.DIM_FEEDFORWARD, "decoder_attention_heads": mask_former.NHEADS, "encoder_layerdrop": 0.0, "decoder_layerdrop": 0.0, "d_model": mask_former.HIDDEN_DIM, "dropout": mask_former.DROPOUT, "attention_dropout": 0.0, "activation_dropout": 0.0, "init_std": 0.02, "init_xavier_std": 1.0, "scale_embedding": False, "auxiliary_loss": False, "dilation": False, # default pretrained config values }, id2label=id2label, label2id=label2id, ) return config class OriginalMaskFormerConfigToImageProcessorConverter: def __call__(self, original_config: object) -> MaskFormerImageProcessor: model = original_config.MODEL model_input = original_config.INPUT dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0]) return MaskFormerImageProcessor( image_mean=(torch.tensor(model.PIXEL_MEAN) / 255).tolist(), image_std=(torch.tensor(model.PIXEL_STD) / 255).tolist(), size=model_input.MIN_SIZE_TEST, max_size=model_input.MAX_SIZE_TEST, num_labels=model.SEM_SEG_HEAD.NUM_CLASSES, ignore_index=dataset_catalog.ignore_label, size_divisibility=32, # 32 is required by swin ) class OriginalMaskFormerCheckpointToOursConverter: def __init__(self, original_model: nn.Module, config: MaskFormerConfig): self.original_model = original_model self.config = config def pop_all(self, renamed_keys: List[Tuple[str, str]], dst_state_dict: StateDict, src_state_dict: StateDict): for src_key, dst_key in renamed_keys: dst_state_dict[dst_key] = src_state_dict.pop(src_key) def replace_backbone(self, dst_state_dict: StateDict, src_state_dict: StateDict, config: MaskFormerConfig): dst_prefix: str = "pixel_level_module.encoder" src_prefix: str = "backbone" renamed_keys = [ ( f"{src_prefix}.patch_embed.proj.weight", f"{dst_prefix}.model.embeddings.patch_embeddings.projection.weight", ), (f"{src_prefix}.patch_embed.proj.bias", f"{dst_prefix}.model.embeddings.patch_embeddings.projection.bias"), (f"{src_prefix}.patch_embed.norm.weight", f"{dst_prefix}.model.embeddings.norm.weight"), (f"{src_prefix}.patch_embed.norm.bias", f"{dst_prefix}.model.embeddings.norm.bias"), ] num_layers = len(config.backbone_config.depths) for layer_idx in range(num_layers): for block_idx in range(config.backbone_config.depths[layer_idx]): renamed_keys.extend( [ # src, dst ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.bias", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_bias_table", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_bias_table", ), ] ) # now we need to handle the attentions # read in weights + bias of input projection layer of cross-attention src_att_weight = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight"] src_att_bias = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias"] size = src_att_weight.shape[0] offset = size // 3 dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.weight" ] = src_att_weight[:offset, :] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.bias" ] = src_att_bias[:offset] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.weight" ] = src_att_weight[offset : offset * 2, :] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.bias" ] = src_att_bias[offset : offset * 2] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.weight" ] = src_att_weight[-offset:, :] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.bias" ] = src_att_bias[-offset:] # let's pop them src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight") src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias") # proj renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.bias", ), ] ) # second norm renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.bias", ), ] ) # mlp renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.bias", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.bias", ), ] ) renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_index", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_index", ) ] ) if layer_idx < num_layers - 1: # patch merging renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.downsample.reduction.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.reduction.weight", ), ( f"{src_prefix}.layers.{layer_idx}.downsample.norm.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.weight", ), ( f"{src_prefix}.layers.{layer_idx}.downsample.norm.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.bias", ), ] ) # hidden states norms renamed_keys.extend( [ ( f"{src_prefix}.norm{layer_idx}.weight", f"{dst_prefix}.hidden_states_norms.{layer_idx}.weight", ), ( f"{src_prefix}.norm{layer_idx}.bias", f"{dst_prefix}.hidden_states_norms.{layer_idx}.bias", ), ] ) self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def replace_pixel_module(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "pixel_level_module.decoder" src_prefix: str = "sem_seg_head.pixel_decoder" self.replace_backbone(dst_state_dict, src_state_dict, self.config) def rename_keys_for_conv(detectron_conv: str, mine_conv: str): return [ (f"{detectron_conv}.weight", f"{mine_conv}.0.weight"), # 2 cuz the have act in the middle -> rename it (f"{detectron_conv}.norm.weight", f"{mine_conv}.1.weight"), (f"{detectron_conv}.norm.bias", f"{mine_conv}.1.bias"), ] renamed_keys = [ (f"{src_prefix}.mask_features.weight", f"{dst_prefix}.mask_projection.weight"), (f"{src_prefix}.mask_features.bias", f"{dst_prefix}.mask_projection.bias"), # the layers in the original one are in reverse order, stem is the last one! ] renamed_keys.extend(rename_keys_for_conv(f"{src_prefix}.layer_4", f"{dst_prefix}.fpn.stem")) # add all the fpn layers (here we need some config parameters to know the size in advance) for src_i, dst_i in zip(range(3, 0, -1), range(0, 3)): renamed_keys.extend( rename_keys_for_conv(f"{src_prefix}.adapter_{src_i}", f"{dst_prefix}.fpn.layers.{dst_i}.proj") ) renamed_keys.extend( rename_keys_for_conv(f"{src_prefix}.layer_{src_i}", f"{dst_prefix}.fpn.layers.{dst_i}.block") ) self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def rename_keys_in_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module.decoder" src_prefix: str = "sem_seg_head.predictor.transformer.decoder" # not sure why we are not popping direcetly here! # here we list all keys to be renamed (original name on the left, our name on the right) rename_keys = [] for i in range(self.config.decoder_config.decoder_layers): # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms rename_keys.append( ( f"{src_prefix}.layers.{i}.self_attn.out_proj.weight", f"{dst_prefix}.layers.{i}.self_attn.out_proj.weight", ) ) rename_keys.append( ( f"{src_prefix}.layers.{i}.self_attn.out_proj.bias", f"{dst_prefix}.layers.{i}.self_attn.out_proj.bias", ) ) rename_keys.append( ( f"{src_prefix}.layers.{i}.multihead_attn.out_proj.weight", f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.weight", ) ) rename_keys.append( ( f"{src_prefix}.layers.{i}.multihead_attn.out_proj.bias", f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.bias", ) ) rename_keys.append((f"{src_prefix}.layers.{i}.linear1.weight", f"{dst_prefix}.layers.{i}.fc1.weight")) rename_keys.append((f"{src_prefix}.layers.{i}.linear1.bias", f"{dst_prefix}.layers.{i}.fc1.bias")) rename_keys.append((f"{src_prefix}.layers.{i}.linear2.weight", f"{dst_prefix}.layers.{i}.fc2.weight")) rename_keys.append((f"{src_prefix}.layers.{i}.linear2.bias", f"{dst_prefix}.layers.{i}.fc2.bias")) rename_keys.append( (f"{src_prefix}.layers.{i}.norm1.weight", f"{dst_prefix}.layers.{i}.self_attn_layer_norm.weight") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm1.bias", f"{dst_prefix}.layers.{i}.self_attn_layer_norm.bias") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm2.weight", f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.weight") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm2.bias", f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.bias") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm3.weight", f"{dst_prefix}.layers.{i}.final_layer_norm.weight") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm3.bias", f"{dst_prefix}.layers.{i}.final_layer_norm.bias") ) return rename_keys def replace_q_k_v_in_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module.decoder" src_prefix: str = "sem_seg_head.predictor.transformer.decoder" for i in range(self.config.decoder_config.decoder_layers): # read in weights + bias of input projection layer of self-attention in_proj_weight = src_state_dict.pop(f"{src_prefix}.layers.{i}.self_attn.in_proj_weight") in_proj_bias = src_state_dict.pop(f"{src_prefix}.layers.{i}.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:] # read in weights + bias of input projection layer of cross-attention in_proj_weight_cross_attn = src_state_dict.pop(f"{src_prefix}.layers.{i}.multihead_attn.in_proj_weight") in_proj_bias_cross_attn = src_state_dict.pop(f"{src_prefix}.layers.{i}.multihead_attn.in_proj_bias") # next, add query, keys and values (in that order) of cross-attention to the state dict dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.q_proj.weight"] = in_proj_weight_cross_attn[:256, :] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.q_proj.bias"] = in_proj_bias_cross_attn[:256] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.k_proj.weight"] = in_proj_weight_cross_attn[ 256:512, : ] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.k_proj.bias"] = in_proj_bias_cross_attn[256:512] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.v_proj.weight"] = in_proj_weight_cross_attn[-256:, :] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.v_proj.bias"] = in_proj_bias_cross_attn[-256:] def replace_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module.decoder" src_prefix: str = "sem_seg_head.predictor.transformer.decoder" renamed_keys = self.rename_keys_in_detr_decoder(dst_state_dict, src_state_dict) # add more renamed_keys.extend( [ (f"{src_prefix}.norm.weight", f"{dst_prefix}.layernorm.weight"), (f"{src_prefix}.norm.bias", f"{dst_prefix}.layernorm.bias"), ] ) self.pop_all(renamed_keys, dst_state_dict, src_state_dict) self.replace_q_k_v_in_detr_decoder(dst_state_dict, src_state_dict) def replace_transformer_module(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module" src_prefix: str = "sem_seg_head.predictor" self.replace_detr_decoder(dst_state_dict, src_state_dict) renamed_keys = [ (f"{src_prefix}.query_embed.weight", f"{dst_prefix}.queries_embedder.weight"), (f"{src_prefix}.input_proj.weight", f"{dst_prefix}.input_projection.weight"), (f"{src_prefix}.input_proj.bias", f"{dst_prefix}.input_projection.bias"), ] self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def replace_instance_segmentation_module(self, dst_state_dict: StateDict, src_state_dict: StateDict): # NOTE in our case we don't have a prefix, thus we removed the "." from the keys later on! dst_prefix: str = "" src_prefix: str = "sem_seg_head.predictor" renamed_keys = [ (f"{src_prefix}.class_embed.weight", f"{dst_prefix}class_predictor.weight"), (f"{src_prefix}.class_embed.bias", f"{dst_prefix}class_predictor.bias"), ] mlp_len = 3 for i in range(mlp_len): renamed_keys.extend( [ (f"{src_prefix}.mask_embed.layers.{i}.weight", f"{dst_prefix}mask_embedder.{i}.0.weight"), (f"{src_prefix}.mask_embed.layers.{i}.bias", f"{dst_prefix}mask_embedder.{i}.0.bias"), ] ) logger.info(f"Replacing keys {pformat(renamed_keys)}") self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def convert(self, mask_former: MaskFormerModel) -> MaskFormerModel: dst_state_dict = TrackedStateDict(mask_former.state_dict()) src_state_dict = self.original_model.state_dict() self.replace_pixel_module(dst_state_dict, src_state_dict) self.replace_transformer_module(dst_state_dict, src_state_dict) logger.info(f"Missed keys are {pformat(dst_state_dict.diff())}") logger.info(f"Not copied keys are {pformat(src_state_dict.keys())}") logger.info("🙌 Done") mask_former.load_state_dict(dst_state_dict) return mask_former def convert_instance_segmentation( self, mask_former: MaskFormerForInstanceSegmentation ) -> MaskFormerForInstanceSegmentation: dst_state_dict = TrackedStateDict(mask_former.state_dict()) src_state_dict = self.original_model.state_dict() self.replace_instance_segmentation_module(dst_state_dict, src_state_dict) mask_former.load_state_dict(dst_state_dict) return mask_former @staticmethod def using_dirs(checkpoints_dir: Path, config_dir: Path) -> Iterator[Tuple[object, Path, Path]]: checkpoints: List[Path] = checkpoints_dir.glob("**/*.pkl") for checkpoint in checkpoints: logger.info(f"💪 Converting {checkpoint.stem}") # find associated config file config: Path = config_dir / checkpoint.parents[0].stem / "swin" / f"{checkpoint.stem}.yaml" yield config, checkpoint def test(original_model, our_model: MaskFormerForInstanceSegmentation, image_processor: MaskFormerImageProcessor): with torch.no_grad(): original_model = original_model.eval() our_model = our_model.eval() im = prepare_img() tr = T.Compose( [ T.Resize((384, 384)), T.ToTensor(), T.Normalize( mean=torch.tensor([123.675, 116.280, 103.530]) / 255.0, std=torch.tensor([58.395, 57.120, 57.375]) / 255.0, ), ], ) x = tr(im).unsqueeze(0) original_model_backbone_features = original_model.backbone(x.clone()) our_model_output: MaskFormerModelOutput = our_model.model(x.clone(), output_hidden_states=True) for original_model_feature, our_model_feature in zip( original_model_backbone_features.values(), our_model_output.encoder_hidden_states ): assert torch.allclose( original_model_feature, our_model_feature, atol=1e-3 ), "The backbone features are not the same." original_model_pixel_out = original_model.sem_seg_head.pixel_decoder.forward_features( original_model_backbone_features ) assert torch.allclose( original_model_pixel_out[0], our_model_output.pixel_decoder_last_hidden_state, atol=1e-4 ), "The pixel decoder feature are not the same" # let's test the full model original_model_out = original_model([{"image": x.squeeze(0)}]) original_segmentation = original_model_out[0]["sem_seg"] our_model_out: MaskFormerForInstanceSegmentationOutput = our_model(x) our_segmentation = image_processor.post_process_segmentation(our_model_out, target_size=(384, 384)) assert torch.allclose( original_segmentation, our_segmentation, atol=1e-3 ), "The segmentation image is not the same." logger.info("✅ Test passed!") def get_name(checkpoint_file: Path): model_name_raw: str = checkpoint_file.stem # model_name_raw is something like maskformer_panoptic_swin_base_IN21k_384_bs64_554k parent_name: str = checkpoint_file.parents[0].stem backbone = "swin" dataset = "" if "coco" in parent_name: dataset = "coco" elif "ade" in parent_name: dataset = "ade" else: raise ValueError(f"{parent_name} must be wrong since we didn't find 'coco' or 'ade' in it ") backbone_types = ["tiny", "small", "base", "large"] backbone_type = list(filter(lambda x: x in model_name_raw, backbone_types))[0] model_name = f"maskformer-{backbone}-{backbone_type}-{dataset}" return model_name if __name__ == "__main__": parser = ArgumentParser( description="Command line to convert the original maskformers (with swin backbone) to our implementations." ) parser.add_argument( "--checkpoints_dir", type=Path, help=( "A directory containing the model's checkpoints. The directory has to have the following structure:" " <DIR_NAME>/<DATASET_NAME>/<CONFIG_NAME>.pkl\n" "Given the files are in the pickle format, please be wary of passing it files you trust." ), ) parser.add_argument( "--configs_dir", type=Path, help=( "A directory containing the model's configs, see detectron2 doc. The directory has to have the following" " structure: <DIR_NAME>/<DATASET_NAME>/<CONFIG_NAME>.yaml" ), ) parser.add_argument( "--pytorch_dump_folder_path", required=True, type=Path, help="Path to the folder to output PyTorch models.", ) parser.add_argument( "--maskformer_dir", required=True, type=Path, help=( "A path to MaskFormer's original implementation directory. You can download from here:" " https://github.com/facebookresearch/MaskFormer" ), ) args = parser.parse_args() checkpoints_dir: Path = args.checkpoints_dir config_dir: Path = args.configs_dir save_directory: Path = args.pytorch_dump_folder_path maskformer_dir: Path = args.maskformer_dir # append the path to the parents to maskformer dir sys.path.append(str(maskformer_dir.parent)) # and import what's needed from MaskFormer.mask_former import add_mask_former_config from MaskFormer.mask_former.mask_former_model import MaskFormer as OriginalMaskFormer if not save_directory.exists(): save_directory.mkdir(parents=True) for config_file, checkpoint_file in OriginalMaskFormerCheckpointToOursConverter.using_dirs( checkpoints_dir, config_dir ): image_processor = OriginalMaskFormerConfigToImageProcessorConverter()(setup_cfg(Args(config_file=config_file))) original_config = setup_cfg(Args(config_file=config_file)) mask_former_kwargs = OriginalMaskFormer.from_config(original_config) original_model = OriginalMaskFormer(**mask_former_kwargs).eval() DetectionCheckpointer(original_model).load(str(checkpoint_file)) config: MaskFormerConfig = OriginalMaskFormerConfigToOursConverter()(original_config) mask_former = MaskFormerModel(config=config).eval() converter = OriginalMaskFormerCheckpointToOursConverter(original_model, config) maskformer = converter.convert(mask_former) mask_former_for_instance_segmentation = MaskFormerForInstanceSegmentation(config=config).eval() mask_former_for_instance_segmentation.model = mask_former mask_former_for_instance_segmentation = converter.convert_instance_segmentation( mask_former_for_instance_segmentation ) test(original_model, mask_former_for_instance_segmentation, image_processor) model_name = get_name(checkpoint_file) logger.info(f"🪄 Saving {model_name}") image_processor.save_pretrained(save_directory / model_name) mask_former_for_instance_segmentation.save_pretrained(save_directory / model_name) image_processor.push_to_hub( repo_path_or_name=save_directory / model_name, commit_message="Add model", use_temp_dir=True, ) mask_former_for_instance_segmentation.push_to_hub( repo_path_or_name=save_directory / model_name, commit_message="Add model", use_temp_dir=True, )

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

{ "file_path": "transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 16139 }

# coding=utf-8 # Copyright 2021 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 os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] # fmt: skip class MBart50Tokenizer(PreTrainedTokenizer): """ Construct a MBart50 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`): Path to the vocabulary file. src_lang (`str`, *optional*): A string representing the source language. tgt_lang (`str`, *optional*): A string representing the target language. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. 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. 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. Examples: ```python >>> from transformers import MBart50Tokenizer >>> tokenizer = MBart50Tokenizer.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO") >>> src_text = " UN Chief Says There Is No Military Solution in Syria" >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria" >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt") >>> # model(**model_inputs) should work ```""" vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file, src_lang=None, tgt_lang=None, eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", 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 mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or [] kwargs["additional_special_tokens"] += [ code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"] ] self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.sp_model_size = len(self.sp_model) self.lang_code_to_id = { code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES) } self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()} self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset self.fairseq_tokens_to_ids.update(self.lang_code_to_id) self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} super().__init__( src_lang=src_lang, tgt_lang=tgt_lang, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) self._src_lang = src_lang if src_lang is not None else "en_XX" self.cur_lang_code_id = self.lang_code_to_id[self._src_lang] self.tgt_lang = tgt_lang self.set_src_lang_special_tokens(self._src_lang) @property def vocab_size(self) -> int: return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token @property def src_lang(self) -> str: return self._src_lang @src_lang.setter def src_lang(self, new_src_lang: str) -> None: self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) 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 = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def get_vocab(self) -> Dict: vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) # 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 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,) 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] * len(self.suffix_tokens) 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 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 MBART-50 sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `[src_lang_code] X [eos]` - `labels`: (for decoder) `[tgt_lang_code] X [eos]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. 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.prefix_tokens + token_ids_0 + self.suffix_tokens # 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.suffix_tokens def _build_translation_inputs( self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs ): """Used by translation pipeline, to prepare inputs for the generate function""" if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs) tgt_lang_id = self.convert_tokens_to_ids(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs def prepare_seq2seq_batch( self, src_texts: List[str], src_lang: str = "en_XX", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "ro_RO", **kwargs, ) -> BatchEncoding: self.src_lang = src_lang self.tgt_lang = tgt_lang return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) def _switch_to_input_mode(self): return self.set_src_lang_special_tokens(self.src_lang) def _switch_to_target_mode(self): return self.set_tgt_lang_special_tokens(self.tgt_lang) def set_src_lang_special_tokens(self, src_lang: str) -> None: """Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos].""" self.cur_lang_code_id = self.lang_code_to_id[src_lang] self.prefix_tokens = [self.cur_lang_code_id] self.suffix_tokens = [self.eos_token_id] def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos].""" self.cur_lang_code_id = self.lang_code_to_id[tgt_lang] self.prefix_tokens = [self.cur_lang_code_id] self.suffix_tokens = [self.eos_token_id] __all__ = ["MBart50Tokenizer"]

transformers/src/transformers/models/mbart50/tokenization_mbart50.py/0

{ "file_path": "transformers/src/transformers/models/mbart50/tokenization_mbart50.py", "repo_id": "transformers", "token_count": 7119 }

# coding=utf-8 # Copyright 2024 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 Mimi checkpoints.""" import argparse import safetensors import torch from transformers import ( EncodecFeatureExtractor, MimiConfig, MimiModel, logging, ) logging.set_verbosity_info() logger = logging.get_logger("transformers.models.mimi") def assert_param_count(model_1, model_2): count_1 = sum(p[1].numel() for p in model_1.named_parameters() if "final_proj" not in p[0]) count_2 = sum(p[1].numel() for p in model_2.named_parameters() if "final_proj" not in p[0]) assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}" def param_count(model): return sum(p[1].numel() for p in model.named_parameters() if "final_proj" not in p[0]) def _grab_best_device(use_gpu=True): if torch.cuda.device_count() > 0 and use_gpu: device = "cuda" else: device = "cpu" return torch.device(device) convert_list = [ # GENERAL ("conv.conv.conv", "conv"), ("convtr.convtr.convtr", "conv"), ("conv.conv", "conv"), ("convtr.convtr", "conv"), # QUANTIZER ("quantizer.rvq_first.vq", "quantizer.semantic_residual_vector_quantizer"), ("quantizer.rvq_first", "quantizer.semantic_residual_vector_quantizer"), ("quantizer.rvq_rest.vq", "quantizer.acoustic_residual_vector_quantizer"), ("quantizer.rvq_rest", "quantizer.acoustic_residual_vector_quantizer"), ("_codebook", "codebook"), ("_initialized", "initialized"), ("embedding_sum", "embed_sum"), # ENCODER PART ("encoder.model", "encoder.layers"), ("decoder.model", "decoder.layers"), # TRANSFORMERS PART ("encoder_transformer.transformer", "encoder_transformer"), ("decoder_transformer.transformer", "decoder_transformer"), ("linear1", "mlp.fc1"), ("linear2", "mlp.fc2"), ("self_attn.out_proj", "self_attn.o_proj"), ("norm1", "input_layernorm"), ("norm2", "post_attention_layernorm"), ("layer_scale_1", "self_attn_layer_scale"), ("layer_scale_2", "mlp_layer_scale"), ] def _convert_model( state_dict, hf_model, convert_list, device, config, unwanted_prefix=None, ): hidden_size = config.hidden_size head_dim = config.head_dim num_heads = int(config.hidden_size // config.head_dim) num_key_value_heads = config.num_key_value_heads key_value_head_dim = config.num_key_value_heads * head_dim # permute for sliced rotary def permute(w, n_heads, dim1=hidden_size, dim2=hidden_size): return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2) for k, v in list(state_dict.items()): new_k = k if unwanted_prefix is None else k[len(unwanted_prefix) :] for old_layer_name, new_layer_name in convert_list: if old_layer_name in new_k: new_k = new_k.replace(old_layer_name, new_layer_name) if "in_proj_weight" in new_k: # split qkv into query key and value mixed_qkv = state_dict.pop(k) qkv_dim = mixed_qkv.size(0) // 3 query_layer = mixed_qkv[:qkv_dim] key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] value_layer = mixed_qkv[qkv_dim * 2 :] state_dict[new_k.replace("in_proj_weight", "q_proj.weight")] = permute(query_layer, num_heads) state_dict[new_k.replace("in_proj_weight", "k_proj.weight")] = permute( key_layer, num_key_value_heads, dim1=key_value_head_dim ) state_dict[new_k.replace("in_proj_weight", "v_proj.weight")] = value_layer else: state_dict[new_k] = state_dict.pop(k) extra_keys = set(state_dict.keys()) - set(hf_model.state_dict().keys()) missing_keys = set(hf_model.state_dict().keys()) - set(state_dict.keys()) if len(extra_keys) != 0: raise ValueError(f"extra keys found: {extra_keys}") if len(missing_keys) != 0: raise ValueError(f"missing keys: {missing_keys}") hf_model.load_state_dict(state_dict, strict=True) n_params = param_count(hf_model) logger.info(f"model loaded: {round(n_params/1e6,1)}M params") hf_model.eval() hf_model.to(device) del state_dict return hf_model @torch.no_grad() def convert_checkpoint( checkpoint_path, pytorch_dump_folder_path, config_path=None, repo_id=None, ): """ Copy/paste/tweak model's weights to transformers design. """ device = _grab_best_device() if config_path is not None: config = MimiConfig.from_pretrained(config_path) else: config = MimiConfig() model = MimiModel(config) feature_extractor = EncodecFeatureExtractor( feature_size=config.audio_channels, sampling_rate=config.sampling_rate, ) feature_extractor.save_pretrained(pytorch_dump_folder_path) original_checkpoint = safetensors.torch.load_file(checkpoint_path) if "best_state" in original_checkpoint: # we might have a training state saved, in which case discard the yaml results and just retain the weights original_checkpoint = original_checkpoint["best_state"] model = _convert_model(original_checkpoint, model, convert_list, device, config) model.save_pretrained(pytorch_dump_folder_path) if repo_id: print("Pushing to the hub...") feature_extractor.push_to_hub(repo_id) model.push_to_hub(repo_id) if __name__ == "__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." ) args = parser.parse_args() convert_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, )

transformers/src/transformers/models/mimi/convert_mimi_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/mimi/convert_mimi_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 2809 }

# Copyright 2024 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 gc import json import math import os from typing import List, Optional import regex as re import torch import torch.nn.functional as F from transformers import ( GenerationConfig, MllamaConfig, MllamaForConditionalGeneration, MllamaImageProcessor, PreTrainedTokenizerFast, ) from transformers.convert_slow_tokenizer import TikTokenConverter from transformers.models.mllama.configuration_mllama import MllamaTextConfig, MllamaVisionConfig from transformers.models.mllama.image_processing_mllama import get_all_supported_aspect_ratios # fmt: off # If a weight needs to be split in two or more keys, use `|` to indicate it. ex: # r"text_model.layers.(\d+).attention.wqkv.weight": r"language_model.model.layers.\1.self_attn.q|k|v|_proj.weight" ORIGINAL_TO_CONVERTED_KEY_MAPPING = { r"text_model.norm.weight": r"language_model.model.norm.weight", r"text_model.output.weight": r"language_model.lm_head.weight", r"text_model.tok_embeddings": r"language_model.model.embed_tokens", r"text_model.learnable_embedding": r"language_model.model.learnable_embedding", r"text_model.rope.freqs": None, # meaning we skip it and don't want it # For every cross attention layer, the layer needs to be updated r"text_model.cross_attention_layers.(\d+).gate_attn": r"language_model.model.layers.\1.cross_attn_attn_gate", r"text_model.cross_attention_layers.(\d+).gate_ffwd": r"language_model.model.layers.\1.cross_attn_mlp_gate", # special key, wqkv needs to be split afterwards r"text_model.cross_attention_layers.(\d+).attention.w(q|k|v|o)": r"language_model.model.layers.\1.cross_attn.\2_proj", r"text_model.cross_attention_layers.(\d+).attention.(q|k)_norm": r"language_model.model.layers.\1.cross_attn.\2_norm", r"text_model.cross_attention_layers.(\d+).attention_norm.weight": r"language_model.model.layers.\1.input_layernorm.weight", r"text_model.cross_attention_layers.(\d+).attention.wk.layer_norm_weight": r"language_model.model.layers.\1.post_attention_layernorm.weight", r"text_model.cross_attention_layers.(\d+).feed_forward.w1.weight": r"language_model.model.layers.\1.mlp.gate_proj.weight", r"text_model.cross_attention_layers.(\d+).feed_forward.w2.weight": r"language_model.model.layers.\1.mlp.down_proj.weight", r"text_model.cross_attention_layers.(\d+).feed_forward.w3.weight": r"language_model.model.layers.\1.mlp.up_proj.weight", r"text_model.cross_attention_layers.(\d+).ffn_norm.weight": r"language_model.model.layers.\1.post_attention_layernorm.weight", # self attention layers r"text_model.layers.(\d+).attention.w(q|k|v|o).weight": r"language_model.model.layers.\1.self_attn.\2_proj.weight", r"text_model.layers.(\d+).attention_norm.weight": r"language_model.model.layers.\1.input_layernorm.weight", r"text_model.layers.(\d+).feed_forward.w1.": r"language_model.model.layers.\1.mlp.gate_proj.", r"text_model.layers.(\d+).feed_forward.w2.": r"language_model.model.layers.\1.mlp.down_proj.", r"text_model.layers.(\d+).feed_forward.w3.": r"language_model.model.layers.\1.mlp.up_proj.", r"text_model.layers.(\d+).ffn_norm.weight": r"language_model.model.layers.\1.post_attention_layernorm.weight", # Vision encoder mapping r"vision_model.vision_encoder.conv1._linear": r"vision_model.patch_embedding", r'vision_model.vision_projection.': r"multi_modal_projector.", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wq": r"vision_model.\1.layers.\2.self_attn.q_proj", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wk": r"vision_model.\1.layers.\2.self_attn.k_proj", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wv": r"vision_model.\1.layers.\2.self_attn.v_proj", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wo": r"vision_model.\1.layers.\2.self_attn.o_proj", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).mlp.c_fc": r"vision_model.\1.layers.\2.mlp.fc1", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).mlp.c_proj": r"vision_model.\1.layers.\2.mlp.fc2", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).ln_1": r"vision_model.\1.layers.\2.input_layernorm", r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).ln_2": r"vision_model.\1.layers.\2.post_attention_layernorm", r"vision_model.vision_encoder.global_transformer.resblocks.(\d+).(gate_ffn|gate_attn)": r"vision_model.global_transformer.layers.\1.\2", r'vision_model.vision_encoder.ln_(pre|post).(weight|bias)': r'vision_model.vision_encoder.layernorm_\1.\2', r'vision_model.vision_encoder.positional_embedding\b': r'vision_model.gated_positional_embedding.embedding', r'vision_model.vision_encoder.gated_positional_embedding\b': r'vision_model.gated_positional_embedding.tile_embedding.weight', r'vision_model.vision_encoder.gated_positional_embedding_gate': r'vision_model.gated_positional_embedding.gate', r"vision_model.vision_encoder.pre_tile_pos_embed.embedding": r"vision_model.pre_tile_positional_embedding.embedding.weight", r"vision_model.vision_encoder.post_tile_pos_embed.embedding": r"vision_model.post_tile_positional_embedding.embedding.weight", r"vision_model.vision_encoder.pre_tile_pos_embed.gate": r"vision_model.pre_tile_positional_embedding.gate", r"vision_model.vision_encoder.post_tile_pos_embed.gate": r"vision_model.post_tile_positional_embedding.gate", r"vision_model.vision_encoder.(?=\w)": r"vision_model.", } # fmt: on CONTEXT_LENGTH = 131072 def convert_old_keys_to_new_keys(state_dict_keys: dict = None): """ This function should be applied only once, on the concatenated keys to efficiently rename using the key mappings. """ output_dict = {} if state_dict_keys is not None: old_text = "\n".join(state_dict_keys) new_text = old_text for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items(): if replacement is None: new_text = re.sub(pattern, "", new_text) # an empty line continue new_text = re.sub(pattern, replacement, new_text) output_dict = dict(zip(old_text.split("\n"), new_text.split("\n"))) return output_dict def permute_for_rope(input_tensor, n_heads, dim1, dim2): """ When you go from the complex ROPE formulation to sin and cos one, you need to permute the query and key weights (to avoid doing it on the fly) """ input_tensor = input_tensor.reshape(dim1, dim2) input_tensor = input_tensor.view(n_heads, dim1 // n_heads // 2, 2, dim2) input_tensor = input_tensor.transpose(1, 2).reshape(dim1, dim2) return input_tensor def pre_compute_positional_embedding(embedding): """ Instead of iterating of the batch of images, and the ratios inside, we pre-compute the positional embeddings depending on the aspect ratio id. This is done to support `torch.compile` and efficient inference / training with different aspect ratios. """ max_num_tiles, *shapes = embedding.shape hidden_size = shapes[-1] supported_aspect_ratios = get_all_supported_aspect_ratios(max_num_tiles) max_aspect_ratio_id = len(supported_aspect_ratios) # we keep 0 index for padding # tile embedding does not have patches num_patches = 1 if len(shapes) == 2 else shapes[1] precomputed_embeddings = torch.zeros( max_aspect_ratio_id + 1, max_num_tiles, num_patches, hidden_size, device=embedding.device, dtype=embedding.dtype, ) for i, (height, width) in enumerate(supported_aspect_ratios): aspect_ratio_id = i + 1 # we keep 0 index for padding current_embedding = embedding[:height, :width].reshape(height * width, num_patches, hidden_size) precomputed_embeddings[aspect_ratio_id, : height * width] = current_embedding precomputed_embeddings = precomputed_embeddings.flatten(1) return precomputed_embeddings def is_param_different_across_shards(key): """ Return `True` if the parameter is different across checkpoint shards and needs to be concatenated. """ patterns = [r"vision_model.patch_embedding.weight",r"vision_model.(transformer|global_transformer).layers.(\d+).self_attn.(q|k|v|o)_proj.weight",r"vision_model.(transformer|global_transformer).layers.(\d+).mlp.fc1.(weight|bias)",r"vision_model.(transformer|global_transformer).layers.(\d+).mlp.fc2.weight", r"multi_modal_projector.(weight|bias)",r"language_model.model.embed_tokens.weight",r"language_model.lm_head.weight",r"language_model.model.layers.(\d+).self_attn.(q|k|v|o)_proj.weight",r"language_model.model.layers.(\d+).cross_attn.(q|k|v|o)_proj.weight",r"language_model.model.layers.(\d+).mlp.(up|down|gate)_proj.weight",r"language_model.model.learnable_embedding.weight"] # fmt: skip return any(re.search(pattern, key) for pattern in patterns) def get_concat_dim(key): """ Return the dimension to concatenate the weights on. """ concat_dim_1 = [r"vision_model.(transformer|global_transformer).layers.(\d+).mlp.fc2.weight",r"vision_model.(transformer|global_transformer).layers.(\d+).self_attn.o_proj.weight",r"language_model.model.layers.(\d+).cross_attn.o_proj.weight",r"language_model.model.layers.(\d+).self_attn.o_proj.weight",r"language_model.model.layers.(\d+).mlp.down_proj.weight"] # fmt: off if any(re.search(pattern, key) for pattern in concat_dim_1): return 1 return 0 def compute_intermediate_size(hidden_dim, multiple_of=1024, ffn_dim_multiplier=1.3): hidden_dim = 4 * int(2 * hidden_dim / 3) hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) return hidden_dim def interpolate_positional_embedding( embeddings: torch.Tensor, vision_tile_size: int, vision_patch_size: int ) -> torch.Tensor: """ This method allows to interpolate the pre-trained position embeddings, to be able to use the model on higher resolution images. """ cls_embedding, positional_embedding = embeddings[:1], embeddings[1:] total_num_patches, dim = positional_embedding.shape # compute current and target number of patches for height and width num_patches = int(round(total_num_patches**0.5)) new_num_patches = vision_tile_size // vision_patch_size # Check if the number of patches is already the desired size if num_patches == new_num_patches: return embeddings positional_embedding = positional_embedding.transpose(0, 1) positional_embedding = positional_embedding.reshape(1, dim, num_patches, num_patches) positional_embedding = F.interpolate( positional_embedding, size=(new_num_patches, new_num_patches), mode="bicubic", align_corners=False, ) positional_embedding = positional_embedding.reshape(dim, -1).transpose(0, 1) embeddings = torch.cat([cls_embedding, positional_embedding], dim=0) return embeddings def write_model( model_path, input_base_path, num_shards, safe_serialization=True, instruct=False, ): os.makedirs(model_path, exist_ok=True) with open(os.path.join(input_base_path, "params.json"), "r") as f: params = json.load(f) params = params.get("model", params) torch_dtype = "bfloat16" # ------------------------------------------------------------ # Text model params and config # ------------------------------------------------------------ # params from config text_vocab_size = params["vocab_size"] text_num_layers = params["n_layers"] text_dim = params["dim"] text_num_heads = params["n_heads"] text_rms_norm_eps = params["norm_eps"] text_rope_theta = params["rope_theta"] cross_attention_num_layers = params["vision_num_cross_attention_layers"] # some constans from original code rope_scaling = { "rope_type": "llama3", "factor": 8.0, "low_freq_factor": 1.0, "high_freq_factor": 4.0, "original_max_position_embeddings": 8192, } max_position_embeddings = CONTEXT_LENGTH # compute additional params for weight conversion text_num_heads_per_shard = text_num_heads // num_shards text_dim_per_head = text_dim // text_num_heads text_intermediate_size = compute_intermediate_size(text_dim, multiple_of=params["multiple_of"]) if params.get("n_kv_heads", None) is not None: text_num_key_value_heads = params["n_kv_heads"] # for GQA / MQA text_num_key_value_heads_per_shard = text_num_key_value_heads // num_shards text_key_value_dim = text_dim_per_head * text_num_key_value_heads else: # compatibility with other checkpoints text_num_key_value_heads = text_num_heads text_num_key_value_heads_per_shard = text_num_heads_per_shard text_key_value_dim = text_dim # cross-attention layers: 20 for 90B, 8 for 11B cross_attention_frequency = math.ceil(text_num_layers / cross_attention_num_layers) text_num_total_layers = text_num_layers + cross_attention_num_layers cross_attention_layers_shift = list( range(cross_attention_frequency - 1, text_num_total_layers, cross_attention_frequency + 1) ) self_attention_layers_shift = [k for k in range(text_num_total_layers) if k not in cross_attention_layers_shift] bos_token_id = 128000 eos_token_id = [128001, 128008, 128009] if instruct else 128001 pad_token_id = 128004 text_config = MllamaTextConfig( num_attention_heads=text_num_heads, vocab_size=text_vocab_size, hidden_size=text_dim, rms_norm_eps=text_rms_norm_eps, rope_theta=text_rope_theta, num_hidden_layers=text_num_total_layers, cross_attention_layers=cross_attention_layers_shift, intermediate_size=text_intermediate_size, max_position_embeddings=max_position_embeddings, rope_scaling=rope_scaling, bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, tie_word_embeddings=False, # Constant set to False torch_dtype=torch_dtype, ) # ------------------------------------------------------------ # Vision model params and config # ------------------------------------------------------------ # params from config vision_tile_size = params["vision_chunk_size"] vision_max_num_tiles = params["vision_max_num_chunks"] # some constants from original code vision_patch_size = 14 vision_num_channels = 3 vision_num_layers = 32 vision_num_layers_global = 8 vision_dim = 1280 vision_num_heads = 16 vision_intermediate_layers_indices = [3, 7, 15, 23, 30] # compute additional params for weight conversion vision_dim_per_head = vision_dim // vision_num_heads vision_num_heads_per_shard = vision_num_heads // num_shards vision_intermediate_size = vision_dim * 4 vision_supported_aspect_ratios = get_all_supported_aspect_ratios(vision_max_num_tiles) vision_config = MllamaVisionConfig( hidden_size=vision_dim, patch_size=vision_patch_size, num_channels=vision_num_channels, intermediate_size=vision_intermediate_size, num_hidden_layers=vision_num_layers, num_attention_heads=vision_num_heads, num_global_layers=vision_num_layers_global, intermediate_layers_indices=vision_intermediate_layers_indices, image_size=vision_tile_size, max_num_tiles=vision_max_num_tiles, supported_aspect_ratios=vision_supported_aspect_ratios, torch_dtype=torch_dtype, ) # save config config = MllamaConfig(vision_config=vision_config, text_config=text_config, torch_dtype=torch_dtype) config.architectures = ["MllamaForConditionalGeneration"] config.save_pretrained(model_path) print("Model config saved successfully...") # ------------------------------------------------------------ # Convert weights # ------------------------------------------------------------ print(f"Fetching all parameters from the checkpoint at {input_base_path}...") if num_shards == 1: if os.path.exists(os.path.join(input_base_path, "consolidated.00.pth")): path = os.path.join(input_base_path, "consolidated.00.pth") else: path = os.path.join(input_base_path, "consolidated.pth") loaded = [torch.load(path, map_location="cpu", mmap=True)] else: loaded = [ torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu", mmap=True) for i in range(num_shards) ] print("Converting model...") all_keys = list(loaded[0].keys()) new_keys = convert_old_keys_to_new_keys(all_keys) state_dict = {} for key in all_keys: new_key = new_keys[key] # In the original model, self-attention layers and cross-attention layers are different lists of layers. # In the converted model, they are merged into one list with corresponding index shift to preserve the order. if ("cross_attention" in key or "text_model.layers" in key) and "language_model" in new_key: shift = cross_attention_layers_shift if "cross_attention" in key else self_attention_layers_shift new_key = re.sub(r"layers.(\d+).", lambda _match: f"layers.{shift[int(_match.groups()[0])]}.", new_key) current_parameter = [chunk.pop(key).contiguous().clone() for chunk in loaded] if not is_param_different_across_shards(new_key): current_parameter = current_parameter[0] concat_dim = get_concat_dim(new_key) # Post-process the current_parameter. if re.search("(k|v|q)_proj.weight", new_key) and "language_model" in new_key: if "q_proj" in new_key: param_num_heads = text_num_heads param_num_head_per_shard = text_num_heads_per_shard param_dim = text_dim else: param_num_heads = text_num_key_value_heads param_num_head_per_shard = text_num_key_value_heads_per_shard param_dim = text_key_value_dim shards = [param.view(param_num_head_per_shard, text_dim_per_head, text_dim) for param in current_parameter] current_parameter = torch.cat(shards, dim=concat_dim) if "cross_attn" not in new_key and "v_proj.weight" not in new_key: current_parameter = permute_for_rope(current_parameter, param_num_heads, param_dim, text_dim) state_dict[new_key] = current_parameter.reshape(param_num_heads * text_dim_per_head, text_dim) elif "vision_model" in new_key and re.search("(k|v|q)_proj", new_key): shards = [ param.view(vision_num_heads_per_shard, vision_dim_per_head, vision_dim) for param in current_parameter ] param = torch.cat(shards, dim=concat_dim) state_dict[new_key] = param.reshape(vision_num_heads * vision_dim_per_head, vision_dim) elif new_key == "vision_model.patch_embedding.weight": current_parameter = torch.cat(current_parameter, dim=concat_dim) state_dict[new_key] = current_parameter.reshape( -1, vision_num_channels, vision_patch_size, vision_patch_size ) elif new_key.endswith("gate"): state_dict[new_key] = current_parameter[0].view(1) elif "vision_model.gated_positional_embedding.embedding" in new_key: current_parameter = interpolate_positional_embedding( current_parameter, vision_tile_size, vision_patch_size ) state_dict[new_key] = current_parameter elif "vision_model.gated_positional_embedding.tile_embedding.weight" in new_key: current_parameter = current_parameter.permute(2, 0, 1, 3).flatten(1) current_parameter = interpolate_positional_embedding( current_parameter, vision_tile_size, vision_patch_size ) current_parameter = current_parameter.reshape( -1, vision_max_num_tiles, vision_max_num_tiles, vision_dim ).permute(1, 2, 0, 3) state_dict[new_key] = pre_compute_positional_embedding(current_parameter) elif "tile_positional_embedding.embedding" in new_key: state_dict[new_key] = pre_compute_positional_embedding(current_parameter) elif new_key != "": if isinstance(current_parameter, list): current_parameter = torch.cat(current_parameter, dim=concat_dim) state_dict[new_key] = current_parameter state_dict["language_model.model.embed_tokens.weight"] = torch.cat( [ state_dict["language_model.model.embed_tokens.weight"], state_dict.pop("language_model.model.learnable_embedding.weight"), ], dim=0, ) del loaded gc.collect() print("Loading the checkpoint in a Mllama model.") with torch.device("meta"): model = MllamaForConditionalGeneration(config) model.load_state_dict(state_dict, strict=True, assign=True) print("Checkpoint loaded successfully.") del model.config._name_or_path print("Saving the model.") model.save_pretrained(model_path, safe_serialization=safe_serialization) del state_dict, model # Safety check: reload the converted model gc.collect() print("Reloading the model to check if it's saved correctly.") MllamaForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map="auto") print("Model reloaded successfully.") # generation config if instruct: print("Saving generation config...") generation_config = GenerationConfig( do_sample=True, temperature=0.6, top_p=0.9, bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, ) generation_config.save_pretrained(model_path) class MllamaConverter(TikTokenConverter): def __init__( self, vocab_file, special_tokens: List[str], pattern: str, model_max_length: int, chat_template: Optional[str] = None, **kwargs, ): super().__init__(vocab_file, pattern=pattern) self.additional_special_tokens = special_tokens tokenizer = self.converted() if chat_template is not None: kwargs["chat_template"] = chat_template self.tokenizer = PreTrainedTokenizerFast( tokenizer_object=tokenizer, model_input_names=["input_ids", "attention_mask"], model_max_length=model_max_length, **kwargs, ) def write_tokenizer(tokenizer_path: str, save_dir: str, instruct: bool = False): model_max_length = CONTEXT_LENGTH pattern = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+" # noqa: W605 # Special tokens num_reserved_special_tokens = 256 special_tokens = [ "<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|finetune_right_pad_id|>", "<|step_id|>", "<|start_header_id|>", "<|end_header_id|>", "<|eom_id|>", # end of message "<|eot_id|>", # end of turn "<|python_tag|>", ] special_tokens += [ f"<|reserved_special_token_{i + 2}|>" for i in range(num_reserved_special_tokens - len(special_tokens)) ] # original tokenizer has <|image|> with 128011 token_id, # however, later in the code it is replaced with 128256 token_id special_tokens.append("<|image|>") # Chat template chat_template = ( "{% for message in messages %}" "{% if loop.index0 == 0 %}" "{{ bos_token }}" "{% endif %}" "{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' }}" "{% if message['content'] is string %}" "{{ message['content'] }}" "{% else %}" "{% for content in message['content'] %}" "{% if content['type'] == 'image' %}" "{{ '<|image|>' }}" "{% elif content['type'] == 'text' %}" "{{ content['text'] }}" "{% endif %}" "{% endfor %}" "{% endif %}" "{{ '<|eot_id|>' }}" "{% endfor %}" "{% if add_generation_prompt %}" "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}" "{% endif %}" ) converter = MllamaConverter( vocab_file=tokenizer_path, pattern=pattern, special_tokens=special_tokens, model_max_length=model_max_length, chat_template=chat_template if instruct else None, bos_token="<|begin_of_text|>", eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>", pad_token="<|finetune_right_pad_id|>", ) tokenizer = converter.tokenizer tokenizer.save_pretrained(save_dir) if instruct: print("Saving chat template...") chat_template_path = os.path.join(save_dir, "chat_template.json") with open(chat_template_path, "w") as f: json.dump({"chat_template": chat_template}, f, indent=2) def write_image_processor(config_path: str, save_dir: str): with open(config_path, "r") as f: params = json.load(f) tile_size = params["vision_chunk_size"] max_image_tiles = params["vision_max_num_chunks"] image_processor = MllamaImageProcessor( do_resize=True, size={"height": tile_size, "width": tile_size}, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.48145466, 0.4578275, 0.40821073], image_std=[0.26862954, 0.26130258, 0.27577711], do_pad=True, max_image_tiles=max_image_tiles, ) image_processor.save_pretrained(save_dir) def main(): parser = argparse.ArgumentParser() parser.add_argument( "--input_dir", default="Llama-3.2-11B-Vision/original", help="Location of LLaMA weights, which contains tokenizer.model and model folders", ) parser.add_argument( "--output_dir", default="Llama-3.2-11B-Vision", help="Location to write HF model and tokenizer", ) parser.add_argument( "--safe_serialization", default=True, type=bool, help="Whether or not to save using `safetensors`." ) parser.add_argument( "--special_tokens", default=None, type=List[str], help="The list of special tokens that should be added to the model.", ) parser.add_argument( "--num_shards", default=1, type=int, help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth", ) parser.add_argument( "--instruct", action="store_true", help="Whether the model is an instruct model", ) args = parser.parse_args() write_model( model_path=args.output_dir, input_base_path=args.input_dir, safe_serialization=args.safe_serialization, num_shards=args.num_shards, instruct=args.instruct, ) write_tokenizer( tokenizer_path=os.path.join(args.input_dir, "tokenizer.model"), save_dir=args.output_dir, instruct=args.instruct, ) write_image_processor( config_path=os.path.join(args.input_dir, "params.json"), save_dir=args.output_dir, ) if __name__ == "__main__": main()

transformers/src/transformers/models/mllama/convert_mllama_weights_to_hf.py/0

{ "file_path": "transformers/src/transformers/models/mllama/convert_mllama_weights_to_hf.py", "repo_id": "transformers", "token_count": 13294 }

# coding=utf-8 # Copyright 2022 Apple 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. # # Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE """TensorFlow 2.0 MobileViT model.""" from __future__ import annotations from typing import Dict, Optional, Tuple, Union import tensorflow as tf from ...activations_tf import get_tf_activation from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...modeling_tf_outputs import ( TFBaseModelOutput, TFBaseModelOutputWithPooling, TFImageClassifierOutputWithNoAttention, TFSemanticSegmenterOutputWithNoAttention, ) from ...modeling_tf_utils import ( TFPreTrainedModel, TFSequenceClassificationLoss, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import logging from .configuration_mobilevit import MobileViTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "MobileViTConfig" # Base docstring _CHECKPOINT_FOR_DOC = "apple/mobilevit-small" _EXPECTED_OUTPUT_SHAPE = [1, 640, 8, 8] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "apple/mobilevit-small" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int: """ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the original TensorFlow repo. It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py """ if min_value is None: min_value = divisor new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_value < 0.9 * value: new_value += divisor return int(new_value) class TFMobileViTConvLayer(keras.layers.Layer): def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, groups: int = 1, bias: bool = False, dilation: int = 1, use_normalization: bool = True, use_activation: Union[bool, str] = True, **kwargs, ) -> None: super().__init__(**kwargs) logger.warning( f"\n{self.__class__.__name__} has backpropagation operations that are NOT supported on CPU. If you wish " "to train/fine-tune this model, you need a GPU or a TPU" ) padding = int((kernel_size - 1) / 2) * dilation self.padding = keras.layers.ZeroPadding2D(padding) if out_channels % groups != 0: raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.") self.convolution = keras.layers.Conv2D( filters=out_channels, kernel_size=kernel_size, strides=stride, padding="VALID", dilation_rate=dilation, groups=groups, use_bias=bias, name="convolution", ) if use_normalization: self.normalization = keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.1, name="normalization") else: self.normalization = None if use_activation: if isinstance(use_activation, str): self.activation = get_tf_activation(use_activation) elif isinstance(config.hidden_act, str): self.activation = get_tf_activation(config.hidden_act) else: self.activation = config.hidden_act else: self.activation = None self.in_channels = in_channels self.out_channels = out_channels def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor: padded_features = self.padding(features) features = self.convolution(padded_features) if self.normalization is not None: features = self.normalization(features, training=training) if self.activation is not None: features = self.activation(features) return features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "convolution", None) is not None: with tf.name_scope(self.convolution.name): self.convolution.build([None, None, None, self.in_channels]) if getattr(self, "normalization", None) is not None: if hasattr(self.normalization, "name"): with tf.name_scope(self.normalization.name): self.normalization.build([None, None, None, self.out_channels]) class TFMobileViTInvertedResidual(keras.layers.Layer): """ Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381 """ def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1, **kwargs ) -> None: super().__init__(**kwargs) expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8) if stride not in [1, 2]: raise ValueError(f"Invalid stride {stride}.") self.use_residual = (stride == 1) and (in_channels == out_channels) self.expand_1x1 = TFMobileViTConvLayer( config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1, name="expand_1x1" ) self.conv_3x3 = TFMobileViTConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=3, stride=stride, groups=expanded_channels, dilation=dilation, name="conv_3x3", ) self.reduce_1x1 = TFMobileViTConvLayer( config, in_channels=expanded_channels, out_channels=out_channels, kernel_size=1, use_activation=False, name="reduce_1x1", ) def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor: residual = features features = self.expand_1x1(features, training=training) features = self.conv_3x3(features, training=training) features = self.reduce_1x1(features, training=training) return residual + features if self.use_residual else features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "expand_1x1", None) is not None: with tf.name_scope(self.expand_1x1.name): self.expand_1x1.build(None) if getattr(self, "conv_3x3", None) is not None: with tf.name_scope(self.conv_3x3.name): self.conv_3x3.build(None) if getattr(self, "reduce_1x1", None) is not None: with tf.name_scope(self.reduce_1x1.name): self.reduce_1x1.build(None) class TFMobileViTMobileNetLayer(keras.layers.Layer): def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1, **kwargs, ) -> None: super().__init__(**kwargs) self.layers = [] for i in range(num_stages): layer = TFMobileViTInvertedResidual( config, in_channels=in_channels, out_channels=out_channels, stride=stride if i == 0 else 1, name=f"layer.{i}", ) self.layers.append(layer) in_channels = out_channels def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor: for layer_module in self.layers: features = layer_module(features, training=training) return features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layers", None) is not None: for layer_module in self.layers: with tf.name_scope(layer_module.name): layer_module.build(None) class TFMobileViTSelfAttention(keras.layers.Layer): def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None: super().__init__(**kwargs) if hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size {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(hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size scale = tf.cast(self.attention_head_size, dtype=tf.float32) self.scale = tf.math.sqrt(scale) self.query = keras.layers.Dense(self.all_head_size, use_bias=config.qkv_bias, name="query") self.key = keras.layers.Dense(self.all_head_size, use_bias=config.qkv_bias, name="key") self.value = keras.layers.Dense(self.all_head_size, use_bias=config.qkv_bias, name="value") self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob) self.hidden_size = hidden_size def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor: batch_size = tf.shape(x)[0] x = tf.reshape(x, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) return tf.transpose(x, perm=[0, 2, 1, 3]) def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: batch_size = tf.shape(hidden_states)[0] 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(self.query(hidden_states)) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) attention_scores = attention_scores / self.scale # Normalize the attention scores to probabilities. attention_probs = stable_softmax(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(attention_probs, training=training) context_layer = tf.matmul(attention_probs, value_layer) context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) context_layer = tf.reshape(context_layer, shape=(batch_size, -1, self.all_head_size)) return context_layer 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.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.hidden_size]) class TFMobileViTSelfOutput(keras.layers.Layer): def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None: super().__init__(**kwargs) self.dense = keras.layers.Dense(hidden_size, name="dense") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.hidden_size = hidden_size def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) 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.hidden_size]) class TFMobileViTAttention(keras.layers.Layer): def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None: super().__init__(**kwargs) self.attention = TFMobileViTSelfAttention(config, hidden_size, name="attention") self.dense_output = TFMobileViTSelfOutput(config, hidden_size, name="output") def prune_heads(self, heads): raise NotImplementedError def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: self_outputs = self.attention(hidden_states, training=training) attention_output = self.dense_output(self_outputs, training=training) return attention_output 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, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) class TFMobileViTIntermediate(keras.layers.Layer): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None: super().__init__(**kwargs) self.dense = keras.layers.Dense(intermediate_size, 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.hidden_size = hidden_size def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(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.hidden_size]) class TFMobileViTOutput(keras.layers.Layer): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None: super().__init__(**kwargs) self.dense = keras.layers.Dense(hidden_size, name="dense") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.intermediate_size = intermediate_size def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = 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.intermediate_size]) class TFMobileViTTransformerLayer(keras.layers.Layer): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None: super().__init__(**kwargs) self.attention = TFMobileViTAttention(config, hidden_size, name="attention") self.intermediate = TFMobileViTIntermediate(config, hidden_size, intermediate_size, name="intermediate") self.mobilevit_output = TFMobileViTOutput(config, hidden_size, intermediate_size, name="output") self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before") self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after") self.hidden_size = hidden_size def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: attention_output = self.attention(self.layernorm_before(hidden_states), training=training) hidden_states = attention_output + hidden_states layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) layer_output = self.mobilevit_output(layer_output, hidden_states, training=training) return layer_output 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, "mobilevit_output", None) is not None: with tf.name_scope(self.mobilevit_output.name): self.mobilevit_output.build(None) if getattr(self, "layernorm_before", None) is not None: with tf.name_scope(self.layernorm_before.name): self.layernorm_before.build([None, None, self.hidden_size]) if getattr(self, "layernorm_after", None) is not None: with tf.name_scope(self.layernorm_after.name): self.layernorm_after.build([None, None, self.hidden_size]) class TFMobileViTTransformer(keras.layers.Layer): def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int, **kwargs) -> None: super().__init__(**kwargs) self.layers = [] for i in range(num_stages): transformer_layer = TFMobileViTTransformerLayer( config, hidden_size=hidden_size, intermediate_size=int(hidden_size * config.mlp_ratio), name=f"layer.{i}", ) self.layers.append(transformer_layer) def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: for layer_module in self.layers: hidden_states = layer_module(hidden_states, training=training) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layers", None) is not None: for layer_module in self.layers: with tf.name_scope(layer_module.name): layer_module.build(None) class TFMobileViTLayer(keras.layers.Layer): """ MobileViT block: https://arxiv.org/abs/2110.02178 """ def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, hidden_size: int, num_stages: int, dilation: int = 1, **kwargs, ) -> None: super().__init__(**kwargs) self.patch_width = config.patch_size self.patch_height = config.patch_size if stride == 2: self.downsampling_layer = TFMobileViTInvertedResidual( config, in_channels=in_channels, out_channels=out_channels, stride=stride if dilation == 1 else 1, dilation=dilation // 2 if dilation > 1 else 1, name="downsampling_layer", ) in_channels = out_channels else: self.downsampling_layer = None self.conv_kxk = TFMobileViTConvLayer( config, in_channels=in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size, name="conv_kxk", ) self.conv_1x1 = TFMobileViTConvLayer( config, in_channels=in_channels, out_channels=hidden_size, kernel_size=1, use_normalization=False, use_activation=False, name="conv_1x1", ) self.transformer = TFMobileViTTransformer( config, hidden_size=hidden_size, num_stages=num_stages, name="transformer" ) self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") self.conv_projection = TFMobileViTConvLayer( config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1, name="conv_projection" ) self.fusion = TFMobileViTConvLayer( config, in_channels=2 * in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size, name="fusion", ) self.hidden_size = hidden_size def unfolding(self, features: tf.Tensor) -> Tuple[tf.Tensor, Dict]: patch_width, patch_height = self.patch_width, self.patch_height patch_area = tf.cast(patch_width * patch_height, "int32") batch_size = tf.shape(features)[0] orig_height = tf.shape(features)[1] orig_width = tf.shape(features)[2] channels = tf.shape(features)[3] new_height = tf.cast(tf.math.ceil(orig_height / patch_height) * patch_height, "int32") new_width = tf.cast(tf.math.ceil(orig_width / patch_width) * patch_width, "int32") interpolate = new_width != orig_width or new_height != orig_height if interpolate: # Note: Padding can be done, but then it needs to be handled in attention function. features = tf.image.resize(features, size=(new_height, new_width), method="bilinear") # number of patches along width and height num_patch_width = new_width // patch_width num_patch_height = new_height // patch_height num_patches = num_patch_height * num_patch_width # convert from shape (batch_size, orig_height, orig_width, channels) # to the shape (batch_size * patch_area, num_patches, channels) features = tf.transpose(features, [0, 3, 1, 2]) patches = tf.reshape( features, (batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width) ) patches = tf.transpose(patches, [0, 2, 1, 3]) patches = tf.reshape(patches, (batch_size, channels, num_patches, patch_area)) patches = tf.transpose(patches, [0, 3, 2, 1]) patches = tf.reshape(patches, (batch_size * patch_area, num_patches, channels)) info_dict = { "orig_size": (orig_height, orig_width), "batch_size": batch_size, "channels": channels, "interpolate": interpolate, "num_patches": num_patches, "num_patches_width": num_patch_width, "num_patches_height": num_patch_height, } return patches, info_dict def folding(self, patches: tf.Tensor, info_dict: Dict) -> tf.Tensor: patch_width, patch_height = self.patch_width, self.patch_height patch_area = int(patch_width * patch_height) batch_size = info_dict["batch_size"] channels = info_dict["channels"] num_patches = info_dict["num_patches"] num_patch_height = info_dict["num_patches_height"] num_patch_width = info_dict["num_patches_width"] # convert from shape (batch_size * patch_area, num_patches, channels) # back to shape (batch_size, channels, orig_height, orig_width) features = tf.reshape(patches, (batch_size, patch_area, num_patches, -1)) features = tf.transpose(features, perm=(0, 3, 2, 1)) features = tf.reshape( features, (batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width) ) features = tf.transpose(features, perm=(0, 2, 1, 3)) features = tf.reshape( features, (batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width) ) features = tf.transpose(features, perm=(0, 2, 3, 1)) if info_dict["interpolate"]: features = tf.image.resize(features, size=info_dict["orig_size"], method="bilinear") return features def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor: # reduce spatial dimensions if needed if self.downsampling_layer: features = self.downsampling_layer(features, training=training) residual = features # local representation features = self.conv_kxk(features, training=training) features = self.conv_1x1(features, training=training) # convert feature map to patches patches, info_dict = self.unfolding(features) # learn global representations patches = self.transformer(patches, training=training) patches = self.layernorm(patches) # convert patches back to feature maps features = self.folding(patches, info_dict) features = self.conv_projection(features, training=training) features = self.fusion(tf.concat([residual, features], axis=-1), training=training) return features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "conv_kxk", None) is not None: with tf.name_scope(self.conv_kxk.name): self.conv_kxk.build(None) if getattr(self, "conv_1x1", None) is not None: with tf.name_scope(self.conv_1x1.name): self.conv_1x1.build(None) if getattr(self, "transformer", None) is not None: with tf.name_scope(self.transformer.name): self.transformer.build(None) if getattr(self, "layernorm", None) is not None: with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, self.hidden_size]) if getattr(self, "conv_projection", None) is not None: with tf.name_scope(self.conv_projection.name): self.conv_projection.build(None) if getattr(self, "fusion", None) is not None: with tf.name_scope(self.fusion.name): self.fusion.build(None) if getattr(self, "downsampling_layer", None) is not None: with tf.name_scope(self.downsampling_layer.name): self.downsampling_layer.build(None) class TFMobileViTEncoder(keras.layers.Layer): def __init__(self, config: MobileViTConfig, **kwargs) -> None: super().__init__(**kwargs) self.config = config self.layers = [] # segmentation architectures like DeepLab and PSPNet modify the strides # of the classification backbones dilate_layer_4 = dilate_layer_5 = False if config.output_stride == 8: dilate_layer_4 = True dilate_layer_5 = True elif config.output_stride == 16: dilate_layer_5 = True dilation = 1 layer_1 = TFMobileViTMobileNetLayer( config, in_channels=config.neck_hidden_sizes[0], out_channels=config.neck_hidden_sizes[1], stride=1, num_stages=1, name="layer.0", ) self.layers.append(layer_1) layer_2 = TFMobileViTMobileNetLayer( config, in_channels=config.neck_hidden_sizes[1], out_channels=config.neck_hidden_sizes[2], stride=2, num_stages=3, name="layer.1", ) self.layers.append(layer_2) layer_3 = TFMobileViTLayer( config, in_channels=config.neck_hidden_sizes[2], out_channels=config.neck_hidden_sizes[3], stride=2, hidden_size=config.hidden_sizes[0], num_stages=2, name="layer.2", ) self.layers.append(layer_3) if dilate_layer_4: dilation *= 2 layer_4 = TFMobileViTLayer( config, in_channels=config.neck_hidden_sizes[3], out_channels=config.neck_hidden_sizes[4], stride=2, hidden_size=config.hidden_sizes[1], num_stages=4, dilation=dilation, name="layer.3", ) self.layers.append(layer_4) if dilate_layer_5: dilation *= 2 layer_5 = TFMobileViTLayer( config, in_channels=config.neck_hidden_sizes[4], out_channels=config.neck_hidden_sizes[5], stride=2, hidden_size=config.hidden_sizes[2], num_stages=3, dilation=dilation, name="layer.4", ) self.layers.append(layer_5) def call( self, hidden_states: tf.Tensor, output_hidden_states: bool = False, return_dict: bool = True, training: bool = False, ) -> Union[tuple, TFBaseModelOutput]: all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.layers): hidden_states = layer_module(hidden_states, training=training) 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 TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layers", None) is not None: for layer_module in self.layers: with tf.name_scope(layer_module.name): layer_module.build(None) @keras_serializable class TFMobileViTMainLayer(keras.layers.Layer): config_class = MobileViTConfig def __init__(self, config: MobileViTConfig, expand_output: bool = True, **kwargs): super().__init__(**kwargs) self.config = config self.expand_output = expand_output self.conv_stem = TFMobileViTConvLayer( config, in_channels=config.num_channels, out_channels=config.neck_hidden_sizes[0], kernel_size=3, stride=2, name="conv_stem", ) self.encoder = TFMobileViTEncoder(config, name="encoder") if self.expand_output: self.conv_1x1_exp = TFMobileViTConvLayer( config, in_channels=config.neck_hidden_sizes[5], out_channels=config.neck_hidden_sizes[6], kernel_size=1, name="conv_1x1_exp", ) self.pooler = keras.layers.GlobalAveragePooling2D(data_format="channels_first", name="pooler") 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 def call( self, pixel_values: tf.Tensor | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFBaseModelOutputWithPooling]: 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 # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. # shape = (batch_size, in_height, in_width, in_channels=num_channels) pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) embedding_output = self.conv_stem(pixel_values, training=training) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training ) if self.expand_output: last_hidden_state = self.conv_1x1_exp(encoder_outputs[0]) # Change to NCHW output format to have uniformity in the modules last_hidden_state = tf.transpose(last_hidden_state, perm=[0, 3, 1, 2]) # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels) pooled_output = self.pooler(last_hidden_state) else: last_hidden_state = encoder_outputs[0] # Change to NCHW output format to have uniformity in the modules last_hidden_state = tf.transpose(last_hidden_state, perm=[0, 3, 1, 2]) pooled_output = None if not return_dict: output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,) # Change to NCHW output format to have uniformity in the modules if not self.expand_output: remaining_encoder_outputs = encoder_outputs[1:] remaining_encoder_outputs = tuple( [tf.transpose(h, perm=(0, 3, 1, 2)) for h in remaining_encoder_outputs[0]] ) remaining_encoder_outputs = (remaining_encoder_outputs,) return output + remaining_encoder_outputs else: return output + encoder_outputs[1:] # Change the other hidden state outputs to NCHW as well if output_hidden_states: hidden_states = tuple([tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1]]) return TFBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "conv_stem", None) is not None: with tf.name_scope(self.conv_stem.name): self.conv_stem.build(None) 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, None, None, None]) if getattr(self, "conv_1x1_exp", None) is not None: with tf.name_scope(self.conv_1x1_exp.name): self.conv_1x1_exp.build(None) class TFMobileViTPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileViTConfig base_model_prefix = "mobilevit" main_input_name = "pixel_values" MOBILEVIT_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 `pixel_values` only and nothing else: `model(pixel_values)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"pixel_values": pixel_values, "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 ([`MobileViTConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ MOBILEVIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]`, `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`MobileViTImageProcessor.__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. 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. """ @add_start_docstrings( "The bare MobileViT model outputting raw hidden-states without any specific head on top.", MOBILEVIT_START_DOCSTRING, ) class TFMobileViTModel(TFMobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig, expand_output: bool = True, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.config = config self.expand_output = expand_output self.mobilevit = TFMobileViTMainLayer(config, expand_output=expand_output, name="mobilevit") @unpack_inputs @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def call( self, pixel_values: tf.Tensor | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], TFBaseModelOutputWithPooling]: output = self.mobilevit(pixel_values, output_hidden_states, return_dict, training=training) return output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "mobilevit", None) is not None: with tf.name_scope(self.mobilevit.name): self.mobilevit.build(None) @add_start_docstrings( """ MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, MOBILEVIT_START_DOCSTRING, ) class TFMobileViTForImageClassification(TFMobileViTPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: MobileViTConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.mobilevit = TFMobileViTMainLayer(config, name="mobilevit") # Classifier head self.dropout = keras.layers.Dropout(config.classifier_dropout_prob) self.classifier = ( keras.layers.Dense(config.num_labels, name="classifier") if config.num_labels > 0 else tf.identity ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=TFImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def call( self, pixel_values: tf.Tensor | None = None, output_hidden_states: Optional[bool] = None, labels: tf.Tensor | None = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[tuple, TFImageClassifierOutputWithNoAttention]: r""" labels (`tf.Tensor` 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.mobilevit( pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training ) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(self.dropout(pooled_output, training=training)) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "mobilevit", None) is not None: with tf.name_scope(self.mobilevit.name): self.mobilevit.build(None) if getattr(self, "classifier", None) is not None: if hasattr(self.classifier, "name"): with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.neck_hidden_sizes[-1]]) class TFMobileViTASPPPooling(keras.layers.Layer): def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int, **kwargs) -> None: super().__init__(**kwargs) self.global_pool = keras.layers.GlobalAveragePooling2D(keepdims=True, name="global_pool") self.conv_1x1 = TFMobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", name="conv_1x1", ) def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor: spatial_size = shape_list(features)[1:-1] features = self.global_pool(features) features = self.conv_1x1(features, training=training) features = tf.image.resize(features, size=spatial_size, method="bilinear") return features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "global_pool", None) is not None: with tf.name_scope(self.global_pool.name): self.global_pool.build([None, None, None, None]) if getattr(self, "conv_1x1", None) is not None: with tf.name_scope(self.conv_1x1.name): self.conv_1x1.build(None) class TFMobileViTASPP(keras.layers.Layer): """ ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587 """ def __init__(self, config: MobileViTConfig, **kwargs) -> None: super().__init__(**kwargs) in_channels = config.neck_hidden_sizes[-2] out_channels = config.aspp_out_channels if len(config.atrous_rates) != 3: raise ValueError("Expected 3 values for atrous_rates") self.convs = [] in_projection = TFMobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=1, use_activation="relu", name="convs.0", ) self.convs.append(in_projection) self.convs.extend( [ TFMobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=3, dilation=rate, use_activation="relu", name=f"convs.{i + 1}", ) for i, rate in enumerate(config.atrous_rates) ] ) pool_layer = TFMobileViTASPPPooling( config, in_channels, out_channels, name=f"convs.{len(config.atrous_rates) + 1}" ) self.convs.append(pool_layer) self.project = TFMobileViTConvLayer( config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu", name="project", ) self.dropout = keras.layers.Dropout(config.aspp_dropout_prob) def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor: # since the hidden states were transposed to have `(batch_size, channels, height, width)` # layout we transpose them back to have `(batch_size, height, width, channels)` layout. features = tf.transpose(features, perm=[0, 2, 3, 1]) pyramid = [] for conv in self.convs: pyramid.append(conv(features, training=training)) pyramid = tf.concat(pyramid, axis=-1) pooled_features = self.project(pyramid, training=training) pooled_features = self.dropout(pooled_features, training=training) return pooled_features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "project", None) is not None: with tf.name_scope(self.project.name): self.project.build(None) if getattr(self, "convs", None) is not None: for conv in self.convs: with tf.name_scope(conv.name): conv.build(None) class TFMobileViTDeepLabV3(keras.layers.Layer): """ DeepLabv3 architecture: https://arxiv.org/abs/1706.05587 """ def __init__(self, config: MobileViTConfig, **kwargs) -> None: super().__init__(**kwargs) self.aspp = TFMobileViTASPP(config, name="aspp") self.dropout = keras.layers.Dropout(config.classifier_dropout_prob) self.classifier = TFMobileViTConvLayer( config, in_channels=config.aspp_out_channels, out_channels=config.num_labels, kernel_size=1, use_normalization=False, use_activation=False, bias=True, name="classifier", ) def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: features = self.aspp(hidden_states[-1], training=training) features = self.dropout(features, training=training) features = self.classifier(features, training=training) return features def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "aspp", None) is not None: with tf.name_scope(self.aspp.name): self.aspp.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC. """, MOBILEVIT_START_DOCSTRING, ) class TFMobileViTForSemanticSegmentation(TFMobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig, **kwargs) -> None: super().__init__(config, **kwargs) self.num_labels = config.num_labels self.mobilevit = TFMobileViTMainLayer(config, expand_output=False, name="mobilevit") self.segmentation_head = TFMobileViTDeepLabV3(config, name="segmentation_head") def hf_compute_loss(self, logits, labels): # upsample logits to the images' original size # `labels` is of shape (batch_size, height, width) label_interp_shape = shape_list(labels)[1:] upsampled_logits = tf.image.resize(logits, size=label_interp_shape, method="bilinear") # compute weighted loss loss_fct = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction="none") def masked_loss(real, pred): unmasked_loss = loss_fct(real, pred) mask = tf.cast(real != self.config.semantic_loss_ignore_index, dtype=unmasked_loss.dtype) masked_loss = unmasked_loss * mask # Reduction strategy in the similar spirit with # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210 reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(mask) return tf.reshape(reduced_masked_loss, (1,)) return masked_loss(labels, upsampled_logits) @unpack_inputs @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSemanticSegmenterOutputWithNoAttention, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: tf.Tensor | None = None, labels: tf.Tensor | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[tuple, TFSemanticSegmenterOutputWithNoAttention]: r""" labels (`tf.Tensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFMobileViTForSemanticSegmentation >>> 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("apple/deeplabv3-mobilevit-small") >>> model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small") >>> inputs = image_processor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> # logits are of shape (batch_size, num_labels, height, width) >>> logits = outputs.logits ```""" 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 labels is not None and not self.config.num_labels > 1: raise ValueError("The number of labels should be greater than one") outputs = self.mobilevit( pixel_values, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, training=training, ) encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] logits = self.segmentation_head(encoder_hidden_states, training=training) loss = None if labels is not None: loss = self.hf_compute_loss(logits=logits, labels=labels) # make logits of shape (batch_size, num_labels, height, width) to # keep them consistent across APIs logits = tf.transpose(logits, perm=[0, 3, 1, 2]) if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSemanticSegmenterOutputWithNoAttention( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "mobilevit", None) is not None: with tf.name_scope(self.mobilevit.name): self.mobilevit.build(None) if getattr(self, "segmentation_head", None) is not None: with tf.name_scope(self.segmentation_head.name): self.segmentation_head.build(None) __all__ = [ "TFMobileViTForImageClassification", "TFMobileViTForSemanticSegmentation", "TFMobileViTModel", "TFMobileViTPreTrainedModel", ]

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

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# coding=utf-8 # Copyright 2024 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 Moshi checkpoints.""" import argparse import safetensors import sentencepiece import torch from transformers import ( AutoFeatureExtractor, GenerationConfig, MimiModel, # initial audio encoder MoshiConfig, MoshiForConditionalGeneration, PreTrainedTokenizerFast, logging, ) from transformers.convert_slow_tokenizer import MoshiConverter logging.set_verbosity_info() logger = logging.get_logger("transformers.models.mimi") def assert_param_count(model_1, model_2): count_1 = sum(p[1].numel() for p in model_1.named_parameters() if "final_proj" not in p[0]) count_2 = sum(p[1].numel() for p in model_2.named_parameters() if "final_proj" not in p[0]) assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}" def param_count(model): return sum(p[1].numel() for p in model.named_parameters() if "final_proj" not in p[0]) def _grab_best_device(use_gpu=True): if torch.cuda.device_count() > 0 and use_gpu: device = "cuda" else: device = "cpu" return torch.device(device) convert_list = [ # GENERAL ("out_norm", "decoder.model.norm"), ("depformer_emb", "depth_decoder.emb"), ("depformer_text_emb", "depth_decoder.text_emb"), ("text_emb", "decoder.model.emb"), ("emb", "embed_tokens"), ("text_linear", "decoder.lm_head"), ("depformer", "depth_decoder"), ("transformer", "decoder.model"), # TRANSFORMERS PART ("gating.linear_in", "mlp.fc1"), ("gating.linear_out", "mlp.fc2"), ("self_attn.out_proj", "self_attn.o_proj.linear"), ("norm1", "input_layernorm"), ("norm2", "post_attention_layernorm"), ("layer_scale_1", "self_attn_layer_scale"), ("layer_scale_2", "mlp_layer_scale"), ("alpha", "weight"), ] def _preprocess_state_dict(state_dict, config): # Moshi original weights are using a gating mechanism # pattern for depth transformer: # stack(gating.{i}.linear_in)->mlp.fc1 # stack(gating.{i}.linear_out)->mlp.fc2 for layer_idx in range(config.depth_decoder_config.num_hidden_layers): linear_layers_in = [ state_dict.pop(f"depformer.layers.{layer_idx}.gating.{i}.linear_in.weight") for i in range(config.num_codebooks) ] linear_layers_out = [ state_dict.pop(f"depformer.layers.{layer_idx}.gating.{i}.linear_out.weight") for i in range(config.num_codebooks) ] state_dict[f"depth_decoder.layers.{layer_idx}.mlp.fc1.weight"] = torch.stack(linear_layers_in) state_dict[f"depth_decoder.layers.{layer_idx}.mlp.fc2.weight"] = torch.stack(linear_layers_out) input_projections = [] lm_heads = [] for codebook_idx in range(config.num_codebooks): input_projections.append(state_dict.pop(f"depformer_in.{codebook_idx}.weight")) lm_heads.append(state_dict.pop(f"linears.{codebook_idx}.weight")) state_dict["depth_decoder.input_projections.weight"] = torch.stack(input_projections, dim=0) state_dict["depth_decoder.lm_heads.weight"] = torch.stack(lm_heads, dim=0) return state_dict def _convert_model( state_dict, hf_model, convert_list, device, config, unwanted_prefix=None, ): hidden_size = config.hidden_size head_dim = config.head_dim num_heads = int(config.hidden_size // config.head_dim) num_key_value_heads = config.num_key_value_heads key_value_head_dim = config.num_key_value_heads * head_dim state_dict = _preprocess_state_dict(state_dict, config) # permute for sliced rotary def permute(w, n_heads, dim1=hidden_size, dim2=hidden_size): return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2) for k, v in list(state_dict.items()): if "audio_encoder" not in k: new_k = k if unwanted_prefix is None else k[len(unwanted_prefix) :] for old_layer_name, new_layer_name in convert_list: if old_layer_name in new_k: new_k = new_k.replace(old_layer_name, new_layer_name) if "alpha" in k: state_dict[k] = state_dict[k].squeeze() if "in_proj_weight" in new_k: # split qkv into query key and value mixed_qkv = state_dict.pop(k) if "depth_decoder" in new_k: mixed_qkv = mixed_qkv.view(config.num_codebooks, -1, mixed_qkv.shape[-1]) qkv_dim = mixed_qkv.size(1) // 3 query_layer = mixed_qkv[:, :qkv_dim] key_layer = mixed_qkv[:, qkv_dim : qkv_dim * 2] value_layer = mixed_qkv[:, qkv_dim * 2 :] state_dict[new_k.replace("in_proj_weight", "q_proj.linear.weight")] = query_layer state_dict[new_k.replace("in_proj_weight", "k_proj.linear.weight")] = key_layer else: qkv_dim = mixed_qkv.size(0) // 3 query_layer = mixed_qkv[:qkv_dim] key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] value_layer = mixed_qkv[qkv_dim * 2 :] state_dict[new_k.replace("in_proj_weight", "q_proj.linear.weight")] = permute( query_layer, num_heads, hidden_size, hidden_size ) state_dict[new_k.replace("in_proj_weight", "k_proj.linear.weight")] = permute( key_layer, num_key_value_heads, key_value_head_dim, hidden_size ) state_dict[new_k.replace("in_proj_weight", "v_proj.linear.weight")] = value_layer elif "o_proj" in new_k and "depth_decoder" in new_k: output_layer = state_dict.pop(k) state_dict[new_k] = output_layer.view(config.num_codebooks, -1, output_layer.shape[-1]) else: state_dict[new_k] = state_dict.pop(k) # Do the last one by hand state_dict["depth_decoder.text_embed_tokens.weight"] = state_dict.pop( "depth_decoder.decoder.model.embed_tokens.weight" ) extra_keys = set(state_dict.keys()) - set(hf_model.state_dict().keys()) missing_keys = set(hf_model.state_dict().keys()) - set(state_dict.keys()) if len(extra_keys) != 0: raise ValueError(f"extra keys found: {extra_keys}") if len(missing_keys) != 0: raise ValueError(f"missing keys: {missing_keys}") hf_model.load_state_dict(state_dict, strict=True) n_params = param_count(hf_model) logger.info(f"model loaded: {round(n_params/1e6,1)}M params") hf_model.eval() hf_model.to(device) del state_dict return hf_model @torch.no_grad() def convert_checkpoint( checkpoint_path, pytorch_dump_folder_path, mimi_repo_id, config_path=None, repo_id=None, ): """ Copy/paste/tweak model's weights to transformers design. """ device = _grab_best_device() mimi_model = MimiModel.from_pretrained(mimi_repo_id, torch_dtype=torch.bfloat16) if config_path is not None: config = MoshiConfig.from_pretrained(config_path) else: audio_encoder_config = mimi_model.config config = MoshiConfig.from_audio_encoder_config(audio_encoder_config) model = MoshiForConditionalGeneration(config).to(torch.bfloat16) depth_decoder_generation_config = GenerationConfig( do_sample=True, temperature=0.8, top_k=250, min_length=config.num_codebooks + 1, max_length=config.num_codebooks + 1, cache_implementation="sliding_window", ) generation_config = GenerationConfig( do_sample=True, temp=0.7, top_k=25, cache_implementation="sliding_window", pad_token_id=config.vocab_size, bos_token_id=config.vocab_size, ) generation_config.depth_decoder_config = depth_decoder_generation_config.to_diff_dict() model.generation_config = generation_config original_checkpoint = safetensors.torch.load_file(checkpoint_path) if "best_state" in original_checkpoint: # we might have a training state saved, in which case discard the yaml results and just retain the weights original_checkpoint = original_checkpoint["best_state"] audio_checkpoint = mimi_model.state_dict() original_checkpoint.update({f"audio_encoder.{key}": value for (key, value) in audio_checkpoint.items()}) model = _convert_model(original_checkpoint, model, convert_list, device, config) model.save_pretrained(pytorch_dump_folder_path) if repo_id: print("Pushing to the hub...") model.push_to_hub(repo_id) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") parser.add_argument( "--tokenizer_vocab_path", required=False, default=None, type=str, help="Path to original tokenizer vocab file" ) parser.add_argument("--mimi_repo_id", required=True, default=None, type=str, help="Repository id to HF Mimi.") 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." ) args = parser.parse_args() # convert tokenizer if args.tokenizer_vocab_path: original_tokenizer = sentencepiece.SentencePieceProcessor(args.tokenizer_vocab_path) tokenizer = MoshiConverter(args.tokenizer_vocab_path).converted() tokenizer = PreTrainedTokenizerFast( tokenizer_object=tokenizer, chat_template=None, unk_token="<unk>", model_input_names=["input_ids", "attention_mask"], clean_up_tokenization_spaces=False, bos_token_id=original_tokenizer.bos_id(), eos_token_id=original_tokenizer.eos_id(), pad_token_id=original_tokenizer.pad_id(), ) tokenizer.save_pretrained(args.pytorch_dump_folder_path) if args.push_to_hub: print("Pushing the tokenizer to the hub...") tokenizer.push_to_hub(args.push_to_hub) # upload feature extractor feature_extractor = AutoFeatureExtractor.from_pretrained(args.mimi_repo_id) feature_extractor.save_pretrained(args.pytorch_dump_folder_path) if args.push_to_hub: print("Pushing the feature extractor to the hub...") feature_extractor.push_to_hub(args.push_to_hub) convert_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.mimi_repo_id, args.config_path, args.push_to_hub, )

transformers/src/transformers/models/moshi/convert_moshi_transformers.py/0

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# coding=utf-8 # Copyright 2024 Meta 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. """ Text/audio processor class for MusicGen Melody """ from typing import List, Optional import numpy as np from ...processing_utils import ProcessorMixin from ...utils import to_numpy class MusicgenMelodyProcessor(ProcessorMixin): r""" Constructs a MusicGen Melody processor which wraps a Wav2Vec2 feature extractor - for raw audio waveform processing - and a T5 tokenizer into a single processor class. [`MusicgenProcessor`] offers all the functionalities of [`MusicgenMelodyFeatureExtractor`] and [`T5Tokenizer`]. See [`~MusicgenProcessor.__call__`] and [`~MusicgenProcessor.decode`] for more information. Args: feature_extractor (`MusicgenMelodyFeatureExtractor`): An instance of [`MusicgenMelodyFeatureExtractor`]. The feature extractor is a required input. tokenizer (`T5Tokenizer`): An instance of [`T5Tokenizer`]. The tokenizer is a required input. """ feature_extractor_class = "MusicgenMelodyFeatureExtractor" tokenizer_class = ("T5Tokenizer", "T5TokenizerFast") def __init__(self, feature_extractor, tokenizer): super().__init__(feature_extractor, tokenizer) # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.get_decoder_prompt_ids def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True): return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps) def __call__(self, audio=None, text=None, **kwargs): """ Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `audio` and `kwargs` arguments to MusicgenMelodyFeatureExtractor's [`~MusicgenMelodyFeatureExtractor.__call__`] if `audio` is not `None` to pre-process the audio. It also forwards the `text` and `kwargs` arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not `None`. Please refer to the doctsring of the above two methods for more information. Args: audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): The audio or batch of audios to be prepared. Each audio can be NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be a mono-stereo signal of shape (T), where T is the sample length of the audio. 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). kwargs (*optional*): Remaining dictionary of keyword arguments that will be passed to the feature extractor and/or the tokenizer. 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`. - **input_features** -- Audio input features to be fed to a model. Returned when `audio` is not `None`. - **attention_mask** -- List of token indices specifying which tokens should be attended to by the model when `text` is not `None`. When only `audio` is specified, returns the timestamps attention mask. """ sampling_rate = kwargs.pop("sampling_rate", None) if audio is None and text is None: raise ValueError("You need to specify either an `audio` or `text` input to process.") if text is not None: inputs = self.tokenizer(text, **kwargs) if audio is not None: audio_inputs = self.feature_extractor(audio, sampling_rate=sampling_rate, **kwargs) if text is None: return audio_inputs elif audio is None: return inputs else: inputs["input_features"] = audio_inputs["input_features"] return inputs # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.batch_decode with padding_mask->attention_mask def batch_decode(self, *args, **kwargs): """ This method is used to decode either batches of audio outputs from the MusicGen model, or batches of token ids from the tokenizer. In the case of decoding token ids, this method forwards all its arguments to T5Tokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ audio_values = kwargs.pop("audio", None) attention_mask = kwargs.pop("attention_mask", None) if len(args) > 0: audio_values = args[0] args = args[1:] if audio_values is not None: return self._decode_audio(audio_values, attention_mask=attention_mask) else: return self.tokenizer.batch_decode(*args, **kwargs) # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.decode def decode(self, *args, **kwargs): """ This method forwards all its arguments to T5Tokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor._decode_audio with padding_mask->attention_mask def _decode_audio(self, audio_values, attention_mask: Optional = None) -> List[np.ndarray]: """ This method strips any padding from the audio values to return a list of numpy audio arrays. """ audio_values = to_numpy(audio_values) bsz, channels, seq_len = audio_values.shape if attention_mask is None: return list(audio_values) attention_mask = to_numpy(attention_mask) # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding** # token (so that the generated audio values are **not** treated as padded tokens) difference = seq_len - attention_mask.shape[-1] padding_value = 1 - self.feature_extractor.padding_value attention_mask = np.pad(attention_mask, ((0, 0), (0, difference)), "constant", constant_values=padding_value) audio_values = audio_values.tolist() for i in range(bsz): sliced_audio = np.asarray(audio_values[i])[ attention_mask[i][None, :] != self.feature_extractor.padding_value ] audio_values[i] = sliced_audio.reshape(channels, -1) return audio_values def get_unconditional_inputs(self, num_samples=1, return_tensors="pt"): """ Helper function to get null inputs for unconditional generation, enabling the model to be used without the feature extractor or tokenizer. Args: num_samples (int, *optional*): Number of audio samples to unconditionally generate. Example: ```python >>> from transformers import MusicgenMelodyForConditionalGeneration, MusicgenMelodyProcessor >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("facebook/musicgen-melody") >>> # get the unconditional (or 'null') inputs for the model >>> processor = MusicgenMelodyProcessor.from_pretrained("facebook/musicgen-melody") >>> unconditional_inputs = processor.get_unconditional_inputs(num_samples=1) >>> audio_samples = model.generate(**unconditional_inputs, max_new_tokens=256) ```""" inputs = self.tokenizer([""] * num_samples, return_tensors=return_tensors, return_attention_mask=True) inputs["attention_mask"][:] = 0 return inputs

transformers/src/transformers/models/musicgen_melody/processing_musicgen_melody.py/0

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# 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. import argparse import os import warnings import flatdict import torch from transformers import LlamaTokenizer, PersimmonConfig, PersimmonForCausalLM try: from transformers import LlamaTokenizerFast tokenizer_class = LlamaTokenizerFast except ImportError as e: warnings.warn(e) warnings.warn( "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion" ) tokenizer_class = LlamaTokenizer """ Sample usage: ``` git clone https://github.com/persimmon-ai-labs/adept-inference wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_base_model_release.tar wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar python src/transformers/models/persimmon/convert_persimmon_weights_to_hf.py --input_dir /path/to/downloaded/persimmon/weights/ --output_dir /output/path ``` Thereafter, models can be loaded via: ```py from transformers import PersimmonForCausalLM, PersimmonTokenizer model = PersimmonForCausalLM.from_pretrained("/output/path") tokenizer = PersimmonTokenizer.from_pretrained("/output/path") ``` Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM). """ KEYS_TO_MODIFY_MAPPING = { "self_attention": "self_attn", "language_model.encoder": "model", "word_embeddings_for_head": "lm_head", "language_model.embedding.word_embeddings": "model.embed_tokens", } KEYS_TO_REMOVE = "rotary_emb.inv_freq" def rename_state_dict(state_dict): model_state_dict = {} for key, value in state_dict.items(): for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items(): if key_to_modify in key: key = key.replace(key_to_modify, new_key) if KEYS_TO_REMOVE in key: continue model_state_dict[key] = value return model_state_dict def convert_persimmon_checkpoint(pytorch_dump_folder_path, ada_lib_path, pt_model_path, safe_serialization=False): import sys sys.path.insert(0, ada_lib_path) model_state_dict_base = torch.load(pt_model_path, map_location="cpu") state_dict = flatdict.FlatDict(model_state_dict_base["model"], ".") state_dict = rename_state_dict(state_dict) transformers_config = PersimmonConfig() model = PersimmonForCausalLM(transformers_config, eos_token_id=71013, bos_token_id=71013).to(torch.bfloat16) model.load_state_dict(state_dict) model.save_pretrained(pytorch_dump_folder_path, safe_serialization=safe_serialization) transformers_config.save_pretrained(pytorch_dump_folder_path) def main(): parser = argparse.ArgumentParser() parser.add_argument( "--input_dir", help="Location of Persimmon weights, which contains tokenizer.model and model folders", ) parser.add_argument( "--pt_model_path", help="Location of Persimmon `model_optim_rng.pt`", ) parser.add_argument( "--output_dir", help="Location to write HF model and tokenizer", ) parser.add_argument( "--ada_lib_path", help="Location to write HF model and tokenizer", ) parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.") args = parser.parse_args() spm_path = os.path.join(args.input_dir, "adept_vocab.model") convert_persimmon_checkpoint( pytorch_dump_folder_path=args.output_dir, pt_model_path=args.pt_model_path, safe_serialization=args.safe_serialization, ada_lib_path=args.ada_lib_path, ) tokenizer = tokenizer_class(spm_path, bos_token="|ENDOFTEXT|", eos_token="|ENDOFTEXT|") tokenizer.save_pretrained(args.output_dir) if __name__ == "__main__": main()

transformers/src/transformers/models/persimmon/convert_persimmon_weights_to_hf.py/0

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# coding=utf-8 # Copyright 2022, UCLA NLP, The Facebook AI Research 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. """PyTorch PLBART 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 BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...generation import GenerationMixin from ...modeling_attn_mask_utils import ( _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa, ) from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqSequenceClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_plbart import PLBartConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "uclanlp/plbart-base" _CONFIG_FOR_DOC = "PLBartConfig" # Copied from transformers.models.mbart.modeling_mbart.shift_tokens_right def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int): """ Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not have a single `decoder_start_token_id` in contrast to other Bart-like models. """ prev_output_tokens = input_ids.clone() 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` prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() prev_output_tokens[:, 0] = decoder_start_tokens return prev_output_tokens # Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->PLBart class PLBartLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # PLBart is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, input_ids: torch.Tensor, 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 ).expand(bsz, -1) return super().forward(positions + self.offset) # Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->PLBart class PLBartScaledWordEmbedding(nn.Embedding): """ This module overrides nn.Embeddings' forward by multiplying with embeddings scale. """ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0): super().__init__(num_embeddings, embedding_dim, padding_idx) self.embed_scale = embed_scale def forward(self, input_ids: torch.Tensor): return super().forward(input_ids) * self.embed_scale # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->PLBart class PLBartAttention(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[PLBartConfig] = 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->PLBart, BART->PLBART class PLBartEncoderLayer(nn.Module): def __init__(self, config: PLBartConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = PLBART_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 PLBart. PLBART_ATTENTION_CLASSES = {"eager": PLBartAttention} # Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->PLBart, BART->PLBART class PLBartDecoderLayer(nn.Module): def __init__(self, config: PLBartConfig): super().__init__() self.embed_dim = config.d_model self.self_attn = PLBART_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 = PLBART_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 # Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->PLBart class PLBartClassificationHead(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) -> 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 PLBartPreTrainedModel(PreTrainedModel): config_class = PLBartConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["PLBartDecoderLayer", "PLBartEncoderLayer"] 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_() PLBART_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 ([`PLBartConfig`]): 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. """ PLBART_GENERATION_EXAMPLE = r""" Mask-filling example: ```python >>> from transformers import AutoTokenizer, PLBartForConditionalGeneration >>> model = PLBartForConditionalGeneration.from_pretrained("uclanlp/plbart-base") >>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base") >>> # en_XX is the language symbol id <LID> for English >>> TXT = "<s> Is 0 the <mask> Fibonacci number ? </s> en_XX" >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="pt").input_ids >>> logits = model(input_ids).logits >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item() >>> probs = logits[0, masked_index].softmax(dim=0) >>> values, predictions = probs.topk(5) >>> tokenizer.decode(predictions).split() ['first', 'same', 'highest', 'result', 'number'] ``` """ PLBART_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`] or [`PLBartMultiTokenizer`] depending on the checkpoint. 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`] or [`PLBartMultiTokenizer`] depending on the checkpoint. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (: obj:*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 (: obj:*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 (: obj:*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 (: obj:*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 (: obj:*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.bart.modeling_bart.BartEncoder with Bart->PLBart class PLBartEncoder(PLBartPreTrainedModel): """ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`PLBartEncoderLayer`]. Args: config: PLBartConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config: PLBartConfig, 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 embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 self.embed_tokens = PLBartScaledWordEmbedding( config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale ) if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight self.embed_positions = PLBartLearnedPositionalEmbedding( config.max_position_embeddings, embed_dim, ) self.layers = nn.ModuleList([PLBartEncoderLayer(config) for _ in range(config.encoder_layers)]) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self._use_sdpa = config._attn_implementation == "sdpa" self.layernorm_embedding = nn.LayerNorm(embed_dim) 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: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = 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, BaseModelOutput]: 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: input = input_ids input_ids = input_ids.view(-1, input_ids.shape[-1]) elif inputs_embeds is not None: input = inputs_embeds[:, :, -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) embed_pos = self.embed_positions(input) embed_pos = embed_pos.to(inputs_embeds.device) 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: if self._use_flash_attention_2: attention_mask = attention_mask if 0 in attention_mask else None elif self._use_sdpa and head_mask is None and not output_attentions: # output_attentions=True & head_mask can not be supported when using SDPA, fall back to # the manual implementation that requires a 4D causal mask in all cases. # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype) else: # [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 ) # Copied from transformers.models.bart.modeling_bart.BartDecoder with Bart->PLBart class PLBartDecoder(PLBartPreTrainedModel): """ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PLBartDecoderLayer`] Args: config: PLBartConfig embed_tokens (nn.Embedding): output embedding """ def __init__(self, config: PLBartConfig, 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 embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 self.embed_tokens = PLBartScaledWordEmbedding( config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale ) if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight self.embed_positions = PLBartLearnedPositionalEmbedding( config.max_position_embeddings, config.d_model, ) self.layers = nn.ModuleList([PLBartDecoderLayer(config) for _ in range(config.decoder_layers)]) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self._use_sdpa = config._attn_implementation == "sdpa" 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: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.LongTensor] = 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, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: 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 = input_ids input_shape = input.shape input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -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) if self._use_flash_attention_2: # 2d mask is passed through the layers attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None elif self._use_sdpa and not output_attentions and cross_attn_head_mask is None: # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on # the manual implementation that requires a 4D causal mask in all cases. attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( attention_mask, input_shape, inputs_embeds, past_key_values_length, ) else: # 4d mask is passed through the layers 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: if self._use_flash_attention_2: encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None elif self._use_sdpa and cross_attn_head_mask is None and not output_attentions: # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on # the manual implementation that requires a 4D causal mask in all cases. # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa( encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1], ) else: # [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, past_key_values_length) positions = positions.to(inputs_embeds.device) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) 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 PLBART Model outputting raw hidden-states without any specific head on top.", PLBART_START_DOCSTRING, ) class PLBartModel(PLBartPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: PLBartConfig): super().__init__(config) padding_idx, vocab_size = config.pad_token_id, config.vocab_size embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 self.shared = PLBartScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale) self.encoder = PLBartEncoder(config, self.shared) self.decoder = PLBartDecoder(config, self.shared) self.init_weights() 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 _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 @add_start_docstrings_to_model_forward(PLBART_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.LongTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = 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.Tensor], Seq2SeqModelOutput]: 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 # different to other models, PLBart automatically creates decoder_input_ids from # input_ids if no decoder_input_ids are provided if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) 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 PLBART Model with a language modeling head. Can be used for code-to-text, text-to-code and code-to-code.", PLBART_START_DOCSTRING, ) class PLBartForConditionalGeneration(PLBartPreTrainedModel, GenerationMixin): base_model_prefix = "model" _keys_to_ignore_on_load_missing = ["final_logits_bias"] _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] def __init__(self, config: PLBartConfig): super().__init__(config) self.model = PLBartModel(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) self.init_weights() 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, mean_resizing: bool = True ) -> nn.Embedding: new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing) 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(PLBART_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @add_end_docstrings(PLBART_GENERATION_EXAMPLE) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.LongTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[List[torch.FloatTensor]] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.Tensor] = 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], 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 decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right(labels, self.config.pad_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]) lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device) 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_decoder_input_ids_from_labels(self, labels: torch.Tensor): return shift_tokens_right(labels, self.config.pad_token_id) @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 @add_start_docstrings( """ PLBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for code classification. """, PLBART_START_DOCSTRING, ) class PLBartForSequenceClassification(PLBartPreTrainedModel): _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] def __init__(self, config: PLBartConfig, **kwargs): super().__init__(config, **kwargs) self.model = PLBartModel(config) self.classification_head = PLBartClassificationHead( config.d_model, config.d_model, config.num_labels, config.classifier_dropout, ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(PLBART_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=Seq2SeqSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification.forward def forward( self, input_ids: 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[List[torch.FloatTensor]] = 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, return_dict: Optional[bool] = None, ) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]: 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 classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False if input_ids is None and inputs_embeds is not None: raise NotImplementedError( f"Passing input embeddings is currently not supported for {self.__class__.__name__}" ) outputs = self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, encoder_outputs=encoder_outputs, 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, ) hidden_states = outputs[0] # last hidden state eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device) if len(torch.unique_consecutive(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.") sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[ :, -1, : ] logits = self.classification_head(sentence_representation) loss = None if labels is not None: labels = labels.to(logits.device) if self.config.problem_type is None: if self.config.num_labels == 1: self.config.problem_type = "regression" elif self.config.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.config.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.config.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 Seq2SeqSequenceClassifierOutput( loss=loss, logits=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, ) # Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->PLBart class PLBartDecoderWrapper(PLBartPreTrainedModel): """ 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 = PLBartDecoder(config) def forward(self, *args, **kwargs): return self.decoder(*args, **kwargs) # Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->PLBart, facebook/bart-base->uclanlp/plbart-base class PLBartForCausalLM(PLBartPreTrainedModel, GenerationMixin): _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 = PLBartDecoderWrapper(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, PLBartForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base") >>> model = PLBartForCausalLM.from_pretrained("uclanlp/plbart-base", 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, ) @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 __all__ = [ "PLBartForCausalLM", "PLBartForConditionalGeneration", "PLBartForSequenceClassification", "PLBartModel", "PLBartPreTrainedModel", ]

transformers/src/transformers/models/plbart/modeling_plbart.py/0

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from typing import Callable, Optional, Tuple import torch import torch.utils.checkpoint from torch import nn from ...cache_utils import Cache from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_utils import ALL_ATTENTION_FUNCTIONS from ...processing_utils import Unpack from ...utils import logging from ..llama.modeling_llama import ( LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaForQuestionAnswering, LlamaForSequenceClassification, LlamaForTokenClassification, LlamaMLP, LlamaModel, apply_rotary_pos_emb, eager_attention_forward, ) from .configuration_qwen2 import Qwen2Config logger = logging.get_logger(__name__) class Qwen2MLP(LlamaMLP): def __init__(self, config): super().__init__(config) self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) class Qwen2Attention(LlamaAttention): def __init__(self, config: Qwen2Config, layer_idx: int): super().__init__(config, layer_idx) self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) sliding_window = None if ( self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers ): sliding_window = self.config.sliding_window attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=sliding_window, # main diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class Qwen2DecoderLayer(LlamaDecoderLayer): def __init__(self, config: Qwen2Config, layer_idx: int): super().__init__() self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx) self.mlp = Qwen2MLP(config) if config.sliding_window and config._attn_implementation != "flash_attention_2": logger.warning_once( f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " "unexpected results may be encountered." ) class Qwen2Model(LlamaModel): pass class Qwen2ForCausalLM(LlamaForCausalLM): pass class Qwen2ForSequenceClassification(LlamaForSequenceClassification): pass class Qwen2ForTokenClassification(LlamaForTokenClassification): pass class Qwen2ForQuestionAnswering(LlamaForQuestionAnswering): pass

transformers/src/transformers/models/qwen2/modular_qwen2.py/0

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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. """PyTorch RoFormer model.""" import math import os from typing import Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...generation import GenerationMixin from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel, SequenceSummary from ...pytorch_utils import apply_chunking_to_forward, 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_roformer import RoFormerConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "junnyu/roformer_chinese_base" _CONFIG_FOR_DOC = "RoFormerConfig" # Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->RoFormer class RoFormerSinusoidalPositionalEmbedding(nn.Embedding): """This module produces sinusoidal positional embeddings of any length.""" def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None: super().__init__(num_positions, embedding_dim) self.weight = self._init_weight(self.weight) @staticmethod def _init_weight(out: nn.Parameter) -> nn.Parameter: """ Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in the 2nd half of the vector. [dim // 2:] """ n_pos, dim = out.shape position_enc = np.array( [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)] ) out.requires_grad = False # set early to avoid an error in pytorch-1.8+ sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1 out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2])) out.detach_() return out @torch.no_grad() def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor: """`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) def load_tf_weights_in_roformer(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name.replace("bert", "roformer")) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: if not pointer.shape == array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class RoFormerEmbeddings(nn.Module): """Construct the embeddings from word and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=inputs_embeds.device) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class RoFormerSelfAttention(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.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) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder self.rotary_value = config.rotary_value 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, attention_mask=None, sinusoidal_pos=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): mixed_query_layer = self.query(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) # 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(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) if sinusoidal_pos is not None: if self.rotary_value: query_layer, key_layer, value_layer = self.apply_rotary_position_embeddings( sinusoidal_pos, query_layer, key_layer, value_layer ) else: query_layer, key_layer = self.apply_rotary_position_embeddings( sinusoidal_pos, query_layer, key_layer ) if past_key_value is not None: key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) 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_layer, 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)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in RoFormerModel forward() function) attention_scores = attention_scores + attention_mask # 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,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs @staticmethod def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, value_layer=None): # https://kexue.fm/archives/8265 # sin [batch_size, num_heads, sequence_length, embed_size_per_head//2] # cos [batch_size, num_heads, sequence_length, embed_size_per_head//2] sin, cos = sinusoidal_pos.chunk(2, dim=-1) # sin [θ0,θ1,θ2......θd/2-1] -> sin_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos) # cos [θ0,θ1,θ2......θd/2-1] -> cos_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos) # rotate_half_query_layer [-q1,q0,-q3,q2......,-qd-1,qd-2] rotate_half_query_layer = torch.stack([-query_layer[..., 1::2], query_layer[..., ::2]], dim=-1).reshape_as( query_layer ) query_layer = query_layer * cos_pos + rotate_half_query_layer * sin_pos # rotate_half_key_layer [-k1,k0,-k3,k2......,-kd-1,kd-2] rotate_half_key_layer = torch.stack([-key_layer[..., 1::2], key_layer[..., ::2]], dim=-1).reshape_as(key_layer) key_layer = key_layer * cos_pos + rotate_half_key_layer * sin_pos if value_layer is not None: # rotate_half_value_layer [-v1,v0,-v3,v2......,-vd-1,vd-2] rotate_half_value_layer = torch.stack([-value_layer[..., 1::2], value_layer[..., ::2]], dim=-1).reshape_as( value_layer ) value_layer = value_layer * cos_pos + rotate_half_value_layer * sin_pos return query_layer, key_layer, value_layer return query_layer, key_layer # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->RoFormer class RoFormerSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) 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 = self.LayerNorm(hidden_states + input_tensor) return hidden_states class RoFormerAttention(nn.Module): def __init__(self, config): super().__init__() self.self = RoFormerSelfAttention(config) self.output = RoFormerSelfOutput(config) self.pruned_heads = set() # Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads 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) # End Copy def forward( self, hidden_states, attention_mask=None, sinusoidal_pos=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): self_outputs = self.self( hidden_states, attention_mask, sinusoidal_pos, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, 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 # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RoFormer class RoFormerIntermediate(nn.Module): def __init__(self, config): 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 # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RoFormer class RoFormerOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) 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 = self.LayerNorm(hidden_states + input_tensor) return hidden_states class RoFormerLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = RoFormerAttention(config) 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 = RoFormerAttention(config) self.intermediate = RoFormerIntermediate(config) self.output = RoFormerOutput(config) def forward( self, hidden_states, attention_mask=None, sinusoidal_pos=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): # 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( hidden_states, attention_mask, sinusoidal_pos, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) 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( attention_output, attention_mask, sinusoidal_pos, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) 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 layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class RoFormerEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.embed_positions = RoFormerSinusoidalPositionalEmbedding( config.max_position_embeddings, config.hidden_size // config.num_attention_heads ) self.layer = nn.ModuleList([RoFormerLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): 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 all_hidden_states = () 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 past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 # [sequence_length, embed_size_per_head] -> [batch_size, num_heads, sequence_length, embed_size_per_head] sinusoidal_pos = self.embed_positions(hidden_states.shape[:-1], past_key_values_length)[None, 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,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, sinusoidal_pos, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask, sinusoidal_pos, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) 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 RoFormerPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.embedding_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class RoFormerLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = RoFormerPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def _tie_weights(self) -> None: self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->RoFormer class RoFormerOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = RoFormerLMPredictionHead(config) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores class RoFormerPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RoFormerConfig load_tf_weights = load_tf_weights_in_roformer base_model_prefix = "roformer" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, 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, RoFormerSinusoidalPositionalEmbedding): pass 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) ROFORMER_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 ([`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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ ROFORMER_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) 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 RoFormer Model transformer outputting raw hidden-states without any specific head on top.", ROFORMER_START_DOCSTRING, ) class RoFormerModel(RoFormerPreTrainedModel): """ 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. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config): super().__init__(config) self.config = config self.embeddings = RoFormerEmbeddings(config) if config.embedding_size != config.hidden_size: self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size) self.encoder = RoFormerEncoder(config) # 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(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, 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, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[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[BaseModelOutputWithPastAndCrossAttentions, Tuple[torch.Tensor]]: 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 self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: 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: 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 # 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 attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_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) # 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.config.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=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) 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] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) if hasattr(self, "embeddings_project"): embedding_output = self.embeddings_project(embedding_output) encoder_outputs = self.encoder( 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, ) sequence_output = encoder_outputs[0] if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=sequence_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("""RoFormer Model with a `language modeling` head on top.""", ROFORMER_START_DOCSTRING) class RoFormerForMaskedLM(RoFormerPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `RoFormerForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roformer = RoFormerModel(config) self.cls = RoFormerOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings self.cls.predictions.bias = new_embeddings.bias @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, 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, head_mask: Optional[torch.FloatTensor] = 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[MaskedLMOutput, Tuple[torch.Tensor]]: 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]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, 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] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] 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, ) def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs): input_shape = input_ids.shape effective_batch_size = input_shape[0] # add a dummy token assert self.config.pad_token_id is not None, "The PAD token should be defined for generation" attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1) dummy_token = torch.full( (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device ) input_ids = torch.cat([input_ids, dummy_token], dim=1) return {"input_ids": input_ids, "attention_mask": attention_mask} @add_start_docstrings( """RoFormer Model with a `language modeling` head on top for CLM fine-tuning.""", ROFORMER_START_DOCSTRING ) class RoFormerForCausalLM(RoFormerPreTrainedModel, GenerationMixin): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `RoFormerForCausalLM` as a standalone, add `is_decoder=True.`") self.roformer = RoFormerModel(config) self.cls = RoFormerOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings self.cls.predictions.bias = new_embeddings.bias @add_start_docstrings_to_model_forward(ROFORMER_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, head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[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, **kwargs, ) -> Union[CausalLMOutputWithCrossAttentions, Tuple[torch.Tensor]]: 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)`. 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 n `[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`). Returns: Example: ```python >>> from transformers import AutoTokenizer, RoFormerForCausalLM, RoFormerConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("junnyu/roformer_chinese_base") >>> config = RoFormerConfig.from_pretrained("junnyu/roformer_chinese_base") >>> config.is_decoder = True >>> model = RoFormerForCausalLM.from_pretrained("junnyu/roformer_chinese_base", config=config) >>> inputs = tokenizer("今天天气非常好。", 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 outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_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, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) lm_loss = None if labels is not None: lm_loss = self.loss_function( prediction_scores, labels, vocab_size=self.config.vocab_size, **kwargs, ) if not return_dict: output = (prediction_scores,) + outputs[1:] 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 _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[:2]) + layer_past[2:], ) return reordered_past class RoFormerClassificationHead(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) self.config = config def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = ACT2FN[self.config.hidden_act](x) x = self.dropout(x) x = self.out_proj(x) return x @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 RoFormerForSequenceClassification(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roformer = RoFormerModel(config) self.classifier = RoFormerClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ROFORMER_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, 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.Tensor]]: 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.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_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[1:] 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( """ 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 RoFormerForMultipleChoice(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) self.roformer = RoFormerModel(config) self.sequence_summary = SequenceSummary(config) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( ROFORMER_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, attention_mask: Optional[torch.FloatTensor] = None, token_type_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[MultipleChoiceModelOutput, Tuple[torch.Tensor]]: 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] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_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] pooled_output = self.sequence_summary(sequence_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[1:] 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( """ 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 RoFormerForTokenClassification(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roformer = RoFormerModel(config) 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(ROFORMER_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, 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.Tensor]]: 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.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_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[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=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 RoFormerForQuestionAnswering(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) config.num_labels = 2 self.num_labels = config.num_labels self.roformer = RoFormerModel(config) 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(ROFORMER_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, 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.Tensor]]: 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.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_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) end_logits = end_logits.squeeze(-1) 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[1:] 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, ) __all__ = [ "RoFormerForCausalLM", "RoFormerForMaskedLM", "RoFormerForMultipleChoice", "RoFormerForQuestionAnswering", "RoFormerForSequenceClassification", "RoFormerForTokenClassification", "RoFormerLayer", "RoFormerModel", "RoFormerPreTrainedModel", "load_tf_weights_in_roformer", ]

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

{ "file_path": "transformers/src/transformers/models/roformer/modeling_roformer.py", "repo_id": "transformers", "token_count": 29539 }

# coding=utf-8 # Copyright 2025 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 RT Detr V2 checkpoints with Timm backbone""" import argparse import json import re from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from torchvision import transforms from transformers import RTDetrImageProcessor, RTDetrV2Config, RTDetrV2ForObjectDetection from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_rt_detr_v2_config(model_name: str) -> RTDetrV2Config: config = RTDetrV2Config() config.num_labels = 80 repo_id = "huggingface/label-files" filename = "coco-detection-mmdet-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()} if model_name == "rtdetr_v2_r18vd": config.backbone_config.hidden_sizes = [64, 128, 256, 512] config.backbone_config.depths = [2, 2, 2, 2] config.backbone_config.layer_type = "basic" config.encoder_in_channels = [128, 256, 512] config.hidden_expansion = 0.5 config.decoder_layers = 3 elif model_name == "rtdetr_v2_r34vd": config.backbone_config.hidden_sizes = [64, 128, 256, 512] config.backbone_config.depths = [3, 4, 6, 3] config.backbone_config.layer_type = "basic" config.encoder_in_channels = [128, 256, 512] config.hidden_expansion = 0.5 config.decoder_layers = 4 # TODO: check this not working elif model_name == "rtdetr_v2_r50vd_m": config.hidden_expansion = 0.5 elif model_name == "rtdetr_v2_r50vd": pass elif model_name == "rtdetr_v2_r101vd": config.backbone_config.depths = [3, 4, 23, 3] config.encoder_ffn_dim = 2048 config.encoder_hidden_dim = 384 config.decoder_in_channels = [384, 384, 384] return config # Define a mapping from original keys to converted keys using regex ORIGINAL_TO_CONVERTED_KEY_MAPPING = { r"backbone.conv1.conv1_1.conv.weight": r"model.backbone.model.embedder.embedder.0.convolution.weight", r"backbone.conv1.conv1_1.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.embedder.embedder.0.normalization.\1", r"backbone.conv1.conv1_2.conv.weight": r"model.backbone.model.embedder.embedder.1.convolution.weight", r"backbone.conv1.conv1_2.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.embedder.embedder.1.normalization.\1", r"backbone.conv1.conv1_3.conv.weight": r"model.backbone.model.embedder.embedder.2.convolution.weight", r"backbone.conv1.conv1_3.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.embedder.embedder.2.normalization.\1", r"backbone.res_layers.(\d+).blocks.(\d+).branch2a.conv.weight": r"model.backbone.model.encoder.stages.\1.layers.\2.layer.0.convolution.weight", r"backbone.res_layers.(\d+).blocks.(\d+).branch2a.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.encoder.stages.\1.layers.\2.layer.0.normalization.\3", r"backbone.res_layers.(\d+).blocks.(\d+).branch2b.conv.weight": r"model.backbone.model.encoder.stages.\1.layers.\2.layer.1.convolution.weight", r"backbone.res_layers.(\d+).blocks.(\d+).branch2b.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.encoder.stages.\1.layers.\2.layer.1.normalization.\3", r"backbone.res_layers.(\d+).blocks.(\d+).branch2c.conv.weight": r"model.backbone.model.encoder.stages.\1.layers.\2.layer.2.convolution.weight", r"backbone.res_layers.(\d+).blocks.(\d+).branch2c.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.encoder.stages.\1.layers.\2.layer.2.normalization.\3", r"encoder.encoder.(\d+).layers.0.self_attn.out_proj.weight": r"model.encoder.encoder.\1.layers.0.self_attn.out_proj.weight", r"encoder.encoder.(\d+).layers.0.self_attn.out_proj.bias": r"model.encoder.encoder.\1.layers.0.self_attn.out_proj.bias", r"encoder.encoder.(\d+).layers.0.linear1.weight": r"model.encoder.encoder.\1.layers.0.fc1.weight", r"encoder.encoder.(\d+).layers.0.linear1.bias": r"model.encoder.encoder.\1.layers.0.fc1.bias", r"encoder.encoder.(\d+).layers.0.linear2.weight": r"model.encoder.encoder.\1.layers.0.fc2.weight", r"encoder.encoder.(\d+).layers.0.linear2.bias": r"model.encoder.encoder.\1.layers.0.fc2.bias", r"encoder.encoder.(\d+).layers.0.norm1.weight": r"model.encoder.encoder.\1.layers.0.self_attn_layer_norm.weight", r"encoder.encoder.(\d+).layers.0.norm1.bias": r"model.encoder.encoder.\1.layers.0.self_attn_layer_norm.bias", r"encoder.encoder.(\d+).layers.0.norm2.weight": r"model.encoder.encoder.\1.layers.0.final_layer_norm.weight", r"encoder.encoder.(\d+).layers.0.norm2.bias": r"model.encoder.encoder.\1.layers.0.final_layer_norm.bias", r"encoder.input_proj.(\d+).conv.weight": r"model.encoder_input_proj.\1.0.weight", r"encoder.input_proj.(\d+).norm.(.*)": r"model.encoder_input_proj.\1.1.\2", r"encoder.fpn_blocks.(\d+).conv(\d+).conv.weight": r"model.encoder.fpn_blocks.\1.conv\2.conv.weight", # r"encoder.fpn_blocks.(\d+).conv(\d+).norm.(.*)": r"model.encoder.fpn_blocks.\1.conv\2.norm.\3", r"encoder.fpn_blocks.(\d+).conv(\d+).norm.(weight|bias|running_mean|running_var)": r"model.encoder.fpn_blocks.\1.conv\2.norm.\3", r"encoder.lateral_convs.(\d+).conv.weight": r"model.encoder.lateral_convs.\1.conv.weight", r"encoder.lateral_convs.(\d+).norm.(.*)": r"model.encoder.lateral_convs.\1.norm.\2", r"encoder.fpn_blocks.(\d+).bottlenecks.(\d+).conv(\d+).conv.weight": r"model.encoder.fpn_blocks.\1.bottlenecks.\2.conv\3.conv.weight", r"encoder.fpn_blocks.(\d+).bottlenecks.(\d+).conv(\d+).norm.(\w+)": r"model.encoder.fpn_blocks.\1.bottlenecks.\2.conv\3.norm.\4", r"encoder.pan_blocks.(\d+).conv(\d+).conv.weight": r"model.encoder.pan_blocks.\1.conv\2.conv.weight", r"encoder.pan_blocks.(\d+).conv(\d+).norm.(weight|bias|running_mean|running_var)": r"model.encoder.pan_blocks.\1.conv\2.norm.\3", r"encoder.pan_blocks.(\d+).bottlenecks.(\d+).conv(\d+).conv.weight": r"model.encoder.pan_blocks.\1.bottlenecks.\2.conv\3.conv.weight", r"encoder.pan_blocks.(\d+).bottlenecks.(\d+).conv(\d+).norm.(weight|bias|running_mean|running_var)": r"model.encoder.pan_blocks.\1.bottlenecks.\2.conv\3.norm.\4", r"encoder.downsample_convs.(\d+).conv.weight": r"model.encoder.downsample_convs.\1.conv.weight", r"encoder.downsample_convs.(\d+).norm.(weight|bias|running_mean|running_var)": r"model.encoder.downsample_convs.\1.norm.\2", r"decoder.decoder.layers.(\d+).self_attn.out_proj.weight": r"model.decoder.layers.\1.self_attn.out_proj.weight", r"decoder.decoder.layers.(\d+).self_attn.out_proj.bias": r"model.decoder.layers.\1.self_attn.out_proj.bias", r"decoder.decoder.layers.(\d+).cross_attn.sampling_offsets.weight": r"model.decoder.layers.\1.encoder_attn.sampling_offsets.weight", r"decoder.decoder.layers.(\d+).cross_attn.sampling_offsets.bias": r"model.decoder.layers.\1.encoder_attn.sampling_offsets.bias", r"decoder.decoder.layers.(\d+).cross_attn.attention_weights.weight": r"model.decoder.layers.\1.encoder_attn.attention_weights.weight", r"decoder.decoder.layers.(\d+).cross_attn.attention_weights.bias": r"model.decoder.layers.\1.encoder_attn.attention_weights.bias", r"decoder.decoder.layers.(\d+).cross_attn.value_proj.weight": r"model.decoder.layers.\1.encoder_attn.value_proj.weight", r"decoder.decoder.layers.(\d+).cross_attn.value_proj.bias": r"model.decoder.layers.\1.encoder_attn.value_proj.bias", r"decoder.decoder.layers.(\d+).cross_attn.output_proj.weight": r"model.decoder.layers.\1.encoder_attn.output_proj.weight", r"decoder.decoder.layers.(\d+).cross_attn.output_proj.bias": r"model.decoder.layers.\1.encoder_attn.output_proj.bias", r"decoder.decoder.layers.(\d+).norm1.weight": r"model.decoder.layers.\1.self_attn_layer_norm.weight", r"decoder.decoder.layers.(\d+).norm1.bias": r"model.decoder.layers.\1.self_attn_layer_norm.bias", r"decoder.decoder.layers.(\d+).norm2.weight": r"model.decoder.layers.\1.encoder_attn_layer_norm.weight", r"decoder.decoder.layers.(\d+).norm2.bias": r"model.decoder.layers.\1.encoder_attn_layer_norm.bias", r"decoder.decoder.layers.(\d+).linear1.weight": r"model.decoder.layers.\1.fc1.weight", r"decoder.decoder.layers.(\d+).linear1.bias": r"model.decoder.layers.\1.fc1.bias", r"decoder.decoder.layers.(\d+).linear2.weight": r"model.decoder.layers.\1.fc2.weight", r"decoder.decoder.layers.(\d+).linear2.bias": r"model.decoder.layers.\1.fc2.bias", r"decoder.decoder.layers.(\d+).norm3.weight": r"model.decoder.layers.\1.final_layer_norm.weight", r"decoder.decoder.layers.(\d+).norm3.bias": r"model.decoder.layers.\1.final_layer_norm.bias", r"decoder.decoder.layers.(\d+).cross_attn.num_points_scale": r"model.decoder.layers.\1.encoder_attn.n_points_scale", r"decoder.dec_score_head.(\d+).weight": r"model.decoder.class_embed.\1.weight", r"decoder.dec_score_head.(\d+).bias": r"model.decoder.class_embed.\1.bias", r"decoder.dec_bbox_head.(\d+).layers.(\d+).(weight|bias)": r"model.decoder.bbox_embed.\1.layers.\2.\3", r"decoder.denoising_class_embed.weight": r"model.denoising_class_embed.weight", r"decoder.query_pos_head.layers.0.weight": r"model.decoder.query_pos_head.layers.0.weight", r"decoder.query_pos_head.layers.0.bias": r"model.decoder.query_pos_head.layers.0.bias", r"decoder.query_pos_head.layers.1.weight": r"model.decoder.query_pos_head.layers.1.weight", r"decoder.query_pos_head.layers.1.bias": r"model.decoder.query_pos_head.layers.1.bias", r"decoder.enc_output.proj.weight": r"model.enc_output.0.weight", r"decoder.enc_output.proj.bias": r"model.enc_output.0.bias", r"decoder.enc_output.norm.weight": r"model.enc_output.1.weight", r"decoder.enc_output.norm.bias": r"model.enc_output.1.bias", r"decoder.enc_score_head.weight": r"model.enc_score_head.weight", r"decoder.enc_score_head.bias": r"model.enc_score_head.bias", r"decoder.enc_bbox_head.layers.(\d+).(weight|bias)": r"model.enc_bbox_head.layers.\1.\2", r"backbone.res_layers.0.blocks.0.short.conv.weight": r"model.backbone.model.encoder.stages.0.layers.0.shortcut.convolution.weight", r"backbone.res_layers.0.blocks.0.short.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.encoder.stages.0.layers.0.shortcut.normalization.\1", r"backbone.res_layers.(\d+).blocks.0.short.conv.conv.weight": r"model.backbone.model.encoder.stages.\1.layers.0.shortcut.1.convolution.weight", r"backbone.res_layers.(\d+).blocks.0.short.conv.norm.(\w+)": r"model.backbone.model.encoder.stages.\1.layers.0.shortcut.1.normalization.\2", # Mapping for subsequent blocks in other stages r"backbone.res_layers.(\d+).blocks.0.short.conv.weight": r"model.backbone.model.encoder.stages.\1.layers.0.shortcut.1.convolution.weight", r"backbone.res_layers.(\d+).blocks.0.short.norm.(weight|bias|running_mean|running_var)": r"model.backbone.model.encoder.stages.\1.layers.0.shortcut.1.normalization.\2", r"decoder.input_proj.(\d+).conv.weight": r"model.decoder_input_proj.\1.0.weight", r"decoder.input_proj.(\d+).norm.(.*)": r"model.decoder_input_proj.\1.1.\2", } def convert_old_keys_to_new_keys(state_dict_keys: dict = None): # Use the mapping to rename keys for original_key, converted_key in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items(): for key in list(state_dict_keys.keys()): new_key = re.sub(original_key, converted_key, key) if new_key != key: state_dict_keys[new_key] = state_dict_keys.pop(key) return state_dict_keys def read_in_q_k_v(state_dict, config): prefix = "" encoder_hidden_dim = config.encoder_hidden_dim # first: transformer encoder for i in range(config.encoder_layers): # 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}encoder.encoder.{i}.layers.0.self_attn.in_proj_weight") in_proj_bias = state_dict.pop(f"{prefix}encoder.encoder.{i}.layers.0.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"model.encoder.encoder.{i}.layers.0.self_attn.q_proj.weight"] = in_proj_weight[ :encoder_hidden_dim, : ] state_dict[f"model.encoder.encoder.{i}.layers.0.self_attn.q_proj.bias"] = in_proj_bias[:encoder_hidden_dim] state_dict[f"model.encoder.encoder.{i}.layers.0.self_attn.k_proj.weight"] = in_proj_weight[ encoder_hidden_dim : 2 * encoder_hidden_dim, : ] state_dict[f"model.encoder.encoder.{i}.layers.0.self_attn.k_proj.bias"] = in_proj_bias[ encoder_hidden_dim : 2 * encoder_hidden_dim ] state_dict[f"model.encoder.encoder.{i}.layers.0.self_attn.v_proj.weight"] = in_proj_weight[ -encoder_hidden_dim:, : ] state_dict[f"model.encoder.encoder.{i}.layers.0.self_attn.v_proj.bias"] = in_proj_bias[-encoder_hidden_dim:] # next: transformer decoder (which is a bit more complex because it also includes cross-attention) for i in range(config.decoder_layers): # read in weights + bias of input projection layer of self-attention in_proj_weight = state_dict.pop(f"{prefix}decoder.decoder.layers.{i}.self_attn.in_proj_weight") in_proj_bias = state_dict.pop(f"{prefix}decoder.decoder.layers.{i}.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] state_dict[f"model.decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] state_dict[f"model.decoder.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 write_model_and_image_processor(model_name, output_dir, push_to_hub, repo_id): """ Copy/paste/tweak model's weights to our RTDETR structure. """ # load default config config = get_rt_detr_v2_config(model_name) # load original model from torch hub model_name_to_checkpoint_url = { "rtdetr_v2_r18vd": "https://github.com/lyuwenyu/storage/releases/download/v0.2/rtdetrv2_r18vd_120e_coco_rerun_48.1.pth", "rtdetr_v2_r34vd": "https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r34vd_120e_coco_ema.pth", "rtdetr_v2_r50vd": "https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r50vd_6x_coco_ema.pth", "rtdetr_v2_r101vd": "https://github.com/lyuwenyu/storage/releases/download/v0.1/rtdetrv2_r101vd_6x_coco_from_paddle.pth", } logger.info(f"Converting model {model_name}...") state_dict = torch.hub.load_state_dict_from_url(model_name_to_checkpoint_url[model_name], map_location="cpu")[ "ema" ]["module"] # rename keys state_dict = convert_old_keys_to_new_keys(state_dict) for key in state_dict.copy().keys(): if key.endswith("num_batches_tracked"): del state_dict[key] # query, key and value matrices need special treatment read_in_q_k_v(state_dict, config) # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them for key in state_dict.copy().keys(): if key.endswith("num_batches_tracked"): del state_dict[key] # for two_stage if "bbox_embed" in key or ("class_embed" in key and "denoising_" not in key): state_dict[key.split("model.decoder.")[-1]] = state_dict[key] # no need in ckpt del state_dict["decoder.anchors"] del state_dict["decoder.valid_mask"] # finally, create HuggingFace model and load state dict model = RTDetrV2ForObjectDetection(config) model.load_state_dict(state_dict) model.eval() # load image processor image_processor = RTDetrImageProcessor() # prepare image img = prepare_img() # preprocess image transformations = transforms.Compose( [ transforms.Resize([640, 640], interpolation=transforms.InterpolationMode.BILINEAR), transforms.ToTensor(), ] ) original_pixel_values = transformations(img).unsqueeze(0) # insert batch dimension encoding = image_processor(images=img, return_tensors="pt") pixel_values = encoding["pixel_values"] assert torch.allclose(original_pixel_values, pixel_values) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) pixel_values = pixel_values.to(device) # Pass image by the model with torch.no_grad(): outputs = model(pixel_values) if model_name == "rtdetr_v2_r18vd": expected_slice_logits = torch.tensor( [[-3.7045, -5.1913, -6.1787], [-4.0106, -9.3450, -5.2043], [-4.1287, -4.7463, -5.8634]] ) expected_slice_boxes = torch.tensor( [[0.2582, 0.5497, 0.4764], [0.1684, 0.1985, 0.2120], [0.7665, 0.4146, 0.4669]] ) elif model_name == "rtdetr_v2_r34vd": expected_slice_logits = torch.tensor( [[-4.6108, -5.9453, -3.8505], [-3.8702, -6.1136, -5.5677], [-3.7790, -6.4538, -5.9449]] ) expected_slice_boxes = torch.tensor( [[0.1691, 0.1984, 0.2118], [0.2594, 0.5506, 0.4736], [0.7669, 0.4136, 0.4654]] ) elif model_name == "rtdetr_v2_r50vd": expected_slice_logits = torch.tensor( [[-4.7881, -4.6754, -6.1624], [-5.4441, -6.6486, -4.3840], [-3.5455, -4.9318, -6.3544]] ) expected_slice_boxes = torch.tensor( [[0.2588, 0.5487, 0.4747], [0.5497, 0.2760, 0.0573], [0.7688, 0.4133, 0.4634]] ) elif model_name == "rtdetr_v2_r101vd": expected_slice_logits = torch.tensor( [[-4.6162, -4.9189, -4.6656], [-4.4701, -4.4997, -4.9659], [-5.6641, -7.9000, -5.0725]] ) expected_slice_boxes = torch.tensor( [[0.7707, 0.4124, 0.4585], [0.2589, 0.5492, 0.4735], [0.1688, 0.1993, 0.2108]] ) else: raise ValueError(f"Unknown rt_detr_v2_name: {model_name}") assert torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits.to(outputs.logits.device), atol=1e-4) assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes.to(outputs.pred_boxes.device), atol=1e-3) if output_dir is not None: Path(output_dir).mkdir(exist_ok=True) print(f"Saving model {model_name} to {output_dir}") model.save_pretrained(output_dir) print(f"Saving image processor to {output_dir}") image_processor.save_pretrained(output_dir) if push_to_hub: # Upload model, image processor and config to the hub logger.info("Uploading PyTorch model and image processor to the hub...") config.push_to_hub( repo_id=repo_id, commit_message="Add config from convert_rt_detr_v2_original_pytorch_checkpoint_to_pytorch.py", ) model.push_to_hub( repo_id=repo_id, commit_message="Add model from convert_rt_detr_v2_original_pytorch_checkpoint_to_pytorch.py", ) image_processor.push_to_hub( repo_id=repo_id, commit_message="Add image processor from convert_rt_detr_v2_original_pytorch_checkpoint_to_pytorch.py", ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_name", default="rtdetr_v2_r18vd", type=str, help="model_name of the checkpoint you'd like to convert.", ) parser.add_argument("--output_dir", default=None, type=str, help="Location to write HF model and image processor") parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.") parser.add_argument( "--repo_id", type=str, help="repo_id where the model will be pushed to.", ) args = parser.parse_args() write_model_and_image_processor(args.model_name, args.output_dir, args.push_to_hub, args.repo_id)

transformers/src/transformers/models/rt_detr_v2/convert_rt_detr_v2_weights_to_hf.py/0

{ "file_path": "transformers/src/transformers/models/rt_detr_v2/convert_rt_detr_v2_weights_to_hf.py", "repo_id": "transformers", "token_count": 9661 }

# coding=utf-8 # Copyright 2022 NVIDIA 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. """TensorFlow SegFormer model.""" from __future__ import annotations import math from typing import Optional, Tuple, Union import tensorflow as tf from ...activations_tf import get_tf_activation from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from ...modeling_tf_outputs import TFBaseModelOutput, TFSemanticSegmenterOutput, TFSequenceClassifierOutput from ...modeling_tf_utils import ( TFPreTrainedModel, TFSequenceClassificationLoss, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import logging from .configuration_segformer import SegformerConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "SegformerConfig" # Base docstring _CHECKPOINT_FOR_DOC = "nvidia/mit-b0" _EXPECTED_OUTPUT_SHAPE = [1, 256, 16, 16] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "nvidia/mit-b0" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" # Copied from transformers.models.convnext.modeling_tf_convnext.TFConvNextDropPath with ConvNext->Segformer class TFSegformerDropPath(keras.layers.Layer): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). References: (1) github.com:rwightman/pytorch-image-models """ def __init__(self, drop_path: float, **kwargs): super().__init__(**kwargs) self.drop_path = drop_path def call(self, x: tf.Tensor, training=None): if training: keep_prob = 1 - self.drop_path shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1) random_tensor = keep_prob + tf.random.uniform(shape, 0, 1) random_tensor = tf.floor(random_tensor) return (x / keep_prob) * random_tensor return x class TFSegformerOverlapPatchEmbeddings(keras.layers.Layer): """Construct the overlapping patch embeddings.""" def __init__(self, patch_size, stride, num_channels, hidden_size, **kwargs): super().__init__(**kwargs) self.padding = keras.layers.ZeroPadding2D(padding=patch_size // 2) self.proj = keras.layers.Conv2D( filters=hidden_size, kernel_size=patch_size, strides=stride, padding="VALID", name="proj" ) self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-05, name="layer_norm") self.num_channels = num_channels self.hidden_size = hidden_size def call(self, pixel_values: tf.Tensor) -> Tuple[tf.Tensor, int, int]: embeddings = self.proj(self.padding(pixel_values)) height = shape_list(embeddings)[1] width = shape_list(embeddings)[2] hidden_dim = shape_list(embeddings)[3] # (batch_size, height, width, num_channels) -> (batch_size, height*width, num_channels) # this can be fed to a Transformer layer embeddings = tf.reshape(embeddings, (-1, height * width, hidden_dim)) embeddings = self.layer_norm(embeddings) return embeddings, height, width def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "proj", None) is not None: with tf.name_scope(self.proj.name): self.proj.build([None, None, None, self.num_channels]) if getattr(self, "layer_norm", None) is not None: with tf.name_scope(self.layer_norm.name): self.layer_norm.build([None, None, self.hidden_size]) class TFSegformerEfficientSelfAttention(keras.layers.Layer): """SegFormer's efficient self-attention mechanism. Employs the sequence reduction process introduced in the [PvT paper](https://arxiv.org/abs/2102.12122).""" def __init__( self, config: SegformerConfig, hidden_size: int, num_attention_heads: int, sequence_reduction_ratio: int, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads if self.hidden_size % self.num_attention_heads != 0: raise ValueError( f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention " f"heads ({self.num_attention_heads})" ) self.attention_head_size = self.hidden_size // self.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(self.all_head_size, name="query") self.key = keras.layers.Dense(self.all_head_size, name="key") self.value = keras.layers.Dense(self.all_head_size, name="value") self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob) self.sr_ratio = sequence_reduction_ratio if sequence_reduction_ratio > 1: self.sr = keras.layers.Conv2D( filters=hidden_size, kernel_size=sequence_reduction_ratio, strides=sequence_reduction_ratio, name="sr" ) self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-05, name="layer_norm") def transpose_for_scores(self, tensor: tf.Tensor) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] # to [batch_size, seq_length, num_attention_heads, attention_head_size] batch_size = shape_list(tensor)[0] 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, height: int, width: int, output_attentions: bool = False, training: bool = False, ) -> Union[tf.Tensor, Tuple[tf.Tensor, tf.Tensor]]: batch_size = shape_list(hidden_states)[0] num_channels = shape_list(hidden_states)[2] query_layer = self.transpose_for_scores(self.query(hidden_states)) if self.sr_ratio > 1: # Reshape to (batch_size, height, width, num_channels) hidden_states = tf.reshape(hidden_states, (batch_size, height, width, num_channels)) # Apply sequence reduction hidden_states = self.sr(hidden_states) # Reshape back to (batch_size, seq_len, num_channels) hidden_states = tf.reshape(hidden_states, (batch_size, -1, num_channels)) hidden_states = self.layer_norm(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) scale = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, scale) # 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(attention_probs, training=training) context_layer = tf.matmul(attention_probs, value_layer) context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) context_layer = tf.reshape(context_layer, (batch_size, -1, self.all_head_size)) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) 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.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.hidden_size]) if getattr(self, "sr", None) is not None: with tf.name_scope(self.sr.name): self.sr.build([None, None, None, self.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.hidden_size]) class TFSegformerSelfOutput(keras.layers.Layer): def __init__(self, config: SegformerConfig, hidden_size: int, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense(hidden_size, name="dense") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.hidden_size = hidden_size def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) 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.hidden_size]) class TFSegformerAttention(keras.layers.Layer): def __init__( self, config: SegformerConfig, hidden_size: int, num_attention_heads: int, sequence_reduction_ratio: int, **kwargs, ): super().__init__(**kwargs) self.self = TFSegformerEfficientSelfAttention( config=config, hidden_size=hidden_size, num_attention_heads=num_attention_heads, sequence_reduction_ratio=sequence_reduction_ratio, name="self", ) self.dense_output = TFSegformerSelfOutput(config, hidden_size=hidden_size, name="output") def call( self, hidden_states: tf.Tensor, height: int, width: int, output_attentions: bool = False ) -> Union[tf.Tensor, Tuple[tf.Tensor, tf.Tensor]]: self_outputs = self.self(hidden_states, height, width, output_attentions) attention_output = self.dense_output(self_outputs[0]) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self", None) is not None: with tf.name_scope(self.self.name): self.self.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) class TFSegformerDWConv(keras.layers.Layer): def __init__(self, dim: int = 768, **kwargs): super().__init__(**kwargs) self.depthwise_convolution = keras.layers.Conv2D( filters=dim, kernel_size=3, strides=1, padding="same", groups=dim, name="dwconv" ) self.dim = dim def call(self, hidden_states: tf.Tensor, height: int, width: int) -> tf.Tensor: batch_size = shape_list(hidden_states)[0] num_channels = shape_list(hidden_states)[-1] hidden_states = tf.reshape(hidden_states, (batch_size, height, width, num_channels)) hidden_states = self.depthwise_convolution(hidden_states) new_height = shape_list(hidden_states)[1] new_width = shape_list(hidden_states)[2] num_channels = shape_list(hidden_states)[3] hidden_states = tf.reshape(hidden_states, (batch_size, new_height * new_width, num_channels)) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "depthwise_convolution", None) is not None: with tf.name_scope(self.depthwise_convolution.name): self.depthwise_convolution.build([None, None, None, self.dim]) class TFSegformerMixFFN(keras.layers.Layer): def __init__( self, config: SegformerConfig, in_features: int, hidden_features: int = None, out_features: int = None, **kwargs, ): super().__init__(**kwargs) out_features = out_features or in_features self.dense1 = keras.layers.Dense(hidden_features, name="dense1") self.depthwise_convolution = TFSegformerDWConv(hidden_features, name="dwconv") 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.dense2 = keras.layers.Dense(out_features, name="dense2") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.hidden_features = hidden_features self.in_features = in_features def call(self, hidden_states: tf.Tensor, height: int, width: int, training: bool = False) -> tf.Tensor: hidden_states = self.dense1(hidden_states) hidden_states = self.depthwise_convolution(hidden_states, height, width) hidden_states = self.intermediate_act_fn(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.dense2(hidden_states) hidden_states = self.dropout(hidden_states, training=training) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense1", None) is not None: with tf.name_scope(self.dense1.name): self.dense1.build([None, None, self.in_features]) if getattr(self, "depthwise_convolution", None) is not None: with tf.name_scope(self.depthwise_convolution.name): self.depthwise_convolution.build(None) if getattr(self, "dense2", None) is not None: with tf.name_scope(self.dense2.name): self.dense2.build([None, None, self.hidden_features]) class TFSegformerLayer(keras.layers.Layer): """This corresponds to the Block class in the original implementation.""" def __init__( self, config, hidden_size: int, num_attention_heads: int, drop_path: float, sequence_reduction_ratio: int, mlp_ratio: int, **kwargs, ): super().__init__(**kwargs) self.layer_norm_1 = keras.layers.LayerNormalization(epsilon=1e-05, name="layer_norm_1") self.attention = TFSegformerAttention( config, hidden_size=hidden_size, num_attention_heads=num_attention_heads, sequence_reduction_ratio=sequence_reduction_ratio, name="attention", ) self.drop_path = TFSegformerDropPath(drop_path) if drop_path > 0.0 else keras.layers.Activation("linear") self.layer_norm_2 = keras.layers.LayerNormalization(epsilon=1e-05, name="layer_norm_2") mlp_hidden_size = int(hidden_size * mlp_ratio) self.mlp = TFSegformerMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size, name="mlp") self.hidden_size = hidden_size def call( self, hidden_states: tf.Tensor, height: int, width: int, output_attentions: bool = False, training: bool = False, ) -> Tuple: self_attention_outputs = self.attention( self.layer_norm_1(hidden_states), # in Segformer, layernorm is applied before self-attention height, width, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection (with stochastic depth) attention_output = self.drop_path(attention_output, training=training) hidden_states = attention_output + hidden_states mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width) # second residual connection (with stochastic depth) mlp_output = self.drop_path(mlp_output, training=training) layer_output = mlp_output + hidden_states outputs = (layer_output,) + outputs return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer_norm_1", None) is not None: with tf.name_scope(self.layer_norm_1.name): self.layer_norm_1.build([None, None, self.hidden_size]) if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "layer_norm_2", None) is not None: with tf.name_scope(self.layer_norm_2.name): self.layer_norm_2.build([None, None, self.hidden_size]) if getattr(self, "mlp", None) is not None: with tf.name_scope(self.mlp.name): self.mlp.build(None) class TFSegformerEncoder(keras.layers.Layer): def __init__(self, config: SegformerConfig, **kwargs): super().__init__(**kwargs) self.config = config # stochastic depth decay rule drop_path_decays = [x.numpy() for x in tf.linspace(0.0, config.drop_path_rate, sum(config.depths))] # patch embeddings embeddings = [] for i in range(config.num_encoder_blocks): embeddings.append( TFSegformerOverlapPatchEmbeddings( patch_size=config.patch_sizes[i], stride=config.strides[i], num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1], hidden_size=config.hidden_sizes[i], name=f"patch_embeddings.{i}", ) ) self.embeddings = embeddings # Transformer blocks blocks = [] cur = 0 for i in range(config.num_encoder_blocks): # each block consists of layers layers = [] if i != 0: cur += config.depths[i - 1] for j in range(config.depths[i]): layers.append( TFSegformerLayer( config, hidden_size=config.hidden_sizes[i], num_attention_heads=config.num_attention_heads[i], drop_path=drop_path_decays[cur + j], sequence_reduction_ratio=config.sr_ratios[i], mlp_ratio=config.mlp_ratios[i], name=f"block.{i}.{j}", ) ) blocks.append(layers) self.block = blocks # Layer norms self.layer_norms = [ keras.layers.LayerNormalization(epsilon=1e-05, name=f"layer_norm.{i}") for i in range(config.num_encoder_blocks) ] def call( self, pixel_values: tf.Tensor, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, training: bool = False, ) -> Union[Tuple, TFBaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None batch_size = shape_list(pixel_values)[0] hidden_states = pixel_values for idx, x in enumerate(zip(self.embeddings, self.block, self.layer_norms)): embedding_layer, block_layer, norm_layer = x # first, obtain patch embeddings hidden_states, height, width = embedding_layer(hidden_states) # second, send embeddings through blocks # (each block consists of multiple layers i.e., list of layers) for i, blk in enumerate(block_layer): layer_outputs = blk( hidden_states, height, width, output_attentions, training=training, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) # third, apply layer norm hidden_states = norm_layer(hidden_states) # fourth, optionally reshape back to (batch_size, height, width, num_channels) if idx != len(self.embeddings) - 1 or (idx == len(self.embeddings) - 1 and self.config.reshape_last_stage): num_channels = shape_list(hidden_states)[-1] hidden_states = tf.reshape(hidden_states, (batch_size, height, width, num_channels)) 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 TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer_norms", None) is not None: for layer, shape in zip(self.layer_norms, self.config.hidden_sizes): with tf.name_scope(layer.name): layer.build([None, None, shape]) if getattr(self, "block", None) is not None: for block in self.block: for layer in block: with tf.name_scope(layer.name): layer.build(None) if getattr(self, "embeddings", None) is not None: for layer in self.embeddings: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFSegformerMainLayer(keras.layers.Layer): config_class = SegformerConfig def __init__(self, config: SegformerConfig, **kwargs): super().__init__(**kwargs) self.config = config # hierarchical Transformer encoder self.encoder = TFSegformerEncoder(config, name="encoder") @unpack_inputs def call( self, pixel_values: tf.Tensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple, TFBaseModelOutput]: 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 # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. # shape = (batch_size, in_height, in_width, in_channels=num_channels) pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) encoder_outputs = self.encoder( pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] # Change to NCHW output format to have uniformity in the modules sequence_output = tf.transpose(sequence_output, perm=[0, 3, 1, 2]) # Change the other hidden state outputs to NCHW as well if output_hidden_states: hidden_states = tuple([tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1]]) if not return_dict: if tf.greater(len(encoder_outputs[1:]), 0): transposed_encoder_outputs = tuple(tf.transpose(v, perm=[0, 3, 1, 2]) for v in encoder_outputs[1:][0]) return (sequence_output,) + (transposed_encoder_outputs,) else: return (sequence_output,) + encoder_outputs[1:] return TFBaseModelOutput( last_hidden_state=sequence_output, hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states, attentions=encoder_outputs.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) class TFSegformerPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = SegformerConfig base_model_prefix = "segformer" main_input_name = "pixel_values" @property def input_signature(self): return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 512, 512), dtype=tf.float32)} SEGFORMER_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. Parameters: config ([`SegformerConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ SEGFORMER_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`SegformerImageProcessor.__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. 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 SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.", SEGFORMER_START_DOCSTRING, ) class TFSegformerModel(TFSegformerPreTrainedModel): def __init__(self, config: SegformerConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.config = config # hierarchical Transformer encoder self.segformer = TFSegformerMainLayer(config, name="segformer") @unpack_inputs @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def call( self, pixel_values: tf.Tensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple, TFBaseModelOutput]: outputs = self.segformer( pixel_values, 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, "segformer", None) is not None: with tf.name_scope(self.segformer.name): self.segformer.build(None) @add_start_docstrings( """ SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden states) e.g. for ImageNet. """, SEGFORMER_START_DOCSTRING, ) class TFSegformerForImageClassification(TFSegformerPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: SegformerConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.segformer = TFSegformerMainLayer(config, name="segformer") # Classifier head self.classifier = keras.layers.Dense(config.num_labels, name="classifier") self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def call( self, pixel_values: tf.Tensor | None = None, labels: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TFSequenceClassifierOutput]: outputs = self.segformer( pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] # convert last hidden states to (batch_size, height*width, hidden_size) batch_size = shape_list(sequence_output)[0] sequence_output = tf.transpose(sequence_output, perm=[0, 2, 3, 1]) sequence_output = tf.reshape(sequence_output, (batch_size, -1, self.config.hidden_sizes[-1])) # global average pooling sequence_output = tf.reduce_mean(sequence_output, axis=1) logits = self.classifier(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=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, "segformer", None) is not None: with tf.name_scope(self.segformer.name): self.segformer.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_sizes[-1]]) class TFSegformerMLP(keras.layers.Layer): """ Linear Embedding. """ def __init__(self, input_dim: int, config: SegformerConfig, **kwargs): super().__init__(**kwargs) self.proj = keras.layers.Dense(config.decoder_hidden_size, name="proj") self.input_dim = input_dim def call(self, hidden_states: tf.Tensor) -> tf.Tensor: height = shape_list(hidden_states)[1] width = shape_list(hidden_states)[2] hidden_dim = shape_list(hidden_states)[-1] hidden_states = tf.reshape(hidden_states, (-1, height * width, hidden_dim)) hidden_states = self.proj(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "proj", None) is not None: with tf.name_scope(self.proj.name): self.proj.build([None, None, self.input_dim]) class TFSegformerDecodeHead(TFSegformerPreTrainedModel): def __init__(self, config: SegformerConfig, **kwargs): super().__init__(config, **kwargs) # linear layers which will unify the channel dimension of each of the encoder blocks to the same config.decoder_hidden_size mlps = [] for i in range(config.num_encoder_blocks): mlp = TFSegformerMLP(config=config, input_dim=config.hidden_sizes[i], name=f"linear_c.{i}") mlps.append(mlp) self.mlps = mlps # the following 3 layers implement the ConvModule of the original implementation self.linear_fuse = keras.layers.Conv2D( filters=config.decoder_hidden_size, kernel_size=1, use_bias=False, name="linear_fuse" ) self.batch_norm = keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="batch_norm") self.activation = keras.layers.Activation("relu") self.dropout = keras.layers.Dropout(config.classifier_dropout_prob) self.classifier = keras.layers.Conv2D(filters=config.num_labels, kernel_size=1, name="classifier") self.config = config def call(self, encoder_hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: all_hidden_states = () for encoder_hidden_state, mlp in zip(encoder_hidden_states, self.mlps): if self.config.reshape_last_stage is False and len(shape_list(encoder_hidden_state)) == 3: height = tf.math.sqrt(tf.cast(shape_list(encoder_hidden_state)[1], tf.float32)) height = width = tf.cast(height, tf.int32) channel_dim = shape_list(encoder_hidden_state)[-1] encoder_hidden_state = tf.reshape(encoder_hidden_state, (-1, height, width, channel_dim)) # unify channel dimension encoder_hidden_state = tf.transpose(encoder_hidden_state, perm=[0, 2, 3, 1]) height, width = shape_list(encoder_hidden_state)[1:3] encoder_hidden_state = mlp(encoder_hidden_state) channel_dim = shape_list(encoder_hidden_state)[-1] encoder_hidden_state = tf.reshape(encoder_hidden_state, (-1, height, width, channel_dim)) # upsample temp_state = tf.transpose(encoder_hidden_states[0], perm=[0, 2, 3, 1]) upsample_resolution = shape_list(temp_state)[1:-1] encoder_hidden_state = tf.image.resize(encoder_hidden_state, size=upsample_resolution, method="bilinear") all_hidden_states += (encoder_hidden_state,) hidden_states = self.linear_fuse(tf.concat(all_hidden_states[::-1], axis=-1)) hidden_states = self.batch_norm(hidden_states, training=training) hidden_states = self.activation(hidden_states) hidden_states = self.dropout(hidden_states, training=training) # logits of shape (batch_size, height/4, width/4, num_labels) logits = self.classifier(hidden_states) return logits def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "linear_fuse", None) is not None: with tf.name_scope(self.linear_fuse.name): self.linear_fuse.build( [None, None, None, self.config.decoder_hidden_size * self.config.num_encoder_blocks] ) if getattr(self, "batch_norm", None) is not None: with tf.name_scope(self.batch_norm.name): self.batch_norm.build([None, None, None, self.config.decoder_hidden_size]) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, None, self.config.decoder_hidden_size]) if getattr(self, "mlps", None) is not None: for layer in self.mlps: with tf.name_scope(layer.name): layer.build(None) @add_start_docstrings( """SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.""", SEGFORMER_START_DOCSTRING, ) class TFSegformerForSemanticSegmentation(TFSegformerPreTrainedModel): def __init__(self, config: SegformerConfig, **kwargs): super().__init__(config, **kwargs) self.segformer = TFSegformerMainLayer(config, name="segformer") self.decode_head = TFSegformerDecodeHead(config, name="decode_head") def hf_compute_loss(self, logits, labels): # upsample logits to the images' original size # `labels` is of shape (batch_size, height, width) label_interp_shape = shape_list(labels)[1:] upsampled_logits = tf.image.resize(logits, size=label_interp_shape, method="bilinear") # compute weighted loss loss_fct = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction="none") def masked_loss(real, pred): unmasked_loss = loss_fct(real, pred) mask = tf.cast(real != self.config.semantic_loss_ignore_index, dtype=unmasked_loss.dtype) masked_loss = unmasked_loss * mask # Reduction strategy in the similar spirit with # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210 reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(mask) return tf.reshape(reduced_masked_loss, (1,)) return masked_loss(labels, upsampled_logits) @unpack_inputs @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFSemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: tf.Tensor, labels: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TFSemanticSegmenterOutput]: r""" labels (`tf.Tensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a (per-pixel) classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFSegformerForSemanticSegmentation >>> 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("nvidia/segformer-b0-finetuned-ade-512-512") >>> model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") >>> inputs = image_processor(images=image, return_tensors="tf") >>> outputs = model(**inputs, training=False) >>> # logits are of shape (batch_size, num_labels, height/4, width/4) >>> logits = outputs.logits >>> list(logits.shape) [1, 150, 128, 128] ```""" 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 ) if labels is not None and not self.config.num_labels > 1: raise ValueError("The number of labels should be greater than one") outputs = self.segformer( pixel_values, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] logits = self.decode_head(encoder_hidden_states) loss = None if labels is not None: loss = self.hf_compute_loss(logits=logits, labels=labels) # make logits of shape (batch_size, num_labels, height, width) to # keep them consistent across APIs logits = tf.transpose(logits, perm=[0, 3, 1, 2]) if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSemanticSegmenterOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "segformer", None) is not None: with tf.name_scope(self.segformer.name): self.segformer.build(None) if getattr(self, "decode_head", None) is not None: with tf.name_scope(self.decode_head.name): self.decode_head.build(None) __all__ = [ "TFSegformerDecodeHead", "TFSegformerForImageClassification", "TFSegformerForSemanticSegmentation", "TFSegformerModel", "TFSegformerPreTrainedModel", ]

transformers/src/transformers/models/segformer/modeling_tf_segformer.py/0

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# coding=utf-8 # Copyright 2024 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 SigLIP checkpoints from the original repository. URL: https://github.com/google-research/big_vision/tree/main """ import argparse import collections from pathlib import Path import numpy as np import requests import torch from huggingface_hub import hf_hub_download from numpy import load from PIL import Image from transformers import SiglipConfig, SiglipImageProcessor, SiglipModel, SiglipProcessor, SiglipTokenizer from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) model_name_to_checkpoint = { # base checkpoints "siglip-base-patch16-224": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_224_63724782.npz", "siglip-base-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_256_60500360.npz", "siglip-base-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_384_68578854.npz", "siglip-base-patch16-512": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_512_68580893.npz", # large checkpoints "siglip-large-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_256_60552751.npz", "siglip-large-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_384_63634585.npz", # multilingual checkpoint "siglip-base-patch16-256-i18n": "/Users/nielsrogge/Documents/SigLIP/webli_i18n_b16_256_66117334.npz", # so400m checkpoints "siglip-so400m-patch14-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_so400m_384_58765454.npz", } model_name_to_image_size = { "siglip-base-patch16-224": 224, "siglip-base-patch16-256": 256, "siglip-base-patch16-384": 384, "siglip-base-patch16-512": 512, "siglip-large-patch16-256": 256, "siglip-large-patch16-384": 384, "siglip-base-patch16-256-i18n": 256, "siglip-so400m-patch14-384": 384, } def get_siglip_config(model_name): config = SiglipConfig() vocab_size = 250000 if "i18n" in model_name else 32000 image_size = model_name_to_image_size[model_name] patch_size = 16 if "patch16" in model_name else 14 # size of the architecture config.vision_config.image_size = image_size config.vision_config.patch_size = patch_size config.text_config.vocab_size = vocab_size if "base" in model_name: pass elif "large" in model_name: config.text_config.hidden_size = 1024 config.text_config.intermediate_size = 4096 config.text_config.num_hidden_layers = 24 config.text_config.num_attention_heads = 16 config.vision_config.hidden_size = 1024 config.vision_config.intermediate_size = 4096 config.vision_config.num_hidden_layers = 24 config.vision_config.num_attention_heads = 16 elif "so400m" in model_name: config.text_config.hidden_size = 1152 config.text_config.intermediate_size = 4304 config.text_config.num_hidden_layers = 27 config.text_config.num_attention_heads = 16 config.vision_config.hidden_size = 1152 config.vision_config.intermediate_size = 4304 config.vision_config.num_hidden_layers = 27 config.vision_config.num_attention_heads = 16 else: raise ValueError("Model not supported") return config def create_rename_keys(config): rename_keys = [] # fmt: off # vision encoder rename_keys.append(("params/img/embedding/kernel", "vision_model.embeddings.patch_embedding.weight")) rename_keys.append(("params/img/embedding/bias", "vision_model.embeddings.patch_embedding.bias")) rename_keys.append(("params/img/pos_embedding", "vision_model.embeddings.position_embedding.weight")) for i in range(config.vision_config.num_hidden_layers): rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/scale", f"vision_model.encoder.layers.{i}.layer_norm1.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/scale", f"vision_model.encoder.layers.{i}.layer_norm2.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"vision_model.encoder.layers.{i}.mlp.fc1.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"vision_model.encoder.layers.{i}.mlp.fc2.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"vision_model.encoder.layers.{i}.self_attn.k_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"vision_model.encoder.layers.{i}.self_attn.k_proj.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"vision_model.encoder.layers.{i}.self_attn.v_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"vision_model.encoder.layers.{i}.self_attn.v_proj.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"vision_model.encoder.layers.{i}.self_attn.q_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"vision_model.encoder.layers.{i}.self_attn.q_proj.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"vision_model.encoder.layers.{i}.self_attn.out_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"vision_model.encoder.layers.{i}.self_attn.out_proj.bias")) rename_keys.append(("params/img/Transformer/encoder_norm/scale", "vision_model.post_layernorm.weight")) rename_keys.append(("params/img/Transformer/encoder_norm/bias", "vision_model.post_layernorm.bias")) rename_keys.append(("params/img/MAPHead_0/probe", "vision_model.head.probe")) rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/scale", "vision_model.head.layernorm.weight")) rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/bias", "vision_model.head.layernorm.bias")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/kernel", "vision_model.head.mlp.fc1.weight")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/bias", "vision_model.head.mlp.fc1.bias")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/kernel", "vision_model.head.mlp.fc2.weight")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/bias", "vision_model.head.mlp.fc2.bias")) rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/kernel", "vision_model.head.attention.out_proj.weight")) rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/bias", "vision_model.head.attention.out_proj.bias")) # text encoder rename_keys.append(("params/txt/Embed_0/embedding", "text_model.embeddings.token_embedding.weight")) rename_keys.append(("params/txt/pos_embedding", "text_model.embeddings.position_embedding.weight")) for i in range(config.text_config.num_hidden_layers): rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/scale", f"text_model.encoder.layers.{i}.layer_norm1.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/bias", f"text_model.encoder.layers.{i}.layer_norm1.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/scale", f"text_model.encoder.layers.{i}.layer_norm2.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/bias", f"text_model.encoder.layers.{i}.layer_norm2.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"text_model.encoder.layers.{i}.mlp.fc1.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"text_model.encoder.layers.{i}.mlp.fc1.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"text_model.encoder.layers.{i}.mlp.fc2.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"text_model.encoder.layers.{i}.mlp.fc2.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"text_model.encoder.layers.{i}.self_attn.k_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"text_model.encoder.layers.{i}.self_attn.k_proj.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"text_model.encoder.layers.{i}.self_attn.v_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"text_model.encoder.layers.{i}.self_attn.v_proj.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"text_model.encoder.layers.{i}.self_attn.q_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"text_model.encoder.layers.{i}.self_attn.q_proj.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"text_model.encoder.layers.{i}.self_attn.out_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"text_model.encoder.layers.{i}.self_attn.out_proj.bias")) rename_keys.append(("params/txt/Encoder_0/encoder_norm/scale", "text_model.final_layer_norm.weight")) rename_keys.append(("params/txt/Encoder_0/encoder_norm/bias", "text_model.final_layer_norm.bias")) rename_keys.append(("params/txt/head/kernel", "text_model.head.weight")) rename_keys.append(("params/txt/head/bias", "text_model.head.bias")) # learned temperature and bias rename_keys.append(("params/t", "logit_scale")) rename_keys.append(("params/b", "logit_bias")) # fmt: on return rename_keys def rename_key(dct, old, new, config): val = dct.pop(old) if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "vision" in new: val = val.reshape(-1, config.vision_config.hidden_size) if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "text" in new: val = val.reshape(-1, config.text_config.hidden_size) if "patch_embedding.weight" in new: val = val.transpose(3, 2, 0, 1) elif new.endswith("weight") and "position_embedding" not in new and "token_embedding" not in new: val = val.T if "position_embedding" in new and "vision" in new: val = val.reshape(-1, config.vision_config.hidden_size) if "position_embedding" in new and "text" in new: val = val.reshape(-1, config.text_config.hidden_size) if new.endswith("bias"): val = val.reshape(-1) dct[new] = torch.from_numpy(val) def read_in_q_k_v_head(state_dict, config): # read in individual input projection layers key_proj_weight = ( state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/kernel") .reshape(-1, config.vision_config.hidden_size) .T ) key_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/bias").reshape(-1) value_proj_weight = ( state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/kernel") .reshape(-1, config.vision_config.hidden_size) .T ) value_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/bias").reshape(-1) query_proj_weight = ( state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/kernel") .reshape(-1, config.vision_config.hidden_size) .T ) query_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/bias").reshape(-1) # next, add them to the state dict as a single matrix + vector state_dict["vision_model.head.attention.in_proj_weight"] = torch.from_numpy( np.concatenate([query_proj_weight, key_proj_weight, value_proj_weight], axis=0) ) state_dict["vision_model.head.attention.in_proj_bias"] = torch.from_numpy( np.concatenate([query_proj_bias, key_proj_bias, value_proj_bias], axis=0) ) # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image def flatten_nested_dict(params, parent_key="", sep="/"): items = [] for k, v in params.items(): new_key = parent_key + sep + k if parent_key else k if isinstance(v, collections.abc.MutableMapping): items.extend(flatten_nested_dict(v, new_key, sep=sep).items()) else: items.append((new_key, v)) return dict(items) @torch.no_grad() def convert_siglip_checkpoint(model_name, pytorch_dump_folder_path, verify_logits=True, push_to_hub=False): """ Copy/paste/tweak model's weights to our SigLIP structure. """ # define default SigLIP configuration config = get_siglip_config(model_name) # get checkpoint checkpoint = model_name_to_checkpoint[model_name] # get vocab file if "i18n" in model_name: vocab_file = "/Users/nielsrogge/Documents/SigLIP/multilingual_vocab/sentencepiece.model" else: vocab_file = "/Users/nielsrogge/Documents/SigLIP/english_vocab/sentencepiece.model" # load original state dict data = load(checkpoint) state_dict = flatten_nested_dict(data) # remove and rename some keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest, config) # qkv matrices of attention pooling head need special treatment read_in_q_k_v_head(state_dict, config) # load HuggingFace model model = SiglipModel(config).eval() model.load_state_dict(state_dict) # create processor # important: make tokenizer not return attention_mask since original one doesn't require it image_size = config.vision_config.image_size size = {"height": image_size, "width": image_size} image_processor = SiglipImageProcessor(size=size) tokenizer = SiglipTokenizer(vocab_file=vocab_file, model_input_names=["input_ids"]) processor = SiglipProcessor(image_processor=image_processor, tokenizer=tokenizer) # verify on dummy images and texts url_1 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-ipod.jpg" image_1 = Image.open(requests.get(url_1, stream=True).raw).convert("RGB") url_2 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-blank.jpg" image_2 = Image.open(requests.get(url_2, stream=True).raw).convert("RGB") texts = ["an apple", "a picture of an apple"] inputs = processor(images=[image_1, image_2], text=texts, return_tensors="pt", padding="max_length") # verify input_ids against original ones if image_size == 224: filename = "siglip_pixel_values.pt" elif image_size == 256: filename = "siglip_pixel_values_256.pt" elif image_size == 384: filename = "siglip_pixel_values_384.pt" elif image_size == 512: filename = "siglip_pixel_values_512.pt" else: raise ValueError("Image size not supported") filepath = hf_hub_download(repo_id="nielsr/test-image", filename=filename, repo_type="dataset") original_pixel_values = torch.load(filepath) filepath = hf_hub_download(repo_id="nielsr/test-image", filename="siglip_input_ids.pt", repo_type="dataset") original_input_ids = torch.load(filepath) if "i18n" not in model_name: assert inputs.input_ids.tolist() == original_input_ids.tolist() print("Mean of original pixel values:", original_pixel_values.mean()) print("Mean of new pixel values:", inputs.pixel_values.mean()) # note: we're testing with original pixel values here since we don't have exact pixel values with torch.no_grad(): outputs = model(input_ids=inputs.input_ids, pixel_values=original_pixel_values) # with torch.no_grad(): # outputs = model(input_ids=inputs.input_ids, pixel_values=inputs.pixel_values) print(outputs.logits_per_image[:3, :3]) probs = torch.sigmoid(outputs.logits_per_image) # these are the probabilities print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'") if verify_logits: if model_name == "siglip-base-patch16-224": expected_slice = torch.tensor( [[-2.9621, -2.1672], [-0.2713, 0.2910]], ) elif model_name == "siglip-base-patch16-256": expected_slice = torch.tensor( [[-3.1146, -1.9894], [-0.7312, 0.6387]], ) elif model_name == "siglip-base-patch16-384": expected_slice = torch.tensor( [[-2.8098, -2.1891], [-0.4242, 0.4102]], ) elif model_name == "siglip-base-patch16-512": expected_slice = torch.tensor( [[-2.7899, -2.2668], [-0.4295, -0.0735]], ) elif model_name == "siglip-large-patch16-256": expected_slice = torch.tensor( [[-1.5827, -0.5801], [-0.9153, 0.1363]], ) elif model_name == "siglip-large-patch16-384": expected_slice = torch.tensor( [[-2.1523, -0.2899], [-0.2959, 0.7884]], ) elif model_name == "siglip-so400m-patch14-384": expected_slice = torch.tensor([[-1.2441, -0.6649], [-0.7060, 0.7374]]) elif model_name == "siglip-base-patch16-256-i18n": expected_slice = torch.tensor( [[-0.9064, 0.1073], [-0.0299, 0.5304]], ) assert torch.allclose(outputs.logits_per_image[:3, :3], expected_slice, atol=1e-4) print("Looks ok!") if pytorch_dump_folder_path is not None: Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving processor to {pytorch_dump_folder_path}") processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: model.push_to_hub(f"nielsr/{model_name}") processor.push_to_hub(f"nielsr/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="siglip-base-patch16-224", type=str, choices=model_name_to_checkpoint.keys(), help="Name of the 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." ) parser.add_argument( "--verify_logits", action="store_false", help="Whether to verify logits against the original implementation.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_siglip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)

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

{ "file_path": "transformers/src/transformers/models/siglip/convert_siglip_to_hf.py", "repo_id": "transformers", "token_count": 8771 }

# Copyright 2024 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 gc import os import re from typing import List import torch from datasets import load_dataset from transformers import ( AutoModelForKeypointDetection, SuperGlueConfig, SuperGlueForKeypointMatching, SuperGlueImageProcessor, ) def prepare_imgs(): dataset = load_dataset("hf-internal-testing/image-matching-test-dataset", split="train") image1 = dataset[0]["image"] image2 = dataset[1]["image"] image3 = dataset[2]["image"] return [[image1, image2], [image3, image2]] def verify_model_outputs(model, model_name, device): images = prepare_imgs() preprocessor = SuperGlueImageProcessor() inputs = preprocessor(images=images, return_tensors="pt").to(device) model.to(device) with torch.no_grad(): outputs = model(**inputs, output_hidden_states=True, output_attentions=True) predicted_matches_values = outputs.matches[0, 0, :10] predicted_matching_scores_values = outputs.matching_scores[0, 0, :10] predicted_number_of_matches = torch.sum(outputs.matches[0][0] != -1).item() if "outdoor" in model_name: expected_max_number_keypoints = 865 expected_matches_shape = torch.Size((len(images), 2, expected_max_number_keypoints)) expected_matching_scores_shape = torch.Size((len(images), 2, expected_max_number_keypoints)) expected_matches_values = torch.tensor( [125, 630, 137, 138, 136, 143, 135, -1, -1, 153], dtype=torch.int64, device=device ) expected_matching_scores_values = torch.tensor( [0.9899, 0.0033, 0.9897, 0.9889, 0.9879, 0.7464, 0.7109, 0, 0, 0.9841], device=device ) expected_number_of_matches = 281 elif "indoor" in model_name: expected_max_number_keypoints = 865 expected_matches_shape = torch.Size((len(images), 2, expected_max_number_keypoints)) expected_matching_scores_shape = torch.Size((len(images), 2, expected_max_number_keypoints)) expected_matches_values = torch.tensor( [125, 144, 137, 138, 136, 155, 135, -1, -1, 153], dtype=torch.int64, device=device ) expected_matching_scores_values = torch.tensor( [0.9694, 0.0010, 0.9006, 0.8753, 0.8521, 0.5688, 0.6321, 0.0, 0.0, 0.7235], device=device ) expected_number_of_matches = 282 assert outputs.matches.shape == expected_matches_shape assert outputs.matching_scores.shape == expected_matching_scores_shape assert torch.allclose(predicted_matches_values, expected_matches_values, atol=1e-4) assert torch.allclose(predicted_matching_scores_values, expected_matching_scores_values, atol=1e-4) assert predicted_number_of_matches == expected_number_of_matches ORIGINAL_TO_CONVERTED_KEY_MAPPING = { r"kenc.encoder.(\d+)": r"keypoint_encoder.encoder.\1.old", r"gnn.layers.(\d+).attn.proj.0": r"gnn.layers.\1.attention.self.query", r"gnn.layers.(\d+).attn.proj.1": r"gnn.layers.\1.attention.self.key", r"gnn.layers.(\d+).attn.proj.2": r"gnn.layers.\1.attention.self.value", r"gnn.layers.(\d+).attn.merge": r"gnn.layers.\1.attention.output.dense", r"gnn.layers.(\d+).mlp.0": r"gnn.layers.\1.mlp.0.linear", r"gnn.layers.(\d+).mlp.1": r"gnn.layers.\1.mlp.0.batch_norm", r"gnn.layers.(\d+).mlp.3": r"gnn.layers.\1.mlp.1", r"final_proj": r"final_projection.final_proj", } def convert_old_keys_to_new_keys(state_dict_keys: List[str], conversion_mapping=ORIGINAL_TO_CONVERTED_KEY_MAPPING): """ This function should be applied only once, on the concatenated keys to efficiently rename using the key mappings. """ output_dict = {} if state_dict_keys is not None: old_text = "\n".join(state_dict_keys) new_text = old_text for pattern, replacement in conversion_mapping.items(): if replacement is None: new_text = re.sub(pattern, "", new_text) # an empty line continue new_text = re.sub(pattern, replacement, new_text) output_dict = dict(zip(old_text.split("\n"), new_text.split("\n"))) return output_dict def replace_state_dict_keys(all_keys, new_keys, original_state_dict): state_dict = {} for key in all_keys: new_key = new_keys[key] state_dict[new_key] = original_state_dict.pop(key).contiguous().clone() return state_dict def convert_state_dict(state_dict, config): converted_to_final_key_mapping = {} def convert_conv_to_linear(keys): for key in keys: state_dict[key] = state_dict[key].squeeze(-1) def qkv_permute_weights_and_biases(keys, num_heads=4): for key in keys: tensor = state_dict[key] shape = tensor.shape dim_out = shape[0] if len(shape) == 2: dim_in = shape[1] tensor = ( tensor.reshape(dim_out // num_heads, num_heads, dim_in).permute(1, 0, 2).reshape(dim_out, dim_in) ) if len(shape) == 1: tensor = tensor.reshape(dim_out // num_heads, num_heads).permute(1, 0).reshape(dim_out) state_dict[key] = tensor def output_permute_weights(keys, num_heads=4): for key in keys: tensor = state_dict[key] dim_in = tensor.shape[1] dim_out = tensor.shape[0] tensor = tensor.reshape(dim_out, dim_in // num_heads, num_heads).permute(0, 2, 1).reshape(dim_out, dim_in) state_dict[key] = tensor conv_keys = [] qkv_permute_keys = [] output_permute_keys = [] # Keypoint Encoder keypoint_encoder_key = "keypoint_encoder.encoder" for i in range(1, len(config.keypoint_encoder_sizes) + 2): old_conv_key = f"{keypoint_encoder_key}.{(i - 1) * 3}.old" new_index = i - 1 new_conv_key = f"{keypoint_encoder_key}.{new_index}." if i < len(config.keypoint_encoder_sizes) + 1: new_conv_key = f"{new_conv_key}linear." converted_to_final_key_mapping[rf"{old_conv_key}\."] = new_conv_key if i < len(config.keypoint_encoder_sizes) + 1: old_batch_norm_key = f"{keypoint_encoder_key}.{(i - 1) * 3 + 1}.old" new_batch_norm_key = f"{keypoint_encoder_key}.{new_index}.batch_norm." converted_to_final_key_mapping[rf"{old_batch_norm_key}\."] = new_batch_norm_key conv_keys.append(f"{old_conv_key}.weight") # Attentional GNN for i in range(len(config.gnn_layers_types)): gnn_layer_key = f"gnn.layers.{i}" ## Attention attention_key = f"{gnn_layer_key}.attention" conv_keys.extend( [ f"{attention_key}.self.query.weight", f"{attention_key}.self.key.weight", f"{attention_key}.self.value.weight", f"{attention_key}.output.dense.weight", ] ) qkv_permute_keys.extend( [ f"{attention_key}.self.query.weight", f"{attention_key}.self.key.weight", f"{attention_key}.self.value.weight", f"{attention_key}.self.query.bias", f"{attention_key}.self.key.bias", f"{attention_key}.self.value.bias", ] ) output_permute_keys.append(f"{attention_key}.output.dense.weight") ## MLP conv_keys.extend([f"{gnn_layer_key}.mlp.0.linear.weight", f"{gnn_layer_key}.mlp.1.weight"]) # Final Projection conv_keys.append("final_projection.final_proj.weight") convert_conv_to_linear(conv_keys) qkv_permute_weights_and_biases(qkv_permute_keys) output_permute_weights(output_permute_keys) all_keys = list(state_dict.keys()) new_keys = convert_old_keys_to_new_keys(all_keys, converted_to_final_key_mapping) state_dict = replace_state_dict_keys(all_keys, new_keys, state_dict) return state_dict def add_keypoint_detector_state_dict(superglue_state_dict): keypoint_detector = AutoModelForKeypointDetection.from_pretrained("magic-leap-community/superpoint") keypoint_detector_state_dict = keypoint_detector.state_dict() for k, v in keypoint_detector_state_dict.items(): superglue_state_dict[f"keypoint_detector.{k}"] = v return superglue_state_dict @torch.no_grad() def write_model( model_path, checkpoint_url, safe_serialization=True, push_to_hub=False, ): os.makedirs(model_path, exist_ok=True) # ------------------------------------------------------------ # SuperGlue config # ------------------------------------------------------------ config = SuperGlueConfig( hidden_size=256, keypoint_encoder_sizes=[32, 64, 128, 256], gnn_layers_types=["self", "cross"] * 9, sinkhorn_iterations=100, matching_threshold=0.0, ) config.architectures = ["SuperGlueForKeypointMatching"] config.save_pretrained(model_path, push_to_hub=push_to_hub) print("Model config saved successfully...") # ------------------------------------------------------------ # Convert weights # ------------------------------------------------------------ print(f"Fetching all parameters from the checkpoint at {checkpoint_url}...") original_state_dict = torch.hub.load_state_dict_from_url(checkpoint_url) print("Converting model...") all_keys = list(original_state_dict.keys()) new_keys = convert_old_keys_to_new_keys(all_keys) state_dict = replace_state_dict_keys(all_keys, new_keys, original_state_dict) state_dict = convert_state_dict(state_dict, config) del original_state_dict gc.collect() state_dict = add_keypoint_detector_state_dict(state_dict) print("Loading the checkpoint in a SuperGlue model...") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") with torch.device(device): model = SuperGlueForKeypointMatching(config) model.load_state_dict(state_dict, strict=True) print("Checkpoint loaded successfully...") del model.config._name_or_path print("Saving the model...") model.save_pretrained(model_path, safe_serialization=safe_serialization) del state_dict, model # Safety check: reload the converted model gc.collect() print("Reloading the model to check if it's saved correctly.") model = SuperGlueForKeypointMatching.from_pretrained(model_path) print("Model reloaded successfully.") model_name = "superglue" if "superglue_outdoor.pth" in checkpoint_url: model_name += "_outdoor" elif "superglue_indoor.pth" in checkpoint_url: model_name += "_indoor" print("Checking the model outputs...") verify_model_outputs(model, model_name, device) print("Model outputs verified successfully.") organization = "magic-leap-community" if push_to_hub: print("Pushing model to the hub...") model.push_to_hub( repo_id=f"{organization}/{model_name}", commit_message="Add model", ) write_image_processor(model_path, model_name, organization, push_to_hub=push_to_hub) def write_image_processor(save_dir, model_name, organization, push_to_hub=False): image_processor = SuperGlueImageProcessor() image_processor.save_pretrained(save_dir) if push_to_hub: print("Pushing image processor to the hub...") image_processor.push_to_hub( repo_id=f"{organization}/{model_name}", commit_message="Add image processor", ) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/weights/superglue_indoor.pth", type=str, help="URL of the original SuperGlue checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model directory.", ) parser.add_argument("--save_model", action="store_true", help="Save model to local") parser.add_argument( "--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub", ) args = parser.parse_args() write_model( args.pytorch_dump_folder_path, args.checkpoint_url, safe_serialization=True, push_to_hub=args.push_to_hub )

transformers/src/transformers/models/superglue/convert_superglue_to_hf.py/0

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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

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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 ...cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter 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, is_torchdynamo_compiling, 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 #################################################### 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() router_mask = router_mask.bool() batch_size, seq_len, num_experts = router_mask.shape idx_mask = router_mask.reshape(batch_size * seq_len, num_experts).sum(dim=0) idx_mask = torch.nonzero(idx_mask, as_tuple=True)[ 0 ].tolist() # length: number of "activated" expert / value: index for idx in idx_mask: next_states[router_mask[:, :, idx]] = getattr(self.experts, "expert_{}".format(idx))( hidden_states[router_mask[:, :, idx]] ) 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, layer_idx: Optional[int] = None, ): 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 self.layer_idx = layer_idx if layer_idx is None and self.is_decoder: logger.warning_once( f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) # 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, cache_position=None): """Compute binned relative position bias""" if device is None: device = self.relative_attention_bias.weight.device if cache_position is None: context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] else: context_position = cache_position[:, None].to(device) 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, cache_position=None, ): """ 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, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder) batch_size, seq_length = hidden_states.shape[:2] # 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 query_states = self.q(hidden_states) query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) if past_key_value is not None: is_updated = past_key_value.is_updated.get(self.layer_idx) if is_cross_attention: # after the first generated id, we can subsequently re-use all key/value_states from cache curr_past_key_value = past_key_value.cross_attention_cache else: curr_past_key_value = past_key_value.self_attention_cache current_states = key_value_states if is_cross_attention else hidden_states if is_cross_attention and past_key_value is not None and is_updated: # reuse k,v, cross_attentions key_states = curr_past_key_value.key_cache[self.layer_idx] value_states = curr_past_key_value.value_cache[self.layer_idx] else: key_states = self.k(current_states) value_states = self.v(current_states) key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) if past_key_value is not None: # save all key/value_states to cache to be re-used for fast auto-regressive generation cache_position = cache_position if not is_cross_attention else None key_states, value_states = curr_past_key_value.update( key_states, value_states, self.layer_idx, {"cache_position": cache_position} ) # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls if is_cross_attention: past_key_value.is_updated[self.layer_idx] = True # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 scores = torch.matmul(query_states, key_states.transpose(3, 2)) if position_bias is None: key_length = key_states.shape[-2] # cache position is 0-indexed so we add 1 to get the real length of queries (aka with past) real_seq_length = query_length if query_length is not None else cache_position[-1] + 1 if not self.has_relative_attention_bias: position_bias = torch.zeros( (1, self.n_heads, 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, cache_position=cache_position ) position_bias = position_bias[:, :, -seq_length:, :] if mask is not None: causal_mask = mask[:, :, :, : key_states.shape[-2]] position_bias = position_bias + causal_mask 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 # (batch_size, n_heads, seq_length, key_length) attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores) attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) # Mask heads if we want to if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(batch_size, -1, self.inner_dim) attn_output = self.o(attn_output) outputs = (attn_output, past_key_value, 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, layer_idx: Optional[int] = None): super().__init__() self.SelfAttention = SwitchTransformersAttention( config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx ) 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, cache_position=None, ): 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, cache_position=cache_position, ) 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, layer_idx: Optional[int] = None): super().__init__() self.EncDecAttention = SwitchTransformersAttention( config, has_relative_attention_bias=False, layer_idx=layer_idx ) 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, cache_position=None, ): 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, cache_position=cache_position, ) 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, layer_idx: Optional[int] = None): 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, layer_idx=layer_idx ) ) if self.is_decoder: self.layer.append(SwitchTransformersLayerCrossAttention(config, layer_idx=layer_idx)) 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, cache_position=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=past_key_value, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states, past_key_value = 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: 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=past_key_value, query_length=cache_position[-1] + 1, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states, past_key_value = cross_attention_outputs[:2] # 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) # 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 + (past_key_value,) + attention_outputs + (router_tuple,) else: outputs = outputs + attention_outputs + (router_tuple,) return outputs # hidden-states, past_key_value, (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 _supports_cache_class = True _supports_static_cache = False _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, layer_idx=i ) ) 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, cache_position=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 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 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 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") # initialize past_key_values return_legacy_cache = False return_self_attention_cache = False if self.is_decoder and (use_cache or past_key_values is not None): if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache): return_self_attention_cache = True past_key_values = EncoderDecoderCache(past_key_values, DynamicCache()) elif not isinstance(past_key_values, EncoderDecoderCache): return_legacy_cache = True logger.warning_once( "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. " "You should pass an instance of `EncoderDecoderCache` instead, e.g. " "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`." ) past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values) elif past_key_values is None: past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache()) elif not self.is_decoder: # do not pass cache object down the line for encoder stack # it messes indexing later in decoder-stack because cache object is modified in-place past_key_values = None past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 if cache_position is None: cache_position = torch.arange( past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device ) if attention_mask is None and not is_torchdynamo_compiling(): # required mask seq length can be calculated via length of past cache mask_seq_length = past_key_values_length + seq_length attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) if self.config.is_decoder: causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values.self_attention_cache if past_key_values is not None else None, output_attentions, ) else: causal_mask = attention_mask[:, None, None, :] causal_mask = causal_mask.to(dtype=inputs_embeds.dtype) causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min # 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 # 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) 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 in enumerate(self.block): 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, causal_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, output_router_logits, return_dict, cache_position, ) else: layer_outputs = layer_module( hidden_states, attention_mask=causal_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_values, use_cache=use_cache, output_attentions=output_attentions, output_router_logits=output_router_logits, return_dict=return_dict, cache_position=cache_position, ) 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, next_decoder_cache = 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] 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,) next_cache = next_decoder_cache if use_cache else None if return_self_attention_cache: next_cache = past_key_values.self_attention_cache if return_legacy_cache: next_cache = past_key_values.to_legacy_cache() if not return_dict: return tuple( v for v in [ hidden_states, next_cache, 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=next_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, router_probs=all_router_probs, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask 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. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. It is used to update the cache in the correct position and to infer the complete sequence length. """ 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, cache_position: Optional[torch.LongTensor] = 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, cache_position=cache_position, ) 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, GenerationMixin): _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, cache_position: Optional[torch.LongTensor] = 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, cache_position=cache_position, ) 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_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 __all__ = [ "SwitchTransformersEncoderModel", "SwitchTransformersForConditionalGeneration", "SwitchTransformersModel", "SwitchTransformersPreTrainedModel", "SwitchTransformersTop1Router", "SwitchTransformersSparseMLP", ]

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": 40394 }

# 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_timm_available, logging, replace_return_docstrings, requires_backends, ) from ...utils.backbone_utils import load_backbone from .configuration_table_transformer import TableTransformerConfig if is_timm_available(): from timm import create_model logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "TableTransformerConfig" _CHECKPOINT_FOR_DOC = "microsoft/table-transformer-detection" @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 # For backwards compatibility we have to use the timm library directly instead of the AutoBackbone API if config.use_timm_backbone: # We default to values which were previously hard-coded. This enables configurability from the config # using backbone arguments, while keeping the default behavior the same. requires_backends(self, ["timm"]) kwargs = getattr(config, "backbone_kwargs", {}) kwargs = {} if kwargs is None else kwargs.copy() out_indices = kwargs.pop("out_indices", (1, 2, 3, 4)) num_channels = kwargs.pop("in_chans", config.num_channels) if config.dilation: kwargs["output_stride"] = kwargs.get("output_stride", 16) backbone = create_model( config.backbone, pretrained=config.use_pretrained_backbone, features_only=True, out_indices=out_indices, in_chans=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 = None if config.backbone is not None: backbone_model_type = config.backbone elif config.backbone_config is not None: backbone_model_type = config.backbone_config.model_type else: raise ValueError("Either `backbone` or `backbone_config` should be provided in the config") 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]): 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, ) -> 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 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 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, ): 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. """ 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() @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: outputs_class, outputs_coord = None, None 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() loss, loss_dict, auxiliary_outputs = self.loss_function( logits, labels, self.device, pred_boxes, self.config, outputs_class, outputs_coord ) 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.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 __all__ = ["TableTransformerForObjectDetection", "TableTransformerModel", "TableTransformerPreTrainedModel"]

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

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# 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. """TimeSformer model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class TimesformerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`TimesformerModel`]. It is used to instantiate a TimeSformer 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 TimeSformer [facebook/timesformer-base-finetuned-k600](https://huggingface.co/facebook/timesformer-base-finetuned-k600) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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. num_channels (`int`, *optional*, defaults to 3): The number of input channels. num_frames (`int`, *optional*, defaults to 8): The number of frames in each video. 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"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`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. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. attention_type (`str`, *optional*, defaults to `"divided_space_time"`): The attention type to use. Must be one of `"divided_space_time"`, `"space_only"`, `"joint_space_time"`. drop_path_rate (`float`, *optional*, defaults to 0): The dropout ratio for stochastic depth. Example: ```python >>> from transformers import TimesformerConfig, TimesformerModel >>> # Initializing a TimeSformer timesformer-base style configuration >>> configuration = TimesformerConfig() >>> # Initializing a model from the configuration >>> model = TimesformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "timesformer" def __init__( self, image_size=224, patch_size=16, num_channels=3, num_frames=8, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-6, qkv_bias=True, attention_type="divided_space_time", drop_path_rate=0, **kwargs, ): super().__init__(**kwargs) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_frames = num_frames 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.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.attention_type = attention_type self.drop_path_rate = drop_path_rate __all__ = ["TimesformerConfig"]

transformers/src/transformers/models/timesformer/configuration_timesformer.py/0

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# 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. """TVP model configuration""" import copy from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import verify_backbone_config_arguments from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) class TvpConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp 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 Tvp [Intel/tvp-base](https://huggingface.co/Intel/tvp-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: backbone_config (`PretrainedConfig` or `dict`, *optional*): The configuration of the backbone model. backbone (`str`, *optional*): Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. use_pretrained_backbone (`bool`, *optional*, defaults to `False`): Whether to use pretrained weights for the backbone. use_timm_backbone (`bool`, *optional*, defaults to `False`): Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers library. backbone_kwargs (`dict`, *optional*): Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. distance_loss_weight (`float`, *optional*, defaults to 1.0): The weight of distance loss. duration_loss_weight (`float`, *optional*, defaults to 0.1): The weight of duration loss. visual_prompter_type (`str`, *optional*, defaults to `"framepad"`): Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad" or "framedownpad" visual_prompter_apply (`str`, *optional*, defaults to `"replace"`): The way of applying visual prompt. Replace means use the value of prompt to change the original value in visual inputs. Should be one of "replace", or "add", or "remove". visual_prompt_size (`int`, *optional*, defaults to 96): The size of visual prompt. max_img_size (`int`, *optional*, defaults to 448): The maximum size of frame. num_frames (`int`, *optional*, defaults to 48): The number of frames extracted from a video. vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`TvpModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers. intermediate_size (`int`, *optional*, defaults to 3072): 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 12): Number of attention heads for each attention layer in the Transformer encoder. 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). max_grid_col_position_embeddings (`int`, *optional*, defaults to 100): The largest number of horizontal patches from a video frame. max_grid_row_position_embeddings (`int`, *optional*, defaults to 100): The largest number of vertical patches from a video frame. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability of hidden layers. 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-12): The epsilon used by 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. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability of attention layers. """ model_type = "tvp" def __init__( self, backbone_config=None, backbone=None, use_pretrained_backbone=False, use_timm_backbone=False, backbone_kwargs=None, distance_loss_weight=1.0, duration_loss_weight=0.1, visual_prompter_type="framepad", visual_prompter_apply="replace", visual_prompt_size=96, max_img_size=448, num_frames=48, vocab_size=30522, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, max_position_embeddings=512, max_grid_col_position_embeddings=100, max_grid_row_position_embeddings=100, hidden_dropout_prob=0.1, hidden_act="gelu", layer_norm_eps=1e-12, initializer_range=0.02, attention_probs_dropout_prob=0.1, **kwargs, ): super().__init__(**kwargs) if backbone_config is None and backbone is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.") backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"]) elif isinstance(backbone_config, dict): backbone_model_type = backbone_config.get("model_type") config_class = CONFIG_MAPPING[backbone_model_type] backbone_config = config_class.from_dict(backbone_config) verify_backbone_config_arguments( use_timm_backbone=use_timm_backbone, use_pretrained_backbone=use_pretrained_backbone, backbone=backbone, backbone_config=backbone_config, backbone_kwargs=backbone_kwargs, ) self.backbone_config = backbone_config self.backbone = backbone self.use_pretrained_backbone = use_pretrained_backbone self.use_timm_backbone = use_timm_backbone self.backbone_kwargs = backbone_kwargs self.distance_loss_weight = distance_loss_weight self.duration_loss_weight = duration_loss_weight self.visual_prompter_type = visual_prompter_type self.visual_prompter_apply = visual_prompter_apply self.visual_prompt_size = visual_prompt_size self.max_img_size = max_img_size self.num_frames = num_frames self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.max_position_embeddings = max_position_embeddings self.max_grid_col_position_embeddings = max_grid_col_position_embeddings self.max_grid_row_position_embeddings = max_grid_row_position_embeddings self.layer_norm_eps = layer_norm_eps self.hidden_dropout_prob = hidden_dropout_prob self.hidden_act = hidden_act self.initializer_range = initializer_range self.attention_probs_dropout_prob = attention_probs_dropout_prob @classmethod def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs): """Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration. Args: backbone_config ([`PretrainedConfig`]): The backbone configuration. Returns: [`TvpConfig`]: An instance of a configuration object """ return cls(backbone_config=backbone_config, **kwargs) def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) if output["backbone_config"] is not None: output["backbone_config"] = self.backbone_config.to_dict() output["model_type"] = self.__class__.model_type return output __all__ = ["TvpConfig"]

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

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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 Swin Transformer + UperNet checkpoints from mmsegmentation. URL: https://github.com/open-mmlab/mmsegmentation/tree/master/configs/swin """ import argparse import json import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation def get_upernet_config(model_name): auxiliary_in_channels = 384 window_size = 7 if "tiny" in model_name: embed_dim = 96 depths = (2, 2, 6, 2) num_heads = (3, 6, 12, 24) elif "small" in model_name: embed_dim = 96 depths = (2, 2, 18, 2) num_heads = (3, 6, 12, 24) elif "base" in model_name: embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) window_size = 12 auxiliary_in_channels = 512 elif "large" in model_name: embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) window_size = 12 auxiliary_in_channels = 768 # set label information num_labels = 150 repo_id = "huggingface/label-files" filename = "ade20k-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()} label2id = {v: k for k, v in id2label.items()} backbone_config = SwinConfig( embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=window_size, out_features=["stage1", "stage2", "stage3", "stage4"], ) config = UperNetConfig( backbone_config=backbone_config, auxiliary_in_channels=auxiliary_in_channels, num_labels=num_labels, id2label=id2label, label2id=label2id, ) return config # 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 # stem rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")) rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")) rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")) rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias")) # stages for i in range(len(config.backbone_config.depths)): for j in range(config.backbone_config.depths[i]): rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")) rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")) if i < 3: rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")) rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")) rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")) rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")) rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")) # decode head rename_keys.extend( [ ("decode_head.conv_seg.weight", "decode_head.classifier.weight"), ("decode_head.conv_seg.bias", "decode_head.classifier.bias"), ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"), ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"), ] ) # fmt: on return rename_keys def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict, backbone_config): num_features = [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))] for i in range(len(backbone_config.depths)): dim = num_features[i] for j in range(backbone_config.depths[i]): # fmt: off # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight") in_proj_bias = state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :] state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"] = in_proj_bias[: dim] state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[ dim : dim * 2, : ] state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.bias"] = in_proj_bias[ dim : dim * 2 ] state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[ -dim :, : ] state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"] = in_proj_bias[-dim :] # fmt: on def correct_unfold_reduction_order(x): out_channel, in_channel = x.shape x = x.reshape(out_channel, 4, in_channel // 4) x = x[:, [0, 2, 1, 3], :].transpose(1, 2).reshape(out_channel, in_channel) return x def reverse_correct_unfold_reduction_order(x): out_channel, in_channel = x.shape x = x.reshape(out_channel, in_channel // 4, 4) x = x[:, :, [0, 2, 1, 3]].transpose(1, 2).reshape(out_channel, in_channel) return x def correct_unfold_norm_order(x): in_channel = x.shape[0] x = x.reshape(4, in_channel // 4) x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel) return x # there was an incompatibility with this version, due to a new implementation of their downsampling operation using nn.Unfold. # was resolved as seen here: # https://github.com/open-mmlab/mmdetection/blob/31c84958f54287a8be2b99cbf87a6dcf12e57753/mmdet/models/utils/ckpt_convert.py#L96. def reverse_correct_unfold_norm_order(x): in_channel = x.shape[0] x = x.reshape(in_channel // 4, 4) x = x[:, [0, 2, 1, 3]].transpose(0, 1).reshape(in_channel) return x def convert_upernet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): model_name_to_url = { "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth", "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth", "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth", "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth", } checkpoint_url = model_name_to_url[model_name] state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", file_name=model_name)[ "state_dict" ] for name, param in state_dict.items(): print(name, param.shape) config = get_upernet_config(model_name) model = UperNetForSemanticSegmentation(config) model.eval() # replace "bn" => "batch_norm" for key in state_dict.copy().keys(): val = state_dict.pop(key) if "bn" in key: key = key.replace("bn", "batch_norm") state_dict[key] = val # rename keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) read_in_q_k_v(state_dict, config.backbone_config) # fix downsample parameters for key, value in state_dict.items(): if "downsample" in key: if "reduction" in key: state_dict[key] = reverse_correct_unfold_reduction_order(value) if "norm" in key: state_dict[key] = reverse_correct_unfold_norm_order(value) model.load_state_dict(state_dict) # verify on image 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).convert("RGB") processor = SegformerImageProcessor() pixel_values = processor(image, return_tensors="pt").pixel_values with torch.no_grad(): outputs = model(pixel_values) logits = outputs.logits print(logits.shape) print("First values of logits:", logits[0, 0, :3, :3]) # assert values if model_name == "upernet-swin-tiny": expected_slice = torch.tensor( [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ) elif model_name == "upernet-swin-small": expected_slice = torch.tensor( [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]] ) elif model_name == "upernet-swin-base": expected_slice = torch.tensor( [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]] ) elif model_name == "upernet-swin-large": expected_slice = torch.tensor( [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]] ) print("Logits:", outputs.logits[0, 0, :3, :3]) assert torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving processor to {pytorch_dump_folder_path}") processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and processor for {model_name} to hub") model.push_to_hub(f"openmmlab/{model_name}") processor.push_to_hub(f"openmmlab/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="upernet-swin-tiny", type=str, choices=[f"upernet-swin-{size}" for size in ["tiny", "small", "base", "large"]], help="Name of the Swin + UperNet 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." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/upernet/convert_swin_upernet_to_pytorch.py/0

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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 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("google-bert/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

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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. - 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. - 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", "google-bert/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("google-bert/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", "google-bert/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 ) __all__ = ["FlaxVisionTextDualEncoderModel"]

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

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# 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. """TF 2.0 ViT model.""" from __future__ import annotations import collections.abc import math 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 TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging 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" class TFViTEmbeddings(keras.layers.Layer): """ Construct the CLS token, position and patch embeddings. """ def __init__(self, config: ViTConfig, **kwargs): super().__init__(**kwargs) self.patch_embeddings = TFViTPatchEmbeddings(config, name="patch_embeddings") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def build(self, input_shape=None): num_patches = self.patch_embeddings.num_patches self.cls_token = self.add_weight( shape=(1, 1, self.config.hidden_size), initializer=get_initializer(self.config.initializer_range), trainable=True, name="cls_token", ) self.position_embeddings = self.add_weight( shape=(1, num_patches + 1, self.config.hidden_size), initializer=get_initializer(self.config.initializer_range), trainable=True, name="position_embeddings", ) if self.built: return self.built = True if getattr(self, "patch_embeddings", None) is not None: with tf.name_scope(self.patch_embeddings.name): self.patch_embeddings.build(None) def interpolate_pos_encoding(self, embeddings, height, width) -> tf.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 """ batch_size, seq_len, dim = shape_list(embeddings) num_patches = seq_len - 1 _, num_positions, _ = shape_list(self.position_embeddings) num_positions -= 1 if num_patches == num_positions and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, :1] patch_pos_embed = self.position_embeddings[:, 1:] h0 = height // self.config.patch_size w0 = width // self.config.patch_size patch_pos_embed = tf.image.resize( images=tf.reshape( patch_pos_embed, shape=(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) ), size=(h0, w0), method="bicubic", ) shape = shape_list(patch_pos_embed) assert h0 == shape[-3] and w0 == shape[-2] patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim)) return tf.concat(values=(class_pos_embed, patch_pos_embed), axis=1) def call( self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False ) -> tf.Tensor: batch_size, num_channels, height, width = shape_list(pixel_values) embeddings = self.patch_embeddings( pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, training=training ) # add the [CLS] token to the embedded patch tokens cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0) embeddings = tf.concat((cls_tokens, embeddings), axis=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, training=training) return embeddings # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py class TFViTPatchEmbeddings(keras.layers.Layer): """ 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: ViTConfig, **kwargs): super().__init__(**kwargs) 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_patches = num_patches self.num_channels = num_channels self.config = config self.projection = keras.layers.Conv2D( filters=hidden_size, kernel_size=patch_size, strides=patch_size, padding="valid", data_format="channels_last", use_bias=True, kernel_initializer=get_initializer(self.config.initializer_range), bias_initializer="zeros", name="projection", ) def call( self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False ) -> tf.Tensor: batch_size, num_channels, height, width = shape_list(pixel_values) if tf.executing_eagerly() and 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." ) if not interpolate_pos_encoding: if tf.executing_eagerly(): 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]})." ) # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. # shape = (batch_size, in_height, in_width, in_channels=num_channels) pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) projection = self.projection(pixel_values) # Change the 2D spatial dimensions to a single temporal dimension. # shape = (batch_size, num_patches, out_channels=embed_dim) num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0]) embeddings = tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1)) return embeddings def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "projection", None) is not None: with tf.name_scope(self.projection.name): self.projection.build([None, None, None, self.num_channels]) class TFViTSelfAttention(keras.layers.Layer): def __init__(self, config: ViTConfig, **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.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, head_mask: 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) mixed_key_layer = self.key(inputs=hidden_states) mixed_value_layer = self.value(inputs=hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) # 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) # 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,) 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]) class TFViTSelfOutput(keras.layers.Layer): """ The residual connection is defined in TFViTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: ViTConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) 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) 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]) class TFViTAttention(keras.layers.Layer): def __init__(self, config: ViTConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFViTSelfAttention(config, name="attention") self.dense_output = TFViTSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them 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) class TFViTIntermediate(keras.layers.Layer): def __init__(self, config: ViTConfig, **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]) class TFViTOutput(keras.layers.Layer): def __init__(self, config: ViTConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) 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 = 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]) class TFViTLayer(keras.layers.Layer): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: ViTConfig, **kwargs): super().__init__(**kwargs) self.attention = TFViTAttention(config, name="attention") self.intermediate = TFViTIntermediate(config, name="intermediate") self.vit_output = TFViTOutput(config, name="output") self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before") self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after") self.config = config def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: attention_outputs = self.attention( # in ViT, layernorm is applied before self-attention input_tensor=self.layernorm_before(inputs=hidden_states), head_mask=head_mask, output_attentions=output_attentions, training=training, ) attention_output = attention_outputs[0] # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(inputs=hidden_states) intermediate_output = self.intermediate(hidden_states=layer_output) # second residual connection is done here layer_output = self.vit_output( hidden_states=intermediate_output, input_tensor=hidden_states, training=training ) outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them 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, "vit_output", None) is not None: with tf.name_scope(self.vit_output.name): self.vit_output.build(None) if getattr(self, "layernorm_before", None) is not None: with tf.name_scope(self.layernorm_before.name): self.layernorm_before.build([None, None, self.config.hidden_size]) if getattr(self, "layernorm_after", None) is not None: with tf.name_scope(self.layernorm_after.name): self.layernorm_after.build([None, None, self.config.hidden_size]) class TFViTEncoder(keras.layers.Layer): def __init__(self, config: ViTConfig, **kwargs): super().__init__(**kwargs) self.layer = [TFViTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_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_outputs = layer_module( hidden_states=hidden_states, head_mask=head_mask[i], output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) # 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] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_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 TFViTMainLayer(keras.layers.Layer): config_class = ViTConfig def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, **kwargs): super().__init__(**kwargs) self.config = config self.embeddings = TFViTEmbeddings(config, name="embeddings") self.encoder = TFViTEncoder(config, name="encoder") self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") self.pooler = TFViTPooler(config, name="pooler") if add_pooling_layer else None def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings.patch_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} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs def call( self, pixel_values: TFModelInputType | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: if pixel_values is None: raise ValueError("You have to specify pixel_values") embedding_output = self.embeddings( pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, training=training, ) # 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, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(inputs=sequence_output) 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 TFBaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) 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, "layernorm", None) is not None: with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, self.config.hidden_size]) if getattr(self, "pooler", None) is not None: with tf.name_scope(self.pooler.name): self.pooler.build(None) class TFViTPreTrainedModel(TFPreTrainedModel): """ 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" VIT_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 `pixel_values` only and nothing else: `model(pixel_values)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"pixel_values": pixel_values, "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> Args: 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ VIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details. head_mask (`np.ndarray` 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**. 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. 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. 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 ViT Model transformer outputting raw hidden-states without any specific head on top.", VIT_START_DOCSTRING, ) class TFViTModel(TFViTPreTrainedModel): def __init__(self, config: ViTConfig, *inputs, add_pooling_layer=True, **kwargs): super().__init__(config, *inputs, **kwargs) self.vit = TFViTMainLayer(config, add_pooling_layer=add_pooling_layer, name="vit") @unpack_inputs @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def call( self, pixel_values: TFModelInputType | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: outputs = self.vit( pixel_values=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, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "vit", None) is not None: with tf.name_scope(self.vit.name): self.vit.build(None) class TFViTPooler(keras.layers.Layer): def __init__(self, config: ViTConfig, **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]) @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 TFViTForImageClassification(TFViTPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: ViTConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.vit = TFViTMainLayer(config, add_pooling_layer=False, name="vit") # Classifier head self.classifier = keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def call( self, pixel_values: TFModelInputType | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: 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` or `np.ndarray` 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). """ outputs = self.vit( pixel_values=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, training=training, ) sequence_output = outputs[0] logits = self.classifier(inputs=sequence_output[:, 0, :]) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=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, "vit", None) is not None: with tf.name_scope(self.vit.name): self.vit.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]) __all__ = ["TFViTForImageClassification", "TFViTModel", "TFViTPreTrainedModel"]

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

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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 VitMatte checkpoints from the original repository. URL: https://github.com/hustvl/ViTMatte """ import argparse import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import VitDetConfig, VitMatteConfig, VitMatteForImageMatting, VitMatteImageProcessor def get_config(model_name): hidden_size = 384 if "small" in model_name else 768 num_attention_heads = 6 if "small" in model_name else 12 backbone_config = VitDetConfig( num_channels=4, image_size=512, pretrain_image_size=224, patch_size=16, hidden_size=hidden_size, num_attention_heads=num_attention_heads, use_absolute_position_embeddings=True, use_relative_position_embeddings=True, window_size=14, # 2, 5, 8, 11 for global attention window_block_indices=[0, 1, 3, 4, 6, 7, 9, 10], residual_block_indices=[2, 5, 8, 11], out_features=["stage12"], ) return VitMatteConfig(backbone_config=backbone_config, hidden_size=hidden_size) # 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 # stem rename_keys.append(("backbone.pos_embed", "backbone.embeddings.position_embeddings")) rename_keys.append(("backbone.patch_embed.proj.weight", "backbone.embeddings.projection.weight")) rename_keys.append(("backbone.patch_embed.proj.bias", "backbone.embeddings.projection.bias")) # fmt: on return rename_keys def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val def convert_vitmatte_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): config = get_config(model_name) # load original state dict model_name_to_filename = { "vitmatte-small-composition-1k": "ViTMatte_S_Com.pth", "vitmatte-base-composition-1k": "ViTMatte_B_Com.pth", "vitmatte-small-distinctions-646": "ViTMatte_S_DIS.pth", "vitmatte-base-distinctions-646": "ViTMatte_B_DIS.pth", } filename = model_name_to_filename[model_name] filepath = hf_hub_download(repo_id="nielsr/vitmatte-checkpoints", filename=filename, repo_type="model") state_dict = torch.load(filepath, map_location="cpu") # rename keys for key in state_dict.copy().keys(): val = state_dict.pop(key) if "backbone.blocks" in key: key = key.replace("backbone.blocks", "backbone.encoder.layer") if "attn" in key: key = key.replace("attn", "attention") if "fusion_blks" in key: key = key.replace("fusion_blks", "fusion_blocks") if "bn" in key: key = key.replace("bn", "batch_norm") state_dict[key] = val # rename keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) # create model processor = VitMatteImageProcessor() model = VitMatteForImageMatting(config) model.eval() # load state dict model.load_state_dict(state_dict) # verify on dummy image + trimap url = "https://github.com/hustvl/ViTMatte/blob/main/demo/bulb_rgb.png?raw=true" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") url = "https://github.com/hustvl/ViTMatte/blob/main/demo/bulb_trimap.png?raw=true" trimap = Image.open(requests.get(url, stream=True).raw) pixel_values = processor(images=image, trimaps=trimap.convert("L"), return_tensors="pt").pixel_values with torch.no_grad(): alphas = model(pixel_values).alphas if model_name == "vitmatte-small-composition-1k": expected_slice = torch.tensor([[0.9977, 0.9987, 0.9990], [0.9980, 0.9998, 0.9998], [0.9983, 0.9998, 0.9998]]) elif model_name == "vitmatte-base-composition-1k": expected_slice = torch.tensor([[0.9972, 0.9971, 0.9981], [0.9948, 0.9987, 0.9994], [0.9963, 0.9992, 0.9995]]) elif model_name == "vitmatte-small-distinctions-646": expected_slice = torch.tensor([[0.9880, 0.9970, 0.9972], [0.9960, 0.9996, 0.9997], [0.9963, 0.9996, 0.9997]]) elif model_name == "vitmatte-base-distinctions-646": expected_slice = torch.tensor([[0.9963, 0.9998, 0.9999], [0.9995, 1.0000, 1.0000], [0.9992, 0.9999, 1.0000]]) assert torch.allclose(alphas[0, 0, :3, :3], expected_slice, atol=1e-4) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and processor for {model_name} to hub") model.push_to_hub(f"hustvl/{model_name}") processor.push_to_hub(f"hustvl/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="vitmatte-small-composition-1k", type=str, choices=[ "vitmatte-small-composition-1k", "vitmatte-base-composition-1k", "vitmatte-small-distinctions-646", "vitmatte-base-distinctions-646", ], help="Name of the VitMatte 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." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_vitmatte_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/vitmatte/convert_vitmatte_to_hf.py/0

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