# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: # Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: # Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li # # 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 ast import base64 import copy import glob import io import json import logging import math import os import pathlib import pickle import random import re import time from dataclasses import dataclass, field from typing import Dict, List, Optional, Sequence import numpy as np import soundfile as sf import tokenizers import torch import transformers import whisper from packaging import version from PIL import Image from safetensors.torch import load_file as safetensor_load_file from scipy.signal import resample from torch.utils.data import Dataset from egogpt import conversation as conversation_lib from egogpt.constants import ( DEFAULT_IMAGE_TOKEN, DEFAULT_SPEECH_TOKEN, IGNORE_INDEX, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, ) from egogpt.mm_utils import ( process_anyres_image, process_highres_image, process_highres_image_crop_split, ) from egogpt.model import * from egogpt.train.llava_trainer import LLaVATrainer from egogpt.utils import process_video_with_decord, process_video_with_decord_byframe local_rank = None IS_TOKENIZER_GREATER_THAN_0_14 = version.parse(tokenizers.__version__) >= version.parse( "0.14" ) def rank0_print(*args): if local_rank == 0: print(*args) @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="facebook/opt-125m") version: Optional[str] = field(default="v0") freeze_backbone: bool = field(default=False) tune_mm_mlp_adapter: bool = field(default=False) tune_speech_generator_only: bool = field(default=False) speech_encoder: Optional[str] = field(default=None) unfreeze_mm_speech_encoder: bool = field(default=False) mm_vision_select_layer: Optional[int] = field( default=-1 ) # default to the last layer pretrain_speech_projector: Optional[str] = field(default=None) speech_projector_type: Optional[str] = field(default="linear") speech_encoder_type: Optional[str] = field(default="whisper") speech_encoder_config: Optional[str] = field( default="models/speech_encoder/large-v3.pt" ) speech_encoder_ds_rate: Optional[int] = field(default=5) speech_encoder_hidden_size: Optional[int] = field(default=1280) tune_mm_mlp_adapter: bool = field(default=False) tune_mm_vision_resampler: bool = field(default=False) vision_tower: Optional[str] = field(default=None) unfreeze_mm_vision_tower: bool = field(default=False) unfreeze_language_model: bool = field(default=False) mm_vision_select_layer: Optional[int] = field( default=-1 ) # default to the last layer pretrain_mm_mlp_adapter: Optional[str] = field(default=None) mm_projector_type: Optional[str] = field(default="linear") mm_use_im_start_end: bool = field(default=False) mm_use_im_patch_token: bool = field(default=True) mm_patch_merge_type: Optional[str] = field(default="flat") mm_vision_select_feature: Optional[str] = field(default="patch") mm_resampler_type: Optional[str] = field(default=None) mm_mask_drop_mode: str = field(default="fixed") mm_mask_drop_skip_percentage: float = field(default=0.0) mm_mask_drop_ratio: float = field(default=0.25) mm_mask_drop_ratio_upper: Optional[float] = field(default=None) mm_mask_drop_ratio_lower: Optional[float] = field(default=None) mm_spatial_pool_stride: Optional[int] = field(default=None) mm_spatial_pool_mode: str = field(default="bilinear") mm_spatial_pool_out_channels: Optional[int] = field(default=None) mm_perceiver_depth: Optional[int] = field(default=3) mm_perceiver_latents: Optional[int] = field(default=32) mm_perceiver_ff_mult: Optional[float] = field(default=4) mm_perceiver_pretrained: Optional[str] = field(default=None) mm_qformer_depth: Optional[int] = field(default=3) mm_qformer_latents: Optional[int] = field(default=32) mm_qformer_pretrained: Optional[str] = field(default=None) rope_scaling_factor: Optional[float] = field(default=None) rope_scaling_type: Optional[str] = field(default=None) s2: Optional[bool] = field(default=False) s2_scales: Optional[str] = field(default="336,672,1008") use_pos_skipping: Optional[bool] = field(default=False) pos_skipping_range: Optional[int] = field(default=4096) mm_newline_position: Optional[str] = field(default="grid") delay_load: Optional[bool] = field(default=True) delay_load_audio: Optional[bool] = field(default=True) add_faster_video: Optional[bool] = field(default=False) faster_token_stride: Optional[int] = field(default=10) @dataclass class DataArguments: data_path: str = field( default=None, metadata={"help": "Path to the training data."} ) lazy_preprocess: bool = False is_multimodal: bool = False image_aspect_ratio: str = "square" image_grid_pinpoints: Optional[str] = field(default=None) image_crop_resolution: Optional[int] = field(default=None) image_split_resolution: Optional[int] = field(default=None) video_fps: Optional[int] = field(default=1) frames_upbound: Optional[int] = field(default=100) force_sample: bool = False @dataclass class TrainingArguments(transformers.TrainingArguments): cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") remove_unused_columns: bool = field(default=False) freeze_mm_mlp_adapter: bool = field(default=False) mpt_attn_impl: Optional[str] = field(default="triton") model_max_length: int = field( default=512, metadata={ "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)." }, ) double_quant: bool = field( default=True, metadata={ "help": "Compress the quantization statistics through double quantization." }, ) quant_type: str = field( default="nf4", metadata={ "help": "Quantization data type to use. Should be one of `fp4` or `nf4`." }, ) bits: int = field(default=16, metadata={"help": "How many bits to use."}) lora_enable: bool = field(default=False) lora_r: int = 64 lora_alpha: int = 16 lora_dropout: float = 0.05 lora_weight_path: str = "" lora_bias: str = "none" speech_projector_lr: Optional[float] = None gradient_checkpointing: bool = field(default=True) mm_speech_encoder_lr: Optional[float] = None diffusion_head_lr: Optional[float] = None group_by_varlen: bool = field(default=False) group_by_modality_length: bool = field(default=False) group_by_modality_length_auto: bool = field(default=False) min_lr_ratio: float = field(default=0.0) sample_independently: bool = field(default=False) freeze_mm_mlp_adapter: bool = field(default=False) mm_projector_lr: Optional[float] = None mm_vision_tower_lr: Optional[float] = None freeze_mm_vision_resampler: bool = field(default=False) def maybe_zero_3(param, ignore_status=False, name=None): from deepspeed import zero from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus if hasattr(param, "ds_id"): if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: if not ignore_status: logging.warning( f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}" ) with zero.GatheredParameters([param]): param = param.data.detach().cpu().clone() else: param = param.detach().cpu().clone() return param # Borrowed from peft.utils.get_peft_model_state_dict def get_peft_state_maybe_zero_3(named_params, bias): if bias == "none": to_return = {k: t for k, t in named_params if "lora_" in k} elif bias == "all": to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k} elif bias == "lora_only": to_return = {} maybe_lora_bias = {} lora_bias_names = set() for k, t in named_params: if "lora_" in k: to_return[k] = t bias_name = k.split("lora_")[0] + "bias" lora_bias_names.add(bias_name) elif "bias" in k: maybe_lora_bias[k] = t for k, t in maybe_lora_bias: if bias_name in lora_bias_names: to_return[bias_name] = t else: raise NotImplementedError to_return = {k: maybe_zero_3(v, ignore_status=True) for k, v in to_return.items()} return to_return def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True): to_return = {k: t for k, t in named_params if "lora_" not in k} if require_grad_only: to_return = {k: t for k, t in to_return.items() if t.requires_grad} to_return = { k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items() } return to_return def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): to_return = { k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match) } to_return = { k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items() } return to_return def find_all_linear_names(model): cls = torch.nn.Linear lora_module_names = set() multimodal_keywords = ["speech_projector", "speech_encoder"] for name, module in model.named_modules(): if any(mm_keyword in name for mm_keyword in multimodal_keywords): continue if isinstance(module, cls): names = name.split(".") lora_module_names.add(names[0] if len(names) == 1 else names[-1]) if "lm_head" in lora_module_names: # needed for 16-bit lora_module_names.remove("lm_head") return list(lora_module_names) def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str): """Collects the state dict and dump to disk.""" if getattr(trainer.args, "tune_mm_mlp_adapter", False): # Only save Adapter keys_to_match = ["speech_projector"] if getattr(trainer.args, "use_im_start_end", False): keys_to_match.extend(["embed_tokens", "embed_in"]) weight_to_save = get_mm_adapter_state_maybe_zero_3( trainer.model.named_parameters(), keys_to_match ) trainer.model.config.save_pretrained(output_dir) current_folder = output_dir.split("/")[-1] parent_folder = os.path.dirname(output_dir) if trainer.args.local_rank == 0 or trainer.args.local_rank == -1: if current_folder.startswith("checkpoint-"): speech_projector_folder = os.path.join( parent_folder, "speech_projector" ) os.makedirs(speech_projector_folder, exist_ok=True) torch.save( weight_to_save, os.path.join(speech_projector_folder, f"{current_folder}.bin"), ) else: torch.save( weight_to_save, os.path.join(output_dir, f"speech_projector.bin") ) return if trainer.deepspeed: torch.cuda.synchronize() trainer.save_model(output_dir) return state_dict = trainer.model.state_dict() if trainer.args.should_save: cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()} del state_dict trainer._save(output_dir, state_dict=cpu_state_dict) # noqa def smart_tokenizer_and_embedding_resize( special_tokens_dict: Dict, tokenizer: transformers.PreTrainedTokenizer, model: transformers.PreTrainedModel, ): """Resize tokenizer and embedding. Note: This is the unoptimized version that may make your embedding size not be divisible by 64. """ num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) model.resize_token_embeddings(len(tokenizer)) if num_new_tokens > 0: input_embeddings = model.get_input_embeddings().weight.data output_embeddings = model.get_output_embeddings().weight.data input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( dim=0, keepdim=True ) output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( dim=0, keepdim=True ) input_embeddings[-num_new_tokens:] = input_embeddings_avg output_embeddings[-num_new_tokens:] = output_embeddings_avg def _tokenize_fn( strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer ) -> Dict: """Tokenize a list of strings.""" tokenized_list = [ tokenizer( text, return_tensors="pt", padding="longest", max_length=tokenizer.model_max_length, truncation=True, ) for text in strings ] input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] input_ids_lens = labels_lens = [ tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list ] return dict( input_ids=input_ids, labels=labels, input_ids_lens=input_ids_lens, labels_lens=labels_lens, ) def _mask_targets(target, tokenized_lens, speakers): # cur_idx = 0 cur_idx = tokenized_lens[0] tokenized_lens = tokenized_lens[1:] target[:cur_idx] = IGNORE_INDEX for tokenized_len, speaker in zip(tokenized_lens, speakers): if speaker == "human": target[cur_idx + 2 : cur_idx + tokenized_len] = IGNORE_INDEX cur_idx += tokenized_len def _add_speaker_and_signal(header, source, get_conversation=True): """Add speaker and start/end signal on each round.""" BEGIN_SIGNAL = "### " END_SIGNAL = "\n" conversation = header for sentence in source: from_str = sentence["from"] if from_str.lower() == "human": from_str = conversation_lib.default_conversation.roles[0] elif from_str.lower() == "gpt": from_str = conversation_lib.default_conversation.roles[1] else: from_str = "unknown" sentence["value"] = ( BEGIN_SIGNAL + from_str + ": " + sentence["value"] + END_SIGNAL ) if get_conversation: conversation += sentence["value"] conversation += BEGIN_SIGNAL return conversation def tokenizer_speech_token( prompt, tokenizer, speech_token_index=SPEECH_TOKEN_INDEX, return_tensors=None ): prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split("")] def insert_separator(X, sep): return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1] input_ids = [] offset = 0 if ( len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id ): offset = 1 input_ids.append(prompt_chunks[0][0]) for x in insert_separator(prompt_chunks, [speech_token_index] * (offset + 1)): input_ids.extend(x[offset:]) if return_tensors is not None: if return_tensors == "pt": return torch.tensor(input_ids, dtype=torch.long) raise ValueError(f"Unsupported tensor type: {return_tensors}") return input_ids def preprocess_multimodal(sources: Sequence[str], data_args: DataArguments) -> Dict: is_multimodal = data_args.is_multimodal if not is_multimodal: return sources # Add speech and image special tokens to the beginning of the conversation for source in sources: for sentence in source: if DEFAULT_SPEECH_TOKEN in sentence["value"]: sentence["value"] = ( sentence["value"].replace(DEFAULT_SPEECH_TOKEN, "").strip() ) sentence["value"] = DEFAULT_SPEECH_TOKEN + "\n" + sentence["value"] sentence["value"] = sentence["value"].strip() if DEFAULT_IMAGE_TOKEN in sentence["value"]: sentence["value"] = ( sentence["value"].replace(DEFAULT_IMAGE_TOKEN, "").strip() ) sentence["value"] = DEFAULT_IMAGE_TOKEN + "\n" + sentence["value"] sentence["value"] = sentence["value"].strip() return sources def preprocess_llama_2( sources, tokenizer: transformers.PreTrainedTokenizer, has_speech: bool = False ) -> Dict: conv = conversation_lib.default_conversation.copy() roles = {"human": conv.roles[0], "gpt": conv.roles[1]} # Apply prompt templates conversations = [] for i, source in enumerate(sources): if roles[source[0]["from"]] != conv.roles[0]: # Skip the first one if it is not from human source = source[1:] conv.messages = [] for j, sentence in enumerate(source): role = roles[sentence["from"]] assert role == conv.roles[j % 2], f"{i}" conv.append_message(role, sentence["value"]) conversations.append(conv.get_prompt()) # Tokenize conversations if has_speech: input_ids = torch.stack( [ tokenizer_speech_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations ], dim=0, ) else: input_ids = tokenizer( conversations, return_tensors="pt", padding="longest", max_length=tokenizer.model_max_length, truncation=True, ).input_ids targets = input_ids.clone() assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2 # Mask targets sep = "[/INST] " for conversation, target in zip(conversations, targets): total_len = int(target.ne(tokenizer.pad_token_id).sum()) rounds = conversation.split(conv.sep2) cur_len = 1 target[:cur_len] = IGNORE_INDEX for i, rou in enumerate(rounds): if rou == "": break parts = rou.split(sep) if len(parts) != 2: break parts[0] += sep if has_speech: round_len = len(tokenizer_speech_token(rou, tokenizer)) instruction_len = len(tokenizer_speech_token(parts[0], tokenizer)) - 2 else: round_len = len(tokenizer(rou).input_ids) instruction_len = len(tokenizer(parts[0]).input_ids) - 2 target[cur_len : cur_len + instruction_len] = IGNORE_INDEX cur_len += round_len target[cur_len:] = IGNORE_INDEX if cur_len < tokenizer.model_max_length: if cur_len != total_len: target[:] = IGNORE_INDEX print( f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." f" (ignored)" ) return dict( input_ids=input_ids, labels=targets, ) def preprocess_llama_3( sources, tokenizer: transformers.PreTrainedTokenizer, has_speech: bool = False ) -> Dict: conv = conversation_lib.default_conversation.copy() roles = {"human": conv.roles[0], "gpt": conv.roles[1]} # Apply prompt templates conversations = [] for i, source in enumerate(sources): if roles[source[0]["from"]] != conv.roles[0]: # Skip the first one if it is not from human source = source[1:] assert len(source) == 2, "now only support single-turn conversation" conv.messages = [] for j, sentence in enumerate(source): role = roles[sentence["from"]] assert role == conv.roles[j % 2], f"{i}" conv.append_message(role, sentence["value"]) conversations.append(conv.get_prompt()) # Tokenize conversations if has_speech: input_ids = torch.stack( [ tokenizer_speech_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations ], dim=0, ) else: input_ids = tokenizer( conversations, return_tensors="pt", padding="longest", max_length=tokenizer.model_max_length, truncation=True, ).input_ids targets = input_ids.clone() assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_3 # Mask targets sep = "<|start_header_id|>" + conv.roles[1] + "<|end_header_id|>\n\n" for conversation, target in zip(conversations, targets): total_len = int(target.ne(tokenizer.pad_token_id).sum()) cur_len = 1 target[:cur_len] = IGNORE_INDEX parts = conversation.split(sep) parts[0] += sep if has_speech: conversation_len = len(tokenizer_speech_token(conversation, tokenizer)) instruction_len = len(tokenizer_speech_token(parts[0], tokenizer)) - 1 else: conversation_len = len(tokenizer(conversation).input_ids) instruction_len = len(tokenizer(parts[0]).input_ids) - 1 target[cur_len : cur_len + instruction_len] = IGNORE_INDEX cur_len += conversation_len target[cur_len:] = IGNORE_INDEX # if cur_len < tokenizer.model_max_length: # if cur_len != total_len: # target[:] = IGNORE_INDEX # print( # f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." # f" (ignored)" # ) return dict( input_ids=input_ids, labels=targets, ) def preprocess_v1( sources, tokenizer: transformers.PreTrainedTokenizer, has_speech: bool = False ) -> Dict: conv = conversation_lib.default_conversation.copy() roles = {"human": conv.roles[0], "gpt": conv.roles[1]} # Apply prompt templates conversations = [] for i, source in enumerate(sources): if roles[source[0]["from"]] != conv.roles[0]: # Skip the first one if it is not from human source = source[1:] conv.messages = [] for j, sentence in enumerate(source): role = roles[sentence["from"]] assert role == conv.roles[j % 2], f"{i}" conv.append_message(role, sentence["value"]) conversations.append(conv.get_prompt()) # Tokenize conversations if has_speech: input_ids = torch.stack( [ tokenizer_speech_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations ], dim=0, ) else: input_ids = tokenizer( conversations, return_tensors="pt", padding="longest", max_length=tokenizer.model_max_length, truncation=True, ).input_ids targets = input_ids.clone() if conv.sep_style == conversation_lib.SeparatorStyle.TWO: # Mask targets sep = conv.sep + conv.roles[1] + ": " for conversation, target in zip(conversations, targets): total_len = int(target.ne(tokenizer.pad_token_id).sum()) rounds = conversation.split(conv.sep2) cur_len = 1 target[:cur_len] = IGNORE_INDEX for i, rou in enumerate(rounds): if rou == "": break parts = rou.split(sep) if len(parts) != 2: break parts[0] += sep if has_speech: round_len = len(tokenizer_speech_token(rou, tokenizer)) instruction_len = ( len(tokenizer_speech_token(parts[0], tokenizer)) - 2 ) else: round_len = len(tokenizer(rou).input_ids) instruction_len = len(tokenizer(parts[0]).input_ids) - 2 if i != 0 and not tokenizer.legacy and IS_TOKENIZER_GREATER_THAN_0_14: round_len -= 1 instruction_len -= 1 target[cur_len : cur_len + instruction_len] = IGNORE_INDEX cur_len += round_len target[cur_len:] = IGNORE_INDEX if cur_len < tokenizer.model_max_length: if cur_len != total_len: target[:] = IGNORE_INDEX print( f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." f" (ignored)" ) elif conv.sep_style == conversation_lib.SeparatorStyle.QWEN2: # Mask targets sep = "<|im_start|>assistant\n" for conversation, target in zip(conversations, targets): total_len = int(target.ne(tokenizer.pad_token_id).sum()) raw_rounds = conversation.split("<|im_end|>\n") cur_len = 0 rounds = [] now_str = "" for rou in raw_rounds: if len(rou) > 0: rou = rou + "<|im_end|>\n" if rou.startswith("<|endoftext|>"): rounds[-1] = rounds[-1] + "<|endoftext|>" rou = rou.replace("<|endoftext|>", "") if len(rou.strip()) == 0: continue if "<|im_start|>assistant\n" in rou: now_str += rou rounds.append(now_str) now_str = "" else: now_str += rou for i, rou in enumerate(rounds): if rou == "": break parts = rou.split(sep) if len(parts) != 2: break parts[0] += sep if has_speech: round_len = len(tokenizer_speech_token(rou, tokenizer)) instruction_len = ( len(tokenizer_speech_token(parts[0], tokenizer)) - 2 ) else: round_len = len(tokenizer(rou).input_ids) instruction_len = len(tokenizer(parts[0]).input_ids) - 2 try: is_legacy = tokenizer.legacy except: is_legacy = True if i != 0 and not is_legacy and IS_TOKENIZER_GREATER_THAN_0_14: round_len -= 1 instruction_len -= 1 target[cur_len : cur_len + instruction_len] = IGNORE_INDEX cur_len += round_len target[cur_len:] = IGNORE_INDEX if cur_len < tokenizer.model_max_length: if cur_len != total_len: target[:] = IGNORE_INDEX print( f"WARNING: tokenization mismatch for QWEN2: {cur_len} vs. {total_len}." f" (ignored)" ) return dict( input_ids=input_ids, labels=targets, ) def preprocess_plain( sources: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, ) -> Dict: # add end signal and concatenate together conversations = [] for source in sources: assert len(source) == 2 assert DEFAULT_SPEECH_TOKEN in source[0]["value"] source[0]["value"] = DEFAULT_SPEECH_TOKEN conversation = ( source[0]["value"] + source[1]["value"] + conversation_lib.default_conversation.sep ) conversations.append(conversation) # tokenize conversations input_ids = [ tokenizer_speech_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations ] targets = copy.deepcopy(input_ids) for target, source in zip(targets, sources): tokenized_len = len(tokenizer_speech_token(source[0]["value"], tokenizer)) target[:tokenized_len] = IGNORE_INDEX return dict(input_ids=input_ids, labels=targets) def preprocess_qwen( sources, tokenizer: transformers.PreTrainedTokenizer, has_speech: bool = False, has_image: bool = False, max_len=2048, system_message: str = "You are a helpful assistant.", ) -> Dict: def split_text(text, keywords): pattern = "(" + "|".join(map(re.escape, keywords)) + ")" parts = re.split(pattern, text) parts = [part for part in parts if part] return parts roles = {"human": "<|im_start|>user", "gpt": "<|im_start|>assistant"} # im_start, im_end = tokenizer.additional_special_tokens_ids im_start = tokenizer("<|im_start|>").input_ids[0] im_end = tokenizer("<|im_end|>").input_ids[0] nl_tokens = tokenizer("\n").input_ids _system = tokenizer("system").input_ids + nl_tokens # Apply prompt templates input_ids, targets = [], [] for i, source in enumerate(sources): if roles[source[0]["from"]] != roles["human"]: source = source[1:] input_id, target = [], [] system = ( [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens ) input_id += system target += [im_start] + [IGNORE_INDEX] * (len(system) - 3) + [im_end] + nl_tokens assert len(input_id) == len(target) for j, sentence in enumerate(source): role = roles[sentence["from"]] splited_sentence = split_text(sentence["value"], ["", ""]) _input_id = [] for part in splited_sentence: _input_id += tokenizer(role).input_ids + nl_tokens # add prefix if "" == part: _input_id += [SPEECH_TOKEN_INDEX] elif "" == part: _input_id += [IMAGE_TOKEN_INDEX] else: _input_id += tokenizer(part).input_ids _input_id += [im_end] + nl_tokens # add suffix input_id += _input_id if role == "<|im_start|>user": _target = ( [im_start] + [IGNORE_INDEX] * (len(_input_id) - 3) + [im_end] + nl_tokens ) elif role == "<|im_start|>assistant": _target = ( [im_start] + [IGNORE_INDEX] * len(tokenizer(role).input_ids) + _input_id[len(tokenizer(role).input_ids) + 1 : -2] + [im_end] + nl_tokens ) else: raise NotImplementedError target += _target assert len(input_id) == len(target) input_ids.append(input_id) targets.append(target) input_ids = torch.tensor(input_ids, dtype=torch.long) targets = torch.tensor(targets, dtype=torch.long) return dict( input_ids=input_ids, # tensor(bs x seq_len) labels=targets, # tensor(bs x seq_len) ) def preprocess( sources: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, has_speech: bool = False, has_image: bool = False, ) -> Dict: """ Given a list of sources, each is a conversation list. This transform: 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; 2. Concatenate conversations together; 3. Tokenize the concatenated conversation; 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. """ if ( conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN ): return preprocess_plain(sources, tokenizer, has_image=has_image) if ( conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_2 ): return preprocess_llama_2( sources, tokenizer, has_speech=has_speech, has_image=has_image ) if conversation_lib.default_conversation.version.startswith("v1"): return preprocess_v1( sources, tokenizer, has_speech=has_speech, has_image=has_image ) if ( conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_3 ): return preprocess_llama_3( sources, tokenizer, has_speech=has_speech, has_image=has_image ) if conversation_lib.default_conversation.version == "qwen": return preprocess_qwen( sources, tokenizer, has_speech=has_speech, has_image=has_image ) raise NotImplementedError class LazySupervisedDataset(Dataset): """Dataset for supervised fine-tuning.""" def __init__( self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, data_args: DataArguments, ): super(LazySupervisedDataset, self).__init__() list_data_dict = json.load(open(data_path, "r")) rank0_print("Formatting inputs...Skip in lazy mode") self.tokenizer = tokenizer self.list_data_dict = list_data_dict self.data_args = data_args self.mel_size = 128 def __len__(self): return len(self.list_data_dict) @property def modality_lengths(self): length_list = [] for sample in self.list_data_dict: cur_len = sum( len(conv["value"].split()) for conv in sample["conversations"] ) assert cur_len > 0, f"Conversation length is 0 for {sample}" if "image" in sample or "video" in sample or self.data_args.early_mix_text: length_list.append(cur_len) else: length_list.append(-cur_len) return length_list def process_audio(self, audio_file, start_frame=None, end_frame=None, fps=20): speech, sample_rate = sf.read(audio_file) if start_frame is not None and end_frame is not None: start_sample = start_frame * sample_rate // fps end_sample = end_frame * sample_rate // fps speech = speech[start_sample:end_sample] if sample_rate != 16000: target_length = int(len(speech) * 16000 / sample_rate) speech = resample(speech, target_length) if speech.ndim > 1: speech = np.mean(speech, axis=1) speech = whisper.pad_or_trim(speech.astype(np.float32)) speech = whisper.log_mel_spectrogram(speech, n_mels=self.mel_size).permute(1, 0) speech_length = torch.LongTensor([speech.shape[0]]) return speech, speech_length def process_image(self, image_file, overwrite_image_aspect_ratio=None): processor = self.data_args.image_processor # print(f"\n\nInspecting the image path, folder = {image_folder}, image={image_file}\n\n") try: image = Image.open(image_file).convert("RGB") except Exception as exn: print(f"Failed to open image {image_file}. Exception:", exn) raise exn image_size = image.size image_aspect_ratio = self.data_args.image_aspect_ratio if overwrite_image_aspect_ratio is not None: image_aspect_ratio = overwrite_image_aspect_ratio if image_aspect_ratio == "highres": image = process_highres_image( image, self.data_args.image_processor, self.data_args.image_grid_pinpoints, ) elif image_aspect_ratio == "anyres" or "anyres_max" in image_aspect_ratio: image = process_anyres_image( image, self.data_args.image_processor, self.data_args.image_grid_pinpoints, ) elif image_aspect_ratio == "crop_split": image = process_highres_image_crop_split(image, self.data_args) elif image_aspect_ratio == "pad": def expand2square(pil_img, background_color): width, height = pil_img.size if width == height: return pil_img elif width > height: result = Image.new(pil_img.mode, (width, width), background_color) result.paste(pil_img, (0, (width - height) // 2)) return result else: result = Image.new(pil_img.mode, (height, height), background_color) result.paste(pil_img, ((height - width) // 2, 0)) return result image = expand2square( image, tuple(int(x * 255) for x in processor.image_mean) ) image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0] else: image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0] return image, image_size, "image" def __getitem__(self, i) -> Dict[str, torch.Tensor]: while True: try: sample = self._get_item(i) # print("process sample",i) break except Exception as e: while True: try: i += 1 random_index = i % len(self.list_data_dict) sample = self._get_item(random_index) # print("something error, process sample",random_index) break except Exception as e: # random_index = random.randint(0, len(self.list_data_dict) - 1) continue return sample def _get_item(self, i) -> Dict[str, torch.Tensor]: sources = self.list_data_dict[i] if isinstance(i, int): sources = [sources] assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME if "image" in sources[0]: image_file = self.list_data_dict[i]["image"] if type(image_file) is list: image = [self.process_image(f) for f in image_file] # Handling multi images # overwrite to process with simple pad if len(image_file) > 1: image = [self.process_image(f, "pad") for f in image_file] image = [[im[0], im[1], "image"] for im in image] else: image = [self.process_image(image_file)] if "video" or "audio" in sources[0]: if "video" in sources[0]: video_file = self.list_data_dict[i]["video"] # video_folder = self.data_args.video_folder # video_file = os.path.join(video_folder, video_file) if not os.path.exists(video_file): print("File {} not exist!".format(video_file)) if "start_frame" in self.list_data_dict[i]: start_frame = self.list_data_dict[i]["start_frame"] end_frame = self.list_data_dict[i]["end_frame"] if self.list_data_dict[i].get( "current_observation_frame", None ): # Customized for egoplan data current_observation_frame = self.list_data_dict[i][ "current_observation_frame" ] else: current_observation_frame = None video = process_video_with_decord_byframe( video_file, start_frame, end_frame, self.data_args, current_observation_frame, ) else: ( video, video_time, frame_time, num_frames, ) = process_video_with_decord(video_file, self.data_args) processor = self.data_args.image_processor processed_video = processor.preprocess(video, return_tensors="pt")[ "pixel_values" ] image = [(processed_video, video[0].size, "video")] if "audio" in sources[0]: audio_file = self.list_data_dict[i]["audio"] # audio_folder = self.data_args.audio_folder # audio_file = os.path.join(audio_folder, audio_file) try: if "start_frame" in self.list_data_dict[i]: start_frame = self.list_data_dict[i]["start_frame"] end_frame = self.list_data_dict[i]["end_frame"] else: start_frame = None end_frame = None audio, audio_length = self.process_audio( audio_file, start_frame, end_frame ) except Exception as e: print("audio error", e) audio = [torch.zeros(3000, 128)] audio_length = torch.tensor([3000]) audio = [audio] sources = preprocess_multimodal( copy.deepcopy([e["conversations"] for e in sources]), self.data_args ) else: sources = copy.deepcopy([e["conversations"] for e in sources]) has_speech = "audio" in self.list_data_dict[i] has_image = ("image" in self.list_data_dict[i]) or ( "video" in self.list_data_dict[i] ) data_dict = preprocess( sources, self.tokenizer, has_speech=has_speech, has_image=has_image ) if isinstance(i, int): data_dict = dict( input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0] ) if "image" or "video" in self.list_data_dict[i]: data_dict["image"] = image # audio exist in the data if "audio" in self.list_data_dict[i]: data_dict["speech"] = audio data_dict["speech_lengths"] = audio_length else: # if no audio, add a dummy audio data_dict["speech"] = [torch.zeros(3000, 128)] data_dict["speech_lengths"] = torch.tensor([3000]) return data_dict @dataclass class DataCollatorForSupervisedDataset(object): """Collate examples for supervised fine-tuning.""" tokenizer: transformers.PreTrainedTokenizer def pad_sequence(self, input_ids, batch_first, padding_value): if self.tokenizer.padding_side == "left": input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] input_ids = torch.nn.utils.rnn.pad_sequence( input_ids, batch_first=batch_first, padding_value=padding_value ) if self.tokenizer.padding_side == "left": input_ids = torch.flip(input_ids, [1]) return input_ids def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: input_ids, labels = tuple( [instance[key] for instance in instances] for key in ("input_ids", "labels") ) input_ids = [ _input_ids[: self.tokenizer.model_max_length] for _input_ids in input_ids ] labels = [_labels[: self.tokenizer.model_max_length] for _labels in labels] if self.tokenizer.pad_token_id is None: if "qwen" in self.tokenizer.name_or_path.lower(): # print("Setting pad token to bos token for qwen model.") self.tokenizer.pad_token_id = 151643 else: self.tokenizer.pad_token_id = ( self.tokenizer.eos_token_id ) # FIXME: this could only be triggered for llama3 model. input_ids = self.pad_sequence( input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id ) labels = self.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) batch = dict( input_ids=input_ids, labels=labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id), ) if "speech" in instances[0]: speeches = [instance["speech"] for instance in instances] speeches_lengths = [instance["speech_lengths"] for instance in instances] batch["speech"] = [au for audio_list in speeches for au in audio_list] batch["speech_lengths"] = [ au for audio_list in speeches_lengths for au in audio_list ] batch["speech_lengths"] = torch.stack(batch["speech_lengths"]) if all( x is not None and x.shape == speeches[0][0].shape for x in batch["speech"] ): batch["speech"] = torch.stack(batch["speech"]) if "image" in instances[0]: images = [instance["image"] for instance in instances] batch["image_sizes"] = [im[1] for im_list in images for im in im_list] batch["modalities"] = [im[2] for im_list in images for im in im_list] images = [im[0] for im_list in images for im in im_list] # if all(x is not None and x.shape == images[0].shape for x in images): # Image: (N, P, C, H, W) # Video: (N, F, C, H, W) # batch["images"] = torch.stack(images) # else: batch["images"] = images return batch def make_supervised_data_module( tokenizer: transformers.PreTrainedTokenizer, data_args ) -> Dict: """Make dataset and collator for supervised fine-tuning.""" train_dataset = LazySupervisedDataset( tokenizer=tokenizer, data_path=data_args.data_path, data_args=data_args ) data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) return dict( train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator ) def train(): global local_rank parser = transformers.HfArgumentParser( (ModelArguments, DataArguments, TrainingArguments) ) model_args, data_args, training_args = parser.parse_args_into_dataclasses() local_rank = training_args.local_rank compute_dtype = ( torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32) ) if "qwen" in model_args.model_name_or_path.lower(): model = EgoGPTQwenForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, attn_implementation="flash_attention_2", torch_dtype=(torch.bfloat16 if training_args.bf16 else None), ) else: model = EgoGPTLlamaForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, attn_implementation="flash_attention_2", torch_dtype=(torch.bfloat16 if training_args.bf16 else None), ) model.config.use_cache = False if model_args.freeze_backbone: model.model.requires_grad_(False) if training_args.gradient_checkpointing: if hasattr(model, "enable_input_require_grads"): model.enable_input_require_grads() else: def make_inputs_require_grad(module, input, output): output.requires_grad_(True) model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) training_args.gradient_checkpointing_kwargs = {"use_reentrant": True} training_args.ddp_find_unused_parameters = True if training_args.lora_enable: from peft import LoraConfig, get_peft_model lora_config = LoraConfig( r=training_args.lora_r, lora_alpha=training_args.lora_alpha, target_modules=find_all_linear_names(model), lora_dropout=training_args.lora_dropout, bias=training_args.lora_bias, task_type="CAUSAL_LM", use_dora=True, ) if training_args.bits == 16: if training_args.bf16: model.to(torch.bfloat16) if training_args.fp16: model.to(torch.float16) rank0_print("Adding LoRA adapters...") model = get_peft_model(model, lora_config) model.to(dtype=compute_dtype, device=training_args.device) if "qwen" in model_args.model_name_or_path.lower(): tokenizer = transformers.AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, model_max_length=training_args.model_max_length, padding_side="right", ) else: tokenizer = transformers.AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, model_max_length=training_args.model_max_length, padding_side="right", use_fast=False, ) if model_args.version == "v0": if tokenizer.pad_token is None: smart_tokenizer_and_embedding_resize( special_tokens_dict=dict(pad_token="[PAD]"), tokenizer=tokenizer, model=model, ) elif model_args.version == "v0.5": tokenizer.pad_token = tokenizer.unk_token else: tokenizer.pad_token = tokenizer.unk_token if model_args.version in conversation_lib.conv_templates: conversation_lib.default_conversation = conversation_lib.conv_templates[ model_args.version ] else: conversation_lib.default_conversation = conversation_lib.conv_templates[ "vicuna_v1" ] model.get_model().initialize_speech_modules( model_args=model_args, fsdp=training_args.fsdp ) speech_encoder = model.get_speech_encoder() speech_encoder.to( dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device, ) if model_args.vision_tower is not None: model.get_model().initialize_vision_modules( model_args=model_args, fsdp=training_args.fsdp ) # import pdb;pdb.set_trace() vision_tower = model.get_vision_tower() vision_tower.to( dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device, ) data_args.image_processor = vision_tower.image_processor model.config.image_aspect_ratio = data_args.image_aspect_ratio if data_args.image_grid_pinpoints is not None: if ( isinstance(data_args.image_grid_pinpoints, str) and "x" in data_args.image_grid_pinpoints ): try: patch_size = data_args.image_processor.size[0] except Exception as e: patch_size = data_args.image_processor.size["shortest_edge"] assert patch_size in [ 224, 336, 384, 448, 512, ], "patch_size should be in [224, 336, 384, 448, 512]" # Use regex to extract the range from the input string matches = re.findall(r"\((\d+)x(\d+)\)", data_args.image_grid_pinpoints) range_start = tuple(map(int, matches[0])) range_end = tuple(map(int, matches[-1])) # Generate a matrix of tuples from (range_start[0], range_start[1]) to (range_end[0], range_end[1]) grid_pinpoints = [ (i, j) for i in range(range_start[0], range_end[0] + 1) for j in range(range_start[1], range_end[1] + 1) ] # Multiply all elements by patch_size data_args.image_grid_pinpoints = [ [dim * patch_size for dim in pair] for pair in grid_pinpoints ] elif isinstance(data_args.image_grid_pinpoints, str): data_args.image_grid_pinpoints = ast.literal_eval( data_args.image_grid_pinpoints ) model.config.image_grid_pinpoints = data_args.image_grid_pinpoints model.config.image_crop_resolution = data_args.image_crop_resolution model.config.image_split_resolution = data_args.image_split_resolution model.config.tokenizer_padding_side = tokenizer.padding_side model.config.tokenizer_model_max_length = tokenizer.model_max_length model.config.mm_newline_position = model_args.mm_newline_position model.config.add_faster_video = model_args.add_faster_video model.config.faster_token_stride = model_args.faster_token_stride model.config.mm_spatial_pool_stride = model_args.mm_spatial_pool_stride data_args.is_multimodal = True model.config.tune_mm_mlp_adapter = ( training_args.tune_mm_mlp_adapter ) = model_args.tune_mm_mlp_adapter if model_args.tune_mm_mlp_adapter: model.requires_grad_(False) if model_args.tune_mm_mlp_adapter: for p in model.get_model().speech_projector.parameters(): p.requires_grad = True for p in model.get_model().mm_projector.parameters(): p.requires_grad = True model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter if training_args.freeze_mm_mlp_adapter: for p in model.get_model().speech_projector.parameters(): p.requires_grad = False for p in model.get_model().mm_projector.parameters(): p.requires_grad = False model.config.freeze_mm_vision_resampler = training_args.freeze_mm_vision_resampler if training_args.freeze_mm_vision_resampler: for p in model.get_model().vision_resampler.parameters(): p.requires_grad = False model.config.unfreeze_mm_speech_encoder = model_args.unfreeze_mm_speech_encoder if model_args.unfreeze_mm_speech_encoder: speech_encoder.requires_grad_(True) model.config.mm_use_im_start_end = ( data_args.mm_use_im_start_end ) = model_args.mm_use_im_start_end model.config.mm_projector_lr = training_args.mm_projector_lr model.config.mm_vision_tower_lr = training_args.mm_vision_tower_lr model.config.speech_projector_lr = training_args.speech_projector_lr model.config.mm_speech_encoder_lr = training_args.mm_speech_encoder_lr model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token training_args.use_im_start_end = model_args.mm_use_im_start_end data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args) # test_data = data_module['train_dataset'].__getitem__(0) trainer = LLaVATrainer( model=model, tokenizer=tokenizer, args=training_args, **data_module ) if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")): trainer.train(resume_from_checkpoint=True) else: trainer.train() trainer.save_state() model.config.use_cache = True if training_args.lora_enable: state_dict = get_peft_state_maybe_zero_3( model.named_parameters(), training_args.lora_bias ) non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3( model.named_parameters() ) if training_args.local_rank == 0 or training_args.local_rank == -1: model.config.save_pretrained(training_args.output_dir) model.save_pretrained(training_args.output_dir, state_dict=state_dict) torch.save( non_lora_state_dict, os.path.join(training_args.output_dir, "non_lora_trainables.bin"), ) else: safe_save_model_for_hf_trainer( trainer=trainer, output_dir=training_args.output_dir ) if __name__ == "__main__": import torch print("number of gpus", torch.cuda.device_count()) train()