From e4c4a7b0a0a21940be56c857a03c2b50f2fa1c81 Mon Sep 17 00:00:00 2001 From: YunyaoZhou Date: Sat, 11 Jan 2025 23:59:03 +0800 Subject: [PATCH] =?UTF-8?q?feat=E2=9C=A8:=20=E6=9B=B4=E6=96=B0=E6=95=B0?= =?UTF-8?q?=E6=8D=AE=E9=9B=86=E5=90=8D=E7=A7=B0=E4=B8=BAOCR-VQA-200K?= =?UTF-8?q?=EF=BC=8C=E4=BC=98=E5=8C=96=E8=AE=AD=E7=BB=83=E8=84=9A=E6=9C=AC?= =?UTF-8?q?=E5=92=8C=E6=8D=9F=E5=A4=B1=E8=AE=A1=E7=AE=97=E9=80=BB=E8=BE=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 3 +- src/dataset_library/OCRVQADataset.py | 2 +- src/dataset_library/factory.py | 2 +- src/model_library/qwen2vl/model.py | 19 ++- src/train.py | 19 +++ src/train.sh | 2 +- src/utils/accelerator.py | 7 +- src/utils/trainer.py | 62 +++++++- uv.lock | 221 +++++++++++++++------------ 9 files changed, 224 insertions(+), 113 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 10891b7..cf02630 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "torch==2.5.1+cu124", "torchaudio==2.5.1+cu124", "torchvision==0.20.1+cu124", - "transformers==4.46.1", + "transformers==4.48.0", "trl==0.13.0", "wheel>=0.45.1", ] @@ -40,6 +40,7 @@ url = "https://pypi.org/simple" [tool.uv] no-build-isolation-package = ["flash-attn"] +concurrent-builds = 4 [[tool.uv.index]] name = "pytorch" diff --git a/src/dataset_library/OCRVQADataset.py b/src/dataset_library/OCRVQADataset.py index dedb432..5c971dd 100644 --- a/src/dataset_library/OCRVQADataset.py +++ b/src/dataset_library/OCRVQADataset.py @@ -107,7 +107,7 @@ class OCRVQADataset(Dataset): } -class OCRVQADatasetForGeneration(Dataset): +class OCRVQADatasetForGeneration(OCRVQADataset): def __getitem__(self, index): sample = self.data[index] diff --git a/src/dataset_library/factory.py b/src/dataset_library/factory.py index f86cd0f..6a4767a 100644 --- a/src/dataset_library/factory.py +++ b/src/dataset_library/factory.py @@ -6,7 +6,7 @@ def get_dataset( dataset_name, base_path="/home/zyy/research/accelerate/dataset" ) -> dict[Literal["train", "test", "generation"], Dataset]: dataset: dict[Literal["train", "test", "generation"], Dataset] = {} - if dataset_name == "OCR_VQA_200K": + if dataset_name == "OCR-VQA-200K": import os.path as osp from .OCRVQADataset import OCRVQADataset, OCRVQADatasetForGeneration diff --git a/src/model_library/qwen2vl/model.py b/src/model_library/qwen2vl/model.py index 522355a..27d5be8 100644 --- a/src/model_library/qwen2vl/model.py +++ b/src/model_library/qwen2vl/model.py @@ -1,5 +1,22 @@ # from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast, Qwen2VLModel, Qwen2VLForConditionalGeneration, logger, DynamicCache, Qwen2VLDecoderLayer, Qwen2VLConfig, Qwen2VLAttention, -from transformers.models.qwen2_vl.modeling_qwen2_vl import * +from transformers.models.qwen2_vl.modeling_qwen2_vl import ( + is_flash_attn_2_available, + Qwen2VLAttention, + Qwen2VLFlashAttention2, + Qwen2VLDecoderLayer, + Qwen2VLModel, + Qwen2VLForConditionalGeneration, + Qwen2VLConfig, + logger, + apply_rotary_pos_emb_vision, + Cache, + apply_multimodal_rotary_pos_emb, + repeat_kv, + is_flash_attn_greater_or_equal_2_10, + math, + Qwen2VLCausalLMOutputWithPast, + Qwen2VisionTransformerPretrainedModel, +) if is_flash_attn_2_available(): from flash_attn import flash_attn_varlen_func diff --git a/src/train.py b/src/train.py index c5c4368..d40410d 100644 --- a/src/train.py +++ b/src/train.py @@ -72,6 +72,24 @@ if __name__ == "__main__": collate_fn_for_train = partial(collate_fn_for_train, processor=processor) collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) + if model_args.model_name_or_path == "VITA-MLLM/VITA-1.5": + # from transformers import + # from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified + + model = Qwen2VLForConditionalGeneration_modified.from_pretrained( + model_args.model_name_or_path, + trust_remote_code=model_args.trust_remote_code, + **model_kwargs, + ) + print(model) + from model_library.qwen2vl import ( + collate_fn_for_train, + collate_fn_for_evaluate, + ) + from functools import partial + + collate_fn_for_train = partial(collate_fn_for_train, processor=processor) + collate_fn_for_evaluate = partial(collate_fn_for_evaluate, processor=processor) ################ # Dataset ################ @@ -104,6 +122,7 @@ if __name__ == "__main__": for dataset_name in script_args.dataset_name: dataset = get_dataset(dataset_name) + print(dataset) model.train() trainer = ContinualTrainer( diff --git a/src/train.sh b/src/train.sh index db3fcf0..9169286 100755 --- a/src/train.sh +++ b/src/train.sh @@ -1,7 +1,7 @@ #!/bin/bash accelerate launch --config_file configs/accelerate_configs/deepspeed_zero2.yaml train.py \ - --dataset_name CHEM \ + --dataset_name OCR-VQA-200K \ --use_peft \ --peft_type MMOELORA \ --model_name_or_path Qwen/Qwen2-VL-7B-Instruct \ diff --git a/src/utils/accelerator.py b/src/utils/accelerator.py index 44d6c33..858784d 100644 --- a/src/utils/accelerator.py +++ b/src/utils/accelerator.py @@ -62,15 +62,12 @@ def create_accelerator_and_postprocess(args): "when using FSDP." ) - def propagate_args_to_deepspeed(auto_find_batch_size=False): + if is_deepspeed_enabled and getattr(args, "hf_deepspeed_config", None) is None: from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig ds_plugin = accelerator.state.deepspeed_plugin ds_plugin.hf_ds_config = HfTrainerDeepSpeedConfig(ds_plugin.hf_ds_config.config) ds_plugin.deepspeed_config = ds_plugin.hf_ds_config.config - ds_plugin.hf_ds_config.trainer_config_process(args, auto_find_batch_size) - - if is_deepspeed_enabled and getattr(args, "hf_deepspeed_config", None) is None: - propagate_args_to_deepspeed() + ds_plugin.hf_ds_config.trainer_config_process(args, auto_find_batch_size=False) return accelerator diff --git a/src/utils/trainer.py b/src/utils/trainer.py index 0e86bca..b5d1017 100644 --- a/src/utils/trainer.py +++ b/src/utils/trainer.py @@ -1,6 +1,11 @@ # _________________________________________________________ from transformers import Trainer +from transformers.trainer import ( + Trainer, + _is_peft_model, + MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, +) class ContinualTrainer(Trainer): @@ -13,8 +18,7 @@ class ContinualTrainer(Trainer): def create_accelerator_and_postprocess(self): if self.accelerator is not None: self.is_deepspeed_enabled = ( - getattr(self.accelerator.state, "deepspeed_plugin", None) - is not None + getattr(self.accelerator.state, "deepspeed_plugin", None) is not None ) self.is_fsdp_enabled = ( getattr(self.accelerator.state, "fsdp_plugin", None) is not None @@ -22,4 +26,56 @@ class ContinualTrainer(Trainer): self.gather_function = self.accelerator.gather_for_metrics return else: - super().create_accelerator_and_postprocess() \ No newline at end of file + super().create_accelerator_and_postprocess() + + def compute_loss( + self, model, inputs, return_outputs=False, num_items_in_batch=None + ): + """ + How the loss is computed by Trainer. By default, all models return the loss in the first element. + + Subclass and override for custom behavior. + """ + if ( + self.label_smoother is not None or self.compute_loss_func is not None + ) and "labels" in inputs: + labels = inputs.pop("labels") + else: + labels = None + if self.model_accepts_loss_kwargs: + loss_kwargs = {} + if num_items_in_batch is not None: + loss_kwargs["num_items_in_batch"] = num_items_in_batch + inputs = {**inputs, **loss_kwargs} + outputs = model(**inputs) + # Save past state if it exists + # TODO: this needs to be fixed and made cleaner later. + if self.args.past_index >= 0: + self._past = outputs[self.args.past_index] + print(labels) + + if labels is not None: + unwrapped_model = self.accelerator.unwrap_model(model) + if _is_peft_model(unwrapped_model): + model_name = unwrapped_model.base_model.model._get_name() + else: + model_name = unwrapped_model._get_name() + # User-defined compute_loss function + if self.compute_loss_func is not None: + loss = self.compute_loss_func( + outputs, labels, num_items_in_batch=num_items_in_batch + ) + elif model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values(): + loss = self.label_smoother(outputs, labels, shift_labels=True) + else: + loss = self.label_smoother(outputs, labels) + else: + if isinstance(outputs, dict) and "loss" not in outputs: + raise ValueError( + "The model did not return a loss from the inputs, only the following keys: " + f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}." + ) + # We don't use .loss here since the model may return tuples instead of ModelOutput. + loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0] + + return (loss, outputs) if return_outputs else loss diff --git a/uv.lock b/uv.lock index 64d3c62..94a35dd 100644 --- a/uv.lock +++ b/uv.lock @@ -1,8 +1,8 @@ version = 1 requires-python = ">=3.11" resolution-markers = [ - "python_full_version < '3.12'", "python_full_version >= '3.12'", + "python_full_version < '3.12'", ] [[package]] @@ -223,7 +223,7 @@ requires-dist = [ { name = "torch", specifier = "==2.5.1+cu124", index = "https://download.pytorch.org/whl/cu124" }, { name = "torchaudio", specifier = "==2.5.1+cu124", index = "https://download.pytorch.org/whl/cu124" }, { name = "torchvision", specifier = "==0.20.1+cu124", index = "https://download.pytorch.org/whl/cu124" }, - { name = "transformers", specifier = "==4.46.1" }, + { name = "transformers", 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