feat✨: 重构模型库，更新数据集处理逻辑，优化导入路径，添加新的化学数据集类
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181
src/dataset_library/chem.py
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181
src/dataset_library/chem.py
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@ -0,0 +1,181 @@
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from PIL import Image
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from torch.utils.data import Dataset
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import json
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import os
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class ChemDataseet(Dataset):
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def __init__(
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self, vis_root, ann_path, vis_processor=None, text_processor=None, split="train"
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):
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"""
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vis_root (string): Root directory of images (e.g. coco/images/)
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ann_root (string): directory to store the annotation file
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"""
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self.vis_root = vis_root
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self.vis_processor = vis_processor
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self.text_processor = text_processor
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if split == "train":
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self.data = self.create_data(ann_path, split=1)[:200]
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elif split == "test":
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self.data = self.create_data(ann_path, split=3)[:200]
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def create_data(self, ann_path, split=1):
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processed_data = []
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with open(ann_path, "r") as f:
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data = json.load(f)
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for k in data.keys():
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if data[k]["split"] != split:
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continue # 1 for training, 2 for validation, 3 for test
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ext = os.path.splitext(data[k]["imageURL"])[1]
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imageFile = k + ext
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assert len(data[k]["questions"]) == len(data[k]["answers"])
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for q, a in zip(data[k]["questions"], data[k]["answers"]):
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if os.path.exists(os.path.join(self.vis_root, imageFile)):
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processed_data.append(
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{
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"question": q,
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"answer": a,
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"image_path": imageFile,
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"image_id": k,
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"title": data[k]["title"],
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"genre": data[k]["genre"],
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}
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)
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return processed_data
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def __len__(self):
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return len(self.data)
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def __getitem__(self, index):
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sample = self.data[index]
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image = Image.open(os.path.join(self.vis_root, sample["image_path"])).convert(
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"RGB"
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)
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question = sample["question"]
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answer = sample["answer"]
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if self.vis_processor is not None:
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image = self.vis_processor(image)
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if self.text_processor is not None:
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question = self.text_processor(question)
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answer = self.text_processor(answer)
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chat = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{
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"type": "text",
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"text": f"[vqa] Based on the image, respond to this question with a short answer: {question}",
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},
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],
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},
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{"role": "assistant", "content": [{"type": "text", "text": answer}]},
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]
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return {
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"image": image,
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"chat": chat,
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"image_id": sample["image_id"],
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}
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class OCRVQADatasetForGeneration(Dataset):
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def __init__(
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self, vis_root, ann_path, vis_processor=None, text_processor=None, split="train"
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):
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"""
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vis_root (string): Root directory of images (e.g. coco/images/)
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ann_root (string): directory to store the annotation file
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"""
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self.vis_root = vis_root
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self.vis_processor = vis_processor
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self.text_processor = text_processor
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if split == "train":
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self.data = self.create_data(ann_path, split=1)[:200]
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elif split == "test":
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self.data = self.create_data(ann_path, split=3)[:200]
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# self.instruction_pool = [
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# "[vqa] {}",
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# "[vqa] Based on the image, respond to this question with a short answer: {}",
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# ]
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def create_data(self, ann_path, split=1):
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processed_data = []
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with open(ann_path, "r") as f:
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data = json.load(f)
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for k in data.keys():
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if data[k]["split"] != split:
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continue # 1 for training, 2 for validation, 3 for test
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ext = os.path.splitext(data[k]["imageURL"])[1]
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imageFile = k + ext
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assert len(data[k]["questions"]) == len(data[k]["answers"])
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for q, a in zip(data[k]["questions"], data[k]["answers"]):
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if os.path.exists(os.path.join(self.vis_root, imageFile)):
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processed_data.append(
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{
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"question": q,
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"answer": a,
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"image_path": imageFile,
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"image_id": k,
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"title": data[k]["title"],
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"genre": data[k]["genre"],
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}
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)
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return processed_data
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def __len__(self):
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return len(self.data)
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def __getitem__(self, index):
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sample = self.data[index]
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image = Image.open(os.path.join(self.vis_root, sample["image_path"])).convert(
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"RGB"
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)
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question = sample["question"]
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answer = sample["answer"]
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if self.vis_processor is not None:
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image = self.vis_processor(image)
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if self.text_processor is not None:
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question = self.text_processor(question)
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answer = self.text_processor(answer)
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chat = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{
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"type": "text",
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"text": f"[vqa] Based on the image, respond to this question with a short answer: {question}",
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},
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],
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}
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# {"role": "assistant", "content": answer},
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]
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return {
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"image": image,
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"chat": chat,
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"answer": answer,
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"image_id": sample["image_id"],
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}
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if __name__ == "__main__":
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dataset = ChemDataseet(
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"/home/zyy/research/accelerate/dataset/chem/images/",
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"/home/zyy/research/accelerate/dataset/chem/qwen_data/conversations_loc_train.jsonl",
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split="train",
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)
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print(len(dataset))
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print(dataset[0])
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dataset = OCRVQADatasetForGeneration(
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"/home/zyy/research/accelerate/dataset/OCR-VQA-200K/images",
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"/home/zyy/research/accelerate/dataset/OCR-VQA-200K/dataset.json",
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split="train",
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)
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print(len(dataset))
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print(dataset[0])
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pass
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@ -51,7 +51,7 @@ if __name__ == "__main__":
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print(model)
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if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct":
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from collatefn_library.qwen2 import (
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from model_library.qwen2 import (
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collate_fn_for_train,
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collate_fn_for_evaluate,
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)
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4
src/model_library/qwen2vl/__init__.py
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4
src/model_library/qwen2vl/__init__.py
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from .collate_fn import collate_fn_for_evaluate, collate_fn_for_train
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from .model import Qwen2VLForConditionalGeneration_modified
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__all__ = ["collate_fn_for_train", "collate_fn_for_evaluate", "Qwen2VLForConditionalGeneration_modified"]
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@ -10,7 +10,6 @@ def collate_fn_for_train(examples, processor: Qwen2VLProcessor):
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]
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# print(texts)
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images = [example["image"] for example in examples]
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# Tokenize the texts and process the images
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batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
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@ -52,7 +51,7 @@ def collate_fn_for_train(examples, processor: Qwen2VLProcessor):
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now_index += 1
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now_index += 1
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batch["labels"] = labels
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# batch["task_id"] = torch.tensor([0] * len(labels), dtype=torch.long)
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batch["task_id"] = torch.tensor([0] * len(labels), dtype=torch.long)
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return batch
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@ -82,46 +81,3 @@ def collate_fn_for_evaluate(examples, processor: Qwen2VLProcessor):
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# answers_ids torch.Size([3, 10])
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# answers_mask torch.Size([3, 10])
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return batch
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if __name__ == "__main__":
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from transformers import Qwen2VLProcessor
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from dataset_library.OCRVQADataset import OCRVQADatasetForGeneration
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processor = Qwen2VLProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
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dataset = OCRVQADatasetForGeneration(
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vis_root="/home/zyy/research/accelerate/dataset/OCR-VQA-200K/images",
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ann_path="/home/zyy/research/accelerate/dataset/OCR-VQA-200K/dataset.json",
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split="train",
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)
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examples = [dataset[i] for i in range(3)]
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# print(collate_fn_for_evaluate(examples, processor))
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
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from accelerate import Accelerator
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from torch.utils.data import DataLoader
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val_dataloader = DataLoader(
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dataset,
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batch_size=3,
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collate_fn=lambda x: collate_fn_for_evaluate(x, processor),
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)
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accelerator = Accelerator()
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model = accelerator.prepare(model)
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val_dataloader = accelerator.prepare(val_dataloader)
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for batch in val_dataloader:
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completion = model.generate(
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input_ids=batch["input_ids"],
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attention_mask=batch["attention_mask"],
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pixel_values=batch["pixel_values"],
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image_grid_thw=batch["image_grid_thw"],
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max_length=100,
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)
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target = batch["answers_ids"]
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generated_text = [out_ids[len(in_ids) :] for out_ids, in_ids in zip(completion, batch["input_ids"])]
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generated_text = processor.tokenizer.batch_decode(generated_text)
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target_text = processor.tokenizer.batch_decode(target)
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print(generated_text, target_text)
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370
src/model_library/qwen2vl/model.py
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370
src/model_library/qwen2vl/model.py
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# from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast, Qwen2VLModel, Qwen2VLForConditionalGeneration, logger, DynamicCache, Qwen2VLDecoderLayer, Qwen2VLConfig, Qwen2VLAttention,
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from transformers.models.qwen2_vl.modeling_qwen2_vl import *
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from transformers.modeling_outputs import BaseModelOutputWithPast
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import torch
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from typing import Optional, List, Union, Tuple
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from torch.nn import CrossEntropyLoss
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class Qwen2VLAttention_modified(Qwen2VLAttention):
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QWEN2_VL_ATTENTION_CLASSES = {
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"eager": Qwen2VLAttention,
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"flash_attention_2": Qwen2VLFlashAttention2,
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"sdpa": Qwen2VLSdpaAttention,
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}
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class Qwen2VLDecoderLayer_modified(Qwen2VLDecoderLayer):
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def __init__(self, config: Qwen2VLConfig, layer_idx: int):
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super().__init__(config, layer_idx)
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class Qwen2VLModel_modified(Qwen2VLModel):
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def __init__(self, config):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[Qwen2VLDecoderLayer_modified(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = (
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output_attentions
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if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states
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if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = (
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return_dict if return_dict is not None else self.config.use_return_dict
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)
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError(
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"You must specify exactly one of input_ids or inputs_embeds"
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)
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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# torch.jit.trace() doesn't support cache objects in the output
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if use_cache and past_key_values is None and not torch.jit.is_tracing():
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past_key_values = DynamicCache()
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if cache_position is None:
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past_seen_tokens = (
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past_key_values.get_seq_length() if past_key_values is not None else 0
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)
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cache_position = torch.arange(
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past_seen_tokens,
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past_seen_tokens + inputs_embeds.shape[1],
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device=inputs_embeds.device,
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)
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# the hard coded `3` is for temporal, height and width.
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if position_ids is None:
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position_ids = cache_position.view(1, 1, -1).expand(
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3, inputs_embeds.shape[0], -1
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)
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elif position_ids.dim() == 2:
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position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1)
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causal_mask = self._update_causal_mask(
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attention_mask,
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inputs_embeds,
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cache_position,
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past_key_values,
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output_attentions,
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)
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hidden_states = inputs_embeds
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# create position embeddings to be shared across the decoder layers
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position_embeddings = self.rotary_emb(hidden_states, position_ids)
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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causal_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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cache_position,
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position_embeddings,
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**kwargs,
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)
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else:
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if not return_dict:
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return tuple(
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v
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for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
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if v is not None
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)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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class Qwen2VLForConditionalGeneration_modified(Qwen2VLForConditionalGeneration):
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def __init__(self, config):
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super().__init__(config)
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self.model = Qwen2VLModel_modified(config)
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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pixel_values: Optional[torch.Tensor] = None,
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pixel_values_videos: Optional[torch.FloatTensor] = None,
|
||||
image_grid_thw: Optional[torch.LongTensor] = None,
|
||||
video_grid_thw: Optional[torch.LongTensor] = None,
|
||||
rope_deltas: Optional[torch.LongTensor] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs,
|
||||
) -> Union[Tuple, Qwen2VLCausalLMOutputWithPast]:
|
||||
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]`.
|
||||
|
||||
Returns:
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from PIL import Image
|
||||
>>> import requests
|
||||
>>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
|
||||
|
||||
>>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
|
||||
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
|
||||
|
||||
>>> messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image"},
|
||||
{"type": "text", "text": "What is shown in this image?"},
|
||||
],
|
||||
},
|
||||
]
|
||||
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
||||
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||||
|
||||
>>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
||||
>>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
|
||||
|
||||
>>> # 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]
|
||||
"The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
|
||||
```"""
|
||||
|
||||
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 None:
|
||||
inputs_embeds = self.model.embed_tokens(input_ids)
|
||||
if pixel_values is not None:
|
||||
pixel_values = pixel_values.type(self.visual.get_dtype())
|
||||
image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
|
||||
n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
|
||||
n_image_features = image_embeds.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}"
|
||||
)
|
||||
image_mask = (
|
||||
(input_ids == self.config.image_token_id)
|
||||
.unsqueeze(-1)
|
||||
.expand_as(inputs_embeds)
|
||||
.to(inputs_embeds.device)
|
||||
)
|
||||
image_embeds = image_embeds.to(
|
||||
inputs_embeds.device, inputs_embeds.dtype
|
||||
)
|
||||
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
|
||||
|
||||
if pixel_values_videos is not None:
|
||||
pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
|
||||
video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
|
||||
n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
|
||||
n_video_features = video_embeds.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}"
|
||||
)
|
||||
video_mask = (
|
||||
(input_ids == self.config.video_token_id)
|
||||
.unsqueeze(-1)
|
||||
.expand_as(inputs_embeds)
|
||||
.to(inputs_embeds.device)
|
||||
)
|
||||
video_embeds = video_embeds.to(
|
||||
inputs_embeds.device, inputs_embeds.dtype
|
||||
)
|
||||
inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
|
||||
|
||||
if attention_mask is not None:
|
||||
attention_mask = attention_mask.to(inputs_embeds.device)
|
||||
|
||||
# if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
|
||||
if (
|
||||
position_ids is None
|
||||
and input_ids is not None
|
||||
and (attention_mask is None or attention_mask.ndim == 2)
|
||||
):
|
||||
# calculate RoPE index once per generation in the pre-fill stage only
|
||||
if (
|
||||
cache_position is not None and cache_position[0] == 0
|
||||
) or self.rope_deltas is None:
|
||||
position_ids, rope_deltas = self.get_rope_index(
|
||||
input_ids, image_grid_thw, video_grid_thw, attention_mask
|
||||
)
|
||||
self.rope_deltas = rope_deltas
|
||||
# then use the prev pre-calculated rope-deltas to get the correct position ids
|
||||
else:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
delta = (
|
||||
cache_position[0] + self.rope_deltas
|
||||
if cache_position is not None
|
||||
else 0
|
||||
)
|
||||
position_ids = torch.arange(seq_length, device=inputs_embeds.device)
|
||||
position_ids = position_ids.view(1, -1).expand(batch_size, -1)
|
||||
if cache_position is not None: # otherwise `deltas` is an int `0`
|
||||
delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
|
||||
position_ids = position_ids.add(delta)
|
||||
position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
|
||||
|
||||
outputs = self.model(
|
||||
input_ids=None,
|
||||
position_ids=position_ids,
|
||||
attention_mask=attention_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,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Upcast to float if we need to compute the loss to avoid potential precision issues
|
||||
logits = logits.float()
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||||
shift_labels = shift_labels.view(-1)
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
loss = loss_fct(shift_logits, shift_labels)
|
||||
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
return (loss,) + output if loss is not None else output
|
||||
|
||||
return Qwen2VLCausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
rope_deltas=self.rope_deltas,
|
||||
)
|
||||
16
src/train.py
16
src/train.py
@ -53,14 +53,16 @@ if __name__ == "__main__":
|
||||
padding_side="left",
|
||||
)
|
||||
|
||||
model = AutoModelForVision2Seq.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
trust_remote_code=model_args.trust_remote_code,
|
||||
**model_kwargs,
|
||||
)
|
||||
|
||||
if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct":
|
||||
from collatefn_library.qwen2 import (
|
||||
# from transformers import Qwen2VLForConditionalGeneration
|
||||
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,
|
||||
)
|
||||
from model_library.qwen2vl import (
|
||||
collate_fn_for_train,
|
||||
collate_fn_for_evaluate,
|
||||
)
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional
|
||||
from trl import ScriptArguments, ModelConfig
|
||||
from transformers import TrainingArguments
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
Loading…
Reference in New Issue
Block a user