105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
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# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import json
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import argparse
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import logging
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from datasets import load_dataset, Dataset
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from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLProcessor
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from qwen_vl_utils import process_vision_info
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from trl import SFTTrainer, SFTConfig
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def train(config):
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dataset = load_dataset("json", data_files=config["dataset"]["labeled_path"])
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dataset = dataset.shuffle(seed=config["dataset"]["seed"])["train"]
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student_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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config["models"]["student"],
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trust_remote_code=True
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)
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processor = Qwen2_5_VLProcessor.from_pretrained(config["models"]["student"])
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def collate_fn(examples):
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texts = []
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images = []
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for example in examples:
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chat = [
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{
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"role": "user",
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"content": [
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{
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"type": "image","image": example["image"]
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},
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{
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"type": "text","text": example["instruction"]
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}
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]
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},
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{
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"role": "assistant",
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"content": example["output"]
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}
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]
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text = processor.apply_chat_template(chat, tokenize=False)
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texts.append(text)
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image, _ = process_vision_info(chat)
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images.append(image)
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batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
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labels = batch["input_ids"].clone()
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labels[labels == processor.tokenizer.pad_token_id] = -100
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if isinstance(processor, Qwen2_5_VLProcessor):
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image_tokens = [151652, 151653, 151655]
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else:
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image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)]
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for image_token_id in image_tokens:
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labels[labels == image_token_id] = -100
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batch["labels"] = labels
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return batch
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training_arguments = SFTConfig(**config["training"])
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training_arguments.gradient_checkpointing_kwargs = dict(use_reentrant=False)
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training_arguments.remove_unused_columns = False
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training_arguments.dataset_kwargs = {"skip_prepare_dataset": True}
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trainer = SFTTrainer(
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model=student_model,
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data_collator=collate_fn,
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processing_class=processor.tokenizer,
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args=training_arguments,
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train_dataset=dataset
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)
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trainer.train()
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trainer.save_model(config["training"]["output_dir"])
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processor.tokenizer.save_pretrained(config["training"]["output_dir"])
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--config', type=str, required=True, help='path to the json config file')
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args = parser.parse_args()
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config = json.load(open(args.config))
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train(config)
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if __name__ == "__main__":
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main() |