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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import os
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from jinja2 import Environment, BaseLoader, FileSystemLoader
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from datasets import load_dataset, Dataset
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from trl import DPOTrainer, DPOConfig
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import copy
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def process_dataset(dataset_path, dataset_seed, env, template):
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examples = []
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with open(dataset_path, 'r') as file:
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examples = json.load(file)
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output_text = {
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"prompt": [],
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"chosen": [],
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"rejected": []
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}
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# use chat template
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for i in range(len(examples)):
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try:
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prompt_message = {"content": examples[i]["prompt"]}
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prompt = template.render(message=prompt_message, add_generation_prompt=False, add_output=False)
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chosen_message = {"content": examples[i]["prompt"], "output": examples[i]["chosen"]}
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chosen = template.render(message=chosen_message, add_generation_prompt=False, add_output=True)
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chosen = chosen[len(prompt):]
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rejected_message = {"content": examples[i]["prompt"], "output": examples[i]["rejected"]}
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rejected = template.render(message=rejected_message, add_generation_prompt=False, add_output=True)
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rejected = rejected[len(prompt):]
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output_text["prompt"].append(prompt)
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output_text["chosen"].append(chosen)
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output_text["rejected"].append(rejected)
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except:
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logging.warning(f"Error processing sample.")
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dataset = Dataset.from_dict(output_text)
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dataset = dataset.shuffle(seed=dataset_seed)
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return dataset
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def train(config):
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dataset_path = config["dataset"]["labeled_path"]
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dataset_seed = config["dataset"]["seed"]
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full_path = config["dataset"]["template"]
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template_dir = os.path.dirname(full_path)
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template_file = os.path.basename(full_path)
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env = Environment(loader=FileSystemLoader(template_dir))
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template = env.get_template(template_file)
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dataset = process_dataset(dataset_path, dataset_seed, env, template)
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student_tokenizer = AutoTokenizer.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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student_model = AutoModelForCausalLM.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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training_arguments = DPOConfig(**config["training"])
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trainer = DPOTrainer(
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student_model,
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ref_model=copy.deepcopy(student_model),
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args=training_arguments,
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train_dataset=dataset,
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processing_class=student_tokenizer
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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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student_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()
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