122 lines
4.4 KiB
Python
122 lines
4.4 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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import random
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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, AutoModelForSequenceClassification, AutoTokenizer
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from trl import PPOConfig, PPOTrainer
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def process_dataset(dataset_path, dataset_seed, env, template, tokenizer, train_ratio):
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examples = []
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try:
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with open(dataset_path, 'r') as file:
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examples = json.load(file)
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except FileNotFoundError:
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print(f"Error: The file '{dataset_path}' was not found.")
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except json.JSONDecodeError:
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print(f"Error: The file '{dataset_path}' is not a valid JSON file.")
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except Exception as e:
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print(f"An unexpected error occurred: {e}")
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output_dataset = []
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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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message = {"content": examples[i]["instruction"]}
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rendered = template.render(message=message, add_generation_prompt=True, add_output=False)
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tokens = tokenizer.encode(rendered)
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sample = {"input_ids": tokens}
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output_dataset.append(sample)
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except:
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logging.warning(f"Error processing sample.")
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random.shuffle(output_dataset)
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random.seed(dataset_seed)
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split_index = int(len(output_dataset) * train_ratio)
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train_list = output_dataset[:split_index]
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eval_list = output_dataset[split_index:]
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return Dataset.from_list(train_list), Dataset.from_list(eval_list)
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def train(config):
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dataset_path = config["dataset"]["instruction_path"]
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dataset_seed = config["dataset"]["seed"]
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train_ratio = config["dataset"]["train_ratio"]
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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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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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train_dataset, eval_dataset = process_dataset(dataset_path, dataset_seed, env, template, tokenizer, train_ratio)
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assert train_dataset[0]["input_ids"][-1] != tokenizer.eos_token_id, "The last token should not be an EOS token"
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print(train_dataset)
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print(eval_dataset)
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reward_model_path = config["models"]["reward"]
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sft_model_path = config["models"]["student"]
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value_model = AutoModelForSequenceClassification.from_pretrained(
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reward_model_path, trust_remote_code=True, num_labels=1
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)
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reward_model = AutoModelForSequenceClassification.from_pretrained(
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reward_model_path, trust_remote_code=True, num_labels=1
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)
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ref_policy = AutoModelForCausalLM.from_pretrained(
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sft_model_path, trust_remote_code=True
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)
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policy = AutoModelForCausalLM.from_pretrained(
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sft_model_path, trust_remote_code=True
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)
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training_arguments = PPOConfig(**config["training"])
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trainer = PPOTrainer(
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config=training_arguments,
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processing_class=tokenizer,
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policy=policy,
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ref_policy=ref_policy,
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reward_model=reward_model,
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value_model=value_model,
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train_dataset=train_dataset,
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eval_dataset=eval_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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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() |