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easydistill/rl/ppo_train.py

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+
+# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import json
+import argparse
+import logging
+import os
+import random
+from jinja2 import Environment, BaseLoader, FileSystemLoader
+from datasets import load_dataset, Dataset
+from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
+from trl import PPOConfig, PPOTrainer
+
+
+def process_dataset(dataset_path, dataset_seed, env, template, tokenizer, train_ratio):
+    examples = []
+    try:
+        with open(dataset_path, 'r') as file:
+            examples = json.load(file)
+    except FileNotFoundError:
+        print(f"Error: The file '{dataset_path}' was not found.")
+    except json.JSONDecodeError:
+        print(f"Error: The file '{dataset_path}' is not a valid JSON file.")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+
+    output_dataset = []
+    # use chat template
+    for i in range(len(examples)):
+        try:
+            message = {"content": examples[i]["instruction"]}
+            rendered = template.render(message=message, add_generation_prompt=True, add_output=False)
+            tokens = tokenizer.encode(rendered)
+            sample = {"input_ids": tokens}
+            output_dataset.append(sample)
+        except:
+            logging.warning(f"Error processing sample.")
+            
+    random.shuffle(output_dataset)
+    random.seed(dataset_seed)
+    split_index = int(len(output_dataset) * train_ratio)
+    train_list = output_dataset[:split_index]
+    eval_list = output_dataset[split_index:]
+    
+    return Dataset.from_list(train_list), Dataset.from_list(eval_list)
+
+
+def train(config):    
+    dataset_path = config["dataset"]["instruction_path"]
+    dataset_seed = config["dataset"]["seed"]
+    train_ratio = config["dataset"]["train_ratio"]
+    
+    full_path = config["dataset"]["template"]
+    template_dir = os.path.dirname(full_path)
+    template_file = os.path.basename(full_path)
+    env = Environment(loader=FileSystemLoader(template_dir))
+    template = env.get_template(template_file)
+    
+    tokenizer = AutoTokenizer.from_pretrained(
+        config["models"]["student"], 
+        trust_remote_code=True
+    )
+    train_dataset, eval_dataset = process_dataset(dataset_path, dataset_seed, env, template, tokenizer, train_ratio)
+    assert train_dataset[0]["input_ids"][-1] != tokenizer.eos_token_id, "The last token should not be an EOS token"
+
+    print(train_dataset)
+    print(eval_dataset)
+    
+    reward_model_path = config["models"]["reward"]
+    sft_model_path = config["models"]["student"]
+    value_model = AutoModelForSequenceClassification.from_pretrained(
+        reward_model_path, trust_remote_code=True, num_labels=1
+    )
+    reward_model = AutoModelForSequenceClassification.from_pretrained(
+        reward_model_path, trust_remote_code=True, num_labels=1
+    )
+    ref_policy = AutoModelForCausalLM.from_pretrained(
+        sft_model_path, trust_remote_code=True
+    )
+    policy = AutoModelForCausalLM.from_pretrained(
+        sft_model_path, trust_remote_code=True
+    )
+
+    training_arguments = PPOConfig(**config["training"])
+    trainer = PPOTrainer(
+        config=training_arguments,
+        processing_class=tokenizer,
+        policy=policy,
+        ref_policy=ref_policy,
+        reward_model=reward_model,
+        value_model=value_model,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset
+    )
+    trainer.train()
+    trainer.save_model(config["training"]["output_dir"])
+    tokenizer.save_pretrained(config["training"]["output_dir"])
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, required=True, help='path to the json config file')
+    args = parser.parse_args()
+    config = json.load(open(args.config))
+    train(config)
+
+
+if __name__ == "__main__":
+    main()