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easydistill/rl/reward_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
+from jinja2 import Environment, FileSystemLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from trl import RewardTrainer, RewardConfig
+from datasets import Dataset
+
+
+def process_dataset(dataset_path, tokenizer, config, template):
+    kwargs = {"padding": "max_length", "truncation": True, "max_length": config["training"]["max_length"], "return_tensors": "pt"}
+    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}")
+
+    print(examples)
+    output_dataset = []
+    # use chat template
+    for i in range(len(examples)):
+        try:
+            chosen_message = {"content": examples[i]["prompt"], "output": examples[i]["chosen"]}
+            prompt_plus_chosen_response = template.render(message=chosen_message, add_generation_prompt=False, add_output=True)
+                        
+            rejected_message = {"content": examples[i]["prompt"], "output": examples[i]["rejected"]}
+            prompt_plus_rejected_response = template.render(message=rejected_message, add_generation_prompt=False, add_output=True)
+            
+            tokens_chosen = tokenizer.encode_plus(prompt_plus_chosen_response, **kwargs)
+            tokens_rejected = tokenizer.encode_plus(prompt_plus_rejected_response, **kwargs)
+            sample = {
+                "input_ids_chosen": tokens_chosen["input_ids"][0], "attention_mask_chosen": tokens_chosen["attention_mask"][0],
+                "input_ids_rejected": tokens_rejected["input_ids"][0], "attention_mask_rejected": tokens_rejected["attention_mask"][0]
+            }
+            output_dataset.append(sample)
+        except:
+            logging.warning(f"Error processing sample.")
+    dataset = Dataset.from_list(output_dataset)
+    return dataset
+
+
+def train(config):
+    dataset_path = config["dataset"]["labeled_path"]
+    student_tokenizer = AutoTokenizer.from_pretrained(
+        config["models"]["student"], 
+        trust_remote_code=True
+    )
+    
+    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)
+    dataset = process_dataset(dataset_path, student_tokenizer, config, template)
+
+    student_model = AutoModelForSequenceClassification.from_pretrained(
+        config["models"]["student"],
+        num_labels=1,
+        trust_remote_code=True
+    )
+    student_model.config.pad_token_id = student_tokenizer.pad_token_id
+
+    training_arguments = RewardConfig(**config["training"])    
+    trainer = RewardTrainer(
+        model=student_model,
+        processing_class=student_tokenizer,
+        args=training_arguments,
+        train_dataset=dataset
+    )
+
+    trainer.train()
+    trainer.save_model(config["training"]["output_dir"])
+    student_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()