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easydistill/rank/infer.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 torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from vllm import LLM, SamplingParams
+from jinja2 import Environment, FileSystemLoader
+from tqdm import tqdm
+from openai import OpenAI
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+def read_json_field(filename, field_name='prompt'):
+    try:
+        with open(filename, 'r') as file:
+            data = json.load(file)
+        output_fields = []
+        for item in data:
+            if field_name in item:
+                output_fields.append(item[field_name])
+        return output_fields
+    except FileNotFoundError:
+        logging.error("The file was not found.")
+    except json.JSONDecodeError:
+        logging.error("There was an error decoding the JSON file.")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+
+def write_data_to_json_file(data, file_path):
+    try:
+        with open(file_path, 'w') as file:
+            json.dump(data, file, ensure_ascii=False, indent=4)
+        logging.info(f"Data successfully written to {file_path}")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+
+def load_tokenizer_and_vllm(config, eos_token=None, is_teacher_model=True):
+    if is_teacher_model:
+        model_path = config["models"]["teacher"]
+    else:
+        model_path = config["models"]["student"]
+    logging.info(f"Loading ckpt and tokenizer: {model_path}")
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer.padding_side = "left"
+    if eos_token:
+        eos_token_id = tokenizer.convert_tokens_to_ids(eos_token)
+        logging.info(f"eos_token {eos_token} from user input")
+    elif hasattr(tokenizer, "eos_token_id") and tokenizer.eos_token_id:
+        logging.info(f"Initial eos_token_id {tokenizer.eos_token_id} from tokenizer")
+        eos_token_id = tokenizer.eos_token_id
+        eos_token = tokenizer.convert_ids_to_tokens(eos_token_id)
+    else:
+        raise ValueError("No available eos_token or eos_token_id.")
+    try:
+        tokenizer.eos_token = eos_token
+        tokenizer.eos_token_id = eos_token_id
+        tokenizer.pad_token = eos_token
+        tokenizer.pad_token_id = eos_token_id
+    except:
+        logging.info(f"[WARNING] Cannot set tokenizer.eos_token")
+    logging.info(f"tokenizer's eos_token: {tokenizer.eos_token}, pad_token: {tokenizer.pad_token}")
+    logging.info(f"tokenizer's eos_token_id: {tokenizer.eos_token_id}, pad_token_id: {tokenizer.pad_token_id}")
+    num_gpus = torch.cuda.device_count()
+    llm = LLM(
+        model=model_path,
+        tensor_parallel_size=num_gpus,
+        enable_chunked_prefill=config["inference"]["enable_chunked_prefill"],
+        gpu_memory_utilization=config["inference"]["gpu_memory_utilization"],
+        trust_remote_code=config["inference"]["trust_remote_code"],
+        dtype=torch.bfloat16,
+        enforce_eager=config["inference"]["enforce_eager"],
+        max_model_len=config["inference"]["max_model_len"],
+    )
+    logging.info("vLLM model loaded successfully")
+    return tokenizer, llm
+
+
+def generate_teacher_student_response_api(data_list, config):
+    client = OpenAI(
+        api_key=config["inference"]["api_key"],
+        base_url=config["inference"]["base_url"]
+    )
+    models = client.models.list()
+    model = models.data[0].id
+    logging.info(model)
+    system_prompt = config["inference"]["system_prompt"]
+    stream = config["inference"]["stream"]
+    
+    # load student model
+    student_tokenizer = AutoTokenizer.from_pretrained(
+        config["models"]["student"], 
+        trust_remote_code=True
+    )
+    student_model = AutoModelForCausalLM.from_pretrained(
+        config["models"]["student"],
+        device_map="auto",
+        trust_remote_code=True
+    ) 
+    outcomes = []
+    for sample in tqdm(data_list, desc="Call remote model and generating responses"):
+        # for teacher model
+        if system_prompt == "":
+            message=[
+                {'role': 'user', 'content': sample}
+            ]
+        else:
+            message=[
+                {'role': 'system', 'content': system_prompt},
+                {'role': 'user', 'content': sample}
+            ]
+        completion = client.chat.completions.create(
+            messages=message,
+            model=model,
+            max_completion_tokens=config["inference"]["max_new_tokens"],
+            stream=stream,
+        )
+        if stream:
+            result = ""
+            for chunk in completion:
+                result += chunk.choices[0].delta.content
+        else:
+            result = completion.choices[0].message.content
+        
+        # for student model
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": sample}
+        ]
+        text = student_tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        model_inputs = student_tokenizer([text], return_tensors="pt").to(student_model.device)
+
+        generated_ids = student_model.generate(
+            **model_inputs,
+            max_new_tokens=config["inference"]["max_new_tokens"]
+        )
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+
+        rejected = student_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        gen_data = {'prompt': sample, 'chosen': result, 'rejected': rejected}
+        outcomes.append(gen_data)
+    write_data_to_json_file(outcomes, config["dataset"]["labeled_path"])
+
+
+def generate_model_response_batch(tokenizer, llm, data_list, config, batch_size=32, is_teacher_model=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)
+    outcomes = []
+    batches = [data_list[i:i + batch_size] for i in range(0, len(data_list), batch_size)]
+    for batch in tqdm(batches, desc="Generating responses"):
+        new_batch = []
+        for sample in batch:
+            message={"role": "user", "content": sample}
+            full_text = template.render(
+                message=message,
+                add_generation_prompt=True,
+                add_output=False
+            )
+            new_batch.append(full_text)
+        model_outputs = llm.generate(
+            new_batch,
+            SamplingParams(
+                n=1,
+                top_k=1,
+                temperature=config["inference"]["temperature"],
+                seed=config["inference"]["seed"],
+                skip_special_tokens=False,
+                ignore_eos=False,
+                max_tokens=config["inference"]["max_new_tokens"]
+            )
+        )
+        model_responses = [output.outputs[0].text for output in model_outputs]
+        if is_teacher_model:
+            gen_data = [{'prompt': batch[i], 'chosen': model_responses[i]} for i in range(len(batch))]
+        else:
+            gen_data = [{'prompt': batch[i], 'rejected': model_responses[i]} for i in range(len(batch))]
+        outcomes = outcomes + gen_data
+    return outcomes
+    
+    
+    
+def merge_outcomes(teacher_outcomes, student_outcomes, config):
+    try:
+        student_dict = {item['prompt']: item['rejected'] for item in student_outcomes}
+        merged_outcomes = []
+        for teacher_item in teacher_outcomes:
+            prompt = teacher_item['prompt']
+            if prompt in student_dict:
+                merged_outcome = {
+                    'prompt': prompt,
+                    'chosen': teacher_item['chosen'],
+                    'rejected': student_dict[prompt]
+                }
+                merged_outcomes.append(merged_outcome)
+        with open(config["dataset"]["labeled_path"], 'w') as file:
+            json.dump(merged_outcomes, file, ensure_ascii=False, indent=4)
+    except Exception as e:
+        print(f"An error occurred: {e}")
+    
+    
+def infer_with_teacher_model(config):
+    logging.info('Generating distillation data from the teacher model!')
+    data_list = read_json_field(config["dataset"]["instruction_path"])
+    try:
+        job_type =  config["job_type"]
+        if job_type == "rank_dpo_api":
+            generate_teacher_student_response_api(data_list, config)
+        elif job_type == "rank_dpo_local":
+            teacher_tokenizer, teacher_llm = load_tokenizer_and_vllm(config, is_teacher_model=True)
+            teacher_outcomes = generate_model_response_batch(teacher_tokenizer, teacher_llm, data_list, config, is_teacher_model=True)
+            del teacher_llm            
+            student_tokenizer, student_llm = load_tokenizer_and_vllm(config, is_teacher_model=False)
+            student_outcomes = generate_model_response_batch(student_tokenizer, student_llm, data_list, config, is_teacher_model=False)
+            del student_llm            
+            merge_outcomes(teacher_outcomes, student_outcomes, config)
+        else:
+            logging.error(f"Invalid job type: {job_type}")
+            raise ValueError(f"Invalid job type: {job_type}")
+    except ValueError as e:
+        logging.error(f"Training job terminated: {e}")
+        return
+
+        
+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))
+    infer_with_teacher_model(config)
+
+
+if __name__ == "__main__":
+    main()