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