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easydistill/synthesis/instruct_synthesis.py
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293
easydistill/synthesis/instruct_synthesis.py
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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 logging
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import os
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from jinja2 import Environment, FileSystemLoader
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from vllm import LLM, SamplingParams
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from tqdm import tqdm
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from openai import OpenAI
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import random
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import re
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from utils import read_json_field, write_data_to_json_file, load_tokenizer_and_vllm
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def extract_answer(content):
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pattern = r'<answer>(.*?)</answer>'
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match = re.search(pattern, content, re.DOTALL)
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if match:
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return match.group(1)
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else:
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return None
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def extract_instruction_response(content):
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instruction_pattern = r'<instruction>(.*?)</instruction>'
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instruction_match = re.search(instruction_pattern, content, re.DOTALL)
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response_pattern = r'<response>(.*?)</response>'
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response_match = re.search(response_pattern, content, re.DOTALL)
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if instruction_match and response_match:
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return instruction_match.group(1), response_match.group(1)
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else:
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return None, None
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def generate_prompt_list(data_list, prompt, num_in_context_samples, num_output_samples):
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if num_in_context_samples > len(data_list):
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raise ValueError("num_in_context_samples cannot be larger than the length of data_list")
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output_list = []
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for _ in range(num_output_samples):
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selected_samples = random.sample(data_list, num_in_context_samples)
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combined_prompts = prompt + "\n" + "".join([sample + "\n" for sample in selected_samples])
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output_list.append(combined_prompts)
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return output_list
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def expand_instruction_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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num_output_samples = config["dataset"]["num_output_samples"]
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num_in_context_samples = config["dataset"]["num_in_context_samples"]
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prompt = config["inference"]["prompt"]
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stream = config["inference"]["stream"]
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logging.info(model)
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prompt_list = generate_prompt_list(data_list, prompt, num_in_context_samples, num_output_samples)
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outcomes = []
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for sample in tqdm(prompt_list, desc="Calling remote model and generating responses"):
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logging.info(sample)
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message = [
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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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result = extract_answer(result)
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if result is not None:
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outcomes.append({"instruction": result})
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write_data_to_json_file(outcomes, config["dataset"]["output_path"])
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def expand_instruction_batch(tokenizer, llm, data_list, config, batch_size=32):
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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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num_output_samples = config["dataset"]["num_output_samples"]
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num_in_context_samples = config["dataset"]["num_in_context_samples"]
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prompt = config["inference"]["prompt"]
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prompt_list = generate_prompt_list(data_list, prompt, num_in_context_samples, num_output_samples)
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outcomes = []
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batches = [prompt_list[i:i + batch_size] for i in range(0, len(prompt_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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logging.info(sample)
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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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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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responses = [output.outputs[0].text for output in outputs]
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for i in range(len(batch)):
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result = extract_answer(responses[i])
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if result is not None:
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outcomes.append({"instruction": result})
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write_data_to_json_file(outcomes, config["dataset"]["output_path"])
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def refine_instruction_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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prompt = config["inference"]["prompt"]
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stream = config["inference"]["stream"]
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logging.info(model)
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outcomes = []
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for sample in tqdm(data_list, desc="Calling remote model and generating responses"):
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sample = prompt + "\n" + sample
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logging.info(sample)
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message = [
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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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result = extract_answer(result)
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if result is not None:
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outcomes.append({"instruction": result})
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write_data_to_json_file(outcomes, config["dataset"]["output_path"])
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def refine_instruction_batch(tokenizer, llm, data_list, config, batch_size=32):
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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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prompt = config["inference"]["prompt"]
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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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sample = prompt + "\n" + sample
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logging.info(sample)
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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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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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responses = [output.outputs[0].text for output in outputs]
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for i in range(len(batch)):
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result = extract_answer(responses[i])
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if result is not None:
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outcomes.append({"instruction": result})
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write_data_to_json_file(outcomes, config["dataset"]["output_path"])
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def instruction_response_extraction_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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prompt = config["inference"]["prompt"]
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stream = config["inference"]["stream"]
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logging.info(model)
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outcomes = []
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for sample in tqdm(data_list, desc="Calling remote model and generating responses"):
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sample = prompt + "\n" + sample
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logging.info(sample)
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message = [
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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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new_instruction, new_response = extract_instruction_response(result)
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if new_instruction is not None and new_response is not None:
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outcomes.append({"instruction": new_instruction, "output": new_response})
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write_data_to_json_file(outcomes, config["dataset"]["output_path"])
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def instruction_response_extraction_batch(tokenizer, llm, data_list, config, batch_size=32):
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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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prompt = config["inference"]["prompt"]
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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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logging.info(sample)
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sample = prompt + "\n" + sample
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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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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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responses = [output.outputs[0].text for output in outputs]
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for i in range(len(batch)):
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new_instruction, new_response = extract_instruction_response(responses[i])
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if new_instruction is not None and new_response is not None:
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outcomes.append({"instruction": new_instruction, "output": new_response})
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write_data_to_json_file(outcomes, config["dataset"]["output_path"])
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