import re
from typing import Dict, Optional
from loguru import logger
from pydantic import PositiveInt
from data_juicer.ops.base_op import OPERATORS, Mapper
from data_juicer.utils.lazy_loader import LazyLoader
from data_juicer.utils.model_utils import (
get_model,
prepare_model,
update_sampling_params,
)
torch = LazyLoader("torch")
vllm = LazyLoader("vllm")
OP_NAME = "optimize_qa_mapper"
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@OPERATORS.register_module(OP_NAME)
class OptimizeQAMapper(Mapper):
"""Mapper to optimize question-answer pairs.
This operator refines and enhances the quality of question-answer pairs. It uses a
Hugging Face model to generate more detailed and accurate questions and answers. The
input is formatted using a template, and the output is parsed using a regular
expression. The system prompt, input template, and output pattern can be customized. If
VLLM is enabled, the operator accelerates inference on CUDA devices."""
# avoid leading whitespace
DEFAULT_SYSTEM_PROMPT = (
"请优化输入的问答对，使【问题】和【回答】都更加详细、准确。"
"必须按照以下标记格式，直接输出优化后的问答对：\n"
"【问题】\n"
"优化后的问题\n"
"【回答】\n"
"优化后的回答"
)
DEFAULT_INPUT_TEMPLATE = "以下是原始问答对：\n{}"
DEFAULT_QA_PAIR_TEMPLATE = "【问题】\n{}\n【回答】\n{}"
DEFAULT_OUTPUT_PATTERN = r".*?【问题】\s*(.*?)\s*【回答】\s*(.*)"
_accelerator = "cuda"
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def __init__(
self,
api_or_hf_model: str = "Qwen/Qwen2.5-7B-Instruct",
is_hf_model: bool = True,
*,
api_endpoint: Optional[str] = None,
response_path: Optional[str] = None,
system_prompt: Optional[str] = None,
input_template: Optional[str] = None,
qa_pair_template: Optional[str] = None,
output_pattern: Optional[str] = None,
try_num: PositiveInt = 3,
enable_vllm: bool = False,
model_params: Optional[Dict] = None,
sampling_params: Optional[Dict] = None,
**kwargs,
):
"""
Initialization method.
:param api_or_hf_model: API or huggingface model name.
:param is_hf_model: If true, use huggingface model. Otherwise, use API.
:param api_endpoint: URL endpoint for the API.
:param response_path: Path to extract content from the API response.
Defaults to 'choices.0.message.content'.
:param system_prompt: System prompt for guiding the optimization task.
:param input_template: Template for building the input for the model.
Please make sure the template contains one placeholder '{}', which
corresponds to the question and answer pair generated by
param `qa_pair_template`.
:param qa_pair_template: Template for formatting the question and
answer pair. Please make sure the template contains two
'{}' to format question and answer.
:param output_pattern: Regular expression pattern to extract question
and answer from model response.
:param try_num: The number of retry attempts when there is an API
call error or output parsing error.
:param enable_vllm: Whether to use VLLM for inference acceleration.
:param model_params: Parameters for initializing the model.
:param sampling_params: Sampling parameters for text generation (e.g.,
{'temperature': 0.9, 'top_p': 0.95}).
:param kwargs: Extra keyword arguments.
"""
super().__init__(**kwargs)
self.system_prompt = system_prompt or self.DEFAULT_SYSTEM_PROMPT
self.input_template = input_template or self.DEFAULT_INPUT_TEMPLATE
self.qa_pair_template = qa_pair_template or self.DEFAULT_QA_PAIR_TEMPLATE
self.output_pattern = output_pattern or self.DEFAULT_OUTPUT_PATTERN
self.try_num = try_num
self.is_hf_model = is_hf_model
self.enable_vllm = enable_vllm
model_params = model_params or {}
sampling_params = sampling_params or {}
sampling_params = update_sampling_params(sampling_params, api_or_hf_model, self.enable_vllm)
if enable_vllm:
assert torch.cuda.device_count() >= 1, "must be executed in CUDA"
# cannot initialize vllm replicas on different GPUs
self.num_proc = 1
if model_params.get("tensor_parallel_size") is None:
tensor_parallel_size = torch.cuda.device_count()
logger.info(
f"Set tensor_parallel_size to \
{tensor_parallel_size} for vllm."
)
model_params["tensor_parallel_size"] = tensor_parallel_size
self.model_key = prepare_model(
model_type="vllm", pretrained_model_name_or_path=api_or_hf_model, **model_params
)
self.sampling_params = vllm.SamplingParams(**sampling_params)
elif is_hf_model:
self.model_key = prepare_model(
model_type="huggingface",
pretrained_model_name_or_path=api_or_hf_model,
return_pipe=True,
**model_params,
)
self.sampling_params = sampling_params
else:
self.sampling_params = sampling_params
self.model_key = prepare_model(
model_type="api",
model=api_or_hf_model,
endpoint=api_endpoint,
response_path=response_path,
**model_params,
)
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def parse_output(self, raw_output):
logger.debug(raw_output)
match = re.match(self.output_pattern, raw_output, re.DOTALL)
if match:
return match.group(1).strip(), match.group(2).strip()
else:
return None, None
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def process_single(self, sample, rank=None):
if self.enable_vllm or self.is_hf_model:
model, _ = get_model(self.model_key, rank, self.use_cuda())
else:
model = get_model(self.model_key, rank, self.use_cuda())
input_prompt = self.build_input(sample)
messages = [{"role": "system", "content": self.system_prompt}, {"role": "user", "content": input_prompt}]
parsed_q, parsed_a = None, None
for _ in range(self.try_num):
try:
if self.enable_vllm:
response = model.chat(messages, self.sampling_params)
output = response[0].outputs[0].text
elif self.is_hf_model:
# model is pipe
response = model(messages, return_full_text=False, **self.sampling_params)
output = response[0]["generated_text"]
else:
output = model(messages, **self.sampling_params)
parsed_q, parsed_a = self.parse_output(output)
if parsed_q or parsed_a:
break
except Exception as e:
logger.warning(f"Exception: {e}")
if parsed_q:
sample[self.query_key] = parsed_q
if parsed_a:
sample[self.response_key] = parsed_a
return sample