import re
from typing import Dict, Optional
from loguru import logger
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
torch = LazyLoader('torch', 'torch')
vllm = LazyLoader('vllm', 'vllm')
OP_NAME = 'generate_qa_from_text_mapper'
# TODO: Extend LLM-based OPs into API-based implementation.
[docs]
@OPERATORS.register_module(OP_NAME)
class GenerateQAFromTextMapper(Mapper):
"""
Mapper to generate question and answer pairs from text.
Recommended model list: [
'alibaba-pai/pai-llama3-8b-doc2qa',
'alibaba-pai/pai-baichuan2-7b-doc2qa',
'alibaba-pai/pai-qwen1_5-4b-doc2qa',
'alibaba-pai/pai-qwen1_5-7b-doc2qa',
'alibaba-pai/pai-qwen1_5-1b8-doc2qa',
'alibaba-pai/pai-qwen1_5-0b5-doc2qa'
]
These recommended models are all trained with Chinese data
and are suitable for Chinese.
"""
_accelerator = 'cuda'
_batched_op = True
[docs]
def __init__(self,
hf_model: str = 'alibaba-pai/pai-qwen1_5-7b-doc2qa',
*,
output_pattern: Optional[str] = None,
enable_vllm: bool = False,
model_params: Optional[Dict] = None,
sampling_params: Optional[Dict] = None,
**kwargs):
"""
Initialization method.
:param hf_model: Hugginface model ID.
:param output_pattern: Regular expression pattern to extract
questions and answers from model response.
: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.
The default data format parsed by this interface is as follows:
Model Input:
蒙古国的首都是乌兰巴托（Ulaanbaatar）
冰岛的首都是雷克雅未克（Reykjavik）
Model Output:
蒙古国的首都是乌兰巴托（Ulaanbaatar）
冰岛的首都是雷克雅未克（Reykjavik）
Human: 请问蒙古国的首都是哪里？
Assistant: 你好，根据提供的信息，蒙古国的首都是乌兰巴托（Ulaanbaatar）。
Human: 冰岛的首都是哪里呢？
Assistant: 冰岛的首都是雷克雅未克（Reykjavik）。
...
"""
super().__init__(**kwargs)
if output_pattern is None:
self.output_pattern = r'Human:(.*?)Assistant:(.*?)(?=Human|$)' # noqa: E501
else:
self.output_pattern = output_pattern
self.enable_vllm = enable_vllm
model_params = model_params or {}
sampling_params = sampling_params or {}
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=hf_model,
**model_params)
self.sampling_params = vllm.SamplingParams(**sampling_params)
else:
self.model_key = prepare_model(
model_type='huggingface',
pretrained_model_name_or_path=hf_model,
return_pipe=True,
**model_params)
self.sampling_params = sampling_params
[docs]
def parse_output(self, raw_output):
logger.debug(raw_output)
qa_list = []
matches = re.findall(self.output_pattern, raw_output, re.DOTALL)
for match in matches:
user, assistant = match
qa_list.append((user.strip(), assistant.strip()))
return qa_list
[docs]
def process_batched(self, samples, rank=None):
model, _ = get_model(self.model_key, rank, self.use_cuda())
input_keys = samples.keys()
num_samples = len(samples[next(iter(input_keys))])
output_keys = input_keys | {self.query_key, self.response_key}
output_samples = {key: [] for key in output_keys}
for i in range(num_samples):
messages = [{'role': 'user', 'content': samples[self.text_key][i]}]
if self.enable_vllm:
response = model.chat(messages, self.sampling_params)
output = response[0].outputs[0].text
else:
# model is pipe
response = model(messages,
return_full_text=False,
**self.sampling_params)
output = response[0]['generated_text']
qa_list = self.parse_output(output)
if len(qa_list) > 0:
for q, a in qa_list:
for input_k in input_keys:
output_samples[input_k].append(samples[input_k][i])
output_samples[self.query_key].append(q)
output_samples[self.response_key].append(a)
else:
logger.warning(
'No question and answer was extracted from current sample!'
)
return output_samples