import json
import random
import re
from typing import Dict, Optional
from loguru import logger
from pydantic import PositiveInt
from data_juicer.utils.lazy_loader import LazyLoader
from data_juicer.utils.model_utils import get_model, prepare_model
from ..base_op import OPERATORS, UNFORKABLE, Mapper
torch = LazyLoader('torch', 'torch')
vllm = LazyLoader('vllm', 'vllm')
rouge = LazyLoader('rouge', 'rouge')
OP_NAME = 'generate_qa_from_examples_mapper'
# TODO: Extend LLM-based OPs into API-based implementation.
[docs]@UNFORKABLE.register_module(OP_NAME)
@OPERATORS.register_module(OP_NAME)
class GenerateQAFromExamplesMapper(Mapper):
"""
Mapper to generate question and answer pairs from examples.
You should configure an empty dataset in your yaml config file:
```
generated_dataset_config:
type: 'EmptyFormatter' # use `RayEmptyFormatter` when enable ray
length: ${The number of generated samples}
feature_keys: ${text key}
```
The number of samples generated is determined by
the length of the empty dataset.
"""
DEFAULT_SYSTEM_PROMPT = (
'请你仔细观察多个示例数据的输入和输出，按照你的理解，总结出相应规矩，然后写出一个新的【问题】和【回答】。'
'注意，新生成的【问题】和【回答】需要满足如下要求：\n'
'1. 生成的【问题】和【回答】不能与输入的【问题】和【回答】一致，但是需要保持格式相同。\n'
'2. 生成的【问题】不一定要局限于输入【问题】的话题或领域，生成的【回答】需要正确回答生成的【问题】。\n'
'3. 提供的【问题】和【回答】可能是多轮对话，生成的【问题】和【回答】也可以是多轮，但是需要保持格式相同。\n'
'4. 生成的【问题】和【回答】必须成对出现，而且【问题】需要在【回答】之前。\n')
DEFAULT_INPUT_TEMPLATE = '{}'
DEFAULT_EXAMPLE_TEMPLATE = '\n如下是一条示例数据：\n{}'
DEFAULT_QA_PAIR_TEMPLATE = '【问题】\n{}\n【回答】\n{}\n'
DEFAULT_OUTPUT_PATTERN = r'【问题】(.*?)【回答】(.*?)(?=【问题】|$)'
_accelerator = 'cuda'
[docs] def __init__(self,
hf_model: str = 'Qwen/Qwen2.5-7B-Instruct',
*,
seed_file: str = '',
example_num: PositiveInt = 3,
similarity_threshold: float = 0.7,
system_prompt: Optional[str] = None,
input_template: Optional[str] = None,
example_template: Optional[str] = None,
qa_pair_template: Optional[str] = None,
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 seed_file: Path to the seed file in chatml format.
:param example_num: The number of selected examples.
Randomly select N examples from "seed_file" and
put them into prompt as QA examples.
:param similarity_threshold: The similarity score threshold
between the generated samples and the seed examples.
Range from 0 to 1. Samples with similarity score less than
this threshold will be kept.
:param system_prompt: System prompt for guiding the generation task.
:param input_template: Template for building the input prompt. It must
include one placeholder '{}', which will be replaced by
`example_num` formatted examples defined by `example_template`.
:param example_template: Template for formatting one QA example. It
must include one placeholder '{}', which will be replaced by one
formatted qa_pair.
:param qa_pair_template: Template for formatting a single QA pair
within each example. Must include two placeholders '{}' for the
question and answer.
: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.
"""
super().__init__(**kwargs)
if not seed_file:
raise ValueError(
'Please provide `seed_file` in chatml format.'
'Example: data-juicer/demos/data/demo-dataset-chatml.jsonl')
self.seed_file = seed_file
self.example_num = example_num
self.similarity_threshold = similarity_threshold
self.similarity_type = 'rouge_l'
self.system_prompt = system_prompt or self.DEFAULT_SYSTEM_PROMPT
self.input_template = input_template or self.DEFAULT_INPUT_TEMPLATE
self.example_template = example_template or self.DEFAULT_EXAMPLE_TEMPLATE # noqa: E501
self.qa_pair_template = qa_pair_template or \
self.DEFAULT_QA_PAIR_TEMPLATE
self.output_pattern = output_pattern or self.DEFAULT_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
self.seed_qa_samples = self._load_seed_qa_samples()
if len(self.seed_qa_samples) == 0:
raise ValueError('No QA data was parsed from the seed file!')
def _load_seed_qa_samples(self):
"""Load QA pairs from chatml format file."""
qa_samples = []
with open(self.seed_file, encoding='utf-8') as f:
lines = f.readlines()
for line in lines:
line = line.strip()
qa_pairs = self._parse_chatml_str(line)
if len(qa_pairs) > 0:
qa_samples.append(qa_pairs)
return qa_samples
def _sample_to_str(self, qa_sample):
return '\n'.join(['\n'.join(qa_pair) for qa_pair in qa_sample]) + '\n'
def _max_rouge_l_score(self, hypothesis, references):
r = rouge.Rouge()
max_score = 0.0
hyp_str = self._sample_to_str(hypothesis)
for reference in references:
ref_str = self._sample_to_str(reference)
scores = r.get_scores(hyp_str, ref_str)
rouge_l_score = scores[0]['rouge-l']['f']
if rouge_l_score > max_score:
max_score = rouge_l_score
return max_score
def _parse_chatml_str(self, sample_str):
user_input = None
assistant_output = None
qa_pairs = []
data = json.loads(sample_str)
for message in data['messages']:
role = message['role']
content = message['content']
if role == 'user':
user_input = content
elif role == 'assistant':
assistant_output = content
qa_pairs.append((user_input, assistant_output))
return qa_pairs
[docs] def parse_output(self, raw_output):
logger.debug(raw_output)
output_qa_pairs = []
matches = re.findall(self.output_pattern, raw_output, re.DOTALL)
for match in matches:
question, answer = match
output_qa_pairs.append((question.strip(), answer.strip()))
return output_qa_pairs
[docs] def process_single(self, sample, rank=None):
model, _ = get_model(self.model_key, rank, self.use_cuda())
random_qa_samples = random.sample(self.seed_qa_samples,
self.example_num)
input_prompt = self.build_input(random_qa_samples)
messages = [{
'role': 'system',
'content': self.system_prompt
}, {
'role': 'user',
'content': input_prompt
}]
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']
output_qa_pairs = self.parse_output(output)
if len(output_qa_pairs) == 0:
logger.warning('Parse model response error! '
'No data generated for the current response!')
sample.update({
self.query_key: '',
self.response_key: '',
self.history_key: self.empty_history()
})
return sample
if self.similarity_type == 'rouge_l':
sim_score = self._max_rouge_l_score(output_qa_pairs,
random_qa_samples)
else:
raise ValueError(
f'Not support similarity type "{self.similarity_type}"!')
if sim_score <= self.similarity_threshold:
query, response = output_qa_pairs[-1]
history = output_qa_pairs[:-1]
if len(history) == 0:
history = self.empty_history()
else:
query = response = ''
history = self.empty_history()
logger.info('Filter this generated sample due to similarity.')
sample.update({
self.query_key: query,
self.response_key: response,
self.history_key: history
})
return sample