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import json
import math
import os
from typing import List, Dict
import yaml
import pdb
from factool.math.tool import python_executor
from factool.utils.base.pipeline import pipeline
class math_pipeline(pipeline):
def __init__(self, foundation_model):
super().__init__('math', foundation_model)
self.tool = python_executor()
with open(os.path.join(self.prompts_path, "claim_extraction.yaml"), 'r') as file:
data = yaml.load(file, Loader=yaml.FullLoader)
self.claim_prompt = data['math']
with open(os.path.join(self.prompts_path, 'query_generation.yaml'), 'r') as file:
data = yaml.load(file, Loader=yaml.FullLoader)
self.query_prompt = data['math']
def _verification(self, exec_results):
classification_results = [True for _ in range(len(exec_results))]
for i in range(len(exec_results)):
if exec_results[i] is not None and 'False' in exec_results[i]:
classification_results[i] = False
return classification_results
async def _claim_extraction(self, samples):
messages_list = [
[
{"role": "system", "content": self.claim_prompt['system']},
{"role": "user", "content": self.claim_prompt['user'].format(input_question=sample['prompt'], input_solution=sample['response'])},
]
for sample in samples
]
return await self.chat.async_run(messages_list, List)
async def _query_generation(self, claims):
messages_list = [
[
{"role": "system", "content": self.query_prompt['system']},
{"role": "user", "content": self.query_prompt['user'].format(math_calculation=claim['math_calculation'], calculated_answer=claim['calculated_answer'])},
]
for claim in claims
]
return await self.chat.async_run(messages_list, Dict)
async def run_with_tool_live(self, samples):
claims_in_responses = await self._claim_extraction(samples)
queries_in_responses = []
exec_results_in_responses = []
verifications_in_responses = []
for claims_in_response in claims_in_responses:
queries = await self._query_generation(claims_in_response)
queries_in_responses.append(queries)
exec_results = []
for query in queries:
try:
exec_results.append(self.tool.run(query['python_snippet']))
except:
exec_results.append('None')
exec_results_in_responses.append(exec_results)
verifications = self._verification(exec_results)
verifications_in_responses.append(verifications)
return claims_in_responses, queries_in_responses, exec_results_in_responses, verifications_in_responses
async def run_with_tool_live_without_claim_extraction(self, claims):
queries = await self._query_generation(claims)
exec_results = []
for query in queries:
try:
exec_results.append(self.tool.run(query['python_snippet']))
except:
exec_results.append(None)
classification_results = self._verification(exec_results)
return queries, exec_results, classification_results
async def run_with_tool_api_call(self, prompts, responses):
batch_size = 5
num_batches = math.ceil(len(prompts) / batch_size)
self.sample_list = [{"prompt": prompt, "response": response, "category": 'math'} for prompt, response in zip(prompts, responses)]
for i in range(num_batches):
print(i)
batch_start = i * batch_size
batch_end = min((i + 1) * batch_size, len(responses))
claims_in_responses, queries_in_responses, exec_results_in_response, verifications_in_responses = await self.run_with_tool_live(self.sample_list[batch_start: batch_end])
for j, (claims_in_response, queries_in_response, exec_results_in_response, verifications_in_response) in enumerate(zip(claims_in_responses, queries_in_responses, exec_results_in_response, verifications_in_responses)):
index = batch_start + j
self.sample_list[index].update({
'claims': claims_in_response,
'queries': queries_in_response,
'execution_results': exec_results_in_response,
'claim_level_factuality': verifications_in_response,
'response_level_factuality': all([verification if verification != None else True for verification in verifications_in_response])
})
return self.sample_list
async def run_with_tool_dataset(self, annotated_dataset_path: str, with_tool_classified_dataset_path: str, rerun: bool = False, rerun_indices: list = []):
data_path = annotated_dataset_path if not rerun else with_tool_classified_dataset_path
with open(data_path, 'r') as f:
data = [json.loads(line) for line in f]
self.sample_list = data if rerun else [claim for sample in data for claim in sample['claims']]
rerun_elements = self.sample_list if not rerun else [self.sample_list[i] for i in rerun_indices]
batch_size = 10
num_batches = math.ceil(len(rerun_elements) / batch_size) # 5
for i in range(num_batches):
print("test1")
print(i)
batch_start = i * batch_size
batch_end = min((i + 1) * batch_size, len(rerun_elements))
batch = rerun_elements[batch_start:batch_end]
queries, exec_results, classification_results = await self.run_with_tool_live_without_claim_extraction(batch)
for j, (query, exec_result, classification_result) in enumerate(zip(queries, exec_results, classification_results)):
index = batch_start + j if not rerun else rerun_indices[batch_start + j]
self.sample_list[index].update({
'query': query,
'exec_result': exec_result,
'with_tool_classification': classification_result,
})
# save everything after each batch to prevent data loss
with open(with_tool_classified_dataset_path, 'w') as f:
for item in self.sample_list:
try:
json_str = json.dumps(item)
except:
continue
f.write(json_str + '\n')
async def run_self_check_live(self, fewshot, batch):
user_prompt_key = 'user_3_shot_CoT' if fewshot else 'user_zero_shot_CoT'
messages_list = [
[
{"role": "system", "content": self.self_check_prompt['system']},
{"role": "user", "content": self.self_check_prompt[user_prompt_key].format(input_calculation=response['math_calculation'], input_calculated_answer=response['calculated_answer'])},
]
for response in batch
]
return await self.chat.async_run(messages_list, Dict)
async def run_self_check_dataset(self, annotated_dataset_path: str, self_check_classified_dataset_path: str, fewshot: bool = False, rerun: bool = False, rerun_indices: list = []):
data_path = annotated_dataset_path if not rerun else self_check_classified_dataset_path
with open(data_path, 'r') as f:
data = [json.loads(line) for line in f]
self.sample_list = data if rerun else [claim for sample in data for claim in sample['claims']]
rerun_elements = self.sample_list if not rerun else [self.sample_list[i] for i in rerun_indices]
batch_size = 10
num_batches = math.ceil(len(rerun_elements) / batch_size)
for i in range(num_batches):
print(i)
batch_start = i * batch_size
batch_end = min((i + 1) * batch_size, len(rerun_elements))
batch = rerun_elements[batch_start:batch_end]
responses = await self.run_self_check_live(fewshot, batch)
for j, response in enumerate(responses):
index = batch_start + j if not rerun else rerun_indices[batch_start + j]
if response is None:
self.sample_list[index].update({
'self_check_classification': 'None',
'self_check_reasoning': 'None'
})
else:
self.sample_list[index].update({
'self_check_classification': response.get('factuality', 'None'),
'self_check_reasoning': response.get('reasoning', 'None')
})
# save everything after each batch to prevent data loss
with open(self_check_classified_dataset_path, 'w') as f:
for item in self.sample_list:
json_str = json.dumps(item)
f.write(json_str + '\n') |