# Copyright (c) 2023 Baidu, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
manager which manage whole procedure of evaluation
"""
import math
import multiprocessing
import os.path
import time
from concurrent.futures import ALL_COMPLETED, Future, ThreadPoolExecutor, wait
from typing import Any, Dict, List, Optional, Sequence, Set, Union
from qianfan import get_config
from qianfan.dataset import Dataset
from qianfan.dataset.consts import (
LLMOutputColumnName,
NewInputChatColumnName,
NewInputPromptColumnName,
OldReferenceColumnName,
)
from qianfan.dataset.data_source import FileDataSource, QianfanDataSource
from qianfan.dataset.data_source.utils import (
_download_file_from_url_streamly,
)
from qianfan.errors import QianfanError
from qianfan.evaluation.consts import QianfanRefereeEvaluatorPromptTemplate
from qianfan.evaluation.evaluation_result import EvaluationResult
from qianfan.evaluation.evaluator import (
LocalEvaluator,
QianfanEvaluator,
QianfanManualEvaluator,
QianfanRefereeEvaluator,
QianfanRuleEvaluator,
)
from qianfan.model import Model, Service
from qianfan.resources import Model as ResourceModel
from qianfan.resources.console.consts import (
EvaluationResultExportTaskStatus,
EvaluationTaskStatus,
)
from qianfan.utils import log_debug, log_error, log_info, log_warn
from qianfan.utils.pydantic import BaseModel, Field, root_validator
from qianfan.utils.utils import generate_letter_num_random_id
[docs]class EvaluationManager(BaseModel):
"""logic control center of evaluation"""
local_evaluators: Optional[List[LocalEvaluator]] = Field(default=None)
qianfan_evaluators: Optional[List[QianfanEvaluator]] = Field(default=None)
task_id: Optional[str] = Field(default=None)
@root_validator
@classmethod
def _check_evaluators(cls, input_dict: Any) -> Any:
"""校验传入的参数"""
if not isinstance(input_dict, dict):
err_msg = (
"the arguments of model validator of EvaluationManager isn't dict,"
f" rather {type(input_dict)}"
)
log_error(err_msg)
raise ValueError(err_msg)
local_evaluators = input_dict.get("local_evaluators", None)
qianfan_evaluators = input_dict.get("qianfan_evaluators", None)
if not local_evaluators and not qianfan_evaluators:
# 如果都没设置，则报错抛出
err_msg = "none of local evaluator and qianfan evaluator has been set"
log_error(err_msg)
raise ValueError(err_msg)
if local_evaluators and qianfan_evaluators:
# 如果同时设置，则报错抛出
err_msg = "both local evaluator and qianfan evaluator has been set"
log_error(err_msg)
raise ValueError(err_msg)
if qianfan_evaluators:
dedup_map: Set[str] = set()
for evaluator in qianfan_evaluators:
type_name = f"{type(evaluator)}"
if type_name in dedup_map:
err_msg = f"multiple {type_name} has been set in qianfan_evaluators"
log_error(err_msg)
raise ValueError
dedup_map.add(type_name)
return input_dict
def _eval_worker(
self,
start: int,
end: int,
input: List[Union[str, List[Dict[str, Any]]]],
reference: List[str],
output: List[str],
) -> List[Dict[str, Any]]:
result_list: List[Dict[str, Any]] = []
if start >= end:
return result_list
assert self.local_evaluators
for i in range(start, end):
result: Dict[str, Any] = {}
for evaluator in self.local_evaluators:
result.update(evaluator.evaluate(input[i], reference[i], output[i]))
result_list.append(result)
return result_list
def _get_eval_task_future(self, dataset: Dataset, **kwargs: Any) -> List[Future]:
if NewInputPromptColumnName in dataset.col_names():
ds_dict = dataset.col_list(
[NewInputPromptColumnName, LLMOutputColumnName, OldReferenceColumnName]
)
else:
ds_dict = dataset.col_list(
[NewInputChatColumnName, LLMOutputColumnName, OldReferenceColumnName]
)
sector_length = math.ceil(len(dataset) / multiprocessing.cpu_count())
pool = ThreadPoolExecutor()
future_list: List[Future] = []
for i in range(multiprocessing.cpu_count()):
if NewInputPromptColumnName in dataset.col_names():
future_list.append(
pool.submit(
self._eval_worker,
i * sector_length,
min((i + 1) * sector_length, len(dataset)),
ds_dict[NewInputPromptColumnName],
ds_dict[LLMOutputColumnName],
ds_dict[OldReferenceColumnName],
)
)
else:
future_list.append(
pool.submit(
self._eval_worker,
i * sector_length,
min((i + 1) * sector_length, len(dataset)),
ds_dict[NewInputChatColumnName],
ds_dict[LLMOutputColumnName],
ds_dict[OldReferenceColumnName],
)
)
return future_list
def _run_evaluator_locally(
self, dataset: Dataset, **kwargs: Any
) -> List[Dict[str, Any]]:
future_list = self._get_eval_task_future(dataset, **kwargs)
wait(future_list, return_when=ALL_COMPLETED)
result_list: List[Dict[str, Any]] = []
for future in future_list:
try:
result = future.result()
result_list.extend(result)
except Exception as e:
err_msg = (
f"an fatal error occurred when evaluate llm output in batch: {e}"
)
log_error(err_msg)
raise e
return result_list
def _get_llm_tags(self, llms: Sequence[Union[Model, Service]]) -> List[str]:
llm_tags: List[str] = []
for llm in llms:
if isinstance(llm, Service):
if llm.model:
llm_key_str = f"{llm.id}_{llm.endpoint}_{llm.model.name}"
else:
llm_key_str = f"{llm.id}_{llm.endpoint}"
elif isinstance(llm, Model):
llm_key_str = f"{llm.id}_{llm.version_id}_{llm.name}"
else:
llm_key_str = ""
llm_tags.append(llm_key_str)
return llm_tags
def _get_batch_inference_task_future(
self, llms: Sequence[Union[Model, Service]], dataset: Dataset, **kwargs: Any
) -> Dict[int, Future]:
future_dict: Dict[int, Future] = {}
pool = ThreadPoolExecutor()
for index in range(len(llms)):
llm = llms[index]
if isinstance(llm, Model):
future_dict[index] = pool.submit(
dataset.test_using_llm,
model_version_id=llm.version_id,
**kwargs,
)
elif isinstance(llm, Service):
model_name = None if not llm.model else llm.model.name
future_dict[index] = pool.submit(
dataset.test_using_llm,
service_model=model_name,
service_endpoint=llm.endpoint,
**kwargs,
)
return future_dict
def _get_qianfan_evaluator_configuration_dict(self) -> Dict[str, Any]:
# 对输入数据做映射
input_argument_dict: Dict[str, Any] = {}
assert self.qianfan_evaluators
for evaluator in self.qianfan_evaluators:
# 如果处理的是人工评估
if isinstance(evaluator, QianfanManualEvaluator):
input_argument_dict["evalMode"] = (
input_argument_dict.get("evalMode", "") + "manual,"
)
# 超过 4 个指标则截断，对齐平台
dimensions = evaluator.evaluation_dimensions[:]
if len(evaluator.evaluation_dimensions) > 4:
log_warn(
"the number of evaluation dimension is more than 4, the"
" dimensions will be truncated"
)
dimensions = dimensions[:4]
# 创建指标字典
input_dimension_list: List[Dict[str, Any]] = []
for dimension in dimensions:
input_dimension_dict: Dict[str, Any] = {
"dimension": dimension.dimension
}
if dimension.description:
input_dimension_dict["description"] = dimension.description
input_dimension_list.append(input_dimension_dict)
input_argument_dict["evaluationDimension"] = input_dimension_list
# 如果处理的是基于规则的评估
if isinstance(evaluator, QianfanRuleEvaluator):
input_argument_dict["evalMode"] = (
input_argument_dict.get("evalMode", "") + "rule,"
)
rule_list: List[str] = []
if evaluator.using_similarity:
rule_list.append("similarity")
if evaluator.using_accuracy:
rule_list.append("accuracy")
if not rule_list:
err_msg = "no rule has been set despite using QianfanRuleEvaluator"
log_error(err_msg)
raise ValueError(err_msg)
input_argument_dict["scoreModes"] = rule_list
# 添加停用词表
if evaluator.stop_words:
input_argument_dict["stopWordsPath"] = evaluator.stop_words
# 如果处理的是基于裁判员的打分
if isinstance(evaluator, QianfanRefereeEvaluator):
input_argument_dict["evalMode"] = (
input_argument_dict.get("evalMode", "") + "model,"
)
input_argument_dict["appId"] = evaluator.app_id
input_argument_dict["prompt"] = {
"templateContent": QianfanRefereeEvaluatorPromptTemplate,
"metric": evaluator.prompt_metrics,
"steps": evaluator.prompt_steps,
"maxScore": evaluator.prompt_max_score,
}
input_argument_dict["evalMode"] = input_argument_dict.get("evalMode", "").strip(
","
)
return input_argument_dict
def _create_qianfan_evaluation_task_and_wait_to_success(
self,
llms: Sequence[Union[Model, Service]],
dataset_id: str,
eval_config: Dict[str, Any],
**kwargs: Any,
) -> EvaluationTaskStatus:
model_objs: List[Model] = llms # type: ignore
for i in range(len(model_objs)):
model_objs[i].auto_complete_info()
resp_body = ResourceModel.create_evaluation_task(
name=f"sdk_eval_{generate_letter_num_random_id(11)}",
version_info=[
{"modelId": model.id, "modelVersionId": model.version_id}
for model in model_objs
],
dataset_id=dataset_id,
eval_config=eval_config,
**kwargs,
).body
eval_id = resp_body["result"]["evalId"]
task_url = f"https://console.bce.baidu.com/qianfan/modelcenter/model/eval/detail/task/{eval_id}"
self.task_id = eval_id
log_info(f"please check webpage {task_url} to get further information")
# 开始轮询任务进度
while True:
eval_info = ResourceModel.get_evaluation_info(eval_id)
eval_state = EvaluationTaskStatus(eval_info["result"]["state"])
log_debug(f"current evaluation task info: {eval_info}")
log_info(f"current eval_state: {eval_state}")
if eval_state not in [
EvaluationTaskStatus.Pending,
EvaluationTaskStatus.Doing,
]:
break
time.sleep(get_config().EVALUATION_ONLINE_POLLING_INTERVAL)
if eval_state not in [
EvaluationTaskStatus.DoingWithManualBegin,
EvaluationTaskStatus.Done,
]:
err_msg = f"can't finish evaluation task and failed with state {eval_state}"
log_error(err_msg)
raise QianfanError(err_msg)
return eval_state
[docs] def eval(
self, llms: Sequence[Union[Model, Service]], dataset: Dataset, **kwargs: Any
) -> Optional[EvaluationResult]:
"""
Evaluate the performance of models on the dataset.
Args:
llms (List[Union[Model, Service]]):
List of models or service to be evaluated.
dataset (Dataset):
The dataset on which models will be evaluated.
**kwargs (Any):
Other keyword arguments.
Returns:
Optional[EvaluationResult]: Evaluation result of models on the dataset.
"""
if len(set(type(llm) for llm in llms)) > 1:
err_msg = "should use only either Model or Service, not both togather"
log_error(err_msg)
raise ValueError(err_msg)
if self.local_evaluators:
llm_tags = self._get_llm_tags(llms)
future_dict = self._get_batch_inference_task_future(llms, dataset, **kwargs)
wait(list(future_dict.values()), return_when=ALL_COMPLETED)
llm_evaluation_result_dict: Dict[int, List[Dict[str, Any]]] = {}
llm_response_list: Dict[int, List[str]] = {}
llm_input_list: List[Any] = []
expected_output_list: List[str] = []
input_column_name: str = ""
for index, future in future_dict.items():
try:
result = future.result()
llm_response_list[index] = result[LLMOutputColumnName][
LLMOutputColumnName
]
llm_evaluation_result_dict[index] = self._run_evaluator_locally(
result
)
if not llm_input_list:
if NewInputPromptColumnName in result.col_names():
llm_input_list = result[NewInputPromptColumnName][
NewInputPromptColumnName
]
input_column_name = NewInputPromptColumnName
else:
llm_input_list = result[NewInputChatColumnName][
NewInputChatColumnName
]
input_column_name = NewInputChatColumnName
if not expected_output_list:
expected_output_list = result[OldReferenceColumnName][
OldReferenceColumnName
]
except Exception as e:
err_msg = (
"an error occurred when doing batch inference in"
f" evaluation: {e}"
)
log_warn(err_msg)
new_response_list: List[List[Dict[str, Any]]] = []
for index, response_list in llm_response_list.items():
for inner_index in range(len(response_list)):
while len(new_response_list) <= inner_index:
new_response_list.append([])
eval_result = llm_evaluation_result_dict[index][inner_index]
new_response_list[inner_index].append(
{
"content": response_list[inner_index],
"llm_tag": llm_tags[index],
**eval_result,
}
)
dataset_data = {
input_column_name: llm_input_list,
OldReferenceColumnName: expected_output_list,
"model_content": new_response_list,
}
return EvaluationResult(
result_dataset=Dataset.create_from_pyobj(dataset_data)
)
if self.qianfan_evaluators:
# 检查是否有不支持的实例
if any([not isinstance(inst, Model) for inst in llms]):
err_msg = "only Model instance can use QianfanEvaluator"
log_error(err_msg)
raise ValueError(err_msg)
# 检查数据集是否在云上
if not dataset.is_dataset_located_in_qianfan():
err_msg = "dataset must be in qianfan, not local storage"
log_error(err_msg)
raise ValueError(err_msg)
# 检查数据集是否是泛文本
if dataset.is_dataset_generic_text():
err_msg = "dataset can't be generic text dataset"
log_error(err_msg)
raise ValueError(err_msg)
input_argument_dict = self._get_qianfan_evaluator_configuration_dict()
qianfan_data_source = dataset.inner_data_source_cache
assert isinstance(qianfan_data_source, QianfanDataSource)
# 提交评估任务
eval_state = self._create_qianfan_evaluation_task_and_wait_to_success(
llms, qianfan_data_source.id, input_argument_dict
)
# 如果是进入人工评估阶段，则返回空
if eval_state == EvaluationTaskStatus.DoingWithManualBegin:
log_warn("can't fetch any metrics due to manual mode has been set")
return None
assert self.task_id
result_list = ResourceModel.get_evaluation_result(self.task_id)["result"]
metric_list: Dict[str, Dict[str, Any]] = {
f'{result["modelName"]}_{result["modelVersion"]}': result[
"effectMetric"
]
for result in result_list
}
export_task_id = ResourceModel.create_evaluation_result_export_task(
self.task_id, **kwargs
)["result"]["exportIDStr"]
polling_interval = get_config().EXPORT_STATUS_POLLING_INTERVAL
while True:
result = ResourceModel.get_evaluation_result_export_task_status(
export_task_id
)["result"]
task_status = EvaluationResultExportTaskStatus(result["state"])
if task_status == EvaluationResultExportTaskStatus.Doing:
log_info(
f"wait evaluation result export task {export_task_id} to be"
" completed"
)
time.sleep(polling_interval)
continue
if task_status == EvaluationResultExportTaskStatus.Fail:
err_msg = "evaluation result export failed"
log_error(err_msg)
raise QianfanError(err_msg)
if task_status == EvaluationResultExportTaskStatus.Done:
log_info("export result succeeded")
break
err_msg = "enter logic unreachable"
log_error(err_msg)
raise Exception(err_msg)
download_url = result["downloadUrl"]
log_info(f"start to download evaluation result file from {download_url}")
local_cache_file_path = "tmp.csv"
try:
_download_file_from_url_streamly(download_url, local_cache_file_path)
# 返回指标信息
return EvaluationResult(
result_dataset=Dataset.load(
FileDataSource(path=local_cache_file_path)
),
metrics=metric_list,
)
finally:
if os.path.exists(local_cache_file_path):
os.remove(local_cache_file_path)
return None