paddlenlp.taskflow.task 源代码

# coding:utf-8
# Copyright (c) 2021  PaddlePaddle Authors. 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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import os
import abc
from abc import abstractmethod
import paddle
from ..utils.env import PPNLP_HOME
from ..utils.log import logger
from .utils import download_check, static_mode_guard, dygraph_mode_guard

[文档]class Task(metaclass=abc.ABCMeta): """ The meta classs of task in Taskflow. The meta class has the five abstract function, the subclass need to inherit from the meta class. Args: task(string): The name of task. model(string): The model name in the task. kwargs (dict, optional): Additional keyword arguments passed along to the specific task. """ def __init__(self, model, task, **kwargs): self.model = model self.task = task self.kwargs = kwargs self._usage = "" # The dygraph model instantce self._model = None # The static model instantce self._input_spec = None self._config = None # The root directory for storing Taskflow related files, default to ~/.paddlenlp. self._home_path = self.kwargs[ 'home_path'] if 'home_path' in self.kwargs else PPNLP_HOME self._task_path = os.path.join(self._home_path, "taskflow", self.task, self.model) self._task_flag = self.kwargs[ 'task_flag'] if 'task_flag' in self.kwargs else self.model download_check(self._task_flag) @abstractmethod def _construct_model(self, model): """ Construct the inference model for the predictor. """ @abstractmethod def _construct_tokenizer(self, model): """ Construct the tokenizer for the predictor. """ @abstractmethod def _preprocess(self, inputs, padding=True, add_special_tokens=True): """ Transform the raw text to the model inputs, two steps involved: 1) Transform the raw text to token ids. 2) Generate the other model inputs from the raw text and token ids. """ @abstractmethod def _run_model(self, inputs): """ Run the task model from the outputs of the `_tokenize` function. """ @abstractmethod def _postprocess(self, inputs): """ The model output is the logits and pros, this function will convert the model output to raw text. """ @abstractmethod def _construct_input_spec(self): """ Construct the input spec for the convert dygraph model to static model. """ def _prepare_static_mode(self): """ Construct the input data and predictor in the PaddlePaddele static mode. """ place = paddle.get_device() if place == 'cpu': self._config.disable_gpu() else: self._config.enable_use_gpu(100, self.kwargs['device_id']) self._config.switch_use_feed_fetch_ops(False) self._config.disable_glog_info() self.predictor = paddle.inference.create_predictor(self._config) self.input_handles = [ self.predictor.get_input_handle(name) for name in self.predictor.get_input_names() ] self.output_handle = [ self.predictor.get_output_handle(name) for name in self.predictor.get_output_names() ] def _get_inference_model(self): """ Return the inference program, inputs and outputs in static mode. """ inference_model_path = os.path.join(self._task_path, "static", "inference") if not os.path.exists(inference_model_path + ".pdiparams"): with dygraph_mode_guard(): self._construct_model(self.model) self._construct_input_spec() self._convert_dygraph_to_static() model_file = inference_model_path + ".pdmodel" params_file = inference_model_path + ".pdiparams" self._config = paddle.inference.Config(model_file, params_file) self._prepare_static_mode() def _convert_dygraph_to_static(self): """ Convert the dygraph model to static model. """ assert self._model is not None, 'The dygraph model must be created before converting the dygraph model to static model.' assert self._input_spec is not None, 'The input spec must be created before converting the dygraph model to static model.'"Converting to the inference model cost a little time.") static_model = paddle.jit.to_static( self._model, input_spec=self._input_spec) save_path = os.path.join(self._task_path, "static", "inference"), save_path)"The inference model save in the path:{}".format(save_path)) def _check_input_text(self, inputs): inputs = inputs[0] if isinstance(inputs, str): if len(inputs) == 0: raise ValueError( "Invalid inputs, input text should not be empty text, please check your input.". format(type(inputs))) inputs = [inputs] elif isinstance(inputs, list): if not (isinstance(inputs[0], str) and len(inputs[0].strip()) > 0): raise TypeError( "Invalid inputs, input text should be list of str, and first element of list should not be empty text.". format(type(inputs[0]))) else: raise TypeError( "Invalid inputs, input text should be str or list of str, but type of {} found!". format(type(inputs))) return inputs
[文档] def help(self): """ Return the usage message of the current task. """ print("Examples:\n{}".format(self._usage))
def __call__(self, *args): inputs = self._preprocess(*args) outputs = self._run_model(inputs) results = self._postprocess(outputs) return results