paddlenlp.taskflow.text_generation 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
# limitations under the License.

import glob
import json
import math
import os
import copy
import itertools

import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ..transformers import GPTForGreedyGeneration
from ..transformers import GPTChineseTokenizer, GPTTokenizer
from ..datasets import load_dataset
from import Stack, Pad, Tuple
from .utils import download_file, add_docstrings, static_mode_guard, dygraph_mode_guard
from .task import Task

usage = r"""

URLS = {
    "gpt-cpm-large-cn": [

[文档]class TextGenerationTask(Task): """ The text generation model to predict the question or chinese poetry. 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, task, model, **kwargs): super().__init__(task=task, model=model, **kwargs) self._static_mode = True self._usage = usage if self._static_mode: download_file(self._task_path, "static" + os.path.sep + "inference.pdiparams", URLS[self.model][0], URLS[self.model][1]) self._get_inference_model() else: self._construct_model(model) self._construct_tokenizer(model) self.kwargs['generation_task'] = task def _construct_input_spec(self): """ Construct the input spec for the convert dygraph model to static model. """ self._input_spec = [ paddle.static.InputSpec( shape=[None, None], dtype="int64", name='token_ids') ] def _construct_model(self, model): """ Construct the inference model for the predictor. """ model_instance = GPTForGreedyGeneration.from_pretrained( self.model, max_predict_len=32) # Load the model parameter for the predict model_instance.eval() self._model = model_instance def _construct_tokenizer(self, model): """ Construct the tokenizer for the predictor. """ if self.model == "gpt-cpm-large-cn": tokenizer_instance = GPTChineseTokenizer.from_pretrained(model) else: tokenizer_instance = GPTTokenizer.from_pretrained(model) self._tokenizer = tokenizer_instance 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. """ inputs = self._check_input_text(inputs) # Get the config from the kwargs batch_size = self.kwargs[ 'batch_size'] if 'batch_size' in self.kwargs else 1 num_workers = self.kwargs[ 'num_workers'] if 'num_workers' in self.kwargs else 0 generation_task = self.kwargs[ 'generation_task'] if 'generation_task' in self.kwargs else 'question_answering' max_seq_len = 32 def select_few_shot_input(model_name, generation_task): pre_input = "" if generation_task not in [ 'question_answering', 'poetry_generation' ]: raise ValueError( "The generation task must be question or poetry") if model_name == "gpt-cpm-large-cn": if generation_task == "question_answering": pre_input = '问题:中国的首都是哪里?答案:北京。\n问题:{} 答案:' else: pre_input = '默写古诗: 大漠孤烟直,长河落日圆。\n{}' return pre_input pre_input = select_few_shot_input(self.model, generation_task) infer_data = [] examples = [] filter_inputs = [] for input_text in inputs: if not (isinstance(input_text, str) and len(input_text) > 0): continue filter_inputs.append(input_text) few_shot_input = pre_input.format(input_text) ids = self._tokenizer(few_shot_input)["input_ids"] examples.append((ids, len(ids))) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=0, dtype="int64"), Stack(dtype='int64'), # seq_len ): fn(samples) batches = [ examples[idx:idx + batch_size] for idx in range(0, len(examples), batch_size) ] outputs = {} outputs['text'] = filter_inputs outputs['data_loader'] = batches self._batchify_fn = batchify_fn return outputs def _run_model(self, inputs): """ Run the task model from the outputs of the `_tokenize` function. """ results = [] lens = [] with static_mode_guard(): for batch in inputs['data_loader']: ids, seq_len = self._batchify_fn(batch) self.input_handles[0].copy_from_cpu(ids) result = self.output_handle[0].copy_to_cpu().tolist() results.extend(result) lens.extend(seq_len.tolist()) inputs['results'] = results inputs['lens'] = lens return inputs def _postprocess(self, inputs): """ The model output is tag ids, this function will convert the model output to raw text. """ batch_out = [] preds = inputs['results'] for index in range(0, len(preds)): seq_len = inputs['lens'][index] single_result = {} single_result['text'] = inputs['text'][index] single_result['answer'] = self._tokenizer.convert_ids_to_string( preds[index][seq_len:-1]) batch_out.append(single_result) return batch_out