# coding=utf-8 # Copyright 2021 The IDEA 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 # 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. from logging import basicConfig import torch from torch import nn import json from tqdm import tqdm import os import numpy as np from transformers import BertTokenizer import pytorch_lightning as pl from pytorch_lightning import trainer, loggers from transformers import AlbertTokenizer from transformers import AutoConfig from transformers.pipelines.base import Pipeline import argparse import copy from fengshen.utils.universal_checkpoint import UniversalCheckpoint import warnings from fengshen.models.unimc.modeling_unimc import ( UniMCDataModel, UniMCLitModel, UniMCPredict, ) class UniMCPipelines(Pipeline): @staticmethod def piplines_args(parent_args): total_parser = parent_args.add_argument_group("piplines args") total_parser.add_argument( '--pretrained_model_path', default='', type=str) total_parser.add_argument('--load_checkpoints_path', default='', type=str) total_parser.add_argument('--train', action='store_true') total_parser.add_argument('--language', default='chinese', type=str) total_parser = UniMCDataModel.add_data_specific_args(total_parser) total_parser = UniversalCheckpoint.add_argparse_args(total_parser) total_parser = UniMCLitModel.add_model_specific_args(total_parser) total_parser = pl.Trainer.add_argparse_args(parent_args) return parent_args def __init__(self, args, model_path): self.args = args self.checkpoint_callback = UniversalCheckpoint(args) self.logger = loggers.TensorBoardLogger(save_dir=args.default_root_dir) self.trainer = pl.Trainer.from_argparse_args(args, logger=self.logger, callbacks=[self.checkpoint_callback]) self.config = AutoConfig.from_pretrained(model_path) if self.config.model_type == 'albert': self.tokenizer = AlbertTokenizer.from_pretrained( model_path) else: self.tokenizer = BertTokenizer.from_pretrained( model_path) if args.language == 'chinese': self.yes_token = self.tokenizer.encode('是')[1] self.no_token = self.tokenizer.encode('非')[1] else: self.yes_token = self.tokenizer.encode('yes')[1] self.no_token = self.tokenizer.encode('no')[1] if args.load_checkpoints_path != '': self.model = UniMCLitModel.load_from_checkpoint( args.load_checkpoints_path, args=args, yes_token=self.yes_token, model_path=model_path) print('load model from: ', args.load_checkpoints_path) else: self.model = UniMCLitModel( args, yes_token=self.yes_token, model_path=model_path) def train(self, train_data, dev_data, process=True): if process: train_data = self.preprocess(train_data) dev_data = self.preprocess(dev_data) data_model = UniMCDataModel( train_data, dev_data, self.yes_token, self.no_token, self.tokenizer, self.args) self.model.num_data = len(train_data) self.trainer.fit(self.model, data_model) def predict(self, test_data, cuda=True, process=True): if process: test_data = self.preprocess(test_data) result = [] start = 0 if cuda: self.model = self.model.cuda() self.model.model.eval() predict_model = UniMCPredict( self.yes_token, self.no_token, self.model, self.tokenizer, self.args) while start < len(test_data): batch_data = test_data[start:start+self.args.batchsize] start += self.args.batchsize batch_result = predict_model.predict(batch_data) result.extend(batch_result) if process: result = self.postprocess(result) return result def preprocess(self, data): for i, line in enumerate(data): if 'task_type' in line.keys() and line['task_type'] == '语义匹配': data[i]['choice'] = ['不能理解为：'+data[i] ['textb'], '可以理解为：'+data[i]['textb']] # data[i]['question']='怎么理解这段话？' data[i]['textb'] = '' if 'task_type' in line.keys() and line['task_type'] == '自然语言推理': data[i]['choice'] = ['不能推断出：'+data[i]['textb'], '很难推断出：'+data[i]['textb'], '可以推断出：'+data[i]['textb']] # data[i]['question']='根据这段话' data[i]['textb'] = '' return data def postprocess(self, data): for i, line in enumerate(data): if 'task_type' in line.keys() and line['task_type'] == '语义匹配': data[i]['textb'] = data[i]['choice'][0].replace('不能理解为：', '') data[i]['choice'] = ['不相似', '相似'] ns = {} for k, v in data[i]['score'].items(): if '不能' in k: k = '不相似' if '可以' in k: k = '相似' ns[k] = v data[i]['score'] = ns data[i]['answer'] = data[i]['choice'][data[i]['label']] if 'task_type' in line.keys() and line['task_type'] == '自然语言推理': data[i]['textb'] = data[i]['choice'][0].replace('不能推断出：', '') data[i]['choice'] = ['矛盾', '自然', '蕴含'] ns = {} for k, v in data[i]['score'].items(): if '不能' in k: k = '矛盾' if '很难' in k: k = '自然' if '可以' in k: k = '蕴含' ns[k] = v data[i]['score'] = ns data[i]['answer'] = data[i]['choice'][data[i]['label']] return data def _forward(self, model_inputs): return self.model(**model_inputs) def _sanitize_parameters(self, return_all_scores=None, function_to_apply=None, top_k="", **tokenizer_kwargs): # Using "" as default argument because we're going to use `top_k=None` in user code to declare # "No top_k" preprocess_params = tokenizer_kwargs postprocess_params = {} if hasattr(self.model.config, "return_all_scores") and return_all_scores is None: return_all_scores = self.model.config.return_all_scores if isinstance(top_k, int) or top_k is None: postprocess_params["top_k"] = top_k postprocess_params["_legacy"] = False elif return_all_scores is not None: warnings.warn( "`return_all_scores` is now deprecated, if want a similar funcionality use `top_k=None` instead of" " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.", UserWarning, ) if return_all_scores: postprocess_params["top_k"] = None else: postprocess_params["top_k"] = 1 if function_to_apply is not None: postprocess_params["function_to_apply"] = function_to_apply return preprocess_params, {}, postprocess_params