# Copyright (c) 2019 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 # # 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. """ELECTRA model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import six import json import paddle.fluid as fluid from chinese_electra_small.model.transformer_encoder import encoder, pre_process_layer class ElectraConfig(object): def __init__(self, config_path): self._config_dict = self._parse(config_path) def _parse(self, config_path): try: with open(config_path) as json_file: config_dict = json.load(json_file) except Exception: raise IOError("Error in parsing electra model config file '%s'" % config_path) else: return config_dict def __getitem__(self, key): return self._config_dict[key] def print_config(self): for arg, value in sorted(six.iteritems(self._config_dict)): print('%s: %s' % (arg, value)) print('------------------------------------------------') class ElectraModel(object): def __init__(self, src_ids, position_ids, sentence_ids, input_mask, config, weight_sharing=True, use_fp16=False): self._emb_size = 128 self._hidden_size = config['hidden_size'] self._n_layer = config['num_hidden_layers'] self._n_head = config['num_attention_heads'] self._voc_size = config['vocab_size'] self._max_position_seq_len = config['max_position_embeddings'] self._sent_types = config['type_vocab_size'] self._hidden_act = config['hidden_act'] self._prepostprocess_dropout = config['hidden_dropout_prob'] self._attention_dropout = config['attention_probs_dropout_prob'] self._weight_sharing = weight_sharing self._word_emb_name = "word_embedding" self._pos_emb_name = "pos_embedding" self._sent_emb_name = "sent_embedding" self._dtype = "float16" if use_fp16 else "float32" # Initialize all weigths by truncated normal initializer, and all biases # will be initialized by constant zero by default. self._param_initializer = fluid.initializer.TruncatedNormal(scale=config['initializer_range']) self._build_model(src_ids, position_ids, sentence_ids, input_mask) def _build_model(self, src_ids, position_ids, sentence_ids, input_mask): # padding id in vocabulary must be set to 0 emb_out = fluid.layers.embedding(input=src_ids, size=[self._voc_size, self._emb_size], dtype=self._dtype, param_attr=fluid.ParamAttr(name=self._word_emb_name, initializer=self._param_initializer), is_sparse=False) position_emb_out = fluid.layers.embedding(input=position_ids, size=[self._max_position_seq_len, self._emb_size], dtype=self._dtype, param_attr=fluid.ParamAttr(name=self._pos_emb_name, initializer=self._param_initializer)) sent_emb_out = fluid.layers.embedding(sentence_ids, size=[self._sent_types, self._emb_size], dtype=self._dtype, param_attr=fluid.ParamAttr(name=self._sent_emb_name, initializer=self._param_initializer)) emb_out = emb_out + position_emb_out emb_out = emb_out + sent_emb_out emb_out = pre_process_layer(emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder') if self._emb_size != self._hidden_size: emb_out = fluid.layers.fc(input=emb_out, size=self._hidden_size, act=None, param_attr=fluid.ParamAttr(name="embeddings_project.w_0", initializer=self._param_initializer), num_flatten_dims=2, bias_attr="embeddings_project.b_0") if self._dtype == "float16": input_mask = fluid.layers.cast(x=input_mask, dtype=self._dtype) self_attn_mask = fluid.layers.matmul(x=input_mask, y=input_mask, transpose_y=True) self_attn_mask = fluid.layers.scale(x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False) n_head_self_attn_mask = fluid.layers.stack(x=[self_attn_mask] * self._n_head, axis=1) n_head_self_attn_mask.stop_gradient = True self._enc_out = encoder(enc_input=emb_out, attn_bias=n_head_self_attn_mask, n_layer=self._n_layer, n_head=self._n_head, d_key=self._hidden_size // self._n_head, d_value=self._hidden_size // self._n_head, d_model=self._hidden_size, d_inner_hid=self._hidden_size * 4, prepostprocess_dropout=self._prepostprocess_dropout, attention_dropout=self._attention_dropout, relu_dropout=0, hidden_act=self._hidden_act, preprocess_cmd="", postprocess_cmd="dan", param_initializer=self._param_initializer, name='encoder') def get_sequence_output(self): return self._enc_out def get_pooled_output(self): """Get the first feature of each sequence for classification""" next_sent_feat = fluid.layers.slice(input=self._enc_out, axes=[1], starts=[0], ends=[1]) return next_sent_feat def get_pretraining_output(self, mask_label, mask_pos, labels): """Get the loss & accuracy for pretraining""" mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32') # extract the first token feature in each sentence next_sent_feat = self.get_pooled_output() reshaped_emb_out = fluid.layers.reshape(x=self._enc_out, shape=[-1, self._hidden_size]) # extract masked tokens' feature mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos) # transform: fc mask_trans_feat = fluid.layers.fc(input=mask_feat, size=self._hidden_size, act=self._hidden_act, param_attr=fluid.ParamAttr(name='mask_lm_trans_fc.w_0', initializer=self._param_initializer), bias_attr=fluid.ParamAttr(name='mask_lm_trans_fc.b_0')) # transform: layer norm mask_trans_feat = pre_process_layer(mask_trans_feat, 'n', name='mask_lm_trans') mask_lm_out_bias_attr = fluid.ParamAttr(name="mask_lm_out_fc.b_0", initializer=fluid.initializer.Constant(value=0.0)) if self._weight_sharing: fc_out = fluid.layers.matmul(x=mask_trans_feat, y=fluid.default_main_program().global_block().var(self._word_emb_name), transpose_y=True) fc_out += fluid.layers.create_parameter(shape=[self._voc_size], dtype=self._dtype, attr=mask_lm_out_bias_attr, is_bias=True) else: fc_out = fluid.layers.fc(input=mask_trans_feat, size=self._voc_size, param_attr=fluid.ParamAttr(name="mask_lm_out_fc.w_0", initializer=self._param_initializer), bias_attr=mask_lm_out_bias_attr) mask_lm_loss = fluid.layers.softmax_with_cross_entropy(logits=fc_out, label=mask_label) mean_mask_lm_loss = fluid.layers.mean(mask_lm_loss) next_sent_fc_out = fluid.layers.fc(input=next_sent_feat, size=2, param_attr=fluid.ParamAttr(name="next_sent_fc.w_0", initializer=self._param_initializer), bias_attr="next_sent_fc.b_0") next_sent_loss, next_sent_softmax = fluid.layers.softmax_with_cross_entropy(logits=next_sent_fc_out, label=labels, return_softmax=True) next_sent_acc = fluid.layers.accuracy(input=next_sent_softmax, label=labels) mean_next_sent_loss = fluid.layers.mean(next_sent_loss) loss = mean_next_sent_loss + mean_mask_lm_loss return next_sent_acc, mean_mask_lm_loss, loss