# -*- coding: UTF-8 -*- # 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. """Ernie model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import from paddle import fluid from paddle.fluid import layers from paddlepalm.backbone.utils.transformer import pre_process_layer, encoder from paddlepalm.interface import backbone class Model(backbone): def __init__(self, config, phase): # self._is_training = phase == 'train' # backbone一般不用关心运行阶段，因为outputs在任何阶段基本不会变 self._emb_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'] if config['sent_type_vocab_size']: self._sent_types = config['sent_type_vocab_size'] else: self._sent_types = config['type_vocab_size'] self._task_types = config['task_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._word_emb_name = "word_embedding" self._pos_emb_name = "pos_embedding" self._sent_emb_name = "sent_embedding" self._task_emb_name = "task_embedding" self._emb_dtype = "float32" self._param_initializer = fluid.initializer.TruncatedNormal( scale=config['initializer_range']) @property def inputs_attr(self): return {"token_ids": [[-1, -1, 1], 'int64'], "position_ids": [[-1, -1, 1], 'int64'], "segment_ids": [[-1, -1, 1], 'int64'], "input_mask": [[-1, -1, 1], 'float32'], "task_ids": [[-1,-1, 1], 'int64']} @property def outputs_attr(self): return {"word_embedding": [[-1, -1, self._emb_size], 'float32'], "embedding_table": [[-1, self._voc_size, self._emb_size], 'float32'], "encoder_outputs": [[-1, -1, self._emb_size], 'float32'], "sentence_embedding": [[-1, self._emb_size], 'float32'], "sentence_pair_embedding": [[-1, self._emb_size], 'float32']} def build(self, inputs, scope_name=""): src_ids = inputs['token_ids'] pos_ids = inputs['position_ids'] sent_ids = inputs['segment_ids'] input_mask = inputs['input_mask'] task_ids = inputs['task_ids'] # 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._emb_dtype, param_attr=fluid.ParamAttr( name=scope_name+self._word_emb_name, initializer=self._param_initializer), is_sparse=False) # fluid.global_scope().find_var('backbone-word_embedding').get_tensor() embedding_table = fluid.default_main_program().global_block().var(scope_name+self._word_emb_name) position_emb_out = fluid.layers.embedding( input=pos_ids, size=[self._max_position_seq_len, self._emb_size], dtype=self._emb_dtype, param_attr=fluid.ParamAttr( name=scope_name+self._pos_emb_name, initializer=self._param_initializer)) sent_emb_out = fluid.layers.embedding( sent_ids, size=[self._sent_types, self._emb_size], dtype=self._emb_dtype, param_attr=fluid.ParamAttr( name=scope_name+self._sent_emb_name, initializer=self._param_initializer)) emb_out = emb_out + position_emb_out emb_out = emb_out + sent_emb_out task_emb_out = fluid.layers.embedding( task_ids, size=[self._task_types, self._emb_size], dtype=self._emb_dtype, param_attr=fluid.ParamAttr( name=scope_name+self._task_emb_name, initializer=self._param_initializer)) emb_out = emb_out + task_emb_out emb_out = pre_process_layer( emb_out, 'nd', self._prepostprocess_dropout, name=scope_name+'pre_encoder') 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 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._emb_size // self._n_head, d_value=self._emb_size // self._n_head, d_model=self._emb_size, d_inner_hid=self._emb_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=scope_name+'encoder') next_sent_feat = fluid.layers.slice( input=enc_out, axes=[1], starts=[0], ends=[1]) next_sent_feat = fluid.layers.reshape(next_sent_feat, [-1, next_sent_feat.shape[-1]]) next_sent_feat = fluid.layers.fc( input=next_sent_feat, size=self._emb_size, act="tanh", param_attr=fluid.ParamAttr( name=scope_name+"pooled_fc.w_0", initializer=self._param_initializer), bias_attr=scope_name+"pooled_fc.b_0") return {'embedding_table': embedding_table, 'word_embedding': emb_out, 'encoder_outputs': enc_out, 'sentence_embedding': next_sent_feat, 'sentence_pair_embedding': next_sent_feat} def postprocess(self, rt_outputs): pass