wehub-resource-sync
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class BiEncoder(nn.Layer):
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"""dual-encoder model
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Attributes:
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state: for question or for context
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question_encoder: used to code the problem
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context_encoder: used to code the context
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"""
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def __init__(self, question_encoder, context_encoder, state=None):
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super(BiEncoder, self).__init__()
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self.state = state
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if self.state is None:
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self.question_encoder = question_encoder
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self.context_encoder = context_encoder
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elif self.state == "FORQUESTION":
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self.question_encoder = question_encoder
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elif self.state == "FORCONTEXT":
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self.context_encoder = context_encoder
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def get_question_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):
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_, cls_embedding = self.question_encoder(input_ids, token_type_ids, position_ids, attention_mask)
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"""cls_embedding = self.emb_reduce_linear(cls_embedding)
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cls_embedding = self.dropout(cls_embedding)
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cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)"""
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return cls_embedding
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def get_context_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):
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_, cls_embedding = self.context_encoder(input_ids, token_type_ids, position_ids, attention_mask)
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"""cls_embedding = self.emb_reduce_linear(cls_embedding)
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cls_embedding = self.dropout(cls_embedding)
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cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)"""
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return cls_embedding
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def forward(
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self,
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question_id,
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question_segments,
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question_attn_mask,
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context_ids,
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context_segments,
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context_attn_mask,
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):
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question_pooled_out = self.get_question_pooled_embedding(question_id, question_segments, question_attn_mask)
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context_pooled_out = self.get_context_pooled_embedding(context_ids, context_segments, context_attn_mask)
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return question_pooled_out, context_pooled_out
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class BiEncoderNllLoss(object):
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"""
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calculate the nll loss for dual-encoder model
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"""
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def calc(self, q_vectors, ctx_vectors, positive_idx_per_question, loss_scale=None):
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scorces = paddle.matmul(q_vectors, paddle.transpose(ctx_vectors, [1, 0]))
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# if len(q_vectors.shape()) > 1:
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q_num = q_vectors.shape[0]
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scores = scorces.reshape([q_num, -1])
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softmax_scorces = F.log_softmax(scores, axis=1)
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loss = F.nll_loss(softmax_scorces, paddle.to_tensor(positive_idx_per_question))
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correct_predictions_count = None
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if loss_scale:
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loss.mul_(loss_scale)
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return loss, correct_predictions_count
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