wehub-resource-sync
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
# Copyright (c) 2021 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 SimCSE(nn.Layer):
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def __init__(self, pretrained_model, dropout=None, margin=0.0, scale=20, output_emb_size=None):
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super().__init__()
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self.ptm = pretrained_model
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self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
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# if output_emb_size is greater than 0, then add Linear layer to reduce embedding_size,
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# we recommend set output_emb_size = 256 considering the trade-off between
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# recall performance and efficiency
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self.output_emb_size = output_emb_size
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if output_emb_size > 0:
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weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02))
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self.emb_reduce_linear = paddle.nn.Linear(768, output_emb_size, weight_attr=weight_attr)
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self.margin = margin
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# Used scaling cosine similarity to ease converge
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self.sacle = scale
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def get_pooled_embedding(
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self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, with_pooler=True
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):
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# Note: cls_embedding is poolerd embedding with act tanh
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sequence_output, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)
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if with_pooler is False:
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cls_embedding = sequence_output[:, 0, :]
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if self.output_emb_size > 0:
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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 cosine_sim(
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self,
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query_input_ids,
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title_input_ids,
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query_token_type_ids=None,
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query_position_ids=None,
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query_attention_mask=None,
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title_token_type_ids=None,
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title_position_ids=None,
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title_attention_mask=None,
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with_pooler=True,
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):
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query_cls_embedding = self.get_pooled_embedding(
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query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask, with_pooler=with_pooler
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)
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title_cls_embedding = self.get_pooled_embedding(
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title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask, with_pooler=with_pooler
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)
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cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding, axis=-1)
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return cosine_sim
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def forward(
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self,
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query_input_ids,
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title_input_ids,
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query_token_type_ids=None,
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query_position_ids=None,
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query_attention_mask=None,
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title_token_type_ids=None,
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title_position_ids=None,
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title_attention_mask=None,
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):
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query_cls_embedding = self.get_pooled_embedding(
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query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask
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)
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title_cls_embedding = self.get_pooled_embedding(
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title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask
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)
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cosine_sim = paddle.matmul(query_cls_embedding, title_cls_embedding, transpose_y=True)
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# subtract margin from all positive samples cosine_sim()
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margin_diag = paddle.full(
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shape=[query_cls_embedding.shape[0]], fill_value=self.margin, dtype=paddle.get_default_dtype()
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)
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cosine_sim = cosine_sim - paddle.diag(margin_diag)
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# scale cosine to ease training converge
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cosine_sim *= self.sacle
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labels = paddle.arange(0, query_cls_embedding.shape[0], dtype="int64")
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labels = paddle.reshape(labels, shape=[-1, 1])
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loss = F.cross_entropy(input=cosine_sim, label=labels)
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return loss
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