wehub-resource-sync
94 lines
3.6 KiB
Python
94 lines
3.6 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.functional as F
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from base_model import SemanticIndexBase
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class SemanticIndexCacheNeg(SemanticIndexBase):
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def __init__(self, pretrained_model, dropout=None, margin=0.3, scale=30, output_emb_size=None):
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super().__init__(pretrained_model, dropout, output_emb_size)
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self.margin = margin
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# Used scaling cosine similarity to ease converge
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self.scale = scale
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def get_pooled_embedding_with_no_grad(
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self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None
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):
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if self.use_fp16:
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if attention_mask is None:
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attention_mask = paddle.unsqueeze(
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(input_ids == self.ptm.pad_token_id).astype(self.ptm.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
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)
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with paddle.no_grad():
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embedding_output = self.ptm.embeddings(
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input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids
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)
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embedding_output = paddle.cast(embedding_output, "float16")
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attention_mask = paddle.cast(attention_mask, "float16")
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with paddle.no_grad():
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encoder_outputs = self.ptm.encoder(embedding_output, attention_mask)
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if self.use_fp16:
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encoder_outputs = paddle.cast(encoder_outputs, "float32")
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cls_embedding = self.ptm.pooler(encoder_outputs)
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else:
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_, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)
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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 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.scale
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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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return cosine_sim, labels, query_cls_embedding, title_cls_embedding
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