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base_model.py
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base_model.py
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# Copyright (c) 2021 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.
import abc
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class SemanticIndexBase(nn.Layer):
def __init__(self, pretrained_model, dropout=None, output_emb_size=None):
super().__init__()
self.ptm = pretrained_model
self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
# if output_emb_size is not None, then add Linear layer to reduce embedding_size,
# we recommend set output_emb_size = 256 considering the trade-off between
# recall performance and efficiency
self.output_emb_size = output_emb_size
if output_emb_size > 0:
weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02))
self.emb_reduce_linear = paddle.nn.Linear(768, output_emb_size, weight_attr=weight_attr)
def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):
_, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)
if self.output_emb_size > 0:
cls_embedding = self.emb_reduce_linear(cls_embedding)
cls_embedding = self.dropout(cls_embedding)
cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)
return cls_embedding
def get_semantic_embedding(self, data_loader):
self.eval()
with paddle.no_grad():
for batch_data in data_loader:
input_ids, token_type_ids = batch_data
text_embeddings = self.get_pooled_embedding(input_ids, token_type_ids=token_type_ids)
yield text_embeddings
def cosine_sim(
self,
query_input_ids,
title_input_ids,
query_token_type_ids=None,
query_position_ids=None,
query_attention_mask=None,
title_token_type_ids=None,
title_position_ids=None,
title_attention_mask=None,
):
query_cls_embedding = self.get_pooled_embedding(
query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask
)
title_cls_embedding = self.get_pooled_embedding(
title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask
)
cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding, axis=-1)
return cosine_sim
@abc.abstractmethod
def forward(self):
pass
class SemanticIndexBaseStatic(nn.Layer):
def __init__(self, pretrained_model, dropout=None, output_emb_size=None):
super().__init__()
self.ptm = pretrained_model
self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
# if output_emb_size is not None, then add Linear layer to reduce embedding_size,
# we recommend set output_emb_size = 256 considering the trade-off between
# recall performance and efficiency
self.output_emb_size = output_emb_size
if output_emb_size > 0:
weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02))
self.emb_reduce_linear = paddle.nn.Linear(768, output_emb_size, weight_attr=weight_attr)
@paddle.jit.to_static(
input_spec=[
paddle.static.InputSpec(shape=[None, None], dtype="int64"),
paddle.static.InputSpec(shape=[None, None], dtype="int64"),
]
)
def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):
_, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)
if self.output_emb_size > 0:
cls_embedding = self.emb_reduce_linear(cls_embedding)
cls_embedding = self.dropout(cls_embedding)
cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)
return cls_embedding
def get_semantic_embedding(self, data_loader):
self.eval()
with paddle.no_grad():
for batch_data in data_loader:
input_ids, token_type_ids = batch_data
text_embeddings = self.get_pooled_embedding(input_ids, token_type_ids=token_type_ids)
yield text_embeddings
def cosine_sim(
self,
query_input_ids,
title_input_ids,
query_token_type_ids=None,
query_position_ids=None,
query_attention_mask=None,
title_token_type_ids=None,
title_position_ids=None,
title_attention_mask=None,
):
query_cls_embedding = self.get_pooled_embedding(
query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask
)
title_cls_embedding = self.get_pooled_embedding(
title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask
)
cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding, axis=-1)
return cosine_sim
def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):
_, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)
if self.output_emb_size > 0:
cls_embedding = self.emb_reduce_linear(cls_embedding)
cls_embedding = self.dropout(cls_embedding)
cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)
return cls_embedding