199 lines
7.2 KiB
Python
199 lines
7.2 KiB
Python
# Copyright (c) 2020 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 math
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dg
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import paddle.fluid.layers as layers
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class Linear(dg.Layer):
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def __init__(self,
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in_features,
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out_features,
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is_bias=True,
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dtype="float32"):
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super(Linear, self).__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.dtype = dtype
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self.weight = fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer())
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self.bias = is_bias
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if is_bias is not False:
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k = math.sqrt(1 / in_features)
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self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k))
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self.linear = dg.Linear(
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in_features,
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out_features,
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param_attr=self.weight,
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bias_attr=self.bias, )
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def forward(self, x):
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x = self.linear(x)
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return x
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class ScaledDotProductAttention(dg.Layer):
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def __init__(self, d_key):
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super(ScaledDotProductAttention, self).__init__()
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self.d_key = d_key
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# please attention this mask is diff from pytorch
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def forward(self,
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key,
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value,
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query,
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mask=None,
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query_mask=None,
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dropout=0.1):
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"""
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Scaled Dot Product Attention.
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Args:
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key (Variable): Shape(B, T, C), dtype: float32. The input key of attention.
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value (Variable): Shape(B, T, C), dtype: float32. The input value of attention.
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query (Variable): Shape(B, T, C), dtype: float32. The input query of attention.
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mask (Variable): Shape(B, len_q, len_k), dtype: float32. The mask of key.
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query_mask (Variable): Shape(B, len_q, 1), dtype: float32. The mask of query.
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dropout (Constant): dtype: float32. The probability of dropout.
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Returns:
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result (Variable), Shape(B, T, C), the result of mutihead attention.
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attention (Variable), Shape(n_head * B, T, C), the attention of key.
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"""
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# Compute attention score
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attention = layers.matmul(
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query, key, transpose_y=True, alpha=self.d_key
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**-0.5) #transpose the last dim in y
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# Mask key to ignore padding
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if mask is not None:
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attention = attention + mask
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attention = layers.softmax(attention)
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attention = layers.dropout(
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attention, dropout, dropout_implementation='upscale_in_train')
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# Mask query to ignore padding
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if query_mask is not None:
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attention = attention * query_mask
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result = layers.matmul(attention, value)
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return result, attention
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class MultiheadAttention(dg.Layer):
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def __init__(self,
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num_hidden,
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d_k,
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d_q,
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num_head=4,
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is_bias=False,
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dropout=0.1,
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is_concat=True):
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super(MultiheadAttention, self).__init__()
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self.num_hidden = num_hidden
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self.num_head = num_head
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self.d_k = d_k
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self.d_q = d_q
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self.dropout = dropout
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self.is_concat = is_concat
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self.key = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
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self.value = Linear(num_hidden, num_head * d_k, is_bias=is_bias)
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self.query = Linear(num_hidden, num_head * d_q, is_bias=is_bias)
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self.scal_attn = ScaledDotProductAttention(d_k)
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if self.is_concat:
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self.fc = Linear(num_head * d_q * 2, num_hidden)
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else:
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self.fc = Linear(num_head * d_q, num_hidden)
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self.layer_norm = dg.LayerNorm(num_hidden)
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def forward(self, key, value, query_input, mask=None, query_mask=None):
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"""
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Multihead Attention.
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Args:
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key (Variable): Shape(B, T, C), dtype: float32. The input key of attention.
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value (Variable): Shape(B, T, C), dtype: float32. The input value of attention.
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query_input (Variable): Shape(B, T, C), dtype: float32. The input query of attention.
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mask (Variable): Shape(B, len_q, len_k), dtype: float32. The mask of key.
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query_mask (Variable): Shape(B, len_q, 1), dtype: float32. The mask of query.
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Returns:
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result (Variable), Shape(B, T, C), the result of mutihead attention.
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attention (Variable), Shape(n_head * B, T, C), the attention of key.
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"""
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batch_size = key.shape[0]
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seq_len_key = key.shape[1]
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seq_len_query = query_input.shape[1]
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# Make multihead attention
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# key & value.shape = (batch_size, seq_len, feature)(feature = num_head * num_hidden_per_attn)
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key = layers.reshape(
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self.key(key), [batch_size, seq_len_key, self.num_head, self.d_k])
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value = layers.reshape(
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self.value(value),
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[batch_size, seq_len_key, self.num_head, self.d_k])
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query = layers.reshape(
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self.query(query_input),
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[batch_size, seq_len_query, self.num_head, self.d_q])
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key = layers.reshape(
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layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
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value = layers.reshape(
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layers.transpose(value, [2, 0, 1, 3]),
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[-1, seq_len_key, self.d_k])
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query = layers.reshape(
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layers.transpose(query, [2, 0, 1, 3]),
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[-1, seq_len_query, self.d_q])
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result, attention = self.scal_attn(
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key, value, query, mask=mask, query_mask=query_mask)
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key = layers.reshape(
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layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
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value = layers.reshape(
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layers.transpose(value, [2, 0, 1, 3]),
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[-1, seq_len_key, self.d_k])
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query = layers.reshape(
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layers.transpose(query, [2, 0, 1, 3]),
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[-1, seq_len_query, self.d_q])
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result, attention = self.scal_attn(
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key, value, query, mask=mask, query_mask=query_mask)
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# concat all multihead result
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result = layers.reshape(
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result, [self.num_head, batch_size, seq_len_query, self.d_q])
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result = layers.reshape(
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layers.transpose(result, [1, 2, 0, 3]),
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[batch_size, seq_len_query, -1])
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if self.is_concat:
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result = layers.concat([query_input, result], axis=-1)
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result = layers.dropout(
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self.fc(result),
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self.dropout,
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dropout_implementation='upscale_in_train')
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result = result + query_input
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result = self.layer_norm(result)
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return result, attention
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