84 lines
3.4 KiB
Python
84 lines
3.4 KiB
Python
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import math
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import numpy as np
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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 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, key, value, query, mask=None, query_mask=None):
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# Compute attention score
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attention = layers.matmul(query, key, transpose_y=True) #transpose the last dim in y
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attention = attention / math.sqrt(self.d_key)
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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 == 0).astype(np.float32)
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mask = mask * (-2 ** 32 + 1)
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attention = attention + mask
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attention = layers.softmax(attention)
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attention = layers.dropout(attention, 0.0)
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# Mask query to ignore padding
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# Not sure how to work
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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, num_hidden, d_k, d_q, num_head=4, dropout=0.1):
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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.key = dg.Linear(num_hidden, num_head * d_k)
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self.value = dg.Linear(num_hidden, num_head * d_k)
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self.query = dg.Linear(num_hidden, num_head * d_q)
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self.scal_attn = ScaledDotProductAttention(d_k)
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self.fc = dg.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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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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# repeat masks h times
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if query_mask is not None:
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query_mask = layers.expand(query_mask, [self.num_head, 1, seq_len_key])
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if mask is not None:
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mask = layers.expand(mask, (self.num_head, 1, 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(self.key(key), [batch_size, seq_len_key, self.num_head, self.d_k])
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value = layers.reshape(self.value(value), [batch_size, seq_len_key, self.num_head, self.d_k])
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query = layers.reshape(self.query(query_input), [batch_size, seq_len_query, self.num_head, self.d_q])
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key = layers.reshape(layers.transpose(key, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
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value = layers.reshape(layers.transpose(value, [2, 0, 1, 3]), [-1, seq_len_key, self.d_k])
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query = layers.reshape(layers.transpose(query, [2, 0, 1, 3]), [-1, seq_len_query, self.d_q])
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result, attention = self.scal_attn(key, value, query, mask=mask, query_mask=query_mask)
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# concat all multihead result
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result = layers.reshape(result, [self.num_head, batch_size, seq_len_query, self.d_q])
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result = layers.reshape(layers.transpose(result, [1,2,0,3]),[batch_size, seq_len_query, -1])
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result = layers.dropout(self.fc(result), self.dropout)
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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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