85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import init
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class SimplifiedScaledDotProductAttention(nn.Module):
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'''
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Scaled dot-product attention
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'''
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def __init__(self, d_model, h,dropout=.1):
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'''
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:param d_model: Output dimensionality of the model
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:param d_k: Dimensionality of queries and keys
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:param d_v: Dimensionality of values
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:param h: Number of heads
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'''
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super(SimplifiedScaledDotProductAttention, self).__init__()
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self.d_model = d_model
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self.d_k = d_model//h
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self.d_v = d_model//h
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self.h = h
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self.fc_o = nn.Linear(h * self.d_v, d_model)
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self.dropout=nn.Dropout(dropout)
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self.init_weights()
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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init.normal_(m.weight, std=0.001)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, queries, keys, values, attention_mask=None, attention_weights=None):
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'''
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Computes
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:param queries: Queries (b_s, nq, d_model)
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:param keys: Keys (b_s, nk, d_model)
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:param values: Values (b_s, nk, d_model)
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:param attention_mask: Mask over attention values (b_s, h, nq, nk). True indicates masking.
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:param attention_weights: Multiplicative weights for attention values (b_s, h, nq, nk).
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:return:
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'''
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b_s, nq = queries.shape[:2]
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nk = keys.shape[1]
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q = queries.view(b_s, nq, self.h, self.d_k).permute(0, 2, 1, 3) # (b_s, h, nq, d_k)
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k = keys.view(b_s, nk, self.h, self.d_k).permute(0, 2, 3, 1) # (b_s, h, d_k, nk)
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v = values.view(b_s, nk, self.h, self.d_v).permute(0, 2, 1, 3) # (b_s, h, nk, d_v)
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att = torch.matmul(q, k) / np.sqrt(self.d_k) # (b_s, h, nq, nk)
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if attention_weights is not None:
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att = att * attention_weights
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if attention_mask is not None:
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att = att.masked_fill(attention_mask, -np.inf)
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att = torch.softmax(att, -1)
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att=self.dropout(att)
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out = torch.matmul(att, v).permute(0, 2, 1, 3).contiguous().view(b_s, nq, self.h * self.d_v) # (b_s, nq, h*d_v)
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out = self.fc_o(out) # (b_s, nq, d_model)
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return out
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if __name__ == '__main__':
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input=torch.randn(50,49,512)
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ssa = SimplifiedScaledDotProductAttention(d_model=512, h=8)
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output=ssa(input,input,input)
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print(output.shape)
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