105 lines
3.6 KiB
Python
105 lines
3.6 KiB
Python
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 Depth_Pointwise_Conv1d(nn.Module):
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def __init__(self, in_ch, out_ch, k):
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super().__init__()
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if (k == 1):
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self.depth_conv = nn.Identity()
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else:
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self.depth_conv = nn.Conv1d(
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in_channels=in_ch,
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out_channels=in_ch,
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kernel_size=k,
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groups=in_ch,
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padding=k // 2
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)
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self.pointwise_conv = nn.Conv1d(
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in_channels=in_ch,
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out_channels=out_ch,
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kernel_size=1,
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groups=1
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)
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def forward(self, x):
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out = self.pointwise_conv(self.depth_conv(x))
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return out
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class MUSEAttention(nn.Module):
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def __init__(self, d_model, d_k, d_v, h, dropout=.1):
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super(MUSEAttention, self).__init__()
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self.fc_q = nn.Linear(d_model, h * d_k)
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self.fc_k = nn.Linear(d_model, h * d_k)
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self.fc_v = nn.Linear(d_model, h * d_v)
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self.fc_o = nn.Linear(h * d_v, d_model)
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self.dropout = nn.Dropout(dropout)
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self.conv1 = Depth_Pointwise_Conv1d(h * d_v, d_model, 1)
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self.conv3 = Depth_Pointwise_Conv1d(h * d_v, d_model, 3)
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self.conv5 = Depth_Pointwise_Conv1d(h * d_v, d_model, 5)
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self.dy_paras = nn.Parameter(torch.ones(3))
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self.softmax = nn.Softmax(-1)
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self.d_model = d_model
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self.d_k = d_k
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self.d_v = d_v
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self.h = h
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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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# Self Attention
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b_s, nq = queries.shape[:2]
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nk = keys.shape[1]
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q = self.fc_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 = self.fc_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 = self.fc_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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v2 = v.permute(0, 1, 3, 2).contiguous().view(b_s, -1, nk) # bs,dim,n
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self.dy_paras = nn.Parameter(self.softmax(self.dy_paras))
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out2 = self.dy_paras[0] * self.conv1(v2) + self.dy_paras[1] * self.conv3(v2) + self.dy_paras[2] * self.conv5(v2)
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out2 = out2.permute(0, 2, 1) # bs.n.dim
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out = out + out2
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return out
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if __name__ == '__main__':
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block = MUSEAttention(d_model=512, d_k=512, d_v=512, h=8).cuda()
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input = torch.rand(64, 64, 512).cuda()
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output = block(input, input, input)
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print(input.size(), output.size())
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