Adding transpose to att_output before concatenate all attention heads
Browse files
Transformer_Implementation_Tutorial.ipynb
CHANGED
@@ -310,6 +310,7 @@
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" att_output = torch.matmul(att_score, V_state)\n",
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" \n",
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" # concatinate all attention heads\n",
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" att_output = att_output.contiguous().view(batch_size, seq_len, self.num_head*self.d_head) \n",
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" \n",
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" # final linear transformation to the concatenated output\n",
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@@ -792,7 +793,6 @@
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" self.num_head = num_head\n",
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" self.dropout = dropout\n",
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" self.bias = bias\n",
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-
"\n",
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" \n",
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" # Encoder stack\n",
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" self.encoder_stack = nn.ModuleList([ TransformerEncoder(\n",
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@@ -820,7 +820,6 @@
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" encoder_output = encoder(embed_input = encoder_output, padding_mask = padding_mask)\n",
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" decoder_output = decoder(embed_input = decoder_output, cross_input =encoder_output, padding_mask=padding_mask)\n",
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" \n",
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-
" \n",
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" return decoder_output"
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]
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},
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" att_output = torch.matmul(att_score, V_state)\n",
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" \n",
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" # concatinate all attention heads\n",
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+
" att_output = att_output.transpose(1, 2)\n",
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" att_output = att_output.contiguous().view(batch_size, seq_len, self.num_head*self.d_head) \n",
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" \n",
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" # final linear transformation to the concatenated output\n",
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" self.num_head = num_head\n",
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" self.dropout = dropout\n",
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" self.bias = bias\n",
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" \n",
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" # Encoder stack\n",
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" self.encoder_stack = nn.ModuleList([ TransformerEncoder(\n",
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" encoder_output = encoder(embed_input = encoder_output, padding_mask = padding_mask)\n",
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" decoder_output = decoder(embed_input = decoder_output, cross_input =encoder_output, padding_mask=padding_mask)\n",
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" \n",
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" return decoder_output"
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]
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},
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