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readability improvements
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@@ -1658,11 +1658,18 @@
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"\n",
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" # Compute scaled dot-product attention (aka self-attention) with a causal mask\n",
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" attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head\n",
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" attn_scores.masked_fill_(self.mask.bool()[:num_tokens, :num_tokens].unsqueeze(0).unsqueeze(0), -torch.inf)\n",
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" # Original mask truncated to the number of tokens and converted to boolean\n",
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" mask_bool = self.mask.bool()[:num_tokens, :num_tokens]\n",
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" # Unsqueeze the mask twice to match dimensions\n",
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" mask_unsqueezed = mask_bool.unsqueeze(0).unsqueeze(0)\n",
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" # Use the unsqueezed mask to fill attention scores\n",
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" attn_scores.masked_fill_(mask_unsqueezed, -torch.inf)\n",
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" \n",
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" attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)\n",
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" attn_weights = self.dropout(attn_weights)\n",
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"\n",
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" context_vec = (attn_weights @ values).transpose(1, 2) # Shape: (b, num_tokens, n_heads, head_dim)\n",
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" # Shape: (b, num_tokens, num_heads, head_dim)\n",
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" context_vec = (attn_weights @ values).transpose(1, 2) \n",
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" \n",
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" # Combine heads, where self.d_out = self.num_heads * self.head_dim\n",
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" context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)\n",
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