50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
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import numpy as np
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import torch
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import torch.nn as nn
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import triton
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class MultiHeadAttention(nn.Module):
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''' Multi-Head Attention module '''
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def __init__(self, n_head, d_model, d_k, d_v):
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super().__init__()
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self.n_head = n_head
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self.d_k = d_k
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self.d_v = d_v
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# linear layers
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self.w_qs = nn.Linear(d_model, n_head * d_k)
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self.w_ks = nn.Linear(d_model, n_head * d_k)
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self.w_vs = nn.Linear(d_model, n_head * d_v)
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self.fc = nn.Linear(n_head * d_v, d_model)
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# initialize weights
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nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
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nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
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nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))
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nn.init.xavier_normal_(self.fc.weight)
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# layer normalization
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self.layer_norm = nn.LayerNorm(d_model)
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def forward(self, q, k, v, mask=None):
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# dimensions
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d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
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sz_b, len_q, _ = q.size()
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sz_b, len_k, _ = k.size()
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sz_b, len_v, _ = v.size()
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# linear transformations
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residual = q
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q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
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k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
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v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
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# scaled dot-product attention
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attn = triton.ops.einsum('blhk,bthk->hblt', q, k, [n_head, sz_b, len_q, len_k])
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attn = attn / np.sqrt(d_k)
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if mask is not None:
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attn = attn.masked_fill(mask[None], -np.inf)
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attn = torch.softmax(attn, dim=3)
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output = triton.ops.einsum('hblt,bthv->blhv', attn, v, [sz_b, len_q, n_head, d_v])
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output = output.view(sz_b, len_q, -1)
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output = self.fc(output)
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# epilogue
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output = self.layer_norm(output + residual)
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return output, attn
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