History prior to this date belonged to the now deprecated ISAAC project, and was deleted to save space

python/examples/attention/optimized.py

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