[PYTHON][EXAMPLES] Added self-attention example using triton.ops.einsum

python/examples/attention/reference.py

@@ -0,0 +1,72 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+class ScaledDotProductAttention(nn.Module):
+    ''' Scaled Dot-Product Attention '''
+
+    def __init__(self, temperature, attn_dropout=0.1):
+        super().__init__()
+        self.temperature = temperature
+        self.softmax = nn.Softmax(dim=2)
+
+    def forward(self, q, k, v, mask=None):
+        attn = torch.bmm(q, k.transpose(1, 2))
+        attn = attn / self.temperature
+        if mask is not None:
+            attn = attn.masked_fill(mask, -np.inf)
+        attn = self.softmax(attn)
+        output = torch.bmm(attn, v)
+        return output, attn
+
+
+
+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)
+        # normalization
+        self.layer_norm = nn.LayerNorm(d_model)
+        # scaled dot-product
+        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
+
+
+    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
+        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk
+        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk
+        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv
+        if mask:
+            mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
+        output, attn = self.attention(q, k, v, mask=mask)
+        # linear transformation
+        output = output.view(n_head, sz_b, len_q, d_v)
+        output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) # b x lq x (n*dv)
+        output = self.fc(output)
+        # normalization
+        output = self.layer_norm(output + residual)
+        return output, attn