Add the implementations for the MultiHeadAttention Layer

Add the implementations for the MultiHeadAttention Layer

Author: calmdown539

examples/singa_peft/examples/model/trans.py

@@ -372,3 +372,75 @@ def matmul4d(x1, x2):
         ys.append(yb)
     y = autograd.cat(ys, axis=0)
     return y
+    
+class MultiHeadAttention(layer.Layer):
+    def __init__(self, d_model=512, n_head=8):
+        super(MultiHeadAttention, self).__init__()
+        self.d_k = d_model // n_head
+        assert (
+                self.d_k * n_head == d_model
+        ), "embed_dim must be divisible by num_heads"
+        self.d_model = d_model
+        self.d_v = self.d_k
+        self.n_head = n_head
+        self.W_Q = Linear3D(d_model, self.d_k * n_head)
+        self.W_K = Linear3D(d_model, self.d_k * n_head)
+        self.W_V = Linear3D(d_model, self.d_v * n_head)
+
+        self.scaled_dot_product_attention = ScaledDotProductAttention(d_model, n_head)
+        self.linear = Linear3D(self.d_v * n_head, d_model)
+        self.add = layer.Add()
+        self.layer_norm = LayerNorm(d_model)
+
+    def forward(self, query, key, value, attn_mask):
+        """
+        Args:
+            query: [batch_size, len_q, d_model]
+            key: [batch_size, len_k, d_model]
+            value: [batch_size, len_v(=len_k), d_model]
+            attn_mask: [batch_size, seq_len, seq_len]
+        Returns:
+        """
+        residual = query
+        batch_size = query.shape[0]
+
+        # (B, S, D) -proj-> (B, S, D_new) -split-> (B, S, H, W) -trans-> (B, H, S, W)
+        Q = self.W_Q(query)
+        Q = autograd.reshape(Q, [batch_size, -1, self.n_head, self.d_k])
+        Q = autograd.transpose(Q, [0, 2, 1, 3])
+
+        K = self.W_K(key)
+        K = autograd.reshape(K, [batch_size, -1, self.n_head, self.d_k])
+        K = autograd.transpose(K, [0, 2, 1, 3])
+
+        V = self.W_V(value)
+        V = autograd.reshape(V, [batch_size, -1, self.n_head, self.d_v])
+        V = autograd.transpose(V, [0, 2, 1, 3])
+
+        # Q: [batch_size, n_heads, len_q, d_k]
+        # K: [batch_size, n_heads, len_k, d_k]
+        # V: [batch_size, n_heads, len_v(=len_k), d_v]
+
+        # attn_mask : [batch_size, n_heads, seq_len, seq_len]
+        attn_mask = MultiHeadAttention._get_attn_mask(attn_mask, self.n_head)
+
+        # context: [batch_size, n_heads, len_q, d_v]
+        # attn: [batch_size, n_heads, seq_len, seq_len]
+        context, attn = self.scaled_dot_product_attention(Q, K, V, attn_mask)
+        context = autograd.transpose(context, [0, 2, 1, 3])
+        # context: [batch_size, len_q, n_heads * d_v]
+        context = autograd.reshape(context, [batch_size, -1, self.n_head * self.d_v])
+
+        output = self.linear(context)
+        output = self.add(output, residual)
+        # [batch_size, len_q, d_model]
+        output = self.layer_norm(output)
+        return output, attn
+
+    @staticmethod
+    def _get_attn_mask(attn_mask, n_head):
+        batch_size, seq_q_len,seq_k_len = attn_mask.shape[0], attn_mask.shape[1], attn_mask.shape[2]
+        attn_mask_np = tensor.to_numpy(attn_mask)
+        attn_mask_np = np.expand_dims(attn_mask_np, axis=1)
+        attn_mask_np = np.broadcast_to(attn_mask_np, (batch_size, n_head, seq_q_len, seq_k_len))
+        return tensor.from_numpy(attn_mask_np)