compute only query vector when reusing kv

llmfoundry/models/layers/attention.py

@@ -460,18 +460,29 @@ def __init__(
             self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
         self.attn_dropout_p = attn_pdrop
 
-        self.Wqkv = build_fc(
-            name=fc_type_name,
-            in_features=self.d_model,
-            out_features=self.d_model + 2 * self.kv_n_heads * self.head_dim,
-            fc_kwargs=fc_type,
-        )
-        # for param init fn; enables shape based init of fused layers
-        fuse_splits = [
-            i * self.head_dim
-            for i in range(1, self.n_heads + 2 * self.kv_n_heads)
-        ]
-        self.Wqkv._fused = (0, fuse_splits)
+        if self.reuse_kv_layer_idx is None:
+            self.Wqkv = build_fc(
+                name=fc_type_name,
+                in_features=self.d_model,
+                out_features=self.d_model + 2 * self.kv_n_heads * self.head_dim,
+                fc_kwargs=fc_type,
+            )
+            # for param init fn; enables shape based init of fused layers
+            fuse_splits = [
+                i * self.head_dim
+                for i in range(1, self.n_heads + 2 * self.kv_n_heads)
+            ]
+            self.Wqkv._fused = (0, fuse_splits)
+        else:
+            self.Wq = build_fc(
+                name=fc_type_name,
+                in_features=self.d_model,
+                out_features=self.d_model,
+                fc_kwargs=fc_type,
+            )
+            # for param init fn; enables shape based init of fused layers
+            fuse_splits = [i * self.head_dim for i in range(1, self.n_heads)]
+            self.Wq._fused = (0, fuse_splits)
 
         if self.qk_ln or self.qk_gn:
             norm_size = self.head_dim if qk_gn else d_model
@@ -480,13 +491,14 @@ def __init__(
                 normalized_shape=norm_size,
                 device=device,
             )
-            if qk_ln:
-                norm_size = self.head_dim * kv_n_heads
-            self.k_ln = build_norm(
-                name=norm_type.lower(),
-                normalized_shape=norm_size,
-                device=device,
-            )
+            if self.reuse_kv_layer_idx is None:
+                if qk_ln:
+                    norm_size = self.head_dim * kv_n_heads
+                self.k_ln = build_norm(
+                    name=norm_type.lower(),
+                    normalized_shape=norm_size,
+                    device=device,
+                )
 
         self.attn_fn = attention_implementations.get(self.attn_impl)
 
@@ -564,6 +576,27 @@ def get_qkv(
             key (torch.Tensor): The key tensor.
             value (torch.Tensor): The value tensor.
         """
+        if self.reuse_kv_layer_idx is not None:
+            if prev_layer_key_value is None:
+                raise ValueError(
+                    'prev_layer_key_value is None, cannot reuse_prev_layer_kv.',
+                )
+            key, value = prev_layer_key_value
+
+            query = self.Wq(x)
+            if self.clip_qkv:
+                query = query.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+
+            if self.qk_ln or self.qk_gn:
+                # Applying layernorm to qk
+                q_shape = query.shape
+                if self.qk_gn:
+                    b, s = query.shape[:2]
+                    query = query.view(b, s, self.n_heads, -1)
+                dtype = query.dtype
+                query = self.q_ln(query).to(dtype).view(q_shape)
+            return query, key, value
+
         qkv = self.Wqkv(x)
 
         if self.clip_qkv:
@@ -589,14 +622,6 @@ def get_qkv(
             query = self.q_ln(query).to(dtype).view(q_shape)
             key = self.k_ln(key).to(dtype).view(k_shape)
 
-        if self.reuse_kv_layer_idx is not None:
-            # TODO: We still compute key and values in the code above, even if we end up reusing previous layer's kv cache. We should avoid this wasteful computation.
-            if prev_layer_key_value is None:
-                raise ValueError(
-                    'prev_layer_key_value is None, cannot reuse_prev_layer_kv.',
-                )
-            key, value = prev_layer_key_value
-
         return query, key, value
 
     def _apply_rotary_embeddings(