[Perf] Decouple torch op from GDA to leverage torch.compile (#27871)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>

vllm/model_executor/layers/kda.py

@@ -40,18 +40,36 @@ logger = init_logger(__name__)
 
 
 def kda_attention(
-    hidden_states: torch.Tensor,
-    output: torch.Tensor,
+    q_proj_states: torch.Tensor,
+    k_proj_states: torch.Tensor,
+    v_proj_states: torch.Tensor,
+    g1: torch.Tensor,
+    g2: torch.Tensor,
+    beta: torch.Tensor,
+    core_attn_out: torch.Tensor,
     layer_name: str,
 ) -> None:
     forward_context: ForwardContext = get_forward_context()
     self = forward_context.no_compile_layers[layer_name]
-    self._forward(hidden_states=hidden_states, output=output)
+    self._forward(
+        q_proj_states=q_proj_states,
+        k_proj_states=k_proj_states,
+        v_proj_states=v_proj_states,
+        g1=g1,
+        g2=g2,
+        beta=beta,
+        core_attn_out=core_attn_out,
+    )
 
 
 def kda_attention_fake(
-    hidden_states: torch.Tensor,
-    output: torch.Tensor,
+    q_proj_states: torch.Tensor,
+    k_proj_states: torch.Tensor,
+    v_proj_states: torch.Tensor,
+    g1: torch.Tensor,
+    g2: torch.Tensor,
+    beta: torch.Tensor,
+    core_attn_out: torch.Tensor,
     layer_name: str,
 ) -> None:
     return
@@ -60,7 +78,7 @@ def kda_attention_fake(
 direct_register_custom_op(
     op_name="kda_attention",
     op_func=kda_attention,
-    mutates_args=["output"],
+    mutates_args=["core_attn_out"],
     fake_impl=kda_attention_fake,
 )
 
@@ -241,37 +259,56 @@ class KimiDeltaAttention(nn.Module, MambaBase):
         hidden_states: torch.Tensor,
         positions: torch.Tensor,
         output: torch.Tensor,
-    ) -> None:
-        return torch.ops.vllm.kda_attention(
-            hidden_states,
-            output,
+    ) -> torch.Tensor:
+        num_tokens = hidden_states.size(0)
+        q = self.q_proj(hidden_states)[0]
+        k = self.k_proj(hidden_states)[0]
+        v = self.v_proj(hidden_states)[0]
+
+        beta = self.b_proj(hidden_states)[0].float().sigmoid()
+        g1 = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
+        g1 = fused_kda_gate(g1, self.A_log, self.head_dim, g_bias=self.dt_bias)
+        beta = beta.unsqueeze(0)
+        g1 = g1.unsqueeze(0)
+
+        g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
+        g2 = rearrange(g_proj_states, "... (h d) -> ... h d", d=self.head_dim)
+
+        core_attn_out = torch.zeros(
+            (1, num_tokens, self.local_num_heads, self.head_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+        torch.ops.vllm.kda_attention(
+            q,
+            k,
+            v,
+            g1,
+            g2,
+            beta,
+            core_attn_out,
             self.prefix,
         )
+        core_attn_out = self.o_norm(core_attn_out, g2)
+        core_attn_out = rearrange(core_attn_out, "1 n h d -> n (h d)")
+
+        return self.o_proj(core_attn_out)[0]
 
     def _forward(
         self,
-        hidden_states: torch.Tensor,
-        output: torch.Tensor,
+        q_proj_states: torch.Tensor,
+        k_proj_states: torch.Tensor,
+        v_proj_states: torch.Tensor,
+        g1: torch.Tensor,
+        g2: torch.Tensor,
+        beta: torch.Tensor,
+        core_attn_out: torch.Tensor,
     ) -> None:
         forward_context = get_forward_context()
         attn_metadata: AttentionMetadata = forward_context.attn_metadata
 
         if attn_metadata is None:
-            # V1 profile run
-            # Mimic the memory allocation in the real run
-            q = torch.empty_like(hidden_states)
-            k = torch.empty_like(hidden_states)
-            v = torch.empty_like(hidden_states)
-            g = hidden_states.new_empty(
-                hidden_states.size(0),
-                self.local_num_heads,
-                self.head_dim,
-                dtype=torch.float32,
-            )
-            beta = torch.empty(
-                hidden_states.size(0), self.local_num_heads, dtype=torch.float32
-            )
-            core_attn_out = torch.empty_like(hidden_states)
+            #     # V1 profile run
             return
 
         assert isinstance(attn_metadata, dict)
@@ -288,10 +325,6 @@ class KimiDeltaAttention(nn.Module, MambaBase):
         conv_state_k = conv_state_k.transpose(-1, -2)
         conv_state_v = conv_state_v.transpose(-1, -2)
 
-        q_proj_states = self.q_proj(hidden_states)[0]
-        k_proj_states = self.k_proj(hidden_states)[0]
-        v_proj_states = self.v_proj(hidden_states)[0]
-
         q_conv_weights = self.q_conv1d.weight.view(
             self.q_conv1d.weight.size(0), self.q_conv1d.weight.size(2)
         )
@@ -374,14 +407,6 @@ class KimiDeltaAttention(nn.Module, MambaBase):
             lambda x: rearrange(x, "n (h d) -> 1 n h d", d=self.head_dim), (q, k, v)
         )
 
-        beta = self.b_proj(hidden_states)[0].float().sigmoid()
-
-        g = self.f_b_proj(self.f_a_proj(hidden_states)[0])[0]
-        g = fused_kda_gate(g, self.A_log, self.head_dim, g_bias=self.dt_bias)
-
-        beta = beta.unsqueeze(0)
-        g = g.unsqueeze(0)
-
         if attn_metadata.num_prefills > 0:
             zero_idx = non_spec_state_indices_tensor[~has_initial_state]
             recurrent_state[zero_idx] = 0
@@ -393,7 +418,7 @@ class KimiDeltaAttention(nn.Module, MambaBase):
                 q=q,
                 k=k,
                 v=v,
-                g=g,
+                g=g1,
                 beta=beta,
                 initial_state=initial_state,
                 output_final_state=True,
@@ -410,17 +435,12 @@ class KimiDeltaAttention(nn.Module, MambaBase):
                 q=q,
                 k=k,
                 v=v,
-                g=g,
+                g=g1,
                 beta=beta,
                 initial_state=recurrent_state,
                 use_qk_l2norm_in_kernel=True,
                 cu_seqlens=non_spec_query_start_loc,
                 ssm_state_indices=non_spec_state_indices_tensor,
             )
-
-        g_proj_states = self.g_b_proj(self.g_a_proj(hidden_states)[0])[0]
-        g = rearrange(g_proj_states, "... (h d) -> ... h d", d=self.head_dim)
-        core_attn_out = self.o_norm(core_attn_out_non_spec, g)
-        core_attn_out = rearrange(core_attn_out, "1 n h d -> n (h d)")
-
-        output[:] = self.o_proj(core_attn_out)[0]
+        assert core_attn_out_non_spec.shape == core_attn_out.shape
+        core_attn_out[:] = core_attn_out_non_spec