Fix trtllm_mla slow concat kernel in MTP (#10777)

python/sglang/srt/layers/attention/trtllm_mla_backend.py

@@ -505,10 +505,7 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
             q_rope_reshaped = q_rope.view(
                 -1, layer.tp_q_head_num, layer.head_dim - layer.v_head_dim
             )
-            if _is_cuda and q_nope.shape[-1] == 512 and q_rope_reshaped.shape[-1] == 64:
-                query = concat_mla_absorb_q(q_nope, q_rope_reshaped)
-            else:
-                query = torch.cat([q_nope, q_rope_reshaped], dim=-1)
+            query = _concat_mla_absorb_q_general(q_nope, q_rope_reshaped)
         else:
             # For FP8 path, we already have the query and rope parts merged because of the quantize_and_rope_for_fp8 function
             query = q.view(-1, layer.tp_q_head_num, layer.head_dim)
@@ -591,7 +588,7 @@ class TRTLLMMLABackend(FlashInferMLAAttnBackend):
             q_rope = q_rope.view(
                 -1, layer.tp_q_head_num, layer.head_dim - layer.v_head_dim
             )
-            q = torch.cat([q, q_rope], dim=-1)
+            q = _concat_mla_absorb_q_general(q, q_rope)
 
         q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
 
@@ -716,3 +713,10 @@ class TRTLLMMLAMultiStepDraftBackend(FlashInferMLAMultiStepDraftBackend):
                 kv_indptr_buf=self.kv_indptr[i],
                 q_indptr_decode_buf=self.q_indptr_decode,
             )
+
+
+def _concat_mla_absorb_q_general(q_nope, q_rope):
+    if _is_cuda and q_nope.shape[-1] == 512 and q_rope.shape[-1] == 64:
+        return concat_mla_absorb_q(q_nope, q_rope)
+    else:
+        return torch.cat([q_nope, q_rope], dim=-1)