opt flashinfer mla cat (#5822)

Co-authored-by: xuyongfei.xyf <xuyongfei.xyf@antgroup.com>

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

@@ -339,22 +339,38 @@ class FlashInferMLAAttnBackend(AttentionBackend):
         layer: RadixAttention,
         forward_batch: ForwardBatch,
         save_kv_cache: bool = True,
+        q_rope: Optional[torch.Tensor] = None,
+        k_rope: Optional[torch.Tensor] = None,
     ):
 
         cache_loc = forward_batch.out_cache_loc
         logits_soft_cap = layer.logit_cap
         prefill_wrapper_paged = self.forward_metadata.prefill_wrapper
-        qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
         k_buf = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
 
         # Save kv cache
         if save_kv_cache and k is not None:
             assert v is not None
             if save_kv_cache:
+                if k_rope is not None:
+                    forward_batch.token_to_kv_pool.set_mla_kv_buffer(
+                        layer, cache_loc, k, k_rope
+                    )
+                else:
                     forward_batch.token_to_kv_pool.set_kv_buffer(layer, cache_loc, k, v)
+        if q_rope is not None:
+            q = q.view(-1, layer.tp_q_head_num, layer.v_head_dim)
+            q_rope = q_rope.view(
+                -1, layer.tp_q_head_num, layer.head_dim - layer.v_head_dim
+            )
 
         if self.forward_metadata.use_ragged:
             # ragged prefill
+            if q_rope is not None:
+                q = torch.cat([q, q_rope], dim=-1)
+            qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
+            if k_rope is not None:
+                k = torch.cat([k, k_rope], dim=-1)
             o = self.prefill_wrapper_ragged.forward(
                 qall,
                 k.view(-1, layer.tp_k_head_num, layer.head_dim),
@@ -365,11 +381,19 @@ class FlashInferMLAAttnBackend(AttentionBackend):
             )
         else:
             # mla paged prefill
-            o = prefill_wrapper_paged.run(
+            if q_rope is None:
+                qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
+                q, q_rope = (
                     qall[:, :, : layer.v_head_dim],
                     qall[:, :, layer.v_head_dim :],
+                )
+            o = q.new_empty(q.shape)
+            o = prefill_wrapper_paged.run(
+                q,
+                q_rope,
                 k_buf[:, :, : layer.v_head_dim],
                 k_buf[:, :, layer.v_head_dim :],
+                out=o,
             )
 
         return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
@@ -382,6 +406,9 @@ class FlashInferMLAAttnBackend(AttentionBackend):
         layer: RadixAttention,
         forward_batch: ForwardBatch,
         save_kv_cache: bool = True,
+        # For multi-head latent attention
+        q_rope: Optional[torch.Tensor] = None,
+        k_rope: Optional[torch.Tensor] = None,
     ):
         decode_wrapper = self.forward_metadata.decode_wrapper
         cache_loc = forward_batch.out_cache_loc
@@ -389,6 +416,14 @@ class FlashInferMLAAttnBackend(AttentionBackend):
         if k is not None:
             assert v is not None
             if save_kv_cache:
+                if k_rope is not None:
+                    forward_batch.token_to_kv_pool.set_mla_kv_buffer(
+                        layer,
+                        cache_loc,
+                        k,
+                        k_rope,
+                    )
+                else:
                     forward_batch.token_to_kv_pool.set_kv_buffer(
                         layer,
                         cache_loc,
@@ -397,15 +432,26 @@ class FlashInferMLAAttnBackend(AttentionBackend):
                     )
 
         # Reshape inputs
+        if q_rope is not None:
+            q_nope = q.view(-1, layer.tp_q_head_num, layer.v_head_dim)
+            q_rope = q_rope.view(
+                -1, layer.tp_q_head_num, layer.head_dim - layer.v_head_dim
+            )
+        else:
             reshaped_q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
+            q_nope = reshaped_q[:, :, : layer.v_head_dim]
+            q_rope = reshaped_q[:, :, layer.v_head_dim :]
+
         k_buffer = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
 
+        o = q_nope.new_empty(q_nope.shape)
         # Direct call to run without the wrapper
         o = decode_wrapper.run(
-            reshaped_q[:, :, : layer.v_head_dim],
-            reshaped_q[:, :, layer.v_head_dim :],
+            q_nope,
+            q_rope,
             k_buffer[:, :, : layer.v_head_dim],
             k_buffer[:, :, layer.v_head_dim :],
+            out=o,
         )
 
         return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)

python/sglang/srt/models/deepseek_v2.py

@@ -777,7 +777,7 @@ class DeepseekV2AttentionMLA(nn.Module):
         q_nope_out = q_nope_out.transpose(0, 1)
         q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
 
-        if self.attention_backend == "fa3":
+        if self.attention_backend == "fa3" or self.attention_backend == "flashinfer":
             attn_output = self.attn_mqa(
                 q_nope_out, k_nope, k_nope, forward_batch, q_rope=q_pe, k_rope=k_pe
             )