[FA3 Attn Backend] Remove Unnecessary Device Sync for FA3 (#4745)

Co-authored-by: Yubo Wang <yubowang2019@gmail.com>

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

@@ -29,11 +29,11 @@ class FlashAttentionMetadata:
 
     cu_seqlens_q: torch.Tensor = None
     cu_seqlens_k: torch.Tensor = None
+    max_seq_len_q: int = 0
     max_seq_len_k: int = 0
     window_size: tuple = (-1, -1)
     page_table: torch.Tensor = None
     cache_seqlens_int32: torch.Tensor = None
-    max_seq_len_q: int = 0
 
 
 class FlashAttentionBackend(AttentionBackend):
@@ -63,7 +63,6 @@ class FlashAttentionBackend(AttentionBackend):
         # Create metadata based on forward mode
         metadata = FlashAttentionMetadata()
 
-        extend_seq_lens = forward_batch.extend_seq_lens
         # Get sequence information
         seqlens_in_batch = forward_batch.seq_lens
         # Precompute int32 version of sequence lengths
@@ -85,15 +84,16 @@ class FlashAttentionBackend(AttentionBackend):
                 0, batch_size + 1, dtype=torch.int32, device=device
             )
         else:
-            extend_no_prefix = not any(forward_batch.extend_prefix_lens)
             # Precompute cumulative sequence lengths
-            if not extend_no_prefix:
+            if any(forward_batch.extend_prefix_lens_cpu):
+                extend_seq_lens = forward_batch.extend_seq_lens
                 metadata.cu_seqlens_q = torch.nn.functional.pad(
                     torch.cumsum(extend_seq_lens, dim=0, dtype=torch.int32), (1, 0)
                 )
+                metadata.max_seq_len_q = max(forward_batch.extend_seq_lens_cpu)
             else:
                 metadata.cu_seqlens_q = metadata.cu_seqlens_k
-            metadata.max_seq_len_q = seqlens_in_batch.max().item()
+                metadata.max_seq_len_q = metadata.max_seq_len_k
         self.forward_metadata = metadata
 
     def forward_extend(
@@ -274,20 +274,26 @@ class FlashAttentionBackend(AttentionBackend):
         seq_lens_cpu: Optional[torch.Tensor],
     ):
         # """Initialize forward metadata for replaying CUDA graph."""
-        seqlens_in_batch = seq_lens[:bs]
         metadata = self.decode_cuda_graph_metadata[bs]
-        metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
+
+        # For CPU operations
+        max_len = seq_lens_cpu[:bs].max().item()
+        metadata.max_seq_len_k = max_len
+
+        # For GPU operations
+        seq_lens_in_batch = seq_lens[:bs]
+        metadata.cache_seqlens_int32 = seq_lens_in_batch.to(torch.int32)
         metadata.cu_seqlens_k = torch.nn.functional.pad(
-            torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)
+            torch.cumsum(seq_lens_in_batch, dim=0, dtype=torch.int32), (1, 0)
         )
-        # Precompute maximum sequence length
-        metadata.max_seq_len_k = seqlens_in_batch.max().item()
+
         # Only zero out the part out of max_len_k
         metadata.page_table[:, metadata.max_seq_len_k :].fill_(0)
         # Then do the copy
         metadata.page_table[:, : metadata.max_seq_len_k].copy_(
             self.req_to_token[req_pool_indices[:bs], : metadata.max_seq_len_k]
         )
+
         self.forward_decode_metadata = metadata
 
     def get_cuda_graph_seq_len_fill_value(self):

python/sglang/srt/managers/schedule_batch.py

@@ -1376,6 +1376,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             if (
                 global_server_args_dict["enable_flashinfer_mla"]
                 or global_server_args_dict["enable_flashmla"]
+                or global_server_args_dict["attention_backend"] == "fa3"
             ):
                 decode_seq_lens = self.seq_lens.cpu()
             else: