perf: Optimize local attention memory allocation in FlashAttentionBackend (#6356)

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

@@ -1434,19 +1434,7 @@ class FlashAttentionBackend(AttentionBackend):
                 self.decode_cuda_graph_metadata[bs] = metadata
 
                 if self.attention_chunk_size is not None:
-                    metadata.local_attn_metadata = FlashAttentionMetadata.LocalAttentionMetadata(
-                        local_query_start_loc=self.decode_cuda_graph_local_attn_metadata[
-                            "local_query_start_loc"
-                        ],
-                        local_seqused_k=self.decode_cuda_graph_local_attn_metadata[
-                            "local_seqused_k"
-                        ],
-                        local_block_table=self.decode_cuda_graph_local_attn_metadata[
-                            "local_block_table"
-                        ],
-                        local_max_query_len=1,
-                        local_max_seq_len=1,
-                    )
+                    self._update_local_attn_metadata_for_capture(metadata, batch_size)
 
         elif forward_mode.is_target_verify():
             if self.topk <= 1:
@@ -1807,6 +1795,62 @@ class FlashAttentionBackend(AttentionBackend):
         )
         metadata.local_attn_metadata = local_metadata
 
+    def _update_local_attn_metadata_for_capture(
+        self, metadata: FlashAttentionMetadata, bs: int
+    ):
+        """Update local attention metadata during CUDA graph capture phase.
+
+        This method calculates the exact buffer sizes needed for local attention metadata
+        during the CUDA graph capture phase, optimizing memory usage by creating views of
+        pre-allocated buffers with exactly the sizes needed.
+        """
+        seq_lens_capture = metadata.cache_seqlens_int32
+        max_seq_len = int(seq_lens_capture.max().item())
+        page_table_capture = metadata.page_table
+
+        cu_seqlens_q_np = metadata.cu_seqlens_q.cpu().numpy()
+        seqlens_np = seq_lens_capture.cpu().numpy()
+        (
+            seqlens_q_local_np,
+            cu_seqlens_q_local_np,
+            seqlens_k_local_np,
+            block_table_local_np,
+        ) = make_local_attention_virtual_batches(
+            self.attention_chunk_size,
+            cu_seqlens_q_np,
+            seqlens_np,
+            page_table_capture,
+            self.page_size,
+        )
+
+        # Get exact dimensions from the calculation
+        q_len = len(cu_seqlens_q_local_np)
+        k_len = len(seqlens_k_local_np)
+        b0 = block_table_local_np.shape[0] if block_table_local_np.shape[0] > 0 else bs
+        b1 = block_table_local_np.shape[1] if block_table_local_np.shape[1] > 0 else 1
+
+        # Create views of the pre-allocated buffers with exactly these sizes
+        # This is the key optimization - we only use the memory we actually need
+        local_query_start_loc = self.decode_cuda_graph_local_attn_metadata[
+            "local_query_start_loc"
+        ][:q_len]
+
+        local_seqused_k = self.decode_cuda_graph_local_attn_metadata["local_seqused_k"][
+            :k_len
+        ]
+
+        local_block_table = self.decode_cuda_graph_local_attn_metadata[
+            "local_block_table"
+        ][:b0, :b1]
+
+        metadata.local_attn_metadata = FlashAttentionMetadata.LocalAttentionMetadata(
+            local_query_start_loc=local_query_start_loc,
+            local_seqused_k=local_seqused_k,
+            local_block_table=local_block_table,
+            local_max_query_len=1,
+            local_max_seq_len=max_seq_len,
+        )
+
     def _update_local_attn_metadata_for_replay(
         self, metadata: FlashAttentionMetadata, bs: int
     ):