[Bugfix] Fix Llama 4 FA3 local attention with paged KV cache (#32902)

Co-authored-by: kpham-sgl <khoa.pham@radixark.ai>

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

@@ -3183,6 +3183,9 @@ class FlashAttentionBackend(AttentionBackend):
         if cu_seqlens_q is None or cache_seqlens_int32 is None or page_table is None:
             metadata.local_attn_metadata = None
             return
+        if self.page_size > 1:
+            # Convert the eager token table to physical page indices.
+            page_table = page_table[:, :: self.page_size] // self.page_size
 
         cu_seqlens_q_np = cu_seqlens_q.cpu().numpy()
         seq_lens_np = cache_seqlens_int32.cpu().numpy()
@@ -3197,6 +3200,7 @@ class FlashAttentionBackend(AttentionBackend):
             seq_lens_np,
             page_table,
             self.page_size,
+            preserve_attn_chunk_size=True,
         )
 
         local_metadata = FlashAttentionMetadata.LocalAttentionMetadata(
@@ -3237,6 +3241,7 @@ class FlashAttentionBackend(AttentionBackend):
             seqlens_np,
             page_table_capture,
             self.page_size,
+            preserve_attn_chunk_size=True,
         )
 
         # Get exact dimensions from the calculation
@@ -3321,6 +3326,7 @@ class FlashAttentionBackend(AttentionBackend):
             seqlens_np,
             sliced_page_table,
             self.page_size,
+            preserve_attn_chunk_size=True,
         )
 
         # Convert back to tensors
@@ -3564,6 +3570,7 @@ def make_local_attention_virtual_batches(
     seq_lens_np: np.ndarray,
     block_table: torch.Tensor,
     page_size: int = 0,
+    preserve_attn_chunk_size: bool = False,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray, torch.Tensor]:
     """
     Take in `query_start_loc_np` and `seq_lens_np` and break the sequences into
@@ -3576,6 +3583,7 @@ def make_local_attention_virtual_batches(
         seq_lens_np: Sequence lengths (numpy array)
         block_table: Block table for KV cache
         page_size: Size of each page in the KV cache
+        preserve_attn_chunk_size: Skip sequence-length-based chunk normalization.
 
     Returns:
         seqlens_q_local: Query sequence lengths for local attention
@@ -3583,11 +3591,9 @@ def make_local_attention_virtual_batches(
         seqlens_k_local: Key sequence lengths for local attention
         block_table_local: Block table for local attention
     """
-    # Adjust attention_chunk_size based on the actual sequence length
+    if not preserve_attn_chunk_size:
-    # to avoid index out of bounds errors
         max_seq_len = seq_lens_np.max()
         effective_chunk_size = min(attn_chunk_size, max_seq_len)
-    # Make sure effective_chunk_size is divisible by page_size
         effective_chunk_size = (effective_chunk_size // page_size) * page_size
         if effective_chunk_size < page_size:
             effective_chunk_size = page_size