[DeepSeek v3.2][Bugfix] get_index_k_scale_buffer support cp

python/sglang/srt/layers/attention/nsa/index_buf_accessor.py

@@ -624,7 +624,7 @@ def _get_k_and_s_triton(
     :param page_indices: (num_pages,), int32/int64
     :param seq_lens: tensor of sequence lens, int64
     :param seq_len_sum: sum of all sequence len, int32
-    :param seq_len_sum: max of sequence len, int32
+    :param max_seq_len: max of sequence len, int32
     :param page_size: int, typically 64
     :param index_head_dim: int, typically 128
     :return: tuple of (k_out, s_out) where

python/sglang/srt/layers/attention/nsa/nsa_indexer.py

@@ -97,6 +97,11 @@ def get_indexer_seq_len_cpu(self) -> torch.Tensor:
         Return: seq lens for each batch.
         """
 
+    def get_indexer_seq_len(self) -> torch.Tensor:
+        """
+        Return: seq lens for each batch.
+        """
+
     def get_nsa_extend_len_cpu(self) -> List[int]:
         """
         Return: extend seq lens for each batch.
@@ -538,11 +543,12 @@ def _get_topk_ragged(
 
         ks, ke = metadata.get_indexer_kvcache_range()
 
-        seq_len_sum = forward_batch.seq_lens_sum
-        max_seq_len = torch.max(forward_batch.seq_lens_cpu).item()
+        indexer_seq_lens_cpu = metadata.get_indexer_seq_len_cpu()
+        seq_len_sum = torch.sum(indexer_seq_lens_cpu).item()
+        max_seq_len = torch.max(indexer_seq_lens_cpu).item()
         k_fp8, k_scale = forward_batch.token_to_kv_pool.get_index_k_scale_buffer(
             layer_id,
-            forward_batch.seq_lens,
+            metadata.get_indexer_seq_len(),
             block_tables,
             seq_len_sum,
             max_seq_len,

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

@@ -138,6 +138,8 @@ class NSAMetadata:
     indexer_k_start_end: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
     # seq lens for each batch.
     indexer_seq_lens_cpu: Optional[torch.Tensor] = None
+    # seq lens for each batch.
+    indexer_seq_lens: Optional[torch.Tensor] = None
     # batch index for each token.
     token_to_batch_idx: Optional[torch.Tensor] = None
 
@@ -194,6 +196,9 @@ def get_cu_seqlens_k(self) -> torch.Tensor:
     def get_indexer_kvcache_range(self) -> Tuple[torch.Tensor, torch.Tensor]:
         return self.attn_metadata.indexer_k_start_end
 
+    def get_indexer_seq_len(self) -> torch.Tensor:
+        return self.attn_metadata.indexer_seq_lens
+
     def get_indexer_seq_len_cpu(self) -> torch.Tensor:
         return self.attn_metadata.indexer_seq_lens_cpu
 
@@ -404,6 +409,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
         bs_idx_cpu = None
         # seq_len_cpu of selected sequences
         indexer_seq_lens_cpu = forward_batch.seq_lens_cpu
+        indexer_seq_lens = forward_batch.seq_lens
 
         if forward_batch.forward_mode.is_decode_or_idle():
             extend_seq_lens_cpu = [1] * batch_size
@@ -504,6 +510,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
                     )
                 )
                 indexer_seq_lens_cpu = indexer_seq_lens_cpu[bs_idx_cpu]
+                indexer_seq_lens = indexer_seq_lens[bs_idx]
                 cache_seqlens_int32 = cache_seqlens_int32[bs_idx]
                 cu_seqlens_k = compute_cu_seqlens(cache_seqlens_int32)
                 max_seqlen_k = (
@@ -641,6 +648,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
             topk_indices_offset=topk_indices_offset,
             indexer_k_start_end=indexer_k_start_end,
             indexer_seq_lens_cpu=indexer_seq_lens_cpu,
+            indexer_seq_lens=indexer_seq_lens,
             token_to_batch_idx=token_to_batch_idx,
         )
         self.forward_metadata = metadata

test/registered/kernels/test_nsa_indexer.py

@@ -132,6 +132,10 @@ def get_indexer_seq_len_cpu(self) -> torch.Tensor:
         """Return: seq lens for each batch."""
         return torch.tensor(self.seq_lens, dtype=torch.int32, device="cpu")
 
+    def get_indexer_seq_len(self) -> torch.Tensor:
+        """Return: seq lens for each batch."""
+        return torch.tensor(self.seq_lens, dtype=torch.int32, device=self.device)
+
     def get_nsa_extend_len_cpu(self) -> List[int]:
         """
         Return: extend seq lens for each batch.