[Deepseek V3.2] Only skip Indexer logits computation when is_extend_without_speculative (#12816)

Signed-off-by: Hao Lu <14827759+hlu1@users.noreply.github.com>

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

@@ -410,6 +410,8 @@ class Indexer(CustomOp):
         metadata: BaseIndexerMetadata,
         return_indices: bool = True,
     ) -> Optional[torch.Tensor]:
+        assert forward_batch.forward_mode.is_extend_without_speculative()
+
         # Fast path: only compute and store k cache, skip all q and weights ops
         key = self._get_k_bf16(x, positions, enable_dual_stream)
         k_fp8, k_scale = act_quant(key, self.block_size, self.scale_fmt)
@@ -555,14 +557,15 @@ class Indexer(CustomOp):
             return None
 
         # Determine if should skip topk based on sequence length
-        should_skip = False
+        # We can only skip the logits computation if cuda graph is not involved
-        if not forward_batch.forward_mode.is_decode_or_idle():
+        skip_logits_computation = False
+        if forward_batch.forward_mode.is_extend_without_speculative():
             if forward_batch.seq_lens_cpu is not None:
                 max_kv_len = forward_batch.seq_lens_cpu.max().item()
-                should_skip = max_kv_len <= self.index_topk
+                skip_logits_computation = max_kv_len <= self.index_topk
 
         # Optimization: fast path when skipping topk computation
-        if should_skip:
+        if skip_logits_computation:
             return self._forward_cuda_k_only(
                 x,
                 positions,

python/sglang/srt/model_executor/forward_batch_info.py

@@ -141,6 +141,13 @@ class ForwardMode(IntEnum):
     def is_split_prefill(self):
         return self == ForwardMode.SPLIT_PREFILL
 
+    def is_extend_without_speculative(self):
+        return (
+            self.is_extend()
+            and not self.is_target_verify()
+            and not self.is_draft_extend()
+        )
+
 
 @total_ordering
 class CaptureHiddenMode(IntEnum):

python/sglang/srt/models/deepseek_v2.py

@@ -308,14 +308,6 @@ def _get_sum_extend_prefix_lens(forward_batch):
     )
 
 
-def _is_extend_without_speculative(forward_batch):
-    return (
-        forward_batch.forward_mode.is_extend()
-        and not forward_batch.forward_mode.is_target_verify()
-        and not forward_batch.forward_mode.is_draft_extend()
-    )
-
-
 def _support_mha_one_shot(attn: DeepseekV2AttentionMLA, forward_batch, backend_name):
     attn_supported = backend_name in ["fa3", "flashinfer", "flashmla"]
     sum_seq_lens = (
@@ -334,7 +326,7 @@ def _handle_attention_backend(
 
     if (
         not disable_ragged
-        and _is_extend_without_speculative(forward_batch)
+        and forward_batch.forward_mode.is_extend_without_speculative()
         and (
             (
                 sum_extend_prefix_lens >= attn.chunked_prefix_cache_threshold
@@ -377,7 +369,7 @@ def handle_attention_fa4(attn, forward_batch):
 
 def handle_attention_trtllm_mla(attn, forward_batch):
     sum_extend_prefix_lens = _get_sum_extend_prefix_lens(forward_batch)
-    if _is_extend_without_speculative(forward_batch) and (
+    if forward_batch.forward_mode.is_extend_without_speculative() and (
         not attn.disable_chunked_prefix_cache or sum_extend_prefix_lens == 0
     ):
         return AttnForwardMethod.MHA_CHUNKED_KV
@@ -386,7 +378,7 @@ def handle_attention_trtllm_mla(attn, forward_batch):
 
 
 def handle_attention_aiter(attn, forward_batch):
-    if _is_extend_without_speculative(forward_batch):
+    if forward_batch.forward_mode.is_extend_without_speculative():
         if is_dp_attention_enabled():
             if sum(forward_batch.extend_prefix_lens_cpu) == 0:
                 return AttnForwardMethod.MHA
@@ -411,7 +403,7 @@ def handle_attention_nsa(attn, forward_batch):
     if forward_batch.forward_mode.is_decode_or_idle():
         return AttnForwardMethod.MLA
 
-    if _is_extend_without_speculative(forward_batch):
+    if forward_batch.forward_mode.is_extend_without_speculative():
         assert forward_batch.seq_lens_cpu is not None
         max_kv_len = forward_batch.seq_lens_cpu.max().item()
 
@@ -439,7 +431,7 @@ def handle_attention_triton(attn, forward_batch):
         return _dispatch_mla_subtype(attn, forward_batch)
 
     if (
-        _is_extend_without_speculative(forward_batch)
+        forward_batch.forward_mode.is_extend_without_speculative()
         and sum(forward_batch.extend_prefix_lens_cpu) == 0
     ):
         return AttnForwardMethod.MHA
@@ -1517,7 +1509,7 @@ class DeepseekV2AttentionMLA(nn.Module):
             )
 
             # NSA Indexer: cache quantized keys, auto-skip topk for sequences <= nsa_index_topk
-            if self.use_nsa and _is_extend_without_speculative(forward_batch):
+            if self.use_nsa:
                 _ = self.indexer(
                     x=hidden_states,
                     q_lora=q_lora,