Cleaning indexer for DeepSeek V3.2 (#11682)

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

@@ -17,7 +17,7 @@ if is_cuda():
     except ImportError as e:
         deep_gemm = e
 
-from sglang.srt.layers.attention.nsa.utils import NSA_DUAL_STREAM, NSA_USE_REAL_INDEXER
+from sglang.srt.layers.attention.nsa.utils import NSA_DUAL_STREAM
 from sglang.srt.layers.dp_attention import get_attention_tp_group
 from sglang.srt.layers.linear import ReplicatedLinear
 from sglang.srt.layers.quantization import deep_gemm_wrapper
@@ -168,43 +168,6 @@ class Indexer(CustomOp):
         self.scale_fmt = scale_fmt
         self.softmax_scale = self.head_dim**-0.5
 
-    def _forward_fake(
-        self,
-        x: torch.Tensor,
-        q_lora: torch.Tensor,
-        positions: torch.Tensor,
-        forward_batch: ForwardBatch,
-        layer_id: int,
-    ):
-        bs = x.shape[0]
-        assert self.index_topk == 2048
-        ans = torch.arange(0, self.index_topk, dtype=torch.int32, device=x.device)[
-            None, ...
-        ].repeat(bs, 1)
-        if forward_batch.forward_mode.is_extend():
-            assert (
-                forward_batch.extend_seq_lens_cpu is not None
-                and forward_batch.seq_lens_cpu is not None
-            )
-            which = 0
-            for i, (kv_len, qo_len) in enumerate(
-                zip(
-                    forward_batch.seq_lens_cpu.tolist(),
-                    forward_batch.extend_seq_lens_cpu,
-                    strict=True,
-                )
-            ):
-                for j in range(kv_len - qo_len, kv_len):
-                    ans[which, j + 1 :] = -1
-                    which += 1
-            assert which == ans.shape[0]
-        else:
-            assert forward_batch.seq_lens_cpu is not None
-            for i, seq_len in enumerate(forward_batch.seq_lens_cpu.tolist()):
-                ans[i, seq_len:] = -1
-
-        return ans
-
     @torch.compile(dynamic=True)
     def _get_logits_head_gate(self, x: torch.Tensor, q_scale: torch.Tensor):
         weights, _ = self.weights_proj(x)
@@ -404,7 +367,7 @@ class Indexer(CustomOp):
 
         return topk_result
 
-    def forward_indexer_bs_1(
+    def forward_indexer(
         self,
         q_fp8: torch.Tensor,
         weights: torch.Tensor,
@@ -485,20 +448,9 @@ class Indexer(CustomOp):
             q_len_start = q_len_end
 
         topk_indices = torch.cat(topk_indices_list, dim=0)
-
         return topk_indices
 
-    def forward_indexer(
-        self,
-        q_fp8: torch.Tensor,
-        weights: torch.Tensor,
-        forward_batch: ForwardBatch,
-        topk: int,
-        layer_id: int,
-    ) -> Optional[torch.Tensor]:
-        return self.forward_indexer_bs_1(q_fp8, weights, forward_batch, topk, layer_id)
-
-    def _forward(
+    def forward_cuda(
         self,
         x: torch.Tensor,
         q_lora: torch.Tensor,
@@ -530,9 +482,6 @@ class Indexer(CustomOp):
         if metadata is None:
             return None
 
-        if not NSA_USE_REAL_INDEXER:  # temporary
-            return self._forward_fake(x, q_lora, positions, forward_batch, layer_id)
-
         query, key = self._get_q_k_bf16(q_lora, x, positions, enable_dual_stream)
 
         if enable_dual_stream:
@@ -588,19 +537,8 @@ class Indexer(CustomOp):
                 topk=self.index_topk,
                 layer_id=layer_id,
             )
-
         return topk_result
 
-    def forward_cuda(
-        self,
-        x: torch.Tensor,
-        q_lora: torch.Tensor,
-        positions: torch.Tensor,
-        forward_batch: ForwardBatch,
-        layer_id: int,
-    ) -> Optional[torch.Tensor]:
-        return self._forward(x, q_lora, positions, forward_batch, layer_id)
-
     def forward_npu(
         self,
         x: torch.Tensor,

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

 # temp NSA debugging environ
 from sglang.srt.utils import get_bool_env_var
 
-NSA_USE_REAL_INDEXER = get_bool_env_var("SGLANG_NSA_USE_REAL_INDEXER", "true")
 NSA_DUAL_STREAM = get_bool_env_var("SGLANG_NSA_DUAL_STREAM", "true")
 NSA_FUSE_TOPK = get_bool_env_var("SGLANG_NSA_FUSE_TOPK", "true")