[RL] DSV4: dispatch indexer topk_transform_512 through DSATopKBackend (#31087)

Signed-off-by: zhihaow6 <zhihaow6@illinois.edu>

python/sglang/srt/layers/attention/deepseek_v4_backend.py

@@ -40,6 +40,7 @@ from sglang.kernels.ops.speculative.dspark.dspark_attn_metadata import (
 )
 from sglang.srt.environ import envs
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
+from sglang.srt.layers.attention.dsa.dsa_topk_backend import DSATopKBackend
 from sglang.srt.layers.attention.dsv4.compressor_v2 import (
     CompressorBackendMixin,
     FusedCompressMetadata,
@@ -528,6 +529,9 @@ class DeepseekV4AttnBackend(
         self.enable_deepseek_v4_fp4_indexer: bool = (
             model_runner.server_args.enable_deepseek_v4_fp4_indexer
         )
+        self.dsa_topk_backend: DSATopKBackend = DSATopKBackend(
+            model_runner.server_args.dsa_topk_backend
+        )
         self.topk = model_runner.server_args.speculative_eagle_topk or 0
         assert self.topk in [0, 1], "MTP Topk > 1 not supported for DeepSeek V4"
         self.mtp_enabled = self.topk > 0

python/sglang/srt/layers/attention/dsv4/indexer.py

@@ -1,6 +1,16 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple, TypeAlias, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Tuple,
+    TypeAlias,
+    Union,
+)
 
 import torch
 import torch.nn as nn
@@ -15,6 +25,7 @@ from sglang.kernels.ops.attention.dsv4 import (
 from sglang.kernels.ops.quantization.fp8_kernel import is_fp8_fnuz
 from sglang.srt.configs.deepseek_v4 import DeepSeekV4Config
 from sglang.srt.environ import envs
+from sglang.srt.layers.attention.dsa.dsa_topk_backend import DSATopKBackend
 from sglang.srt.layers.attention.dsv4.compressor import Compressor
 from sglang.srt.layers.attention.dsv4.metadata import (
     NonPagedIndexerPlan,
@@ -245,18 +256,17 @@ def fp8_paged_mqa_logits_torch_sm120(
     return logits
 
 
-def topk_transform_512_pytorch_vectorized(
+def _topk_transform_512_vectorized(
     scores: torch.Tensor,
     seq_lens: torch.Tensor,
     page_tables: torch.Tensor,
     out_page_indices: torch.Tensor,
     page_size: int,
     out_raw_indices: Optional[torch.Tensor] = None,
+    topk_op: Callable[..., Tuple[torch.Tensor, torch.Tensor]] = torch.topk,
+    topk_op_kwargs: Optional[Dict[str, object]] = None,
+    contiguous_topk_input: bool = False,
 ) -> None:
-    """Vectorized PyTorch fallback for topk_transform_512.
-    All helper tensors (arange, zeros) are cached to avoid device-tensor
-    creation during HIP/CUDA graph capture."""
-
     TOPK = out_page_indices.shape[1]
     batch_size = scores.shape[0]
     max_seq_len = scores.shape[1]
@@ -283,9 +293,13 @@ def topk_transform_512_pytorch_vectorized(
     masked_scores.masked_fill_(~valid_mask, float("-inf"))
 
     actual_k = min(TOPK, max_seq_len)
-    _, raw_indices = torch.topk(
-        masked_scores, k=actual_k, dim=1, largest=True, sorted=False
+    topk_kwargs = (
+        {"dim": 1, "largest": True, "sorted": False}
+        if topk_op_kwargs is None
+        else topk_op_kwargs
     )
+    topk_input = masked_scores.contiguous() if contiguous_topk_input else masked_scores
+    _, raw_indices = topk_op(topk_input, actual_k, **topk_kwargs)
     raw_indices = raw_indices.to(torch.int32)
 
     if actual_k < TOPK:
@@ -330,10 +344,67 @@ def topk_transform_512_pytorch_vectorized(
         out_raw_indices.copy_(raw_indices)
 
 
+def topk_transform_512_pytorch_vectorized(
+    scores: torch.Tensor,
+    seq_lens: torch.Tensor,
+    page_tables: torch.Tensor,
+    out_page_indices: torch.Tensor,
+    page_size: int,
+    out_raw_indices: Optional[torch.Tensor] = None,
+) -> None:
+    """Vectorized PyTorch fallback for topk_transform_512.
+    All helper tensors (arange, zeros) are cached to avoid device-tensor
+    creation during HIP/CUDA graph capture."""
+
+    _topk_transform_512_vectorized(
+        scores,
+        seq_lens,
+        page_tables,
+        out_page_indices,
+        page_size,
+        out_raw_indices,
+        topk_op=torch.topk,
+        topk_op_kwargs={"dim": 1, "largest": True, "sorted": False},
+    )
+
+
+def topk_transform_512_flashinfer_unfused(
+    scores: torch.Tensor,
+    seq_lens: torch.Tensor,
+    page_tables: torch.Tensor,
+    out_page_indices: torch.Tensor,
+    page_size: int,
+    out_raw_indices: Optional[torch.Tensor] = None,
+) -> None:
+    import flashinfer
+
+    from sglang.srt.layers.attention.dsa.dsa_topk_backend import (
+        _flashinfer_tie_break_value,
+    )
+
+    _topk_transform_512_vectorized(
+        scores,
+        seq_lens,
+        page_tables,
+        out_page_indices,
+        page_size,
+        out_raw_indices,
+        topk_op=flashinfer.top_k,
+        topk_op_kwargs={
+            "sorted": False,
+            "deterministic": envs.SGLANG_DSA_TOPK_FLASHINFER_DETERMINISTIC.get(),
+            "tie_break": _flashinfer_tie_break_value(),
+            "dsa_graph_safe": True,
+        },
+        contiguous_topk_input=True,
+    )
+
+
 class C4IndexerBackendMixin:
     def __init__(self):
         super().__init__()
         self.debug_use_external_c4_sparse_indices: bool = False
+        self.dsa_topk_backend: DSATopKBackend = DSATopKBackend.SGL_KERNEL
 
     def _forward_prepare_multi_stream(
         self,
@@ -732,7 +803,10 @@ class C4IndexerBackendMixin:
         elif core_metadata.c4_sparse_raw_indices is not None:
             raw_indices = core_metadata.c4_sparse_raw_indices
 
-        if envs.SGLANG_TOPK_TRANSFORM_512_TORCH.get():
+        if (
+            envs.SGLANG_TOPK_TRANSFORM_512_TORCH.get()
+            or self.dsa_topk_backend.is_torch()
+        ):
             topk_transform_512_pytorch_vectorized(
                 logits,
                 c4_seq_lens,
@@ -741,6 +815,15 @@ class C4IndexerBackendMixin:
                 indexer_metadata.c4_page_size,
                 raw_indices,
             )
+        elif self.dsa_topk_backend.is_flashinfer():
+            topk_transform_512_flashinfer_unfused(
+                logits,
+                c4_seq_lens,
+                page_table,
+                c4_sparse_page_indices,
+                indexer_metadata.c4_page_size,
+                raw_indices,
+            )
         elif envs.SGLANG_OPT_USE_TOPK_V2.get() and raw_indices is None:
             topk_transform_512_v2(
                 logits,