CustomOp: grouped topk (#29575)

Signed-off-by: Xinyu Chen <xinyu1.chen@intel.com>

tests/kernels/moe/test_grouped_topk.py

@@ -9,8 +9,8 @@ import pytest
 import torch
 
 from vllm.model_executor.layers.fused_moe.fused_moe import (
+    GroupedTopk,
     fused_grouped_topk,
-    grouped_topk,
 )
 from vllm.platforms import current_platform
 
@@ -50,15 +50,17 @@ def test_grouped_topk(
 
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "0")
-        baseline_topk_weights, baseline_topk_ids = grouped_topk(
-            hidden_states=hidden_states,
-            gating_output=gating_output,
+        grouped_topk = GroupedTopk(
             topk=topk,
             renormalize=renormalize,
             num_expert_group=num_expert_group,
             topk_group=topk_group,
             scoring_func=scoring_func,
             routed_scaling_factor=routed_scaling_factor,
+        )
+        baseline_topk_weights, baseline_topk_ids = grouped_topk(
+            hidden_states=hidden_states,
+            gating_output=gating_output,
             e_score_correction_bias=e_score_correction_bias,
         )
 

vllm/model_executor/layers/fused_moe/__init__.py

@@ -77,11 +77,11 @@ if HAS_TRITON:
         BatchedTritonExperts,
     )
     from vllm.model_executor.layers.fused_moe.fused_moe import (
+        GroupedTopk,
         TritonExperts,
         fused_experts,
         fused_topk,
         get_config_file_name,
-        grouped_topk,
     )
     from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
         TritonOrDeepGemmExperts,
@@ -91,7 +91,7 @@ if HAS_TRITON:
         "fused_topk",
         "fused_experts",
         "get_config_file_name",
-        "grouped_topk",
+        "GroupedTopk",
         "cutlass_moe_fp8",
         "cutlass_moe_fp4",
         "cutlass_moe_w4a8_fp8",

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -16,6 +16,7 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.logger import init_logger
+from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.batch_invariant import (
     vllm_is_batch_invariant,
 )
@@ -1286,6 +1287,57 @@ def grouped_topk(
     return topk_weights.to(torch.float32), topk_ids.to(torch.int32)
 
 
+@CustomOp.register("grouped_topk")
+class GroupedTopk(CustomOp):
+    """GroupedTopk used by the Deepseek-V2 and Deepseek-V3 model."""
+
+    def __init__(
+        self,
+        topk: int,
+        renormalize: bool,
+        num_expert_group: int = 0,
+        topk_group: int = 0,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+    ) -> None:
+        super().__init__()
+        self.native_impl = grouped_topk
+        self.topk = topk
+        self.renormalize = renormalize
+        self.num_expert_group = num_expert_group
+        self.topk_group = topk_group
+        self.scoring_func = scoring_func
+        self.routed_scaling_factor = routed_scaling_factor
+
+    def forward_native(
+        self,
+        hidden_states: torch.Tensor,
+        gating_output: torch.Tensor,
+        e_score_correction_bias: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.native_impl(
+            hidden_states,
+            gating_output,
+            self.topk,
+            self.renormalize,
+            self.num_expert_group,
+            self.topk_group,
+            self.scoring_func,
+            self.routed_scaling_factor,
+            e_score_correction_bias,
+        )
+
+    def forward_cuda(
+        self,
+        hidden_states: torch.Tensor,
+        gating_output: torch.Tensor,
+        e_score_correction_bias: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.forward_native(
+            hidden_states, gating_output, e_score_correction_bias
+        )
+
+
 @torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
 def eplb_map_to_physical_and_record(
     topk_ids: torch.Tensor,

vllm/model_executor/layers/fused_moe/layer.py

@@ -67,7 +67,7 @@ else:
         return topk_ids
 
     eplb_map_to_physical_and_record = _eplb_map_to_physical_and_record
-from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk
+from vllm.model_executor.layers.fused_moe.fused_moe import GroupedTopk
 from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa: E501
     rocm_aiter_grouped_topk,
 )
@@ -1594,19 +1594,26 @@ class FusedMoE(CustomOp):
                 grouped_topk_impl = partial(
                     rocm_aiter_grouped_topk,
                     num_fused_shared_experts=self.num_fused_shared_experts,
+                    topk=self.top_k,
+                    renormalize=self.renormalize,
+                    num_expert_group=self.num_expert_group,
+                    topk_group=self.topk_group,
+                    scoring_func=self.scoring_func,
+                    routed_scaling_factor=self.routed_scaling_factor,
                 )
             else:
-                grouped_topk_impl = grouped_topk
+                grouped_topk_impl = GroupedTopk(
+                    topk=self.top_k,
+                    renormalize=self.renormalize,
+                    num_expert_group=self.num_expert_group,
+                    topk_group=self.topk_group,
+                    scoring_func=self.scoring_func,
+                    routed_scaling_factor=self.routed_scaling_factor,
+                )
 
             topk_weights, topk_ids = grouped_topk_impl(
                 hidden_states=hidden_states,
                 gating_output=router_logits,
-                topk=self.top_k,
-                renormalize=self.renormalize,
-                num_expert_group=self.num_expert_group,
-                topk_group=self.topk_group,
-                scoring_func=self.scoring_func,
-                routed_scaling_factor=self.routed_scaling_factor,
                 e_score_correction_bias=self.e_score_correction_bias,
             )
         elif self.e_score_correction_bias is not None: