Enable grouped_topk kernel registration.Hook up the existing grouped_topk kernel to the kernel registry. by xiaolong-intel · Pull Request #39145 · vllm-project/vllm

xiaolong-intel · 2026-04-07T02:35:15Z

Purpose

Enable grouped_topk kernel registration.Hook up the existing grouped_topk kernel to the kernel registry.

Test Plan

I wrote test cases in https://github.com/xiaolong-intel/vllm-xpu-kernels/blob/grouped_topk/tests/test_grouped_topk.py.Tested the consistency of the forward_xpu operator with the torch version of grouped_topk on B60

Test Result

test cases:

test result:

All test cases passed successfully

Documentation Update

add forward_xpu dispatch in https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/router/grouped_topk_router.py
Added the forward_xpu interface implementation in https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/xpu_fused_moe.py

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
(Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

…on on Intel GPUs Signed-off-by: root <xiaolong.guo@intel.com>

github-actions · 2026-04-07T02:35:26Z

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gemini-code-assist

Code Review

This pull request introduces XPU support for fused grouped top-k routing, adding the xpu_fused_grouped_topk function and integrating it into the model executor. Review feedback points out critical parameter mismatches regarding the num_fused_shared_experts parameter in both the main implementation and the fake operator registration, which would cause runtime failures. Furthermore, the reviewer advised against wildcard imports and flagged potential logic inconsistencies in the scoring function handling.

gemini-code-assist · 2026-04-07T02:37:30Z

+def xpu_fused_grouped_topk(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    num_expert_group: int,
+    topk_group: int,
+    scoring_func: str = "softmax",
+    routed_scaling_factor: float = 1.0,
+    e_score_correction_bias: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    assert hidden_states.size(0) == gating_output.size(0), (
+        "Number of tokens mismatch")
+    if scoring_func == "softmax":
+        scores = torch.softmax(gating_output, dim=-1)
+    elif scoring_func == "sigmoid":
+        scores = gating_output
+    else:   
+        raise ValueError(f"Unsupported scoring function: {scoring_func}")
+    return torch.ops._moe_C.fused_grouped_topk(hidden_states, scores, topk,
+                                  renormalize, num_expert_group, topk_group,
+                                  scoring_func, routed_scaling_factor,
+                                  e_score_correction_bias)


There is a critical argument mismatch between the definition of xpu_fused_grouped_topk and its usage in grouped_topk_router.py. The parameter num_fused_shared_experts is missing from both the function signature and the internal call to torch.ops._moe_C.fused_grouped_topk. Additionally, the scoring_func logic appears inconsistent: softmax is applied in Python while sigmoid is passed as raw logits, which might lead to incorrect results depending on the kernel's internal handling of these activations.

def xpu_fused_grouped_topk( hidden_states: torch.Tensor, gating_output: torch.Tensor, topk: int, renormalize: bool, num_expert_group: int, topk_group: int, scoring_func: str = "softmax", routed_scaling_factor: float = 1.0, e_score_correction_bias: Optional[torch.Tensor] = None, num_fused_shared_experts: int = 0, ) -> tuple[torch.Tensor, torch.Tensor]: assert hidden_states.size(0) == gating_output.size(0), ( "Number of tokens mismatch") if scoring_func == "softmax": scores = torch.softmax(gating_output, dim=-1) elif scoring_func == "sigmoid": # Ensure consistency with how the kernel expects activations. # If the kernel handles sigmoid internally, passing raw logits is correct. scores = gating_output else: raise ValueError(f"Unsupported scoring function: {scoring_func}") return torch.ops._moe_C.fused_grouped_topk( hidden_states, scores, topk, renormalize, num_expert_group, topk_group, scoring_func, routed_scaling_factor, e_score_correction_bias, num_fused_shared_experts)

gemini-code-assist · 2026-04-07T02:37:31Z

+    def _fused_grouped_topk_fake(
+        hidden_states: torch.Tensor,
+        gating_output: torch.Tensor,
+        topk: int,
+        renormalize: bool,
+        num_expert_group: int,
+        topk_group: int,
+        scoring_func: str,
+        routed_scaling_factor: float,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:


The fake implementation _fused_grouped_topk_fake is missing the num_fused_shared_experts parameter. This will cause a TypeError during torch compilation or fake tensor propagation when the operator is called with the expected 10 arguments from the router.

@register_fake("_moe_C::fused_grouped_topk") def _fused_grouped_topk_fake( hidden_states: torch.Tensor, gating_output: torch.Tensor, topk: int, renormalize: bool, num_expert_group: int, topk_group: int, scoring_func: str, routed_scaling_factor: float, e_score_correction_bias: Optional[torch.Tensor] = None, num_fused_shared_experts: int = 0, ) -> tuple[torch.Tensor, torch.Tensor]:

jikunshang

pls fix pre-commit issues.

jikunshang · 2026-04-07T03:18:11Z


 import torch
 from vllm_xpu_kernels.flash_attn_interface import flash_attn_varlen_func
-
+from vllm_xpu_kernels.fused_moe_interface import xpu_fused_moe


why import this

I want to import _C _xpu_C _moe_C through vllm_xpu_kernels.fused_moe_interface.py 😂

jikunshang · 2026-04-07T03:19:51Z

@@ -17,6 +17,9 @@
 from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
    rocm_aiter_grouped_topk,
 )
+from vllm.model_executor.layers.fused_moe.xpu_fused_moe import (
+    xpu_fused_grouped_topk,


this may break cuda. please don't import this.

Sorry,my fault. Maybe I can introduce it this way？

if current_platform() is 'xpu': from vllm.model_executor.layers.fused_moe.xpu_fused_moe import ( xpu_fused_grouped_topk,

jikunshang · 2026-04-07T03:20:16Z

@@ -74,6 +74,33 @@ def _int4_gemm_w4a16_fake(
        N = q_weight.size(1)
        return torch.empty((M, N), dtype=input.dtype, device=input.device)

+if hasattr(torch.ops._moe_C, "fused_grouped_topk"):


better register like deepseek_scaling_rope op

…nces Signed-off-by: root <xiaolong.guo@intel.com>

Signed-off-by: xiaolong-intel <xiaolong.guo@intel.com>

jikunshang · 2026-04-09T09:06:26Z

thanks contributing.

please follow https://docs.vllm.ai/en/stable/contributing/?h=lint#linting fix pre commit issue.
we should not merge this, unless a) kernel side pr merged, b) vllm-xpu-kernel have a latest release, c) we bump up vllm-xpu-kenrel dependency.

jikunshang · 2026-04-09T09:03:49Z

+    topk_weights = torch.empty(
+        (num_tokens, topk),
+        device=hidden_states.device,
+        dtype=torch.float32,


should we use float32?

On this point, I referred to NV's implementation; the weight return value of grouped_topk is of float32 type.

vllm/csrc/moe/grouped_topk_kernels.cu

Line 1030 in e80e633

torch::Tensor topk_values = torch::empty(

xiaolong-intel · 2026-04-09T09:10:07Z

thanks contributing.

please follow https://docs.vllm.ai/en/stable/contributing/?h=lint#linting fix pre commit issue.

we should not merge this, unless a) kernel side pr merged, b) vllm-xpu-kernel have a latest release, c) we bump up vllm-xpu-kenrel dependency.

Haha, thank you for your guidance. This is my first time submitting a PR, and I have learned a lot.
Then let's wait until the vllm-xpu-kernels are ready before talking about it.

jinyouzhi · 2026-05-21T06:04:55Z

    from vllm_xpu_kernels.fused_moe_interface import xpu_fused_moe

-
+from typing import Optional, Tuple


clean this line.

jinyouzhi · 2026-05-21T06:05:14Z

 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING,Optional


no need to change.

jinyouzhi · 2026-05-21T06:06:12Z

                rocm_aiter_grouped_topk,
                num_fused_shared_experts=self.num_fused_shared_experts,
            )
        else:


add dispatch logic for xpu

…_topk kernel to the kernel registry. Signed-off-by: root <xiaolong.guo@intel.com> Signed-off-by: <xiaolong.guo@intel.com>

mergify · 2026-05-21T06:49:53Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @xiaolong-intel.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: Liangqiusong <xiaolong.guo@intel.com>

xiaolong-intel · 2026-05-21T07:00:23Z

thanks contributing.

please follow https://docs.vllm.ai/en/stable/contributing/?h=lint#linting fix pre commit issue.

we should not merge this, unless a) kernel side pr merged, b) vllm-xpu-kernel have a latest release, c) we bump up vllm-xpu-kenrel dependency.

Hello Kunshang:
The fused_grouped_topk operator already exists in vllm_xpu_kernel, but the current vllm codebase lacks the registration and dispatch path to actually invoke it. The purpose of this PR is to add the calling mechanism for fused_grouped_topk on the vLLM side.
Accordingly, I have updated the PR title to: "Enable grouped_topk kernel registration. Hook up the existing grouped_topk kernel to the kernel registry."
With this clarified scope, this PR can now be decoupled from vllm-project/vllm-xpu-kernels#253. Any further optimizations made inside vllm_xpu_kernel are independent of the registration logic here and will not affect how vLLM calls this kernel.

mergify · 2026-05-23T08:01:36Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @xiaolong-intel.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Added the xpu_grouped_topk feature to support the grouped_topk functi…

6e46642

…on on Intel GPUs Signed-off-by: root <xiaolong.guo@intel.com>

xiaolong-intel requested review from mgoin and pavanimajety as code owners April 7, 2026 02:35

mergify Bot added the intel-gpu Related to Intel GPU label Apr 7, 2026

gemini-code-assist Bot reviewed Apr 7, 2026

View reviewed changes

xiaolong-intel mentioned this pull request Apr 7, 2026

optimizing grouped_topk_multi_group SYCL kernel for MoE routing vllm-project/vllm-xpu-kernels#247

Closed

jikunshang requested changes Apr 7, 2026

View reviewed changes

xiaolong-intel marked this pull request as draft April 9, 2026 02:52

Change the way operators are registered and remove unnecessary refere…

333ece9

…nces Signed-off-by: root <xiaolong.guo@intel.com>

xiaolong-intel force-pushed the xpu_grouped_topk branch from 75f030c to 333ece9 Compare April 9, 2026 08:53

Merge branch 'main' into xpu_grouped_topk

f5edf9c

Signed-off-by: xiaolong-intel <xiaolong.guo@intel.com>

xiaolong-intel marked this pull request as ready for review April 9, 2026 08:57

xiaolong-intel requested a review from jikunshang April 9, 2026 08:57

jikunshang reviewed Apr 9, 2026

View reviewed changes

jinyouzhi reviewed May 21, 2026

View reviewed changes

Enable grouped_topk kernel registration. Hook up the existing grouped…

170d36e

…_topk kernel to the kernel registry. Signed-off-by: root <xiaolong.guo@intel.com> Signed-off-by: <xiaolong.guo@intel.com>

xiaolong-intel requested a review from zyongye as a code owner May 21, 2026 06:48

mergify Bot added the needs-rebase label May 21, 2026

Merge branch 'main' into xpu_grouped_topk

defd1fc

Signed-off-by: Liangqiusong <xiaolong.guo@intel.com>

mergify Bot removed the needs-rebase label May 21, 2026

xiaolong-intel changed the title ~~Added the xpu_grouped_topk feature to support the grouped_topk functi…~~ Enable grouped_topk kernel registration.Hook up the existing grouped_topk kernel to the kernel registry. May 21, 2026

mergify Bot added the needs-rebase label May 23, 2026

		from vllm_xpu_kernels.fused_moe_interface import xpu_fused_moe


		from typing import Optional, Tuple

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Purpose

Test Plan

Test Result

Documentation Update

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xiaolong-intel commented Apr 7, 2026 •

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