Port code from 43276

[qianlihuang]

This ports the small follow-up fixes from #43276 onto m2-gate-function.

The CUDA changes are mostly ai-review-driven safety fixes.
The Python changes complete the enable_router_pdl plumbing so MiniMax-M2 can opt into router PDL while keeping the default behavior unchanged.

H800

CUDA_VISIBLE_DEVICES=0 python3 - <<'PY'
import torch
import torch.nn.functional as F
from vllm import _custom_ops as ops

torch.manual_seed(0)
H, E = 3072, 256

for precision, matmul_precision in [("default", None), ("fp32_high", "high")]:
    if matmul_precision is not None:
        torch.set_float32_matmul_precision(matmul_precision)
    else:
        torch.set_float32_matmul_precision("highest")

    for dtype, atol in [(torch.bfloat16, 2e-2), (torch.float32, 2e-4)]:
        for M in [1, 2, 3, 4, 7, 8, 15, 16, 31, 32]:
            x = torch.randn(M, H, device="cuda", dtype=dtype).contiguous()
            w = torch.randn(E, H, device="cuda", dtype=torch.float32).contiguous()
            out = ops.fp32_router_gemm(x, w)
            ref = F.linear(x.float(), w)
            err = (out - ref).abs()
            print(f"prec={precision} {dtype} M={M}",
                  "max_abs=", err.max().item(),
                  "mean_abs=", err.mean().item(),
                  "pass=", bool(torch.allclose(out, ref, atol=atol, rtol=0)))
PY

prec=default torch.bfloat16 M=1 max_abs= 3.814697265625e-05 mean_abs= 9.896233677864075e-06 pass= True
prec=default torch.bfloat16 M=2 max_abs= 6.103515625e-05 mean_abs= 1.0900897905230522e-05 pass= True
prec=default torch.bfloat16 M=3 max_abs= 4.673004150390625e-05 mean_abs= 1.0053938240162097e-05 pass= True
prec=default torch.bfloat16 M=4 max_abs= 3.814697265625e-05 mean_abs= 1.0253861546516418e-05 pass= True
prec=default torch.bfloat16 M=7 max_abs= 6.103515625e-05 mean_abs= 1.2296851309656631e-05 pass= True
prec=default torch.bfloat16 M=8 max_abs= 5.340576171875e-05 mean_abs= 1.2036529369652271e-05 pass= True
prec=default torch.bfloat16 M=15 max_abs= 5.53131103515625e-05 mean_abs= 1.2322170732659288e-05 pass= True
prec=default torch.bfloat16 M=16 max_abs= 6.103515625e-05 mean_abs= 1.2327080185059458e-05 pass= True
prec=default torch.bfloat16 M=31 max_abs= 6.103515625e-05 mean_abs= 9.407965080754366e-06 pass= True
prec=default torch.bfloat16 M=32 max_abs= 6.103515625e-05 mean_abs= 9.411050996277481e-06 pass= True
prec=default torch.float32 M=1 max_abs= 3.0517578125e-05 mean_abs= 8.818693459033966e-06 pass= True
prec=default torch.float32 M=2 max_abs= 4.57763671875e-05 mean_abs= 9.83034260571003e-06 pass= True
prec=default torch.float32 M=3 max_abs= 4.57763671875e-05 mean_abs= 9.48490014707204e-06 pass= True
prec=default torch.float32 M=4 max_abs= 5.340576171875e-05 mean_abs= 9.826384484767914e-06 pass= True
prec=default torch.float32 M=7 max_abs= 5.7220458984375e-05 mean_abs= 1.2004881682514679e-05 pass= True
prec=default torch.float32 M=8 max_abs= 5.340576171875e-05 mean_abs= 1.2546559446491301e-05 pass= True
prec=default torch.float32 M=15 max_abs= 6.103515625e-05 mean_abs= 1.2333870472502895e-05 pass= True
prec=default torch.float32 M=16 max_abs= 7.62939453125e-05 mean_abs= 1.221662387251854e-05 pass= True
prec=default torch.float32 M=31 max_abs= 7.62939453125e-05 mean_abs= 9.509156370768324e-06 pass= True
prec=default torch.float32 M=32 max_abs= 7.62939453125e-05 mean_abs= 9.350329492008314e-06 pass= True
prec=fp32_high torch.bfloat16 M=1 max_abs= 3.0517578125e-05 mean_abs= 8.903443813323975e-06 pass= True
prec=fp32_high torch.bfloat16 M=2 max_abs= 0.044440269470214844 mean_abs= 0.009015086106956005 pass= False
prec=fp32_high torch.bfloat16 M=3 max_abs= 0.0346832275390625 mean_abs= 0.008942199870944023 pass= False
prec=fp32_high torch.bfloat16 M=4 max_abs= 0.04113006591796875 mean_abs= 0.009151562117040157 pass= False
prec=fp32_high torch.bfloat16 M=7 max_abs= 0.04279804229736328 mean_abs= 0.008998721837997437 pass= False
prec=fp32_high torch.bfloat16 M=8 max_abs= 0.0445556640625 mean_abs= 0.009134004823863506 pass= False
prec=fp32_high torch.bfloat16 M=15 max_abs= 0.03853607177734375 mean_abs= 0.009143132716417313 pass= False
prec=fp32_high torch.bfloat16 M=16 max_abs= 0.04340171813964844 mean_abs= 0.00932237133383751 pass= False
prec=fp32_high torch.bfloat16 M=31 max_abs= 0.04761219024658203 mean_abs= 0.009131493046879768 pass= False
prec=fp32_high torch.bfloat16 M=32 max_abs= 0.04586029052734375 mean_abs= 0.009119704365730286 pass= False
prec=fp32_high torch.float32 M=1 max_abs= 3.4332275390625e-05 mean_abs= 8.806586265563965e-06 pass= True
prec=fp32_high torch.float32 M=2 max_abs= 0.05176544189453125 mean_abs= 0.013077953830361366 pass= False
prec=fp32_high torch.float32 M=3 max_abs= 0.051513671875 mean_abs= 0.01334131509065628 pass= False
prec=fp32_high torch.float32 M=4 max_abs= 0.05275726318359375 mean_abs= 0.013239540159702301 pass= False
prec=fp32_high torch.float32 M=7 max_abs= 0.06198310852050781 mean_abs= 0.013133209198713303 pass= False
prec=fp32_high torch.float32 M=8 max_abs= 0.06512451171875 mean_abs= 0.013000346720218658 pass= False
prec=fp32_high torch.float32 M=15 max_abs= 0.06135368347167969 mean_abs= 0.013118076138198376 pass= False
prec=fp32_high torch.float32 M=16 max_abs= 0.061614990234375 mean_abs= 0.012958699837327003 pass= False
prec=fp32_high torch.float32 M=31 max_abs= 0.06546783447265625 mean_abs= 0.012923624366521835 pass= False
prec=fp32_high torch.float32 M=32 max_abs= 0.07131576538085938 mean_abs= 0.012893503531813622 pass= False

CUDA_VISIBLE_DEVICES=0 TRTLLM_ENABLE_PDL=0 \
python3 benchmarks/kernels/benchmark_router_gemm.py \
  --model MiniMaxAI/MiniMax-M2.7 \
  --max-batch-size 32 \
  --trust-remote-code

CUDA_VISIBLE_DEVICES=0 TRTLLM_ENABLE_PDL=1 \
python3 benchmarks/kernels/benchmark_router_gemm.py \
  --model MiniMaxAI/MiniMax-M2.7 \
  --max-batch-size 32 \
  --trust-remote-code

MiniMaxAI/MiniMax-M2.7 router gemm throughput:
   batch_size  PyTorch (TFLOPs)  vLLM (TFLOPs)
0         1.0          0.311737       0.761627
1         2.0          0.263098       1.422127
2         4.0          0.523085       2.584606
3         8.0          0.784099       4.342279
4        16.0          1.513443       6.493178
5        32.0          3.489885       7.984414
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MiniMaxAI/MiniMax-M2.7 router gemm throughput:
   batch_size  PyTorch (TFLOPs)  vLLM (TFLOPs)
0         1.0          0.311601       0.916041
1         2.0          0.263055       1.716123
2         4.0          0.523220       3.037290
3         8.0          0.783896       4.967528
4        16.0          1.513612       7.038150
5        32.0          3.495769       8.374800

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[Copilot]

Pull request overview

Note

Copilot was unable to run its full agentic suite in this review.

This PR adds optional Programmatic Dependent Launch (PDL) support to fused MoE top-k routing on supported NVIDIA GPUs, and tightens/extends the fp32 router GEMM path (including bf16 inputs and extra validation).

Changes:

• Introduce an enable_pdl/enable_router_pdl plumbing path from model → FusedMoE → router → fused top-k ops.
• Update routing kernels to participate in a PDL chain (CUDA 12+ / SM90+ guarded) and adjust launch behavior accordingly.
• Add stricter validation + device guarding to fp32_router_gemm, and allow bf16 inputs for the fp32 specialized routing GEMM dispatch.

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
vllm/model_executor/models/minimax_m2.py	Adds platform/env-based gating to enable router PDL for MiniMax M2.
vllm/model_executor/layers/fused_moe/router/router_factory.py	Threads a new enable_pdl flag through router construction.
vllm/model_executor/layers/fused_moe/router/gate_linear.py	Extends fp32 router GEMM dispatch eligibility to bf16 inputs.
vllm/model_executor/layers/fused_moe/router/fused_topk_router.py	Adds enable_pdl argument and forwards it only to non-ROCm routes.
vllm/model_executor/layers/fused_moe/router/fused_topk_bias_router.py	Plumbs enable_pdl through bias routing and changes default to False.
vllm/model_executor/layers/fused_moe/layer.py	Adds enable_router_pdl init arg and forwards it to router factory.
vllm/_custom_ops.py	Registers a fake implementation for _moe_C::fp32_router_gemm when present.
csrc/moe/topk_softmax_kernels.cu	Adds CUDA-version-guarded PDL wait/launch_dependents and PDL launch path.
csrc/moe/fp32_router_gemm_entry.cu	Adds device/shape/contiguity checks, handles 0-token case, and sets device guard.
csrc/moe/fp32_router_gemm.cu	Adjusts PDL asm placement and gates host launch attribute by env.

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