[Ondre119]

Overview

Add an optional router GEMM → fused top-k → weight accumulation chain to benchmark_fused_topk.py, with eager and CUDA Graph measurements. This adapts Seungryong Kim's harness as a benchmark follow-up to #65059, with the author's permission.

Claims

• --mode chain measures the dependent chain with configurable shapes, layer counts, scoring function, and independent GEMM/gating dtypes. The existing standalone mode and default report filenames are preserved.
• Check accumulated weights against Torch before timing both eager execution and graph replay. Allocate inputs and buffers outside the timed region.
• Append raw JSONL samples with separate GPU event and host submission intervals, shape/dtype settings, run/round labels, and the loaded MoE binary's SHA-256 for paired comparisons.

Validation

RTX 4090 (SM89), driver 595.71.05, Torch 2.14.0 / CUDA 13.0, installed vLLM 0.33.0. The benchmark and CLI test files were copied byte-for-byte from this branch; this did not rebuild main's CUDA extensions.

Check	Result
CLI tests	11 passed, locally and on the CUDA host
Chain correctness	108 eager/graph cases passed: 2 scoring functions × 3 GEMM dtypes × 3 gating dtypes × 3 token counts × 2 execution modes
Raw chain measurements	324 samples: 3 per case
Default standalone mode	96 configurations completed for Torch/vLLM; original fused-topk-perf-softmax.csv produced. AITER is unavailable on this NVIDIA host.
File-scoped pre-commit hooks	Passed

Chain settings: hidden 4096, 256 experts, top-k 8, 48 layers, tokens 1/16/48, softmax/sigmoid, BF16/FP16/FP32 for each dtype; seed 0; correctness tolerance rtol=1e-4, atol=1e-4. Each shape used 1000 ms warm-up, 100 graph replays or 20 eager calls per sample, and 3 samples per mode.

.venv/bin/python -m pytest tests/benchmarks/test_fused_topk_cli.py -q \
  --confcutdir=tests/benchmarks
pre-commit run --files benchmarks/kernels/benchmark_fused_topk.py \
  tests/benchmarks/test_fused_topk_cli.py docs/benchmarking/cli.md \
  .buildkite/test_areas/benchmarks.yaml

mkdir -p results/standalone
.venv/bin/python benchmarks/kernels/benchmark_fused_topk.py \
  --save-path results/standalone

# Repeat for both scoring functions and all 3 × 3 GEMM/gating dtype pairs.
.venv/bin/python benchmarks/kernels/benchmark_fused_topk.py \
  --mode chain --num-tokens 1 16 48 --num-experts 256 --topk 8 \
  --hidden-size 4096 --num-layers 48 \
  --scoring-func softmax --gemm-dtype bfloat16 --dtype float32 \
  --warmup-ms 1000 --repeats 3 --replays 100 --eager-iters 20 \
  --label integrated-installed --round 0 --output-json results/chain.jsonl

The local CPU check used --confcutdir=tests/benchmarks to exclude repository-wide GPU/model fixtures. The CLI tests are selected by the existing Benchmarks CLI CI step, whose source dependencies now include this script. GPU chain validation was run manually on the SM89 host; no timing threshold is added to CI.

Details

• CUDA events measure the full chain interval, including graph dependency/scheduling gaps. Inputs and buffers are reused without flushing L2. Eager intervals may include GPU idle time waiting for Python submissions. Host intervals exclude the final synchronization. us_per_layer divides the complete chain interval by the layer count.
• This is a synthetic benchmark: hidden states are fixed across layers, router weights differ, and the consumer accumulates routing weights. It excludes expert compute and model loading. This PR makes no before/after speedup or model-throughput claim; model evaluation is not applicable because inference/model behavior is unchanged.
• Duplicate checks on 2026-11-03 found no open PR adding this chain benchmark. [Bugfix][Kernel] Only set PDL launch attr for MoE top-k gating on SM90+ #65059 is merged. [Perf][MoE] Select full-warp top-k rows with redux.sync and keep selections in registers (SM80+) #62912 and [Perf][Kernel] Faster full-warp MoE top-k selection with redux.sync #64731 optimize top-k selection and overlap the standalone --dtype option; neither adds the dependent chain. The CLI spelling and dtype choices here match that work, with the original BF16 report stem retained for compatibility.
• MoE binary SHA-256 used for validation: 99682a5814b3081de7e4e95cc2c1cf1ba8f6139514ac4c5563dbad16c229a4c0.
• Attribution: Seungryong Kim, with his requested Co-authored-by trailer. AI assistance (Codex) was used for implementation, documentation, and validation.

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Pull Request Checklist

• I used vLLM's /pr-checklist skill. (Mandatory for agents, optional for humans).

• AI assistance was used during the creation of this PR.

• Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.

• Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.

• Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.

• Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.

[claude]

Claude Code Review

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[mergify]

Documentation preview: https://vllm--65298.org.readthedocs.build/en/65298/

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