[Bugfix] Fix gridDim.y overflow for large row counts

[JasonLi314]

Purpose

Fixes #45099. Prior code mixed up cuda gridDim.x and gridDim.y, mapping the row dimension (mn) to gridDim.y, which CUDA caps at 65535. This fix:

• Assigns row dimension mn to gridDim.x instead of gridDim.y, as grid x limit is capped at 2^31 - 1.
• Fixed the launch guard, which previously checked both grid dims against INT32_MAX. Now grid x is checked at INT32_MAX while grid y is checked against 65535.

This bug doesn't surface in most cases as mn is often small, but this edge case can be hit by the DeepSeek-V4 MTP draft path in the issue above, which quantizes a 3D residual so mn = tokens * hc_mult and exceeds 65535 during profile run.

Test Plan

Added new unit test, able to reproduce error CUDA error: invalid configuration argument (cudaErrorInvalidConfiguration) in unit test without the fix.

Notice: unit test was able to reproduce CUDA error: invalid configuration argument which is a slightly different err message than the one from profile run CUDA error: invalid argument. This is most likely caused by different cuda versions, but regardless both errors come from gridDim.y being too large at kernel launch.

Test Result

Running new unit test without the fix

$ pytest tests/kernels/quantization/test_per_token_group_quant.py -v --tb=short
...

====================================================== FAILURES ======================================================
___________________________________ test_per_token_group_quant_fp8_packed_large_mn ___________________________________
tests/kernels/quantization/test_per_token_group_quant.py:376: in test_per_token_group_quant_fp8_packed_large_mn
    ref_q, ref_s = fp8_utils.per_token_group_quant_fp8(x, group_size, use_ue8m0=True)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
vllm/model_executor/layers/quantization/utils/fp8_utils.py:574: in per_token_group_quant_fp8
    x_s = torch.empty(shape, device=x.device, dtype=torch.float32)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   torch.AcceleratorError: CUDA error: invalid configuration argument
E   Search for `cudaErrorInvalidConfiguration' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
E   CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
E   For debugging consider passing CUDA_LAUNCH_BLOCKING=1
E   Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

...

============================================== short test summary info ===============================================
FAILED tests/kernels/quantization/test_per_token_group_quant.py::test_per_token_group_quant_fp8_packed_large_mn - torch.AcceleratorError: CUDA error: invalid configuration argument
==================================== 1 failed, 119 passed, 16 warnings in 27.20s =====================================

---

Running new unit test with the fix

$ pytest tests/kernels/quantization/test_per_token_group_quant.py -v --tb=short
...
tests/kernels/quantization/test_per_token_group_quant.py::test_per_token_group_quant_fp8_packed_large_mn PASSED [ 95%]
...
========================================= 120 passed, 16 warnings in 26.80s ==========================================

---

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[JasonLi314]

PR is ready for review. Please take a look when available. Thanks!

[tlrmchlsmth]

nit: could improve this reason str (but I see that it is consistent with other reasons in this file)

[tlrmchlsmth]

Thanks for the fix!

[JasonLi314]

The earlier CI failures came from unrelated upstream issues on a stale branch, not from this fix. Rebased to latest commit. Re-compiled and ran unit test again after rebase.

This PR only changes a CUDA quantization kernel, while the failed CI runs hit an API server startup bug already tracked in issues #45596 and #45597.

Also improved pytest reason field based on comment above.

[JasonLi314]

Hi @tlrmchlsmth, thanks for the review. Quick CI update:

This PR only touches quantization, and quantization & kernel checks have passed. There are couple CI failures that are unrelated to this change:

Infrastructure

• arm CPU Docker build failed pulling a PyTorch dependency (CDN returned http 503)
• one CPU multimodal job timed out

Upstream dependency (NIXL)

five NIXL PD jobs fail at startup because the installed NIXL package is missing its native extension, probably because NIXL 1.13.0 was just released. Couple similar NIXL related issues:

• prior fix when NIXL packaging last broke PD CI: [CI][NIXL] Fix PD CI breakage: pin nixl-cu{12,13} versions #39851
• later bump to newer NIXL versions: [PD] Bump NIXL connector dependency to 1.x #42364
• open follow-up to tighten version constraints: [CI][NIXL] Pin nixl < 1.2.0 pending validation #44143

Main branch (unrelated)

• spec decode + LoRA job hit a GPU crash in an unrelated LoRA kernel; the FP8 parts of that same job passed
• one e2e test failed on a whitespace-only output mismatch

I do not think rerunning will help here. Could you force merge when you have a moment? Thanks!

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @JasonLi314.

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

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @JasonLi314.

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

[mergify]

Hi @JasonLi314, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

[JasonLi314]

Need to force merge.

2 CI failed and neither of them are related to this change.

• extract-hidden-states-integration-2-gpus: test_extract_hidden_states_tp2 timed out at 60s. Log shows a HuggingFace 429 at startup eating 27s, so the TP2 engine was still booting when pytest killed it. This is KV connector / speculative decoding.
• cudagraph: test_cudagraph_compilation_combo[FA2-FULL-0-True] failed because GPU memory didn't drop after teardown (stuck ~10.7 GiB for 120s). CUDA graph + FA2 integration test.

[JasonLi314]

All tests passed. Please merge thx.