[Bugfix][ROCm] Fix FP8 per-tensor scale rank mismatch causing Inductor assertion failure

[nehmathe2]

Purpose

Fix AssertionError in Inductor's aten._scaled_mm lowering due to mismatched scale tensor ranks when AITER is enabled (VLLM_ROCM_USE_AITER=1) and skinny GEMMs are disabled ( VLLM_ROCM_USE_SKINNY_GEMM=0) on ROCm.

requantize_with_max_scale returns a 0-D weight scale, but AITER's _rocm_aiter_per_tensor_quant_fake always returns a 1-D activation scale. When torch.compile lowers aten._scaled_mm, it asserts that scale_a and scale_b have matching ranks, causing EngineCore to fail on startup.

This patch normalizes both scales to 1-D before they are stored, ensuring consistent ranks at compile time.

Test Plan

Run vLLM with a static FP8 per-tensor quantized model on ROCm with AITER enabled and skinny GEMMs disabled:

AMDGCN_USE_BUFFER_OPS=1 VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_SKINNY_GEMM=0 USE_FASTSAFETENSOR=1 SAFETENSORS_FAST_GPU=1 vllm serve amd/Llama-3.1-8B-Instruct-FP8-KV --max-model-len 12000 --gpu-memory-utilization 0.92 --no-enable-prefix-caching --disable-log-stats --kv-cache-dtype fp8 --async-scheduling --attention-backend ROCM_AITER_FA

Verify the model loads, compiles, and serves requests successfully.

Test Result

Before:

torch._inductor.exc.InductorError: LoweringException: AssertionError:
  target: aten._scaled_mm.default
  File "torch/_inductor/template_heuristics/triton.py", line 1879, in adjust_kernel_inputs
    assert len(scale_a.get_size()) == len(scale_b.get_size())

EngineCore fails to start during benchmark run

After:

Model loads and compiles successfully and benchmark completes.
Validated on MI355X with Llama-3.1-8B-Instruct-FP8-KV.

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[nehmathe2]

hi @tjtanaa @BadrBasowid,

As suggested by @divakar-amd, I have opened a new PR regarding the same issue/fix in PR 41293.

Please let me know if this PR is ready to merge now. Thanks

[AndreasKaratzas]

would view work as well? if so then probably better to use it as it would not do any copy ..

[divakar-amd]

would view work as well? if so then probably better to use it as it would not do any copy ..

Since we are converting a 0-D tensor to 1-D, this should be fine. It would implicitly call view and avoid a copy

[mergify]

Hi @nehmathe2, 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.

[AndreasKaratzas]

@tjtanaa PTAL

[nehmathe2]

would view work as well? if so then probably better to use it as it would not do any copy ..

Since we are converting a 0-D tensor to 1-D, this should be fine. It would implicitly call view and avoid a copy

Yes, this should work. I tested the changes using view with the model amd/Llama-3.1-8B-Instruct-FP8-KV and it works.

[tjtanaa]

LGTM