[Bugfix] FusedMoE: coerce shape-(1,) per-tensor scales to 0-D scalar …

[V-3604]

Fixes #43297

Purpose

I checked open and merged PR's for _load_per_tensor_weight_scale and issue #43297, no existing PR addressed this issue.

FusedMoE._load_per_tensor_weight_scale assumed per-tensor weight scales arrive as 0-D scalars. llm-compressor NVFP4 presets emit them as shape-(1,) tensors by default (torch.tensor([x]) rather than torch.tensor(x)).
PyTorch's copy_() path rejects the (1,) -> () broadcast:

RuntimeError: output with shape [] doesn't match the broadcast shape [1]

This crashes server init for any compressed-tensors NVFP4 MoE artifact produced by llm-compressor before any inference runs.

Fix: call .view([]) on loaded_weight in both the w1/w3 and w2 assignment, paths. .view([]) coerces a singleton tensor to a 0-D scalar in-place (no copy), and raises error if the tensor has more than one element.

Test Plan

Standalone reproducer (torch only, no GPU or model weights needed):

import torch, torch.nn as nn
param = nn.Parameter(torch.empty(4, 2, dtype=torch.float32), requires_grad=False)
slot = param.data[0][0]
slot.copy_(torch.tensor([0.5]))        # RuntimeError before fix
slot.copy_(torch.tensor([0.5]).view([]))  # passes after fix

Test Result

## AI Assistance disclosure Local test file and PR descrioption written with AI assistance. All changes reviewed and validated by V-3604.

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[gemini-code-assist]

Code Review

This pull request updates the _load_per_tensor_weight_scale method in vllm/model_executor/layers/fused_moe/layer.py to coerce weight scales into 0-D scalars using .view([]), preventing broadcast errors when quantization tools provide shape-(1,) tensors. The reviewer suggested extending this fix to other methods like _load_single_value and _load_combined_w13_weight_scale where similar issues with per-tensor scales, such as input_scale, are likely to occur.

[V-3604]

Added TestPerTensorScaleLoading in tests/kernels/moe/test_moe_weight_loading_padded.py covering both _load_per_tensor_weight_scale (w1/w3 and w2) and _load_single_value with shape-(1,) inputs, plus a 0-D regression case and a numel>1 case that confirms .view([]) still fails loudly. 5/5 pass locally.

platform linux -- Python 3.12.3, pytest-9.0.3, pluggy-1.6.0 -- /home/pc/vllm-fork/.venv/bin/python
cachedir: .pytest_cache
hypothesis profile 'default'
rootdir: /home/logyx/vllm-fork
configfile: pyproject.toml
plugins: asyncio-1.4.0, shard-0.1.2, cov-7.1.0, schemathesis-4.20.2, anyio-4.13.0, typeguard-4.5.2, buildkite-test-collector-0.1.9, timeout-2.4.0, mock-3.15.1, forked-1.6.0, hypothesis-6.153.6, rerunfailures-16.3
asyncio: mode=Mode.STRICT, debug=False, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collecting ... collected 5 items
Running 5 items in this shard: tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_w1_w3_accept_shape_one_scale, tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_w2_accepts_shape_one_scale, tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_single_value_accepts_shape_one, tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_scalar_input_still_works, tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_multi_element_loaded_weight_raises

tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_w1_w3_accept_shape_one_scale PASSED [ 20%]
tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_w2_accepts_shape_one_scale PASSED [ 40%]
tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_single_value_accepts_shape_one PASSED [ 60%]
tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_scalar_input_still_works PASSED [ 80%]
tests/kernels/moe/test_moe_weight_loading_padded.py::TestPerTensorScaleLoading::test_multi_element_loaded_weight_raises PASSED [100%]

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @V-3604.

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

[mgoin]

Nice, LGTM!

[V-3604]

@mgoin thanks. Both red checks trace to one failure: buildkite/ci/pr is the build-level rollup for #72490, and the only failing job under it is distributed-compile-unit-tests-2xh100, which hit a CUDA OOM in test_sequence_parallelism_pass (NCCL Cuda failure 2 'out of memory', 1 failed / 93 passed). Unrelated to this change. A retry of that job should clear both.