QMoE CUDA: Rename build options, refactor PrePack, add GPU kernels

[tianleiwu]

Description

Follow-up refinements to QMoE CUDA EP (#28467): rename build options for consistency, fix CodeQL warnings, refactor PrePack from nested lambdas into named helper methods, and replace CPU data-transformation loops with GPU kernels.

Motivation and Context

PR #28467 introduced the QMoE operator with a 373-line PrePack function containing 5 nested lambdas that performed weight/scale prepacking at model load time. Reviewer feedback requested:

1. Rename onnxruntime_ENABLE_CUDA_* cmake options to onnxruntime_USE_* for naming consistency.
2. Fix CodeQL empty-except warnings in test code.
3. Extract lambdas into named private methods for readability and testability.
4. Replace CPU loops (block-scale swizzle, FP4 col-to-row repack) with GPU kernels to avoid unnecessary CPU↔GPU round-trips during model loading.

Key Changes

Commit	Scope	Description
594642a	Build system	Rename ENABLE_CUDA_FP4_QMOE→USE_FP4_QMOE, ENABLE_CUDA_FP8_QMOE→USE_FP8_QMOE in cmake, C++ defines, and 340+ generated .cu files
594642a	Test	Fix CodeQL empty-except warning in test_qmoe_cuda.py
e8d364b	QMoE operator	Extract 5 lambdas into private helper methods: PrePackTransposeAndPack, PrePackCopyToGpu, PrePackSwizzleBlockScales, PrePackRepackFP4Weights, PrePackComputeBias
e8d364b	CUDA kernels	Add QMoERepackFP4ColToRowKernel — repacks column-major FP4 packed weights to row-major on GPU (replaces per-expert CPU loop)
e8d364b	CUDA kernels	PrePackSwizzleBlockScales now calls existing LaunchQMoEBlockScaleInterleave GPU kernel (replaces CPU SwizzleMXFPXBlockScalesToGpu loop)

Impact

• No behavioral change — all transformations produce identical output tensors.
• Model load only — PrePack runs once during InferenceSession::Initialize(), not on the inference hot path.
• Performance: Eliminates CPU→GPU→CPU→GPU round-trips for block-scale swizzling and FP4 weight repacking. Data stays on GPU throughout.

Testing

• Build verified with onnxruntime_USE_FP4_QMOE=ON onnxruntime_USE_FP8_QMOE=ON (CUDA 13.0, SM90).
• All new symbols confirmed linked in libonnxruntime_providers_cuda.so via nm.
• Existing test_qmoe_fp4_cuda.py, test_qmoe_wfp4afp8_cuda.py, test_qmoe_cuda.py cover the affected code paths.

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