[W8A8 Block Linear Refactor][2/N] Remove W8A8Fp8BlockLinearOp and adopt Fp8 block linear kernel selections.

[maralbahari]

Purpose

This PR refactors block scaled linear kernel into kernel abstraction.

changes:

• Introduces MMLinearKernel base interface for all linear kernels.
• Introduces Params, Fp8Params and Int8Params, classes to access layer params in structured format.
• Introduces DynamicMMLinearKernel which is a type of MMLinearKernel with two main properties of base and fallback kernels that are variant of MMLinearKernel. this class switches between base and fallback
  implementations at runtime.
• Removing the legacy W8A8BlockFp8LinearOp class.
• Unifying kernel selection for both block and non-block quantization
• Updating all consumers (fp8.py, modelopt.py, tests, benchmarks)

Test Plan

Cuda platfrom
run ci/cd tests.

ROCm platform:
lm_eval score RedHatAI/Qwen3-30B-A3B-FP8-block

Test Result

ROCm platform:
lm_eval score RedHatAI/Qwen3-30B-A3B-FP8-block, without AITER

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.8196	±	0.0106
		strict-match	5	exact_match	↑	0.8954	±	0.0084

W8A8 Block Linear Refactor PRs:

• [1/N] [W8A8 Block Linear Refactor][1/N] Keep all quantization types into QuantFP8 class. #33047: Moves all the quantization ops into the same QuantFP8 class. (merged)
• [2/N]: [W8A8 Block Linear Refactor][2/N] Remove W8A8Fp8BlockLinearOp and adopt Fp8 block linear kernel selections. #33892: Extract block scaled mm linear kernels into kernel abstraction and removes the W8A8Fp8BlockLinearOp class and updates all code paths and files that use this class.
• [3/N] [W8A8 Block Linear Refactor][3/N] Make all scaled MM kernels inherit from common generic base. #33893: Applies base inheritance to the remaining ScaledMM kernels for consistent code and improved maintainability of linear kernel classes.

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[gemini-code-assist]

Code Review

This pull request introduces a significant and well-designed refactoring of the FP8 block-scaled linear kernel integration. By removing the monolithic W8A8BlockFp8LinearOp and introducing a new kernel abstraction layer with MMLinearKernel, the code becomes much more modular, maintainable, and extensible. The new kernel selection mechanism in init_fp8_linear_kernel is clear and correctly dispatches to different kernel implementations based on the quantization configuration. The changes are consistently applied across benchmarks, tests, and model implementation files.

I've found a few issues, including a critical one that would cause a runtime error, and a couple of high-severity issues related to correctness in tests and code robustness. After addressing these, this PR will be a great improvement to the codebase.

[mergify]

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

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

[mergify]

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

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pre-commit run --all-files

Then, commit the changes and push to your branch.

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Tip

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[mergify]

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

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

[maralbahari]

@LucasWilkinson after including input_dtype to FP8LinearLayerConfig. as discussed we can assume that the activation dtype is same as model_config.dtype. However, XXLinearMethod objects do not have vllm_config access then.. I used get_current_vllm_config() to access the model config.
then the tests in quantization/test_fp8.py::test_fp8_reloading and quantization/test_modelopt.py were failing because the vllm_config is not set correctly by this default_vllm_config pytest fixture. I had to make this changes to the fixture and then set model_config as well in the unit cases as needed.

[LucasWilkinson]

LGTM! Thanks for the contribution and cleanups! this helps alot

[mergify]

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

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, @maralbahari.

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