Use FusedMoEQuantConfig everywhere

[bnellnm]

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[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @bnellnm, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request undertakes a significant refactoring of the Mixture of Experts (MoE) implementation to centralize and standardize how quantization configurations are handled. By introducing a dedicated FusedMoEQuantConfig class, it streamlines parameter passing across various MoE kernels and their modular components. This change enhances code maintainability, improves testability, and lays the groundwork for more robust quantization support within the MoE system.

Highlights

• Quantization Configuration Centralization: Introduced a new FusedMoEQuantConfig class to centralize and standardize quantization parameters across all Mixture of Experts (MoE) kernels. This replaces numerous individual boolean flags (e.g., use_fp8_w8a8, per_channel_quant) with a single, structured configuration object.
• Modular MoE Architecture Enhancement: The modular MoE kernel architecture has been significantly refactored to consistently utilize FusedMoEQuantConfig. This includes updates to FusedMoEPrepareAndFinalize and FusedMoEPermuteExpertsUnpermute base classes, and their concrete implementations (e.g., TritonExperts, DeepGemmExperts, PplxPrepareAndFinalize, DeepEPPrepareAndFinalize).
• Improved Quantization Test Coverage: New dedicated test files (tests/kernels/moe/test_block_fp8.py and tests/kernels/moe/test_block_int8.py) have been added to thoroughly test block-wise FP8 and INT8 quantization for fused MoE operations, including integration with DeepGemm kernels.
• Standardized MoE Test Utilities: A new utility file (tests/kernels/moe/utils.py) has been introduced, providing helper functions like make_quantized_test_activations, make_test_weights, triton_moe, and batched_moe to streamline and standardize MoE kernel testing with quantization.
• Refactored Quantization Helpers: Core quantization helper functions, such as moe_kernel_quantize_input and native_w8a8_block_matmul, have been updated and/or moved to tests/kernels/quant_utils.py to support the new FusedMoEQuantConfig and improve code organization.

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[gemini-code-assist]

Code Review

The pull request focuses on using FusedMoEQuantConfig everywhere, which involves removing redundant parameters and ensuring consistency in quantization configurations across different MoE implementations. The changes include modifications in test files to accommodate the new configuration and ensure correct functionality with various quantization settings. The code review identified areas for improved documentation, consistency, and clarity, particularly regarding the use of environment variables and the rationale behind certain design choices.

[gemini-code-assist]

Consider adding docstrings to explain the purpose of each field in the BatchedMMConfig dataclass.

[gemini-code-assist]

The block size is hardcoded here. Consider adding it as a parameter to the test function.

[gemini-code-assist]

Consider adding a comment to explain why these sizes trigger wrong answers.