gpt-oss: Add MXFP8 x MXFP4 CUTLASS MOE for SM100 and BF16 x MXFP4 CUTLASS for SM90 + SwigluBias Activation

[djmmoss]

📌 Description

This PR adds MXFP8 x MXFP4 CUTLASS MOE with SwigluBias for SM100 GPUs.

It also adds BF16 x MXFP4 CUTLASS MOE with SwigluBias for SM 90 - A follow up will be needed to expose this

🔍 Related Issues

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Reviewer Notes

[gemini-code-assist]

Summary of Changes

Hello @djmmoss, 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 introduces support for MXFP8 x MXFP4 CUTLASS Mixture of Experts (MoE) operations, specifically targeting SM100 GPUs. I've also added a new SwigluBias activation function, which can be configured with per-expert parameters. These changes involve significant updates to the quantization pipeline, activation kernels, and the GEMM profiler backend to ensure efficient and accurate mixed-precision computations.

Highlights

• New Mixed-Precision MoE Support: I've added comprehensive support for MXFP8 x MXFP4 CUTLASS Mixture of Experts (MoE) operations, specifically optimized for SM100 GPUs.
• Introduction of SwigluBias Activation: A new SwigluBias activation function has been introduced, allowing for per-expert alpha, beta, and limit parameters to fine-tune activation behavior.
• Enhanced Quantization Functions: The underlying quantization logic and kernels have been updated to seamlessly handle the new MXFP8 and MXFP4 data types, including refactoring of cvt_quant_get_sf_out_offset and quantize_with_block_size.
• Improved Groupwise Scaling for WFP4A16: I've improved the handling of WFP4A16 groupwise scaling, with precise adjustments to stride and pointer calculations within the kernels.
• Refactored Activation Kernels: The activation kernels (doGatedActivationKernel, doActivationKernel) have been refactored to use generic ActFn adaptors, making them more flexible and enabling the integration of SwigluBiasAdaptor.
• Profiler Backend Updates: The GemmProfilerBackend has been updated to correctly account for WFP4A16 quantization and the new SwigluBias parameters when calculating workspace sizes.
• Updated PyTorch Bindings: The PyTorch bindings (flashinfer_cutlass_fused_moe_sm100_ops.cu) have been modified to pass the new ActivationParams struct and handle swiglu_alpha, swiglu_beta, and swiglu_limit tensors from Python.

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[gemini-code-assist]

Code Review

This pull request adds support for MXFP8 x MXFP4 CUTLASS MoE with SwigluBias activation for SM100 GPUs. The changes are extensive, including new template instantiations, refactoring of quantization kernels for better generality, and the addition of activation adaptors to handle the new SwigluBias activation. The code is well-structured, and the new features seem to be implemented correctly. I have one suggestion to improve maintainability by refactoring a piece of duplicated code.

[gemini-code-assist]

The logic for extracting SwiGLU parameters (gate_alpha, gate_beta, gate_limit) is very similar to the logic in doGatedActivationKernel at lines 2068-2079. To improve maintainability and reduce code duplication, consider refactoring this into a helper function. This function could take activation_params and the expert index as input and return a struct or tuple with the gate parameters.