[PyTorch] Add grouped linear op and experimental fusion for grouped MLP

[timmoon10]

Description

This PR adds a grouped linear op, which can be used in the grouped MLP block in Mixture-of-Experts models. It also adds an experimental fused operation for a grouped MLP block, using a CuTe DSL kernel that computes an MXFP8 grouped GEMM and SwiGLU.

Type of change

• Documentation change (change only to the documentation, either a fix or a new content)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Infra/Build change
• Code refactoring

Changes

• Add a grouped linear operation
• Add a post-scaled SwiGLU op and add support for interleaving SwiGLU gate and linear units
• Add a fused operation for grouped MLP

Checklist:

• I have read and followed the contributing guidelines
• The functionality is complete
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

[greptile-apps]

_{9 files reviewed, 4 comments}

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[greptile-apps]

Incorrect grad-required flags

In ForwardGroupedMLP_CuTeGEMMSwiGLU_MXFP8.fuser_forward, swiglu_ctx.input_requires_grad and swiglu_ctx.extra_input_requires_grad are set to True unconditionally (and input_requires_grad is set to requires_grad unconditionally). This will make ScaledSwiGLU.fuser_backward compute grad_input and grad_extra_input even when neither input_ nor scales require grads, which violates autograd semantics and can raise (e.g., scales.detach() passed into the fused kernel, but extra_input_requires_grad=True forces a gradient).

This should be set based on the actual requirements:

• input_requires_grad = input_.requires_grad
• swiglu_ctx.extra_input_requires_grad = scales.requires_grad
• and for FC weights, check each parameter’s requires_grad (not just weight0).

[greptile-apps]

_{4 files reviewed, no comments}

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