[Common, PyTorch] Improve mHC to match DeepSeek's implementation

[kainzhong]

Description

Some enhancement for mHC to better align with DeepSeek's tilelang implementation: https://github.com/deepseek-ai/TileKernels/tree/main/tile_kernels/mhc

Fixes # (issue)

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 mhc_generate_mix_and_aggregate API that does projection, scale, sinkhorn and aggregate together
• Allow mhc_fused_projection to accept arguments with mixed dtype: x.dtype=bf16, phi.dtype=fp32, which matches DeepSeek's implementation
• mhc_fused_projection now outputs fp32 regardless of the input dtype, matching DeepSeek's implementation
• Add fuse_grad_x_acc optimization (default to False), which will reuse the same grad_x buffer to accumulate the initial mHC input x's gradient for mhc_fused_expand_combine, mhc_fused_aggregate and mhc_fused_projection
• Support norm_weight for mhc_fused_projection, which would be equivalent to apply RMSNorm in the unfused manner with elementwise_affine=True, which would be the learnable per-element affine parameters for RMSNorm
• Refactor some kernel code to avoid duplication. I just realized if you make a triton kernel constexpr, it can be used as a macro in if branches since triton will not compile if it knows in compile time that some branch will not be taken
• Fix the bug that grid will exceed CUDA's limitation when M is too large and the autotune candidate is BLOCK_SIZE_M=1. Such invalid configs will be pruned now.
• Improve projection op by using TMA if on Hopper+

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