[Kernel]Add streamK for block-quantized CUTLASS kernels

[Hongbosherlock]

Thanks to #11868

Currently only supports scale_a block shapes of 1x128 and scale_b block shapes of 128x128 (for deepseek v3)

This CUTLASS GEMM is slower when K is very large, such as m128-n1536-k7168 in deepseek v3.
To address this, I explored using both Stream-K and Split-K strategies to accelerate the GEMM computation.

Among these approaches, Stream-K demonstrated superior performance in most cases, achieving over 60% performance improvement compared to the baseline.

To achieve overall better performance, use Stream-K only when K > 3N.

Below are the test results based on the GEMM shape of DeepSeek-V3, tested on H800 with TP-4.

[----------------- scaled-torch.float8_e4m3fn-gemm ------------------]
                           |  cutlass_fp8_fp8_fp16_scaled_mm_blockwise
  -------------------------------------------------------------------------------  
                                             cutlass-base             streamk                                 
1 threads: ----------------------------------------------------------------------
      MKN=(128x7168x1536)  |                    28.5                  17.1                large K       
      MKN=(128x1536x6144)  |                    10.4                  10.7
      MKN=(128x4096x7168)  |                    19.3                  19.3
      MKN=(128x7168x9216)  |                    32.7                  32.7
      MKN=(128x4608x7168)  |                    20.6                  20.6
      MKN=(128x7168x1024)  |                    26.4                  17                  large K
      MKN=(128x512x7168)   |                    10.4                  10.8
      MKN=(256x7168x4096)  |                    28.9                  29.3
      MKN=(256x7168x2048)  |                    29.8                  22                  large K

Times are in microseconds (us).

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[robertgshaw2-redhat]

cc @LucasWilkinson

[LucasWilkinson]

@Hongbosherlock Awesome thanks for the contribution! thats some nice speedups! Left a couple nits.

[Hongbosherlock]

@Hongbosherlock Awesome thanks for the contribution! thats some nice speedups! Left a couple nits.

Thank you for the feedback! I’ll take a look at the nits and address them ASAP.

[Hongbosherlock]

Hi @LucasWilkinson
Thank you for the feedback! I’ve addressed the nits and verified the performance improvements.
Could you please check if it’s ready to be merged?

[LucasWilkinson]

LGTM, thanks for the hardwork!