[feat] enable SM100 CUTLASS block scaled group gemm for smaller batch sizes

[djmmoss]

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• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
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• The test results, such as pasting the results comparison before and after, or e2e results
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Purpose

Enable CUTLASS block scaled grouped GEMM for small batch sizes.

Performance for the smaller sizes is roughly 1.2x-1.4x better than standard triton.

Test Plan

lm_eval --model vllm --model_args pretrained=/scratch/models/DeepSeek-R1,tensor_parallel_size=4,max_model_len=2048,gpu_memory_utilization=0.9,max_num_seqs=32 --trust_remote_code --tasks gsm8k --num_fewshot 5 --batch_size auto

Test Result

vllm (pretrained=/scratch/models/DeepSeek-R1,tensor_parallel_size=4,max_model_len=2048,gpu_memory_utilization=0.9,max_num_seqs=32,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9507|±  | 0.006|
|     |       |strict-match    |     5|exact_match|↑  |0.9500|±  | 0.006|

(Optional) Documentation Update

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[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 significantly enhances the performance of grouped GEMM operations by extending the applicability of the highly optimized CUTLASS block scaled grouped GEMM kernel. By relaxing the input dimension constraints, the system can now leverage this faster implementation for a wider range of batch sizes, particularly benefiting smaller ones, leading to notable speedups in relevant computations.

Highlights

• Performance Optimization: Enabled the use of CUTLASS block scaled grouped GEMM for smaller batch sizes by removing the minimum batch size (M >= 128) requirement in the _valid_cutlass_block_scaled_grouped_gemm_shape validation function. This change is expected to yield performance improvements of approximately 1.2x-1.4x for these smaller batch sizes.

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[gemini-code-assist]

Code Review

This pull request enables the CUTLASS block-scaled grouped GEMM kernel for smaller batch sizes by removing the minimum batch size check. This change improves performance for smaller batch sizes. The review includes a suggestion to remove an unused parameter for better code maintainability.

[mgoin]

It would be helpful to have a kernel level result like benchmark_cutlass_fp4_moe.py but this result seems reasonable to me for Blackwell, thanks