[Draft] [FP8] CUTLASS FP8 matrix multiply

[pcmoritz]

This is a relatively clean port of the CUTLASS FP8 code from https://github.com/NVIDIA/cutlass/blob/033d9efd2db0bbbcf3b3b0650acde6c472f3948e/examples/54_hopper_fp8_warp_specialized_gemm/54_hopper_fp8_warp_specialized_gemm.cu

No scaling yet and some of the numerics might not be right. I tried to focus on improving the small batch size matrix multiplication performance for bs x 4096 x 4096 multiplications.

With @mgoin 's benchmark script from #4118 (comment), https://github.com/mgoin/torch-fp8/blob/main/benchmark_scaled_mm.py with the diff in the appendix, I'm getting the following results:

This is comparing this code (FP8) and the scaled_mm code, both with static scaling, with FP16

M, N, K, Cosine similarity, FP16 time, FP8 time, FP8 scaled_mm, Speedup, Speedup scaled_mm
1, 4096, 4096, 0.9995, 0.1176, 0.1511, 0.2035, 0.778, 0.578
2, 4096, 4096, 0.9995, 0.1173, 0.1374, 0.1962, 0.853, 0.598
4, 4096, 4096, 0.9990, 0.1145, 0.1379, 0.1876, 0.830, 0.610
8, 4096, 4096, 0.9990, 0.1171, 0.1329, 0.1875, 0.881, 0.624
16, 4096, 4096, 0.9990, 0.1204, 0.1302, 0.1887, 0.925, 0.638
32, 4096, 4096, 0.9995, 0.1209, 0.1219, 0.1819, 0.992, 0.665
64, 4096, 4096, 0.9995, 0.1464, 0.1174, 0.1804, 1.247, 0.811
128, 4096, 4096, 0.9995, 0.1666, 0.1190, 0.1803, 1.399, 0.924
256, 4096, 4096, 0.0000, 0.2359, 0.2165, 0.1806, 1.090, 1.307

The TL;DR: Some improvements, but not nearly there yet.

appendix:

diff benchmark_scaled_mm2.py benchmark_scaled_mm.py
2a3
> from vllm import _C
14a16
>     y = torch.empty_like(x)
20a23,24
>     workspace = torch.zeros(32 * 1024 * 1024, dtype=torch.uint8, device='cuda')
> 
24c28,29
<         y, _ = torch._scaled_mm(x_fp8, w_fp8, out_dtype=dtype, scale_a=x_inv_s, scale_b=w_inv_s)
---
>         # y, _ = torch._scaled_mm(x_fp8, w_fp8, out_dtype=dtype, scale_a=x_inv_s, scale_b=w_inv_s)
>         _C.ops.fp8_scaled_gemm(y, x_fp8, w_fp8, workspace)
32c37,38
<         y, _ = torch._scaled_mm(x_fp8, w_fp8, out_dtype=dtype, scale_a=x_inv_s, scale_b=w_inv_s)
---
>         _C.ops.fp8_scaled_gemm(y, x_fp8, w_fp8, workspace)
>         # y, _ = torch._scaled_mm(x_fp8, w_fp8, out_dtype=dtype, scale_a=x_inv_s, scale_b=w_inv_s)
41c47
<         x_fp8, x_inv_s = to_float8(x, dtype=qdtype)
---
>         # x_fp8, x_inv_s = to_float8(x, dtype=qdtype)
75c81
<     print("M, N, K, Cosine similarity, FP16 time, FP8 time, FP8 dynamic quant time, Speedup, Speedup dynamic quant")
---
>     print("M, N, K, Cosine similarity, FP16 time, FP8 time, FP8 scaled_mm, Speedup, Speedup scaled_mm")

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