[Kernel] [FP8] Improve FP8 linear layer performance

[pcmoritz]

This PR improves the FP8 performance of linear layers, which had been lacking before (#4118 (comment) and #4118 (comment)).

We noticed that CUBLASLt can find a better algorithm if the first dimension of the matrix is at least 16. So this PR enlarges matrices appropriately during quantization. This improves FP8 performance and removes the performance regression vs. FP16, in many cases exceeding FP16 performance.

Here are benchmarks on llama3 70b (ITL numbers for 1000 input and 50 output tokens at fixed qps and at TP 4), all FP8 measurements are for dynamic quantization:

qps = 1: 24 ms (FP8, this PR), 32 ms (FP8, previous main), 26 ms (FP16)
qps = 2: 26 ms (FP8, this PR), 34ms (FP8, previous main), 28 ms (FP16) 
qps = 4: 33 ms (FP8, this PR), 44 ms (FP8, previous main), 36 ms (FP16)
qps = 6: 46 ms (FP8, this PR), 56 ms (FP8, previous main), 54 ms (FP16)
qps = 8: 85 ms (FP8, this PR), 85 ms (FP8, previous main), 138 ms (FP16)

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[tlrmchlsmth]

What is the perf effect when padding to 32 vs 16? (I ask because here it's 32 and in the PR description it's 16)

[pcmoritz]

So when I write my own wrappers for CUBLASLt, I'm getting the following error when calling the cublasLtMatmulAlgoGetHeuristic with FP8:

[2024-05-08 19:00:00][cublasLt][1533][Info][cublasLtMatmulAlgoGetHeuristic] Unsupported M dimension for FP8 matrix multiplication. M must be divisible by 16. Got 2.

(this is with highest logging CUBLASLT_LOG_LEVEL=5) -- that's why I wrote 16 in the description.

For the setting we are using however, 32 is actually the best setting -- I tried them both and with 16 it is much closer to what it was previously. It is however possible that this will change in the future (e.g. once we use FP8 outputs I think things will change).

[pcmoritz]

I clarified this in the description now -- I wrote 16 since I didn't want to bias people for the future :)

[courage17340]

Hello, I have two questions:

1. I never saw the M must be divisible by 16 error when testing cublasLt. In fact, I can perform 1 x 1024 x 16 matmul with torch._scaled_mm. But it seems that there are some constraints on N, cublasLt requires N % 8 == 0, while torch requires N % 16 == 0. I guess your error is also on N, because cublasLt api is col major and we pass N as M to it when using row major tensors.
2. In my experiment, matmul is slower when M is in range [1, 16], and is faster in range [17, 32], so maybe 17 is a better choice instead of 32?

[pcmoritz]

Thanks for the suggestion, let me try if 17 is better than 32 :)

[pcmoritz]

I found the performance of 17 to be exactly the same as the performance of 32, so I'll switch to 17 since it uses less memory. Thanks for the suggestion @courage17340 :)

[robertgshaw2-redhat]

kinda wild - I suspect we will be able to improve performance significantly with our kernels

[mgoin]

This makes sense! :)

[comaniac]

Great!