[Kernel] Switch fp8 layers to use the CUTLASS kernels by tlrmchlsmth · Pull Request #5183 · vllm-project/vllm

tlrmchlsmth · 2024-06-01T18:55:56Z

Switching from torch._scaled_mm to vLLM's cutlass fp8 kernels when supported as we are seeing 5-15% improvement in e2e performance on neuralmagic/Meta-Llama-3-8B-Instruct-FP8

see https://docs.google.com/spreadsheets/d/1GiAnmzyGHgZ6zL_LDSTm35Bdrt4A8AaFEurDlISYYA4/ for some quick e2e benchmarks and #5144 for comparisons across different GEMM sizes.

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robertgshaw2-redhat · 2024-06-01T18:58:46Z

@tlrmchlsmth models:

tlrmchlsmth · 2024-06-01T19:20:51Z

Just ran a quick sanity check for correctness. Output looks good on all three. I tried tensor_parallel_size=2 as well for the 70B model, and that looks good

robertgshaw2-redhat · 2024-06-01T19:21:49Z

-        return torch.narrow(output, 0, 0, x.shape[0])
+
+        # We use the CUTLASS kernels by default but they don't support bias yet
+        if bias is None:


Do we also do a branch if we are on ada lovelace and CUDA 12.1?

We will need to if on CUDA < 12.4. We also need a branch if on CUDA 11.8. @comaniac do you know if torch._scaled_mm is supported in that case?

I only know that it only supports SM89+. We can try to call this op with torch+cu118 to test out.

The cutlass kernels need at least SM89 as well, for the record.

Yeah that makes sense. Older architectures don't have native FP8 so we can't get speedup from them, which seems not necessary to be covered.

Note: we already have a mechanism for determining if a LinearMethod can run on a specific cuda arch. The LinearMethod exposes get_min_capability which is called during model loading.

https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/fp8.py#L46

pcmoritz · 2024-06-01T20:04:49Z

Did you run benchmarks to compare the end-to-end performance? ITL for different qps

robertgshaw2-redhat · 2024-06-01T20:06:47Z

Did you run benchmarks to compare the end-to-end performance? ITL for different qps

Not yet. But obviously need this before we merge

comaniac · 2024-06-01T20:34:30Z

I'll do a benchmark on Monday anyways. btw it'd be great if this PR is rebased onto the latest main that includes all required changes (it's likely the case already I suppose

tlrmchlsmth · 2024-06-01T21:04:15Z

btw it'd be great if this PR is rebased onto the latest main that includes all required changes (it's likely the case already I suppose

It's on a very recent main (from this morning) so it's good to use as is. In particular both #5144 and #5137 were needed for the switchover and they are both in.

comaniac · 2024-06-06T16:47:53Z

@tlrmchlsmth @robertgshaw2-neuralmagic per offline discussion, this PR should be ok to go at least for now?

tlrmchlsmth · 2024-06-06T17:00:19Z

Yeah, let's get it landed. It needs to check a few more cases for falling back to scaled_mm. I'll get to that today and then mark it ready for review

robertgshaw2-redhat

LGTM :)

pcmoritz

Great work and thanks for adding the benchmarks :)

comaniac

LGTM. Thanks!
I also did some benchmarks with this PR. Note that all results are in TP=4 on H100 and with chunked prefill enabled (this is just my own requirement). Prompts are 550 tokens, decoding 150 tokens.

Model	QPS	scaled_mm-ITL	cutlass-ITL	scaled_mm-TTFT	cutlass-TTFT
Llama-3-70B	1	17.3	16.3	68.7	68.7
Llama-3-70B	4	22.7	21.2	72.3	72.6
Llama-3-70B	8	35.9	33.6	83.1	81.2
Mixtral-8x7B	1	9.1	8.9	43.1	40.7
Mixtral-8x7B	4	11.4	10.7	42.6	38.4
Mixtral-8x7B	8	15.6	14.3	43.4	42.8

cli99 · 2024-06-07T20:13:43Z

@tlrmchlsmth Awesome work! Was trying this but ran into a problem when checking the cutlass fp8 support. Made a fix that works in my case in #5352.

Bug description: With torch 2.4.0.dev20240603+cu121, cutlass_fp8_supported outputs False, and the (capability, version) before the comparison is (90, 11111111112) This PR fixes the support check for FP8 CUTLASS ( cutlass_fp8_supported) which was introduced in #5183.

) Switching from torch._scaled_mm to vLLM's cutlass fp8 kernels when supported as we are seeing 5-15% improvement in e2e performance on neuralmagic/Meta-Llama-3-8B-Instruct-FP8 see https://docs.google.com/spreadsheets/d/1GiAnmzyGHgZ6zL_LDSTm35Bdrt4A8AaFEurDlISYYA4/ for some quick e2e benchmarks and vllm-project#5144 for comparisons across different GEMM sizes.