[Kernel] Make static FP8 scaling more robust

[pcmoritz]

Previously FP8 static scaling works if the scales are overestimating the maxima of all activation tensors during computation. However this will not always be the case even if the scales were calibrated very carefully. For example, with the activations in my checkpoint

https://huggingface.co/pcmoritz/Mixtral-8x7B-v0.1-fp8-act-scale

(which was calibrated on https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k), I'm getting the following mostly random performance on MMLU:

|      Groups      |Version|Filter|n-shot|Metric|Value |   |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu              |N/A    |none  |     0|acc   |0.2295|±  |0.0035|
| - humanities     |N/A    |none  |     5|acc   |0.2421|±  |0.0062|
| - other          |N/A    |none  |     5|acc   |0.2398|±  |0.0076|
| - social_sciences|N/A    |none  |     5|acc   |0.2171|±  |0.0074|
| - stem           |N/A    |none  |     5|acc   |0.2125|±  |0.0073|

With the fix in this PR where the scaled activations are clamped between [-std::numeric_limits<c10::Float8_e4m3fn>::max(), std::numeric_limits<c10::Float8_e4m3fn>::max()] to make sure there are no NaNs, the performance is

|      Groups      |Version|Filter|n-shot|Metric|Value |   |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu              |N/A    |none  |     0|acc   |0.7008|±  |0.0036|
| - humanities     |N/A    |none  |     5|acc   |0.6453|±  |0.0065|
| - other          |N/A    |none  |     5|acc   |0.7692|±  |0.0072|
| - social_sciences|N/A    |none  |     5|acc   |0.8083|±  |0.0070|
| - stem           |N/A    |none  |     5|acc   |0.6115|±  |0.0083|

This is not perfect yet but is getting very close to the FP16 / dynamic activation scale performance.

Before merging this PR, we should optimize the scaled_fp8_conversion function to use intrinsics, currently it incurs some noticeable slowdown.

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[comaniac]

LGTM! Hopefully we can later use this method for kv-cache as well.

[mgoin]

Here is my suggestion using intrinsics. I saw the same deterministic output on my smoke test, but it is worth doing another eval in your setup.

Suggested change

	template<typename scalar_t>
	__device__ __forceinline__ c10::Float8_e4m3fn scaled_fp8_conversion(const scalar_t val, const float scale) {
	float x = static_cast<float>(val) / scale;
	float r = max(-FP8_E4M3_MAX, min(x, FP8_E4M3_MAX));
	return static_cast<c10::Float8_e4m3fn>(r);
	}
	template<typename scalar_t>
	__device__ __forceinline__ c10::Float8_e4m3fn scaled_fp8_conversion(const scalar_t val, const float scale) {
	float x = __fdiv_rn(static_cast<float>(val), scale);
	float r = fmaxf(-FP8_E4M3_MAX, fminf(x, FP8_E4M3_MAX));
	return static_cast<c10::Float8_e4m3fn>(r);
	}

[pcmoritz]

Unfortunately this doesn't seem to make it faster. The only thing that is fast so far is

template<typename scalar_t>
__device__ __forceinline__ c10::Float8_e4m3fn scaled_fp8_conversion(const scalar_t val, const float scale) {
  float x = static_cast<float>(val) / scale;
  return __nv_cvt_float_to_fp8(x, __NV_SATFINITE, __NV_E4M3);
}

but it doesn't seem to be correct, e.g. I'm seeing

from vllm import _custom_ops as ops
import torch

t = 10000 * torch.ones(1, dtype=torch.float16, device="cuda")
ops.scaled_fp8_quant(t, torch.ones(1, device="cuda"))

returns

(tensor([128.], device='cuda:0', dtype=torch.float8_e4m3fn),
 tensor([1.], device='cuda:0'))

but it should be 448.0 (the max of float8_e4m3). If you have any ideas what is going wrong here I'd love to know :D

[pcmoritz]

This also works but is not super fast either:

float x = static_cast<float>(input[i]) / *scale;
auto r = __nv_cvt_float_to_fp8(x, __NV_SATFINITE, __NV_E4M3);
out[i] = c10::Float8_e4m3fn(r, c10::Float8_e4m3fn::from_bits());

[pcmoritz]

I don't think the performance difference I was seeing was actually from this -- I implemented fmax and fmin now but kept the division since it didn't seem to make a difference for performance @mgoin :)

[mgoin]

Nice - we should work harder on the load/store overlapping around the core operation

[robertgshaw2-neuralmagic]

I think this PR will resolve #4614

[mgoin]

Thanks for the back and forth, LGTM! I'm still experimenting with faster quantization using pipelining

[robertgshaw2-neuralmagic]

Approved, but can you make a small change to re-run automation?