[Kernel] Implement fallback for FP8 channelwise using torch._scaled_mm

[tlrmchlsmth]

Support channelwise quantization in situations where the CUTLASS kernels are not supported by applying the rescale operation after torch._scaled_mm.

lm_eval results

lm_eval --model vllm --model_args pretrained="nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors",dtype=auto,gpu_memory_utilization=0.6

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.7445|±  | 0.012|
|     |       |strict-match    |     5|exact_match|↑  |0.7453|±  | 0.012|

Performance results

Ran the following on an H100

benchmarks/benchmark_latency.py \
--model nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors \
--batch-size 16 --input-len X --output-len Y

	input len 1024, output len 1	input len 16, output len 512
scaled_mm	0.4578757907	4.427179264
cutlass	0.3145771995	3.786522313

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[robertgshaw2-neuralmagic]

Could you just add a comment here that describes the math and why what we are doing works?

[tlrmchlsmth]

/ready

[mgoin]

Thank you for writing it out, I've not understood it for a while :)