[Misc] Pass cutlass_fp8_supported correctly in fbgemm_fp8

[zeyugao]

Only relying on the compute capability is not enough to determine whether we can use fp8 activation. We can re-use the cutlass_fp8_supported function that also takes cuda version into consideration.

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[robertgshaw2-neuralmagic]

We have the option to fall back to torch._scaled_mm when cutlass is not supported (this is for Ada Lovelace -- which we are about to turn on for cutlass)

[zeyugao]

So does it mean that this logic will be factored? Currently, it will not automatically fallback to another fp8 compuation method, resulting

void cutlass::arch::Mma<cutlass::gemm::GemmShape<16, 8, 32>, 32, cutlass::float_e4m3_t, cutlass::layout::RowMajor, cutlass::float_e4m3_t, cutlass::layout::ColumnMajor, float, cutlass::layout::RowMajor, Operator_>::operator()(cutlass::Array<float, 4, true> &, const cutlass::Array<cutlass::float_e4m3_t, 16, false> &, const cutlass::Array<cutlass::float_e4m3_t, 8, false> &, const cutlass::Array<float, 4, true> &) const [with Operator_ = cutlass::arch::OpMultiplyAdd] not implemented

[robertgshaw2-neuralmagic]

Oh I see, in line 149, we should not hardcode cutlass_fp8_supported=True, then , in apply_fp8_linear we will fall back to torch._scaled_mm for lovelace

[robertgshaw2-neuralmagic]

can you save this as a variable in __init__, I just want to avoid footguns where it might be expensive in the future to call this on the hotpath

[zeyugao]

Done

[zeyugao]

Oh, thanks. I have updated the code accordingly.

[robertgshaw2-neuralmagic]

Thanks for the contribution!