FP8 quantization-aware training for linear layers.

Mirror of fp4linear.py but for FP8 (e4m3). The forward pass quantizes both activations and weights to FP8 and runs torch._scaled_mm; the backward pass is a bf16 straight-through estimator so the high-precision master weights stay trainable. Falls back to a bf16 fake-quant forward wherever the device has no e4m3fn _scaled_mm (pre-sm89 CUDA, pre-CDNA4 ROCm); the capability gate and the FP8 constants are shared with fp8_config so the inference and QAT paths cannot drift.

`fastvideo.layers.fp8linear.fp8_linear_forward`

fp8_linear_forward(x: torch.Tensor) -> torch.Tensor

Source

def fp8_linear_forward(self, x: torch.Tensor) -> torch.Tensor:
    # pass config **positionally**; autograd.Function.apply ignores kwargs
    return _LinearFWD8BWD16Fn.apply(x, self.weight, self.bias, "tensor"), None