[float8] Add fnuz fp8 dtypes to Float8Layout (#2351)

This should give us AMD perf on vLLM. With Phi-4-mini-instruct on MI300x with TorchAO FP8 rowwise quant on the MLP I see the following, which is about a 5% speedup: 
```
Avg latency: 1.080369415456274 seconds
10% percentile latency: 1.075335633114446 seconds
25% percentile latency: 1.0811904482543468 seconds
50% percentile latency: 1.082176529977005 seconds
75% percentile latency: 1.0826280051842332 seconds
90% percentile latency: 1.0831242799758911 seconds
99% percentile latency: 1.0836151059856638 seconds
```

For comparison, here is the baseline Phi-4-mini-instruct on MI300x:
```
Avg latency: 1.148340248184589 seconds
10% percentile latency: 1.1391733552212826 seconds
25% percentile latency: 1.14905939399614 seconds
50% percentile latency: 1.150204271019902 seconds
75% percentile latency: 1.1523984443047084 seconds
90% percentile latency: 1.1536207939614542 seconds
99% percentile latency: 1.1548575214319863 seconds
```

Previously, these checks were failing on the unsigned zero ROCm fp8 dtypes, causing us to call `.dequantize()` and then do a bfloat16 mm, which was slower than the bf16 baseline (~2s).

Author: jcaip

torchao/dtypes/floatx/float8_layout.py

@@ -363,7 +363,7 @@ def check_aqt(aqt: Union[torch.Tensor, AffineQuantizedTensor]) -> bool:
         return (
             isinstance(aqt, AffineQuantizedTensor)
             and isinstance(aqt._layout, Float8Layout)
-            and aqt.tensor_impl.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]
+            and _is_float8_type(aqt.tensor_impl.dtype)
             and (aqt.shape == aqt.block_size or _is_rowwise_scaled(aqt))
         )
 
@@ -442,7 +442,7 @@ def _linear_fp_act_fp8_weight_check(
         # weight is float8 quantized affine quantized tensor
         isinstance(weight_tensor, AffineQuantizedTensor)
         and isinstance(weight_tensor._layout, Float8Layout)
-        and weight_tensor.tensor_impl.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]
+        and _is_float8_type(weight_tensor.tensor_impl.dtype)
         and (
             weight_tensor.shape == weight_tensor.block_size
             or _is_rowwise_scaled(weight_tensor)