make smoothquant more PT2 friendly

[vkuzo]

torchao's smoothquant recently broke after a change to PyTorch core: pytorch/pytorch#145733 . We should make the updates suggested by @anijain2305 in that issue to our code. I actually think we should go a bit farther and go with something like

#
# before
#
class _ActQuantizer:
    def __init__(self, target_dtype, quant_min=-127):
        self.target_dtype = target_dtype
        self.quant_min = quant_min

    def dynamic_quantize(self, input):
        return to_affine_quantized_intx(
            input,
            MappingType.SYMMETRIC,
            _get_per_token_block_size(input),
            self.target_dtype,
            self.quant_min,
        )

    def static_quantize(self, input, scale, zero_point):
        return to_affine_quantized_intx_static(
            input,
            scale,
            zero_point,
            list(input.shape),
            self.target_dtype,
            self.quant_min,
        )

#
# after
#
@dataclass
class _ActQuantConfig:
    target_dtype: torch.dtype
    quant_min: int = -127

# then, logic elsewhere chooses whether to call static or dynamic quant based on the contents of an instance of `_ActQuantConfig`

My feedback here is similar in spirit to #1595 - IMO it's simpler and safer to pass around dumb config objects and use them to choose which function to call, instead of encoding the "which function to call" information in the config as a callable object.

[vkuzo]

we should also unpin the nightlies (undo most of #1608) when this is fixed