Expose FakeQuantizeConfigs in QAT quantizers

[andrewor14]

Summary: This commit exposes the activation and weight FakeQuantizeConfigs in the existing QAT quantizers. These are helpful for implementing advanced functionality based on the quantization schemes represented by these quantizers, such as composing QAT + LoRA.

Test Plan:
python test/quantization/test_qat.py

>>> from torchao.quantization.qat.linear import Int8DynActInt4WeightQATQuantizer
>>> q = Int8DynActInt4WeightQATQuantizer()
>>> q.get_activation_fake_quantize_config()
FakeQuantizeConfig(dtype=torch.int8, granularity=PerToken(), mapping_type=<MappingType.ASYMMETRIC: 3>, scale_precision=torch.float32, zero_point_precision=torch.float32, zero_point_domain=<ZeroPointDomain.INT: 1>, is_dynamic=True, range_learning=False)
>>> q.get_weight_fake_quantize_config()
FakeQuantizeConfig(dtype=<TorchAODType.INT4: 4>, granularity=PerGroup(group_size=256), mapping_type=<MappingType.SYMMETRIC: 1>, scale_precision=torch.float32, zero_point_precision=torch.float32, zero_point_domain=<ZeroPointDomain.INT: 1>, is_dynamic=True, range_learning=False)

>>> from torchao.quantization.qat.linear import Int4WeightOnlyQATQuantizer
>>> q = Int4WeightOnlyQATQuantizer()
>>> q.get_activation_fake_quantize_config()
>>> q.get_weight_fake_quantize_config()
FakeQuantizeConfig(dtype=torch.uint4, granularity=PerGroup(group_size=256), mapping_type=<MappingType.ASYMMETRIC: 3>, scale_precision=torch.bfloat16, zero_point_precision=torch.bfloat16, zero_point_domain=<ZeroPointDomain.FLOAT: 2>, is_dynamic=True, range_learning=False)

[pytorch-bot]

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As of commit 504590d with merge base 59dab15 ():

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