Make FakeQuantizer expose useful config details (#1717)

**Summary:** Expose useful config details when printing
FakeQuantizer, which appears when printing QAT prepared models
containing linear layers.

Before:
```
>>> print(prepared_model.layers[0].attn.qproj)
FakeQuantizedLinear(
  in_features=4096, out_features=4096, bias=False
  (activation_fake_quantizer): FakeQuantizer()
  (weight_fake_quantizer): FakeQuantizer()
)
```

After:

```
>>> print(prepared_model.layers[0].attn.qproj)
FakeQuantizedLinear(
  in_features=4096, out_features=4096, bias=False
  (activation_fake_quantizer): FakeQuantizer(FakeQuantizeConfig(dtype=torch.int8, granularity=PerToken(), mapping_type=<MappingType.ASYMMETRIC: 3>, scale_precision=torch.float32, zero_point_precision=torch.int32, zero_point_domain=<ZeroPointDomain.INT: 1>, is_dynamic=True, range_learning=False))
  (weight_fake_quantizer): FakeQuantizer(FakeQuantizeConfig(dtype=torch.int4, granularity=PerGroup(group_size=32), mapping_type=<MappingType.SYMMETRIC: 1>, scale_precision=torch.float32, zero_point_precision=torch.int32, zero_point_domain=<ZeroPointDomain.INT: 1>, is_dynamic=True, range_learning=False))
)
```

**Test Plan:**
python test/quantization/test_qat.py -k test_fake_quantizer_repr

Author: andrewor14

test/quantization/test_qat.py

@@ -31,6 +31,9 @@
 from torchao.quantization.qat.embedding import (
     FakeQuantizedEmbedding,
 )
+from torchao.quantization.qat.fake_quantizer import (
+    FakeQuantizer,
+)
 from torchao.quantization.qat.linear import (
     FakeQuantizedLinear,
     Int4WeightOnlyQATLinear,
@@ -1348,6 +1351,21 @@ def test_fake_quantize_config_torch_intx(self):
         out2 = linear2(*x2)
         torch.testing.assert_close(out1, out2, atol=0, rtol=0)
 
+    @unittest.skipIf(
+        not TORCH_VERSION_AT_LEAST_2_6, "skipping when torch version is 2.6 or lower"
+    )
+    def test_fake_quantizer_repr(self):
+        """
+        Test that `repr(FakeQuantizer(config))` exposes useful config details.
+        """
+        config = FakeQuantizeConfig(torch.int4, group_size=128)
+        fake_quantizer = FakeQuantizer(config)
+        fake_quantizer_repr = repr(fake_quantizer)
+        self.assertTrue("dtype=torch.int4" in fake_quantizer_repr)
+        self.assertTrue("group_size=128" in fake_quantizer_repr)
+        self.assertTrue("PerGroup" in fake_quantizer_repr)
+        self.assertTrue("MappingType.SYMMETRIC" in fake_quantizer_repr)
+
 
 if __name__ == "__main__":
     unittest.main()

torchao/quantization/qat/fake_quantizer.py

@@ -134,3 +134,9 @@ def _should_compute_qparams(self) -> bool:
         Return whether we need to compute new scales and zero points.
         """
         return self.config.is_dynamic or self.scale is None or self.zero_point is None
+
+    def __repr__(self) -> str:
+        """
+        Return a human readable representation of this `FakeQuantizer` with config details.
+        """
+        return "FakeQuantizer(%s)" % self.config