[Quantization] support pass MappingType for TorchAoConfig

src/diffusers/quantizers/quantization_config.py

@@ -47,6 +47,16 @@ class QuantizationMethod(str, Enum):
     TORCHAO = "torchao"
 
 
+if is_torchao_available:
+    from torchao.quantization.quant_primitives import MappingType
+
+    class TorchAoJSONEncoder(json.JSONEncoder):
+        def default(self, obj):
+            if isinstance(obj, MappingType):
+                return obj.name
+            return super().default(obj)
+
+
 @dataclass
 class QuantizationConfigMixin:
     """
@@ -673,4 +683,6 @@ def __repr__(self):
         ```
         """
         config_dict = self.to_dict()
-        return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"
+        return (
+            f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True, cls=TorchAoJSONEncoder)}\n"
+        )

tests/quantization/torchao/test_torchao.py

@@ -76,6 +76,7 @@ def forward(self, input, *args, **kwargs):
 if is_torchao_available():
     from torchao.dtypes import AffineQuantizedTensor
     from torchao.quantization.linear_activation_quantized_tensor import LinearActivationQuantizedTensor
+    from torchao.quantization.quant_primitives import MappingType
     from torchao.utils import get_model_size_in_bytes
 
 
@@ -122,6 +123,19 @@ def test_repr(self):
         quantization_repr = repr(quantization_config).replace(" ", "").replace("\n", "")
         self.assertEqual(quantization_repr, expected_repr)
 
+        quantization_config = TorchAoConfig("int4dq", group_size=64, act_mapping_type=MappingType.SYMMETRIC)
+        expected_repr = """TorchAoConfig {
+            "modules_to_not_convert": null,
+            "quant_method": "torchao",
+            "quant_type": "int4dq",
+            "quant_type_kwargs": {
+                "act_mapping_type": "SYMMETRIC",
+                "group_size": 64
+            }
+        }""".replace(" ", "").replace("\n", "")
+        quantization_repr = repr(quantization_config).replace(" ", "").replace("\n", "")
+        self.assertEqual(quantization_repr, expected_repr)
+
 
 # Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners
 @require_torch