Fix TorchAoConfig not JSON serializable (#36206)

**Summary:** TorchAoConfig optionally contains a
`torchao.dtypes.Layout` object which is a dataclass and not
JSON serializable, and so the following fails:

```
import json
from torchao.dtypes import TensorCoreTiledLayout
from transformers import TorchAoConfig

config = TorchAoConfig("int4_weight_only", layout=TensorCoreTiledLayout())

config.to_json_string()

json.dumps(config.to_dict())
```

This also causes `quantized_model.save_pretrained(...)` to
fail because the first step of this call is to JSON serialize
the config. Fixes https://github.com/pytorch/ao/issues/1704.

**Test Plan:**
python tests/quantization/torchao_integration/test_torchao.py -k test_json_serializable

Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

src/transformers/utils/quantization_config.py

@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import copy
+import dataclasses
 import importlib.metadata
 import json
 import os
@@ -1539,6 +1540,21 @@ class TorchAoConfig(QuantizationConfigMixin):
         config_dict = self.to_dict()
         return f"{self.__class__.__name__} {json.dumps(config_dict, indent=2, sort_keys=True)}\n"
 
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Serializes this instance to a Python dictionary, converting any `torchao.dtypes.Layout`
+        dataclasses to simple dicts.
+
+        Returns:
+            `Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
+        """
+        d = super().to_dict()
+        if "quant_type_kwargs" in d and "layout" in d["quant_type_kwargs"]:
+            layout = d["quant_type_kwargs"]["layout"]
+            layout = dataclasses.asdict(layout)
+            d["quant_type_kwargs"]["layout"] = layout
+        return d
+
 
 @dataclass
 class BitNetConfig(QuantizationConfigMixin):

tests/quantization/torchao_integration/test_torchao.py

@@ -31,8 +31,10 @@ if is_torch_available():
     import torch
 
 if is_torchao_available():
-    from torchao.dtypes import AffineQuantizedTensor
-    from torchao.dtypes.affine_quantized_tensor import TensorCoreTiledLayoutType
+    from torchao.dtypes import (
+        AffineQuantizedTensor,
+        TensorCoreTiledLayout,
+    )
 
 
 def check_torchao_quantized(test_module, qlayer, batch_size=1, context_size=1024):
@@ -40,7 +42,7 @@ def check_torchao_quantized(test_module, qlayer, batch_size=1, context_size=1024
     test_module.assertTrue(isinstance(weight, AffineQuantizedTensor))
     test_module.assertEqual(weight.quant_min, 0)
     test_module.assertEqual(weight.quant_max, 15)
-    test_module.assertTrue(isinstance(weight.layout_type, TensorCoreTiledLayoutType))
+    test_module.assertTrue(isinstance(weight.layout, TensorCoreTiledLayout))
 
 
 def check_forward(test_module, model, batch_size=1, context_size=1024):
@@ -82,6 +84,16 @@ class TorchAoConfigTest(unittest.TestCase):
         quantization_config = TorchAoConfig("int4_weight_only", modules_to_not_convert=["conv"], group_size=8)
         repr(quantization_config)
 
+    def test_json_serializable(self):
+        """
+        Check that the config dict can be JSON serialized.
+        """
+        quantization_config = TorchAoConfig("int4_weight_only", group_size=32, layout=TensorCoreTiledLayout())
+        d = quantization_config.to_dict()
+        self.assertIsInstance(d["quant_type_kwargs"]["layout"], dict)
+        self.assertTrue("inner_k_tiles" in d["quant_type_kwargs"]["layout"])
+        quantization_config.to_json_string(use_diff=False)
+
 
 @require_torch_gpu
 @require_torchao