🚨🚨🚨 [`Quantization`] Store the original dtype in the config as a private attribute 🚨🚨🚨 (#26761)

* First step

* fix

* add adjustements for gptq

* change to `_pre_quantization_dtype`

* Update src/transformers/modeling_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fix serialization

* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fixup

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/configuration_utils.py

@@ -854,6 +854,9 @@ class PretrainedConfig(PushToHubMixin):
                 else self.quantization_config
             )
 
+            # pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
+            _ = serializable_config_dict.pop("_pre_quantization_dtype", None)
+
         self.dict_torch_dtype_to_str(serializable_config_dict)
 
         if "_flash_attn_2_enabled" in serializable_config_dict:
@@ -896,6 +899,9 @@ class PretrainedConfig(PushToHubMixin):
                 else self.quantization_config
             )
 
+            # pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
+            _ = output.pop("_pre_quantization_dtype", None)
+
         self.dict_torch_dtype_to_str(output)
 
         return output

src/transformers/modeling_utils.py

@@ -2178,8 +2178,25 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 "`.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the"
                 " model has already been set to the correct devices and casted to the correct `dtype`."
             )
-        else:
-            return super().to(*args, **kwargs)
+        elif getattr(self, "quantization_method", None) == QuantizationMethod.GPTQ:
+            # For GPTQ models, we prevent users from casting the model to another dytpe to restrict unwanted behaviours.
+            # the correct API should be to load the model with the desired dtype directly through `from_pretrained`.
+            dtype_present_in_args = False
+
+            if "dtype" not in kwargs:
+                for arg in args:
+                    if isinstance(arg, torch.dtype):
+                        dtype_present_in_args = True
+                        break
+            else:
+                dtype_present_in_args = True
+
+            if dtype_present_in_args:
+                raise ValueError(
+                    "You cannot cast a GPTQ model in a new `dtype`. Make sure to load the model using `from_pretrained` using the desired"
+                    " `dtype` by passing the correct `torch_dtype` argument."
+                )
+        return super().to(*args, **kwargs)
 
     def half(self, *args):
         # Checks if the model is quantized
@@ -3165,6 +3182,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         if hasattr(model, "quantization_method"):
             model.is_quantized = True
 
+            # We store the original dtype for quantized models as we cannot easily retrieve it
+            # once the weights have been quantized
+            # Note that once you have loaded a quantized model, you can't change its dtype so this will
+            # remain a single source of truth
+            config._pre_quantization_dtype = torch_dtype
+
         if isinstance(device_map, str):
             special_dtypes = {}
             if load_in_8bit or load_in_4bit:

tests/quantization/bnb/test_4bit.py

@@ -156,6 +156,14 @@ class Bnb4BitTest(Base4bitTest):
         linear = get_some_linear_layer(self.model_4bit)
         self.assertTrue(linear.weight.__class__ == Params4bit)
 
+    def test_original_dtype(self):
+        r"""
+        A simple test to check if the model succesfully stores the original dtype
+        """
+        self.assertTrue(hasattr(self.model_4bit.config, "_pre_quantization_dtype"))
+        self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
+        self.assertTrue(self.model_4bit.config._pre_quantization_dtype == torch.float16)
+
     def test_linear_are_4bit(self):
         r"""
         A simple test to check if the model conversion has been done correctly by checking on the

tests/quantization/bnb/test_mixed_int8.py

@@ -186,6 +186,14 @@ class MixedInt8Test(BaseMixedInt8Test):
 
         _ = config.to_json_string()
 
+    def test_original_dtype(self):
+        r"""
+        A simple test to check if the model succesfully stores the original dtype
+        """
+        self.assertTrue(hasattr(self.model_8bit.config, "_pre_quantization_dtype"))
+        self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
+        self.assertTrue(self.model_8bit.config._pre_quantization_dtype == torch.float16)
+
     def test_memory_footprint(self):
         r"""
         A simple test to check if the model conversion has been done correctly by checking on the

tests/quantization/gptq/test_gptq.py

@@ -145,6 +145,26 @@ class GPTQTest(unittest.TestCase):
 
         self.assertAlmostEqual(self.mem_fp16 / mem_quantized, self.EXPECTED_RELATIVE_DIFFERENCE)
 
+    def test_device_and_dtype_assignment(self):
+        r"""
+        Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error.
+        Checks also if other models are casted correctly.
+        """
+        # This should work
+        _ = self.quantized_model.to(0)
+
+        with self.assertRaises(ValueError):
+            # Tries with a `dtype``
+            self.quantized_model.to(torch.float16)
+
+    def test_original_dtype(self):
+        r"""
+        A simple test to check if the model succesfully stores the original dtype
+        """
+        self.assertTrue(hasattr(self.quantized_model.config, "_pre_quantization_dtype"))
+        self.assertFalse(hasattr(self.model_fp16.config, "_pre_quantization_dtype"))
+        self.assertTrue(self.quantized_model.config._pre_quantization_dtype == torch.float16)
+
     def test_quantized_layers_class(self):
         """
         Simple test to check if the model conversion has been done correctly by checking on