remove to restriction for 4-bit model

src/transformers/modeling_utils.py

@@ -2861,46 +2861,56 @@ def get_memory_footprint(self, return_buffers=True):
     def cuda(self, *args, **kwargs):
         if getattr(self, "quantization_method", None) == QuantizationMethod.HQQ:
             raise ValueError("`.cuda` is not supported for HQQ-quantized models.")
-        # Checks if the model has been loaded in 8-bit
+        # Checks if the model has been loaded in 4-bit or 8-bit with BNB
         if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
-            raise ValueError(
-                "Calling `cuda()` is not supported for `4-bit` or `8-bit` quantized 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`."
-            )
+            if getattr(self, "is_loaded_in_8bit", False):
+                raise ValueError(
+                    "Calling `cuda()` is not supported for `8-bit` quantized 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`."
+                )
+            elif version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.2"):
+                raise ValueError(
+                    "Calling `cuda()` is not supported for `4-bit` quantized 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`. "
+                    "However, if you still want to move the model, you need to install bitsandbytes >= 0.43.2 "
+                )
         else:
             return super().cuda(*args, **kwargs)
 
     @wraps(torch.nn.Module.to)
     def to(self, *args, **kwargs):
+        # For BNB/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 = "dtype" in kwargs
+
+        if not dtype_present_in_args:
+            for arg in args:
+                if isinstance(arg, torch.dtype):
+                    dtype_present_in_args = True
+                    break
+
         if getattr(self, "quantization_method", None) == QuantizationMethod.HQQ:
             raise ValueError("`.to` is not supported for HQQ-quantized models.")
-        # Checks if the model has been loaded in 8-bit
+        # Checks if the model has been loaded in 4-bit or 8-bit with BNB
         if getattr(self, "quantization_method", None) == QuantizationMethod.BITS_AND_BYTES:
             if getattr(self, "is_loaded_in_4bit", False):
-                if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.0"):
+                if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.2"):
                     raise ValueError(
                         "`.to` is not supported for `4-bit`. Please use the model as it is, since the"
                         " model has already been set to the correct devices and casted to the correct `dtype`. "
-                        "However, if you still want to move the model, you need to install bitsandbytes >= 0.43.0 "
+                        "However, if you still want to move the model, you need to install bitsandbytes >= 0.43.2 "
+                    )
+                elif dtype_present_in_args:
+                    raise ValueError(
+                        "You cannot cast a bitsandbytes 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."
                     )
             else:
                 raise ValueError(
                     "`.to` is not supported for `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`."
                 )
         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"

tests/quantization/bnb/test_4bit.py

@@ -256,29 +256,56 @@ def test_generate_quality_dequantize(self):
 
         self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
 
+    def test_device_assignment(self):
+        if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.2"):
+            self.skipTest(reason="This test requires bitsandbytes >= 0.43.2")
+
+        mem_before = self.model_4bit.get_memory_footprint()
+
+        # Move to CPU
+        self.model_4bit.to("cpu")
+        self.assertEqual(self.model_4bit.device.type, "cpu")
+        self.assertAlmostEqual(self.model_4bit.get_memory_footprint(), mem_before)
+
+        # Move back to CUDA device
+        self.model_4bit.to(0)
+        self.assertEqual(self.model_4bit.device, torch.device(0))
+        self.assertAlmostEqual(self.model_4bit.get_memory_footprint(), mem_before)
+
     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.
+        Test whether trying to cast (or assigning a device to) a model after converting it in 4-bit will throw an error.
         Checks also if other models are casted correctly.
         """
-        with self.assertRaises(ValueError):
-            # Tries with `str`
-            self.model_4bit.to("cpu")
+
+        # Moving with `to` or `cuda` is not supported with versions < 0.43.2.
+        if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.43.2"):
+            with self.assertRaises(ValueError):
+                # Tries with `str`
+                self.model_4bit.to("cpu")
+
+            with self.assertRaises(ValueError):
+                # Tries with a `device`
+                self.model_4bit.to(torch.device("cuda:0"))
+
+            with self.assertRaises(ValueError):
+                # Tries with `cuda`
+                self.model_4bit.cuda()
 
         with self.assertRaises(ValueError):
-            # Tries with a `dtype``
+            # Tries with a `dtype`
             self.model_4bit.to(torch.float16)
 
         with self.assertRaises(ValueError):
-            # Tries with a `device`
-            self.model_4bit.to(torch.device("cuda:0"))
+            # Tries with a `dtype` and `device`
+            self.model_4bit.to(device="cuda:0", dtype=torch.float16)
 
         with self.assertRaises(ValueError):
-            # Tries with a `device`
+            # Tries with a cast
             self.model_4bit.float()
 
         with self.assertRaises(ValueError):
-            # Tries with a `device`
+            # Tries with a cast
             self.model_4bit.half()
 
         # Test if we did not break anything
@@ -287,6 +314,9 @@ def test_device_and_dtype_assignment(self):
         self.model_fp16 = self.model_fp16.to(torch.float32)
         _ = self.model_fp16.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
 
+        # Check that this does not throw an error
+        _ = self.model_fp16.cuda()
+
         # Check this does not throw an error
         _ = self.model_fp16.to("cpu")