Fixing cuda device check

Summary:
Previous cuda device check is not general enough, this adds a better check that works for
more cases like "cuda:0"

Test Plan:
python test/quantization/test_quant_api.py -k test_int4wo_quantized_model_to_device

Reviewers:

Subscribers:

Tasks:

Tags:

test/quantization/test_quant_api.py

@@ -642,17 +642,19 @@ def test_int8wo_quantized_model_to_device(self):
     @unittest.skipIf(TORCH_VERSION_AFTER_2_5, "Test currently doesn't work for 2.5+")
     def test_int4wo_quantized_model_to_device(self):
         # TODO: change initial model to "cpu"
-        m = ToyLinearModel().eval().to(torch.bfloat16).to("cuda")
-        m_copy = copy.deepcopy(m)
-        example_inputs = m.example_inputs(dtype=torch.bfloat16, device="cuda")
-
-        quantize_(m, int4_weight_only())
-        ref = m(*example_inputs)
-
-        example_inputs_cuda = (example_inputs[0].to("cuda"),)
-        m.to(device="cuda")
-        cuda_res = m(*example_inputs_cuda)
-        self.assertEqual(cuda_res.cpu(), ref)
+        devices = ["cuda", "cuda:0"]
+        for device in devices:
+            m = ToyLinearModel().eval().to(torch.bfloat16).to(device)
+            m_copy = copy.deepcopy(m)
+            example_inputs = m.example_inputs(dtype=torch.bfloat16, device=device)
+
+            quantize_(m, int4_weight_only())
+            ref = m(*example_inputs)
+
+            example_inputs_cuda = (example_inputs[0].to(device),)
+            m.to(device=device)
+            cuda_res = m(*example_inputs_cuda)
+            self.assertEqual(cuda_res.cpu(), ref)
 
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Test only enabled for 2.4+")
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")

torchao/dtypes/affine_quantized_tensor.py

@@ -21,6 +21,7 @@
     _register_layout_cls,
     _get_layout_tensor_constructor,
     LayoutType,
+    is_device,
 )
 from typing import ClassVar
 from dataclasses import dataclass
@@ -544,7 +545,7 @@ def from_plain(
     def to(self, *args, **kwargs):
         kwargs = self._get_to_kwargs(*args, **kwargs)
         device = kwargs["device"]
-        if device != "cuda" and (isinstance(device, torch.device) and device.type != "cuda"):
+        if not is_device("cuda", device):
             raise ValueError(f"TensorCoreTiledAQTLayout is only available for cuda device, can't convert to {device}")
         return self.__class__(
             self.packed_weight.to(device),

torchao/dtypes/utils.py

@@ -1,5 +1,5 @@
 import torch
-from typing import Dict, Callable
+from typing import Dict, Callable, Union
 from collections import defaultdict
 import functools
 from dataclasses import dataclass
@@ -89,3 +89,6 @@ def _get_layout_tensor_constructor(cls: Callable, layout_type_class: type(Layout
         raise ValueError(f"layout_name: {layout_type_class} is not supported yet for {cls}")
 
     return _LAYOUT_CONSTRUCTOR_TABLE[cls][layout_type_class]
+
+def is_device(target_device_str: str, device: Union[str, torch.device]):
+    return torch.device(device).type == target_device_str