[xpu][test] Port 2 test/dtypes_{floatx, bitpacking} UT files to intel XPU (#3368)

* enable test/dtypes/test_bitpacking.py on intel xpu

* enable test/dtypes/test_floatx.py

* enable test/dtypes/test_floatx.py

* fix format issue

* fix format issue

* update _DEVICES

Author: zxd1997066

test/dtypes/test_bitpacking.py

@@ -8,9 +8,11 @@
 from torch.utils._triton import has_triton
 
 from torchao.dtypes.uintx.bitpacking import pack, pack_cpu, unpack, unpack_cpu
+from torchao.utils import get_current_accelerator_device
 
 bit_widths = (1, 2, 3, 4, 5, 6, 7)
 dimensions = (0, -1, 1)
+_DEVICE = get_current_accelerator_device()
 
 
 @pytest.fixture(autouse=True)
@@ -30,40 +32,46 @@ def test_CPU(bit_width, dim):
     assert unpacked.allclose(test_tensor)
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
 @pytest.mark.parametrize("bit_width", bit_widths)
 @pytest.mark.parametrize("dim", dimensions)
 def test_GPU(bit_width, dim):
-    test_tensor = torch.randint(0, 2**bit_width, (32, 32, 32), dtype=torch.uint8).cuda()
+    test_tensor = torch.randint(0, 2**bit_width, (32, 32, 32), dtype=torch.uint8).to(
+        _DEVICE
+    )
     packed = pack(test_tensor, bit_width, dim=dim)
     unpacked = unpack(packed, bit_width, dim=dim)
     assert unpacked.allclose(test_tensor)
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
 @pytest.mark.skipif(not has_triton(), reason="unsupported without triton")
 @pytest.mark.parametrize("bit_width", bit_widths)
 @pytest.mark.parametrize("dim", dimensions)
 def test_compile(bit_width, dim):
     torch._dynamo.config.specialize_int = True
     torch.compile(pack, fullgraph=True)
     torch.compile(unpack, fullgraph=True)
-    test_tensor = torch.randint(0, 2**bit_width, (32, 32, 32), dtype=torch.uint8).cuda()
+    test_tensor = torch.randint(0, 2**bit_width, (32, 32, 32), dtype=torch.uint8).to(
+        _DEVICE
+    )
     packed = pack(test_tensor, bit_width, dim=dim)
     unpacked = unpack(packed, bit_width, dim=dim)
     assert unpacked.allclose(test_tensor)
 
 
 # these test cases are for the example pack walk through in the bitpacking.py file
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not torch.accelerator.is_available(), reason="GPU not available")
 def test_pack_example():
     test_tensor = torch.tensor(
         [0x30, 0x29, 0x17, 0x5, 0x20, 0x16, 0x9, 0x22], dtype=torch.uint8
-    ).cuda()
+    ).to(_DEVICE)
     shard_4, shard_2 = pack(test_tensor, 6)
     print(shard_4, shard_2)
-    assert torch.tensor([0, 105, 151, 37], dtype=torch.uint8).cuda().allclose(shard_4)
-    assert torch.tensor([39, 146], dtype=torch.uint8).cuda().allclose(shard_2)
+    assert (
+        torch.tensor([0, 105, 151, 37], dtype=torch.uint8).to(_DEVICE).allclose(shard_4)
+    )
+    assert torch.tensor([39, 146], dtype=torch.uint8).to(_DEVICE).allclose(shard_2)
     unpacked = unpack([shard_4, shard_2], 6)
     assert unpacked.allclose(test_tensor)
 

test/dtypes/test_floatx.py

@@ -33,10 +33,11 @@
     quantize_,
 )
 from torchao.testing.utils import skip_if_rocm
-from torchao.utils import is_fbcode
+from torchao.utils import get_current_accelerator_device, is_fbcode
 
-_DEVICES = ["cpu"] + (["cuda"] if torch.cuda.is_available() else [])
 _Floatx_DTYPES = [(3, 2), (2, 2)]
+_DEVICE = get_current_accelerator_device()
+_DEVICES = ["cpu"] + ([_DEVICE] if torch.accelerator.is_available() else [])
 
 
 class TestFloatxTensorCoreAQTTensorImpl(TestCase):
@@ -87,7 +88,7 @@ def test_from_scaled_tc_floatx_compile(self, ebits, mbits, device):
         )
         torch.testing.assert_close(actual, expected)
 
-    @unittest.skipIf(not torch.cuda.is_available(), reason="CUDA not available")
+    @unittest.skipIf(not torch.accelerator.is_available(), reason="GPU not available")
     @parametrize("ebits,mbits", _Floatx_DTYPES)
     def test_to_copy_device(self, ebits, mbits):
         from torchao.quantization.quant_primitives import (
@@ -101,8 +102,8 @@ def test_to_copy_device(self, ebits, mbits):
         _layout = FloatxTensorCoreLayout(ebits, mbits)
         floatx_tensor_impl = FloatxTensorCoreAQTTensorImpl.from_plain(
             x, scale, None, _layout
-        ).cuda()
-        assert floatx_tensor_impl.device.type == "cuda"
+        ).to(_DEVICE)
+        assert floatx_tensor_impl.device.type == _DEVICE.type
         floatx_tensor_impl = floatx_tensor_impl.cpu()
         assert floatx_tensor_impl.device.type == "cpu"
 
@@ -114,7 +115,7 @@ def test_to_copy_device(self, ebits, mbits):
     @skip_if_rocm("ROCm enablement in progress")
     def test_fpx_weight_only(self, ebits, mbits, bias, dtype):
         N, OC, IC = 4, 256, 64
-        device = "cuda"
+        device = _DEVICE
 
         linear = torch.nn.Linear(IC, OC, bias=bias, device=device, dtype=dtype)
         fpx_linear = copy.deepcopy(linear)