[Testing] Add MPS to NATIVE_DEVICES (#153835)

This would allow me to enable more opinfo tests against MPS device eventually and supposed to be a very simple test, but actually required minor adjustments to lots of test files, namely:
- Introduce `all_mps_types_and` that is very similar to `all_types_and`, but skips `float64`
- Decorate lots of tests with `@dtypesIfMPS(*all_mps_types())`
- Skip `test_from_dlpack_noncontinguous` as it currently crashes (need to be fixed)
- Add lots of `expectedFailureIfMPS`
- Delete all `@onlyNativeDeviceTypesAnd("mps")`

<sarcasm> I love how well documented this variable are </sarcasm>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153835
Approved by: https://github.com/Skylion007

test/test_view_ops.py

@@ -11,15 +11,16 @@ from torch.testing import make_tensor
 from torch.testing._internal.common_device_type import (
     dtypes,
     dtypesIfMPS,
+    expectedFailureMPS,
     instantiate_device_type_tests,
     onlyCPU,
     onlyNativeDeviceTypes,
-    onlyNativeDeviceTypesAnd,
     skipLazy,
     skipMeta,
     skipXLA,
 )
 from torch.testing._internal.common_dtype import (
+    all_mps_types_and,
     all_types_and,
     all_types_and_complex_and,
     complex_types,
@@ -157,8 +158,11 @@ class TestViewOps(TestCase):
     @skipIfTorchDynamo("TorchDynamo fails with unknown reason")
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half, torch.bool))
+    @dtypesIfMPS(*integral_types_and(torch.cfloat, torch.float, torch.half, torch.bool))
     def test_view_dtype_new(self, device, dtype):
         dtypes = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}
+        if device.startswith("mps"):
+            del dtypes[torch.float64]
         del dtypes[torch.bool]
 
         def generate_inputs():
@@ -271,6 +275,7 @@ class TestViewOps(TestCase):
     # has a greater element size than the original dtype
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
+    @dtypesIfMPS(*all_mps_types_and(torch.bool))
     def test_view_dtype_upsize_errors(self, device, dtype):
         dtype_size = torch._utils._element_size(dtype)
 
@@ -372,6 +377,7 @@ class TestViewOps(TestCase):
 
     @onlyNativeDeviceTypes
     @dtypes(*complex_types(), torch.complex32)
+    @dtypesIfMPS(torch.cfloat, torch.chalf)
     def test_view_as_real(self, device, dtype):
         def fn(contiguous_input=True):
             t = torch.randn(3, 4, dtype=dtype, device=device)
@@ -398,9 +404,7 @@ class TestViewOps(TestCase):
 
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
-    @dtypesIfMPS(
-        *integral_types_and(torch.half, torch.bfloat16, torch.bool, torch.float32)
-    )
+    @dtypesIfMPS(*all_mps_types_and(torch.bool))
     def test_view_tensor_split(self, device, dtype):
         a = make_tensor((40, 30), dtype=dtype, device=device, low=-9, high=9)
         a_split_dim0 = a.tensor_split(7, 0)
@@ -412,6 +416,7 @@ class TestViewOps(TestCase):
 
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
+    @dtypesIfMPS(*all_mps_types_and(torch.cfloat, torch.bool))
     def test_view_tensor_hsplit(self, device, dtype):
         t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9)
         t_hsplit = torch.hsplit(t, 2)
@@ -422,6 +427,7 @@ class TestViewOps(TestCase):
 
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
+    @dtypesIfMPS(*all_mps_types_and(torch.cfloat, torch.bool))
     def test_view_tensor_vsplit(self, device, dtype):
         t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9)
         t_vsplit = torch.vsplit(t, 2)
@@ -432,6 +438,7 @@ class TestViewOps(TestCase):
 
     @onlyNativeDeviceTypes
     @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
+    @dtypesIfMPS(*all_mps_types_and(torch.cfloat, torch.bool))
     def test_view_tensor_dsplit(self, device, dtype):
         t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9)
         t_dsplit = torch.dsplit(t, 2)
@@ -440,9 +447,9 @@ class TestViewOps(TestCase):
         t[2, 2, 2] = 7
         self.assertEqual(t_dsplit[1][2, 2, 0], t[2, 2, 2])
 
-    @onlyNativeDeviceTypesAnd("mps")
+    @onlyNativeDeviceTypes
     @dtypes(*all_types_and(torch.half, torch.bfloat16))
-    @dtypesIfMPS(*integral_types_and(torch.half, torch.bool, torch.float32))
+    @dtypesIfMPS(*all_mps_types_and(torch.bool))
     def test_imag_noncomplex(self, device, dtype):
         t = torch.ones((5, 5), dtype=dtype, device=device)
 
@@ -451,6 +458,7 @@ class TestViewOps(TestCase):
 
     @onlyNativeDeviceTypes
     @dtypes(*complex_types())
+    @dtypesIfMPS(torch.cfloat)
     def test_real_imag_view(self, device, dtype):
         def compare_with_numpy(contiguous_input=True):
             t = torch.randn(3, 3, dtype=dtype, device=device)
@@ -481,6 +489,7 @@ class TestViewOps(TestCase):
         self.assertEqual(a[5:].imag, a.imag[5:])
 
     @onlyNativeDeviceTypes
+    @expectedFailureMPS
     @dtypes(*complex_types())
     def test_conj_imag_view(self, device, dtype) -> None:
         t = _make_tensor((4, 5), dtype, device)
@@ -512,6 +521,12 @@ class TestViewOps(TestCase):
             all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool),
         )
     )
+    @dtypesIfMPS(
+        *product(
+            [torch.cfloat, torch.chalf],
+            all_mps_types_and(torch.cfloat, torch.chalf, torch.bool),
+        )
+    )
     @suppress_warnings
     def test_set_real_imag(self, device, dtypes):
         x = torch.randn(10, dtype=dtypes[0], device=device)