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Revert D34856571: [pytorch][PR] Replace get_all_ type macros with the ATen dispatch macros.
Test Plan: revert-hammer Differential Revision: D34856571 (3ded7b1da3) Original commit changeset: 0dca038bcad5 Original Phabricator Diff: D34856571 (3ded7b1da3) fbshipit-source-id: 594553fa0b710d78beba59d5d2b646f1f1270386 (cherry picked from commit 8090eb9b12dcf452a9e7dc01792a66fb91b563b6)
This commit is contained in:
Nikita Shulga
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PyTorch MergeBot
PyTorch MergeBot
parent
f14a0be302
commit
ef066f0832
@ -16,7 +16,7 @@ from torch.testing._internal.common_utils import (
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from torch.testing._internal.common_device_type import \
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(instantiate_device_type_tests, onlyCPU, dtypes, onlyNativeDeviceTypes, skipMeta)
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from torch.testing._internal.common_dtype import (
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all_types_and_complex_and, complex_types, all_types_and, floating_and_complex_types_and,
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get_all_dtypes, get_all_int_dtypes, get_all_fp_dtypes, get_all_complex_dtypes
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)
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# TODO: replace this with make_tensor() in common_utils.py
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@ -121,14 +121,14 @@ class TestViewOps(TestCase):
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else:
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return x.transpose(dim0, dim1)
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@dtypes(*all_types_and(torch.half, torch.bfloat16))
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@dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes()))
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def test_conj_self(self, device, dtype):
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t = torch.ones(5, 5, device=device)
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s = t.conj()
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self.assertTrue(s is t)
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@onlyNativeDeviceTypes
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@dtypes(*all_types_and_complex_and(torch.half, torch.bool))
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@dtypes(*get_all_dtypes(include_bfloat16=False))
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def test_view_dtype_new(self, device, dtype):
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dtypes = torch_to_numpy_dtype_dict.copy()
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del dtypes[torch.bool]
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@ -210,18 +210,18 @@ class TestViewOps(TestCase):
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# because view(dtype) does not support backward yet
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# TODO: Remove this when autograd support is added
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if dtype.is_floating_point or dtype.is_complex:
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for view_dtype in floating_and_complex_types_and(torch.half, torch.bfloat16):
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for view_dtype in [*get_all_fp_dtypes(), *get_all_complex_dtypes()]:
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t = make_tensor((5, 5, 64), dtype=dtype, device=device, low=-5, high=5, requires_grad=True)
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self.assertFalse(t.view(view_dtype).requires_grad)
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# Test the extra error checks that happen when the view dtype
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# has a greater element size than the original dtype
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@onlyNativeDeviceTypes
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*get_all_dtypes())
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def test_view_dtype_upsize_errors(self, device, dtype):
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dtype_size = torch._utils._element_size(dtype)
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for view_dtype in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool):
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for view_dtype in get_all_dtypes():
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view_dtype_size = torch._utils._element_size(view_dtype)
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if view_dtype_size <= dtype_size:
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continue
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@ -302,7 +302,7 @@ class TestViewOps(TestCase):
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self.assertEqual(res.shape, torch.Size([0]))
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@onlyNativeDeviceTypes
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@dtypes(*complex_types(), torch.complex32)
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@dtypes(*get_all_complex_dtypes(include_complex32=True))
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def test_view_as_real(self, device, dtype):
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def fn(contiguous_input=True):
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t = torch.randn(3, 4, dtype=dtype, device=device)
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@ -340,7 +340,7 @@ class TestViewOps(TestCase):
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self.assertEqual(res.shape, torch.Size([2]))
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@onlyNativeDeviceTypes
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*get_all_dtypes())
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def test_view_tensor_split(self, device, dtype):
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a = make_tensor((40, 30), dtype=dtype, device=device, low=-9, high=9)
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a_split_dim0 = a.tensor_split(7, 0)
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@ -351,7 +351,7 @@ class TestViewOps(TestCase):
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self.assertTrue(self.is_view_of(a, a_split_dim1_tensor))
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@onlyNativeDeviceTypes
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*get_all_dtypes())
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def test_view_tensor_hsplit(self, device, dtype):
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t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9)
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t_hsplit = torch.hsplit(t, 2)
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@ -361,7 +361,7 @@ class TestViewOps(TestCase):
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self.assertEqual(t_hsplit[1][2, 0, 2], t[2, 2, 2])
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@onlyNativeDeviceTypes
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*get_all_dtypes())
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def test_view_tensor_vsplit(self, device, dtype):
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t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9)
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t_vsplit = torch.vsplit(t, 2)
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@ -371,7 +371,7 @@ class TestViewOps(TestCase):
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self.assertEqual(t_vsplit[1][0, 2, 2], t[2, 2, 2])
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@onlyNativeDeviceTypes
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*get_all_dtypes())
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def test_view_tensor_dsplit(self, device, dtype):
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t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9)
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t_dsplit = torch.dsplit(t, 2)
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@ -381,7 +381,7 @@ class TestViewOps(TestCase):
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self.assertEqual(t_dsplit[1][2, 2, 0], t[2, 2, 2])
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@onlyNativeDeviceTypes
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@dtypes(*all_types_and(torch.half, torch.bfloat16))
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@dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes()))
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def test_imag_noncomplex(self, device, dtype):
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t = torch.ones((5, 5), dtype=dtype, device=device)
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@ -389,7 +389,7 @@ class TestViewOps(TestCase):
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torch.imag(t)
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@onlyNativeDeviceTypes
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@dtypes(*complex_types())
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@dtypes(*get_all_complex_dtypes())
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def test_real_imag_view(self, device, dtype):
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def compare_with_numpy(contiguous_input=True):
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t = torch.randn(3, 3, dtype=dtype, device=device)
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@ -420,7 +420,7 @@ class TestViewOps(TestCase):
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self.assertEqual(a[5:].imag, a.imag[5:])
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@onlyNativeDeviceTypes
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@dtypes(*complex_types())
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@dtypes(*get_all_complex_dtypes())
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def test_conj_imag_view(self, device, dtype) -> None:
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t = _make_tensor((4, 5,), dtype, device)
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t_numpy_conj = torch.from_numpy(t.cpu().numpy().conj()).to(device=device)
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@ -445,7 +445,7 @@ class TestViewOps(TestCase):
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self.assertEqual(torch.add(b, c), b.add_(c))
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@onlyNativeDeviceTypes
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@dtypes(*product(complex_types(), all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)))
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@dtypes(*product(get_all_complex_dtypes(), get_all_dtypes()))
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@suppress_warnings
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def test_set_real_imag(self, device, dtypes):
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x = torch.randn(10, dtype=dtypes[0], device=device)
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@ -1255,7 +1255,7 @@ class TestOldViewOps(TestCase):
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scalar = torch.tensor(5, device=device)
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self.assertEqual(scalar, scalar.T)
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*(torch.testing.get_all_dtypes()))
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def test_transposes(self, device, dtype):
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for op in ("T", "H", "mT", "mH", "adjoint"):
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shapes = ((), (2, 3), (2, 3, 4)) if op[0] == "m" or op == "adjoint" else ((), (2, 3),)
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@ -1271,7 +1271,7 @@ class TestOldViewOps(TestCase):
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t2 = t2.conj()
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self.assertEqual(t2, t1)
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*(torch.testing.get_all_dtypes()))
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def test_transposes_errors(self, device, dtype):
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for op in ("H", "mT", "mH", "adjoint"):
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shapes = ((2,), (2, 3, 4)) if op == "H" else ((2,),)
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@ -1397,7 +1397,8 @@ class TestOldViewOps(TestCase):
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self.assertEqual(np_res, torch_res)
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# TODO: are these view ops?
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@dtypes(*all_types_and_complex_and(torch.half))
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@dtypes(*(get_all_int_dtypes() + get_all_fp_dtypes(include_bfloat16=False) +
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get_all_complex_dtypes()))
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def test_atleast(self, device, dtype):
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self._test_atleast_dim(torch.atleast_1d, np.atleast_1d, device, dtype)
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self._test_atleast_dim(torch.atleast_2d, np.atleast_2d, device, dtype)
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@ -1534,7 +1535,7 @@ class TestOldViewOps(TestCase):
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self.assertEqual(res1, res2_numpy)
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# Skip BFloat16 since numpy does not support it
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@dtypes(*all_types_and_complex_and(torch.half, torch.bool))
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@dtypes(*get_all_dtypes(include_bfloat16=False))
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def test_broadcast_to(self, device, dtype):
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def can_broadcast(s0, s1):
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# s0.dim() <= s1.dim(), reverse s0 and s1 to compare trailing dimension
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@ -1637,7 +1638,7 @@ class TestOldViewOps(TestCase):
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self.assertEqual(tensor.view(6, 2, 1), contig_tensor.view(6, 2, 1))
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self.assertEqual(tensor.view(1, 6, 2, 1), contig_tensor.view(1, 6, 2, 1))
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@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
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@dtypes(*get_all_dtypes())
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def test_reshape_view_semantics(self, device, dtype):
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tensor = make_tensor((15, 4), dtype=dtype, device=device)
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target = (20, 3)
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@ -1664,7 +1665,7 @@ class TestOldViewOps(TestCase):
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@onlyNativeDeviceTypes
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# Skip BFloat16 since numpy does not support it
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@dtypes(*all_types_and_complex_and(torch.half, torch.bool))
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@dtypes(*get_all_dtypes(include_bfloat16=False))
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def test_tensor_split_sections(self, device, dtype):
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input_sizes = [
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(0,),
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@ -1695,7 +1696,7 @@ class TestOldViewOps(TestCase):
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@onlyNativeDeviceTypes
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# Skip BFloat16 since numpy does not support it
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@dtypes(*all_types_and_complex_and(torch.half, torch.bool))
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@dtypes(*get_all_dtypes(include_bfloat16=False))
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def test_tensor_split_indices(self, device, dtype):
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input_sizes = [
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(0,),
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@ -1774,20 +1775,20 @@ class TestOldViewOps(TestCase):
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def test_resize_all_dtypes_and_devices(self, device):
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shape = (2, 2)
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for dt in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool):
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for dt in get_all_dtypes():
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x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=dt, device=device)
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x.resize_(shape)
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self.assertEqual(shape, x.shape)
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def test_resize_as_all_dtypes_and_devices(self, device):
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for dt in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool):
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for dt in get_all_dtypes():
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x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=dt, device=device)
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y = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=dt, device=device)
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x.resize_as_(y)
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self.assertEqual(y.shape, x.shape)
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def test_view_all_dtypes_and_devices(self, device):
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for dt in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool):
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for dt in get_all_dtypes():
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x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=dt, device=device)
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self.assertEqual(x.view(6).shape, [6])
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