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[codemod][lint][fbcode/c*] Enable BLACK by default
Test Plan: manual inspection & sandcastle Reviewed By: zertosh Differential Revision: D30279364 fbshipit-source-id: c1ed77dfe43a3bde358f92737cd5535ae5d13c9a
This commit is contained in:
Zsolt Dollenstein
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Facebook GitHub Bot
Facebook GitHub Bot
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aac3c7bd06
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b004307252
@ -1,12 +1,13 @@
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import torch
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import numpy as np
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from itertools import product
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from torch.testing._internal.common_utils import \
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(TestCase, run_tests)
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from torch.testing._internal.common_device_type import \
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(instantiate_device_type_tests, onlyCPU, dtypes)
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import numpy as np
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import torch
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from torch.testing._internal.common_device_type import (
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instantiate_device_type_tests,
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onlyCPU,
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dtypes,
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)
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from torch.testing._internal.common_utils import TestCase, run_tests
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# For testing handling NumPy objects and sending tensors to / accepting
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# arrays from NumPy.
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@ -17,7 +18,7 @@ class TestNumPyInterop(TestCase):
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@onlyCPU
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def test_numpy_non_writeable(self, device):
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arr = np.zeros(5)
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arr.flags['WRITEABLE'] = False
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arr.flags["WRITEABLE"] = False
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self.assertWarns(UserWarning, lambda: torch.from_numpy(arr))
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@onlyCPU
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@ -95,7 +96,7 @@ class TestNumPyInterop(TestCase):
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x = get_castable_tensor((sz1, sz2), dtp)
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y = x.numpy()
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check2d(x, y)
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self.assertTrue(y.flags['C_CONTIGUOUS'])
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self.assertTrue(y.flags["C_CONTIGUOUS"])
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# with storage offset
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xm = get_castable_tensor((sz1 * 2, sz2), dtp)
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@ -103,13 +104,13 @@ class TestNumPyInterop(TestCase):
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y = x.numpy()
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self.assertTrue(x.storage_offset() > 0)
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check2d(x, y)
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self.assertTrue(y.flags['C_CONTIGUOUS'])
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self.assertTrue(y.flags["C_CONTIGUOUS"])
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# non-contiguous 2D
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x = get_castable_tensor((sz2, sz1), dtp).t()
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y = x.numpy()
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check2d(x, y)
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self.assertFalse(y.flags['C_CONTIGUOUS'])
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self.assertFalse(y.flags["C_CONTIGUOUS"])
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# with storage offset
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xm = get_castable_tensor((sz2 * 2, sz1), dtp)
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@ -221,12 +222,14 @@ class TestNumPyInterop(TestCase):
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self.assertEqual(torch.from_numpy(x).shape, (2, 0))
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# check ill-sized strides raise exception
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x = np.array([3., 5., 8.])
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x = np.array([3.0, 5.0, 8.0])
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x.strides = (3,)
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self.assertRaises(ValueError, lambda: torch.from_numpy(x))
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def test_from_list_of_ndarray_warning(self, device):
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warning_msg = r"Creating a tensor from a list of numpy.ndarrays is extremely slow"
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warning_msg = (
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r"Creating a tensor from a list of numpy.ndarrays is extremely slow"
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)
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with self.assertWarnsOnceRegex(UserWarning, warning_msg):
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torch.tensor([np.array([0]), np.array([1])], device=device)
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@ -275,7 +278,7 @@ class TestNumPyInterop(TestCase):
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]
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for tp, dtype in zip(types, dtypes):
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# Only concrete class can be given where "Type[number[_64Bit]]" is expected
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if np.dtype(dtype).kind == 'u': # type: ignore[misc]
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if np.dtype(dtype).kind == "u": # type: ignore[misc]
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# .type expects a XxxTensor, which have no type hints on
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# purpose, so ignore during mypy type checking
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x = torch.tensor([1, 2, 3, 4]).type(tp) # type: ignore[call-overload]
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@ -304,7 +307,7 @@ class TestNumPyInterop(TestCase):
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asarray = np.asarray(x, dtype=dtype)
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self.assertEqual(asarray.dtype, dtype)
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# Only concrete class can be given where "Type[number[_64Bit]]" is expected
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if np.dtype(dtype).kind == 'u': # type: ignore[misc]
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if np.dtype(dtype).kind == "u": # type: ignore[misc]
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wrapped_x = np.array([1, -2, 3, -4], dtype=dtype)
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for i in range(len(x)):
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self.assertEqual(asarray[i], wrapped_x[i])
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@ -318,7 +321,7 @@ class TestNumPyInterop(TestCase):
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for tp, dtype in zip(float_types, float_dtypes):
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x = torch.tensor([1, 2, 3, 4]).type(tp) # type: ignore[call-overload]
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array = np.array([1, 2, 3, 4], dtype=dtype)
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for func in ['sin', 'sqrt', 'ceil']:
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for func in ["sin", "sqrt", "ceil"]:
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ufunc = getattr(np, func)
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res_x = ufunc(x)
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res_array = ufunc(array)
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@ -331,14 +334,21 @@ class TestNumPyInterop(TestCase):
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x = torch.tensor([1, 2, 3, 4]).type(tp) # type: ignore[call-overload]
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array = np.array([1, 2, 3, 4], dtype=dtype)
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geq2_x = np.greater_equal(x, 2)
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geq2_array = np.greater_equal(array, 2).astype('uint8')
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geq2_array = np.greater_equal(array, 2).astype("uint8")
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self.assertIsInstance(geq2_x, torch.ByteTensor)
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for i in range(len(x)):
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self.assertEqual(geq2_x[i], geq2_array[i])
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@onlyCPU
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def test_multiplication_numpy_scalar(self, device) -> None:
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for np_dtype in [np.float32, np.float64, np.int32, np.int64, np.int16, np.uint8]:
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for np_dtype in [
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np.float32,
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np.float64,
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np.int32,
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np.int64,
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np.int16,
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np.uint8,
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]:
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for t_dtype in [torch.float, torch.double]:
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# mypy raises an error when np.floatXY(2.0) is called
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# even though this is valid code
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@ -356,8 +366,11 @@ class TestNumPyInterop(TestCase):
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@onlyCPU
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def test_parse_numpy_int(self, device):
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# Only concrete class can be given where "Type[number[_64Bit]]" is expected
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self.assertRaisesRegex(RuntimeError, "Overflow",
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lambda: torch.mean(torch.randn(1, 1), np.uint64(-1))) # type: ignore[call-overload]
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self.assertRaisesRegex(
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RuntimeError,
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"Overflow",
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lambda: torch.mean(torch.randn(1, 1), np.uint64(-1)),
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) # type: ignore[call-overload]
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# https://github.com/pytorch/pytorch/issues/29252
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for nptype in [np.int16, np.int8, np.uint8, np.int32, np.int64]:
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scalar = 3
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@ -367,7 +380,10 @@ class TestNumPyInterop(TestCase):
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# np integral type can be treated as a python int in native functions with
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# int parameters:
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self.assertEqual(torch.ones(5).diag(scalar), torch.ones(5).diag(np_val))
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self.assertEqual(torch.ones([2, 2, 2, 2]).mean(scalar), torch.ones([2, 2, 2, 2]).mean(np_val))
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self.assertEqual(
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torch.ones([2, 2, 2, 2]).mean(scalar),
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torch.ones([2, 2, 2, 2]).mean(np_val),
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)
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# numpy integral type parses like a python int in custom python bindings:
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self.assertEqual(torch.Storage(np_val).size(), scalar) # type: ignore[attr-defined]
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@ -384,25 +400,40 @@ class TestNumPyInterop(TestCase):
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self.assertEqual((np_val + t).dtype, t.dtype)
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def test_has_storage_numpy(self, device):
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for dtype in [np.float32, np.float64, np.int64,
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np.int32, np.int16, np.uint8]:
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for dtype in [np.float32, np.float64, np.int64, np.int32, np.int16, np.uint8]:
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arr = np.array([1], dtype=dtype)
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.float32).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.double).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.int).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.long).storage())
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self.assertIsNotNone(torch.tensor(arr, device=device, dtype=torch.uint8).storage())
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self.assertIsNotNone(
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torch.tensor(arr, device=device, dtype=torch.float32).storage()
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)
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self.assertIsNotNone(
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torch.tensor(arr, device=device, dtype=torch.double).storage()
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)
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self.assertIsNotNone(
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torch.tensor(arr, device=device, dtype=torch.int).storage()
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)
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self.assertIsNotNone(
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torch.tensor(arr, device=device, dtype=torch.long).storage()
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)
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self.assertIsNotNone(
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torch.tensor(arr, device=device, dtype=torch.uint8).storage()
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)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_numpy_scalar_cmp(self, device, dtype):
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if dtype.is_complex:
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tensors = (torch.tensor(complex(1, 3), dtype=dtype, device=device),
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torch.tensor([complex(1, 3), 0, 2j], dtype=dtype, device=device),
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torch.tensor([[complex(3, 1), 0], [-1j, 5]], dtype=dtype, device=device))
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tensors = (
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torch.tensor(complex(1, 3), dtype=dtype, device=device),
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torch.tensor([complex(1, 3), 0, 2j], dtype=dtype, device=device),
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torch.tensor(
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[[complex(3, 1), 0], [-1j, 5]], dtype=dtype, device=device
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),
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)
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else:
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tensors = (torch.tensor(3, dtype=dtype, device=device),
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torch.tensor([1, 0, -3], dtype=dtype, device=device),
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torch.tensor([[3, 0, -1], [3, 5, 4]], dtype=dtype, device=device))
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tensors = (
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torch.tensor(3, dtype=dtype, device=device),
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torch.tensor([1, 0, -3], dtype=dtype, device=device),
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torch.tensor([[3, 0, -1], [3, 5, 4]], dtype=dtype, device=device),
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)
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for tensor in tensors:
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if dtype == torch.bfloat16:
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@ -411,17 +442,24 @@ class TestNumPyInterop(TestCase):
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continue
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np_array = tensor.cpu().numpy()
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for t, a in product((tensor.flatten()[0], tensor.flatten()[0].item()),
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(np_array.flatten()[0], np_array.flatten()[0].item())):
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for t, a in product(
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(tensor.flatten()[0], tensor.flatten()[0].item()),
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(np_array.flatten()[0], np_array.flatten()[0].item()),
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):
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self.assertEqual(t, a)
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if dtype == torch.complex64 and torch.is_tensor(t) and type(a) == np.complex64:
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if (
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dtype == torch.complex64
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and torch.is_tensor(t)
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and type(a) == np.complex64
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):
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# TODO: Imaginary part is dropped in this case. Need fix.
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# https://github.com/pytorch/pytorch/issues/43579
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self.assertFalse(t == a)
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else:
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self.assertTrue(t == a)
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instantiate_device_type_tests(TestNumPyInterop, globals())
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if __name__ == '__main__':
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if __name__ == "__main__":
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run_tests()
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