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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
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
aac3c7bd06
commit
b004307252
@ -1,17 +1,27 @@
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import torch
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import numpy as np
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from itertools import product, combinations, permutations, chain
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from functools import partial
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import random
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import warnings
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from functools import partial
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from itertools import product, combinations, permutations, chain
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import numpy as np
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import torch
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from torch._six import nan
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from torch.testing._internal.common_utils import (
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TestCase, run_tests, make_tensor, torch_to_numpy_dtype_dict)
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from torch.testing._internal.common_device_type import (
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instantiate_device_type_tests, onlyCPU, onlyCUDA, dtypes, onlyOnCPUAndCUDA,
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dtypesIfCPU, dtypesIfCUDA, largeTensorTest)
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instantiate_device_type_tests,
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onlyCPU,
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onlyCUDA,
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dtypes,
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onlyOnCPUAndCUDA,
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dtypesIfCPU,
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dtypesIfCUDA,
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largeTensorTest,
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)
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from torch.testing._internal.common_utils import (
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TestCase,
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run_tests,
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make_tensor,
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torch_to_numpy_dtype_dict,
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)
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# TODO: replace with make_tensor
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def _generate_input(shape, dtype, device, with_extremal):
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@ -24,17 +34,19 @@ def _generate_input(shape, dtype, device, with_extremal):
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x = torch.randn(*shape, device=device) * random.randint(30, 100)
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x = x.to(torch.bfloat16)
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else:
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x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(30, 100)
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x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(
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30, 100
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)
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x[torch.randn(*shape) > 0.5] = 0
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if with_extremal and dtype.is_floating_point:
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# Use extremal values
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x[torch.randn(*shape) > 0.5] = float('nan')
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x[torch.randn(*shape) > 0.5] = float('inf')
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x[torch.randn(*shape) > 0.5] = float('-inf')
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x[torch.randn(*shape) > 0.5] = float("nan")
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x[torch.randn(*shape) > 0.5] = float("inf")
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x[torch.randn(*shape) > 0.5] = float("-inf")
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elif with_extremal and dtype.is_complex:
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x[torch.randn(*shape) > 0.5] = complex('nan')
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x[torch.randn(*shape) > 0.5] = complex('inf')
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x[torch.randn(*shape) > 0.5] = complex('-inf')
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x[torch.randn(*shape) > 0.5] = complex("nan")
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x[torch.randn(*shape) > 0.5] = complex("inf")
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x[torch.randn(*shape) > 0.5] = complex("-inf")
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elif dtype == torch.bool:
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x = torch.zeros(shape, dtype=dtype, device=device)
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x[torch.randn(*shape) > 0.5] = True
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@ -43,6 +55,7 @@ def _generate_input(shape, dtype, device, with_extremal):
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return x
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class TestShapeOps(TestCase):
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# TODO: update to work on CUDA, too
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@ -65,7 +78,7 @@ class TestShapeOps(TestCase):
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tensor0D = torch.tensor(list0D)
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self.assertEqual(tensor0D.tolist(), list0D)
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table1D = [1., 2., 3.]
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table1D = [1.0, 2.0, 3.0]
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tensor1D = torch.tensor(table1D)
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storage = torch.Storage(table1D)
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self.assertEqual(tensor1D.tolist(), table1D)
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@ -96,19 +109,29 @@ class TestShapeOps(TestCase):
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fn(x, 0, 5)
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# Mismatch in size of `source` and `destination`
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with self.assertRaisesRegex(RuntimeError, "movedim: Invalid source or destination dims:"):
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fn(x, (1, 0), (0, ))
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with self.assertRaisesRegex(
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RuntimeError, "movedim: Invalid source or destination dims:"
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):
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fn(x, (1, 0), (0,))
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with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `source`"):
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with self.assertRaisesRegex(
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RuntimeError, "movedim: repeated dim in `source`"
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):
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fn(x, (0, 0), (0, 1))
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with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `source`"):
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with self.assertRaisesRegex(
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RuntimeError, "movedim: repeated dim in `source`"
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):
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fn(x, (0, 1, 0), (0, 1, 2))
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with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `destination`"):
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with self.assertRaisesRegex(
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RuntimeError, "movedim: repeated dim in `destination`"
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):
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fn(x, (0, 1), (1, 1))
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with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `destination`"):
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with self.assertRaisesRegex(
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RuntimeError, "movedim: repeated dim in `destination`"
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):
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fn(x, (0, 1, 2), (1, 0, 1))
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@dtypes(torch.int64, torch.float, torch.complex128)
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@ -131,8 +154,12 @@ class TestShapeOps(TestCase):
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# Integer `source` and `destination`
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torch_fn = partial(fn, source=src_dim, destination=dst_dim)
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np_fn = partial(np.moveaxis, source=src_dim, destination=dst_dim)
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self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
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np_fn = partial(
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np.moveaxis, source=src_dim, destination=dst_dim
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)
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self.compare_with_numpy(
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torch_fn, np_fn, x, device=None, dtype=None
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)
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if nd == 0:
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continue
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@ -142,9 +169,13 @@ class TestShapeOps(TestCase):
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sequence[random_idx] = sequence[random_idx] - nd
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return tuple(src_sequence)
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for src_sequence in permutations(range(nd), r=random.randint(1, nd)):
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for src_sequence in permutations(
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range(nd), r=random.randint(1, nd)
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):
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# Sequence `source` and `destination`
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dst_sequence = tuple(random.sample(range(nd), len(src_sequence)))
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dst_sequence = tuple(
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random.sample(range(nd), len(src_sequence))
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)
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# Randomly change a dim to a negative dim representation of itself.
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random_prob = random.random()
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@ -160,9 +191,15 @@ class TestShapeOps(TestCase):
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random_idx = random.randint(0, len(src_sequence) - 1)
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src_sequence = make_index_negative(src_sequence, random_idx)
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torch_fn = partial(fn, source=src_sequence, destination=dst_sequence)
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np_fn = partial(np.moveaxis, source=src_sequence, destination=dst_sequence)
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self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
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torch_fn = partial(
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fn, source=src_sequence, destination=dst_sequence
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)
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np_fn = partial(
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np.moveaxis, source=src_sequence, destination=dst_sequence
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)
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self.compare_with_numpy(
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torch_fn, np_fn, x, device=None, dtype=None
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)
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# Move dim to same position
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x = torch.randn(2, 3, 5, 7, 11)
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@ -207,10 +244,7 @@ class TestShapeOps(TestCase):
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def test_diagonal_multidim(self, device, dtype):
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x = torch.randn(10, 11, 12, 13, dtype=dtype, device=device)
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xn = x.numpy()
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for args in [(2, 2, 3),
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(2,),
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(-2, 1, 2),
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(0, -2, -1)]:
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for args in [(2, 2, 3), (2,), (-2, 1, 2), (0, -2, -1)]:
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result = torch.diagonal(x, *args)
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expected = xn.diagonal(*args)
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self.assertEqual(expected.shape, result.shape)
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@ -223,9 +257,19 @@ class TestShapeOps(TestCase):
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self.assertEqual(expected, result)
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@onlyOnCPUAndCUDA
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@dtypesIfCPU(*torch.testing.get_all_dtypes(include_complex=False, include_bool=False, include_half=False,
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include_bfloat16=False))
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@dtypesIfCUDA(*torch.testing.get_all_dtypes(include_complex=False, include_bool=False, include_bfloat16=False))
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@dtypesIfCPU(
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*torch.testing.get_all_dtypes(
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include_complex=False,
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include_bool=False,
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include_half=False,
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include_bfloat16=False,
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)
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)
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@dtypesIfCUDA(
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*torch.testing.get_all_dtypes(
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include_complex=False, include_bool=False, include_bfloat16=False
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)
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)
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def test_trace(self, device, dtype):
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def test(shape):
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tensor = make_tensor(shape, device, dtype, low=-9, high=9)
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@ -265,14 +309,22 @@ class TestShapeOps(TestCase):
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max_vals = max_vals.cpu().numpy()
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# Use NumPy implementation as reference
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X_clamped = torch.tensor(np.clip(X.cpu().numpy(), a_min=min_vals, a_max=max_vals), device=device)
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X_clamped = torch.tensor(
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np.clip(X.cpu().numpy(), a_min=min_vals, a_max=max_vals), device=device
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)
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return X, X_clamped
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# Tests clamp and its alias, clip
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@dtypes(torch.int64, torch.float32)
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def test_clamp(self, device, dtype):
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op_list = (torch.clamp, torch.Tensor.clamp, torch.Tensor.clamp_,
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torch.clip, torch.Tensor.clip, torch.Tensor.clip_)
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op_list = (
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torch.clamp,
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torch.Tensor.clamp,
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torch.Tensor.clamp_,
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torch.clip,
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torch.Tensor.clip,
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torch.Tensor.clip_,
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)
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# min/max argument product
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args = product((-10, None), (10, None))
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@ -282,10 +334,9 @@ class TestShapeOps(TestCase):
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if min_val is None and max_val is None:
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continue
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X, Y_expected = self.generate_clamp_baseline(device, dtype,
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min_vals=min_val,
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max_vals=max_val,
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with_nans=False)
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X, Y_expected = self.generate_clamp_baseline(
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device, dtype, min_vals=min_val, max_vals=max_val, with_nans=False
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)
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# Test op
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X1 = X.clone() # So that the in-place ops do not change X
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@ -299,8 +350,14 @@ class TestShapeOps(TestCase):
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self.assertEqual(Y_expected, Y_out)
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def test_clamp_propagates_nans(self, device):
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op_list = (torch.clamp, torch.Tensor.clamp, torch.Tensor.clamp_,
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torch.clip, torch.Tensor.clip, torch.Tensor.clip_)
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op_list = (
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torch.clamp,
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torch.Tensor.clamp,
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torch.Tensor.clamp_,
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torch.clip,
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torch.Tensor.clip,
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torch.Tensor.clip_,
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)
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# min/max argument product
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args = product((-10, None), (10, None))
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@ -310,10 +367,13 @@ class TestShapeOps(TestCase):
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if min_val is None and max_val is None:
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continue
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X, Y_expected = self.generate_clamp_baseline(device, torch.float,
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min_vals=min_val,
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max_vals=max_val,
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with_nans=True)
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X, Y_expected = self.generate_clamp_baseline(
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device,
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torch.float,
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min_vals=min_val,
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max_vals=max_val,
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with_nans=True,
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)
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Y_expected = torch.isnan(Y_expected)
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# Test op
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@ -329,7 +389,7 @@ class TestShapeOps(TestCase):
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def test_clamp_raises_arg_errors(self, device):
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X = torch.randn(100, dtype=torch.float, device=device)
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error_msg = 'At least one of \'min\' or \'max\' must not be None'
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error_msg = "At least one of 'min' or 'max' must not be None"
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with self.assertRaisesRegex(RuntimeError, error_msg):
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X.clamp()
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with self.assertRaisesRegex(RuntimeError, error_msg):
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@ -364,18 +424,22 @@ class TestShapeOps(TestCase):
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self.assertEqual(in_t.flip(p_dims), out_t)
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if len(p_dims) > 0:
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# Wrap 1st dim
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self.assertEqual(in_t.flip((-n + p_dims[0],) + p_dims[1:]), out_t)
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self.assertEqual(
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in_t.flip((-n + p_dims[0],) + p_dims[1:]), out_t
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)
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def gen_data():
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# Basic tests
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data = make_from_data([1, 2, 3, 4, 5, 6, 7, 8]).view(2, 2, 2)
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nonctg = make_from_size((2, 2, 2), noncontiguous=True).copy_(data)
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dims_result = ((0, make_from_data([5, 6, 7, 8, 1, 2, 3, 4]).view(2, 2, 2)),
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(1, make_from_data([3, 4, 1, 2, 7, 8, 5, 6]).view(2, 2, 2)),
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(2, make_from_data([2, 1, 4, 3, 6, 5, 8, 7]).view(2, 2, 2)),
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((0, 1), make_from_data([7, 8, 5, 6, 3, 4, 1, 2]).view(2, 2, 2)),
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((0, 1, 2), make_from_data([8, 7, 6, 5, 4, 3, 2, 1]).view(2, 2, 2)))
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dims_result = (
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(0, make_from_data([5, 6, 7, 8, 1, 2, 3, 4]).view(2, 2, 2)),
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(1, make_from_data([3, 4, 1, 2, 7, 8, 5, 6]).view(2, 2, 2)),
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(2, make_from_data([2, 1, 4, 3, 6, 5, 8, 7]).view(2, 2, 2)),
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((0, 1), make_from_data([7, 8, 5, 6, 3, 4, 1, 2]).view(2, 2, 2)),
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((0, 1, 2), make_from_data([8, 7, 6, 5, 4, 3, 2, 1]).view(2, 2, 2)),
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)
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for in_tensor, (dims, out_tensor) in product((data, nonctg), dims_result):
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yield in_tensor, dims, out_tensor
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@ -388,7 +452,9 @@ class TestShapeOps(TestCase):
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yield in_t, 1, in_t
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# Transposed
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in_t = make_from_data([1, 2, 3, 4, 5, 6, 7, 8]).view(2, 2, 2).transpose(0, 1)
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in_t = (
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make_from_data([1, 2, 3, 4, 5, 6, 7, 8]).view(2, 2, 2).transpose(0, 1)
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)
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dims = (0, 1, 2)
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out_t = make_from_data([8, 7, 4, 3, 6, 5, 2, 1]).view(2, 2, 2)
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yield in_t, dims, out_t
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@ -431,7 +497,9 @@ class TestShapeOps(TestCase):
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size = [2, 3, 4]
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data = make_from_size(size)
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possible_dims = range(len(size))
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test_dims = chain(combinations(possible_dims, 1), combinations(possible_dims, 2))
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test_dims = chain(
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combinations(possible_dims, 1), combinations(possible_dims, 2)
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)
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for dims in test_dims:
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self.assertEqual(size, list(data.flip(dims).size()))
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@ -450,7 +518,6 @@ class TestShapeOps(TestCase):
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self.assertRaises(IndexError, lambda: data.flip(0, 1, 2, 3))
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self.assertRaises(IndexError, lambda: data.flip(3))
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def _rand_shape(self, dim, min_size, max_size):
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return tuple(torch.randint(min_size, max_size + 1, (dim,)))
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@ -471,9 +538,9 @@ class TestShapeOps(TestCase):
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self.compare_with_numpy(torch_fn, np_fn, data)
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@onlyCUDA # CPU is too slow
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@largeTensorTest('17GB') # 4 tensors of 4GB (in, out) x (torch, numpy) + 1GB
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@largeTensorTest("17GB") # 4 tensors of 4GB (in, out) x (torch, numpy) + 1GB
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def test_flip_large_tensor(self, device):
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t_in = torch.empty(2**32 + 1, dtype=torch.uint8).random_()
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t_in = torch.empty(2 ** 32 + 1, dtype=torch.uint8).random_()
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torch_fn = partial(torch.flip, dims=(0,))
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np_fn = partial(np.flip, axis=0)
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self.compare_with_numpy(torch_fn, np_fn, t_in)
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@ -534,7 +601,9 @@ class TestShapeOps(TestCase):
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# test tensor with more than 2D
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data = torch.arange(1, 9, device=device).view(2, 2, 2)
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self.assertEqual(torch.tensor([2, 4, 1, 3, 6, 8, 5, 7]).view(2, 2, 2), data.rot90(1, [1, 2]))
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self.assertEqual(
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torch.tensor([2, 4, 1, 3, 6, 8, 5, 7]).view(2, 2, 2), data.rot90(1, [1, 2])
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)
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self.assertEqual(data.rot90(1, [1, -1]), data.rot90(1, [1, 2]))
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# test for errors
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@ -580,7 +649,9 @@ class TestShapeOps(TestCase):
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return torch.randint(2, shape, device=device, dtype=dtype)
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else:
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# windows does not work for bfloat16 randing
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return torch.randint(2, shape, device=device, dtype=torch.float).to(dtype)
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return torch.randint(2, shape, device=device, dtype=torch.float).to(
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dtype
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)
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for shape in shapes:
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tensor = gen_nontrivial_input(shape, dtype, device)
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@ -588,20 +659,28 @@ class TestShapeOps(TestCase):
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dst2 = tensor.nonzero(as_tuple=False)
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dst3 = torch.empty([], dtype=torch.long, device=device)
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torch.nonzero(tensor, out=dst3)
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if self.device_type != 'xla':
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if self.device_type != "xla":
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# xla does not raise runtime error
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self.assertRaisesRegex(
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RuntimeError,
|
||||
"scalar type Long",
|
||||
lambda: torch.nonzero(tensor, out=torch.empty([], dtype=torch.float, device=device))
|
||||
lambda: torch.nonzero(
|
||||
tensor, out=torch.empty([], dtype=torch.float, device=device)
|
||||
),
|
||||
)
|
||||
if self.device_type == 'cuda':
|
||||
if self.device_type == "cuda":
|
||||
self.assertRaisesRegex(
|
||||
RuntimeError,
|
||||
"on the same device",
|
||||
lambda: torch.nonzero(tensor, out=torch.empty([], dtype=torch.long))
|
||||
lambda: torch.nonzero(
|
||||
tensor, out=torch.empty([], dtype=torch.long)
|
||||
),
|
||||
)
|
||||
np_array = tensor.cpu().numpy() if dtype != torch.bfloat16 else tensor.float().cpu().numpy()
|
||||
np_array = (
|
||||
tensor.cpu().numpy()
|
||||
if dtype != torch.bfloat16
|
||||
else tensor.float().cpu().numpy()
|
||||
)
|
||||
np_result = torch.from_numpy(np.stack(np_array.nonzero())).t()
|
||||
self.assertEqual(dst1.cpu(), np_result, atol=0, rtol=0)
|
||||
self.assertEqual(dst2.cpu(), np_result, atol=0, rtol=0)
|
||||
@ -620,7 +699,9 @@ class TestShapeOps(TestCase):
|
||||
with self.assertRaises(RuntimeError):
|
||||
torch.nonzero(t, as_tuple=True, out=out)
|
||||
|
||||
self.assertEqual(torch.nonzero(t, as_tuple=False, out=out), torch.nonzero(t, out=out))
|
||||
self.assertEqual(
|
||||
torch.nonzero(t, as_tuple=False, out=out), torch.nonzero(t, out=out)
|
||||
)
|
||||
|
||||
# Verifies that JIT script cannot handle the as_tuple kwarg
|
||||
# See Issue https://github.com/pytorch/pytorch/issues/45499.
|
||||
@ -648,7 +729,9 @@ class TestShapeOps(TestCase):
|
||||
def test_nonzero_discontiguous(self, device):
|
||||
shape = (4, 4)
|
||||
tensor = torch.randint(2, shape, device=device)
|
||||
tensor_nc = torch.empty(shape[0], shape[1] * 2, device=device)[:, ::2].copy_(tensor)
|
||||
tensor_nc = torch.empty(shape[0], shape[1] * 2, device=device)[:, ::2].copy_(
|
||||
tensor
|
||||
)
|
||||
dst1 = tensor.nonzero(as_tuple=False)
|
||||
dst2 = tensor_nc.nonzero(as_tuple=False)
|
||||
self.assertEqual(dst1, dst2, atol=0, rtol=0)
|
||||
@ -659,7 +742,9 @@ class TestShapeOps(TestCase):
|
||||
self.assertEqual(data_ptr, dst3.data_ptr())
|
||||
self.assertEqual(dst1, dst3, atol=0, rtol=0)
|
||||
# discontiguous out
|
||||
dst4 = torch.empty(dst1.size(0), dst1.size(1) * 2, dtype=torch.long, device=device)[:, ::2]
|
||||
dst4 = torch.empty(
|
||||
dst1.size(0), dst1.size(1) * 2, dtype=torch.long, device=device
|
||||
)[:, ::2]
|
||||
data_ptr = dst4.data_ptr()
|
||||
strides = dst4.stride()
|
||||
torch.nonzero(tensor, out=dst4)
|
||||
@ -672,7 +757,8 @@ class TestShapeOps(TestCase):
|
||||
nz = x.nonzero()
|
||||
self.assertFalse(nz.requires_grad)
|
||||
|
||||
|
||||
instantiate_device_type_tests(TestShapeOps, globals())
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
run_tests()
|
||||
|
||||
Reference in New Issue
Block a user