mirror of
https://github.com/zebrajr/pytorch.git
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c5482e33e9
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23315 Test Plan: - [namedtensor ci] gh-metadata: pytorch pytorch 23315 gh/zou3519/79/head Imported from OSS Differential Revision: D16494414 Pulled By: zou3519 fbshipit-source-id: d2d6beb45db9288e5df707b68b6046d783ca9f97
529 lines
19 KiB
Python
529 lines
19 KiB
Python
import unittest
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from common_utils import TestCase, run_tests
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from common_cuda import TEST_CUDA
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from collections import namedtuple
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import itertools
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import torch
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import sys
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def namedtensor_enabled():
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return '-DBUILD_NAMEDTENSOR' in torch.__config__.show()
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skipIfNamedTensorDisabled = \
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unittest.skipIf(not namedtensor_enabled(),
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'PyTorch not compiled with namedtensor support')
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def pass_name_to_python_arg_parser(name):
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x = torch.empty(2, names=(name,))
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def flatten(lst):
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return [item for sublist in lst for item in sublist]
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Function = namedtuple('TestCase', ['name', 'lambd'])
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class TestNamedTensor(TestCase):
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def test_trivial(self):
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pass
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def _test_factory(self, factory, device):
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x = factory([], device=device)
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self.assertEqual(x.names, ())
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x = factory(1, 2, 3, device=device)
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self.assertEqual(x.names, (None, None, None))
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x = factory(1, 2, 3, names=None, device=device)
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self.assertEqual(x.names, (None, None, None))
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x = factory(1, 2, 3, names=('N', 'T', 'D'), device=device)
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self.assertEqual(x.names, ('N', 'T', 'D'))
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x = factory(1, 2, 3, names=('N', None, 'D'), device=device)
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self.assertEqual(x.names, ('N', None, 'D'))
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with self.assertRaisesRegex(RuntimeError,
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'must contain alphabetical characters and/or underscore'):
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x = factory(2, names=('?',), device=device)
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with self.assertRaisesRegex(RuntimeError, 'Number of names'):
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x = factory(2, 1, names=('N',), device=device)
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with self.assertRaisesRegex(TypeError, 'invalid combination of arguments'):
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x = factory(2, 1, names='N', device=device)
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with self.assertRaisesRegex(RuntimeError, 'construct a tensor with duplicate names'):
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x = factory(2, 1, 1, names=('N', 'C', 'N'), device=device)
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# Tests for tagged names
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x = factory(2, 3, 1, names=('C.in', 'H', 'C.out'), device=device)
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self.assertEqual(x.names, ('C.in', 'H', 'C.out'))
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with self.assertRaisesRegex(RuntimeError, 'construct a tensor with duplicate names'):
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x = factory(2, 1, 1, names=('C.in', 'H', 'C.in'), device=device)
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with self.assertRaisesRegex(
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RuntimeError,
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'with duplicate names unless they are tagged and have different tags'):
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x = factory(2, 1, 1, names=('C.in', 'H', 'C'), device=device)
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def test_empty(self):
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self._test_factory(torch.empty, 'cpu')
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def test_has_names(self):
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unnamed = torch.empty(2, 3)
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none_named = torch.empty(2, 3, names=(None, None))
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partially_named = torch.empty(2, 3, names=('N', None))
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fully_named = torch.empty(2, 3, names=('N', 'C'))
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self.assertFalse(unnamed.has_names())
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self.assertFalse(none_named.has_names())
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self.assertTrue(partially_named.has_names())
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self.assertTrue(fully_named.has_names())
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def test_copy_transpose(self):
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# This type of copy is special-cased and therefore needs its own test
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def _test(self_names, other_names, expected_names):
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x = torch.empty(2, 5, names=self_names)
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y = torch.empty(5, 2).t().set_names_(other_names)
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x.copy_(y)
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self.assertEqual(x.names, expected_names)
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_test(('N', 'C'), ('N', 'C'), ('N', 'C'))
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_test(('N', None), ('N', 'C'), ('N', 'C'))
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_test(None, ('N', 'C'), ('N', 'C'))
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def test_set_names_(self):
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tensor = torch.empty(1, 1, names=('N', 'C'))
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self.assertEqual(tensor.set_names_(None).names, (None, None))
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self.assertEqual(tensor.set_names_(['H', 'W']).names, ('H', 'W'))
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with self.assertRaisesRegex(RuntimeError, 'Number of names'):
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tensor.set_names_(['N', 'C', 'W'])
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with self.assertRaisesRegex(RuntimeError, 'duplicate names'):
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tensor.set_names_(['N', 'N'])
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def test_set_names_property(self):
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tensor = torch.empty(1, 1, names=('N', 'C'))
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tensor.names = None
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self.assertEqual(tensor.names, (None, None))
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tensor.names = ('N', 'W')
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self.assertEqual(tensor.names, ('N', 'W'))
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with self.assertRaisesRegex(RuntimeError, 'Number of names'):
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tensor.names = ['N', 'C', 'W']
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with self.assertRaisesRegex(RuntimeError, 'duplicate names'):
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tensor.names = ['N', 'N']
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@unittest.skipIf(not TEST_CUDA, 'no CUDA')
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def test_empty_cuda(self):
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self._test_factory(torch.empty, 'cuda')
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def test_size(self):
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t = torch.empty(2, 3, 5, names=('N', None, 'C'))
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self.assertEqual(t.size('N'), 2)
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self.assertEqual(t.size('C'), 5)
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with self.assertRaisesRegex(RuntimeError, 'Please look up dimensions by name*'):
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t.size(None)
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with self.assertRaisesRegex(RuntimeError, 'Name \'channels\' not found in '):
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t.size('channels')
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with self.assertRaisesRegex(RuntimeError, 'Name \'N\' not found in '):
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torch.empty(2, 3, 4).size('N')
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def test_stride(self):
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t = torch.empty(2, 3, 5, names=('N', None, 'C'))
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self.assertEqual(t.stride('N'), 3 * 5)
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self.assertEqual(t.stride('C'), 1)
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with self.assertRaisesRegex(RuntimeError, 'Please look up dimensions by name'):
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t.stride(None)
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with self.assertRaisesRegex(RuntimeError, 'Name \'channels\' not found in '):
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t.stride('channels')
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with self.assertRaisesRegex(RuntimeError, 'Name \'N\' not found in '):
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torch.empty(2, 3, 4).stride('N')
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def test_info_smoke(self):
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# Smoke test for info functions / methods / attributes on named tensors.
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tensor = torch.empty(1, 1, names=('N', 'D'))
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tensor.device
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tensor.dtype
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tensor.get_device()
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tensor.is_complex()
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tensor.is_floating_point()
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tensor.is_nonzero()
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torch.is_same_size(tensor, tensor)
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torch.is_signed(tensor)
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tensor.layout
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tensor.numel()
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tensor.dim()
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tensor.element_size()
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tensor.is_contiguous()
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tensor.is_cuda
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tensor.is_leaf
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tensor.is_pinned()
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tensor.is_shared()
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tensor.is_sparse
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tensor.ndimension()
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tensor.nelement()
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tensor.shape
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tensor.size()
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tensor.size(1)
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tensor.storage()
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tensor.storage_offset()
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tensor.storage_type()
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tensor.stride()
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tensor.stride(1)
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tensor.data
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tensor.data_ptr()
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tensor.ndim
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tensor.item()
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tensor.type()
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def test_split_fns_propagates_names(self):
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fns = [
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lambda x: x.split(1, 0),
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lambda x: x.split([1, 1], 1),
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lambda x: x.chunk(2, 0),
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]
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for device in torch.testing.get_all_device_types():
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orig_tensor = torch.empty(2, 2, names=('N', 'D'), device=device)
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for fn in fns:
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splits = fn(orig_tensor)
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for split in splits:
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self.assertEqual(split.names, orig_tensor.names)
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def test_binary_ops(self):
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def test_basic(op):
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a = torch.empty(2, 3, names=('N', 'C'))
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b = torch.empty(2, 3, names=('C', 'N'))
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c = torch.empty(3, names=('C',))
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d = torch.empty(3, names=('W',))
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self.assertEqual(op(a, a).names, ('N', 'C'))
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self.assertEqual(op(a, c).names, ('N', 'C'))
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with self.assertRaisesRegex(RuntimeError, "do not match"):
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op(a, d)
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with self.assertRaisesRegex(RuntimeError, "do not match"):
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op(a, b)
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def test_wildcard(op):
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a = torch.empty(2, 3, names=('N', 'C'))
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c = torch.empty(2, 3, names=(None, 'C'))
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self.assertEqual(op(a, c).names, ('N', 'C'))
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b = torch.empty(2, 3)
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self.assertEqual(op(a, b).names, ('N', 'C'))
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d = torch.empty(2, 3, names=('C', None))
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with self.assertRaisesRegex(RuntimeError, "misaligned"):
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op(d, c)
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def method(name, *args, **kwargs):
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return [Function(name, lambda a, b: getattr(a, name)(b, *args, **kwargs))]
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def out_function(name, *args, **kwargs):
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out_fn = getattr(torch, name)
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def fn(a, b):
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result = a.new_empty([0])
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out_fn(a, b, *args, out=result, **kwargs)
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return result
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return [Function(name, fn)]
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def fn_method_and_inplace(name, *args, **kwargs):
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return (
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method(name, *args, **kwargs) +
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method(name + '_', *args, **kwargs) +
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out_function(name, *args, **kwargs)
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)
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tests = [
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fn_method_and_inplace('add'),
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fn_method_and_inplace('div'),
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fn_method_and_inplace('mul'),
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fn_method_and_inplace('sub'),
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method('copy_'),
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]
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tests = flatten(tests)
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for _, op in tests:
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test_basic(op)
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test_wildcard(op)
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def test_unary_propagate_names_fns(self):
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def _test(testcase, names=('N', 'D'), device='cpu'):
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sizes = [2] * len(names)
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tensor = torch.empty(sizes, names=names, device=device)
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out = testcase.lambd(tensor)
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self.assertEqual(out.names, tensor.names,
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message=testcase.name)
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def fn(name, *args, **kwargs):
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return [Function(name, lambda t: getattr(torch, name)(t, *args, **kwargs))]
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def method(name, *args, **kwargs):
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return [Function(name, lambda t: getattr(t, name)(*args, **kwargs))]
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def out_function(name, *args, **kwargs):
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out_fn = getattr(torch, name)
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def fn(tensor):
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result = tensor.new_empty([0])
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out_fn(tensor, *args, out=result, **kwargs)
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return result
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return [Function(name + '_out', fn)]
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def fn_method_and_inplace(name, *args, **kwargs):
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return (
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method(name, *args, **kwargs) +
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method(name + '_', *args, **kwargs) +
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out_function(name, *args, **kwargs)
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)
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# All of these operate on 2x2 tensors.
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tests = [
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# unary pointwise
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fn_method_and_inplace('abs'),
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fn_method_and_inplace('acos'),
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fn_method_and_inplace('asin'),
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fn_method_and_inplace('atan'),
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fn_method_and_inplace('ceil'),
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fn_method_and_inplace('clamp', -1, 1),
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fn_method_and_inplace('clamp_min', -2),
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fn_method_and_inplace('clamp_max', 2),
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method('clone'),
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method('cauchy_'),
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fn_method_and_inplace('cos'),
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fn_method_and_inplace('cosh'),
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fn_method_and_inplace('digamma'),
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fn_method_and_inplace('erf'),
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fn_method_and_inplace('erfc'),
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fn_method_and_inplace('erfinv'),
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fn_method_and_inplace('exp'),
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fn_method_and_inplace('expm1'),
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method('exponential_'),
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fn_method_and_inplace('floor'),
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fn_method_and_inplace('frac'),
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method('geometric_', p=0.5),
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fn_method_and_inplace('lgamma'),
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fn_method_and_inplace('log'),
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fn_method_and_inplace('log10'),
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fn_method_and_inplace('log1p'),
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fn_method_and_inplace('log2'),
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method('log_normal_'),
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fn_method_and_inplace('neg'),
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method('normal_'),
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[Function('polygamma', lambda t: torch.polygamma(1, t))],
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method('polygamma_', 1),
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fn_method_and_inplace('reciprocal'),
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method('random_', 0, 1),
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method('random_', 1),
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method('random_'),
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fn_method_and_inplace('round'),
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fn_method_and_inplace('rsqrt'),
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fn_method_and_inplace('sigmoid'),
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fn_method_and_inplace('sign'),
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fn_method_and_inplace('sin'),
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fn_method_and_inplace('sinh'),
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fn_method_and_inplace('sqrt'),
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fn_method_and_inplace('tan'),
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fn_method_and_inplace('tanh'),
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fn_method_and_inplace('trunc'),
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method('uniform_'),
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method('zero_'),
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method('fill_', 1),
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method('fill_', torch.tensor(3.14)),
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# conversions
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method('to', dtype=torch.long),
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method('to', device='cpu'),
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method('to', torch.empty([])),
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method('bool'),
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method('byte'),
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method('char'),
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method('cpu'),
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method('double'),
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method('float'),
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method('long'),
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method('half'),
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method('int'),
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method('short'),
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method('type', dtype=torch.long),
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# views
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method('narrow', 0, 0, 1),
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]
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tests = flatten(tests)
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for testcase, device in itertools.product(tests, torch.testing.get_all_device_types()):
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_test(testcase, device=device)
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def test_reduction_fns(self):
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def test_simple_reduce(op_name, device):
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t = torch.empty(2, 3, 5, names=('N', 'C', 'L'), device=device)
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op = getattr(torch.Tensor, op_name)
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self.assertEqual(op(t, 1).names, ['N', 'L'])
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self.assertEqual(op(t, 'C').names, ['N', 'L'])
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with self.assertRaisesRegex(RuntimeError, 'Please look up dimensions by name'):
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op(t, None)
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with self.assertRaisesRegex(RuntimeError, 'Name \'H\' not found'):
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op(t, 'H')
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def test_complete_reduce(op_name, device):
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t = torch.empty(2, 3, 5, names=('N', 'C', 'L'), device=device)
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op = getattr(torch.Tensor, op_name)
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self.assertEqual(op(t).names, [])
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def test_multidim_reduce(op_name, device):
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t = torch.empty(2, 3, 5, names=('N', 'C', 'L'), device=device)
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op = getattr(torch.Tensor, op_name)
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self.assertEqual(op(t, [1, 2]).names, ['N'])
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self.assertEqual(op(t, ['C', 'L']).names, ['N'])
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with self.assertRaisesRegex(RuntimeError, 'Please look up dimensions by name'):
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op(t, [None, 'C'])
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def test_out_variant(op_name, device):
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t = torch.empty(2, 3, 5, names=('N', 'C', 'L'), device=device)
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out = t.new_empty([0])
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getattr(torch, op_name)(t, 'C', out=out)
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self.assertEqual(out.names, ['N', 'L'])
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def test_keepdim(op_name, device):
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t = torch.empty(2, 3, 5, names=('N', 'C', 'L'), device=device)
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op = getattr(torch.Tensor, op_name)
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self.assertEqual(op(t, 'C', keepdim=True).names, ['N', 'C', 'L'])
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Case = namedtuple('Case', [
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'op_name',
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'supports_complete_reduce',
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'supports_multidim_reduce',
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])
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tests = [
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Case(op_name='sum', supports_complete_reduce=True, supports_multidim_reduce=True),
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Case(op_name='prod', supports_complete_reduce=True, supports_multidim_reduce=False),
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]
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for testcase, device in itertools.product(tests, torch.testing.get_all_device_types()):
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op_name = testcase.op_name
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test_simple_reduce(op_name, device)
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test_keepdim(op_name, device)
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test_out_variant(op_name, device)
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if testcase.supports_complete_reduce:
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test_complete_reduce(op_name, device)
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if testcase.supports_multidim_reduce:
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test_multidim_reduce(op_name, device)
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def test_using_seen_interned_string_doesnt_bump_refcount(self):
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def see_name():
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seen_name = 'N'
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pass_name_to_python_arg_parser(seen_name)
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see_name()
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seen_name = 'N'
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old_refcnt = sys.getrefcount(seen_name)
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pass_name_to_python_arg_parser(seen_name)
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new_refcnt = sys.getrefcount(seen_name)
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self.assertEqual(new_refcnt, old_refcnt)
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def test_using_unseen_interned_string_bumps_refcount_permanently(self):
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# Please don't use this as a name in a different test.
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unseen_name = 'abcdefghi'
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old_refcnt = sys.getrefcount(unseen_name)
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pass_name_to_python_arg_parser(unseen_name)
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new_refcnt = sys.getrefcount(unseen_name)
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self.assertEqual(new_refcnt, old_refcnt + 1)
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def test_using_unseen_uninterned_string_refcounts(self):
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# Please don't use this as a name in a different test.
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# non-compile-time constants are not interned
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unseen_name = ''.join(['abc', 'def', 'ghi', 'jkl'])
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interned_unseen_name = 'abcdefghijkl'
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self.assertFalse(unseen_name is interned_unseen_name)
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old_uninterned_refcnt = sys.getrefcount(unseen_name)
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old_interned_refcnt = sys.getrefcount(interned_unseen_name)
|
|
|
|
pass_name_to_python_arg_parser(unseen_name)
|
|
|
|
new_uninterned_refcnt = sys.getrefcount(unseen_name)
|
|
new_interned_refcnt = sys.getrefcount(interned_unseen_name)
|
|
|
|
# Internally, PyTorch should not hold a reference to the uninterned string
|
|
self.assertEqual(new_uninterned_refcnt, old_uninterned_refcnt)
|
|
|
|
# Instead, we should hold a new reference to the interned version.
|
|
self.assertEqual(new_interned_refcnt, old_interned_refcnt + 1)
|
|
|
|
def _test_select(self, device):
|
|
x = torch.empty(2, 3, 4, 5, names=('N', 'C', 'H', 'W'), device=device)
|
|
y = x.select(1, 1)
|
|
self.assertEqual(y.names, ('N', 'H', 'W'))
|
|
|
|
y = x.select('C', 1)
|
|
self.assertEqual(y.names, ('N', 'H', 'W'))
|
|
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, 'Please look up dimensions by name'):
|
|
y = x.select(None, 1)
|
|
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, 'Name \'C.in\' not found in'):
|
|
y = x.select('C.in', 1)
|
|
|
|
x = torch.empty(2, 3, 4, 5, names=('N', 'C.in', 'H', 'W'), device=device)
|
|
y = x.select('C', 1)
|
|
self.assertEqual(y.names, ('N', 'H', 'W'))
|
|
|
|
x = torch.empty(2, 3, 4, 5, names=('C.out', 'C.in', 'H', 'W'), device=device)
|
|
y = x.select('C.in', 1)
|
|
self.assertEqual(y.names, ('C.out', 'H', 'W'))
|
|
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, 'Name \'C\' could refer to multiple dimensions'):
|
|
y = x.select('C', 1)
|
|
|
|
|
|
def test_select(self):
|
|
self._test_select('cpu')
|
|
|
|
@unittest.skipIf(not TEST_CUDA, 'no CUDA')
|
|
def test_select_cuda(self):
|
|
self._test_select('cuda')
|
|
|
|
def _test_as_strided(self, device):
|
|
x = torch.empty(2, 3, 4, 5, names=('N', 'C', 'H', 'W'), device=device)
|
|
y = x.as_strided([2 * 3 * 4 * 5], [1])
|
|
self.assertEqual(y.names, (None,))
|
|
|
|
def test_as_strided(self):
|
|
self._test_as_strided('cpu')
|
|
|
|
@unittest.skipIf(not TEST_CUDA, 'no CUDA')
|
|
def test_as_strided_cuda(self):
|
|
self._test_as_strided('cuda')
|
|
|
|
# Disable all tests if named tensor is not available.
|
|
for attr in dir(TestNamedTensor):
|
|
if attr.startswith('test_'):
|
|
new_test = skipIfNamedTensorDisabled(getattr(TestNamedTensor, attr))
|
|
setattr(TestNamedTensor, attr, new_test)
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|