已合并
【API一致性任务】test: add consistency validation cases for torch.BoolStorage / torch_npu.npu.BoolStorage (#2955) #42273
luoxiaoyan2024创建于 7月21日
【API一致性任务】test: add consistency validation cases for torch.BoolStorage / torch_npu.npu.BoolStorage (#2955) #42273
已合并
共 1 个文件变更+149-0
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| 1 | +# Copyright (c) 2026 Huawei Technologies Co., Ltd | ||
| 2 | +# All rights reserved. | ||
| 3 | +# | ||
| 4 | +# Licensed under the BSD 3-Clause License (the "License"); | ||
| 5 | +# you may not use this file except in compliance with the License. | ||
| 6 | +# You may obtain a copy of the License at | ||
| 7 | +# | ||
| 8 | +# https://opensource.org/licenses/BSD-3-Clause | ||
| 9 | +# | ||
| 10 | +# Unless required by applicable law or agreed to in writing, software | ||
| 11 | +# distributed under the License is distributed on an "AS IS" BASIS, | ||
| 12 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| 13 | +# See the License for the specific language governing permissions and | ||
| 14 | +# limitations under the License. | ||
| 15 | + | ||
| 16 | +""" | ||
| 17 | +Add consistency validation cases for torch.BoolStorage / torch_npu.npu.BoolStorage | ||
| 18 | +on NPU (#2955). | ||
| 19 | + | ||
| 20 | +The PyTorch community lacks a dedicated NPU consistency test for the BoolStorage | ||
| 21 | +storage class. Upstream only checks ``torch.BoolStorage().element_size()`` in | ||
| 22 | +``test_torch.py::test_element_size`` (a CPU-only property check), and the existing | ||
| 23 | +``test/npu/test_storage.py`` exercises storage via tensor ``.storage()`` rather than | ||
| 24 | +the BoolStorage class itself. This file validates the BoolStorage class behavior | ||
| 25 | +(construction from size / sequence, empty object, element_size, indexing, out-of- | ||
| 26 | +bounds access, fill_, tolist, roundtrip) on both CPU and NPU, and asserts an NPU | ||
| 27 | +bool tensor's ``.storage()`` is a ``torch_npu.npu.BoolStorage`` instance with the | ||
| 28 | +expected dtype and data consistency. | ||
| 29 | +""" | ||
| 30 | + | ||
| 31 | +import torch | ||
| 32 | +from torch.testing._internal.common_utils import TestCase, run_tests | ||
| 33 | +import torch_npu.npu as npu | ||
| 34 | + | ||
| 35 | + | ||
| 36 | +class TestBoolStorage(TestCase): | ||
| 37 | + | ||
| 38 | + # ---------------- CPU: torch.BoolStorage ---------------- | ||
| 39 | + def test_cpu_bool_storage_basic(self): | ||
| 40 | + # Core CPU BoolStorage behavior (construction by size, indexing, fill_). | ||
| 41 | + s = torch.BoolStorage(3) | ||
| 42 | + s[0] = True | ||
| 43 | + s[1] = False | ||
| 44 | + s[2] = True | ||
| 45 | + self.assertEqual(s.tolist(), [True, False, True]) | ||
| 46 | + self.assertEqual(s.size(), 3) | ||
| 47 | + self.assertEqual(s.element_size(), 1) | ||
| 48 | + self.assertEqual(torch.BoolStorage().element_size(), 1) | ||
| 49 | + | ||
| 50 | + # fill_ then tolist | ||
| 51 | + s.fill_(False) | ||
| 52 | + self.assertEqual(s.tolist(), [False, False, False]) | ||
| 53 | + | ||
| 54 | + # roundtrip through torch.BoolTensor (CPU storage -> CPU tensor) | ||
| 55 | + t = torch.BoolTensor(s) | ||
| 56 | + self.assertEqual(t.dtype, torch.bool) | ||
| 57 | + self.assertEqual(t.tolist(), [False, False, False]) | ||
| 58 | + | ||
| 59 | + def test_cpu_bool_storage_from_sequence(self): | ||
| 60 | + # Minimal sequence construction (single element). | ||
| 61 | + s1 = torch.BoolStorage([True]) | ||
| 62 | + self.assertEqual(s1.tolist(), [True]) | ||
| 63 | + self.assertEqual(s1.size(), 1) | ||
| 64 | + | ||
| 65 | + # Sequence construction (multiple elements). | ||
| 66 | + s2 = torch.BoolStorage([True, False, True]) | ||
| 67 | + self.assertEqual(s2.tolist(), [True, False, True]) | ||
| 68 | + self.assertEqual(s2.size(), 3) | ||
| 69 | + | ||
| 70 | + def test_cpu_bool_storage_empty(self): | ||
| 71 | + # Empty object construction. | ||
| 72 | + s = torch.BoolStorage() | ||
| 73 | + self.assertEqual(s.size(), 0) | ||
| 74 | + self.assertEqual(s.tolist(), []) | ||
| 75 | + | ||
| 76 | + # Explicit zero-size construction. | ||
| 77 | + s0 = torch.BoolStorage(0) | ||
| 78 | + self.assertEqual(s0.size(), 0) | ||
| 79 | + self.assertEqual(s0.tolist(), []) | ||
| 80 | + | ||
| 81 | + def test_cpu_bool_storage_out_of_bounds(self): | ||
| 82 | + s = torch.BoolStorage(3) | ||
| 83 | + # Read beyond the end raises IndexError. | ||
| 84 | + with self.assertRaises(IndexError): | ||
| 85 | + _ = s[3] | ||
| 86 | + # Write beyond the end raises IndexError. | ||
| 87 | + with self.assertRaises(IndexError): | ||
| 88 | + s[3] = True | ||
| 89 | + | ||
| 90 | + # ---------------- NPU: torch_npu.npu.BoolStorage ---------------- | ||
| 91 | + def test_npu_bool_storage_basic(self): | ||
| 92 | + if not torch.npu.is_available(): | ||
| 93 | + self.skipTest("NPU not available") | ||
| 94 | + | ||
| 95 | + self.assertTrue(hasattr(npu, "BoolStorage")) | ||
| 96 | + ns = npu.BoolStorage(4) | ||
| 97 | + ns[0] = True | ||
| 98 | + ns[1] = False | ||
| 99 | + ns[2] = True | ||
| 100 | + ns[3] = False | ||
| 101 | + self.assertEqual(ns.tolist(), [True, False, True, False]) | ||
| 102 | + self.assertEqual(ns.dtype, torch.bool) | ||
| 103 | + self.assertEqual(ns.element_size(), 1) | ||
| 104 | + self.assertEqual(ns.size(), 4) | ||
| 105 | + | ||
| 106 | + ns.fill_(True) | ||
| 107 | + self.assertEqual(ns.tolist(), [True, True, True, True]) | ||
| 108 | + | ||
| 109 | + def test_npu_bool_storage_from_sequence(self): | ||
| 110 | + if not torch.npu.is_available(): | ||
| 111 | + self.skipTest("NPU not available") | ||
| 112 | + | ||
| 113 | + # Minimal sequence construction on NPU. | ||
| 114 | + ns = npu.BoolStorage([True, False]) | ||
| 115 | + self.assertEqual(ns.tolist(), [True, False]) | ||
| 116 | + self.assertEqual(ns.size(), 2) | ||
| 117 | + | ||
| 118 | + def test_npu_bool_storage_empty(self): | ||
| 119 | + if not torch.npu.is_available(): | ||
| 120 | + self.skipTest("NPU not available") | ||
| 121 | + | ||
| 122 | + ns = npu.BoolStorage() | ||
| 123 | + self.assertEqual(ns.size(), 0) | ||
| 124 | + self.assertEqual(ns.tolist(), []) | ||
| 125 | + | ||
| 126 | + def test_npu_bool_storage_out_of_bounds(self): | ||
| 127 | + if not torch.npu.is_available(): | ||
| 128 | + self.skipTest("NPU not available") | ||
| 129 | + | ||
| 130 | + ns = npu.BoolStorage(2) | ||
| 131 | + with self.assertRaises(IndexError): | ||
| 132 | + _ = ns[2] | ||
| 133 | + with self.assertRaises(IndexError): | ||
| 134 | + ns[2] = True | ||
| 135 | + | ||
| 136 | + def test_npu_tensor_storage_consistency(self): | ||
| 137 | + if not torch.npu.is_available(): | ||
| 138 | + self.skipTest("NPU not available") | ||
| 139 | + | ||
| 140 | + # An NPU bool tensor's storage is a torch_npu.npu.BoolStorage instance, | ||
| 141 | + # with matching dtype and data consistency. | ||
| 142 | + x = torch.tensor([True, False, True], device="npu") | ||
| 143 | + self.assertIsInstance(x.storage(), npu.BoolStorage) | ||
| 144 | + self.assertEqual(x.storage().dtype, torch.bool) | ||
| 145 | + self.assertEqual(x.storage().tolist(), [True, False, True]) | ||
| 146 | + | ||
| 147 | + | ||
| 148 | +if __name__ == "__main__": | ||
| 149 | + run_tests() | ||
【openlibing.ci】检测到当前PR中存在代码检查告警抑制 2 处，详情见下表，请Committer检视合理性。 / Detected 2 code check alert suppression(s) in this PR, see table below. Committers please review.
import torch_npu # noqa: F401import torch_npu.testing # noqa: F401