已合并
test(fx): add graph_module internal API alignment test cases [v2.12.0] #43640
zkx创建于 23 天前
test(fx): add graph_module internal API alignment test cases [v2.12.0] #43640
已合并
共 1 个文件变更+231-0
| @@ -0,0 +1,231 @@ | |||
| 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 validation cases for torch.fx.graph_module APIs on NPU: | ||
| 18 | +1. PyTorch community lacks dedicated test cases for internal | ||
| 19 | + graph_module APIs, so this file is added. | ||
| 20 | +2. This file validates _exec_with_source, _forward_from_src, | ||
| 21 | + _CodeOnlyModule, _copy_attr, and _WrappedCall. | ||
| 22 | +""" | ||
| 23 | + | ||
| 24 | +import torch | ||
| 25 | + | ||
| 26 | +from torch.testing._internal.common_utils import TestCase, run_tests | ||
| 27 | +from torch.fx.graph_module import ( | ||
| 28 | + _exec_with_source, | ||
| 29 | + _forward_from_src, | ||
| 30 | + _CodeOnlyModule, | ||
| 31 | + _copy_attr, | ||
| 32 | + _WrappedCall, | ||
| 33 | + _loader, | ||
| 34 | +) | ||
| 35 | + | ||
| 36 | + | ||
| 37 | +device_type = acc.type if (acc := torch.accelerator.current_accelerator()) else "cpu" | ||
| 38 | + | ||
| 39 | + | ||
| 40 | +class TestExecWithSource(TestCase): | ||
| 41 | + """Test _exec_with_source function.""" | ||
| 42 | + | ||
| 43 | + def test_exec_with_source_basic(self): | ||
| 44 | + src = "x = 42" | ||
| 45 | + g = {} | ||
| 46 | + _exec_with_source(src, g) | ||
| 47 | + self.assertEqual(g["x"], 42) | ||
| 48 | + | ||
| 49 | + def test_exec_with_source_multiple(self): | ||
| 50 | + src = "a = 1\nb = 2\nc = a + b" | ||
| 51 | + g = {} | ||
| 52 | + _exec_with_source(src, g) | ||
| 53 | + self.assertEqual(g["a"], 1) | ||
| 54 | + self.assertEqual(g["b"], 2) | ||
| 55 | + self.assertEqual(g["c"], 3) | ||
| 56 | + | ||
| 57 | + def test_exec_with_source_invalid_syntax(self): | ||
| 58 | + src = "x = " | ||
| 59 | + g = {} | ||
| 60 | + with self.assertRaises(SyntaxError): | ||
| 61 | + _exec_with_source(src, g) | ||
| 62 | + | ||
| 63 | + def test_exec_with_source_invalid_globals(self): | ||
| 64 | + src = "x = 42" | ||
| 65 | + with self.assertRaises((AttributeError, TypeError)): | ||
| 66 | + _exec_with_source(src, None) | ||
| 67 | + | ||
| 68 | + def test_exec_with_source_co_fields(self): | ||
| 69 | + src = "x = 42" | ||
| 70 | + g = {} | ||
| 71 | + co_fields = {"co_filename": "test_mod.py", "co_firstlineno": 10, "co_name": "test_exec"} | ||
| 72 | + _exec_with_source(src, g, co_fields) | ||
| 73 | + self.assertEqual(g["x"], 42) | ||
| 74 | + cache_keys = list(_loader.eval_cache.keys()) | ||
| 75 | + self.assertTrue(any("test_mod.py:10 in test_exec" in k for k in cache_keys)) | ||
| 76 | + | ||
| 77 | + | ||
| 78 | +class TestForwardFromSrc(TestCase): | ||
| 79 | + """Test _forward_from_src function.""" | ||
| 80 | + | ||
| 81 | + def test_forward_from_src_basic(self): | ||
| 82 | + src = ( | ||
| 83 | + "import torch\n" | ||
| 84 | + "def forward(self, x):\n" | ||
| 85 | + " return x + 1\n" | ||
| 86 | + ) | ||
| 87 | + fn = _forward_from_src(src, {}) | ||
| 88 | + x = torch.tensor(1.0).to(device_type) | ||
| 89 | + result = fn(None, x) | ||
| 90 | + self.assertEqual(result, torch.tensor(2.0).to(device_type)) | ||
| 91 | + | ||
| 92 | + def test_forward_from_src_with_imports(self): | ||
| 93 | + src = ( | ||
| 94 | + "import torch\n" | ||
| 95 | + "def forward(self, x):\n" | ||
| 96 | + " return torch.relu(x)\n" | ||
| 97 | + ) | ||
| 98 | + fn = _forward_from_src(src, {}) | ||
| 99 | + t = torch.tensor([-1.0, 0.0, 1.0]).to(device_type) | ||
| 100 | + result = fn(None, t) | ||
| 101 | + expected = torch.tensor([0.0, 0.0, 1.0]).to(device_type) | ||
| 102 | + self.assertEqual(result, expected) | ||
| 103 | + | ||
| 104 | + def test_forward_from_src_missing_forward(self): | ||
| 105 | + src = "x = 1" | ||
| 106 | + with self.assertRaises((KeyError, SyntaxError)): | ||
| 107 | + _forward_from_src(src, {}) | ||
| 108 | + | ||
| 109 | + def test_forward_from_src_co_fields(self): | ||
| 110 | + src = ( | ||
| 111 | + "def forward(self, x):\n" | ||
| 112 | + " return x + 1\n" | ||
| 113 | + ) | ||
| 114 | + co_fields = {"co_filename": "fwd_mod.py", "co_firstlineno": 5, "co_name": "forward_src"} | ||
| 115 | + fn = _forward_from_src(src, {}, co_fields) | ||
| 116 | + x = torch.tensor(1.0).to(device_type) | ||
| 117 | + result = fn(None, x) | ||
| 118 | + self.assertEqual(result, torch.tensor(2.0).to(device_type)) | ||
| 119 | + cache_keys = list(_loader.eval_cache.keys()) | ||
| 120 | + self.assertTrue(any("fwd_mod.py:5 in forward_src" in k for k in cache_keys)) | ||
| 121 | + | ||
| 122 | + | ||
| 123 | +class TestCodeOnlyModule(TestCase): | ||
| 124 | + """Test _CodeOnlyModule class.""" | ||
| 125 | + | ||
| 126 | + def test_code_only_module_basic(self): | ||
| 127 | + body = {"a": 1, "b": "test"} | ||
| 128 | + m = _CodeOnlyModule(body) | ||
| 129 | + self.assertEqual(m.a, 1) | ||
| 130 | + self.assertEqual(m.b, "test") | ||
| 131 | + | ||
| 132 | + def test_code_only_module_empty(self): | ||
| 133 | + m = _CodeOnlyModule({}) | ||
| 134 | + self.assertIsInstance(m, torch.nn.Module) | ||
| 135 | + | ||
| 136 | + | ||
| 137 | +class TestCopyAttr(TestCase): | ||
| 138 | + """Test _copy_attr function.""" | ||
| 139 | + | ||
| 140 | + def test_copy_attr_tensor(self): | ||
| 141 | + src_mod = torch.nn.Module() | ||
| 142 | + dst_mod = torch.nn.Module() | ||
| 143 | + src_mod.register_buffer("weight", torch.ones(3, 4).to(device_type)) | ||
| 144 | + _copy_attr(src_mod, dst_mod, "weight") | ||
| 145 | + self.assertTrue(hasattr(dst_mod, "weight")) | ||
| 146 | + self.assertEqual(dst_mod.weight, torch.ones(3, 4).to(device_type)) | ||
| 147 | + | ||
| 148 | + def test_copy_attr_parameter(self): | ||
| 149 | + src_mod = torch.nn.Module() | ||
| 150 | + dst_mod = torch.nn.Module() | ||
| 151 | + src_mod.register_parameter( | ||
| 152 | + "param", torch.nn.Parameter(torch.zeros(2, 2).to(device_type))) | ||
| 153 | + _copy_attr(src_mod, dst_mod, "param") | ||
| 154 | + self.assertTrue(hasattr(dst_mod, "param")) | ||
| 155 | + self.assertEqual(dst_mod.param, torch.zeros(2, 2).to(device_type)) | ||
| 156 | + | ||
| 157 | + def test_copy_attr_nested(self): | ||
| 158 | + src_mod = torch.nn.Module() | ||
| 159 | + dst_mod = torch.nn.Module() | ||
| 160 | + child = torch.nn.Module() | ||
| 161 | + child.register_buffer("buf", torch.ones(2).to(device_type)) | ||
| 162 | + src_mod.add_module("child", child) | ||
| 163 | + _copy_attr(src_mod, dst_mod, "child.buf") | ||
| 164 | + self.assertTrue(hasattr(dst_mod.child, "buf")) | ||
| 165 | + self.assertEqual(dst_mod.child.buf, torch.ones(2).to(device_type)) | ||
| 166 | + | ||
| 167 | + def test_copy_attr_npu_tensor(self): | ||
| 168 | + src_mod = torch.nn.Module() | ||
| 169 | + dst_mod = torch.nn.Module() | ||
| 170 | + src_mod.register_buffer("npu_buf", torch.ones(3).to(device_type)) | ||
| 171 | + _copy_attr(src_mod, dst_mod, "npu_buf") | ||
| 172 | + self.assertTrue(hasattr(dst_mod, "npu_buf")) | ||
| 173 | + self.assertEqual(dst_mod.npu_buf.device.type, device_type) | ||
| 174 | + | ||
| 175 | + def test_copy_attr_missing_attribute(self): | ||
| 176 | + src_mod = torch.nn.Module() | ||
| 177 | + dst_mod = torch.nn.Module() | ||
| 178 | + with self.assertRaises(AttributeError): | ||
| 179 | + _copy_attr(src_mod, dst_mod, "nonexistent") | ||
| 180 | + | ||
| 181 | + def test_copy_attr_existing_parent(self): | ||
| 182 | + src_mod = torch.nn.Module() | ||
| 183 | + child = torch.nn.Module() | ||
| 184 | + child.register_buffer("buf", torch.ones(2).to(device_type)) | ||
| 185 | + src_mod.add_module("child", child) | ||
| 186 | + dst_mod = torch.nn.Module() | ||
| 187 | + dst_mod.add_module("child", torch.nn.Module()) | ||
| 188 | + _copy_attr(src_mod, dst_mod, "child.buf") | ||
| 189 | + self.assertTrue(hasattr(dst_mod.child, "buf")) | ||
| 190 | + self.assertEqual(dst_mod.child.buf, torch.ones(2).to(device_type)) | ||
| 191 | + | ||
| 192 | + | ||
| 193 | +class TestWrappedCall(TestCase): | ||
| 194 | + """Test _WrappedCall class.""" | ||
| 195 | + | ||
| 196 | + def test_wrapped_call_basic(self): | ||
| 197 | + class SimpleMod(torch.nn.Module): | ||
| 198 | + def forward(self, x): | ||
| 199 | + return x * 2 | ||
| 200 | + | ||
| 201 | + mod = SimpleMod() | ||
| 202 | + wrapped = _WrappedCall(SimpleMod, None) | ||
| 203 | + t = torch.tensor(3.0).to(device_type) | ||
| 204 | + result = wrapped(mod, t) | ||
| 205 | + self.assertEqual(result, torch.tensor(6.0).to(device_type)) | ||
| 206 | + | ||
| 207 | + def test_wrapped_call_with_cls_call(self): | ||
| 208 | + class SimpleMod(torch.nn.Module): | ||
| 209 | + def forward(self, x): | ||
| 210 | + return x + 1 | ||
| 211 | + | ||
| 212 | + mod = SimpleMod() | ||
| 213 | + wrapped = _WrappedCall(SimpleMod, SimpleMod.forward) | ||
| 214 | + t = torch.tensor(5.0).to(device_type) | ||
| 215 | + result = wrapped(mod, t) | ||
| 216 | + self.assertEqual(result, torch.tensor(6.0).to(device_type)) | ||
| 217 | + | ||
| 218 | + def test_wrapped_call_error_path(self): | ||
| 219 | + class BadMod(torch.nn.Module): | ||
| 220 | + def forward(self, x): | ||
| 221 | + return x.undefined_attr | ||
| 222 | + | ||
| 223 | + mod = BadMod() | ||
| 224 | + wrapped = _WrappedCall(BadMod, None) | ||
| 225 | + t = torch.tensor(1.0).to(device_type) | ||
| 226 | + with self.assertRaises(AttributeError): | ||
| 227 | + wrapped(mod, t) | ||
| 228 | + | ||
| 229 | + | ||
| 230 | +if __name__ == "__main__": | ||
| 231 | + run_tests() | ||