已合并
test: add ShapeEnv API alignment tests for symbolic_shapes #38160
liuhaodong-2026创建于 6月10日
test: add ShapeEnv API alignment tests for symbolic_shapes #38160
已合并
共 1 个文件变更+102-0
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| 1 | +""" | ||
| 2 | +Add validation cases for torch.fx.experimental.symbolic_shapes.ShapeEnv APIs on NPU: | ||
| 3 | +1. PyTorch community lacks sufficient and direct API validations for these ShapeEnv | ||
| 4 | + symbolic expression methods, so this file is added. | ||
| 5 | +2. This file validates ShapeEnv.deserialize_symexpr, ShapeEnv.evaluate_symexpr, | ||
| 6 | + ShapeEnv.evaluate_guards_expression, ShapeEnv.evaluate_guards_for_args, | ||
| 7 | + ShapeEnv.evaluate_sym_node (extendable). | ||
| 8 | +""" | ||
| 9 | + | ||
| 10 | +import torch | ||
| 11 | +from torch._dynamo.source import LocalSource, TensorProperty, TensorPropertySource | ||
| 12 | +from torch._subclasses.fake_tensor import FakeTensorMode | ||
| 13 | +from torch.fx.experimental.symbolic_shapes import DimDynamic, ShapeEnv | ||
| 14 | +from torch.testing._internal.common_utils import run_tests, TestCase | ||
| 15 | + | ||
| 16 | +device_type = acc.type if (acc := torch.accelerator.current_accelerator()) else "cpu" | ||
| 17 | + | ||
| 18 | + | ||
| 19 | +class TestShapeEnvAPIs(TestCase): | ||
| 20 | + | ||
| 21 | + def test_deserialize_symexpr_constant(self): | ||
| 22 | + env = ShapeEnv() | ||
| 23 | + self.assertEqual(int(env.deserialize_symexpr("5")), 5) | ||
| 24 | + | ||
| 25 | + def test_deserialize_symexpr_expression(self): | ||
| 26 | + env = ShapeEnv() | ||
| 27 | + self.assertEqual(int(env.deserialize_symexpr("2 + 3")), 5) | ||
| 28 | + | ||
| 29 | + def test_deserialize_symexpr_with_symbol(self): | ||
| 30 | + env = ShapeEnv() | ||
| 31 | + source = TensorPropertySource(LocalSource("x"), TensorProperty.SIZE, 0) | ||
| 32 | + s = env.create_symbol(5, source=source, dynamic_dim=DimDynamic.DYNAMIC) | ||
| 33 | + self.assertEqual(str(env.deserialize_symexpr(str(s))), str(s)) | ||
| 34 | + | ||
| 35 | + def test_evaluate_symexpr_constant(self): | ||
| 36 | + env = ShapeEnv() | ||
| 37 | + self.assertEqual(env.evaluate_symexpr("10"), 10) | ||
| 38 | + | ||
| 39 | + def test_evaluate_symexpr_addition(self): | ||
| 40 | + env = ShapeEnv() | ||
| 41 | + self.assertEqual(env.evaluate_symexpr("3 + 4"), 7) | ||
| 42 | + | ||
| 43 | + def test_evaluate_symexpr_multiplication(self): | ||
| 44 | + env = ShapeEnv() | ||
| 45 | + self.assertEqual(env.evaluate_symexpr("6 * 7"), 42) | ||
| 46 | + | ||
| 47 | + def test_evaluate_guards_expression_true(self): | ||
| 48 | + env = ShapeEnv() | ||
| 49 | + self.assertTrue(env.evaluate_guards_expression("True", [])) | ||
| 50 | + | ||
| 51 | + def test_evaluate_guards_expression_false(self): | ||
| 52 | + env = ShapeEnv() | ||
| 53 | + self.assertFalse(env.evaluate_guards_expression("False", [])) | ||
| 54 | + | ||
| 55 | + def test_evaluate_guards_expression_returns_bool(self): | ||
| 56 | + env = ShapeEnv() | ||
| 57 | + self.assertIsInstance(env.evaluate_guards_expression("True", []), bool) | ||
| 58 | + | ||
| 59 | + def test_evaluate_guards_for_args_basic(self): | ||
| 60 | + env = ShapeEnv() | ||
| 61 | + fake_mode = FakeTensorMode(shape_env=env, allow_non_fake_inputs=True) | ||
| 62 | + with fake_mode: | ||
| 63 | + placeholder = torch.empty(3, 4) | ||
| 64 | + real_tensor = torch.randn(3, 4).to(device_type) | ||
| 65 | + result = env.evaluate_guards_for_args([placeholder], [real_tensor]) | ||
| 66 | + self.assertIsInstance(result, bool) | ||
| 67 | + | ||
| 68 | + def test_evaluate_guards_for_args_multi_placeholders(self): | ||
| 69 | + env = ShapeEnv() | ||
| 70 | + fake_mode = FakeTensorMode(shape_env=env, allow_non_fake_inputs=True) | ||
| 71 | + with fake_mode: | ||
| 72 | + p0 = torch.empty(3, 4) | ||
| 73 | + p1 = torch.empty(5, 6) | ||
| 74 | + real0 = torch.randn(3, 4).to(device_type) | ||
| 75 | + real1 = torch.randn(5, 6).to(device_type) | ||
| 76 | + result = env.evaluate_guards_for_args([p0, p1], [real0, real1]) | ||
| 77 | + self.assertIsInstance(result, bool) | ||
| 78 | + | ||
| 79 | + def test_evaluate_sym_node_basic(self): | ||
| 80 | + env = ShapeEnv() | ||
| 81 | + source = TensorPropertySource(LocalSource("x"), TensorProperty.SIZE, 0) | ||
| 82 | + s = env.create_symbol(5, source=source, dynamic_dim=DimDynamic.DYNAMIC) | ||
| 83 | + sym = env.create_symintnode(s, hint=5, source=source) | ||
| 84 | + self.assertEqual(env.evaluate_sym_node(sym.node, size_oblivious=False), 5) | ||
| 85 | + | ||
| 86 | + def test_evaluate_sym_node_size_oblivious(self): | ||
| 87 | + env = ShapeEnv() | ||
| 88 | + source = TensorPropertySource(LocalSource("x"), TensorProperty.SIZE, 0) | ||
| 89 | + s = env.create_symbol(5, source=source, dynamic_dim=DimDynamic.DYNAMIC) | ||
| 90 | + sym = env.create_symintnode(s, hint=5, source=source) | ||
| 91 | + self.assertEqual(env.evaluate_sym_node(sym.node, size_oblivious=True), 5) | ||
| 92 | + | ||
| 93 | + def test_evaluate_sym_node_different_hint(self): | ||
| 94 | + env = ShapeEnv() | ||
| 95 | + source = TensorPropertySource(LocalSource("y"), TensorProperty.SIZE, 0) | ||
| 96 | + s = env.create_symbol(7, source=source, dynamic_dim=DimDynamic.DYNAMIC) | ||
| 97 | + sym = env.create_symintnode(s, hint=7, source=source) | ||
| 98 | + self.assertEqual(env.evaluate_sym_node(sym.node, size_oblivious=False), 7) | ||
| 99 | + | ||
| 100 | + | ||
| 101 | +if __name__ == "__main__": | ||
| 102 | + run_tests() | ||