已合并
test(fx): add NPU tests for torch.fx graph APIs #35514
zhouzirui1234创建于 5月13日
test(fx): add NPU tests for torch.fx graph APIs #35514
已合并
共 1 个文件变更+109-0
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| 1 | +""" | ||
| 2 | +Add validation cases for torch.fx Graph APIs on NPU: | ||
| 3 | + | ||
| 4 | +1. This file adds lightweight direct validations for torch.fx Graph APIs on NPU. | ||
| 5 | +2. This file validates torch.fx.Graph.inserting_after, | ||
| 6 | + torch.fx.Graph.inserting_before, torch.fx.graph.magic_methods.format, | ||
| 7 | + and torch.fx.graph.inplace_methods.format. | ||
| 8 | +""" | ||
| 9 | + | ||
| 10 | +import operator | ||
| 11 | + | ||
| 12 | +import torch | ||
| 13 | +from torch.testing._internal.common_utils import run_tests, TestCase | ||
| 14 | +from torch.fx import GraphModule, symbolic_trace | ||
| 15 | + | ||
| 16 | +device_type = acc.type if (acc := torch.accelerator.current_accelerator()) else "cpu" | ||
| 17 | + | ||
| 18 | + | ||
| 19 | +class TestFxGraphApiNPU(TestCase): | ||
| 20 | + def test_graph_inserting_after(self): | ||
| 21 | + graph = torch.fx.Graph() | ||
| 22 | + x = graph.placeholder("x") | ||
| 23 | + neg = graph.call_function(torch.neg, (x,)) | ||
| 24 | + with graph.inserting_after(neg): | ||
| 25 | + relu = graph.call_function(torch.relu, (neg,)) | ||
| 26 | + graph.output(relu) | ||
| 27 | + graph.lint() | ||
| 28 | + | ||
| 29 | + nodes = list(graph.nodes) | ||
| 30 | + # Validate both insertion order and data dependency. | ||
| 31 | + self.assertEqual(nodes.index(relu), nodes.index(neg) + 1) | ||
| 32 | + self.assertEqual(neg.args, (x,)) | ||
| 33 | + self.assertEqual(relu.args, (neg,)) | ||
| 34 | + self.assertEqual(relu.target, torch.relu) | ||
| 35 | + | ||
| 36 | + gm = GraphModule(torch.nn.Module(), graph) | ||
| 37 | + input_tensor = torch.randn(2, 3).to(device_type) | ||
| 38 | + self.assertEqual(gm(input_tensor), torch.relu(torch.neg(input_tensor))) | ||
| 39 | + | ||
| 40 | + def test_graph_inserting_before(self): | ||
| 41 | + graph = torch.fx.Graph() | ||
| 42 | + x = graph.placeholder("x") | ||
| 43 | + relu = graph.call_function(torch.relu, (x,)) | ||
| 44 | + graph.output(relu) | ||
| 45 | + with graph.inserting_before(relu): | ||
| 46 | + neg = graph.call_function(torch.neg, (x,)) | ||
| 47 | + relu.args = (neg,) | ||
| 48 | + graph.lint() | ||
| 49 | + | ||
| 50 | + nodes = list(graph.nodes) | ||
| 51 | + # Validate both insertion order and rewritten input dependency. | ||
| 52 | + self.assertLess(nodes.index(neg), nodes.index(relu)) | ||
| 53 | + self.assertEqual(nodes.index(neg), nodes.index(relu) - 1) | ||
| 54 | + self.assertEqual(neg.args, (x,)) | ||
| 55 | + self.assertEqual(relu.args, (neg,)) | ||
| 56 | + self.assertEqual(neg.target, torch.neg) | ||
| 57 | + | ||
| 58 | + gm = GraphModule(torch.nn.Module(), graph) | ||
| 59 | + input_tensor = torch.randn(2, 3).to(device_type) | ||
| 60 | + self.assertEqual(gm(input_tensor), torch.relu(torch.neg(input_tensor))) | ||
| 61 | + | ||
| 62 | + def test_magic_methods_format_codegen(self): | ||
| 63 | + class MyModule(torch.nn.Module): | ||
| 64 | + def forward(self, x): | ||
| 65 | + return x << 3, x >> 3 | ||
| 66 | + | ||
| 67 | + input_tensor = torch.LongTensor(10).random_(0, 1024).to(device_type) | ||
| 68 | + gm = symbolic_trace(MyModule()) | ||
| 69 | + gm.graph.lint() | ||
| 70 | + | ||
| 71 | + # Symbolic tracing should create proxy nodes for shift operators. | ||
| 72 | + nodes = list(gm.graph.nodes) | ||
| 73 | + x_node = next(node for node in nodes if node.op == "placeholder") | ||
| 74 | + lshift_node = next(node for node in nodes if node.target == operator.lshift) | ||
| 75 | + rshift_node = next(node for node in nodes if node.target == operator.rshift) | ||
| 76 | + self.assertEqual(lshift_node.args, (x_node, 3)) | ||
| 77 | + self.assertEqual(rshift_node.args, (x_node, 3)) | ||
| 78 | + | ||
| 79 | + self.assertIn("x << 3", gm.code) | ||
| 80 | + self.assertIn("x >> 3", gm.code) | ||
| 81 | + | ||
| 82 | + expected = MyModule()(input_tensor) | ||
| 83 | + self.assertEqual(gm(input_tensor), expected) | ||
| 84 | + | ||
| 85 | + def test_inplace_methods_format_codegen(self): | ||
| 86 | + graph = torch.fx.Graph() | ||
| 87 | + a = graph.placeholder("a") | ||
| 88 | + b = graph.placeholder("b") | ||
| 89 | + # Build imul directly because tracing "a *= b" lowers to operator.mul. | ||
| 90 | + imul = graph.call_function(operator.imul, (a, b), {}) | ||
| 91 | + graph.output(a) | ||
| 92 | + graph.lint() | ||
| 93 | + | ||
| 94 | + self.assertEqual(imul.args, (a, b)) | ||
| 95 | + self.assertEqual(imul.target, operator.imul) | ||
| 96 | + | ||
| 97 | + gm = GraphModule(torch.nn.Module(), graph) | ||
| 98 | + gm.recompile() | ||
| 99 | + self.assertIn("a *= b", gm.code) | ||
| 100 | + | ||
| 101 | + input_tensor = torch.ones(2, 3).to(device_type) | ||
| 102 | + scale = torch.full((2, 3), 3.0).to(device_type) | ||
| 103 | + output = gm(input_tensor, scale) | ||
| 104 | + self.assertEqual(output, torch.full((2, 3), 3.0).to(device_type)) | ||
| 105 | + self.assertEqual(input_tensor, torch.full((2, 3), 3.0).to(device_type)) | ||
| 106 | + | ||
| 107 | + | ||
| 108 | +if __name__ == "__main__": | ||
| 109 | + run_tests() | ||