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| 1 | +# Copyright (c) Meta Platforms, Inc. and affiliates. |
| 2 | +# All rights reserved. |
| 3 | +# |
| 4 | +# This source code is licensed under the BSD-style license found in the |
| 5 | +# LICENSE file in the root directory of this source tree. |
| 6 | + |
| 7 | +# pyre-unsafe |
| 8 | + |
| 9 | + |
| 10 | +import unittest |
| 11 | + |
| 12 | +import torch |
| 13 | +import torch.nn as nn |
| 14 | +import torch.nn.functional as F |
| 15 | +from executorch.devtools.inspector._intermediate_output_capturer import ( |
| 16 | + IntermediateOutputCapturer, |
| 17 | +) |
| 18 | + |
| 19 | +from executorch.exir import EdgeCompileConfig, EdgeProgramManager, to_edge |
| 20 | +from torch.export import export, ExportedProgram |
| 21 | +from torch.fx import GraphModule |
| 22 | + |
| 23 | + |
| 24 | +class TestIntermediateOutputCapturer(unittest.TestCase): |
| 25 | + @classmethod |
| 26 | + def setUpClass(cls): |
| 27 | + class TestModule(nn.Module): |
| 28 | + def __init__(self): |
| 29 | + super(TestModule, self).__init__() |
| 30 | + self.conv = nn.Conv2d( |
| 31 | + in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1 |
| 32 | + ) |
| 33 | + self.conv.weight = nn.Parameter( |
| 34 | + torch.tensor( |
| 35 | + [[[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]]] |
| 36 | + ) |
| 37 | + ) |
| 38 | + self.conv.bias = nn.Parameter(torch.tensor([0.0])) |
| 39 | + |
| 40 | + self.linear = nn.Linear(in_features=4, out_features=2) |
| 41 | + self.linear.weight = nn.Parameter( |
| 42 | + torch.tensor([[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]) |
| 43 | + ) |
| 44 | + self.linear.bias = nn.Parameter(torch.tensor([0.0, 0.0])) |
| 45 | + self.bias = nn.Parameter(torch.tensor([0.5, -0.5]), requires_grad=False) |
| 46 | + self.scale = nn.Parameter(torch.tensor([2.0, 0.5]), requires_grad=False) |
| 47 | + |
| 48 | + def forward(self, x): |
| 49 | + x = self.conv(x) |
| 50 | + x = x.view(x.size(0), -1) |
| 51 | + x = self.linear(x) |
| 52 | + x = x + self.bias |
| 53 | + x = x - 0.1 |
| 54 | + x = x * self.scale |
| 55 | + x = x / (self.scale + 1.0) |
| 56 | + x = F.relu(x) |
| 57 | + x = torch.sigmoid(x) |
| 58 | + x1, x2 = torch.split(x, 1, dim=1) |
| 59 | + return x1, x2 |
| 60 | + |
| 61 | + cls.model = TestModule() |
| 62 | + cls.input = torch.tensor([[[[1.0, 2.0], [3.0, 4.0]]]], requires_grad=True) |
| 63 | + cls.aten_model: ExportedProgram = export(cls.model, (cls.input,), strict=True) |
| 64 | + cls.edge_program_manager: EdgeProgramManager = to_edge( |
| 65 | + cls.aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True) |
| 66 | + ) |
| 67 | + cls.graph_module: GraphModule = cls.edge_program_manager._edge_programs[ |
| 68 | + "forward" |
| 69 | + ].module() |
| 70 | + cls.capturer = IntermediateOutputCapturer(cls.graph_module) |
| 71 | + cls.intermediate_outputs = cls.capturer.run_and_capture(cls.input) |
| 72 | + |
| 73 | + def test_keying_with_debug_handle_tuple(self): |
| 74 | + for key in self.intermediate_outputs.keys(): |
| 75 | + self.assertIsInstance(key, tuple) |
| 76 | + |
| 77 | + def test_tensor_cloning_and_detaching(self): |
| 78 | + for output in self.intermediate_outputs.values(): |
| 79 | + if isinstance(output, torch.Tensor): |
| 80 | + self.assertFalse(output.requires_grad) |
| 81 | + self.assertTrue(output.is_leaf) |
| 82 | + |
| 83 | + def test_placeholder_nodes_are_skipped(self): |
| 84 | + for node in self.graph_module.graph.nodes: |
| 85 | + if node.op == "placeholder": |
| 86 | + self.assertNotIn( |
| 87 | + node.meta.get("debug_handle"), self.intermediate_outputs |
| 88 | + ) |
| 89 | + |
| 90 | + def test_multiple_outputs_capture(self): |
| 91 | + outputs = self.capturer.run_and_capture(self.input) |
| 92 | + for output in outputs.values(): |
| 93 | + if isinstance(output, tuple): |
| 94 | + self.assertEqual(len(output), 2) |
| 95 | + for part in output: |
| 96 | + self.assertIsInstance(part, torch.Tensor) |
| 97 | + |
| 98 | + def test_capture_correct_outputs(self): |
| 99 | + expected_outputs_with_handles = { |
| 100 | + (10,): torch.tensor([[[[7.7000, 6.7000], [4.7000, 3.7000]]]]), |
| 101 | + (11,): torch.tensor([[7.7000, 6.7000, 4.7000, 3.7000]]), |
| 102 | + (12,): torch.tensor( |
| 103 | + [[0.1000, 0.5000], [0.2000, 0.6000], [0.3000, 0.7000], [0.4000, 0.8000]] |
| 104 | + ), |
| 105 | + (13,): torch.tensor([[5.0000, 14.1200]]), |
| 106 | + (14,): torch.tensor([[5.5000, 13.6200]]), |
| 107 | + (15,): torch.tensor([[5.4000, 13.5200]]), |
| 108 | + (16,): torch.tensor([[10.8000, 6.7600]]), |
| 109 | + (17,): torch.tensor([3.0000, 1.5000]), |
| 110 | + (18,): torch.tensor([[3.6000, 4.5067]]), |
| 111 | + (19,): torch.tensor([[3.6000, 4.5067]]), |
| 112 | + (20,): torch.tensor([[0.9734, 0.9891]]), |
| 113 | + (21,): [torch.tensor([[0.9734]]), torch.tensor([[0.9891]])], |
| 114 | + (22,): torch.tensor([[0.9734]]), |
| 115 | + (23,): torch.tensor([[0.9891]]), |
| 116 | + } |
| 117 | + self.assertEqual( |
| 118 | + len(self.intermediate_outputs), len(expected_outputs_with_handles) |
| 119 | + ) |
| 120 | + |
| 121 | + for debug_handle, expected_output in expected_outputs_with_handles.items(): |
| 122 | + actual_output = self.intermediate_outputs.get(debug_handle) |
| 123 | + self.assertIsNotNone(actual_output) |
| 124 | + if isinstance(expected_output, list): |
| 125 | + self.assertIsInstance(actual_output, list) |
| 126 | + self.assertEqual(len(actual_output), len(expected_output)) |
| 127 | + for actual, expected in zip(actual_output, expected_output): |
| 128 | + self.assertTrue( |
| 129 | + torch.allclose(actual, expected, rtol=1e-4, atol=1e-5) |
| 130 | + ) |
| 131 | + else: |
| 132 | + self.assertTrue( |
| 133 | + torch.allclose(actual_output, expected_output, rtol=1e-4, atol=1e-5) |
| 134 | + ) |
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