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https://github.com/pytorch/pytorch.git
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c96bd3de06
Partially addresses #123062 Ran lintrunner on: - `test/fx` with command: ```bash lintrunner -a --take UFMT --all-files ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/123622 Approved by: https://github.com/ezyang
208 lines
7.4 KiB
Python
208 lines
7.4 KiB
Python
# Owner(s): ["module: fx"]
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import os
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import sys
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import unittest
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import torch
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from torch._dynamo.eval_frame import is_dynamo_supported
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from torch.fx.passes.utils.source_matcher_utils import (
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check_subgraphs_connected,
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get_source_partitions,
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)
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from torch.testing._internal.jit_utils import JitTestCase
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class TestSourceMatcher(JitTestCase):
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@unittest.skipIf(not is_dynamo_supported(), "Dynamo not supported")
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def test_module_partitioner_linear_relu_linear(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(3, 3)
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self.relu = torch.nn.ReLU()
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self.linear2 = torch.nn.Linear(3, 5)
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def forward(self, x):
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x = self.linear1(x)
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x = self.linear1(x)
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x = self.relu(x)
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x = self.linear2(x)
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return x
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inputs = (torch.randn(3, 3),)
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gm, _ = torch._dynamo.export(M(), aten_graph=True)(*inputs)
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gm.graph.eliminate_dead_code()
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module_partitions = get_source_partitions(
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gm.graph, [torch.nn.Linear, torch.nn.ReLU]
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)
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self.assertEqual(len(module_partitions), 2)
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self.assertEqual(len(module_partitions[torch.nn.Linear]), 3)
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self.assertEqual(len(module_partitions[torch.nn.ReLU]), 1)
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self.assertFalse(
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check_subgraphs_connected(
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module_partitions[torch.nn.Linear][0],
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module_partitions[torch.nn.ReLU][0],
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)
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)
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self.assertTrue(
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check_subgraphs_connected(
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module_partitions[torch.nn.Linear][1],
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module_partitions[torch.nn.ReLU][0],
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)
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)
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self.assertFalse(
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check_subgraphs_connected(
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module_partitions[torch.nn.Linear][2],
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module_partitions[torch.nn.ReLU][0],
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)
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)
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@unittest.skipIf(not is_dynamo_supported(), "Dynamo not supported")
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def test_module_partitioner_conv_relu_maxpool(self):
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class M(torch.nn.Module):
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def __init__(self, constant_tensor: torch.Tensor) -> None:
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super().__init__()
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self.constant_tensor = constant_tensor
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self.conv1 = torch.nn.Conv2d(
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in_channels=3, out_channels=16, kernel_size=3, padding=1
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)
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self.conv2 = torch.nn.Conv2d(
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in_channels=16, out_channels=16, kernel_size=3, padding=1
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)
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self.conv3 = torch.nn.Conv2d(
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in_channels=16, out_channels=16, kernel_size=3, padding=1
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)
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self.relu = torch.nn.ReLU()
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self.maxpool = torch.nn.MaxPool2d(kernel_size=3)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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a = self.conv1(x)
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b = self.conv2(a)
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c = a + self.constant_tensor
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z = self.conv3(b + c)
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return self.maxpool(self.relu(z))
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inputs = (torch.randn(1, 3, 256, 256),)
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gm, _ = torch._dynamo.export(M(torch.ones(1, 16, 256, 256)), aten_graph=True)(
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*inputs
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)
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gm.graph.eliminate_dead_code()
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module_partitions = get_source_partitions(
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gm.graph, [torch.nn.Conv2d, torch.nn.ReLU, torch.nn.MaxPool2d]
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)
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self.assertEqual(len(module_partitions), 3)
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self.assertEqual(len(module_partitions[torch.nn.Conv2d]), 3)
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self.assertEqual(len(module_partitions[torch.nn.ReLU]), 1)
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self.assertEqual(len(module_partitions[torch.nn.MaxPool2d]), 1)
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self.assertFalse(
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check_subgraphs_connected(
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module_partitions[torch.nn.Conv2d][0],
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module_partitions[torch.nn.ReLU][0],
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)
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)
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self.assertFalse(
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check_subgraphs_connected(
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module_partitions[torch.nn.Conv2d][1],
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module_partitions[torch.nn.ReLU][0],
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)
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)
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self.assertTrue(
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check_subgraphs_connected(
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module_partitions[torch.nn.Conv2d][2],
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module_partitions[torch.nn.ReLU][0],
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)
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)
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self.assertFalse(
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check_subgraphs_connected(
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module_partitions[torch.nn.MaxPool2d][0],
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module_partitions[torch.nn.ReLU][0],
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)
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)
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self.assertTrue(
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check_subgraphs_connected(
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module_partitions[torch.nn.ReLU][0],
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module_partitions[torch.nn.MaxPool2d][0],
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)
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)
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@unittest.skipIf(not is_dynamo_supported(), "Dynamo not supported")
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def test_module_partitioner_functional_conv_relu_conv(self):
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class FunctionalConv2d(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.stride = (1, 1)
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self.padding = (0, 0)
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self.dilation = (1, 1)
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self.groups = 1
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def forward(self, x, weight, bias):
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return torch.nn.functional.conv2d(
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x,
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weight,
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bias,
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self.stride,
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self.padding,
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self.dilation,
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self.groups,
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)
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = FunctionalConv2d()
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self.conv2 = FunctionalConv2d()
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def forward(self, x, weight, bias):
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x = self.conv1(x, weight, bias)
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x = torch.nn.functional.relu(x)
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x = self.conv2(x, weight, bias)
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return x
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inputs = (torch.randn(1, 3, 5, 5), torch.rand(3, 3, 3, 3), torch.rand(3))
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gm, _ = torch._dynamo.export(M(), aten_graph=True)(*inputs)
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gm.graph.eliminate_dead_code()
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module_partitions = get_source_partitions(
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gm.graph, [torch.nn.functional.conv2d]
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)
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self.assertEqual(len(module_partitions), 1)
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self.assertEqual(len(module_partitions[torch.nn.functional.conv2d]), 2)
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@unittest.skipIf(not is_dynamo_supported(), "Dynamo not supported")
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def test_module_partitioner_functional_linear_relu_linear(self):
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class M(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, weight, bias):
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x = torch.nn.functional.linear(x, weight, bias)
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x = torch.nn.functional.linear(x, weight, bias)
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x = torch.nn.functional.relu(x)
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x = torch.nn.functional.linear(x, weight, bias)
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x = torch.nn.functional.linear(x, weight, bias)
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x = torch.nn.functional.relu(x)
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return x
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inputs = (torch.randn(1, 5), torch.rand((5, 5)), torch.zeros(5))
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gm, _ = torch._dynamo.export(M(), aten_graph=True)(*inputs)
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gm.graph.eliminate_dead_code()
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module_partitions = get_source_partitions(
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gm.graph, [torch.nn.functional.linear, torch.nn.functional.relu]
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)
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self.assertEqual(len(module_partitions), 2)
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self.assertEqual(len(module_partitions[torch.nn.functional.linear]), 4)
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self.assertEqual(len(module_partitions[torch.nn.functional.relu]), 2)
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