mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 12:54:11 +08:00
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136964 Approved by: https://github.com/justinchuby, https://github.com/albanD
118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
# Owner(s): ["module: unknown"]
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from copy import copy
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import torch
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from torch import nn
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from torch.testing._internal.common_utils import (
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run_tests,
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skipIfTorchDynamo,
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TestCase,
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xfailIfTorchDynamo,
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)
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from torch.utils.checkpoint import checkpoint
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from torch.utils.module_tracker import ModuleTracker
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class TestModuleTracker(TestCase):
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# "https://github.com/pytorch/pytorch/issues/127112
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@xfailIfTorchDynamo
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def test_module_hierarchy(self):
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seen_fw = []
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seen_bw = []
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class Foo(nn.Module):
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def forward(self, x):
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x = x["a"].relu_()
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seen_fw.append((copy(tracker.parents), tracker.is_bw))
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x.register_hook(
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lambda grad: seen_bw.append((copy(tracker.parents), tracker.is_bw))
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)
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return {"a": torch.mm(x, x)}
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class Mod(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.a = Foo()
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self.b = nn.ModuleDict({"nest": Foo()})
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self.c = nn.ModuleList([Foo()])
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def forward(self, x):
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x = self.c[0](x)
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return self.b["nest"](self.a(x))
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mod = Mod()
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with ModuleTracker() as tracker:
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mod({"a": torch.randn(10, 10, requires_grad=True).clone()})[
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"a"
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].sum().backward()
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mod({"a": torch.randn(10, 10, requires_grad=True).clone()})[
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"a"
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].sum().backward()
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self.assertEqual(
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seen_fw,
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[
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({"Global", "Mod", "Mod.c.0"}, False),
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({"Global", "Mod", "Mod.a"}, False),
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({"Global", "Mod", "Mod.b.nest"}, False),
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({"Global", "Mod", "Mod.c.0"}, False),
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({"Global", "Mod", "Mod.a"}, False),
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({"Global", "Mod", "Mod.b.nest"}, False),
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],
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)
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self.assertEqual(
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seen_bw,
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[
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({"Global", "Mod", "Mod.b.nest"}, True),
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({"Global", "Mod", "Mod.a"}, True),
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({"Global", "Mod", "Mod.c.0"}, True),
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({"Global", "Mod", "Mod.b.nest"}, True),
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({"Global", "Mod", "Mod.a"}, True),
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({"Global", "Mod", "Mod.c.0"}, True),
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],
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)
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@skipIfTorchDynamo("unexplained 3.13+ recursion error")
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def test_confused_hierarchy(self):
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class MyMod(nn.Module):
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def __init__(self):
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super().__init__()
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self.inner = nn.Linear(2, 2)
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self.ran = False
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def forward(self, inp):
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if not self.ran:
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self.ran = True
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return self(inp)
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else:
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self.ran = False
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return self.inner(inp)
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mod = MyMod()
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inp = torch.rand(1, 2, requires_grad=True)
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# Should not fail
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with ModuleTracker():
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res = mod(inp)
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res.sum().backward()
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# Should not fail
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with ModuleTracker():
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res = checkpoint(lambda inp: mod(inp), inp)
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res.sum().backward()
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def test_bw_detection(self):
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mod = nn.Linear(2, 2)
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with ModuleTracker() as tracker:
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mod(torch.rand(2, requires_grad=True)).sum().backward()
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self.assertFalse(tracker.is_bw)
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self.assertEqual(tracker.parents, {"Global"})
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if __name__ == "__main__":
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run_tests()
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