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https://github.com/pytorch/pytorch.git
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699ae72f51
get_state_dict and set_state_dict currently do not appropriately handle the buffers. This PR fixes thie issue. Fixes, https://github.com/pytorch/pytorch/issues/119535. Differential Revision: [D53616762](https://our.internmc.facebook.com/intern/diff/D53616762/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/119573 Approved by: https://github.com/wz337, https://github.com/LucasLLC
112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
# mypy: ignore-errors
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# Owner(s): ["oncall: distributed"]
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from typing import Tuple
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import torch
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import torch.nn as nn
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class UnitModule(nn.Module):
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def __init__(self, device: torch.device):
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super().__init__()
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self.l1 = nn.Linear(100, 100, device=device)
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self.seq = nn.Sequential(
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nn.ReLU(),
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nn.Linear(100, 100, device=device),
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nn.ReLU(),
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)
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self.l2 = nn.Linear(100, 100, device=device)
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def forward(self, x):
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return self.l2(self.seq(self.l1(x)))
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class CompositeModel(nn.Module):
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def __init__(self, device: torch.device):
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super().__init__()
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self.l1 = nn.Linear(100, 100, device=device)
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self.u1 = UnitModule(device)
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self.u2 = UnitModule(device)
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self.l2 = nn.Linear(100, 100, device=device)
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def forward(self, x):
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return self.l2(self.u2(self.u1(self.l1(x))))
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class UnitParamModule(nn.Module):
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def __init__(self, device: torch.device):
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super().__init__()
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self.l = nn.Linear(100, 100, device=device)
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self.seq = nn.Sequential(
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nn.ReLU(),
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nn.Linear(100, 100, device=device),
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nn.ReLU(),
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)
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self.p = nn.Parameter(torch.randn((100, 100), device=device))
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def forward(self, x):
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return torch.mm(self.seq(self.l(x)), self.p)
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class CompositeParamModel(nn.Module):
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def __init__(self, device: torch.device):
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super().__init__()
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self.l = nn.Linear(100, 100, device=device)
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self.u1 = UnitModule(device)
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self.u2 = UnitModule(device)
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self.p = nn.Parameter(torch.randn((100, 100), device=device))
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self.register_buffer(
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"buffer", torch.randn((100, 100), device=device), persistent=True
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)
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def forward(self, x):
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a = self.u2(self.u1(self.l(x)))
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b = self.p
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return torch.mm(a, b)
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class FakeSequential(nn.Module):
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# Define this class to achieve a desired nested wrapping using the module
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# wrap policy with `nn.Sequential`
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def __init__(self, *modules: Tuple[nn.Module, ...]) -> None:
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super().__init__()
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self._module_sequence = list(modules)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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for module in self._module_sequence:
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x = module(x)
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return x
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class NestedSequentialModel(nn.Module):
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def __init__(self, device: torch.device) -> None:
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super().__init__()
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# This nested structure exercises traversal order to catch differences
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# between valid traversals (e.g. BFS and DFS variations).
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self.seq1 = nn.Sequential(
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nn.Linear(1, 1, device=device),
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FakeSequential(
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nn.Linear(1, 1, device=device),
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nn.ReLU(),
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FakeSequential(
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nn.Linear(1, 1, device=device),
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),
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nn.ReLU(),
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),
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nn.Linear(1, 2, device=device),
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)
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self.lin = nn.Linear(2, 2, device=device)
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self.seq2 = nn.Sequential(
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nn.ReLU(),
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nn.Linear(2, 3, device=device),
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FakeSequential(
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nn.Linear(3, 2, bias=False, device=device),
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nn.Linear(2, 4, bias=False, device=device),
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),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.seq2(self.lin(self.seq1(x)))
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