78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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from torch import nn
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from pytorch_lightning.utilities import find_shared_parameters, set_shared_parameters
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from tests.helpers import BoringModel
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class ParameterSharingModule(BoringModel):
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def __init__(self):
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super().__init__()
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self.layer_1 = nn.Linear(32, 10, bias=False)
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self.layer_2 = nn.Linear(10, 32, bias=False)
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self.layer_3 = nn.Linear(32, 10, bias=False)
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self.layer_3.weight = self.layer_1.weight
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def forward(self, x):
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x = self.layer_1(x)
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x = self.layer_2(x)
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x = self.layer_3(x)
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return x
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@pytest.mark.parametrize(
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["model", "expected_shared_params"],
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[(BoringModel, []), (ParameterSharingModule, [["layer_1.weight", "layer_3.weight"]])],
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)
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def test_find_shared_parameters(model, expected_shared_params):
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assert expected_shared_params == find_shared_parameters(model())
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def test_set_shared_parameters():
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model = ParameterSharingModule()
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set_shared_parameters(model, [["layer_1.weight", "layer_3.weight"]])
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assert torch.all(torch.eq(model.layer_1.weight, model.layer_3.weight))
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class SubModule(nn.Module):
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def __init__(self, layer):
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super().__init__()
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self.layer = layer
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def forward(self, x):
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return self.layer(x)
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class NestedModule(BoringModel):
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def __init__(self):
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super().__init__()
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self.layer = nn.Linear(32, 10, bias=False)
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self.net_a = SubModule(self.layer)
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self.layer_2 = nn.Linear(10, 32, bias=False)
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self.net_b = SubModule(self.layer)
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def forward(self, x):
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x = self.net_a(x)
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x = self.layer_2(x)
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x = self.net_b(x)
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return x
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model = NestedModule()
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set_shared_parameters(model, [["layer.weight", "net_a.layer.weight", "net_b.layer.weight"]])
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assert torch.all(torch.eq(model.net_a.layer.weight, model.net_b.layer.weight))
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