140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
# Copyright The Lightning AI 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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import torch.nn as nn
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from lightning.fabric.utilities.load import (
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_lazy_load,
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_materialize_tensors,
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_move_state_into,
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_NotYetLoadedTensor,
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)
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def test_lazy_load_module(tmp_path):
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model0 = nn.Linear(2, 2)
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torch.save(model0.state_dict(), tmp_path / "model.pt")
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model1 = nn.Linear(2, 2)
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checkpoint = _lazy_load(tmp_path / "model.pt")
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model1.load_state_dict(checkpoint)
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assert isinstance(checkpoint["weight"], _NotYetLoadedTensor)
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assert type(model0.weight.data) is torch.Tensor
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assert torch.equal(model0.weight, model1.weight)
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assert torch.equal(model0.bias, model1.bias)
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class ATensor(torch.Tensor):
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pass
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def test_lazy_load_tensor(tmp_path):
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"""Test that lazy load can handle different classes of tensors."""
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expected = {
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"tensor": torch.rand(2),
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"parameter": nn.Parameter(torch.rand(3)),
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"subclass": torch.Tensor._make_subclass(ATensor, torch.rand(4)),
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}
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torch.save(expected, tmp_path / "data.pt")
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loaded = _lazy_load(tmp_path / "data.pt")
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for t0, t1 in zip(expected.values(), loaded.values()):
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assert isinstance(t1, _NotYetLoadedTensor)
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t1_materialized = _materialize_tensors(t1)
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assert type(t0) == type(t1_materialized)
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assert torch.equal(t0, t1_materialized)
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def test_lazy_load_mixed_state(tmp_path):
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model0 = nn.Linear(2, 2)
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optim0 = torch.optim.Adam(model0.parameters())
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checkpoint = {
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"int": 1,
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"dict": {"a": 1, "b": 2},
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"list": [1, 2, 3],
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"pickled_model": model0,
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"model": model0.state_dict(),
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"optimizer": optim0.state_dict(),
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}
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torch.save(checkpoint, tmp_path / "checkpoint.pt")
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model1 = nn.Linear(2, 2)
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optim1 = torch.optim.Adam(model0.parameters())
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loaded_checkpoint = _lazy_load(tmp_path / "checkpoint.pt")
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model1.load_state_dict(loaded_checkpoint["model"])
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optim1.load_state_dict(loaded_checkpoint["optimizer"])
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def test_lazy_load_raises():
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with pytest.raises(FileNotFoundError, match="foo' does not exist"):
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_lazy_load("foo")
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def test_materialize_tensors(tmp_path):
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# Single tensor
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tensor = torch.tensor([1, 2])
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torch.save(tensor, tmp_path / "tensor.pt")
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loaded = _lazy_load(tmp_path / "tensor.pt")
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materialized = _materialize_tensors(loaded)
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assert torch.equal(materialized, tensor)
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assert type(tensor) == type(materialized)
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# Collection of tensors
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collection = {
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"tensor": torch.tensor([1, 2]),
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"nested": {"int": 1, "list": [torch.tensor([3.0]), torch.tensor([4])]},
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}
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torch.save(collection, tmp_path / "collection.pt")
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loaded = _lazy_load(tmp_path / "collection.pt")
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materialized = _materialize_tensors(loaded)
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assert torch.equal(materialized["tensor"], collection["tensor"])
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assert torch.equal(materialized["nested"]["list"][0], collection["nested"]["list"][0])
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assert torch.equal(materialized["nested"]["list"][1], collection["nested"]["list"][1])
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assert materialized["nested"]["int"] == 1
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def test_move_state_into():
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# all keys from the source
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source = {"apple": 1, "cocofruit": 2}
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destination = {"banana": 100}
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_move_state_into(source, destination)
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assert source == {}
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assert destination == {"apple": 1, "cocofruit": 2, "banana": 100}
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# subset of keys from the source
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source = {"apple": 1, "cocofruit": 2}
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destination = {"banana": 100}
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keys = {"apple"}
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_move_state_into(source, destination, keys=keys)
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assert source == {"cocofruit": 2}
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assert destination == {"apple": 1, "banana": 100}
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# with stateful objects in destination
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class Fruit:
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count = 1
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def state_dict(self):
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return {"count": self.count}
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def load_state_dict(self, state_dict):
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self.count = state_dict["count"]
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source = {"cocofruit": 2, "banana": {"count": 100}}
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destination = {"banana": Fruit()}
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_move_state_into(source, destination)
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assert source == {}
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assert destination["cocofruit"] == 2
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assert destination["banana"].count == 100
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