72 lines
2.9 KiB
Python
72 lines
2.9 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 os
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import pytest
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import torch
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from tests_fabric.helpers.models import BoringLite
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from tests_fabric.helpers.runif import RunIf
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class ShardedSaveAndLoad(BoringLite):
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def get_optimizer(self, module):
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optimizer = super().get_optimizer(module)
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if self.with_fairscale_oss:
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from fairscale.optim import OSS
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optimizer = OSS(params=optimizer.param_groups, optim=type(optimizer), **optimizer.defaults)
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return optimizer
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def run(self, tmpdir, with_fairscale_oss=False):
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self.with_fairscale_oss = with_fairscale_oss
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super().run()
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from fairscale.nn import ShardedDataParallel
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from fairscale.optim import OSS
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# the model and optimizer is wrapped correctly
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assert isinstance(self.model._forward_module, ShardedDataParallel)
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assert isinstance(self.optimizer.optimizer, OSS)
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self.model.cpu()
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checkpoint_path = os.path.join(tmpdir, "checkpoint.ckpt")
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# need to broadcast because tmpdir is different on each process
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checkpoint_path = self.broadcast(checkpoint_path)
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checkpoint = {"model": self.model.state_dict(), "optimizer": self.optimizer.state_dict()}
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self.save(checkpoint, checkpoint_path)
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self.barrier() # ensure the checkpoint is saved before load
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loaded_checkpoint = self.load(checkpoint_path)
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new_model = self.get_model()
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new_model.load_state_dict(loaded_checkpoint["model"])
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# Assert model parameters are identical after loading
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for trained_param, loaded_param in zip(self.model.parameters(), new_model.parameters()):
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assert torch.equal(trained_param, loaded_param)
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@RunIf(fairscale=True)
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@pytest.mark.parametrize("accelerator", ["cpu", pytest.param("cuda", marks=RunIf(min_cuda_gpus=2))])
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@pytest.mark.parametrize("strategy", (pytest.param("ddp_sharded", marks=RunIf(standalone=True)), "ddp_sharded_spawn"))
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@pytest.mark.parametrize("with_fairscale_oss", (True, False))
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def test_fairscale_multi_process_checkpoint_state_consolidation(with_fairscale_oss, strategy, accelerator, tmpdir):
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"""Test that the sharded optimizer states get consolidated when saving the checkpoint, and that the loaded
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weights is identical to the saved one."""
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lite = ShardedSaveAndLoad(strategy=strategy, accelerator=accelerator, devices=2)
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lite.run(tmpdir, with_fairscale_oss=with_fairscale_oss)
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