126 lines
4.5 KiB
Python
126 lines
4.5 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 os
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from unittest import mock
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import pytest
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import torch
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from pytorch_lightning import callbacks, Trainer
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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def test_disabled_checkpointing(tmpdir):
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# no callback
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trainer = Trainer(max_epochs=3, enable_checkpointing=False)
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assert not trainer.checkpoint_callbacks
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trainer.fit(BoringModel())
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assert not trainer.checkpoint_callbacks
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@mock.patch("torch.save")
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@pytest.mark.parametrize(
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["epochs", "val_check_interval", "expected"], [(1, 1.0, 1), (2, 1.0, 2), (1, 0.25, 4), (2, 0.3, 6)]
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)
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def test_default_checkpoint_freq(save_mock, tmpdir, epochs: int, val_check_interval: float, expected: int):
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=epochs,
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enable_model_summary=False,
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val_check_interval=val_check_interval,
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limit_val_batches=1,
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enable_progress_bar=False,
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)
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trainer.fit(model)
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# make sure types are correct
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assert save_mock.call_count == expected
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@mock.patch("torch.save")
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@pytest.mark.parametrize(
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["k", "epochs", "val_check_interval", "expected"], [(1, 1, 1.0, 1), (2, 2, 1.0, 2), (2, 1, 0.25, 4), (2, 2, 0.3, 6)]
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)
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@pytest.mark.parametrize("save_last", (False, True))
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def test_top_k(save_mock, tmpdir, k: int, epochs: int, val_check_interval: float, expected: int, save_last: bool):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.last_coeff = 10.0
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def training_step(self, batch, batch_idx):
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loss = self.step(torch.ones(32))
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loss = loss / (loss + 0.0000001)
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loss += self.last_coeff
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self.log("my_loss", loss)
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self.last_coeff *= 0.999
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return loss
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model = TestModel()
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trainer = Trainer(
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callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss", save_top_k=k, save_last=save_last)],
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default_root_dir=tmpdir,
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max_epochs=epochs,
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enable_model_summary=False,
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val_check_interval=val_check_interval,
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)
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trainer.fit(model)
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if save_last:
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# last epochs are saved every step (so double the save calls)
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expected = expected * 2
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assert save_mock.call_count == expected
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@mock.patch("torch.save")
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@RunIf(min_gpus=2, standalone=True)
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@pytest.mark.parametrize(["k", "epochs", "val_check_interval", "expected"], [(1, 1, 1.0, 1), (2, 2, 0.3, 4)])
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def test_top_k_ddp(save_mock, tmpdir, k, epochs, val_check_interval, expected):
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class TestModel(BoringModel):
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def training_step(self, batch, batch_idx):
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local_rank = int(os.getenv("LOCAL_RANK"))
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self.log("my_loss", batch_idx * (1 + local_rank), on_epoch=True)
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return super().training_step(batch, batch_idx)
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def training_epoch_end(self, outputs) -> None:
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local_rank = int(os.getenv("LOCAL_RANK"))
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if self.trainer.is_global_zero:
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self.log("my_loss_2", (1 + local_rank), on_epoch=True, rank_zero_only=True)
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data = str(self.global_rank)
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obj = [[data], (data,), set(data)]
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out = self.trainer.strategy.broadcast(obj)
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assert obj == [[str(self.global_rank)], (str(self.global_rank),), set(str(self.global_rank))]
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assert out == [["0"], ("0",), set("0")]
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model = TestModel()
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trainer = Trainer(
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callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss_step", save_top_k=k, mode="max")],
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default_root_dir=tmpdir,
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enable_progress_bar=False,
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max_epochs=epochs,
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enable_model_summary=False,
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val_check_interval=val_check_interval,
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strategy="ddp",
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accelerator="gpu",
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devices=2,
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limit_train_batches=64,
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limit_val_batches=32,
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)
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trainer.fit(model)
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if os.getenv("LOCAL_RANK") == "0":
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assert save_mock.call_count == expected
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