lightning/tests/checkpointing/test_checkpoint_callback_frequency.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from unittest import mock

import pytest
import torch

from pytorch_lightning import callbacks, Trainer
from tests.helpers import BoringModel
from tests.helpers.runif import RunIf


def test_disabled_checkpointing(tmpdir):
    # no callback
    trainer = Trainer(max_epochs=3, enable_checkpointing=False)
    assert not trainer.checkpoint_callbacks
    trainer.fit(BoringModel())
    assert not trainer.checkpoint_callbacks


@mock.patch("torch.save")
@pytest.mark.parametrize(
    ["epochs", "val_check_interval", "expected"], [(1, 1.0, 1), (2, 1.0, 2), (1, 0.25, 4), (2, 0.3, 6)]
)
def test_default_checkpoint_freq(save_mock, tmpdir, epochs: int, val_check_interval: float, expected: int):

    model = BoringModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=epochs,
        enable_model_summary=False,
        val_check_interval=val_check_interval,
        limit_val_batches=1,
        enable_progress_bar=False,
    )
    trainer.fit(model)

    # make sure types are correct
    assert save_mock.call_count == expected


@mock.patch("torch.save")
@pytest.mark.parametrize(
    ["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)]
)
@pytest.mark.parametrize("save_last", (False, True))
def test_top_k(save_mock, tmpdir, k: int, epochs: int, val_check_interval: float, expected: int, save_last: bool):
    class TestModel(BoringModel):
        def __init__(self):
            super().__init__()
            self.last_coeff = 10.0

        def training_step(self, batch, batch_idx):
            loss = self.step(torch.ones(32))
            loss = loss / (loss + 0.0000001)
            loss += self.last_coeff
            self.log("my_loss", loss)
            self.last_coeff *= 0.999
            return loss

    model = TestModel()
    trainer = Trainer(
        callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss", save_top_k=k, save_last=save_last)],
        default_root_dir=tmpdir,
        max_epochs=epochs,
        enable_model_summary=False,
        val_check_interval=val_check_interval,
    )
    trainer.fit(model)

    if save_last:
        # last epochs are saved every step (so double the save calls)
        expected = expected * 2
    assert save_mock.call_count == expected


@mock.patch("torch.save")
@RunIf(standalone=True, min_gpus=2)
@pytest.mark.parametrize(["k", "epochs", "val_check_interval", "expected"], [(1, 1, 1.0, 1), (2, 2, 0.3, 4)])
def test_top_k_ddp(save_mock, tmpdir, k, epochs, val_check_interval, expected):
    class TestModel(BoringModel):
        def training_step(self, batch, batch_idx):
            local_rank = int(os.getenv("LOCAL_RANK"))
            self.log("my_loss", batch_idx * (1 + local_rank), on_epoch=True)
            return super().training_step(batch, batch_idx)

        def training_epoch_end(self, outputs) -> None:
            local_rank = int(os.getenv("LOCAL_RANK"))
            if self.trainer.is_global_zero:
                self.log("my_loss_2", (1 + local_rank), on_epoch=True, rank_zero_only=True)
            data = str(self.global_rank)
            obj = [[data], (data,), set(data)]
            out = self.trainer.strategy.broadcast(obj)
            assert obj == [[str(self.global_rank)], (str(self.global_rank),), set(str(self.global_rank))]
            assert out == [["0"], ("0",), set("0")]

    model = TestModel()
    trainer = Trainer(
        callbacks=[callbacks.ModelCheckpoint(dirpath=tmpdir, monitor="my_loss_step", save_top_k=k, mode="max")],
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=epochs,
        enable_model_summary=False,
        val_check_interval=val_check_interval,
        strategy="ddp",
        accelerator="gpu",
        devices=2,
        limit_train_batches=64,
        limit_val_batches=32,
    )
    trainer.fit(model)
    if os.getenv("LOCAL_RANK") == "0":
        assert save_mock.call_count == expected