151 lines
5.2 KiB
Python
151 lines
5.2 KiB
Python
# 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, seed_everything, Trainer
|
|
from tests.helpers import BoringModel
|
|
from tests.helpers.runif import RunIf
|
|
|
|
|
|
@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
|
|
def test_mc_called(tmpdir):
|
|
seed_everything(1234)
|
|
|
|
# -----------------
|
|
# TRAIN LOOP ONLY
|
|
# -----------------
|
|
train_step_only_model = BoringModel()
|
|
train_step_only_model.validation_step = None
|
|
|
|
# no callback
|
|
trainer = Trainer(max_epochs=3, checkpoint_callback=False)
|
|
trainer.fit(train_step_only_model)
|
|
assert len(trainer.dev_debugger.checkpoint_callback_history) == 0
|
|
|
|
# -----------------
|
|
# TRAIN + VAL LOOP ONLY
|
|
# -----------------
|
|
val_train_model = BoringModel()
|
|
# no callback
|
|
trainer = Trainer(max_epochs=3, checkpoint_callback=False)
|
|
trainer.fit(val_train_model)
|
|
assert len(trainer.dev_debugger.checkpoint_callback_history) == 0
|
|
|
|
|
|
@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,
|
|
weights_summary=None,
|
|
val_check_interval=val_check_interval,
|
|
limit_val_batches=1,
|
|
progress_bar_refresh_rate=0,
|
|
)
|
|
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,
|
|
weights_summary=None,
|
|
val_check_interval=val_check_interval,
|
|
)
|
|
trainer.fit(model)
|
|
|
|
if save_last:
|
|
# last epochs are saved every step (so double the save calls) and once `on_train_end`
|
|
expected = expected * 2 + 1
|
|
assert save_mock.call_count == expected
|
|
|
|
|
|
@mock.patch("torch.save")
|
|
@RunIf(special=True, min_gpus=2)
|
|
def test_top_k_ddp_0(save_mock, tmpdir):
|
|
_top_k_ddp(save_mock, tmpdir, k=1, epochs=1, val_check_interval=1.0, expected=1)
|
|
|
|
|
|
@mock.patch("torch.save")
|
|
@RunIf(special=True, min_gpus=2)
|
|
def test_top_k_ddp_1(save_mock, tmpdir):
|
|
_top_k_ddp(save_mock, tmpdir, k=2, epochs=2, val_check_interval=0.3, expected=4)
|
|
|
|
|
|
def _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.training_type_plugin.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,
|
|
progress_bar_refresh_rate=0,
|
|
max_epochs=epochs,
|
|
weights_summary=None,
|
|
val_check_interval=val_check_interval,
|
|
accelerator="ddp",
|
|
gpus=2,
|
|
limit_train_batches=64,
|
|
limit_val_batches=32,
|
|
)
|
|
trainer.fit(model)
|
|
if os.getenv("LOCAL_RANK") == "0":
|
|
assert save_mock.call_count == expected
|