lightning/tests/loops/test_loops.py

513 lines
18 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 copy import deepcopy
from dataclasses import dataclass
from typing import Any, Dict, Iterator
from unittest import mock
from unittest.mock import ANY
import pytest
import torch
from pytorch_lightning import Trainer
from pytorch_lightning.loops import Loop, TrainingBatchLoop
from pytorch_lightning.trainer.progress import BaseProgress
from tests.helpers import BoringModel
from tests.helpers.runif import RunIf
class NestedLoop(Loop):
def __init__(self):
super().__init__()
self.child_loop0 = None
self.child_loop1 = None
@property
def done(self) -> bool:
return False
def connect(self, child0, child1):
self.child_loop0 = child0
self.child_loop1 = child1
def reset(self) -> None:
pass
def advance(self, *args, **kwargs):
pass
@pytest.mark.parametrize("loop_name", ["fit_loop", "validate_loop", "test_loop", "predict_loop"])
def test_connect_loops_direct(loop_name):
"""Test Trainer referenes in loops on assignment."""
loop = NestedLoop()
assert loop.trainer is None
trainer = Trainer()
# trainer.loop = loop
setattr(trainer, loop_name, loop)
assert loop.trainer is trainer
def test_connect_loops_recursive():
"""Test Trainer references in a nested loop assigned to a Trainer."""
main_loop = NestedLoop()
child0 = NestedLoop()
child1 = NestedLoop()
main_loop.connect(child0, child1)
assert main_loop.trainer is None
assert main_loop.child_loop0.trainer is None
trainer = Trainer()
trainer.fit_loop = main_loop
assert child0.trainer is trainer
assert child1.trainer is trainer
def test_connect_subloops(tmpdir):
"""Test connecting individual subloops by calling `trainer.x.y.connect()`"""
model = BoringModel()
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
epoch_loop = trainer.fit_loop.epoch_loop
new_batch_loop = TrainingBatchLoop()
epoch_loop.connect(batch_loop=new_batch_loop)
assert epoch_loop.batch_loop is new_batch_loop
assert new_batch_loop.trainer is None
trainer.fit(model)
assert new_batch_loop.trainer is trainer
class CustomException(Exception):
pass
def test_loop_restore():
class Simple(Loop):
def __init__(self, dataset: Iterator):
super().__init__()
self.iteration_count = 0
self.dataset = dataset
@property
def skip(self) -> bool:
return False
@property
def done(self) -> bool:
return self.iteration_count > len(self.dataset)
def reset(self) -> None:
self.iter_dataset = iter(self.dataset)
if self.restarting:
for _ in range(self.iteration_count):
next(self.iter_dataset)
self.iteration_count += 1
else:
self.outputs = []
def advance(self) -> None:
value = next(self.iter_dataset)
if self.iteration_count == 5:
raise CustomException
self.outputs.append(value)
def on_advance_end(self) -> None:
self.iteration_count += 1
def state_dict(self) -> Dict:
return {"iteration_count": self.iteration_count, "outputs": self.outputs}
def load_state_dict(self, state_dict: Dict) -> None:
self.iteration_count = state_dict["iteration_count"]
self.outputs = state_dict["outputs"]
trainer = Trainer()
data = range(10)
loop = Simple(data)
loop.trainer = trainer
try:
loop.run()
state_dict = {}
except CustomException:
state_dict = loop.state_dict()
loop = Simple(data)
loop.trainer = trainer
loop.load_state_dict(state_dict)
loop.restarting = True
loop.run()
assert not loop.restarting
assert loop.outputs == list(range(10))
def test_loop_hierarchy():
@dataclass
class SimpleProgress(BaseProgress):
increment: int = 0
class Simple(Loop):
def __init__(self, a):
super().__init__()
self.a = a
self.progress = SimpleProgress()
def advance(self, *args: Any, **kwargs: Any) -> None:
loop = getattr(self, "loop_child", None)
if not loop:
return
loop.run()
def on_advance_end(self):
self.progress.increment += 1
@property
def done(self) -> bool:
return self.progress.increment > 0
def reset(self) -> None:
...
def on_save_checkpoint(self) -> Dict:
return {"a": self.a}
def on_load_checkpoint(self, state_dict: Dict) -> None:
self.a = state_dict["a"]
loop_parent = Simple(1)
loop_child = Simple(2)
loop_parent.loop_child = loop_child
# check the trainer reference is propagated
loop_parent.trainer = Trainer()
assert loop_child.trainer is loop_parent.trainer
state_dict = loop_parent.state_dict()
assert state_dict == {
"state_dict": {"a": 1},
"progress": {"increment": 0},
"loop_child.state_dict": {"a": 2},
"loop_child.progress": {"increment": 0},
}
state_dict["loop_child.state_dict"]["a"] = 3
# check restarting after `load_state_dict`
loop_parent.load_state_dict(state_dict)
assert loop_parent.restarting
loop_parent.run()
# check the new state after `run`
state_dict = loop_parent.state_dict()
assert state_dict == {
"state_dict": {"a": 1},
"progress": {"increment": 1},
"loop_child.state_dict": {"a": 3},
"loop_child.progress": {"increment": 1},
}
loop_parent_copy = deepcopy(loop_parent)
assert loop_parent_copy.state_dict() == loop_parent.state_dict()
assert loop_parent_copy.on_save_checkpoint() == state_dict["state_dict"]
assert loop_parent_copy.loop_child.on_save_checkpoint() == state_dict["loop_child.state_dict"]
loop_parent = Simple(1)
loop_child = Simple(2)
loop_parent.loop_child = loop_child
loop_parent.load_state_dict(state_dict)
assert loop_parent.progress.increment == 1
assert loop_parent.loop_child.progress.increment == 1
del loop_parent.loop_child
state_dict = loop_parent.state_dict()
assert state_dict == {"state_dict": {"a": 1}, "progress": {"increment": 1}}
@mock.patch.dict(os.environ, {"PL_FAULT_TOLERANT_TRAINING": "1"})
@pytest.mark.parametrize("stop_epoch", (1, 2))
@pytest.mark.parametrize("stop_batch", (1, 2))
@pytest.mark.parametrize("n_dataloaders,stop_dataloader", [(2, 0), (2, 1), (3, 2)])
@RunIf(min_torch="1.7.0")
def test_loop_restart_progress_multiple_dataloaders(tmpdir, n_dataloaders, stop_dataloader, stop_epoch, stop_batch):
n_batches = 5
n_epochs = 3
class ValidationModel(BoringModel):
def __init__(self):
super().__init__()
def validation_step(self, batch, batch_idx, dataloader_idx):
if self.current_epoch == stop_epoch and batch_idx == stop_batch and dataloader_idx == stop_dataloader:
raise CustomException
return super().validation_step(batch, batch_idx)
def val_dataloader(self):
return [super(ValidationModel, self).val_dataloader() for _ in range(n_dataloaders)]
model = ValidationModel()
model.validation_epoch_end = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=n_epochs,
limit_train_batches=1,
limit_val_batches=n_batches,
num_sanity_val_steps=0,
)
# simulate a failure
try:
trainer.fit(model)
except CustomException:
pass
ckpt_path = str(tmpdir / ".pl_auto_save.ckpt")
checkpoint = torch.load(ckpt_path)["loops"]["fit_loop"]
total_dataloader = stop_epoch * n_dataloaders + stop_dataloader
expected = {
"total": {"ready": total_dataloader + 1, "started": None, "processed": None, "completed": total_dataloader},
"current": {"ready": stop_dataloader + 1, "started": None, "processed": None, "completed": stop_dataloader},
}
assert checkpoint["epoch_loop.val_loop.dataloader_progress"] == expected
trainer.fit_loop.load_state_dict(checkpoint, restart_progress=False)
# `nbe_`: non-breaking epoch, as in, no exception will be raised. `be_`: breaking epoch
nbe_total_val_batch = stop_epoch * n_dataloaders * n_batches
be_total_val_batch = stop_dataloader * n_batches + stop_batch
total_val_batch = nbe_total_val_batch + be_total_val_batch
expected = {
"total": {
"ready": total_val_batch + 1,
"started": total_val_batch + 1,
"processed": total_val_batch,
"completed": total_val_batch,
},
"current": {
"ready": stop_batch + 1,
"started": stop_batch + 1,
"processed": stop_batch,
"completed": stop_batch,
},
}
assert trainer.fit_loop.epoch_loop.val_loop.epoch_loop.batch_progress.state_dict() == expected
trainer.fit_loop.load_state_dict(checkpoint)
expected = {
"total": {
"ready": total_val_batch + 1,
"started": total_val_batch + 1,
"processed": total_val_batch,
"completed": total_val_batch,
},
"current": {"ready": stop_batch, "started": stop_batch, "processed": stop_batch, "completed": stop_batch},
}
assert trainer.fit_loop.epoch_loop.val_loop.epoch_loop.batch_progress.state_dict() == expected
@mock.patch.dict(os.environ, {"PL_FAULT_TOLERANT_TRAINING": "1"})
@pytest.mark.parametrize("accumulate_grad_batches", (1, 2, 3))
@pytest.mark.parametrize("n_optimizers", (1, 3, 5))
@pytest.mark.parametrize("stop_epoch", (1, 2))
@pytest.mark.parametrize("stop_batch", (1, 2))
@pytest.mark.parametrize("stop_optimizer", (1, 2))
@RunIf(min_torch="1.7.0")
def test_loop_state_on_exception(accumulate_grad_batches, stop_epoch, stop_batch, stop_optimizer, n_optimizers, tmpdir):
stop_optimizer = stop_optimizer if stop_optimizer < n_optimizers else 0
n_epochs = 3
n_batches = 3
class TestModel(BoringModel):
def __init__(self):
super().__init__()
if n_optimizers > 1:
self.configure_optimizers = self.configure_optimizers_multiple
def training_step(self, batch, batch_idx, optimizer_idx=0):
if self.trainer.current_epoch == stop_epoch and batch_idx == stop_batch and optimizer_idx == stop_optimizer:
raise CustomException
return super().training_step(batch, batch_idx)
def configure_optimizers_multiple(self):
optimizers = [torch.optim.Adam(self.layer.parameters(), lr=0.1) for _ in range(n_optimizers)]
lr_scheduler_0 = torch.optim.lr_scheduler.StepLR(optimizers[0], step_size=1)
lr_scheduler_1 = torch.optim.lr_scheduler.StepLR(optimizers[1], step_size=1)
# no scheduler for optimizer_2
lr_schedulers = [lr_scheduler_0, {"scheduler": lr_scheduler_1, "interval": "step"}]
return optimizers, lr_schedulers
model = TestModel()
model.training_epoch_end = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=n_epochs,
limit_train_batches=n_batches,
limit_val_batches=0,
accumulate_grad_batches=accumulate_grad_batches,
progress_bar_refresh_rate=0,
logger=False,
checkpoint_callback=False,
)
# simulate a failure
try:
trainer.fit(model)
except CustomException:
pass
ckpt_path = str(tmpdir / ".pl_auto_save.ckpt")
checkpoint = torch.load(ckpt_path)
optim_progress = trainer.fit_loop.epoch_loop.batch_loop.optim_progress
sch_progress = trainer.fit_loop.epoch_loop.scheduler_progress
# `nbe_`: non-breaking epoch, as in, no exception will be raised. `be_`: breaking epoch
nbe_batches_completed = stop_epoch * n_batches
be_batches_completed = stop_batch
be_batches_ready = stop_batch + 1
# lightning applies leftover accumulated gradients when the epoch ends
has_leftover_accumulation_batches = n_batches % accumulate_grad_batches != 0
# number of batches that will call `optimizer.step()` during non-breaking and breaking epochs
nbe_stepping_batches = nbe_batches_completed // accumulate_grad_batches
be_stepping_batches = be_batches_completed // accumulate_grad_batches
nbe_total_opt_steps = (nbe_stepping_batches + has_leftover_accumulation_batches) * n_optimizers
does_last_be_batch_step = be_batches_ready % accumulate_grad_batches == 0 or has_leftover_accumulation_batches
be_total_opt_steps = be_stepping_batches * n_optimizers + does_last_be_batch_step * stop_optimizer
assert optim_progress.optimizer_steps == nbe_total_opt_steps + be_total_opt_steps
assert optim_progress.optimizer.step.current.completed == be_total_opt_steps
has_opt_stepped_in_be = stop_batch + 1 >= accumulate_grad_batches
nbe_total_zero_grad = (nbe_stepping_batches + has_leftover_accumulation_batches) * n_optimizers
does_last_be_batch_zero_grad = be_batches_completed % accumulate_grad_batches == 0
# `max` because the first batch always zero-grads
be_total_zero_grad = max(1, be_stepping_batches) * n_optimizers + stop_optimizer * does_last_be_batch_zero_grad
assert optim_progress.optimizer.zero_grad.total.completed == nbe_total_zero_grad + be_total_zero_grad
assert optim_progress.optimizer.zero_grad.current.completed == be_total_zero_grad
nbe_sch_steps = stop_epoch
be_sch_steps = 0 # the current epoch did not complete
if n_optimizers > 1:
# assumes that the scheduler config is unchanged
# `* 1` because there is only one step-level scheduler
nbe_sch_steps = stop_epoch + nbe_stepping_batches + has_leftover_accumulation_batches * 1
# `0 +` for the epoch-level scheduler
be_sch_steps = 0 + be_stepping_batches
assert sch_progress.total.completed == nbe_sch_steps + be_sch_steps
assert sch_progress.current.completed == be_sch_steps
expected = {
"state_dict": ANY,
"epoch_progress": {
"total": {
"ready": stop_epoch + 1,
"started": stop_epoch + 1,
"processed": stop_epoch,
"completed": stop_epoch,
},
"current": {
"ready": stop_epoch + 1,
"started": stop_epoch + 1,
"processed": stop_epoch,
"completed": stop_epoch,
},
},
"epoch_loop.state_dict": ANY,
"epoch_loop.batch_progress": {
"total": {
"ready": nbe_batches_completed + be_batches_completed + 1,
"started": nbe_batches_completed + be_batches_completed + 1,
"processed": nbe_batches_completed + be_batches_completed,
"completed": nbe_batches_completed + be_batches_completed,
},
"current": {
"ready": stop_batch + 1,
"started": stop_batch + 1,
"processed": stop_batch,
"completed": stop_batch,
},
},
"epoch_loop.scheduler_progress": {
"total": {
"ready": nbe_sch_steps + be_sch_steps,
"started": None,
"processed": None,
"completed": nbe_sch_steps + be_sch_steps,
},
"current": {"ready": be_sch_steps, "started": None, "processed": None, "completed": be_sch_steps},
},
"epoch_loop.batch_loop.state_dict": ANY,
"epoch_loop.batch_loop.optim_progress": {
"optimizer_idx": stop_optimizer,
"optimizer": {
"step": {
"total": {
"ready": nbe_total_opt_steps + be_total_opt_steps + has_opt_stepped_in_be,
"started": None,
"processed": None,
"completed": nbe_total_opt_steps + be_total_opt_steps,
},
"current": {
"ready": be_total_opt_steps + has_opt_stepped_in_be,
"started": None,
"processed": None,
"completed": be_total_opt_steps,
},
},
"zero_grad": {
"total": {
"ready": nbe_total_zero_grad + be_total_zero_grad,
"started": nbe_total_zero_grad + be_total_zero_grad,
"processed": None,
"completed": nbe_total_zero_grad + be_total_zero_grad,
},
"current": {
"ready": be_total_zero_grad,
"started": be_total_zero_grad,
"processed": None,
"completed": be_total_zero_grad,
},
},
},
},
"epoch_loop.val_loop.state_dict": ANY,
"epoch_loop.val_loop.dataloader_progress": ANY,
"epoch_loop.val_loop.epoch_loop.state_dict": ANY,
"epoch_loop.val_loop.epoch_loop.batch_progress": ANY,
"epoch_loop.val_loop._results": ANY,
"epoch_loop._results": ANY,
}
assert checkpoint["loops"]["fit_loop"] == expected
trainer.fit_loop.load_state_dict(checkpoint["loops"]["fit_loop"], restart_progress=False)
assert trainer.fit_loop.state_dict() == checkpoint["loops"]["fit_loop"]
trainer.fit_loop.load_state_dict(checkpoint["loops"]["fit_loop"])
state_dict = trainer.fit_loop.state_dict()
assert state_dict != checkpoint["loops"]["fit_loop"]
assert state_dict["epoch_progress"]["total"]["started"] == stop_epoch + 1
assert state_dict["epoch_progress"]["current"]["started"] == stop_epoch