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test_logger_connector.py
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test_logger_connector.py
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# 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.
"""
Tests to ensure that the training loop works with a dict (1.0)
"""
from copy import deepcopy
from typing import Any, Callable
import pytest
import torch
from torch.utils.data import DataLoader
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.core.step_result import Result
from pytorch_lightning.metrics import Accuracy
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.trainer.connectors.logger_connector.callback_hook_validator import CallbackHookNameValidator
from pytorch_lightning.trainer.connectors.logger_connector.metrics_holder import MetricsHolder
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers.boring_model import BoringModel, RandomDataset
from tests.helpers.runif import RunIf
def decorator_with_arguments(fx_name: str = '', hook_fx_name: str = None) -> Callable:
def decorator(func: Callable) -> Callable:
def wrapper(self, *args, **kwargs) -> Any:
# Set information
self._current_fx_name = fx_name
self._current_hook_fx_name = hook_fx_name
self._results = Result()
result = func(self, *args, **kwargs)
# cache metrics
self.trainer.logger_connector.cache_logged_metrics()
return result
return wrapper
return decorator
def test__logger_connector__epoch_result_store__train(tmpdir, monkeypatch):
"""
Tests that LoggerConnector will properly capture logged information
and reduce them
"""
monkeypatch.setenv("PL_DEV_DEBUG", "1")
class TestModel(BoringModel):
train_losses = []
@decorator_with_arguments(fx_name="training_step")
def training_step(self, batch, batch_idx):
output = self.layer(batch)
loss = self.loss(batch, output)
self.train_losses.append(loss)
self.log("train_loss", loss, on_step=True, on_epoch=True)
return {"loss": loss}
def training_step_end(self, *_):
self.train_results = deepcopy(self.trainer.logger_connector.cached_results)
model = TestModel()
model.training_epoch_end = None
model.val_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=2,
limit_val_batches=4,
max_epochs=1,
log_every_n_steps=1,
weights_summary=None,
)
trainer.fit(model)
train_results = model.train_results
assert len(train_results(fx_name="training_step", dl_idx=0, opt_idx=0)) == 2
generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=0, split_idx=0)["train_loss"]
assert generated == model.train_losses[0]
generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=1, split_idx=0)["train_loss"]
assert generated == model.train_losses[1]
assert train_results.has_reduced is not True
train_results.has_batch_loop_finished = True
assert train_results.has_reduced is True
generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, reduced=True)['train_loss_epoch'].item()
excepted = torch.stack(model.train_losses).mean().item()
assert generated == excepted
def test__logger_connector__epoch_result_store__train__ttbt(tmpdir):
"""
Tests that LoggerConnector will properly capture logged information with ttbt
and reduce them
"""
truncated_bptt_steps = 2
sequence_size = 30
batch_size = 30
x_seq = torch.rand(batch_size, sequence_size, 1)
y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist()
class MockSeq2SeqDataset(torch.utils.data.Dataset):
def __getitem__(self, i):
return x_seq, y_seq_list
def __len__(self):
return 1
class TestModel(BoringModel):
train_losses = []
def __init__(self):
super().__init__()
self.test_hidden = None
self.layer = torch.nn.Linear(2, 2)
@decorator_with_arguments(fx_name="training_step")
def training_step(self, batch, batch_idx, hiddens):
self.test_hidden = torch.rand(1)
x_tensor, y_list = batch
assert x_tensor.shape[1] == truncated_bptt_steps, "tbptt split Tensor failed"
y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype)
assert y_tensor.shape[1] == truncated_bptt_steps, "tbptt split list failed"
pred = self(x_tensor.view(batch_size, truncated_bptt_steps))
loss = torch.nn.functional.mse_loss(pred, y_tensor.view(batch_size, truncated_bptt_steps))
self.train_losses.append(loss)
self.log('a', loss, on_epoch=True)
return {'loss': loss, 'hiddens': self.test_hidden}
def on_train_epoch_start(self) -> None:
self.test_hidden = None
def train_dataloader(self):
return torch.utils.data.DataLoader(
dataset=MockSeq2SeqDataset(),
batch_size=batch_size,
shuffle=False,
sampler=None,
)
def training_step_end(self, *_):
self.train_results = deepcopy(self.trainer.logger_connector.cached_results)
model = TestModel()
model.training_epoch_end = None
model.example_input_array = torch.randn(5, truncated_bptt_steps)
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=10,
limit_val_batches=0,
truncated_bptt_steps=truncated_bptt_steps,
max_epochs=1,
log_every_n_steps=1,
weights_summary=None,
)
trainer.fit(model)
train_results = model.train_results
generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, batch_idx=0)
assert len(generated) == len(model.train_losses)
# assert reduction didn't happen yet
assert train_results.has_reduced is False
# Launch reduction
train_results.has_batch_loop_finished = True
# assert reduction did happen
assert train_results.has_reduced is True
generated = train_results(fx_name="training_step", dl_idx=0, opt_idx=0, reduced=True)['a_epoch'].item()
assert generated == torch.stack(model.train_losses).mean().item()
@pytest.mark.parametrize('num_dataloaders', [1, 2])
def test__logger_connector__epoch_result_store__test_multi_dataloaders(tmpdir, monkeypatch, num_dataloaders):
"""
Tests that LoggerConnector will properly capture logged information in multi dataloaders scenario
"""
monkeypatch.setenv("PL_DEV_DEBUG", "1")
class TestModel(BoringModel):
test_losses = {dl_idx: [] for dl_idx in range(num_dataloaders)}
@decorator_with_arguments(fx_name="test_step")
def test_step(self, batch, batch_idx, dl_idx=0):
output = self.layer(batch)
loss = self.loss(batch, output)
self.test_losses[dl_idx].append(loss)
self.log("test_loss", loss, on_step=True, on_epoch=True)
return {"test_loss": loss}
def on_test_batch_end(self, *args, **kwargs):
# save objects as it will be reset at the end of epoch.
self.batch_results = deepcopy(self.trainer.logger_connector.cached_results)
def on_test_epoch_end(self):
# save objects as it will be reset at the end of epoch.
self.reduce_results = deepcopy(self.trainer.logger_connector.cached_results)
def test_dataloader(self):
return [super().test_dataloader()] * num_dataloaders
model = TestModel()
model.test_epoch_end = None
limit_test_batches = 4
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=0,
limit_val_batches=0,
limit_test_batches=limit_test_batches,
max_epochs=1,
log_every_n_steps=1,
weights_summary=None,
)
trainer.test(model)
test_results = model.batch_results
generated = test_results(fx_name="test_step")
assert len(generated) == num_dataloaders
for dl_idx in range(num_dataloaders):
generated = test_results(fx_name="test_step", dl_idx=dl_idx)
assert len(generated) == limit_test_batches
test_results = model.reduce_results
for dl_idx in range(num_dataloaders):
expected = torch.stack(model.test_losses[dl_idx]).mean()
generated = test_results(fx_name="test_step", dl_idx=dl_idx, reduced=True)["test_loss_epoch"]
torch.testing.assert_allclose(generated, expected)
def test_call_back_validator(tmpdir):
funcs_name = sorted([f for f in dir(Callback) if not f.startswith('_')])
callbacks_func = [
'on_after_backward',
'on_batch_end',
'on_batch_start',
'on_before_accelerator_backend_setup',
'on_before_zero_grad',
'on_epoch_end',
'on_epoch_start',
'on_fit_end',
'on_fit_start',
'on_init_end',
'on_init_start',
'on_keyboard_interrupt',
'on_load_checkpoint',
'on_pretrain_routine_end',
'on_pretrain_routine_start',
'on_sanity_check_end',
'on_sanity_check_start',
'on_save_checkpoint',
'on_test_batch_end',
'on_test_batch_start',
'on_test_end',
'on_test_epoch_end',
'on_test_epoch_start',
'on_test_start',
'on_train_batch_end',
'on_train_batch_start',
'on_train_end',
'on_train_epoch_end',
'on_train_epoch_final_end',
'on_train_epoch_start',
'on_train_start',
'on_validation_batch_end',
'on_validation_batch_start',
'on_validation_end',
'on_validation_epoch_end',
'on_validation_epoch_start',
'on_validation_start',
'setup',
'teardown',
]
not_supported = [
"on_before_accelerator_backend_setup",
"on_fit_end",
"on_fit_start",
"on_init_end",
"on_init_start",
"on_keyboard_interrupt",
"on_load_checkpoint",
"on_pretrain_routine_end",
"on_pretrain_routine_start",
"on_sanity_check_end",
"on_sanity_check_start",
"on_save_checkpoint",
"on_test_end",
"on_train_end",
"on_validation_end",
"setup",
"teardown",
]
assert (
funcs_name == sorted(callbacks_func)
), """Detected new callback function.
Need to add its logging permission to CallbackHookNameValidator and update this test"""
validator = CallbackHookNameValidator()
for func_name in funcs_name:
# This summarizes where and what is currently possible to log using `self.log`
is_stage = "train" in func_name or "test" in func_name or "validation" in func_name
is_start = "start" in func_name or "batch" in func_name
on_step = is_stage and is_start
on_epoch = True
# creating allowed condition
allowed = (
is_stage or "batch" in func_name or "epoch" in func_name or "grad" in func_name or "backward" in func_name
)
allowed = (
allowed and "pretrain" not in func_name
and func_name not in ["on_train_end", "on_test_end", "on_validation_end"]
)
if allowed:
validator.check_logging_in_callbacks(current_hook_fx_name=func_name, on_step=on_step, on_epoch=on_epoch)
if not is_start and is_stage:
with pytest.raises(MisconfigurationException, match="function supports only"):
validator.check_logging_in_callbacks(
current_hook_fx_name=func_name, on_step=True, on_epoch=on_epoch
)
else:
assert func_name in not_supported
with pytest.raises(MisconfigurationException, match="function doesn't support"):
validator.check_logging_in_callbacks(current_hook_fx_name=func_name, on_step=on_step, on_epoch=on_epoch)
# should not fail
validator.check_logging_in_callbacks(current_hook_fx_name=None, on_step=None, on_epoch=None)
@RunIf(min_gpus=2)
def test_epoch_results_cache_dp(tmpdir):
root_device = torch.device("cuda", 0)
class TestModel(BoringModel):
def training_step(self, *args, **kwargs):
result = super().training_step(*args, **kwargs)
self.log("train_loss_epoch", result["loss"], on_step=False, on_epoch=True)
return result
def training_step_end(self, training_step_outputs): # required for dp
loss = training_step_outputs["loss"].mean()
return loss
def training_epoch_end(self, outputs):
assert all(out["loss"].device == root_device for out in outputs)
assert self.trainer.callback_metrics["train_loss_epoch"].device == root_device
def validation_step(self, *args, **kwargs):
val_loss = torch.rand(1, device=torch.device("cuda", 1))
self.log("val_loss_epoch", val_loss, on_step=False, on_epoch=True)
return val_loss
def validation_epoch_end(self, outputs):
assert all(loss.device == root_device for loss in outputs)
assert self.trainer.callback_metrics["val_loss_epoch"].device == root_device
def test_step(self, *args, **kwargs):
test_loss = torch.rand(1, device=torch.device("cuda", 1))
self.log("test_loss_epoch", test_loss, on_step=False, on_epoch=True)
return test_loss
def test_epoch_end(self, outputs):
assert all(loss.device == root_device for loss in outputs)
assert self.trainer.callback_metrics["test_loss_epoch"].device == root_device
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=4)
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=4)
def test_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=4)
model = TestModel()
trainer = Trainer(
default_root_dir=tmpdir,
accelerator="dp",
gpus=2,
limit_train_batches=2,
limit_val_batches=2,
max_epochs=1,
)
trainer.fit(model)
trainer.test(model, ckpt_path=None)
@pytest.mark.parametrize('to_float', [False, True])
def test_metrics_holder(to_float, tmpdir):
device = "cuda" if torch.cuda.is_available() else "cpu"
preds = torch.tensor([[0.9, 0.1]], device=device)
def is_float(value: Any) -> bool:
return isinstance(value, float)
excepted_function = is_float if to_float else torch.is_tensor
targets = torch.tensor([1], device=device)
acc = Accuracy().to(device)
metric_holder = MetricsHolder(to_float=to_float)
metric_holder.update({
"x": 1,
"y": torch.tensor(2),
"z": acc(preds, targets),
})
metric_holder.convert(False, device)
metrics = metric_holder.metrics
assert excepted_function(metrics["x"])
assert excepted_function(metrics["y"])
assert excepted_function(metrics["z"])
def test_logging_to_progress_bar_with_reserved_key(tmpdir):
""" Test that logging a metric with a reserved name to the progress bar raises a warning. """
class TestModel(BoringModel):
def training_step(self, *args, **kwargs):
output = super().training_step(*args, **kwargs)
self.log("loss", output["loss"], prog_bar=True)
return output
model = TestModel()
trainer = Trainer(
default_root_dir=tmpdir,
max_steps=2,
)
with pytest.warns(UserWarning, match="The progress bar already tracks a metric with the .* 'loss'"):
trainer.fit(model)
@pytest.mark.parametrize("add_dataloader_idx", [False, True])
def test_auto_add_dataloader_idx(tmpdir, add_dataloader_idx):
""" test that auto_add_dataloader_idx argument works """
class TestModel(BoringModel):
def val_dataloader(self):
dl = super().val_dataloader()
return [dl, dl]
def validation_step(self, *args, **kwargs):
output = super().validation_step(*args[:-1], **kwargs)
if add_dataloader_idx:
name = "val_loss"
else:
name = f"val_loss_custom_naming_{args[-1]}"
self.log(name, output["x"], add_dataloader_idx=add_dataloader_idx)
return output
model = TestModel()
model.validation_epoch_end = None
trainer = Trainer(default_root_dir=tmpdir, max_steps=5)
trainer.fit(model)
logged = trainer.logged_metrics
# Check that the correct keys exist
if add_dataloader_idx:
assert 'val_loss/dataloader_idx_0' in logged
assert 'val_loss/dataloader_idx_1' in logged
else:
assert 'val_loss_custom_naming_0' in logged
assert 'val_loss_custom_naming_1' in logged