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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
EarlyStoppingCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainerState,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
from transformers.trainer_callback import ExportableState
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS, TRAINER_STATE_NAME
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class MyTestExportableCallback(TrainerCallback, ExportableState):
def __init__(self, my_test_state="test"):
self.my_test_state = my_test_state
def state(self):
return {
"args": {
"my_test_state": self.my_test_state,
},
}
class MyTestTrainerCallback(TrainerCallback):
"A callback that registers the events that goes through."
def __init__(self, my_test_state="test"):
self.events = []
self.my_test_state = my_test_state
def on_init_end(self, args, state, control, **kwargs):
self.events.append("on_init_end")
def on_train_begin(self, args, state, control, **kwargs):
self.events.append("on_train_begin")
def on_train_end(self, args, state, control, **kwargs):
self.events.append("on_train_end")
def on_epoch_begin(self, args, state, control, **kwargs):
self.events.append("on_epoch_begin")
def on_epoch_end(self, args, state, control, **kwargs):
self.events.append("on_epoch_end")
def on_step_begin(self, args, state, control, **kwargs):
self.events.append("on_step_begin")
def on_pre_optimizer_step(self, args, state, control, **kwargs):
self.events.append("on_pre_optimizer_step")
def on_optimizer_step(self, args, state, control, **kwargs):
self.events.append("on_optimizer_step")
def on_step_end(self, args, state, control, **kwargs):
self.events.append("on_step_end")
def on_evaluate(self, args, state, control, **kwargs):
self.events.append("on_evaluate")
def on_predict(self, args, state, control, **kwargs):
self.events.append("on_predict")
def on_save(self, args, state, control, **kwargs):
self.events.append("on_save")
def on_log(self, args, state, control, **kwargs):
self.events.append("on_log")
def on_prediction_step(self, args, state, control, **kwargs):
self.events.append("on_prediction_step")
@require_torch
class TrainerCallbackTest(unittest.TestCase):
def setUp(self):
self.output_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.output_dir)
def get_trainer(self, a=0, b=0, train_len=64, eval_len=64, callbacks=None, disable_tqdm=False, **kwargs):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
train_dataset = RegressionDataset(length=train_len)
eval_dataset = RegressionDataset(length=eval_len)
config = RegressionModelConfig(a=a, b=b)
model = RegressionPreTrainedModel(config)
args = TrainingArguments(self.output_dir, disable_tqdm=disable_tqdm, report_to=[], **kwargs)
return Trainer(
model,
args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=callbacks,
)
def check_callbacks_equality(self, cbs1, cbs2):
self.assertEqual(len(cbs1), len(cbs2))
# Order doesn't matter
cbs1 = sorted(cbs1, key=lambda cb: cb.__name__ if isinstance(cb, type) else cb.__class__.__name__)
cbs2 = sorted(cbs2, key=lambda cb: cb.__name__ if isinstance(cb, type) else cb.__class__.__name__)
for cb1, cb2 in zip(cbs1, cbs2):
if isinstance(cb1, type) and isinstance(cb2, type):
self.assertEqual(cb1, cb2)
elif isinstance(cb1, type) and not isinstance(cb2, type):
self.assertEqual(cb1, cb2.__class__)
elif not isinstance(cb1, type) and isinstance(cb2, type):
self.assertEqual(cb1.__class__, cb2)
else:
self.assertEqual(cb1, cb2)
def get_expected_events(self, trainer):
expected_events = ["on_init_end", "on_train_begin"]
step = 0
train_dl_len = len(trainer.get_eval_dataloader())
evaluation_events = ["on_prediction_step"] * len(trainer.get_eval_dataloader()) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs):
expected_events.append("on_epoch_begin")
for _ in range(train_dl_len):
step += 1
expected_events += ["on_step_begin", "on_pre_optimizer_step", "on_optimizer_step", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log")
if trainer.args.eval_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0 or step == trainer.state.max_steps:
expected_events.append("on_save")
expected_events.append("on_epoch_end")
if trainer.args.eval_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def test_init_callback(self):
trainer = self.get_trainer()
expected_callbacks = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
# Callbacks passed at init are added to the default callbacks
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback])
expected_callbacks.append(MyTestTrainerCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
trainer = self.get_trainer(disable_tqdm=True)
expected_callbacks = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
def test_add_remove_callback(self):
expected_callbacks = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
trainer = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(DefaultFlowCallback)
expected_callbacks.remove(DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer = self.get_trainer()
cb = trainer.pop_callback(DefaultFlowCallback)
self.assertEqual(cb.__class__, DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer.add_callback(DefaultFlowCallback)
expected_callbacks.insert(0, DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
# We can also add, pop, or remove by instance
trainer = self.get_trainer()
cb = trainer.callback_handler.callbacks[0]
trainer.remove_callback(cb)
expected_callbacks.remove(DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer = self.get_trainer()
cb1 = trainer.callback_handler.callbacks[0]
cb2 = trainer.pop_callback(cb1)
self.assertEqual(cb1, cb2)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
trainer.add_callback(cb1)
expected_callbacks.insert(0, DefaultFlowCallback)
self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
def test_event_flow(self):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
with warnings.catch_warnings():
warnings.simplefilter(action="ignore", category=UserWarning)
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback])
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
# Independent log/save/eval
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], logging_steps=5)
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], save_steps=5)
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], eval_steps=5, eval_strategy="steps")
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], eval_strategy="epoch")
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
# A bit of everything
trainer = self.get_trainer(
callbacks=[MyTestTrainerCallback],
logging_steps=3,
save_steps=10,
eval_steps=5,
eval_strategy="steps",
)
trainer.train()
events = trainer.callback_handler.callbacks[-2].events
self.assertEqual(events, self.get_expected_events(trainer))
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning") as warn_mock:
trainer = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback],
)
assert str(MyTestTrainerCallback) in warn_mock.call_args[0][0]
def test_stateful_callbacks(self):
# Use something with non-defaults
cb = EarlyStoppingCallback(early_stopping_patience=5, early_stopping_threshold=0.2)
trainer = self.get_trainer(
callbacks=[cb],
load_best_model_at_end=True,
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
)
trainer.train()
# Create a new trainer with defaults
trainer = self.get_trainer(
callbacks=[EarlyStoppingCallback()],
load_best_model_at_end=True,
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
restore_callback_states_from_checkpoint=True,
)
# Load it back in and verify values
checkpoint = os.path.join(self.output_dir, "checkpoint-2")
trainer.train(resume_from_checkpoint=checkpoint)
cb = [
callback for callback in trainer.callback_handler.callbacks if isinstance(callback, EarlyStoppingCallback)
][0]
assert cb.early_stopping_patience == 5
assert cb.early_stopping_threshold == 0.2
def test_stateful_mixed_callbacks(self):
# Use two callbacks, one stateful one not
# Use something with non-defaults
cbs = [
MyTestTrainerCallback(my_test_state="another value"),
EarlyStoppingCallback(early_stopping_patience=5, early_stopping_threshold=0.2),
]
trainer = self.get_trainer(
callbacks=cbs,
load_best_model_at_end=True,
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
)
trainer.train()
# Create a new trainer with defaults
trainer = self.get_trainer(
callbacks=[EarlyStoppingCallback(), MyTestTrainerCallback()],
load_best_model_at_end=True,
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
restore_callback_states_from_checkpoint=True,
)
# Load it back in and verify values
checkpoint = os.path.join(self.output_dir, "checkpoint-2")
trainer.train(resume_from_checkpoint=checkpoint)
cbs = [
callback
for callback in trainer.callback_handler.callbacks
if isinstance(callback, (EarlyStoppingCallback, MyTestTrainerCallback))
]
assert len(cbs) == 2
my_test, early_stopping = cbs
assert early_stopping.early_stopping_patience == 5
assert early_stopping.early_stopping_threshold == 0.2
assert my_test.my_test_state == "test"
def test_stateful_duplicate_callbacks(self):
# Use something with non-defaults
cbs = [MyTestExportableCallback("first"), MyTestExportableCallback("second")]
trainer = self.get_trainer(
callbacks=cbs,
load_best_model_at_end=True,
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
)
trainer.train()
# Create a new trainer with defaults
trainer = self.get_trainer(
callbacks=[MyTestExportableCallback(), MyTestExportableCallback()],
load_best_model_at_end=True,
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
restore_callback_states_from_checkpoint=True,
)
# Load it back in and verify values
checkpoint = os.path.join(self.output_dir, "checkpoint-2")
trainer.train(resume_from_checkpoint=checkpoint)
cbs = [
callback
for callback in trainer.callback_handler.callbacks
if isinstance(callback, MyTestExportableCallback)
]
assert len(cbs) == 2
assert cbs[0].my_test_state == "first"
assert cbs[1].my_test_state == "second"
def test_missing_stateful_callback(self):
cb = EarlyStoppingCallback()
trainer = self.get_trainer(
callbacks=[cb],
load_best_model_at_end=True,
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
)
trainer.train()
# Create a new trainer with defaults
trainer = self.get_trainer(
save_strategy="steps",
eval_strategy="steps",
save_steps=2,
eval_steps=2,
max_steps=2,
restore_callback_states_from_checkpoint=True,
)
# Load it back in and verify values
checkpoint = os.path.join(self.output_dir, "checkpoint-2")
# warning should be emitted for not-present callbacks
with patch("transformers.trainer.logger.warning") as warn_mock:
trainer.train(resume_from_checkpoint=checkpoint)
assert "EarlyStoppingCallback" in warn_mock.call_args[0][0]
def test_stateful_control(self):
trainer = self.get_trainer(
max_steps=2,
save_strategy="steps",
save_steps=2,
)
trainer.train()
# Load it back in and verify values
trainer = self.get_trainer(max_steps=2, restore_callback_states_from_checkpoint=True)
checkpoint = os.path.join(self.output_dir, "checkpoint-2")
trainer.state = TrainerState.load_from_json(os.path.join(checkpoint, TRAINER_STATE_NAME))
trainer._load_callback_state()
assert trainer.control.should_training_stop
def test_no_duplicate_save_on_epoch_save_strategy(self):
times_saved = 0
class OnEndCallback(TrainerCallback):
def on_step_end(self, args: TrainingArguments, state: TrainerState, control, **kwargs):
nonlocal times_saved
if control.should_save:
times_saved += 1
def on_epoch_end(self, args: TrainingArguments, state: TrainerState, control, **kwargs):
nonlocal times_saved
if control.should_save:
times_saved += 1
trainer = self.get_trainer(max_steps=2, save_strategy="epoch", callbacks=[OnEndCallback])
trainer.train()
assert times_saved == 1
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