246 lines
10 KiB
Python
246 lines
10 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import shutil
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import tempfile
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import unittest
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from unittest.mock import patch
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from transformers import (
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DefaultFlowCallback,
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IntervalStrategy,
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PrinterCallback,
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ProgressCallback,
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Trainer,
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TrainerCallback,
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TrainingArguments,
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is_torch_available,
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)
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from transformers.testing_utils import require_torch
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if is_torch_available():
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from transformers.trainer import DEFAULT_CALLBACKS
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from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
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class MyTestTrainerCallback(TrainerCallback):
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"A callback that registers the events that goes through."
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def __init__(self):
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self.events = []
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def on_init_end(self, args, state, control, **kwargs):
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self.events.append("on_init_end")
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def on_train_begin(self, args, state, control, **kwargs):
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self.events.append("on_train_begin")
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def on_train_end(self, args, state, control, **kwargs):
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self.events.append("on_train_end")
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def on_epoch_begin(self, args, state, control, **kwargs):
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self.events.append("on_epoch_begin")
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def on_epoch_end(self, args, state, control, **kwargs):
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self.events.append("on_epoch_end")
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def on_step_begin(self, args, state, control, **kwargs):
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self.events.append("on_step_begin")
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def on_step_end(self, args, state, control, **kwargs):
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self.events.append("on_step_end")
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def on_evaluate(self, args, state, control, **kwargs):
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self.events.append("on_evaluate")
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def on_predict(self, args, state, control, **kwargs):
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self.events.append("on_predict")
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def on_save(self, args, state, control, **kwargs):
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self.events.append("on_save")
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def on_log(self, args, state, control, **kwargs):
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self.events.append("on_log")
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def on_prediction_step(self, args, state, control, **kwargs):
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self.events.append("on_prediction_step")
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@require_torch
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class TrainerCallbackTest(unittest.TestCase):
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def setUp(self):
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self.output_dir = tempfile.mkdtemp()
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def tearDown(self):
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shutil.rmtree(self.output_dir)
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def get_trainer(self, a=0, b=0, train_len=64, eval_len=64, callbacks=None, disable_tqdm=False, **kwargs):
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# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
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# its set to False since the tests later on depend on its value.
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train_dataset = RegressionDataset(length=train_len)
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eval_dataset = RegressionDataset(length=eval_len)
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config = RegressionModelConfig(a=a, b=b)
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model = RegressionPreTrainedModel(config)
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args = TrainingArguments(self.output_dir, disable_tqdm=disable_tqdm, report_to=[], **kwargs)
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return Trainer(
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model,
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args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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callbacks=callbacks,
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)
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def check_callbacks_equality(self, cbs1, cbs2):
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self.assertEqual(len(cbs1), len(cbs2))
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# Order doesn't matter
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cbs1 = sorted(cbs1, key=lambda cb: cb.__name__ if isinstance(cb, type) else cb.__class__.__name__)
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cbs2 = sorted(cbs2, key=lambda cb: cb.__name__ if isinstance(cb, type) else cb.__class__.__name__)
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for cb1, cb2 in zip(cbs1, cbs2):
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if isinstance(cb1, type) and isinstance(cb2, type):
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self.assertEqual(cb1, cb2)
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elif isinstance(cb1, type) and not isinstance(cb2, type):
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self.assertEqual(cb1, cb2.__class__)
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elif not isinstance(cb1, type) and isinstance(cb2, type):
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self.assertEqual(cb1.__class__, cb2)
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else:
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self.assertEqual(cb1, cb2)
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def get_expected_events(self, trainer):
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expected_events = ["on_init_end", "on_train_begin"]
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step = 0
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train_dl_len = len(trainer.get_eval_dataloader())
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evaluation_events = ["on_prediction_step"] * len(trainer.get_eval_dataloader()) + ["on_log", "on_evaluate"]
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for _ in range(trainer.state.num_train_epochs):
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expected_events.append("on_epoch_begin")
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for _ in range(train_dl_len):
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step += 1
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expected_events += ["on_step_begin", "on_step_end"]
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if step % trainer.args.logging_steps == 0:
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expected_events.append("on_log")
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if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
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expected_events += evaluation_events.copy()
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if step % trainer.args.save_steps == 0:
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expected_events.append("on_save")
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expected_events.append("on_epoch_end")
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if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
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expected_events += evaluation_events.copy()
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expected_events += ["on_log", "on_train_end"]
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return expected_events
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def test_init_callback(self):
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trainer = self.get_trainer()
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expected_callbacks = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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# Callbacks passed at init are added to the default callbacks
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trainer = self.get_trainer(callbacks=[MyTestTrainerCallback])
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expected_callbacks.append(MyTestTrainerCallback)
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
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trainer = self.get_trainer(disable_tqdm=True)
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expected_callbacks = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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def test_add_remove_callback(self):
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expected_callbacks = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
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trainer = self.get_trainer()
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# We can add, pop, or remove by class name
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trainer.remove_callback(DefaultFlowCallback)
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expected_callbacks.remove(DefaultFlowCallback)
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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trainer = self.get_trainer()
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cb = trainer.pop_callback(DefaultFlowCallback)
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self.assertEqual(cb.__class__, DefaultFlowCallback)
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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trainer.add_callback(DefaultFlowCallback)
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expected_callbacks.insert(0, DefaultFlowCallback)
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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# We can also add, pop, or remove by instance
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trainer = self.get_trainer()
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cb = trainer.callback_handler.callbacks[0]
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trainer.remove_callback(cb)
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expected_callbacks.remove(DefaultFlowCallback)
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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trainer = self.get_trainer()
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cb1 = trainer.callback_handler.callbacks[0]
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cb2 = trainer.pop_callback(cb1)
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self.assertEqual(cb1, cb2)
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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trainer.add_callback(cb1)
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expected_callbacks.insert(0, DefaultFlowCallback)
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self.check_callbacks_equality(trainer.callback_handler.callbacks, expected_callbacks)
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def test_event_flow(self):
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import warnings
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# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
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warnings.simplefilter(action="ignore", category=UserWarning)
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trainer = self.get_trainer(callbacks=[MyTestTrainerCallback])
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trainer.train()
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events = trainer.callback_handler.callbacks[-2].events
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self.assertEqual(events, self.get_expected_events(trainer))
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# Independent log/save/eval
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trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], logging_steps=5)
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trainer.train()
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events = trainer.callback_handler.callbacks[-2].events
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self.assertEqual(events, self.get_expected_events(trainer))
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trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], save_steps=5)
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trainer.train()
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events = trainer.callback_handler.callbacks[-2].events
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self.assertEqual(events, self.get_expected_events(trainer))
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trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], eval_steps=5, evaluation_strategy="steps")
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trainer.train()
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events = trainer.callback_handler.callbacks[-2].events
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self.assertEqual(events, self.get_expected_events(trainer))
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trainer = self.get_trainer(callbacks=[MyTestTrainerCallback], evaluation_strategy="epoch")
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trainer.train()
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events = trainer.callback_handler.callbacks[-2].events
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self.assertEqual(events, self.get_expected_events(trainer))
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# A bit of everything
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trainer = self.get_trainer(
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callbacks=[MyTestTrainerCallback],
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logging_steps=3,
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save_steps=10,
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eval_steps=5,
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evaluation_strategy="steps",
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)
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trainer.train()
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events = trainer.callback_handler.callbacks[-2].events
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self.assertEqual(events, self.get_expected_events(trainer))
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# warning should be emitted for duplicated callbacks
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with patch("transformers.trainer_callback.logger.warning") as warn_mock:
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trainer = self.get_trainer(
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callbacks=[MyTestTrainerCallback, MyTestTrainerCallback],
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)
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assert str(MyTestTrainerCallback) in warn_mock.call_args[0][0]
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