def test_tensorboard_logging_parity(
self,
summary_writer,
mmf,
lightning,
logistics,
logistics_logs,
report_logs,
trainer_logs,
mkdirs,
):
# mmf trainer
mmf_trainer = get_mmf_trainer(
max_updates=8,
batch_size=2,
max_epochs=None,
log_interval=3,
tensorboard=True,
)
def _add_scalars_mmf(log_dict, iteration):
self.mmf_tensorboard_logs.append({iteration: log_dict})
mmf_trainer.load_metrics()
logistics_callback = LogisticsCallback(mmf_trainer.config, mmf_trainer)
logistics_callback.snapshot_timer = MagicMock(return_value=None)
logistics_callback.train_timer = Timer()
logistics_callback.tb_writer.add_scalars = _add_scalars_mmf
mmf_trainer.logistics_callback = logistics_callback
mmf_trainer.callbacks = [logistics_callback]
mmf_trainer.early_stop_callback = MagicMock(return_value=None)
mmf_trainer.on_update_end = logistics_callback.on_update_end
mmf_trainer.training_loop()
# lightning_trainer
trainer = get_lightning_trainer(
max_steps=8,
batch_size=2,
prepare_trainer=False,
log_every_n_steps=3,
val_check_interval=9,
tensorboard=True,
)
def _add_scalars_lightning(log_dict, iteration):
self.lightning_tensorboard_logs.append({iteration: log_dict})
def _on_fit_start_callback():
trainer.tb_writer.add_scalars = _add_scalars_lightning
callback = LightningLoopCallback(trainer)
run_lightning_trainer_with_callback(
trainer, callback, on_fit_start_callback=_on_fit_start_callback)
self.assertEqual(len(self.mmf_tensorboard_logs),
len(self.lightning_tensorboard_logs))
for mmf, lightning in zip(self.mmf_tensorboard_logs,
self.lightning_tensorboard_logs):
self.assertDictEqual(mmf, lightning)
def configure_callbacks(self):
self.checkpoint_callback = CheckpointCallback(self.config, self)
self.early_stop_callback = EarlyStoppingCallback(self.config, self)
self.logistics_callback = LogisticsCallback(self.config, self)
self.lr_scheduler_callback = LRSchedulerCallback(self.config, self)
# Add callbacks for execution during events
self.callbacks.append(self.checkpoint_callback)
self.callbacks.append(self.logistics_callback)
self.callbacks.append(self.lr_scheduler_callback)
def configure_callbacks(self):
self.checkpoint_callback = CheckpointCallback(self.config, self)
self.early_stop_callback = EarlyStoppingCallback(self.config, self)
self.logistics_callback = LogisticsCallback(self.config, self)
self.lr_scheduler_callback = LRSchedulerCallback(self.config, self)
# Add callbacks for execution during events
self.callbacks.append(self.lr_scheduler_callback)
# checkpoint_callback needs to be called after lr_scheduler_callback so that
# lr_scheduler_callback._scheduler.step() happens before saving checkpoints
# (otherwise the saved last_epoch in scheduler would be wrong)
self.callbacks.append(self.checkpoint_callback)
self.callbacks.append(self.logistics_callback)
def configure_callbacks(self):
self.checkpoint_callback = CheckpointCallback(self.config, self)
self.early_stop_callback = EarlyStoppingCallback(self.config, self)
self.logistics_callback = LogisticsCallback(self.config, self)
self.lr_scheduler_callback = LRSchedulerCallback(self.config, self)
# Reset callbacks as they are class variables and would be shared between
# multiple interactive shell calls to `run`
self.callbacks = []
# Add callbacks for execution during events
self.callbacks.append(self.lr_scheduler_callback)
# checkpoint_callback needs to be called after lr_scheduler_callback so that
# lr_scheduler_callback._scheduler.step() happens before saving checkpoints
# (otherwise the saved last_epoch in scheduler would be wrong)
self.callbacks.append(self.checkpoint_callback)
self.callbacks.append(self.logistics_callback)
def setUp(self):
self.tmpdir = tempfile.mkdtemp()
self.trainer = argparse.Namespace()
self.config = load_yaml(os.path.join("configs", "defaults.yaml"))
self.config = OmegaConf.merge(
self.config,
{
"model": "simple",
"model_config": {},
"training": {
"checkpoint_interval": 1,
"evaluation_interval": 10,
"early_stop": {
"criteria": "val/total_loss"
},
"batch_size": 16,
"log_interval": 10,
"logger_level": "info",
},
"env": {
"save_dir": self.tmpdir
},
},
)
# Keep original copy for testing purposes
self.trainer.config = deepcopy(self.config)
registry.register("config", self.trainer.config)
setup_logger()
self.report = Mock(spec=Report)
self.report.dataset_name = "abcd"
self.report.dataset_type = "test"
self.trainer.model = SimpleModule()
self.trainer.val_loader = torch.utils.data.DataLoader(
NumbersDataset(), batch_size=self.config.training.batch_size)
self.trainer.optimizer = torch.optim.Adam(
self.trainer.model.parameters(), lr=1e-01)
self.trainer.device = "cpu"
self.trainer.num_updates = 0
self.trainer.current_iteration = 0
self.trainer.current_epoch = 0
self.trainer.max_updates = 0
self.trainer.meter = Meter()
self.cb = LogisticsCallback(self.config, self.trainer)
def test_validation_parity(self, summarize_report_fn, test_reporter, sw,
mkdirs):
mmf_trainer = get_mmf_trainer(max_updates=8,
batch_size=2,
max_epochs=None,
evaluation_interval=3)
mmf_trainer.load_metrics()
logistics_callback = LogisticsCallback(mmf_trainer.config, mmf_trainer)
logistics_callback.snapshot_timer = Timer()
logistics_callback.train_timer = Timer()
mmf_trainer.logistics_callback = logistics_callback
mmf_trainer.callbacks.append(logistics_callback)
mmf_trainer.early_stop_callback = MagicMock(return_value=None)
mmf_trainer.on_validation_end = logistics_callback.on_validation_end
mmf_trainer.training_loop()
calls = summarize_report_fn.call_args_list
self.assertEqual(3, len(calls))
self.assertEqual(len(self.ground_truths), len(calls))
self._check_values(calls)
def configure_callbacks(self):
self.checkpoint_callback = CheckpointCallback(self.config, self)
self.early_stop_callback = EarlyStoppingCallback(self.config, self)
self.logistics_callback = LogisticsCallback(self.config, self)
self.lr_scheduler_callback = LRSchedulerCallback(self.config, self)
# Reset callbacks as they are class variables and would be shared between
# multiple interactive shell calls to `run`
self.callbacks = []
# Add callbacks for execution during events
self.callbacks.append(self.lr_scheduler_callback)
# checkpoint_callback needs to be called after lr_scheduler_callback so that
# lr_scheduler_callback._scheduler.step() happens before saving checkpoints
# (otherwise the saved last_epoch in scheduler would be wrong)
self.callbacks.append(self.checkpoint_callback)
self.callbacks.append(self.logistics_callback)
# Add all customized callbacks defined by users
for callback in self.config.training.get("callbacks", []):
callback_type = callback.type
callback_param = callback.params
callback_cls = registry.get_callback_class(callback_type)
self.callbacks.append(
callback_cls(self.config, self, **callback_param))