def __init__(self, config: Config, model: torch.nn.Module):
optimizer: torch.optim.Optimizer = create_optimizer(
config.optimizer, model)
self.scheduler: torch.optim.lr_scheduler = (create_scheduler(
config.scheduler, optimizer) if config.scheduler else Scheduler())
self.optimizer = precision.wrap_optimizer(optimizer)
self.config = config
def set_up_training(self, state: TrainingState,
training_data: BatchIterator):
if cuda.CUDA_ENABLED:
state.model.cuda()
state.scheduler.prepare(training_data, self.config.epochs)
if cuda.DISTRIBUTED_WORLD_SIZE > 1:
device_id = torch.cuda.current_device()
state.model = DistributedModel(
module=state.model,
device_ids=[device_id],
output_device=device_id,
broadcast_buffers=False,
)
state.optimizer = precision.wrap_optimizer(state.optimizer)
state.start_time = time.time()
def train(
self,
train_iter: BatchIterator,
eval_iter: BatchIterator,
model: Model,
metric_reporter: MetricReporter,
train_config: PyTextConfig,
rank: int = 0,
) -> Tuple[torch.nn.Module, Any]:
"""
Train and eval a model, the model states will be modified. This function
iterates epochs specified in config, and for each epoch do:
1. Train model using training data, aggregate and report training results
2. Adjust learning rate if scheduler is specified
3. Evaluate model using evaluation data
4. Calculate metrics based on evaluation results and select best model
Args:
train_iter (BatchIterator): batch iterator of training data
eval_iter (BatchIterator): batch iterator of evaluation data
model (Model): model to be trained
metric_reporter (MetricReporter): compute metric based on training
output and report results to console, file.. etc
train_config (PyTextConfig): training config
training_result (Optional): only meaningful for Hogwild training. default
is None
rank (int): only used in distributed training, the rank of the current
training thread, evaluation will only be done in rank 0
Returns:
model, best_metric: the trained model together with the best metric
"""
with timing.time("pre-training"):
world_size = 1
if cuda.CUDA_ENABLED:
model = model.cuda()
world_size = cuda.DISTRIBUTED_WORLD_SIZE
if world_size > 1:
device_id = torch.cuda.current_device()
model = DistributedModel(
module=model,
device_ids=[device_id],
output_device=device_id,
broadcast_buffers=False,
)
best_metric = None
last_best_epoch = 0
if self.lr_scheduler:
self.lr_scheduler.prepare(train_iter, self.config.epochs)
self.optimizer = precision.wrap_optimizer(self.optimizer)
def training_pre_batch_callback():
if world_size > 1:
# replace optimizer.zero_grad() here to work with DDP
# in cases where some parameters don't receive grads at each step
# loss.backward will set grad for params in the computation graph
# we can thus follow which params are left out and call .backward
# on them manually
for p in model.parameters():
if p.grad is not None:
p.grad.detach_()
p.grad = None
else:
self.optimizer.zero_grad()
def training_backprop(loss):
with timing.time("loss.backward"):
precision.backward(self.optimizer, loss)
if world_size > 1:
# DDP fix when some parameters don't receive grads
for p in model.parameters():
if p.requires_grad and p.grad is None:
p.backward(torch.zeros_like(p.data))
if self.lr_scheduler:
self.lr_scheduler.step_batch()
if self.config.max_clip_norm is not None:
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), self.config.max_clip_norm)
else:
grad_norm = None
with timing.time("optimizer.step"):
self.optimizer.step()
# grad_norm could be used to check grads sync in distributed training
return grad_norm
time_start = time.time()
best_model_state = None
for epoch in range(1, self.config.epochs + 1):
sys.stdout.flush()
if self.config.target_time_limit_seconds > 0 and epoch > 1:
time_elapsed = time.time() - time_start
mean_epoch_time = time_elapsed / float(epoch - 1)
expected_next_epoch_time = time_elapsed + mean_epoch_time
if expected_next_epoch_time > self.config.target_time_limit_seconds:
print(
f"Training stopped after {epoch - 1} epochs and "
f"{int(time_elapsed)} seconds, due to the target max training "
f"time of {self.config.target_time_limit_seconds} seconds."
)
break
print(f"Rank {rank} worker: Starting epoch #{epoch}")
model.train()
lrs = (str(lr) for lr in learning_rates(self.optimizer))
print(f"Learning rate(s): {', '.join(lrs)}")
with timing.time("epoch train"):
self._run_epoch(
Stage.TRAIN,
epoch,
train_iter,
model,
metric_reporter,
pre_batch=training_pre_batch_callback,
backprop=training_backprop,
rank=rank,
num_samples_to_log_progress=self.config.
num_samples_to_log_progress,
)
if not self.config.do_eval:
continue
with timing.time("epoch eval"):
model.eval(Stage.EVAL)
with torch.no_grad():
eval_metric = self._run_epoch(
Stage.EVAL,
epoch,
eval_iter,
model,
metric_reporter,
rank=rank,
num_samples_to_log_progress=(
self.config.num_samples_to_log_progress),
)
# Step the learning rate scheduler(s)
if self.lr_scheduler:
assert eval_metric is not None
self.lr_scheduler.step_epoch(
metrics=metric_reporter.get_model_select_metric(
eval_metric),
epoch=epoch,
)
# choose best model.
if metric_reporter.compare_metric(eval_metric, best_metric):
with timing.time("save checkpoint model"):
last_best_epoch = epoch
best_metric = eval_metric
# Only rank = 0 trainer saves modules.
if train_config.save_module_checkpoints and rank == 0:
model.save_modules(
base_path=train_config.modules_save_dir,
suffix=f"-ep{epoch}",
)
if rank == 0:
print(f"Rank {rank} worker: Found a better model!")
model_state = model.state_dict()
# save to cpu to avoid multiple model copies in gpu memory
if cuda.CUDA_ENABLED:
for key, state in model_state.items():
model_state[key] = state.cpu()
best_model_state = model_state
if self.config.early_stop_after > 0 and (
epoch - last_best_epoch == self.config.early_stop_after):
print(f"Rank {rank} worker: Eval metric hasn't changed for " +
f"{self.config.early_stop_after} epochs. Stopping now.")
break
sys.stdout.flush()
if rank == 0 and best_model_state is not None:
if cuda.CUDA_ENABLED:
for key, state in best_model_state.items():
best_model_state[key] = state.cuda()
model.load_state_dict(best_model_state)
return model, best_metric
