def train_from_state(
self,
state: TrainingState,
training_data: BatchIterator,
eval_data: BatchIterator,
metric_reporter: MetricReporter,
train_config: PyTextConfig,
) -> Tuple[torch.nn.Module, Any]:
"""
Train and eval a model from a given training state 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:
training_state (TrainingState): contrains stateful information to be
able to restore a training job
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
Returns:
model, best_metric: the trained model together with the best metric
"""
training_data = self.set_up_training(state, training_data)
model = state.model
rank = state.rank
trainable_params = sum(p.numel() for p in state.model.parameters()
if p.requires_grad)
print(f"Model :{model}")
print(f"Num trainable parameters: {trainable_params}")
self.sparsifier.initialize(self, state, eval_data, metric_reporter,
train_config)
while self.continue_training(state):
self.sparsifier.op_pre_epoch(self, state)
state.epoch += 1
state.epochs_since_last_improvement += 1
lrs = learning_rates(state.optimizer)
print(f"\nWorker {state.rank} starting epoch {state.epoch}")
print(f"Learning rate(s): {', '.join(map(str, lrs))}")
with timing.time("train epoch"):
state.stage = Stage.TRAIN
state.model.train()
print(f"start training epoch {state.epoch}")
epoch_data = training_data
if self.config.num_batches_per_epoch:
# We want to limit the number of batches in the epoch;
# equivalent to epoch_data[:num_batches_per_epoch] for iterators.
# In this case we set the training data iterator to cycle earlier
# in the training process, so when it reaches the end it will
# loop back to the beginning.
epoch_data = itertools.islice(
epoch_data, self.config.num_batches_per_epoch)
self.run_epoch(state, epoch_data, metric_reporter)
if not self.config.do_eval:
continue
with timing.time("eval epoch"):
state.stage = Stage.EVAL
model.eval(Stage.EVAL)
print(f"start evaluating epoch {state.epoch}")
with torch.no_grad():
eval_metric = self.run_epoch(state, eval_data,
metric_reporter)
# Step the learning rate scheduler(s)
assert eval_metric is not None
state.scheduler.step_epoch(
metrics=metric_reporter.get_model_select_metric(eval_metric),
epoch=state.epoch,
)
# Did we train a better model?
better_model = metric_reporter.compare_metric(
eval_metric, state.best_model_metric)
if better_model:
self.update_best_model(state, train_config, eval_metric)
if better_model or train_config.save_all_checkpoints:
self.save_checkpoint(state, train_config)
if self.optimizer.finalize():
should_update_model = True
eval_metric = None
if self.config.do_eval:
state.stage = Stage.EVAL
model.eval(Stage.EVAL)
print(f"start evaluating finalized state")
with torch.no_grad():
eval_metric = self.run_epoch(state, eval_data,
metric_reporter)
should_update_model = metric_reporter.compare_metric(
eval_metric, state.best_model_metric)
if should_update_model:
self.update_best_model(state, train_config, eval_metric)
if should_update_model or train_config.save_all_checkpoints:
self.save_checkpoint(state, train_config)
# Only bother loading the best model for master worker
if (rank == 0 and state.best_model_state is not None
and self.config.load_best_model_after_train):
self.load_best_model(state)
return state.model, state.best_model_metric
def train(
self,
train_iter: BatchIterator,
eval_iter: BatchIterator,
model: Model,
metric_reporter: MetricReporter,
train_config: PyTextConfig,
optimizer: torch.optim.Optimizer,
scheduler=None,
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
optimizer (torch.optim.Optimizer): torch optimizer to be used
scheduler (Optional[torch.optim.lr_scheduler]): learning rate scheduler,
default is None
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
"""
timer = time_utils.StageTimer()
world_size = 1
if cuda_utils.CUDA_ENABLED:
model = model.cuda()
world_size = cuda_utils.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,
)
timer.add_stage(stage="init_distributed_model")
best_metric = None
last_best_epoch = 0
scheduler = self._prepare_scheduler(train_iter, scheduler)
timer.add_stage(stage="pre_training")
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:
optimizer.zero_grad()
def training_backprop(loss, timer):
loss.backward()
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))
timer.add_stage("backward")
if scheduler:
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
optimizer.step()
timer.add_stage("update_grads")
# grad_norm could be used to check grads sync in distributed training
return grad_norm
time_start = time.time()
for epoch in range(1, self.config.epochs + 1):
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(optimizer))
print(f"Learning rate(s): {', '.join(lrs)}")
self._run_epoch(
Stage.TRAIN,
epoch,
train_iter,
model,
metric_reporter,
pre_batch=training_pre_batch_callback,
backprop=training_backprop,
rank=rank,
)
timer.add_stage(stage=f"epoch_train")
model.eval(Stage.EVAL)
with torch.no_grad():
eval_metric = self._run_epoch(Stage.EVAL,
epoch,
eval_iter,
model,
metric_reporter,
rank=rank)
timer.add_stage(stage=f"epoch_eval")
# Step the learning rate scheduler(s)
if scheduler:
assert eval_metric is not None
scheduler.step(
metrics=metric_reporter.get_model_select_metric(
eval_metric),
epoch=epoch,
)
# choose best model.
if metric_reporter.compare_metric(eval_metric, best_metric):
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_utils.CUDA_ENABLED:
for key, state in model_state.items():
model_state[key] = state.cpu()
best_model_state = model_state
timer.add_stage(stage=f"epoch_save/load_module")
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:
if cuda_utils.CUDA_ENABLED:
for key, state in best_model_state.items():
best_model_state[key] = state.cuda()
model.load_state_dict(best_model_state)
timer.report("Trainer train timer")
return model, best_metric
def train(
self,
train_iter: BatchIterator,
eval_iter: BatchIterator,
model: Model,
metric_reporter: MetricReporter,
train_config: PyTextConfig,
optimizers: List[torch.optim.Optimizer],
scheduler=None,
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
optimizers (List[torch.optim.Optimizer]): a list of torch optimizers, in
most of the case only contains one optimizer
scheduler (Optional[torch.optim.lr_scheduler]): learning rate scheduler,
default is None
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
"""
if cuda_utils.CUDA_ENABLED:
model = model.cuda()
if cuda_utils.DISTRIBUTED_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
best_model_state = None
scheduler = self._prepare_scheduler(train_iter, scheduler)
def training_pre_batch_callback():
optimizer_zero_grad(optimizers)
def training_backprop(loss):
loss.backward()
if scheduler:
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
optimizer_step(optimizers)
# grad_norm could be used to check grads sync in distributed training
return grad_norm
for epoch in range(1, self.config.epochs + 1):
print(f"Rank {rank} worker: Starting epoch #{epoch}")
model.train()
lrs = (str(lr) for lr in learning_rates(optimizers))
print(f"Learning rate(s): {', '.join(lrs)}")
self._run_epoch(
Stage.TRAIN,
epoch,
train_iter,
model,
metric_reporter,
pre_batch=training_pre_batch_callback,
backprop=training_backprop,
rank=rank,
)
model.eval(Stage.EVAL)
eval_metric = self._run_epoch(Stage.EVAL,
epoch,
eval_iter,
model,
metric_reporter,
rank=rank)
# Step the learning rate scheduler(s)
if scheduler:
assert eval_metric is not None
scheduler.step(
metrics=metric_reporter.get_model_select_metric(
eval_metric),
epoch=epoch,
)
# choose best model.
if metric_reporter.compare_metric(eval_metric, best_metric):
print(
f"Rank {rank} worker: Found a better model! Saving the model state."
)
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}")
best_model_state = copy.deepcopy(model.state_dict())
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()
model.load_state_dict(best_model_state)
return model, best_metric
def train(
self,
training_data: BatchIterator,
eval_data: 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
"""
state = TrainingState(model=model,
optimizer=self.optimizer,
scheduler=self.scheduler,
rank=rank)
self.set_up_training(state, training_data)
while self.continue_training(state):
state.epoch += 1
state.epochs_since_last_improvement += 1
print(f"Worker {state.rank} starting epoch {state.epoch}",
flush=True)
lrs = learning_rates(state.optimizer)
print(f"Learning rate(s): {', '.join(map(str, lrs))}")
with timing.time("train epoch"):
state.stage = Stage.TRAIN
state.model.train()
self.run_epoch(state, training_data, metric_reporter)
if not self.config.do_eval:
continue
with timing.time("eval epoch"):
state.stage = Stage.EVAL
model.eval(Stage.EVAL)
with torch.no_grad():
eval_metric = self.run_epoch(state, eval_data,
metric_reporter)
# Step the learning rate scheduler(s)
assert eval_metric is not None
state.scheduler.step_epoch(
metrics=metric_reporter.get_model_select_metric(eval_metric),
epoch=state.epoch,
)
# Did we train a better model?
if metric_reporter.compare_metric(eval_metric,
state.best_model_metric):
state.epochs_since_last_improvement = 0
state.best_model_metric = eval_metric
self.save_checkpoint(state, train_config)
# Only bother loading the best model for master worker
if rank == 0 and state.best_model_state is not None:
self.load_best_model(state)
return state.model, state.best_model_metric
def train(
self,
train_iter: BatchIterator,
eval_iter: BatchIterator,
model: Model,
metric_reporter: MetricReporter,
train_config: PyTextConfig,
optimizers: List[torch.optim.Optimizer],
scheduler=None,
rank: int = 0,
) -> Tuple[torch.nn.Module, Any]:
if cuda_utils.CUDA_ENABLED:
model = model.cuda()
if cuda_utils.DISTRIBUTED_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
best_model_path = None
scheduler = self._prepare_scheduler(train_iter, scheduler)
def training_pre_batch_callback():
optimizer_zero_grad(optimizers)
def training_backprop(loss):
loss.backward()
if scheduler:
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
optimizer_step(optimizers)
# grad_norm could be used to check grads sync in distributed training
return grad_norm
len_sched_ix = 0
# Used since we need the infinite iterator (only created and called once)
def batch_generator_for_epoch(it):
n = len(it)
while n > 0:
yield next(it)
n -= 1
for epoch in range(self.config.start_epoch, self.config.epochs + 1):
# Set the dialogue length in the fields, to be used by the postprocessor
while self.config.length_schedule_per_epoch \
and len_sched_ix < len(self.config.length_schedule_per_epoch) \
and epoch >= self.config.length_schedule_per_epoch[len_sched_ix][0]:
train_iter.max_n_turns = \
self.config.length_schedule_per_epoch[len_sched_ix][1]
eval_iter.max_n_turns = \
self.config.length_schedule_per_epoch[len_sched_ix][1]
len_sched_ix += 1
LOG.info(f"\nRank {rank} worker: Starting epoch #{epoch}")
model.train()
lrs = (str(lr) for lr in learning_rates(optimizers))
LOG.info(f"Learning rate(s): {', '.join(lrs)}")
self._run_epoch(
Stage.TRAIN,
epoch,
batch_generator_for_epoch(train_iter),
model,
metric_reporter,
pre_batch=training_pre_batch_callback,
backprop=training_backprop,
rank=rank,
)
model.eval(Stage.EVAL)
with torch.no_grad():
eval_metric = self._run_epoch(
Stage.EVAL,
epoch,
batch_generator_for_epoch(eval_iter),
model,
metric_reporter,
rank=rank)
# Step the learning rate scheduler(s)
if scheduler:
assert eval_metric is not None
scheduler.step(
metrics=metric_reporter.get_model_select_metric(
eval_metric),
epoch=epoch,
)
# choose best model.
if metric_reporter.compare_metric(eval_metric, best_metric):
LOG.info(
f"Rank {rank} worker: Found a better model! Saving the model state for epoch #{epoch}."
)
last_best_epoch = epoch
best_metric = eval_metric
# Only rank = 0 trainer saves modules.
if train_config.save_module_checkpoints and rank == 0:
best_model_path = os.path.join(
train_config.modules_save_dir, "best_model")
optimizer, = optimizers # PyText only ever returns a single optimizer in this list
torch.save(
ModelState(
epoch=epoch,
parameters=model.state_dict(),
optimizer=optimizer.state_dict(),
), best_model_path)
if (self.config.early_stop_after > 0 and
(epoch - last_best_epoch == self.config.early_stop_after)):
LOG.info(
f"Rank {rank} worker: Eval metric hasn't changed for "
f"{self.config.early_stop_after} epochs. Stopping now.")
break
sys.stdout.flush()
train_iter.close()
eval_iter.close()
model.load_state_dict(torch.load(best_model_path).parameters)
return model, best_metric
