def checkpoint(self, train_state, step):
"""Saves checkpoint.
Syncs the model state across replicas if needed.
Args:
train_state: TrainSate; A flax struct that keeps model state and optimizer
state.
step: int; Number of steps passes so far during training.
Returns:
train_state
"""
checkpoint_flag = False
if self.hparams.get('ckpnt_steps', None) and self.hparams.checkpoint:
if step in self.hparams.get('ckpnt_steps'):
checkpoint_flag = True
elif ((step % self.checkpoint_frequency == 0) or
(step == self.total_steps)) and self.hparams.checkpoint:
checkpoint_flag = True
if checkpoint_flag:
# Sync model state across replicas.
train_state = pipeline_utils.sync_model_state_across_replicas(
train_state)
if jax.host_id() == 0:
pipeline_utils.save_checkpoint(self.experiment_dir,
train_state,
keep=self.hparams.keep_ckpts)
return train_state
def maybe_eval_and_log(self, eval_summary, master, step, tick,
train_metrics, train_summary):
"""Maybe evaluate and log based on the current step value."""
if (step % self.eval_frequency == 0) or (step == self.total_steps):
del eval_summary
del train_summary
train_metrics = common_utils.get_metrics(train_metrics)
train_summary = pipeline_utils.compute_global_mean_metrics(
train_metrics)
tock = time.time()
steps_per_sec = self.eval_frequency / (tock - tick)
tick = tock
# log train summary
if master:
self.write_train_summary(step=step,
metric_dict=train_metrics,
summary=train_summary,
steps_per_sec=steps_per_sec)
# reset metric accumulation for next evaluation cycle
del train_metrics
train_metrics = []
# sync model state across replicas
self.train_state = pipeline_utils.sync_model_state_across_replicas(
self.train_state)
# evaluate and log the results
eval_summary, _ = self.eval(step, self.train_state)
return eval_summary, train_metrics, train_summary, tick
def maybe_eval_and_log(self, eval_env_ids, eval_summary, master, step,
tick, train_metrics, train_summary):
if (step % self.eval_frequency == 0) or (step == self.total_steps):
train_metrics = jax.device_get(train_metrics)
train_metrics = common_utils.stack_forest(train_metrics)
train_summary = pipeline_utils.compute_global_mean_metrics(
train_metrics)
tock = time.time()
steps_per_sec = self.eval_frequency / (tock - tick)
tick = tock
# Log train summary:
if master:
self.write_train_summary(step=step,
metric_dict=train_metrics,
summary=train_summary,
steps_per_sec=steps_per_sec)
# Reset metric accumulation for next evaluation cycle:
train_metrics = []
# Sync model state across replicas:
self.train_state = pipeline_utils.sync_model_state_across_replicas(
self.train_state)
# Evaluate and log the results:
eval_summary, self.train_state = self.eval(step, self.train_state,
eval_env_ids)
return eval_summary, train_metrics, train_summary, tick
def train(self):
"""Training loop."""
master = jax.host_id() == 0
train_metrics = []
train_summary, eval_summary = None, None
tick = time.time()
@jax.pmap
def get_reps(train_state, flax_module, batch):
with nn.stochastic(train_state.rng):
with nn.stateful(train_state.model_state):
_, reps, _ = flax_module(batch['inputs'],
train=True,
return_activations=True)
return reps
# Prepare arguments for layer sampling:
sample_batch = self.get_next_batch(self.task.dataset.data_iters.train)
reps = get_reps(self.train_state, self.train_state.optimizer.target,
sample_batch)
layer_keys, mixup_layers = pipeline_utils.get_sample_layer_params(
self.hparams, reps)
# Train loop:
for step in range(self.start_step + 1, self.total_steps + 1):
train_batch = self.get_next_batch(
self.task.dataset.data_iters.train)
sampled_layer = pipeline_utils.sample_layer(
layer_keys, mixup_layers=mixup_layers)
self.train_state, t_metrics = self.pmapped_train_step(
self.train_state, train_batch, sampled_layer)
train_metrics.append(t_metrics)
eval_summary, train_metrics, train_summary, tick = self.maybe_eval_and_log(
eval_summary, master, step, tick, train_metrics, train_summary)
# sync and save
self.checkpoint(self.train_state, step)
if master:
# evaluate and log the results
# sync model state across replicas
self.train_state = pipeline_utils.sync_model_state_across_replicas(
self.train_state)
eval_summary, self.train_state = self.eval(step, self.train_state,
'test')
# wait until computations are done before exiting (for timing!)
jax.random.normal(jax.random.PRNGKey(0), ()).block_until_ready()
# return the train and eval summary after last step for regresesion testing
return train_summary, eval_summary
def eval(self, step, train_state, split_name='validation'):
"""Evaluation loop.
Args:
step: int; Training step.
train_state: TrainState; Object containing training state.
split_name: str; Name of the dataset split to evaluate on.
Returns:
eval_summart, train_state
"""
train_state = pipeline_utils.sync_model_state_across_replicas(train_state)
eval_summary, eval_metrics = self.eval_split(
train_state=train_state, split_name=split_name)
# log eval summary
master = jax.host_id() == 0
if master:
self.write_eval_summary(
step=step, metric_dict=eval_metrics, summary=eval_summary)
return eval_summary, train_state
def _train_loop(self, environments, start_step, end_step, master):
"""Training loop.
Trains the model on the given environment set for (end_step - start_step)
number of steps.
Args:
environments: dict; A dictionary from environment name to environment data
iterator.
start_step: int; Staring step in the loop.
end_step: int; End step in the loop.
master: bool; Is this the host device? If yes, log and checkpoint.
Returns:
Evaluation summaries and metrics.
"""
# Initialize return values.
train_metrics = []
train_summary, eval_summary = None, None
tick = time.time()
eval_env_ids = list(
map(int, self.task.dataset.data_iters.validation.keys()))
train_env_ids, train_iters = list(zip(*dict(environments).items()))
train_env_ids = list(map(int, train_env_ids))
for step in range(start_step + 1, end_step + 1):
# Get next batch.
train_batch = self.get_next_batch(train_iters)
# Run train step and get the metrics and the new train state.
self.train_state, t_metrics = self.pmapped_train_step(
self.train_state, train_batch, train_env_ids)
train_metrics.append(t_metrics)
if (step % self.eval_frequency == 0) or (step == end_step):
train_metrics = common_utils.get_metrics(train_metrics)
train_summary = pipeline_utils.compute_global_mean_metrics(
train_metrics)
tock = time.time()
steps_per_sec = self.eval_frequency / (tock - tick)
tick = tock
# Log train summary:
if master:
self.write_train_summary(step=step,
metric_dict=train_metrics,
summary=train_summary,
steps_per_sec=steps_per_sec)
# Reset metric accumulation for next evaluation cycle.
train_metrics = []
# Sync model state across replicas.
self.train_state = pipeline_utils.sync_model_state_across_replicas(
self.train_state)
# Evaluate and log the results.
eval_summary, self.train_state = self.eval(
step, self.train_state, eval_env_ids)
# Sync and save.
self.checkpoint(self.train_state, step)
return eval_summary, train_summary
