def _get_temp_gcs_path():
path = f"gs://{GCS_TEST_BUCKET}/" + "".join(
random.choice(string.ascii_lowercase) for i in range(16)
)
gfile.mkdir(path)
yield path + "/file.name"
gfile.rmtree(path)
def __init__(self, logdir, flush_secs=2, is_dummy=False, dummy_time=None):
self._name_to_tf_name = {}
self._tf_names = set()
self.is_dummy = is_dummy
self.logdir = logdir
self.flush_secs = flush_secs # TODO
self._writer = None
self._dummy_time = dummy_time
if is_dummy:
self.dummy_log = defaultdict(list)
else:
'''
if not os.path.exists(self.logdir):
os.makedirs(self.logdir)
'''
gfile.mkdir(self.logdir)
hostname = socket.gethostname()
filename = os.path.join(
self.logdir,
'events.out.tfevents.{}.{}'.format(int(self._time()),
hostname))
#self._writer = open(filename, 'wb')
self._writer = gfile.GFile(filename, 'wb')
self._write_event(
event_pb2.Event(wall_time=self._time(),
step=0,
file_version='brain.Event:2'))
def _make_root_class_dirs(self):
"""Make dataset root dir and subdir for each class."""
if not gfile.exists(self.new_dataset_dir):
gfile.makedirs(self.new_dataset_dir)
for class_name in self.fg_classes:
class_dir = path.join(self.new_dataset_dir, class_name, '')
if not gfile.exists(class_dir):
gfile.mkdir(class_dir)
def _make_directory(path):
"""Helper function: create directory if it doesn't exist yet."""
if not gfile.exists(path):
log("Creating directory %s" % path)
gfile.mkdir(path)
def mkdir(path: str):
gfile.mkdir(path)
def eval_checkpoints(
checkpoint_dir,
hps,
rng,
eval_num_batches,
model_cls,
dataset_builder,
dataset_meta_data,
hessian_eval_config,
min_global_step=None,
max_global_step=None,
):
"""Evaluate the Hessian of the given checkpoints.
Iterates over all checkpoints in the specified directory, loads the checkpoint
then evaluates the Hessian on the given checkpoint. A list of dicts will be
saved to cns at checkpoint_dir/hessian_eval_config['name'].
Args:
checkpoint_dir: Directory of checkpoints to load.
hps: (tf.HParams) Model, initialization and training hparams.
rng: (jax.random.PRNGKey) Rng seed used in model initialization and data
shuffling.
eval_num_batches: (int) The batch size used for evaluating on
validation, and test sets. Set to None to evaluate on the whole test set.
model_cls: One of the model classes (not an instance) defined in model_lib.
dataset_builder: dataset builder returned by datasets.get_dataset.
dataset_meta_data: dict of meta_data about the dataset.
hessian_eval_config: a dict specifying the configuration of the Hessian
eval.
min_global_step: Lower bound on what steps to filter checkpoints. Set to
None to evaluate all checkpoints in the directory.
max_global_step: Upper bound on what steps to filter checkpoints.
"""
rng, init_rng = jax.random.split(rng)
rng = jax.random.fold_in(rng, jax.process_index())
rng, data_rng = jax.random.split(rng)
initializer = initializers.get_initializer('noop')
loss_name = 'cross_entropy'
metrics_name = 'classification_metrics'
model = model_cls(hps, dataset_meta_data, loss_name, metrics_name)
# Maybe run the initializer.
unreplicated_params, unreplicated_batch_stats = init_utils.initialize(
model.flax_module,
initializer, model.loss_fn,
hps.input_shape,
hps.output_shape, hps, init_rng,
None)
# Fold in a the unreplicated batch_stats and rng into the loss used by
# hessian eval.
def batch_loss(params, batch_rng):
batch, rng = batch_rng
return model.training_cost(
params, batch, batch_stats=unreplicated_batch_stats, dropout_rng=rng)[0]
batch_stats = jax_utils.replicate(unreplicated_batch_stats)
if jax.process_index() == 0:
utils.log_pytree_shape_and_statistics(unreplicated_params)
logging.info('train_size: %d,', hps.train_size)
logging.info(hps)
# Save the hessian computation hps to the experiment directory
exp_dir = os.path.join(checkpoint_dir, hessian_eval_config['name'])
if not gfile.exists(exp_dir):
gfile.mkdir(exp_dir)
if min_global_step == 0:
hparams_fname = os.path.join(exp_dir, 'hparams.json')
with gfile.GFile(hparams_fname, 'w') as f:
f.write(hps.to_json())
config_fname = os.path.join(exp_dir, 'hconfig.json')
with gfile.GFile(config_fname, 'w') as f:
f.write(json.dumps(hessian_eval_config))
optimizer_init_fn, optimizer_update_fn = optimizers.get_optimizer(hps)
unreplicated_optimizer_state = optimizer_init_fn(unreplicated_params)
# Note that we do not use the learning rate.
# The optimizer state is a list of all the optax transformation states, and
# we inject the learning rate into all states that will accept it.
for state in unreplicated_optimizer_state:
if (isinstance(state, optax.InjectHyperparamsState) and
'learning_rate' in state.hyperparams):
state.hyperparams['learning_rate'] = jax_utils.replicate(1.0)
optimizer_state = jax_utils.replicate(unreplicated_optimizer_state)
params = jax_utils.replicate(unreplicated_params)
data_rng = jax.random.fold_in(data_rng, 0)
assert hps.batch_size % (jax.device_count()) == 0
dataset = dataset_builder(
data_rng,
hps.batch_size,
eval_batch_size=hps.batch_size, # eval iterators not used.
hps=hps,
)
# pmap functions for the training loop
evaluate_batch_pmapped = jax.pmap(model.evaluate_batch, axis_name='batch')
if jax.process_index() == 0:
logging.info('Starting eval!')
logging.info('Number of hosts: %d', jax.process_count())
hessian_evaluator = hessian_eval.CurvatureEvaluator(
params,
hessian_eval_config,
dataset=dataset,
loss=batch_loss)
if min_global_step is None:
suffix = ''
else:
suffix = '{}_{}'.format(min_global_step, max_global_step)
pytree_path = os.path.join(checkpoint_dir, hessian_eval_config['name'],
suffix)
logger = utils.MetricLogger(pytree_path=pytree_path)
for checkpoint_path, step in iterate_checkpoints(checkpoint_dir,
min_global_step,
max_global_step):
unreplicated_checkpoint_state = dict(
params=unreplicated_params,
optimizer_state=unreplicated_optimizer_state,
batch_stats=unreplicated_batch_stats,
global_step=0,
preemption_count=0,
sum_train_cost=0.0)
ckpt = checkpoint.load_checkpoint(
checkpoint_path,
target=unreplicated_checkpoint_state)
results, _ = checkpoint.replicate_checkpoint(
ckpt,
pytree_keys=['params', 'optimizer_state', 'batch_stats'])
params = results['params']
optimizer_state = results['optimizer_state']
batch_stats = results['batch_stats']
# pylint: disable=protected-access
batch_stats = trainer_utils.maybe_sync_batchnorm_stats(batch_stats)
# pylint: enable=protected-access
report, _ = trainer.eval_metrics(params, batch_stats, dataset,
eval_num_batches, eval_num_batches,
evaluate_batch_pmapped)
if jax.process_index() == 0:
logging.info('Global Step: %d', step)
logging.info(report)
row = {}
grads, updates = [], []
hess_evecs, cov_evecs = [], []
stats, hess_evecs, cov_evecs = hessian_evaluator.evaluate_spectrum(
params, step)
row.update(stats)
if hessian_eval_config[
'compute_stats'] or hessian_eval_config['compute_interps']:
grads, updates = hessian_evaluator.compute_dirs(
params, optimizer_state, optimizer_update_fn)
row.update(hessian_evaluator.evaluate_stats(params, grads,
updates, hess_evecs,
cov_evecs, step))
row.update(hessian_evaluator.compute_interpolations(params, grads,
updates, hess_evecs,
cov_evecs, step))
if jax.process_index() == 0:
logger.append_pytree(row)
