def test_adam(self):
init_fn, update_fn = optimizers.get_optimizer(
ConfigDict({
'optimizer': 'adam',
'l2_decay_factor': None,
'batch_size': 50,
'total_accumulated_batch_size': 100, # Use gradient accumulation.
'opt_hparams': {
'beta1': 0.9,
'beta2': 0.999,
'epsilon': 1e-7,
'weight_decay': 0.0,
}
}))
del update_fn
optimizer_state = init_fn({'foo': jnp.ones(10)})
# Test that we can extract 'count'.
chex.assert_type(extract_field(optimizer_state, 'count'), int)
# Test that we can extract 'nu'.
chex.assert_shape(extract_field(optimizer_state, 'nu')['foo'], (10,))
# Test that we can extract 'mu'.
chex.assert_shape(extract_field(optimizer_state, 'mu')['foo'], (10,))
# Test that attemptping to extract a nonexistent field "abc" returns None.
chex.assert_equal(extract_field(optimizer_state, 'abc'), None)
def train(train_dir,
model,
dataset_builder,
initializer,
num_train_steps,
hps,
rng,
eval_batch_size,
eval_num_batches,
eval_train_num_batches,
eval_frequency,
checkpoint_steps,
early_stopping_target_name=None,
early_stopping_target_value=None,
early_stopping_mode=None,
eval_steps=None,
metrics_logger=None,
init_logger=None,
training_metrics_config=None,
callback_configs=None,
external_checkpoint_path=None):
"""Main training loop.
Trains the given network on the specified dataset for the given number of
epochs. Saves the training curve in train_dir/r=3/results.tsv.
Args:
train_dir: (str) Path of the training directory.
model: (BaseModel) Model object to be trained.
dataset_builder: dataset builder returned by datasets.get_dataset.
initializer: Must have API as defined in initializers.py
num_train_steps: (int) Number of steps to train on.
hps: (tf.HParams) Model, initialization and training hparams.
rng: (jax.random.PRNGKey) Rng seed used in model initialization and data
shuffling.
eval_batch_size: the evaluation batch size. If None, use hps.batch_size.
eval_num_batches: (int) The number of batches used for evaluating on
validation and test sets. Set to None to evaluate on the whole train set.
eval_train_num_batches: (int) The number of batches for evaluating on train.
Set to None to evaluate on the whole training set.
eval_frequency: (int) Evaluate every k steps.
checkpoint_steps: List of integers indicating special steps to save
checkpoints at. These checkpoints do not get used for preemption recovery.
early_stopping_target_name: A string naming the metric to use to perform
early stopping. If this metric reaches the value
`early_stopping_target_value`, training will stop. Must include the
dataset split (ex: validation/error_rate).
early_stopping_target_value: A float indicating the value at which to stop
training.
early_stopping_mode: One of "above" or "below", indicates if we should stop
when the metric is above or below the threshold value. Example: if
"above", then training will stop when
`report[early_stopping_target_name] >= early_stopping_target_value`.
eval_steps: List of integers indicating which steps to perform evals. If
provided, eval_frequency will be ignored. Performing an eval implies
saving a checkpoint that will be used to resume training in the case of
preemption.
metrics_logger: Used to log all eval metrics during training. See
utils.MetricLogger for API definition.
init_logger: Used for black box initializers that have learning curves.
training_metrics_config: Dict specifying the configuration of the
training_metrics_grabber. Set to None to skip logging of advanced training
metrics.
callback_configs: List of configs specifying general callbacks to run
during the eval phase. Empty list means no callbacks are run. See
callbacks.py for details on what is expected in a config.
external_checkpoint_path: (str) If this argument is set, we will load the
optimizer_state, params, batch_stats, and training_metrics from the
checkpoint at this location.
Yields:
metrics: A dictionary of all eval metrics from the given epoch.
"""
# NOTE: the initialization RNG should *not* be per-host, as this will create
# different sets of weights per host. However, all other RNGs should be
# per-host.
# TODO(znado,gilmer,gdahl): implement replicating the same initialization
# across hosts.
rng, init_rng = jax.random.split(rng)
rng = jax.random.fold_in(rng, jax.process_index())
rng, data_rng = jax.random.split(rng)
# only used if checkpoints_steps is non-empty.
checkpoint_dir = os.path.join(train_dir, 'checkpoints')
# For logging / processing off the main thread
pool = multiprocessing.pool.ThreadPool()
if jax.process_index() == 0:
logging.info('Let the training begin!')
logging.info('Dataset input shape: %r', hps.input_shape)
logging.info('Hyperparameters: %s', hps)
if eval_batch_size is None:
eval_batch_size = hps.batch_size
if callback_configs is None:
callback_configs = []
# 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, init_logger,
model.get_fake_batch(hps))
if jax.process_index() == 0:
utils.log_pytree_shape_and_statistics(unreplicated_params)
logging.info('train_size: %d,', hps.train_size)
# Note that global_step refers to the number of gradients calculations, not
# the number of model updates. This means when using gradient accumulation,
# one must supply configs where the number of steps are in units of gradient
# calculations, not model updates, and in post processing one must divide
# global_step by grad_accum_step_multiplier to get the number of updates.
#
# If using gradient accumulation, stretch the learning rate schedule by the
# number of gradient calculations per weight update.
stretch_factor = 1
if hps.get('total_accumulated_batch_size') is not None:
stretch_factor = hps.total_accumulated_batch_size // hps.batch_size
lr_fn = schedules.get_schedule_fn(hps.lr_hparams,
num_train_steps,
stretch_factor=stretch_factor)
optimizer_init_fn, optimizer_update_fn = optimizers.get_optimizer(
hps, model)
unreplicated_optimizer_state = optimizer_init_fn(unreplicated_params)
(unreplicated_metrics_state, metrics_update_fn,
metrics_summary_fn) = None, None, None
if training_metrics_config is not None:
(metrics_init_fn, metrics_update_fn,
metrics_summary_fn) = make_training_metrics(num_train_steps,
**training_metrics_config)
unreplicated_metrics_state = metrics_init_fn(unreplicated_params)
(optimizer_state, params, batch_stats, metrics_state, global_step,
sum_train_cost, preemption_count,
is_restored) = checkpoint.replicate_and_maybe_restore_checkpoint(
unreplicated_optimizer_state,
unreplicated_params,
unreplicated_batch_stats,
unreplicated_metrics_state,
train_dir=train_dir,
external_checkpoint_path=external_checkpoint_path)
if is_restored:
preemption_count += 1
# Fold the restored step into the dataset RNG so that we will get a
# different shuffle each time we restore, so that we do not repeat a
# previous dataset ordering again after restoring. This is not the only
# difference in shuffling each pre-emption, because we often times reshuffle
# the input files each time in a non-deterministic manner.
#
# Note that if we are pre-empted more than once per epoch then we will
# retrain more on the beginning of the training split, because each time we
# restore we refill the shuffle buffer with the first `shuffle_buffer_size`
# elements from the training split to continue training.
#
# Also note that for evaluating on the training split, because we are
# reshuffling each time, we will get a new eval_train split each time we are
# pre-empted.
data_rng = jax.random.fold_in(data_rng, global_step)
assert hps.batch_size % (jax.device_count()) == 0
assert eval_batch_size % (jax.device_count()) == 0
dataset = dataset_builder(
data_rng,
hps.batch_size,
eval_batch_size=eval_batch_size,
hps=hps,
)
update_fn = functools.partial(update,
training_cost=model.training_cost,
grad_clip=hps.get('grad_clip'),
optimizer_update_fn=optimizer_update_fn,
metrics_update_fn=metrics_update_fn)
# in_axes = (
# optimizer_state = 0,
# params = 0,
# batch_stats = 0,
# metrics_state = 0,
# batch = 0,
# step = None,
# lr = None,
# rng = None,
# local_device_index = 0,
# running_train_cost = 0,
# training_cost,
# grad_clip,
# optimizer_update_fn,
# metrics_state_update_fn)
# Also, we can donate buffers for 'optimizer', 'batch_stats',
# 'batch' and 'training_metrics_state' for update's pmapped computation.
update_pmapped = jax.pmap(update_fn,
axis_name='batch',
in_axes=(0, 0, 0, 0, 0, None, None, None, 0, 0),
donate_argnums=(0, 1, 2, 8))
# During eval, we can donate the 'batch' buffer. We don't donate the
# 'params' and 'batch_stats' buffers as we don't re-assign those values in
# eval, we do that only in train.
evaluate_batch_pmapped = jax.pmap(model.evaluate_batch,
axis_name='batch',
donate_argnums=(2, ))
start_time = time.time()
start_step = global_step
prev_eval_step = start_step
def get_step_frequency(cur_step):
return float(cur_step - start_step) / (time.time() - start_time)
if jax.process_index() == 0:
trainer_utils.log_message('Starting training!', pool, xm_work_unit)
# Numpy array of range(0, local_device_count) to send to each device to be
# folded into the RNG inside each train step to get a unique per-device RNG.
local_device_indices = np.arange(jax.local_device_count())
# Start at the resumed step and continue until we have finished the number of
# training steps. If building a dataset iterator using a tf.data.Dataset, in
# the case of a batch size that does not evenly divide the training dataset
# size, if using `ds.batch(..., drop_remainer=True)` on the training dataset
# then the final batch in this iterator will be a partial batch. However, if
# `drop_remainer=False`, then this iterator will always return batches of the
# same size, and the final batch will have elements from the start of the
# (num_epochs + 1)-th epoch.
train_iter = itertools.islice(dataset.train_iterator_fn(), global_step,
num_train_steps)
eval_callbacks = []
rng, callback_rng = jax.random.split(rng)
callback_rngs = jax.random.split(callback_rng, len(callback_configs))
for callback_rng, config in zip(callback_rngs, callback_configs):
eval_callback = callbacks.get_callback(config['callback_name'])(
model, params, batch_stats, optimizer_state, dataset, hps, config,
train_dir, callback_rng)
eval_callbacks.append(eval_callback)
train_iter = trainer_utils.prefetch_input_pipeline(
train_iter, hps.num_device_prefetches)
eval_start_time = start_time
eval_start_step = start_step
for _ in range(start_step, num_train_steps):
with jax.profiler.StepTraceAnnotation('train', step_num=global_step):
# NOTE(dsuo): to properly profile each step, we must include batch
# creation in the StepTraceContext (as opposed to putting `train_iter`
# directly in the top-level for loop).
batch = next(train_iter)
if global_step in checkpoint_steps and jax.process_index() == 0:
checkpoint.save_unreplicated_checkpoint_background(
checkpoint_dir,
optimizer_state,
params,
batch_stats,
metrics_state,
global_step,
preemption_count,
sum_train_cost,
max_to_keep=None)
lr = lr_fn(global_step)
optimizer_state, params, batch_stats, sum_train_cost, metrics_state, grad_norm = update_pmapped(
optimizer_state, params, batch_stats, metrics_state, batch,
global_step, lr, rng, local_device_indices, sum_train_cost)
global_step += 1
# TODO(gdahl, gilmer): consider moving this test up.
# NB: Since this test is after we increment global_step, having 0 in
# eval_steps does nothing.
if trainer_utils.should_eval(global_step, eval_frequency,
eval_steps):
train_steps_per_sec = (global_step - eval_start_step) / (
time.time() - eval_start_time)
eval_start_step = global_step
eval_start_time = time.time()
batch_stats = trainer_utils.maybe_sync_batchnorm_stats(
batch_stats)
report, eval_time = eval_metrics(params, batch_stats, dataset,
eval_num_batches,
eval_train_num_batches,
evaluate_batch_pmapped)
mean_train_cost = sum_train_cost.mean().item() / max(
1, global_step - prev_eval_step)
report.update(
learning_rate=float(lr),
global_step=global_step,
epoch=global_step * hps.batch_size // hps.train_size,
train_steps_per_sec=train_steps_per_sec,
overall_steps_per_sec=get_step_frequency(global_step),
eval_time=eval_time,
grad_norm=np.mean(grad_norm),
preemption_count=preemption_count,
train_cost=mean_train_cost)
for eval_callback in eval_callbacks:
callback_metrics = eval_callback.run_eval(
params, batch_stats, optimizer_state, global_step)
if set(callback_metrics.keys()).intersection(
set(report.keys())):
raise ValueError(
'There was a collision between the callback'
'metrics and the standard eval metrics keys')
report.update(callback_metrics)
yield report
if jax.process_index() == 0:
trainer_utils.log_eta(pool, xm_work_unit, global_step,
train_steps_per_sec, num_train_steps,
start_time, eval_frequency,
eval_steps, eval_time)
trainer_utils.log_epoch_report(report, metrics_logger)
trainer_utils.maybe_log_training_metrics(
metrics_state, metrics_summary_fn, metrics_logger)
checkpoint.save_unreplicated_checkpoint_background(
train_dir, optimizer_state, params, batch_stats,
metrics_state, global_step, preemption_count,
sum_train_cost)
sum_train_cost = jnp.zeros(jax.local_device_count())
prev_eval_step = global_step
early_stopping_condition = trainer_utils.check_for_early_stopping(
early_stopping_target_name, early_stopping_target_value,
early_stopping_mode, report)
if early_stopping_condition:
comparison_string = '>=' if early_stopping_mode == 'above' else '<='
logging.info(
'Early stopping because metric %s=%f, reached the target value '
'of %s %f.', early_stopping_target_name,
report[early_stopping_target_name], comparison_string,
early_stopping_target_value)
return
# Always log and checkpoint on host 0 at the end of training.
# If we moved where in the loop body evals happen then we would not need this
# test.
if prev_eval_step != num_train_steps:
train_steps_per_sec = (global_step - eval_start_step) / (
time.time() - eval_start_time)
batch_stats = trainer_utils.maybe_sync_batchnorm_stats(batch_stats)
report, eval_time = eval_metrics(params, batch_stats, dataset,
eval_num_batches,
eval_train_num_batches,
evaluate_batch_pmapped)
lr = lr_fn(global_step)
# Correct the average for the final partial epoch.
mean_train_cost = sum_train_cost.mean().item() / max(
1, global_step - prev_eval_step)
report.update(learning_rate=float(lr),
global_step=global_step,
epoch=global_step * hps.batch_size // hps.train_size,
train_steps_per_sec=train_steps_per_sec,
overall_steps_per_sec=get_step_frequency(global_step),
eval_time=eval_time,
grad_norm=np.mean(grad_norm),
preemption_count=preemption_count,
train_cost=mean_train_cost)
yield report
if jax.process_index() == 0:
trainer_utils.log_eta(pool, xm_work_unit, global_step,
train_steps_per_sec, num_train_steps,
start_time, eval_frequency, eval_steps,
eval_time)
trainer_utils.log_epoch_report(report, metrics_logger)
trainer_utils.maybe_log_training_metrics(metrics_state,
metrics_summary_fn,
metrics_logger)
checkpoint.save_unreplicated_checkpoint_background(
train_dir, optimizer_state, params, batch_stats, metrics_state,
global_step, preemption_count, sum_train_cost)
# To make sure the last checkpoint was correctly saved.
checkpoint.wait_for_checkpoint_save()
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)
def test_hessian_free_optimizer(self):
"""Tests the Hessian-free optimizer."""
model_str = 'autoencoder'
model_cls = models.get_model(model_str)
model_hps = models.get_model_hparams(model_str)
loss = 'sigmoid_binary_cross_entropy'
metrics = 'binary_autoencoder_metrics'
input_shape = (2, 2, 1)
output_shape = (4, )
hps = copy.copy(model_hps)
hps.update({
'optimizer': 'hessian_free',
'opt_hparams': {
'weight_decay': 0.0,
},
'hid_sizes': [2],
'activation_function': ['id'],
'input_shape': input_shape,
'output_shape': output_shape
})
model = model_cls(hps, {}, loss, metrics)
inputs = jnp.array([[[1, 0], [1, 1]], [[1, 0], [0, 1]]])
targets = inputs.reshape(tuple([inputs.shape[0]] + list(output_shape)))
batch = {'inputs': inputs, 'targets': targets}
def forward_fn(variables, inputs):
logits = model.flax_module.apply(variables, inputs, train=True)
return logits
def opt_cost(variables):
return model.loss_fn(forward_fn(variables, inputs), targets)
init_fn, update_fn = optimizers.get_optimizer(hps, model)
params = {
'Dense_0': {
'kernel': jnp.array([[-1., 2.], [2., 0.], [-1., 3.], [-2.,
2.]]),
'bias': jnp.array([0., 0.])
},
'Dense_1': {
'kernel': jnp.array([[4., 2., -2., 4.], [-3., 1., 2., -4.]]),
'bias': jnp.array([0., 0., 0., 0.])
}
}
variables = {'params': params}
grad_fn = jax.grad(opt_cost)
grads = grad_fn(variables)['params']
outputs = forward_fn(variables, batch['inputs'])
n = inputs.shape[0]
m = outputs.shape[-1]
d = ravel_pytree(params)[0].shape[0]
v = np.ones(d)
state = init_fn(params)
partial_forward_fn = partial(forward_fn, inputs=batch['inputs'])
partial_loss_fn = partial(model.loss_fn, targets=batch['targets'])
matmul_fn = partial(gvp, variables, outputs, state.inner_state.damping,
partial_forward_fn, partial_loss_fn)
jacobian = jax.jacfwd(partial_forward_fn)(variables)['params']
jacobian_tensor = np.concatenate(
(jacobian['Dense_0']['bias'].reshape(
n, m, -1), jacobian['Dense_0']['kernel'].reshape(
n, m, -1), jacobian['Dense_1']['bias'].reshape(n, m, -1),
jacobian['Dense_1']['kernel'].reshape(n, m, -1)),
axis=2)
ggn_matrix = 0
for i in range(n):
jacobian_matrix = jacobian_tensor[i]
hessian = jax.hessian(partial_loss_fn)(outputs[i, None])[0, :,
0, :]
ggn_matrix += np.transpose(
jacobian_matrix) @ hessian @ jacobian_matrix
ggn_matrix /= n
ggn_matrix += state.inner_state.damping * np.identity(d)
expected = ggn_matrix @ v
# Test the gvp function
self.assertAlmostEqual(jnp.linalg.norm(matmul_fn(v) - expected),
0,
places=4)
update_pmapped = jax.pmap(update_fn,
axis_name='batch',
in_axes=(None, None, None, 0, None))
batch_shard = data_utils.shard(batch)
state.hyperparams['learning_rate'] = 1.0
p, state = update_pmapped(grads, state, params, batch_shard, None)
# Test the damping parameter update
self.assertEqual(state.inner_state.damping, 3 / 2)
# Test the search direction
self.assertAlmostEqual(jnp.linalg.norm(
ravel_pytree(p)[0] +
jnp.linalg.inv(ggn_matrix) @ ravel_pytree(grads)[0]),
0,
places=4)
def test_adam(self):
"""Test Adam preconditioning."""
lr = 1e-3
beta1 = 0.9
beta2 = 0.999
epsilon = 1e-7
opt_hparams = FrozenConfigDict({
'beta1': beta1,
'beta2': beta2,
'epsilon': epsilon
})
hparams = FrozenConfigDict({
'optimizer': 'adam',
'opt_hparams': opt_hparams,
'l2_decay_factor': 0.0,
'batch_size': 50,
'total_accumulated_batch_size': 50,
})
init_fn, update_fn = optimizers.get_optimizer(hparams)
params = {'foo': 1.0, 'bar': {'baz': 3.0}}
gradients = [{
'foo': 0.5,
'bar': {
'baz': 0.1
}
}, {
'foo': 0.2,
'bar': {
'baz': 0.6
}
}]
optimizer_state = init_fn(params)
optimizer_state.base_state.hyperparams['learning_rate'] = lr
for gradient in gradients:
updates, optimizer_state = update_fn(gradient, optimizer_state,
params)
params = optax.apply_updates(params, updates)
# yes bias correction
expected_preconditioner = _calculate_adam_preconditioner(
gradients, beta2, epsilon, bias_correct=True)
preconditioner = make_diag_preconditioner(
'adam', opt_hparams, optimizer_state,
FrozenConfigDict(dict(bias_correction=True)))
self.assertTrue(
pytree_allclose(expected_preconditioner, preconditioner))
# no bias correction
expected_preconditioner = _calculate_adam_preconditioner(
gradients, beta2, epsilon, bias_correct=False)
preconditioner = make_diag_preconditioner(
'adam', opt_hparams, optimizer_state,
FrozenConfigDict(dict(bias_correction=False)))
self.assertTrue(
pytree_allclose(expected_preconditioner, preconditioner))