def create_session(checkpoint_path, n_cpu_threads=-1):
"""Creates a MonitoredSession.
Args:
checkpoint_path (string): Path either to checkpoint directory or
directly to a checkpoint file.
n_cpu_threads (int): Number of CPU threads. If negative, we
assume either GPU decoding or that all
CPU cores can be used.
Returns:
A TensorFlow MonitoredSession.
"""
try:
if os.path.isdir(checkpoint_path):
checkpoint_path = saver.latest_checkpoint(checkpoint_path)
else:
logging.info("%s is not a directory. Interpreting as direct "
"path to checkpoint..." % checkpoint_path)
return training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
config=session_config(n_cpu_threads)))
except tf.errors.NotFoundError as e:
logging.fatal(
"Could not find all variables of the computation "
"graph in the T2T checkpoint file. This means that the "
"checkpoint does not correspond to the model specified in "
"SGNMT. Please double-check pred_src_vocab_size, "
"pred_trg_vocab_size, and all the t2t_* parameters. "
"Also make sure that the checkpoint exists and is readable")
raise AttributeError("Could not initialize TF session.")
def create_session(self, checkpoint_dir):
"""Creates a MonitoredSession for this predictor."""
checkpoint_path = saver.latest_checkpoint(checkpoint_dir)
return training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
config=self._session_config()))
def _create_session(self):
"""Creates a MonitoredSession for restoring model"""
checkpoint_path = saver.latest_checkpoint(self.config.output_path)
return training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
config=self._session_config()))
def predict(self, input_fn, predict_keys=None, hooks=None):
"""Returns predictions for given features.
Args:
input_fn: Input function returning features which is a dictionary of
string feature name to `Tensor` or `SparseTensor`. If it returns a
tuple, first item is extracted as features. Prediction continues until
`input_fn` raises an end-of-input exception (`OutOfRangeError` or
`StopIteration`).
predict_keys: list of `str`, name of the keys to predict. It is used if
the `EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used
then rest of the predictions will be filtered from the dictionary. If
`None`, returns all.
hooks: List of `SessionRunHook` subclass instances. Used for callbacks
inside the prediction call.
Yields:
Evaluated values of `predictions` tensors.
Raises:
ValueError: Could not find a trained model in model_dir.
ValueError: if batch length of predictions are not same.
ValueError: If there is a conflict between `predict_keys` and
`predictions`. For example if `predict_keys` is not `None` but
`EstimatorSpec.predictions` is not a `dict`.
"""
hooks = list(hooks or [])
# Check that model has been trained.
checkpoint_path = saver.latest_checkpoint(self._model_dir)
if not checkpoint_path:
raise ValueError('Could not find trained model in model_dir: {}.'.format(
self._model_dir))
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
training.create_global_step(g)
features = self._get_features_from_input_fn(input_fn)
estimator_spec = self._call_model_fn(features, None,
model_fn_lib.ModeKeys.FIT)
predictions = self._extract_keys(estimator_spec.predictions, predict_keys)
with training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
scaffold=estimator_spec.scaffold,
config=config_pb2.ConfigProto(allow_soft_placement=True)),
hooks=hooks) as mon_sess:
while not mon_sess.should_stop():
preds_evaluated = mon_sess.run(predictions)
if not isinstance(predictions, dict):
for pred in preds_evaluated:
yield pred
else:
for i in range(self._extract_batch_length(preds_evaluated)):
yield {
key: value[i]
for key, value in six.iteritems(preds_evaluated)
}
def _create_session(self):
"""Creates a MonitoredSession for this predictor."""
try:
checkpoint_path = saver.latest_checkpoint(self._checkpoint_dir)
return training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
config=self._session_config()))
except tf.errors.NotFoundError as e:
logging.fatal(
"Could not find all variables of the computation "
"graph in the MoE checkpoint file. This means that the "
"checkpoint does not correspond to the model specification.")
raise AttributeError("Could not initialize TF session for MoE.")
def create_session(self):
"""Creates a MonitoredSession for this predictor."""
try:
checkpoint_path = saver.latest_checkpoint(self._checkpoint_dir)
return training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
config=self._session_config()))
except tf.errors.NotFoundError as e:
logging.fatal(
"Could not find all variables of the computation "
"graph in the T2T checkpoint file. This means that the "
"checkpoint does not correspond to the model specified in "
"SGNMT. Please double-check pred_src_vocab_size, "
"pred_trg_vocab_size, and all the t2t_* parameters.")
raise AttributeError("Could not initialize TF session.")
def create_session():
"""Creates a MonitoredSession for this predictor."""
if not FLAGS.checkpoint_path:
raise AttributeError("Please set --checkpoint_path")
try:
if os.path.isdir(FLAGS.checkpoint_path):
checkpoint_path = saver.latest_checkpoint(FLAGS.checkpoint_path)
else:
checkpoint_path = FLAGS.checkpoint_path
tf.logging.info("%s is not a directory. Interpreting as direct "
"path to checkpoint..." % checkpoint_path)
return training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
config=session_config()))
except tf.errors.NotFoundError as e:
tf.logging.fatal("Could not find all variables of the computation "
"graph in the T2T checkpoint file. This means that the "
"checkpoint does not correspond to the specified model")
raise AttributeError("Could not initialize TF session.")
def predict(self,
input_fn,
predict_keys=None,
hooks=None,
checkpoint_path=None,
yield_single_examples=True):
"""Yields predictions for given features.
Args:
input_fn: A function that constructs the features. Prediction continues
until `input_fn` raises an end-of-input exception (`OutOfRangeError` or
`StopIteration`).
See @{$get_started/premade_estimators#create_input_functions} for more
information. The function should construct and return one of
the following:
* A 'tf.data.Dataset' object: Outputs of `Dataset` object must have
same constraints as below.
* features: A `Tensor` or a dictionary of string feature name to
`Tensor`. features are consumed by `model_fn`. They should satisfy
the expectation of `model_fn` from inputs.
* A tuple, in which case the first item is extracted as features.
predict_keys: list of `str`, name of the keys to predict. It is used if
the `EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used
then rest of the predictions will be filtered from the dictionary. If
`None`, returns all.
hooks: List of `SessionRunHook` subclass instances. Used for callbacks
inside the prediction call.
checkpoint_path: Path of a specific checkpoint to predict. If `None`, the
latest checkpoint in `model_dir` is used.
yield_single_examples: If False, yield the whole batch as returned by the
model_fn instead of decomposing the batch into individual elements. This
is useful if model_fn return some tensor with first dimension not
equal to the batch size
Yields:
Evaluated values of `predictions` tensors.
Raises:
ValueError: Could not find a trained model in model_dir.
ValueError: if batch length of predictions are not same and
yield_single_examples is True.
ValueError: If there is a conflict between `predict_keys` and
`predictions`. For example if `predict_keys` is not `None` but
`EstimatorSpec.predictions` is not a `dict`.
"""
hooks = _check_hooks_type(hooks)
# Check that model has been trained.
if not checkpoint_path:
checkpoint_path = saver.latest_checkpoint(self._model_dir)
if not checkpoint_path:
raise ValueError('Could not find trained model in model_dir: {}.'.format(
self._model_dir))
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
self._create_and_assert_global_step(g)
features, input_hooks = self._get_features_from_input_fn(
input_fn, model_fn_lib.ModeKeys.PREDICT)
estimator_spec = self._call_model_fn(
features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
predictions = self._extract_keys(estimator_spec.predictions, predict_keys)
all_hooks = list(input_hooks)
all_hooks.extend(hooks)
all_hooks.extend(list(estimator_spec.prediction_hooks or []))
with training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
master=self._config.master,
scaffold=estimator_spec.scaffold,
config=self._session_config),
hooks=all_hooks) as mon_sess:
while not mon_sess.should_stop():
preds_evaluated = mon_sess.run(predictions)
if not yield_single_examples:
yield preds_evaluated
elif not isinstance(predictions, dict):
for pred in preds_evaluated:
yield pred
else:
for i in range(self._extract_batch_length(preds_evaluated)):
yield {
key: value[i]
for key, value in six.iteritems(preds_evaluated)
}
def PlaceGraph(metagraph,
cluster=None,
allotted_time=3600,
hparams=None,
verbose=False):
"""Place the provided metagraph.
Args:
metagraph: the metagraph to place.
cluster: an optional set of hardware resource to optimize the placement for.
If none is specified, we'll optimize the placement for the hardware
available on the local machine.
allotted_time: the maximum amount to time in seconds to spend optimizing
the placement.
hparams: hyperparameters used to fine tune the placer.
verbose: prints debug information if True.
Returns:
The placed metagraph.
"""
if cluster is None:
cluster = gcluster.Cluster()
# Optimize the metagraph to speedup the placement
rewriter_config = rewriter_config_pb2.RewriterConfig()
rewriter_config.optimizers.append("pruning")
rewriter_config.optimizers.append("constfold")
rewriter_config.optimizers.append("arithmetic")
rewriter_config.optimizers.append("dependency")
rewriter_config.optimizers.append("pruning")
optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config,
metagraph,
verbose=verbose,
cluster=cluster)
optimized_metagraph = meta_graph_pb2.MetaGraphDef()
optimized_metagraph.CopyFrom(metagraph)
optimized_metagraph.graph_def.CopyFrom(optimized_graph)
item = gitem.Item(optimized_metagraph)
# Measure the runtime achievable with the original placement.
try:
_, original_run_time, _ = cluster.MeasureCosts(item)
if verbose:
print("Runtime for original placement: " + str(original_run_time))
except errors.OpError as e:
if verbose:
print("Original placement isn't feasible: " + str(e))
original_run_time = hparams.failing_signal
if hparams is None:
hparams = hierarchical_controller.hierarchical_controller_hparams()
# We run with a single child
hparams.num_children = 1
with tf_ops.Graph().as_default():
# Place all the nodes of the controller on the CPU. We don't want them to
# fight for accelerator memory with the model to optimize.
with tf_ops.device("/device:CPU:0"):
model = hierarchical_controller.HierarchicalController(
hparams, item, cluster)
ops = model.build_controller()
session_creator = training.ChiefSessionCreator()
with training.MonitoredSession(
session_creator=session_creator) as sess:
start_time = time.time()
current_time = start_time
while current_time - start_time < allotted_time:
grouping_actions = model.generate_grouping(sess)
input_to_seq2seq = model.create_group_embeddings(
grouping_actions, verbose=verbose)
model.generate_placement(input_to_seq2seq, sess)
try:
run_time = model.eval_placement(sess, verbose=verbose)
except errors.OpError as e:
if verbose:
print("Failed to run graph:" + str(e))
run_time = hparams.failing_signal
updated = model.update_reward(sess,
run_time,
verbose=verbose)
if updated and run_time < original_run_time:
if verbose:
print("Found better placement, with runtime " +
str(run_time))
model.export_placement(metagraph)
model.process_reward(sess)
current_time = time.time()
return metagraph
def custom_predict(self,
perturb,
ranker,
input_fn,
predict_keys=None,
hooks=None,
checkpoint_path=None,
yield_single_examples=True):
if not checkpoint_path:
checkpoint_path = checkpoint_management.latest_checkpoint(
ranker._model_dir)
if not checkpoint_path:
logging.info(
'Could not find trained model in model_dir: {}, running '
'initialization to predict.'.format(ranker._model_dir))
with tf.Graph().as_default() as g:
self.perturb_on = tf.compat.v1.placeholder(tf.bool)
random_seed.set_random_seed(ranker._config.tf_random_seed)
ranker._create_and_assert_global_step(g)
features, input_hooks = ranker._get_features_from_input_fn(
input_fn, ModeKeys.PREDICT)
estimator_spec = ranker._call_model_fn(features, None,
ModeKeys.PREDICT,
ranker.config)
# Call to warm_start has to be after model_fn is called.
ranker._maybe_warm_start(checkpoint_path)
predictions = estimator_spec.predictions
all_hooks = list(input_hooks)
all_hooks.extend(list([]))
self.grad_variable_pair_tensor = calculate_grad_var_pair(self)
with training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
master=ranker._config.master,
scaffold=estimator_spec.scaffold,
config=ranker._session_config),
hooks=all_hooks) as mon_sess:
while not mon_sess.should_stop():
[
preds_evaluated,
temp_query_features_evaluated,
temp_answer_features_evaluated,
temp_embedded_features_evaluated,
temp_labels_evaluated,
temp_normalized_features_evaluated,
self.grad_variable_pair_evaluated,
] = mon_sess.run([
predictions,
self.query_features,
self.answer_features,
self.embedded_features_tensor,
self.labels_tensor,
self.normalized_features,
self.grad_variable_pair_tensor,
], {self.perturb_on: perturb})
# Save values for tensors during first nonperturbed evaluation to be
# used in next execution.
if self.first_eval:
self.query_features_evaluated = temp_query_features_evaluated
self.answer_features_evaluated = temp_answer_features_evaluated
self.embedded_features_evaluated = temp_embedded_features_evaluated
self.labels_evaluated = temp_labels_evaluated
self.normalized_features_evaluated = temp_normalized_features_evaluated
self.first_eval = False
if not yield_single_examples:
yield preds_evaluated
elif not isinstance(predictions, dict):
for pred in preds_evaluated:
yield pred
else:
for i in range(
self._extract_batch_length(preds_evaluated)):
yield {
key: value[i]
for key, value in six.iteritems(
preds_evaluated)
}
def predict_with_guide(self,
input_fn,
predict_keys=None,
hooks=None,
checkpoint_path=None,
latest_filename=None,
yield_single_examples=True):
hooks = estimator_lib._check_hooks_type(hooks)
checkpoint_path = self._checkpoint_path(checkpoint_path,
latest_filename)
with ops.Graph().as_default() as g:
random_seed.set_random_seed(self._config.tf_random_seed)
self._create_and_assert_global_step(g)
features, labels, input_hooks = self._get_features_and_labels_from_input_fn(
input_fn, model_fn_lib.ModeKeys.EVAL)
features_ph = {
key: array_ops.placeholder(value.dtype, value.shape, name=key)
for key, value in features.items()
}
labels_ph = array_ops.placeholder(labels.dtype,
labels.shape,
name="labels")
feed_guide_hook = FeedGuideHook(features_ph, labels_ph, features,
labels, self.model_dir)
estimator_spec = self._call_model_fn(features_ph, labels_ph,
model_fn_lib.ModeKeys.PREDICT,
self.config)
if isinstance(predict_keys, list):
predict_keys += list(
self.params["model_instances"][0].metrics_dict.keys())
elif predict_keys is None:
# Evaluating volume don't need metrics in model, we use XXXPred to generate 3D predict
predict_keys = [
x for x in estimator_spec.predictions
if x not in self.params["model_instances"][0].metrics_dict
]
predict_keys.extend(
list(self.params["model_instances"][0].metrics_eval))
else:
raise TypeError(
"predict_keys must be None(for 3d eval) or a list(for 2d eval, "
"for example [\"Names\", \"Indices\"])")
predictions = self._extract_keys(estimator_spec.predictions,
predict_keys)
feed_guide_hook.predictions = predictions
all_hooks = list(input_hooks) + [feed_guide_hook]
all_hooks.extend(hooks)
all_hooks.extend(list(estimator_spec.prediction_hooks or []))
with training.MonitoredSession(
session_creator=training.ChiefSessionCreator(
checkpoint_filename_with_path=checkpoint_path,
master=self._config.master,
scaffold=estimator_spec.scaffold,
config=self._session_config),
hooks=all_hooks) as mon_sess:
while not mon_sess.should_stop():
preds_evaluated = mon_sess.run(predictions)
if not yield_single_examples:
yield preds_evaluated
elif not isinstance(predictions, dict):
for pred in preds_evaluated:
yield pred
else:
for i in range(
self._extract_batch_length(preds_evaluated)):
yield {
key: value[i]
for key, value in six.iteritems(
preds_evaluated)
}