def fetch_differentiable_fixed_embeddings(comp, state, stride,
during_training):
"""Looks up fixed features with separate, differentiable, embedding lookup.
Args:
comp: Component whose fixed features we wish to look up.
state: live MasterState object for the component.
stride: Tensor containing current batch * beam size.
during_training: True if this is being called from a training code path.
This controls, e.g., the use of feature ID dropout.
Returns:
state handle: updated state handle to be used after this call
fixed_embeddings: list of NamedTensor objects
"""
_validate_embedded_fixed_features(comp)
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
state.handle, indices, ids, weights, num_steps = (
dragnn_ops.bulk_fixed_features(state.handle,
component=comp.name,
num_channels=num_channels))
fixed_embeddings = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
differentiable_or_constant = ('constant' if feature_spec.is_constant
else 'differentiable')
tf.logging.info('[%s] Adding %s fixed feature "%s"', comp.name,
differentiable_or_constant, feature_spec.name)
if during_training and feature_spec.dropout_id >= 0:
ids[channel], weights[
channel] = network_units.apply_feature_id_dropout(
ids[channel], weights[channel], feature_spec)
size = stride * num_steps * feature_spec.size
fixed_embedding = network_units.embedding_lookup(
comp.get_variable(network_units.fixed_embeddings_name(channel)),
indices[channel], ids[channel], weights[channel], size)
if feature_spec.is_constant:
fixed_embedding = tf.stop_gradient(fixed_embedding)
fixed_embeddings.append(
network_units.NamedTensor(fixed_embedding, feature_spec.name))
return state.handle, fixed_embeddings
def fetch_differentiable_fixed_embeddings(comp, state, stride, during_training):
"""Looks up fixed features with separate, differentiable, embedding lookup.
Args:
comp: Component whose fixed features we wish to look up.
state: live MasterState object for the component.
stride: Tensor containing current batch * beam size.
during_training: True if this is being called from a training code path.
This controls, e.g., the use of feature ID dropout.
Returns:
state handle: updated state handle to be used after this call
fixed_embeddings: list of NamedTensor objects
"""
_validate_embedded_fixed_features(comp)
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
state.handle, indices, ids, weights, num_steps = (
dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels))
fixed_embeddings = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
differentiable_or_constant = ('constant' if feature_spec.is_constant else
'differentiable')
tf.logging.info('[%s] Adding %s fixed feature "%s"', comp.name,
differentiable_or_constant, feature_spec.name)
if during_training and feature_spec.dropout_id >= 0:
ids[channel], weights[channel] = network_units.apply_feature_id_dropout(
ids[channel], weights[channel], feature_spec)
size = stride * num_steps * feature_spec.size
fixed_embedding = network_units.embedding_lookup(
comp.get_variable(network_units.fixed_embeddings_name(channel)),
indices[channel], ids[channel], weights[channel], size)
if feature_spec.is_constant:
fixed_embedding = tf.stop_gradient(fixed_embedding)
fixed_embeddings.append(
network_units.NamedTensor(fixed_embedding, feature_spec.name))
return state.handle, fixed_embeddings
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0,
'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel],
size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(
network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids
def extract_fixed_feature_ids(comp, state, stride):
"""Extracts fixed feature IDs.
Args:
comp: Component whose fixed feature IDs we wish to extract.
state: Live MasterState object for the component.
stride: Tensor containing current batch * beam size.
Returns:
state handle: Updated state handle to be used after this call.
ids: List of [stride * num_steps, 1] feature IDs per channel. Missing IDs
(e.g., due to batch padding) are set to -1.
"""
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
for feature_spec in comp.spec.fixed_feature:
check.Eq(feature_spec.size, 1, 'All features must have size=1')
check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')
state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels)
size = stride * num_steps
fixed_ids = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
feature_spec.name)
# The +1 and -1 increments ensure that missing IDs default to -1.
#
# TODO(googleuser): This formula breaks if multiple IDs are extracted at some
# step. Try using tf.unique() to enforce the unique-IDS precondition.
sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
sums = tf.expand_dims(sums, axis=1)
fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
return state.handle, fixed_ids