def _add_control_flow_ops(ops, control_ios):
"""Complete `ops` so that the tranformed graph is valid.
Partially copying a graph can lead to a malformed graph. For instance,
copying half of a while construct is likely to result in an invalid graph.
This function attempts to add missing ops so that the transformation result
in a valid graph.
Args:
ops: list of ops (modifed in-place).
control_ios: object created by a call to `util.ControlOutputs`.
"""
# Find while contexts.
control_flow_contexts = set()
for op in ops:
cfc = op._control_flow_context # pylint: disable=protected-access
if cfc:
control_flow_contexts.add(cfc)
# Find new ops.
new_ops = []
for cfc in control_flow_contexts:
if cfc.IsWhileContext():
new_ops += select.get_walks_intersection_ops(
[enter_t.op for enter_t in cfc.loop_enters],
[exit_t.op for exit_t in cfc.loop_exits],
control_ios=control_ios)
# Add new ops.
new_ops_set = set(new_ops)
ops_set = frozenset(ops)
for op in new_ops_set:
if op not in ops_set:
ops.append(op)
def _add_control_flow_ops(ops, control_ios):
"""Complete `ops` so that the transformed graph is valid.
Partially copying a graph can lead to a malformed graph. For instance,
copying half of a while construct is likely to result in an invalid graph.
This function attempts to add missing ops so that the transformation result
in a valid graph.
Args:
ops: list of ops (modifed in-place).
control_ios: object created by a call to `util.ControlOutputs`.
"""
# Find while contexts.
control_flow_contexts = set()
for op in ops:
cfc = op._control_flow_context # pylint: disable=protected-access
if cfc:
control_flow_contexts.add(cfc)
# Find new ops.
new_ops = []
for cfc in control_flow_contexts:
if cfc.IsWhileContext():
new_ops += select.get_walks_intersection_ops(
[enter_t.op for enter_t in cfc.loop_enters],
[exit_t.op for exit_t in cfc.loop_exits],
control_ios=control_ios)
# Add new ops.
new_ops_set = set(new_ops)
ops_set = frozenset(ops)
for op in new_ops_set:
if op not in ops_set:
ops.append(op)
def clone_replace(f, replace):
flatten_target_ts = util.flatten_tree(f)
graph = util.get_unique_graph(flatten_target_ts, check_types=(tf_ops.Tensor))
control_ios = util.ControlOutputs(graph)
ops = select.get_walks_intersection_ops(list(iterkeys(replace)),
flatten_target_ts,
control_ios=control_ios)
if not ops:
# this happens with disconnected inputs
return f
else:
return tf.contrib.graph_editor.graph_replace(f, replace)
def clone_replace(f, replace):
flatten_target_ts = util.flatten_tree(f)
graph = util.get_unique_graph(flatten_target_ts, check_types=(tf_ops.Tensor))
control_ios = util.ControlOutputs(graph)
ops = select.get_walks_intersection_ops(list(iterkeys(replace)),
flatten_target_ts,
control_ios=control_ios)
if not ops:
# this happens with disconnected inputs
return f
else:
return tf.contrib.graph_editor.graph_replace(f, replace)
def graph_replace(target_ts,
replacement_ts,
dst_scope="",
src_scope="",
reuse_dst_scope=False):
"""Create a new graph which compute the targets from the replaced Tensors.
Args:
target_ts: a single tf.Tensor or an iterable of tf.Tensor.
replacement_ts: dictionary mapping from original tensors to replaced tensors
dst_scope: the destination scope.
src_scope: the source scope.
reuse_dst_scope: if True the dst_scope is re-used if it already exists.
Otherwise, the scope is given a unique name based on the one given
by appending an underscore followed by a digit (default).
Returns:
A single tf.Tensor or a list of target tf.Tensor, depending on
the type of the input argument `target_ts`.
The returned tensors are recomputed using the tensors from replacement_ts.
Raises:
ValueError: if the targets are not connected to replacement_ts.
"""
# Identify operations in the graph that will change.
# Start forward walk at Tensors that will be replaced, and
# backward walk at the target output Tensors.
flatten_target_ts = util.flatten_tree(target_ts)
# Construct the forward control dependencies edges so that
# the get_walks_intersection_ops can also traverse the
# control dependencies.
graph = util.get_unique_graph(flatten_target_ts,
check_types=(tf_ops.Tensor))
control_ios = util.ControlOutputs(graph)
ops = select.get_walks_intersection_ops(list(iterkeys(replacement_ts)),
flatten_target_ts,
control_ios=control_ios)
if not ops:
raise ValueError("Targets and replacements are not connected!")
# Complete ops to avoid malformed control flow.
# TODO (fkp): Consider moving this function deeper (in the transformer?). id:1300
# https://github.com/imdone/tensorflow/issues/1301
_add_control_flow_ops(ops, control_ios)
# Create a copy of the relevant subgraph
unused_sgv_, info = copy_with_input_replacements(ops, replacement_ts, None,
dst_scope, src_scope,
reuse_dst_scope)
# Return the transformed targets but keep the original if the transformed
# counterpart cannot be found
missing_fn = lambda original_t: original_t
return info.transformed(target_ts, missing_fn)
def graph_replace(target_ts, replacement_ts, dst_scope="",
src_scope="", reuse_dst_scope=False):
"""Create a new graph which compute the targets from the replaced Tensors.
Args:
target_ts: a single tf.Tensor or an iterable of tf.Tensor.
replacement_ts: dictionary mapping from original tensors to replaced tensors
dst_scope: the destination scope.
src_scope: the source scope.
reuse_dst_scope: if True the dst_scope is re-used if it already exists.
Otherwise, the scope is given a unique name based on the one given
by appending an underscore followed by a digit (default).
Returns:
A single tf.Tensor or a list of target tf.Tensor, depending on
the type of the input argument `target_ts`.
The returned tensors are recomputed using the tensors from replacement_ts.
Raises:
ValueError: if the targets are not connected to replacement_ts.
"""
# Identify operations in the graph that will change.
# Start forward walk at Tensors that will be replaced, and
# backward walk at the target output Tensors.
flatten_target_ts = util.flatten_tree(target_ts)
# Construct the forward control dependencies edges so that
# the get_walks_intersection_ops can also traverse the
# control dependencies.
graph = util.get_unique_graph(flatten_target_ts, check_types=(tf_ops.Tensor))
control_ios = util.ControlOutputs(graph)
ops = select.get_walks_intersection_ops(list(iterkeys(replacement_ts)),
flatten_target_ts,
control_ios=control_ios)
if not ops:
raise ValueError("Targets and replacements are not connected!")
# Complete ops to avoid malformed control flow.
# TODO(fkp): Consider moving this function deeper (in the transformer?).
_add_control_flow_ops(ops, control_ios)
# Create a copy of the relevant subgraph
unused_sgv_, info = copy_with_input_replacements(
ops, replacement_ts, None, dst_scope, src_scope, reuse_dst_scope)
# Return the transformed targets but keep the original if the transformed
# counterpart cannot be found
missing_fn = lambda original_t: original_t
return info.transformed(target_ts, missing_fn)
def graph_replace(target_ts, replacement_ts, dst_scope="",
src_scope="", reuse_dst_scope=False):
"""Create a new graph which compute the targets from the replaced Tensors.
Args:
target_ts: a single tf.Tensor or an iterabble of tf.Tensor.
replacement_ts: dictionary mapping from original tensors to replaced tensors
dst_scope: the destination scope.
src_scope: the source scope.
reuse_dst_scope: if True the dst_scope is re-used if it already exists.
Otherwise, the scope is given a unique name based on the one given
by appending an underscore followed by a digit (default).
Returns:
A single tf.Tensor or a list of target tf.Tensor, depending on
the type of the input argument `target_ts`.
The returned tensors are recomputed using the tensors from replacement_ts.
Raises:
ValueError: if the targets are not connected to replacement_ts.
"""
# Identify operations in the graph that will change.
# Start forward walk at Tensors that will be replaced, and
# backward walk at the target output Tensors.
flatten_target_ts = util.flatten_tree(target_ts)
# Construct the forward control dependencies edges so that
# the get_walks_intersection_ops can also traverse the
# control dependencies.
graph = util.get_unique_graph(flatten_target_ts, check_types=(tf_ops.Tensor))
control_ios = util.ControlOutputs(graph)
ops = select.get_walks_intersection_ops(replacement_ts.keys(),
flatten_target_ts,
control_ios=control_ios)
if not ops:
raise ValueError("Targets and replacements are not connected!")
# Create a copy of the relevant subgraph
_, info = copy_with_input_replacements(
ops, replacement_ts, None, dst_scope, src_scope, reuse_dst_scope)
# Return the transformed targets
return info.transformed(target_ts)
