def __init__(self, inside_ops=(), passthrough_ts=()):
"""Create a subgraph containing the given ops and the "passthrough" tensors.
Args:
inside_ops: an object convertible to a list of tf.Operation. This list
defines all the operations in the subgraph.
passthrough_ts: an object convertible to a list of tf.Tensor. This list
define all the "passthrough" tensors. A passthrough tensor is a tensor
which goes directly from the input of the subgraph to it output, without
any intermediate operations. All the non passthrough tensors are
silently ignored.
Raises:
TypeError: if inside_ops cannot be converted to a list of tf.Operation or
if passthrough_ts cannot be converted to a list of tf.Tensor.
"""
inside_ops = util.make_list_of_op(inside_ops)
passthrough_ts = util.make_list_of_t(passthrough_ts)
ops_and_ts = inside_ops + passthrough_ts
if ops_and_ts:
self._graph = util.get_unique_graph(ops_and_ts)
else:
self._graph = None
self._ops = inside_ops
# Compute inside and outside tensor
inputs, outputs, insides = select.compute_boundary_ts(inside_ops)
# Compute passthrough tensors, silently ignoring the non-passthrough ones.
all_tensors = frozenset(inputs + outputs + list(insides))
self._passthrough_ts = [t for t in passthrough_ts if t not in all_tensors]
# Set inputs and outputs.
self._input_ts = inputs + self._passthrough_ts
self._output_ts = outputs + self._passthrough_ts
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 __init__(self, inside_ops=(), passthrough_ts=()):
"""Create a subgraph containing the given ops and the "passthrough" tensors.
Args:
inside_ops: an object convertible to a list of `tf.Operation`. This list
defines all the operations in the subgraph.
passthrough_ts: an object convertible to a list of `tf.Tensor`. This list
define all the "passthrough" tensors. A passthrough tensor is a tensor
which goes directly from the input of the subgraph to it output, without
any intermediate operations. All the non passthrough tensors are
silently ignored.
Raises:
TypeError: if inside_ops cannot be converted to a list of `tf.Operation`
or if `passthrough_ts` cannot be converted to a list of `tf.Tensor`.
"""
inside_ops = util.make_list_of_op(inside_ops)
passthrough_ts = util.make_list_of_t(passthrough_ts)
ops_and_ts = inside_ops + passthrough_ts
if ops_and_ts:
self._graph = util.get_unique_graph(ops_and_ts)
self._ops = inside_ops
# Compute inside and outside tensor
inputs, outputs, insides = select.compute_boundary_ts(inside_ops)
# Compute passthrough tensors, silently ignoring the non-passthrough ones.
all_tensors = frozenset(inputs + outputs + list(insides))
self._passthrough_ts = [
t for t in passthrough_ts if t not in all_tensors
]
# Set inputs and outputs.
self._input_ts = inputs + self._passthrough_ts
self._output_ts = outputs + self._passthrough_ts
else:
self._graph = None
self._passthrough_ts = []
self._input_ts = []
self._output_ts = []
self._ops = []
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)
