def __init__(
self,
sgv,
dst_graph, # type: Graph
dst_scope,
src_scope):
self.sgv = sgv
self.sgv_inputs_set = frozenset(sgv.inputs)
self.ops = frozenset(sgv.ops)
self.control_outputs = util.ControlOutputs(sgv.graph)
self.graph = sgv.graph # type: Graph
self.scope = src_scope
self.graph_ = dst_graph
self.scope_ = dst_scope
self.transformed_ops = {}
self.transformed_ts = {}
self.collections = dict(
(key, self.graph.get_collection_by_name(key))
for key in self.graph.get_all_collection_keys())
self.cyclic_ops = []
self.transform_original_op_handler = transform_op_if_inside_handler
# The graph is transformed op by op, in the same order the original ops
# were created. However, this is sometimes not possible due to cycles
# (i.e. while loops). So when the transformer creates a new op whose
# inputs do not exist yet, temporary placeholders are created and stored
# in this `tmp_cyclic_ts` container. During a second pass,
# those temporary tensors are replaced by the proper transformed tensors
# (see the function `_finalize_cycles`).
self.tmp_cyclic_ts = []
def graph_replace(target_ts,
replacement_ts,
dst_scope="",
src_scope="",
reuse_dst_scope=False):
"""Create a new graph which computes the targets from the replaced Tensors.
Args:
target_ts: a single gde.Tensor or an iterable of gde.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 gde.Tensor or a list of target gde.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 = _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=(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
def missing_fn(original_t):
return original_t
return info.transformed(target_ts, missing_fn)
