def get_within_boundary_ops(ops, seed_ops, boundary_ops=(), inclusive=True, control_inputs=False, control_outputs=None, control_ios=None): """Return all the `tf.Operation` within the given boundary. Args: ops: an object convertible to a list of `tf.Operation`. those ops define the set in which to perform the operation (if a `tf.Graph` is given, it will be converted to the list of all its operations). seed_ops: the operations from which to start expanding. boundary_ops: the ops forming the boundary. inclusive: if `True`, the result will also include the boundary ops. control_inputs: A boolean indicating whether control inputs are enabled. control_outputs: An instance of `util.ControlOutputs` or `None`. If not `None`, control outputs are enabled. control_ios: An instance of `util.ControlOutputs` or `None`. If not `None`, both control inputs and control outputs are enabled. This is equivalent to set control_inputs to True and control_outputs to the `util.ControlOutputs` instance. Returns: All the `tf.Operation` surrounding the given ops. Raises: TypeError: if `ops` or `seed_ops` cannot be converted to a list of `tf.Operation`. ValueError: if the boundary is intersecting with the seeds. """ control_inputs, control_outputs = check_cios(control_inputs, control_outputs, control_ios) ops = util.make_list_of_op(ops) seed_ops = util.make_list_of_op(seed_ops, allow_graph=False) boundary_ops = set(util.make_list_of_op(boundary_ops)) res = set(seed_ops) if boundary_ops & res: raise ValueError("Boundary is intersecting with the seeds.") wave = set(seed_ops) while wave: new_wave = set() ops_io = get_ops_ios(wave, control_inputs, control_outputs) for op in ops_io: if op in res: continue if op in boundary_ops: if inclusive: res.add(op) else: new_wave.add(op) res.update(new_wave) wave = new_wave return [op for op in ops if op in res]
def get_ops_ios(ops, control_inputs=False, control_outputs=None, control_ios=None): """Return all the `tf.Operation` which are connected to an op in ops. Args: ops: an object convertible to a list of `tf.Operation`. control_inputs: A boolean indicating whether control inputs are enabled. control_outputs: An instance of `util.ControlOutputs` or `None`. If not `None`, control outputs are enabled. control_ios: An instance of `util.ControlOutputs` or `None`. If not `None`, both control inputs and control outputs are enabled. This is equivalent to set `control_inputs` to `True` and `control_outputs` to the `util.ControlOutputs` instance. Returns: All the `tf.Operation` surrounding the given ops. Raises: TypeError: if `ops` cannot be converted to a list of `tf.Operation`. """ control_inputs, control_outputs = check_cios(control_inputs, control_outputs, control_ios) ops = util.make_list_of_op(ops) res = [] for op in ops: util.concatenate_unique(res, [t.op for t in op.inputs]) for t in op.outputs: util.concatenate_unique(res, t.consumers()) if control_outputs is not None: util.concatenate_unique(res, control_outputs.get(op)) if control_inputs: util.concatenate_unique(res, op.control_inputs) return res
def compute_boundary_ts(ops): """Compute the tensors at the boundary of a set of ops. This function looks at all the tensors connected to the given ops (in/out) and classify them into three categories: 1) input tensors: tensors whose generating operation is not in ops. 2) output tensors: tensors whose consumer operations are not in ops 3) inside tensors: tensors which are neither input nor output tensors. Note that a tensor can be both an inside tensor and an output tensor if it is consumed by operations both outside and inside of `ops`. Args: ops: an object convertible to a list of tf.Operation. Returns: A tuple `(outside_input_ts, outside_output_ts, inside_ts)` where: `outside_input_ts` is a Python list of input tensors; `outside_output_ts` is a python list of output tensors; `inside_ts` is a python list of inside tensors. Since a tensor can be both an inside tensor and an output tensor, `outside_output_ts` and `inside_ts` might intersect. Raises: TypeError: if ops cannot be converted to a list of tf.Operation. """ ops = util.make_list_of_op(ops) input_ts = _get_input_ts(ops) output_ts = _get_output_ts(ops) output_ts_set = frozenset(output_ts) ops_set = frozenset(ops) # Compute inside tensors. inside_ts = [] only_inside_ts = [] for t in input_ts: # Skip if the input tensor is not also an output tensor. if t not in output_ts_set: continue # Mark as "inside". inside_ts.append(t) # Mark as "only inside" if the tensor is not both inside and output. consumers = frozenset(t.consumers()) if consumers - ops_set: continue only_inside_ts.append(t) inside_ts_set = frozenset(inside_ts) only_inside_ts_set = frozenset(only_inside_ts) outside_output_ts = [t for t in output_ts if t not in only_inside_ts_set] outside_input_ts = [t for t in input_ts if t not in inside_ts_set] return outside_input_ts, outside_output_ts, inside_ts
def _get_output_ts(ops): """Compute the list of unique output tensors of all the op in ops. Args: ops: an object convertible to a list of tf.Operation. Returns: The list of unique output tensors of all the op in ops. Raises: TypeError: if ops cannot be converted to a list of tf.Operation. """ ops = util.make_list_of_op(ops) ts = [] for op in ops: ts += op.outputs return ts
def filter_ops_from_regex(ops, regex): """Get all the operations that match the given regex. Args: ops: an object convertible to a list of `tf.Operation`. regex: a regular expression matching the operation's name. For example, `"^foo(/.*)?$"` will match all the operations in the "foo" scope. Returns: A list of `tf.Operation`. Raises: TypeError: if ops cannot be converted to a list of `tf.Operation`. """ ops = util.make_list_of_op(ops) regex_obj = make_regex(regex) return filter_ops(ops, lambda op: regex_obj.search(op.name))
def filter_ops(ops, positive_filter): """Get the ops passing the given filter. Args: ops: an object convertible to a list of tf.Operation. positive_filter: a function deciding where to keep an operation or not. If True, all the operations are returned. Returns: A list of selected tf.Operation. Raises: TypeError: if ops cannot be converted to a list of tf.Operation. """ ops = util.make_list_of_op(ops) if positive_filter is not True: # pylint: disable=g-explicit-bool-comparison ops = [op for op in ops if positive_filter(op)] return ops
def filter_ts_from_regex(ops, regex): r"""Get all the tensors linked to ops that match the given regex. Args: ops: an object convertible to a list of tf.Operation. regex: a regular expression matching the tensors' name. For example, "^foo(/.*)?:\d+$" will match all the tensors in the "foo" scope. Returns: A list of tf.Tensor. Raises: TypeError: if ops cannot be converted to a list of tf.Operation. """ ops = util.make_list_of_op(ops) regex_obj = make_regex(regex) return filter_ts(ops, positive_filter=lambda op: regex_obj.search(op.name))
def filter_ts(ops, positive_filter): """Get all the tensors which are input or output of an op in ops. Args: ops: an object convertible to a list of `tf.Operation`. positive_filter: a function deciding whether to keep a tensor or not. If `True`, all the tensors are returned. Returns: A list of `tf.Tensor`. Raises: TypeError: if ops cannot be converted to a list of `tf.Operation`. """ ops = util.make_list_of_op(ops) ts = _get_input_ts(ops) util.concatenate_unique(ts, _get_output_ts(ops)) if positive_filter is not True: ts = [t for t in ts if positive_filter(t)] return ts
def _get_input_ts(ops): """Compute the list of unique input tensors of all the op in ops. Args: ops: an object convertible to a list of `tf.Operation`. Returns: The list of unique input tensors of all the op in ops. Raises: TypeError: if ops cannot be converted to a list of `tf.Operation`. """ ops = util.make_list_of_op(ops) ts = [] ts_set = set() for op in ops: for t in op.inputs: if t not in ts_set: ts.append(t) ts_set.add(t) return ts
def add_control_inputs(op, cops): """Add the control inputs cops to op. Warning: this function is directly manipulating the internals of the tf.Graph. Args: op: a tf.Operation to which the control inputs are added. cops: an object convertible to a list of `tf.Operation`. Raises: TypeError: if op is not a tf.Operation ValueError: if any cop in cops is already a control input of op. """ if not isinstance(op, _tf_ops.Operation): raise TypeError("Expected a tf.Operation, got: {}", type(op)) cops = _util.make_list_of_op(cops, allow_graph=False) for cop in cops: if cop in op.control_inputs: raise ValueError("{} is already a control_input of {}".format( cop.name, op.name)) op._add_control_inputs(cops) # pylint: disable=protected-access
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 remove_control_inputs(op, cops): """Remove the control inputs cops from co. Warning: this function is directly manipulating the internals of the `tf.Graph`. Args: op: a `tf.Operation` from which to remove the control inputs. cops: an object convertible to a list of `tf.Operation`. Raises: TypeError: if op is not a `tf.Operation`. ValueError: if any cop in cops is not a control input of op. """ if not isinstance(op, _tf_ops.Operation): raise TypeError("Expected a tf.Operation, got: {}", type(op)) cops = _util.make_list_of_op(cops, allow_graph=False) for cop in cops: if cop not in op.control_inputs: raise ValueError("{} is not a control_input of {}".format( op.name, cop.name)) control_inputs = [cop for cop in op.control_inputs if cop not in cops] # pylint: disable=protected-access op._remove_all_control_inputs() op._add_control_inputs(control_inputs)
def select_ops(*args, **kwargs): """Helper to select operations. Args: *args: list of 1) regular expressions (compiled or not) or 2) (array of) `tf.Operation`. `tf.Tensor` instances are silently ignored. **kwargs: 'graph': `tf.Graph` in which to perform the regex query.This is required when using regex. 'positive_filter': an elem if selected only if `positive_filter(elem)` is `True`. This is optional. 'restrict_ops_regex': a regular expression is ignored if it doesn't start with the substring "(?#ops)". Returns: A list of `tf.Operation`. Raises: TypeError: if the optional keyword argument graph is not a `tf.Graph` or if an argument in args is not an (array of) `tf.Operation` or an (array of) `tf.Tensor` (silently ignored) or a string or a regular expression. ValueError: if one of the keyword arguments is unexpected or if a regular expression is used without passing a graph as a keyword argument. """ # get keywords arguments graph = None positive_filter = None restrict_ops_regex = False for k, v in iteritems(kwargs): if k == "graph": graph = v if graph is not None and not isinstance(graph, tf_ops.Graph): raise TypeError("Expected a tf.Graph, got: {}".format( type(graph))) elif k == "positive_filter": positive_filter = v elif k == "restrict_ops_regex": restrict_ops_regex = v elif k == "restrict_ts_regex": pass else: raise ValueError("Wrong keywords argument: {}.".format(k)) ops = [] for arg in args: if can_be_regex(arg): if graph is None: raise ValueError( "Use the keyword argument 'graph' to use regex.") regex = make_regex(arg) if regex.pattern.startswith("(?#ts)"): continue if restrict_ops_regex and not regex.pattern.startswith("(?#ops)"): continue ops_ = filter_ops_from_regex(graph, regex) for op_ in ops_: if op_ not in ops: if positive_filter is None or positive_filter(op_): ops.append(op_) else: ops_aux = util.make_list_of_op(arg, ignore_ts=True) if positive_filter is not None: ops_aux = [op for op in ops_aux if positive_filter(op)] ops_aux = [op for op in ops_aux if op not in ops] ops += ops_aux return ops
def get_forward_walk_ops(seed_ops, inclusive=True, within_ops=None, within_ops_fn=None, stop_at_ts=(), control_outputs=None): """Do a forward graph walk and return all the visited ops. Args: seed_ops: an iterable of operations from which the forward graph walk starts. If a list of tensors is given instead, the seed_ops are set to be the consumers of those tensors. inclusive: if True the given seed_ops are also part of the resulting set. within_ops: an iterable of `tf.Operation` within which the search is restricted. If `within_ops` is `None`, the search is performed within the whole graph. within_ops_fn: if provided, a function on ops that should return True iff the op is within the graph traversal. This can be used along within_ops, in which case an op is within if it is also in within_ops. stop_at_ts: an iterable of tensors at which the graph walk stops. control_outputs: a `util.ControlOutputs` instance or None. If not `None`, it will be used while walking the graph forward. Returns: A Python set of all the `tf.Operation` ahead of `seed_ops`. Raises: TypeError: if `seed_ops` or `within_ops` cannot be converted to a list of `tf.Operation`. """ _, control_outputs = check_cios(False, control_outputs) if not util.is_iterable(seed_ops): seed_ops = [seed_ops] if not seed_ops: return [] if isinstance(seed_ops[0], tf_ops.Tensor): ts = util.make_list_of_t(seed_ops, allow_graph=False) seed_ops = util.get_consuming_ops(ts) else: seed_ops = util.make_list_of_op(seed_ops, allow_graph=False) seed_ops = frozenset(seed_ops) stop_at_ts = frozenset(util.make_list_of_t(stop_at_ts)) if within_ops: within_ops = util.make_list_of_op(within_ops, allow_graph=False) within_ops = frozenset(within_ops) seed_ops &= within_ops def is_within(op): return (within_ops is None or op in within_ops) and (within_ops_fn is None or within_ops_fn(op)) result = list(seed_ops) wave = set(seed_ops) while wave: new_wave = set() for op in wave: for new_t in op.outputs: if new_t in stop_at_ts: continue for new_op in new_t.consumers(): if new_op not in result and is_within(new_op): new_wave.add(new_op) if control_outputs is not None: for new_op in control_outputs.get(op): if new_op not in result and is_within(new_op): new_wave.add(new_op) util.concatenate_unique(result, new_wave) wave = new_wave if not inclusive: result = [op for op in result if op not in seed_ops] return result
def _reroute_ts(ts0, ts1, mode, can_modify=None, cannot_modify=None): """Reroute the end of the tensors in each pair (t0,t1) in ts0 x ts1. This function is the back-bone of the Graph-Editor. It is essentially a thin wrapper on top of the tf.Operation._update_input. Given a pair of tensor t0, t1 in ts0 x ts1, this function re-route the end of t0 and t1 in three possible ways: 1) The reroute mode is "a<->b" or "b<->a": the tensors' end are swapped. After this operation, the previous consumers of t0 are now consumers of t1 and vice-versa. 2) The reroute mode is "a->b": the tensors' end of t0 are re-routed to the tensors's end of t1 (which are left dangling). After this operation, the previous consumers of t0 are still consuming t0 but the previous consumers of t1 are not also consuming t0. The tensor t1 has no consumer. 3) The reroute mode is "b->a": this mode is the symmetric of the "a->b" mode. Note that this function is re-routing the end of two tensors, not the start. Re-routing the start of two tensors is not supported by this library. The reason for that is the following: TensorFlow, by design, creates a strong bond between an op and its output tensor. This Graph editor follows this design and treats an operation A and its generating tensors {t_i} as an entity which cannot be broken. In other words, an op cannot be detached from any of its output tensors, ever. But it is possible to detach an op from its input tensors, which is what this function concerns itself with. Warning: this function is directly manipulating the internals of the tf.Graph. Args: ts0: an object convertible to a list of `tf.Tensor`. ts1: an object convertible to a list of `tf.Tensor`. mode: what to do with those tensors: "a->b" or "b<->a" for swaping and "a->b" or "b->a" for one direction re-routing. can_modify: iterable of operations which can be modified. Any operation outside within_ops will be left untouched by this function. cannot_modify: iterable of operations which cannot be modified. Any operation within cannot_modify will be left untouched by this function. Returns: The number of individual modifications made by the function. Raises: TypeError: if `ts0` or `ts1` cannot be converted to a list of `tf.Tensor`. TypeError: if `can_modify` or `cannot_modify` is not `None` and cannot be converted to a list of `tf.Operation`. """ a2b, b2a = _RerouteMode.check(mode) ts0 = _util.make_list_of_t(ts0) ts1 = _util.make_list_of_t(ts1) _check_ts_compatibility(ts0, ts1) if cannot_modify is not None: cannot_modify = frozenset(_util.make_list_of_op(cannot_modify)) if can_modify is not None: can_modify = frozenset(_util.make_list_of_op(can_modify)) nb_update_inputs = 0 precomputed_consumers = [] # precompute consumers to avoid issue with repeated tensors: for t0, t1 in zip(ts0, ts1): consumers0 = set(t0.consumers()) consumers1 = set(t1.consumers()) precomputed_consumers.append((consumers0, consumers1)) for t0, t1, consumers in zip(ts0, ts1, precomputed_consumers): if t0 is t1: continue # Silently ignore identical tensors. consumers0, consumers1 = consumers if a2b: nb_update_inputs += _reroute_t(t0, t1, consumers1, can_modify, cannot_modify) if b2a: nb_update_inputs += _reroute_t(t1, t0, consumers0, can_modify, cannot_modify) return nb_update_inputs