6ca1a727a7
The former fetches an OpDef for a given name, and the latter allows to force-sync the contents of the Python registry with the C++ runtime. The Python registry can be out-of-date e.g. when loading a Graph using ops from an op library which has not yet been imported. PiperOrigin-RevId: 270249444
504 lines
15 KiB
Python
504 lines
15 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Classes and methods for processing debugger-decorated graphs."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.core.framework import graph_pb2
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from tensorflow.python.framework import op_def_registry
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from tensorflow.python.platform import tf_logging as logging
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def parse_node_or_tensor_name(name):
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"""Get the node name from a string that can be node or tensor name.
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Args:
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name: An input node name (e.g., "node_a") or tensor name (e.g.,
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"node_a:0"), as a str.
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Returns:
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1) The node name, as a str. If the input name is a tensor name, i.e.,
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consists of a colon, the final colon and the following output slot
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will be stripped.
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2) If the input name is a tensor name, the output slot, as an int. If
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the input name is not a tensor name, None.
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"""
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if ":" in name and not name.endswith(":"):
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node_name = name[:name.rfind(":")]
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output_slot = int(name[name.rfind(":") + 1:])
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return node_name, output_slot
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else:
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return name, None
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def get_node_name(element_name):
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node_name, _ = parse_node_or_tensor_name(element_name)
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return node_name
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def get_output_slot(element_name):
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"""Get the output slot number from the name of a graph element.
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If element_name is a node name without output slot at the end, 0 will be
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assumed.
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Args:
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element_name: (`str`) name of the graph element in question.
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Returns:
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(`int`) output slot number.
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"""
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_, output_slot = parse_node_or_tensor_name(element_name)
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return output_slot if output_slot is not None else 0
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def is_copy_node(node_name):
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"""Determine whether a node name is that of a debug Copy node.
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Such nodes are inserted by TensorFlow core upon request in
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RunOptions.debug_options.debug_tensor_watch_opts.
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Args:
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node_name: Name of the node.
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Returns:
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A bool indicating whether the input argument is the name of a debug Copy
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node.
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"""
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return node_name.startswith("__copy_")
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def is_debug_node(node_name):
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"""Determine whether a node name is that of a debug node.
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Such nodes are inserted by TensorFlow core upon request in
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RunOptions.debug_options.debug_tensor_watch_opts.
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Args:
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node_name: Name of the node.
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Returns:
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A bool indicating whether the input argument is the name of a debug node.
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"""
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return node_name.startswith("__dbg_")
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def parse_debug_node_name(node_name):
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"""Parse the name of a debug node.
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Args:
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node_name: Name of the debug node.
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Returns:
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1. Name of the watched node, as a str.
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2. Output slot index of the watched tensor, as an int.
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3. Index of the debug node, as an int.
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4. Name of the debug op, as a str, e.g, "DebugIdentity".
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Raises:
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ValueError: If the input node name is not a valid debug node name.
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"""
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prefix = "__dbg_"
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name = node_name
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if not name.startswith(prefix):
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raise ValueError("Invalid prefix in debug node name: '%s'" % node_name)
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name = name[len(prefix):]
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if name.count("_") < 2:
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raise ValueError("Invalid debug node name: '%s'" % node_name)
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debug_op = name[name.rindex("_") + 1:]
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name = name[:name.rindex("_")]
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debug_op_index = int(name[name.rindex("_") + 1:])
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name = name[:name.rindex("_")]
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if name.count(":") != 1:
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raise ValueError("Invalid tensor name in debug node name: '%s'" % node_name)
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watched_node_name = name[:name.index(":")]
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watched_output_slot = int(name[name.index(":") + 1:])
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return watched_node_name, watched_output_slot, debug_op_index, debug_op
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class GraphTracingReachedDestination(Exception):
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pass
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class DFSGraphTracer(object):
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"""Graph input tracer using depth-first search."""
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def __init__(self,
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input_lists,
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skip_node_names=None,
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destination_node_name=None):
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"""Constructor of _DFSGraphTracer.
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Args:
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input_lists: A list of dicts. Each dict is an adjacency (input) map from
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the recipient node name as the key and the list of input node names
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as the value.
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skip_node_names: Optional: a list of node names to skip tracing.
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destination_node_name: Optional: destination node name. If not `None`, it
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should be the name of a destination not as a str and the graph tracing
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will raise GraphTracingReachedDestination as soon as the node has been
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reached.
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Raises:
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GraphTracingReachedDestination: if stop_at_node_name is not None and
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the specified node is reached.
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"""
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self._input_lists = input_lists
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self._skip_node_names = skip_node_names
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self._inputs = []
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self._visited_nodes = []
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self._depth_count = 0
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self._depth_list = []
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self._destination_node_name = destination_node_name
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def trace(self, graph_element_name):
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"""Trace inputs.
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Args:
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graph_element_name: Name of the node or an output tensor of the node, as a
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str.
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Raises:
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GraphTracingReachedDestination: if destination_node_name of this tracer
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object is not None and the specified node is reached.
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"""
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self._depth_count += 1
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node_name = get_node_name(graph_element_name)
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if node_name == self._destination_node_name:
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raise GraphTracingReachedDestination()
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if node_name in self._skip_node_names:
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return
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if node_name in self._visited_nodes:
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return
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self._visited_nodes.append(node_name)
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for input_list in self._input_lists:
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if node_name not in input_list:
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continue
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for inp in input_list[node_name]:
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if get_node_name(inp) in self._visited_nodes:
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continue
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self._inputs.append(inp)
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self._depth_list.append(self._depth_count)
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self.trace(inp)
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self._depth_count -= 1
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def inputs(self):
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return self._inputs
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def depth_list(self):
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return self._depth_list
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def _infer_device_name(graph_def):
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"""Infer device name from a partition GraphDef."""
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device_name = None
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for node in graph_def.node:
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if node.device:
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device_name = node.device
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break
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if device_name is None:
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logging.warn(
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"Failed to infer device name from partition GraphDef: none of the "
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"nodes of the GraphDef has a non-empty device name.")
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return device_name
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class DebugGraph(object):
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"""Represents a debugger-decorated graph."""
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def __init__(self, debug_graph_def, device_name=None):
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self._debug_graph_def = debug_graph_def
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self._non_debug_graph_def = None
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self._node_attributes = {}
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self._node_inputs = {}
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self._node_reversed_ref_inputs = {}
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self._node_ctrl_inputs = {}
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self._node_recipients = {}
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self._node_ctrl_recipients = {}
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self._node_devices = {}
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self._node_op_types = {}
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self._copy_send_nodes = []
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self._ref_args = {}
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self._device_name = device_name
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if not self._device_name:
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self._device_name = _infer_device_name(debug_graph_def)
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for node in debug_graph_def.node:
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self._process_debug_graph_node(node)
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self._prune_non_control_edges_of_debug_ops()
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self._prune_control_edges_of_debug_ops()
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self._prune_nodes_from_input_and_recipient_maps(self._get_copy_nodes())
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self._populate_recipient_maps()
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def _process_debug_graph_node(self, node):
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"""Process a node from the debug GraphDef.
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Args:
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node: (NodeDef) A partition-graph node to be processed.
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Raises:
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ValueError: If duplicate node names are encountered.
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"""
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if is_debug_node(node.name):
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# This is a debug node. Parse the node name and retrieve the
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# information about debug watches on tensors. But do not include
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# the node in the graph.
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return
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if node.name in self._node_inputs:
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raise ValueError("Duplicate node name on device %s: '%s'" %
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(self._device_name, node.name))
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self._node_attributes[node.name] = node.attr
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self._node_inputs[node.name] = []
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self._node_ctrl_inputs[node.name] = []
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self._node_recipients[node.name] = []
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self._node_ctrl_recipients[node.name] = []
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if node.name not in self._node_devices:
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self._node_devices[node.name] = set()
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self._node_devices[node.name].add(
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node.device if node.device else self._device_name)
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self._node_op_types[node.name] = node.op
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self._ref_args[node.name] = self._get_ref_args(node)
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for inp in node.input:
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if is_copy_node(inp) and (node.op == "_Send" or node.op == "_Retval"):
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self._copy_send_nodes.append(node.name)
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if inp.startswith("^"):
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cinp = inp[1:]
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self._node_ctrl_inputs[node.name].append(cinp)
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else:
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self._node_inputs[node.name].append(inp)
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def _get_ref_args(self, node):
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"""Determine whether an input of an op is ref-type.
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Args:
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node: A `NodeDef`.
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Returns:
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A list of the arg names (as strs) that are ref-type.
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"""
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op_def = op_def_registry.get(node.op)
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if op_def is None:
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return []
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ref_args = []
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for i, output_arg in enumerate(op_def.output_arg):
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if output_arg.is_ref:
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arg_name = node.name if i == 0 else ("%s:%d" % (node.name, i))
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ref_args.append(arg_name)
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return ref_args
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def _get_copy_nodes(self):
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"""Find all Copy nodes in the loaded graph."""
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copy_nodes = []
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for node in self._node_inputs:
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if is_copy_node(node):
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copy_nodes.append(node)
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return copy_nodes
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def _prune_non_control_edges_of_debug_ops(self):
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"""Prune (non-control) edges related to debug ops.
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Prune the Copy ops and associated _Send ops inserted by the debugger out
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from the non-control inputs and output recipients map. Replace the inputs
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and recipients with original ones.
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"""
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for node in self._node_inputs:
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inputs = self._node_inputs[node]
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for i, inp in enumerate(inputs):
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if is_copy_node(inp):
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# Find the input to the Copy node, which should be the original
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# input to the node.
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orig_inp = self._node_inputs[inp][0]
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inputs[i] = orig_inp
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def _prune_control_edges_of_debug_ops(self):
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"""Prune control edges related to the debug ops."""
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for node in self._node_ctrl_inputs:
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ctrl_inputs = self._node_ctrl_inputs[node]
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debug_op_inputs = []
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for ctrl_inp in ctrl_inputs:
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if is_debug_node(ctrl_inp):
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debug_op_inputs.append(ctrl_inp)
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for debug_op_inp in debug_op_inputs:
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ctrl_inputs.remove(debug_op_inp)
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def _populate_recipient_maps(self):
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"""Populate the map from node name to recipient(s) of its output(s).
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This method also populates the input map based on reversed ref edges.
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"""
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for node in self._node_inputs:
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inputs = self._node_inputs[node]
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for inp in inputs:
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inp = get_node_name(inp)
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if inp not in self._node_recipients:
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self._node_recipients[inp] = []
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self._node_recipients[inp].append(node)
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if inp in self._ref_args:
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if inp not in self._node_reversed_ref_inputs:
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self._node_reversed_ref_inputs[inp] = []
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self._node_reversed_ref_inputs[inp].append(node)
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for node in self._node_ctrl_inputs:
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ctrl_inputs = self._node_ctrl_inputs[node]
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for ctrl_inp in ctrl_inputs:
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if ctrl_inp in self._copy_send_nodes:
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continue
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if ctrl_inp not in self._node_ctrl_recipients:
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self._node_ctrl_recipients[ctrl_inp] = []
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self._node_ctrl_recipients[ctrl_inp].append(node)
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def _prune_nodes_from_input_and_recipient_maps(self, nodes_to_prune):
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"""Prune nodes out of input and recipient maps.
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Args:
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nodes_to_prune: (`list` of `str`) Names of the nodes to be pruned.
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"""
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for node in nodes_to_prune:
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del self._node_inputs[node]
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del self._node_ctrl_inputs[node]
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del self._node_recipients[node]
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del self._node_ctrl_recipients[node]
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def _reconstruct_non_debug_graph_def(self):
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"""Reconstruct non-debug GraphDef.
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Non-debug GraphDef means the original GraphDef without the Copy* and Debug
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nodes inserted by the debugger.
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"""
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if self._non_debug_graph_def:
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return
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self._non_debug_graph_def = graph_pb2.GraphDef()
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for node in self._debug_graph_def.node:
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if is_copy_node(node.name) or is_debug_node(node.name):
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continue
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new_node = self._non_debug_graph_def.node.add()
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new_node.CopyFrom(node)
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# Redo the list of inputs, because in _debug_graph_def, the list can
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# consist of Copy* and Debug* nodes inserted by the debugger. Those will
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# be replaced with the original inputs here.
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del new_node.input[:]
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for inp in self._node_inputs[node.name]:
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new_node.input.append(inp)
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for ctrl_inp in self._node_ctrl_inputs[node.name]:
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new_node.input.append("^" + ctrl_inp)
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@property
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def device_name(self):
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return self._device_name
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@property
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def debug_graph_def(self):
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"""The debugger-decorated GraphDef."""
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return self._debug_graph_def
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@property
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def non_debug_graph_def(self):
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"""The GraphDef without the Copy* and Debug* nodes added by the debugger."""
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self._reconstruct_non_debug_graph_def()
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return self._non_debug_graph_def
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@property
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def node_devices(self):
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return self._node_devices
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@property
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def node_op_types(self):
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return self._node_op_types
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@property
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def node_attributes(self):
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return self._node_attributes
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@property
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def node_inputs(self):
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return self._node_inputs
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@property
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def node_ctrl_inputs(self):
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return self._node_ctrl_inputs
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@property
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def node_reversed_ref_inputs(self):
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return self._node_reversed_ref_inputs
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@property
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def node_recipients(self):
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return self._node_recipients
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@property
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def node_ctrl_recipients(self):
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return self._node_ctrl_recipients
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def reconstruct_non_debug_graph_def(debug_graph_def):
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"""Reconstruct original (non-debugger-decorated) partition GraphDef.
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This method strips the input `tf.compat.v1.GraphDef` of the Copy* and
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Debug*-type nodes inserted by the debugger.
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The reconstructed partition graph is identical to the original (i.e.,
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non-debugger-decorated) partition graph except in the following respects:
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1) The exact names of the runtime-inserted internal nodes may differ.
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These include _Send, _Recv, _HostSend, _HostRecv, _Retval ops.
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2) As a consequence of 1, the nodes that receive input directly from such
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send- and recv-type ops will have different input names.
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3) The parallel_iteration attribute of while-loop Enter ops are set to 1.
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Args:
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debug_graph_def: The debugger-decorated `tf.compat.v1.GraphDef`, with the
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debugger-inserted Copy* and Debug* nodes.
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Returns:
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The reconstructed `tf.compat.v1.GraphDef` stripped of the debugger-inserted
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nodes.
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"""
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return DebugGraph(debug_graph_def).non_debug_graph_def
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