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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""TensorFlow Debugger (tfdbg) Utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from six.moves import xrange # pylint: disable=redefined-builtin
def add_debug_tensor_watch(run_options,
node_name,
output_slot=0,
debug_ops="DebugIdentity",
debug_urls=None,
global_step=-1):
"""Add watch on a `Tensor` to `RunOptions`.
N.B.: Under certain circumstances, the `Tensor` may not be actually watched
(e.g., if the node of the `Tensor` is constant-folded during runtime).
Args:
run_options: An instance of `config_pb2.RunOptions` to be modified.
node_name: (`str`) name of the node to watch.
output_slot: (`int`) output slot index of the tensor from the watched node.
debug_ops: (`str` or `list` of `str`) name(s) of the debug op(s). Can be a
`list` of `str` or a single `str`. The latter case is equivalent to a
`list` of `str` with only one element.
debug_urls: (`str` or `list` of `str`) URL(s) to send debug values to,
e.g., `file:///tmp/tfdbg_dump_1`, `grpc://localhost:12345`.
global_step: (`int`) Optional global_step count for this debug tensor
watch.
"""
watch_opts = run_options.debug_options.debug_tensor_watch_opts
run_options.debug_options.global_step = global_step
watch = watch_opts.add()
watch.node_name = node_name
watch.output_slot = output_slot
if isinstance(debug_ops, str):
debug_ops = [debug_ops]
watch.debug_ops.extend(debug_ops)
if debug_urls:
if isinstance(debug_urls, str):
debug_urls = [debug_urls]
watch.debug_urls.extend(debug_urls)
def watch_graph(run_options,
graph,
debug_ops="DebugIdentity",
debug_urls=None,
node_name_regex_whitelist=None,
op_type_regex_whitelist=None,
global_step=-1):
"""Add debug watches to `RunOptions` for a TensorFlow graph.
To watch all `Tensor`s on the graph, let both `node_name_regex_whitelist`
and `op_type_regex_whitelist` be the default (`None`).
N.B.: Under certain circumstances, not all specified `Tensor`s will be
actually watched (e.g., nodes that are constant-folded during runtime will
not be watched).
Args:
run_options: An instance of `config_pb2.RunOptions` to be modified.
graph: An instance of `ops.Graph`.
debug_ops: (`str` or `list` of `str`) name(s) of the debug op(s) to use.
debug_urls: URLs to send debug values to. Can be a list of strings,
a single string, or None. The case of a single string is equivalent to
a list consisting of a single string, e.g., `file:///tmp/tfdbg_dump_1`,
`grpc://localhost:12345`.
node_name_regex_whitelist: Regular-expression whitelist for node_name,
e.g., `"(weight_[0-9]+|bias_.*)"`
op_type_regex_whitelist: Regular-expression whitelist for the op type of
nodes, e.g., `"(Variable|Add)"`.
If both `node_name_regex_whitelist` and `op_type_regex_whitelist`
are set, the two filtering operations will occur in a logical `AND`
relation. In other words, a node will be included if and only if it
hits both whitelists.
global_step: (`int`) Optional global_step count for this debug tensor
watch.
"""
if isinstance(debug_ops, str):
debug_ops = [debug_ops]
if node_name_regex_whitelist:
node_name_pattern = re.compile(node_name_regex_whitelist)
else:
node_name_pattern = None
if op_type_regex_whitelist:
op_type_pattern = re.compile(op_type_regex_whitelist)
else:
op_type_pattern = None
ops = graph.get_operations()
for op in ops:
# Skip nodes without any output tensors.
if not op.outputs:
continue
node_name = op.name
op_type = op.type
if node_name_pattern and not node_name_pattern.match(node_name):
continue
if op_type_pattern and not op_type_pattern.match(op_type):
continue
for slot in xrange(len(op.outputs)):
add_debug_tensor_watch(
run_options,
node_name,
output_slot=slot,
debug_ops=debug_ops,
debug_urls=debug_urls,
global_step=global_step)
def watch_graph_with_blacklists(run_options,
graph,
debug_ops="DebugIdentity",
debug_urls=None,
node_name_regex_blacklist=None,
op_type_regex_blacklist=None,
global_step=-1):
"""Add debug tensor watches, blacklisting nodes and op types.
This is similar to `watch_graph()`, but the node names and op types are
blacklisted, instead of whitelisted.
N.B.: Under certain circumstances, not all specified `Tensor`s will be
actually watched (e.g., nodes that are constant-folded during runtime will
not be watched).
Args:
run_options: An instance of `config_pb2.RunOptions` to be modified.
graph: An instance of `ops.Graph`.
debug_ops: (`str` or `list` of `str`) name(s) of the debug op(s) to use.
debug_urls: URL(s) to send ebug values to, e.g.,
`file:///tmp/tfdbg_dump_1`, `grpc://localhost:12345`.
node_name_regex_blacklist: Regular-expression blacklist for node_name.
This should be a string, e.g., `"(weight_[0-9]+|bias_.*)"`.
op_type_regex_blacklist: Regular-expression blacklist for the op type of
nodes, e.g., `"(Variable|Add)"`.
If both node_name_regex_blacklist and op_type_regex_blacklist
are set, the two filtering operations will occur in a logical `OR`
relation. In other words, a node will be excluded if it hits either of
the two blacklists; a node will be included if and only if it hits
neither of the blacklists.
global_step: (`int`) Optional global_step count for this debug tensor
watch.
"""
if isinstance(debug_ops, str):
debug_ops = [debug_ops]
if node_name_regex_blacklist:
node_name_pattern = re.compile(node_name_regex_blacklist)
else:
node_name_pattern = None
if op_type_regex_blacklist:
op_type_pattern = re.compile(op_type_regex_blacklist)
else:
op_type_pattern = None
ops = graph.get_operations()
for op in ops:
# Skip nodes without any output tensors.
if not op.outputs:
continue
node_name = op.name
op_type = op.type
if node_name_pattern and node_name_pattern.match(node_name):
continue
if op_type_pattern and op_type_pattern.match(op_type):
continue
for slot in xrange(len(op.outputs)):
add_debug_tensor_watch(
run_options,
node_name,
output_slot=slot,
debug_ops=debug_ops,
debug_urls=debug_urls,
global_step=global_step)
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