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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.
# ==============================================================================
"""CLI Backend for the Node Stepper Part of the Debugger."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np # pylint: disable=unused-import
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.debug.cli import cli_shared
from tensorflow.python.debug.cli import command_parser
from tensorflow.python.debug.cli import debugger_cli_common
from tensorflow.python.debug.cli import tensor_format
from tensorflow.python.debug.lib import stepper
RL = debugger_cli_common.RichLine
class NodeStepperCLI(object):
"""Command-line-interface backend of Node Stepper."""
# Possible states of an element in the transitive closure of the stepper's
# fetch(es).
# State where the element is already continued-to and a TensorHandle is
# available for the tensor.
STATE_CONT = "H"
# State where an intermediate dump of the tensor is available.
STATE_DUMPED_INTERMEDIATE = "I"
# State where the element is already overridden.
STATE_OVERRIDDEN = "O"
# State where the element is a placeholder (and hence cannot be continued to)
STATE_IS_PLACEHOLDER = "P"
# State where a variable's value has been updated during the lifetime of
# this NodeStepperCLI instance.
STATE_DIRTY_VARIABLE = "D"
STATE_UNFEEDABLE = "U"
NEXT_NODE_POINTER_STR = "-->"
_MESSAGE_TEMPLATES = {
"NOT_IN_CLOSURE":
"%s is not in the transitive closure of this stepper instance.",
"MULTIPLE_TENSORS":
"Node %s has more than one output tensor. "
"Please use full tensor name.",
}
_UPDATED_ATTRIBUTE = "bold"
_STATE_COLORS = {
STATE_CONT: "green",
STATE_DIRTY_VARIABLE: "magenta",
STATE_DUMPED_INTERMEDIATE: "blue",
STATE_OVERRIDDEN: "yellow",
STATE_IS_PLACEHOLDER: "cyan",
STATE_UNFEEDABLE: "red",
}
_FEED_COLORS = {
stepper.NodeStepper.FEED_TYPE_CLIENT: "white",
stepper.NodeStepper.FEED_TYPE_HANDLE: "green",
stepper.NodeStepper.FEED_TYPE_OVERRIDE: "yellow",
stepper.NodeStepper.FEED_TYPE_DUMPED_INTERMEDIATE: "blue",
}
def __init__(self, node_stepper):
self._node_stepper = node_stepper
# Command parsers for the stepper.
self.arg_parsers = {}
# Parser for "list_sorted_nodes".
ap = argparse.ArgumentParser(
description="List the state of the sorted transitive closure of the "
"stepper.",
usage=argparse.SUPPRESS)
ap.add_argument(
"-l",
"--lower_bound",
dest="lower_bound",
type=int,
default=-1,
help="Lower-bound index (0-based)")
ap.add_argument(
"-u",
"--upper_bound",
dest="upper_bound",
type=int,
default=-1,
help="Upper-bound index (0-based)")
self.arg_parsers["list_sorted_nodes"] = ap
# Parser for "cont".
ap = argparse.ArgumentParser(
description="Continue to a tensor or op.", usage=argparse.SUPPRESS)
ap.add_argument(
"target_name",
type=str,
help="Name of the Tensor or Op to continue to.")
ap.add_argument(
"-i",
"--invalidate_from_updated_variables",
dest="invalidate_from_updated_variables",
action="store_true",
help="Whether to invalidate the cached "
"tensor handles and intermediate tensor handles affected "
"by Variable updates in this continue call.")
ap.add_argument(
"-r",
"--restore_variable_values",
dest="restore_variable_values",
action="store_true",
help="Restore all variables in the transitive closure of the cont "
"target to their initial values (i.e., values when this stepper "
"instance was created.")
self.arg_parsers["cont"] = ap
# Parser for "step".
ap = argparse.ArgumentParser(
description="Step to the next tensor or op in the sorted transitive "
"closure of the stepper's fetch(es).",
usage=argparse.SUPPRESS)
ap.add_argument(
"-t",
"--num_times",
dest="num_times",
type=int,
default=1,
help="Number of times to step (>=1)")
self.arg_parsers["step"] = ap
# Parser for "print_tensor".
ap = argparse.ArgumentParser(
description="Print the value of a tensor, from cached TensorHandle or "
"client-provided overrides.",
usage=argparse.SUPPRESS)
ap.add_argument(
"tensor_name",
type=str,
help="Name of the tensor, followed by any slicing indices, "
"e.g., hidden1/Wx_plus_b/MatMul:0, "
"hidden1/Wx_plus_b/MatMul:0[1, :]")
ap.add_argument(
"-r",
"--ranges",
dest="ranges",
type=str,
default="",
help="Numerical ranges to highlight tensor elements in. "
"Examples: -r 0,1e-8, -r [-0.1,0.1], "
"-r \"[[-inf, -0.1], [0.1, inf]]\"")
ap.add_argument(
"-a",
"--all",
dest="print_all",
action="store_true",
help="Print the tensor in its entirety, i.e., do not use ellipses.")
self.arg_parsers["print_tensor"] = ap
# Parser for inject_value.
ap = argparse.ArgumentParser(
description="Inject (override) the value of a Tensor.",
usage=argparse.SUPPRESS)
ap.add_argument(
"tensor_name",
type=str,
help="Name of the Tensor of which the value is to be overridden.")
ap.add_argument(
"tensor_value_str",
type=str,
help="A string representing the value of the tensor, without any "
"whitespaces, e.g., np.zeros([10,100])")
self.arg_parsers["inject_value"] = ap
self._initialize_state()
def _initialize_state(self):
"""Initialize the state of this stepper CLI."""
# Get the elements in the sorted transitive closure, as a list of str.
self._sorted_nodes = self._node_stepper.sorted_nodes()
self._closure_elements = self._node_stepper.closure_elements()
self._placeholders = self._node_stepper.placeholders()
self._completed_nodes = set()
self._calculate_next()
def _calculate_next(self):
"""Calculate the next target for "step" action based on current state."""
override_names = self._node_stepper.override_names()
next_i = -1
for i in xrange(len(self._sorted_nodes)):
if (i > next_i and (self._sorted_nodes[i] in self._completed_nodes) or
(self._sorted_nodes[i] in override_names)):
next_i = i
next_i += 1
self._next = next_i
def list_sorted_nodes(self, args, screen_info=None):
"""List the sorted transitive closure of the stepper's fetches."""
# TODO(cais): Use pattern such as del args, del screen_info python/debug.
_ = args
_ = screen_info
parsed = self.arg_parsers["list_sorted_nodes"].parse_args(args)
if parsed.lower_bound != -1 and parsed.upper_bound != -1:
index_range = [
max(0, parsed.lower_bound),
min(len(self._sorted_nodes), parsed.upper_bound)
]
verbose = False
else:
index_range = [0, len(self._sorted_nodes)]
verbose = True
handle_node_names = self._node_stepper.handle_node_names()
intermediate_tensor_names = self._node_stepper.intermediate_tensor_names()
override_names = self._node_stepper.override_names()
dirty_variable_names = [
dirty_variable.split(":")[0]
for dirty_variable in self._node_stepper.dirty_variables()
]
lines = []
if verbose:
lines.extend(
["Topologically-sorted transitive input(s) and fetch(es):", ""])
for i, element_name in enumerate(self._sorted_nodes):
if i < index_range[0] or i >= index_range[1]:
continue
# TODO(cais): Use fixed-width text to show node index.
if i == self._next:
node_prefix = RL(" ") + RL(self.NEXT_NODE_POINTER_STR, "bold")
else:
node_prefix = RL(" ")
node_prefix += "(%d / %d)" % (i + 1, len(self._sorted_nodes)) + " ["
node_prefix += self._get_status_labels(
element_name,
handle_node_names,
intermediate_tensor_names,
override_names,
dirty_variable_names)
lines.append(node_prefix + "] " + element_name)
output = debugger_cli_common.rich_text_lines_from_rich_line_list(lines)
if verbose:
output.extend(self._node_status_label_legend())
return output
def _get_status_labels(self,
element_name,
handle_node_names,
intermediate_tensor_names,
override_names,
dirty_variable_names):
"""Get a string of status labels for a graph element.
A status label indicates that a node has a certain state in this
node-stepper CLI invocation. For example, 1) that the node has been
continued-to and a handle to its output tensor is available to the node
stepper; 2) the node is a Variable and its value has been altered, e.g.,
by continuing to a variable-updating node, since the beginning of this
node-stepper invocation (i.e., "dirty variable").
Args:
element_name: (str) name of the graph element.
handle_node_names: (list of str) Names of the nodes of which the output
tensors' handles are available.
intermediate_tensor_names: (list of str) TOOD(cais): document.
override_names: (list of str) Names of the tensors of which the values
are overridden.
dirty_variable_names: (list of str) Names of the dirty variables.
Returns:
(RichLine) The rich text string of status labels that currently apply to
the graph element.
"""
status = RL()
node_name = element_name.split(":")[0]
status += (RL(self.STATE_IS_PLACEHOLDER,
self._STATE_COLORS[self.STATE_IS_PLACEHOLDER])
if node_name in self._placeholders else " ")
status += (RL(self.STATE_UNFEEDABLE,
self._STATE_COLORS[self.STATE_UNFEEDABLE])
if not self._node_stepper.is_feedable(str(element_name))
else " ")
status += (RL(self.STATE_CONT, self._STATE_COLORS[self.STATE_CONT])
if element_name in handle_node_names else " ")
intermediate_node_names = [
tensor_name.split(":")[0] for tensor_name in intermediate_tensor_names]
status += (RL(self.STATE_DUMPED_INTERMEDIATE,
self._STATE_COLORS[self.STATE_DUMPED_INTERMEDIATE])
if element_name in intermediate_node_names else " ")
slots = self._node_stepper.output_slots_in_closure(element_name)
has_override = any(element_name + ":%d" % slot in override_names
for slot in slots)
status += (RL(self.STATE_OVERRIDDEN,
self._STATE_COLORS[self.STATE_OVERRIDDEN])
if has_override else " ")
status += (RL(self.STATE_DIRTY_VARIABLE,
self._STATE_COLORS[self.STATE_DIRTY_VARIABLE])
if element_name in dirty_variable_names else " ")
return status
def _node_status_label_legend(self):
"""Get legend for node-status labels.
Returns:
(debugger_cli_common.RichTextLines) Legend text.
"""
return debugger_cli_common.rich_text_lines_from_rich_line_list([
"",
"Legend:",
(RL(" ") +
RL(self.STATE_IS_PLACEHOLDER,
self._STATE_COLORS[self.STATE_IS_PLACEHOLDER]) +
" - Placeholder"),
(RL(" ") +
RL(self.STATE_UNFEEDABLE,
self._STATE_COLORS[self.STATE_UNFEEDABLE]) +
" - Unfeedable"),
(RL(" ") +
RL(self.STATE_CONT,
self._STATE_COLORS[self.STATE_CONT]) +
" - Already continued-to; Tensor handle available from output "
"slot(s)"),
(RL(" ") +
RL(self.STATE_DUMPED_INTERMEDIATE,
self._STATE_COLORS[self.STATE_DUMPED_INTERMEDIATE]) +
" - Unfeedable"),
(RL(" ") +
RL(self.STATE_OVERRIDDEN,
self._STATE_COLORS[self.STATE_OVERRIDDEN]) +
" - Has overriding (injected) tensor value"),
(RL(" ") +
RL(self.STATE_DIRTY_VARIABLE,
self._STATE_COLORS[self.STATE_DIRTY_VARIABLE]) +
" - Dirty variable: Variable already updated this node stepper.")])
def cont(self, args, screen_info=None):
"""Continue-to action on the graph."""
_ = screen_info
parsed = self.arg_parsers["cont"].parse_args(args)
# Determine which node is being continued to, so the _next pointer can be
# set properly.
node_name = parsed.target_name.split(":")[0]
if node_name not in self._sorted_nodes:
return cli_shared.error(self._MESSAGE_TEMPLATES["NOT_IN_CLOSURE"] %
parsed.target_name)
self._next = self._sorted_nodes.index(node_name)
cont_result = self._node_stepper.cont(
parsed.target_name,
invalidate_from_updated_variables=(
parsed.invalidate_from_updated_variables),
restore_variable_values=parsed.restore_variable_values)
self._completed_nodes.add(parsed.target_name.split(":")[0])
screen_output = debugger_cli_common.RichTextLines(
["Continued to %s:" % parsed.target_name, ""])
screen_output.extend(self._report_last_feed_types())
screen_output.extend(self._report_last_updated())
screen_output.extend(
tensor_format.format_tensor(
cont_result, parsed.target_name, include_metadata=True))
# Generate windowed view of the sorted transitive closure on which the
# stepping is occurring.
lower_bound = max(0, self._next - 2)
upper_bound = min(len(self._sorted_nodes), self._next + 3)
final_output = self.list_sorted_nodes(
["-l", str(lower_bound), "-u", str(upper_bound)])
final_output.extend(debugger_cli_common.RichTextLines([""]))
final_output.extend(screen_output)
# Re-calculate the target of the next "step" action.
self._calculate_next()
return final_output
def _report_last_feed_types(self):
"""Generate a report of the feed types used in the cont/step call.
Returns:
(debugger_cli_common.RichTextLines) A RichTextLines representation of the
feeds used in the last cont/step call.
"""
feed_types = self._node_stepper.last_feed_types()
out = ["Stepper used feeds:"]
if feed_types:
for feed_name in feed_types:
feed_info = RL(" %s : " % feed_name)
feed_info += RL(feed_types[feed_name],
self._FEED_COLORS[feed_types[feed_name]])
out.append(feed_info)
else:
out.append(" (No feeds)")
out.append("")
return debugger_cli_common.rich_text_lines_from_rich_line_list(out)
def _report_last_updated(self):
"""Generate a report of the variables updated in the last cont/step call.
Returns:
(debugger_cli_common.RichTextLines) A RichTextLines representation of the
variables updated in the last cont/step call.
"""
last_updated = self._node_stepper.last_updated()
if not last_updated:
return debugger_cli_common.RichTextLines([])
rich_lines = [RL("Updated:", self._UPDATED_ATTRIBUTE)]
sorted_last_updated = sorted(list(last_updated))
for updated in sorted_last_updated:
rich_lines.append(" %s" % updated)
rich_lines.append("")
return debugger_cli_common.rich_text_lines_from_rich_line_list(rich_lines)
def step(self, args, screen_info=None):
"""Step once.
Args:
args: (list of str) command-line arguments for the "step" command.
screen_info: Information about screen.
Returns:
(RichTextLines) Screen output for the result of the stepping action.
"""
parsed = self.arg_parsers["step"].parse_args(args)
if parsed.num_times < 0:
return debugger_cli_common.RichTextLines(
"ERROR: Invalid number of times to step: %d" % parsed.num_times)
for _ in xrange(parsed.num_times):
if self._next >= len(self._sorted_nodes):
return debugger_cli_common.RichTextLines(
"ERROR: Cannot step any further because the end of the sorted "
"transitive closure has been reached.")
else:
screen_output = self.cont([self._sorted_nodes[self._next]], screen_info)
return screen_output
def print_tensor(self, args, screen_info=None):
"""Print the value of a tensor that the stepper has access to."""
parsed = self.arg_parsers["print_tensor"].parse_args(args)
if screen_info and "cols" in screen_info:
np_printoptions = {"linewidth": screen_info["cols"]}
else:
np_printoptions = {}
# Determine if any range-highlighting is required.
highlight_options = cli_shared.parse_ranges_highlight(parsed.ranges)
tensor_name, tensor_slicing = (
command_parser.parse_tensor_name_with_slicing(parsed.tensor_name))
tensor_names = self._resolve_tensor_names(tensor_name)
if not tensor_names:
return cli_shared.error(
self._MESSAGE_TEMPLATES["NOT_IN_CLOSURE"] % tensor_name)
elif len(tensor_names) > 1:
return cli_shared.error(
self._MESSAGE_TEMPLATES["MULTIPLE_TENSORS"] % tensor_name)
else:
tensor_name = tensor_names[0]
try:
tensor_value = self._node_stepper.get_tensor_value(tensor_name)
except ValueError as e:
return debugger_cli_common.RichTextLines([str(e)])
return cli_shared.format_tensor(
tensor_value,
tensor_name,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=highlight_options)
def inject_value(self, args, screen_info=None):
"""Inject value to a given tensor.
Args:
args: (list of str) command-line arguments for the "step" command.
screen_info: Information about screen.
Returns:
(RichTextLines) Screen output for the result of the stepping action.
"""
_ = screen_info # Currently unused.
if screen_info and "cols" in screen_info:
np_printoptions = {"linewidth": screen_info["cols"]}
else:
np_printoptions = {}
parsed = self.arg_parsers["inject_value"].parse_args(args)
tensor_names = self._resolve_tensor_names(parsed.tensor_name)
if not tensor_names:
return cli_shared.error(
self._MESSAGE_TEMPLATES["NOT_IN_CLOSURE"] % parsed.tensor_name)
elif len(tensor_names) > 1:
return cli_shared.error(
self._MESSAGE_TEMPLATES["MULTIPLE_TENSORS"] % parsed.tensor_name)
else:
tensor_name = tensor_names[0]
tensor_value = eval(parsed.tensor_value_str) # pylint: disable=eval-used
try:
self._node_stepper.override_tensor(tensor_name, tensor_value)
lines = [
"Injected value \"%s\"" % parsed.tensor_value_str,
" to tensor \"%s\":" % tensor_name, ""
]
tensor_lines = tensor_format.format_tensor(
tensor_value,
tensor_name,
include_metadata=True,
np_printoptions=np_printoptions).lines
lines.extend(tensor_lines)
except ValueError:
lines = [
"ERROR: Failed to inject value to tensor %s" % parsed.tensor_name
]
return debugger_cli_common.RichTextLines(lines)
# TODO(cais): Implement list_inputs
# TODO(cais): Implement list_outputs
# TODO(cais): Implement node_info
def _resolve_tensor_names(self, element_name):
"""Resolve tensor name from graph element name.
Args:
element_name: (str) Name of the graph element to resolve.
Returns:
(list) Name of the tensor(s). If element_name is the name of a tensor in
the transitive closure, return [element_name]. If element_name is the
name of a node in the transitive closure, return the list of output
tensors from the node that are in the transitive closure. Otherwise,
return empty list.
"""
if element_name in self._closure_elements and ":" in element_name:
return [element_name]
if (element_name in self._sorted_nodes or
(element_name in self._closure_elements and ":" not in element_name)):
slots = self._node_stepper.output_slots_in_closure(element_name)
return [(element_name + ":%d" % slot) for slot in slots]
else:
return []