def print_tensor(self, args, screen_info=None):
"""Command handler for print_tensor.
Print value of a given dumped tensor.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
if screen_info and "cols" in screen_info:
np_printoptions = {"linewidth": screen_info["cols"]}
else:
np_printoptions = {}
parsed = self._arg_parsers["print_tensor"].parse_args(args)
node_name, output_slot = debug_data.parse_node_or_tensor_name(
parsed.tensor_name)
if output_slot is None:
return self._error("\"%s\" is not a valid tensor name" %
parsed.tensor_name)
if not self._debug_dump.node_exists(node_name):
return self._error(
"Node \"%s\" does not exist in partition graphs" % node_name)
watch_keys = self._debug_dump.debug_watch_keys(node_name)
# Find debug dump data that match the tensor name (node name + output
# slot).
matching_data = []
for watch_key in watch_keys:
debug_tensor_data = self._debug_dump.watch_key_to_data(watch_key)
for datum in debug_tensor_data:
if datum.output_slot == output_slot:
matching_data.append(datum)
if not matching_data:
return self._error(
"Tensor \"%s\" did not generate any dumps." % parsed.tensor_name)
# TODO(cais): In the case of multiple dumps from the same tensor, require
# explicit specification of the DebugOp and the temporal order.
if len(matching_data) > 1:
return self._error(
"print_tensor logic for multiple dumped records has not been "
"implemented.")
return tensor_format.format_tensor(
matching_data[0].get_tensor(),
matching_data[0].watch_key,
include_metadata=True,
np_printoptions=np_printoptions)
def print_tensor(self, args, screen_info=None):
"""Command handler for print_tensor.
Print value of a given dumped tensor.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
if screen_info and "cols" in screen_info:
np_printoptions = {"linewidth": screen_info["cols"]}
else:
np_printoptions = {}
parsed = self._arg_parsers["print_tensor"].parse_args(args)
node_name, output_slot = debug_data.parse_node_or_tensor_name(
parsed.tensor_name)
if output_slot is None:
return self._error("\"%s\" is not a valid tensor name" %
parsed.tensor_name)
if not self._debug_dump.node_exists(node_name):
return self._error(
"Node \"%s\" does not exist in partition graphs" % node_name)
watch_keys = self._debug_dump.debug_watch_keys(node_name)
# Find debug dump data that match the tensor name (node name + output
# slot).
matching_data = []
for watch_key in watch_keys:
debug_tensor_data = self._debug_dump.watch_key_to_data(watch_key)
for datum in debug_tensor_data:
if datum.output_slot == output_slot:
matching_data.append(datum)
if not matching_data:
return self._error("Tensor \"%s\" did not generate any dumps." %
parsed.tensor_name)
# TODO(cais): In the case of multiple dumps from the same tensor, require
# explicit specification of the DebugOp and the temporal order.
if len(matching_data) > 1:
return self._error(
"print_tensor logic for multiple dumped records has not been "
"implemented.")
return tensor_format.format_tensor(matching_data[0].get_tensor(),
matching_data[0].watch_key,
include_metadata=True,
np_printoptions=np_printoptions)
def _get_node(self, element):
"""Get the node of a graph element.
Args:
element: A graph element (Op, Tensor or Node)
Returns:
The node associated with element in the graph.
"""
node_name, _ = debug_data.parse_node_or_tensor_name(element.name)
return self._sess.graph.as_graph_element(node_name)
def _get_node(self, element):
"""Get the node of a graph element.
Args:
element: A graph element (Op, Tensor or Node)
Returns:
The node associated with element in the graph.
"""
node_name, _ = debug_data.parse_node_or_tensor_name(element.name)
return self._sess.graph.as_graph_element(node_name)
def testParseTensorName(self):
node_name, slot = debug_data.parse_node_or_tensor_name(
"namespace1/node_2:3")
self.assertEqual("namespace1/node_2", node_name)
self.assertEqual(3, slot)
def testParseNodeName(self):
node_name, slot = debug_data.parse_node_or_tensor_name(
"namespace1/node_1")
self.assertEqual("namespace1/node_1", node_name)
self.assertIsNone(slot)
def _dfs_from_node(self,
lines,
node_name,
tracker,
max_depth,
depth,
unfinished,
include_control=False,
show_op_type=False):
"""Perform depth-first search (DFS) traversal of a node's input tree.
Args:
lines: Text lines to append to, as a list of str.
node_name: Name of the node, as a str. This arg is updated during the
recursion.
tracker: A callable that takes one str as the node name input and
returns a list of str as the inputs/outputs.
This makes it this function general enough to be used with both
node-input and node-output tracking.
max_depth: Maximum recursion depth, as an int.
depth: Current recursion depth. This arg is updated during the
recursion.
unfinished: A stack of unfinished recursion depths, as a list of int.
include_control: Whether control dependencies are to be included as
inputs (and marked as such).
show_op_type: Whether op type of the input nodes are to be displayed
alongside the nodes' names.
"""
# Make a shallow copy of the list because it may be extended later.
all_inputs = copy.copy(tracker(node_name, is_control=False))
is_ctrl = [False] * len(all_inputs)
if include_control:
# Sort control inputs or recipients in in alphabetical order of the node
# names.
ctrl_inputs = sorted(tracker(node_name, is_control=True))
all_inputs.extend(ctrl_inputs)
is_ctrl.extend([True] * len(ctrl_inputs))
if not all_inputs:
if depth == 1:
lines.append(" [None]")
return
unfinished.append(depth)
# Create depth-dependent hanging indent for the line.
hang = ""
for k in xrange(depth):
if k < depth - 1:
if k + 1 in unfinished:
hang += HANG_UNFINISHED
else:
hang += HANG_FINISHED
else:
hang += HANG_SUFFIX
if all_inputs and depth > max_depth:
lines.append(hang + ELLIPSIS)
unfinished.pop()
return
hang += DEPTH_TEMPLATE % depth
for i in xrange(len(all_inputs)):
inp = all_inputs[i]
if is_ctrl[i]:
ctrl_str = CTRL_LABEL
else:
ctrl_str = ""
op_type_str = ""
if show_op_type:
op_type_str = OP_TYPE_TEMPLATE % self._debug_dump.node_op_type(inp)
if i == len(all_inputs) - 1:
unfinished.pop()
lines.append(hang + ctrl_str + op_type_str + inp)
# Recursive call.
# The input's/output's name can be a tensor name, in the case of node
# with >1 output slots.
inp_node_name, _ = debug_data.parse_node_or_tensor_name(inp)
self._dfs_from_node(
lines,
inp_node_name,
tracker,
max_depth,
depth + 1,
unfinished,
include_control=include_control,
show_op_type=show_op_type)
def _list_inputs_or_outputs(self,
recursive,
node_name,
depth,
control,
op_type,
do_outputs=False):
"""Helper function used by list_inputs and list_outputs.
Format a list of lines to display the inputs or output recipients of a
given node.
Args:
recursive: Whether the listing is to be done recursively, as a boolean.
node_name: The name of the node in question, as a str.
depth: Maximum recursion depth, applies only if recursive == True, as an
int.
control: Whether control inputs or control recipients are included, as a
boolean.
op_type: Whether the op types of the nodes are to be included, as a
boolean.
do_outputs: Whether recipients, instead of input nodes are to be
listed, as a boolean.
Returns:
Input or recipient tree formatted as a RichTextLines object.
"""
if do_outputs:
tracker = self._debug_dump.node_recipients
type_str = "Recipients of"
short_type_str = "recipients"
else:
tracker = self._debug_dump.node_inputs
type_str = "Inputs to"
short_type_str = "inputs"
lines = []
# Check if this is a tensor name, instead of a node name.
node_name, _ = debug_data.parse_node_or_tensor_name(node_name)
# Check if node exists.
if not self._debug_dump.node_exists(node_name):
return cli_shared.error(
"There is no node named \"%s\" in the partition graphs" % node_name)
if recursive:
max_depth = depth
else:
max_depth = 1
if control:
include_ctrls_str = ", control %s included" % short_type_str
else:
include_ctrls_str = ""
lines.append("%s node \"%s\" (Depth limit = %d%s):" %
(type_str, node_name, max_depth, include_ctrls_str))
self._dfs_from_node(lines, node_name, tracker, max_depth, 1, [], control,
op_type)
# Include legend.
lines.append("")
lines.append("Legend:")
lines.append(" (d): recursion depth = d.")
if control:
lines.append(" (Ctrl): Control input.")
if op_type:
lines.append(" [Op]: Input node has op type Op.")
# TODO(cais): Consider appending ":0" at the end of 1st outputs of nodes.
return debugger_cli_common.RichTextLines(lines)
def print_tensor(self, args, screen_info=None):
"""Command handler for print_tensor.
Print value of a given dumped tensor.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
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))
node_name, output_slot = debug_data.parse_node_or_tensor_name(
tensor_name)
if (self._debug_dump.loaded_partition_graphs()
and not self._debug_dump.node_exists(node_name)):
output = cli_shared.error(
"Node \"%s\" does not exist in partition graphs" % node_name)
_add_main_menu(output,
node_name=None,
enable_list_tensors=True,
enable_print_tensor=False)
return output
watch_keys = self._debug_dump.debug_watch_keys(node_name)
if output_slot is None:
output_slots = set()
for watch_key in watch_keys:
output_slots.add(int(watch_key.split(":")[1]))
if len(output_slots) == 1:
# There is only one dumped tensor from this node, so there is no
# ambiguity. Proceed to show the only dumped tensor.
output_slot = list(output_slots)[0]
else:
# There are more than one dumped tensors from this node. Indicate as
# such.
# TODO(cais): Provide an output screen with command links for
# convenience.
lines = [
"Node \"%s\" generated debug dumps from %s output slots:" %
(node_name, len(output_slots)),
"Please specify the output slot: %s:x." % node_name
]
output = debugger_cli_common.RichTextLines(lines)
_add_main_menu(output,
node_name=node_name,
enable_list_tensors=True,
enable_print_tensor=False)
return output
# Find debug dump data that match the tensor name (node name + output
# slot).
matching_data = []
for watch_key in watch_keys:
debug_tensor_data = self._debug_dump.watch_key_to_data(watch_key)
for datum in debug_tensor_data:
if datum.output_slot == output_slot:
matching_data.append(datum)
if not matching_data:
# No dump for this tensor.
output = cli_shared.error(
"Tensor \"%s\" did not generate any dumps." %
parsed.tensor_name)
elif len(matching_data) == 1:
# There is only one dump for this tensor.
if parsed.number <= 0:
output = cli_shared.format_tensor(
matching_data[0].get_tensor(),
matching_data[0].watch_key,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=highlight_options)
else:
output = cli_shared.error(
"Invalid number (%d) for tensor %s, which generated one dump."
% (parsed.number, parsed.tensor_name))
_add_main_menu(output,
node_name=node_name,
enable_print_tensor=False)
else:
# There are more than one dumps for this tensor.
if parsed.number < 0:
lines = [
"Tensor \"%s\" generated %d dumps:" %
(parsed.tensor_name, len(matching_data))
]
for i, datum in enumerate(matching_data):
rel_time = (datum.timestamp - self._debug_dump.t0) / 1000.0
lines.append("#%d [%.3f ms] %s" %
(i, rel_time, datum.watch_key))
lines.append("")
lines.append(
"Use the -n (--number) flag to specify which dump to print."
)
lines.append("For example:")
lines.append(" print_tensor %s -n 0" % parsed.tensor_name)
output = debugger_cli_common.RichTextLines(lines)
elif parsed.number >= len(matching_data):
output = cli_shared.error(
"Specified number (%d) exceeds the number of available dumps "
"(%d) for tensor %s" %
(parsed.number, len(matching_data), parsed.tensor_name))
else:
output = cli_shared.format_tensor(
matching_data[parsed.number].get_tensor(),
matching_data[parsed.number].watch_key +
" (dump #%d)" % parsed.number,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=highlight_options)
_add_main_menu(output,
node_name=node_name,
enable_print_tensor=False)
return output
def node_info(self, args, screen_info=None):
"""Command handler for node_info.
Query information about a given node.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
# TODO(cais): Add annotation of substrings for node names, to facilitate
# on-screen highlighting/selection of node names.
_ = screen_info
parsed = self._arg_parsers["node_info"].parse_args(args)
# Get a node name, regardless of whether the input is a node name (without
# output slot attached) or a tensor name (with output slot attached).
node_name, unused_slot = debug_data.parse_node_or_tensor_name(
parsed.node_name)
if not self._debug_dump.node_exists(node_name):
return cli_shared.error(
"There is no node named \"%s\" in the partition graphs" % node_name)
# TODO(cais): Provide UI glossary feature to explain to users what the
# term "partition graph" means and how it is related to TF graph objects
# in Python. The information can be along the line of:
# "A tensorflow graph defined in Python is stripped of unused ops
# according to the feeds and fetches and divided into a number of
# partition graphs that may be distributed among multiple devices and
# hosts. The partition graphs are what's actually executed by the C++
# runtime during a run() call."
lines = ["Node %s" % node_name]
lines.append("")
lines.append(" Op: %s" % self._debug_dump.node_op_type(node_name))
lines.append(" Device: %s" % self._debug_dump.node_device(node_name))
# List node inputs (non-control and control).
inputs = self._debug_dump.node_inputs(node_name)
ctrl_inputs = self._debug_dump.node_inputs(node_name, is_control=True)
input_lines = self._format_neighbors("input", inputs, ctrl_inputs)
lines.extend(input_lines)
# List node output recipients (non-control and control).
recs = self._debug_dump.node_recipients(node_name)
ctrl_recs = self._debug_dump.node_recipients(node_name, is_control=True)
rec_lines = self._format_neighbors("recipient", recs, ctrl_recs)
lines.extend(rec_lines)
# Optional: List attributes of the node.
if parsed.attributes:
lines.extend(self._list_node_attributes(node_name))
# Optional: List dumps available from the node.
if parsed.dumps:
lines.extend(self._list_node_dumps(node_name))
return debugger_cli_common.RichTextLines(lines)
def _dfs_from_node(self,
lines,
node_name,
tracker,
max_depth,
depth,
unfinished,
include_control=False,
show_op_type=False):
"""Perform depth-first search (DFS) traversal of a node's input tree.
Args:
lines: Text lines to append to, as a list of str.
node_name: Name of the node, as a str. This arg is updated during the
recursion.
tracker: A callable that takes one str as the node name input and
returns a list of str as the inputs/outputs.
This makes it this function general enough to be used with both
node-input and node-output tracking.
max_depth: Maximum recursion depth, as an int.
depth: Current recursion depth. This arg is updated during the
recursion.
unfinished: A stack of unfinished recursion depths, as a list of int.
include_control: Whether control dependencies are to be included as
inputs (and marked as such).
show_op_type: Whether op type of the input nodes are to be displayed
alongside the the nodes' names.
"""
# Make a shallow copy of the list because it may be extended later.
all_inputs = copy.copy(tracker(node_name, is_control=False))
is_ctrl = [False] * len(all_inputs)
if include_control:
# Sort control inputs or recipients in in alphabetical order of the node
# names.
ctrl_inputs = sorted(tracker(node_name, is_control=True))
all_inputs.extend(ctrl_inputs)
is_ctrl.extend([True] * len(ctrl_inputs))
if not all_inputs:
if depth == 1:
lines.append(" [None]")
return
unfinished.append(depth)
# Create depth-dependent hanging indent for the line.
hang = ""
for k in xrange(depth):
if k < depth - 1:
if k + 1 in unfinished:
hang += HANG_UNFINISHED
else:
hang += HANG_FINISHED
else:
hang += HANG_SUFFIX
if all_inputs and depth > max_depth:
lines.append(hang + ELLIPSIS)
unfinished.pop()
return
hang += DEPTH_TEMPLATE % depth
for i in xrange(len(all_inputs)):
inp = all_inputs[i]
if is_ctrl[i]:
ctrl_str = CTRL_LABEL
else:
ctrl_str = ""
op_type_str = ""
if show_op_type:
op_type_str = OP_TYPE_TEMPLATE % self._debug_dump.node_op_type(
inp)
if i == len(all_inputs) - 1:
unfinished.pop()
lines.append(hang + ctrl_str + op_type_str + inp)
# Recursive call.
# The input's/output's name can be a tensor name, in the case of node
# with >1 output slots.
inp_node_name, _ = debug_data.parse_node_or_tensor_name(inp)
self._dfs_from_node(lines,
inp_node_name,
tracker,
max_depth,
depth + 1,
unfinished,
include_control=include_control,
show_op_type=show_op_type)
def _list_inputs_or_outputs(self,
recursive,
node_name,
depth,
control,
op_type,
do_outputs=False):
"""Helper function used by list_inputs and list_outputs.
Format a list of lines to display the inputs or output recipients of a
given node.
Args:
recursive: Whether the listing is to be done recursively, as a boolean.
node_name: The name of the node in question, as a str.
depth: Maximum recursion depth, applies only if recursive == True, as an
int.
control: Whether control inputs or control recipients are included, as a
boolean.
op_type: Whether the op types of the nodes are to be included, as a
boolean.
do_outputs: Whether recipients, instead of input nodes are to be
listed, as a boolean.
Returns:
Input or recipient tree formatted as a RichTextLines object.
"""
if do_outputs:
tracker = self._debug_dump.node_recipients
type_str = "Recipients of"
short_type_str = "recipients"
else:
tracker = self._debug_dump.node_inputs
type_str = "Inputs to"
short_type_str = "inputs"
lines = []
# Check if this is a tensor name, instead of a node name.
node_name, _ = debug_data.parse_node_or_tensor_name(node_name)
# Check if node exists.
if not self._debug_dump.node_exists(node_name):
return self._error(
"There is no node named \"%s\" in the partition graphs" %
node_name)
if recursive:
max_depth = depth
else:
max_depth = 1
if control:
include_ctrls_str = ", control %s included" % short_type_str
else:
include_ctrls_str = ""
lines.append("%s node \"%s\" (Depth limit = %d%s):" %
(type_str, node_name, max_depth, include_ctrls_str))
self._dfs_from_node(lines, node_name, tracker, max_depth, 1, [],
control, op_type)
# Include legend.
lines.append("")
lines.append("Legend:")
lines.append(" (d): recursion depth = d.")
if control:
lines.append(" (Ctrl): Control input.")
if op_type:
lines.append(" [Op]: Input node has op type Op.")
# TODO(cais): Consider appending ":0" at the end of 1st outputs of nodes.
return debugger_cli_common.RichTextLines(lines)
def print_tensor(self, args, screen_info=None):
"""Command handler for print_tensor.
Print value of a given dumped tensor.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
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 there parsed.tensor_name contains any indexing (slicing).
if parsed.tensor_name.count("[") == 1 and parsed.tensor_name.endswith(
"]"):
tensor_name = parsed.tensor_name[:parsed.tensor_name.index("[")]
tensor_slicing = parsed.tensor_name[parsed.tensor_name.index("["):]
else:
tensor_name = parsed.tensor_name
tensor_slicing = ""
node_name, output_slot = debug_data.parse_node_or_tensor_name(
tensor_name)
if output_slot is None:
return self._error("\"%s\" is not a valid tensor name" %
parsed.tensor_name)
if not self._debug_dump.node_exists(node_name):
return self._error(
"Node \"%s\" does not exist in partition graphs" % node_name)
watch_keys = self._debug_dump.debug_watch_keys(node_name)
# Find debug dump data that match the tensor name (node name + output
# slot).
matching_data = []
for watch_key in watch_keys:
debug_tensor_data = self._debug_dump.watch_key_to_data(watch_key)
for datum in debug_tensor_data:
if datum.output_slot == output_slot:
matching_data.append(datum)
if not matching_data:
# No dump for this tensor.
return self._error("Tensor \"%s\" did not generate any dumps." %
parsed.tensor_name)
elif len(matching_data) == 1:
# There is only one dump for this tensor.
if parsed.number <= 0:
return self._format_tensor(matching_data[0].get_tensor(),
matching_data[0].watch_key,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing)
else:
return self._error(
"Invalid number (%d) for tensor %s, which generated one dump."
% (parsed.number, parsed.tensor_name))
else:
# There are more than one dumps for this tensor.
if parsed.number < 0:
lines = [
"Tensor \"%s\" generated %d dumps:" %
(parsed.tensor_name, len(matching_data))
]
for i, datum in enumerate(matching_data):
rel_time = (datum.timestamp - self._debug_dump.t0) / 1000.0
lines.append("#%d [%.3f ms] %s" %
(i, rel_time, datum.watch_key))
lines.append("")
lines.append(
"Use the -n (--number) flag to specify which dump to print."
)
lines.append("For example:")
lines.append(" print_tensor %s -n 0" % parsed.tensor_name)
return debugger_cli_common.RichTextLines(lines)
elif parsed.number >= len(matching_data):
return self._error(
"Specified number (%d) exceeds the number of available dumps "
"(%d) for tensor %s" %
(parsed.number, len(matching_data), parsed.tensor_name))
else:
return self._format_tensor(
matching_data[parsed.number].get_tensor(),
matching_data[parsed.number].watch_key +
" (dump #%d)" % parsed.number,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing)
def node_info(self, args, screen_info=None):
"""Command handler for node_info.
Query information about a given node.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
# TODO(cais): Add annotation of substrings for node names, to facilitate
# on-screen highlighting/selection of node names.
_ = screen_info
parsed = self._arg_parsers["node_info"].parse_args(args)
# Get a node name, regardless of whether the input is a node name (without
# output slot attached) or a tensor name (with output slot attached).
node_name, unused_slot = debug_data.parse_node_or_tensor_name(
parsed.node_name)
if not self._debug_dump.node_exists(node_name):
return self._error(
"There is no node named \"%s\" in the partition graphs" %
node_name)
# TODO(cais): Provide UI glossary feature to explain to users what the
# term "partition graph" means and how it is related to TF graph objects
# in Python. The information can be along the line of:
# "A tensorflow graph defined in Python is stripped of unused ops
# according to the feeds and fetches and divided into a number of
# partition graphs that may be distributed among multiple devices and
# hosts. The partition graphs are what's actually executed by the C++
# runtime during a run() call."
lines = ["Node %s" % node_name]
lines.append("")
lines.append(" Op: %s" % self._debug_dump.node_op_type(node_name))
lines.append(" Device: %s" % self._debug_dump.node_device(node_name))
# List node inputs (non-control and control).
inputs = self._debug_dump.node_inputs(node_name)
ctrl_inputs = self._debug_dump.node_inputs(node_name, is_control=True)
input_lines = self._format_neighbors("input", inputs, ctrl_inputs)
lines.extend(input_lines)
# List node output recipients (non-control and control).
recs = self._debug_dump.node_recipients(node_name)
ctrl_recs = self._debug_dump.node_recipients(node_name,
is_control=True)
rec_lines = self._format_neighbors("recipient", recs, ctrl_recs)
lines.extend(rec_lines)
# Optional: List attributes of the node.
if parsed.attributes:
lines.extend(self._list_node_attributes(node_name))
# Optional: List dumps available from the node.
if parsed.dumps:
lines.extend(self._list_node_dumps(node_name))
return debugger_cli_common.RichTextLines(lines)
def print_tensor(self, args, screen_info=None):
"""Command handler for print_tensor.
Print value of a given dumped tensor.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
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))
node_name, output_slot = debug_data.parse_node_or_tensor_name(tensor_name)
if (self._debug_dump.loaded_partition_graphs() and
not self._debug_dump.node_exists(node_name)):
output = cli_shared.error(
"Node \"%s\" does not exist in partition graphs" % node_name)
_add_main_menu(
output,
node_name=None,
enable_list_tensors=True,
enable_print_tensor=False)
return output
watch_keys = self._debug_dump.debug_watch_keys(node_name)
if output_slot is None:
output_slots = set()
for watch_key in watch_keys:
output_slots.add(int(watch_key.split(":")[1]))
if len(output_slots) == 1:
# There is only one dumped tensor from this node, so there is no
# ambiguity. Proceed to show the only dumped tensor.
output_slot = list(output_slots)[0]
else:
# There are more than one dumped tensors from this node. Indicate as
# such.
# TODO(cais): Provide an output screen with command links for
# convenience.
lines = [
"Node \"%s\" generated debug dumps from %s output slots:" %
(node_name, len(output_slots)),
"Please specify the output slot: %s:x." % node_name
]
output = debugger_cli_common.RichTextLines(lines)
_add_main_menu(
output,
node_name=node_name,
enable_list_tensors=True,
enable_print_tensor=False)
return output
# Find debug dump data that match the tensor name (node name + output
# slot).
matching_data = []
for watch_key in watch_keys:
debug_tensor_data = self._debug_dump.watch_key_to_data(watch_key)
for datum in debug_tensor_data:
if datum.output_slot == output_slot:
matching_data.append(datum)
if not matching_data:
# No dump for this tensor.
output = cli_shared.error("Tensor \"%s\" did not generate any dumps." %
parsed.tensor_name)
elif len(matching_data) == 1:
# There is only one dump for this tensor.
if parsed.number <= 0:
output = cli_shared.format_tensor(
matching_data[0].get_tensor(),
matching_data[0].watch_key,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=highlight_options)
else:
output = cli_shared.error(
"Invalid number (%d) for tensor %s, which generated one dump." %
(parsed.number, parsed.tensor_name))
_add_main_menu(output, node_name=node_name, enable_print_tensor=False)
else:
# There are more than one dumps for this tensor.
if parsed.number < 0:
lines = [
"Tensor \"%s\" generated %d dumps:" % (parsed.tensor_name,
len(matching_data))
]
font_attr_segs = {}
for i, datum in enumerate(matching_data):
rel_time = (datum.timestamp - self._debug_dump.t0) / 1000.0
lines.append("#%d [%.3f ms] %s" % (i, rel_time, datum.watch_key))
command = "print_tensor %s -n %d" % (parsed.tensor_name, i)
font_attr_segs[len(lines) - 1] = [(
len(lines[-1]) - len(datum.watch_key), len(lines[-1]),
debugger_cli_common.MenuItem(None, command))]
lines.append("")
lines.append(
"You can use the -n (--number) flag to specify which dump to "
"print.")
lines.append("For example:")
lines.append(" print_tensor %s -n 0" % parsed.tensor_name)
output = debugger_cli_common.RichTextLines(
lines, font_attr_segs=font_attr_segs)
elif parsed.number >= len(matching_data):
output = cli_shared.error(
"Specified number (%d) exceeds the number of available dumps "
"(%d) for tensor %s" %
(parsed.number, len(matching_data), parsed.tensor_name))
else:
output = cli_shared.format_tensor(
matching_data[parsed.number].get_tensor(),
matching_data[parsed.number].watch_key + " (dump #%d)" %
parsed.number,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=highlight_options)
_add_main_menu(output, node_name=node_name, enable_print_tensor=False)
return output
def testParseNodeName(self):
node_name, slot = debug_data.parse_node_or_tensor_name("namespace1/node_1")
self.assertEqual("namespace1/node_1", node_name)
self.assertIsNone(slot)
def testParseTensorName(self):
node_name, slot = debug_data.parse_node_or_tensor_name(
"namespace1/node_2:3")
self.assertEqual("namespace1/node_2", node_name)
self.assertEqual(3, slot)
def print_tensor(self, args, screen_info=None):
"""Command handler for print_tensor.
Print value of a given dumped tensor.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
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))
node_name, output_slot = debug_data.parse_node_or_tensor_name(tensor_name)
if output_slot is None:
return cli_shared.error("\"%s\" is not a valid tensor name" %
parsed.tensor_name)
if (self._debug_dump.loaded_partition_graphs() and
not self._debug_dump.node_exists(node_name)):
return cli_shared.error(
"Node \"%s\" does not exist in partition graphs" % node_name)
watch_keys = self._debug_dump.debug_watch_keys(node_name)
# Find debug dump data that match the tensor name (node name + output
# slot).
matching_data = []
for watch_key in watch_keys:
debug_tensor_data = self._debug_dump.watch_key_to_data(watch_key)
for datum in debug_tensor_data:
if datum.output_slot == output_slot:
matching_data.append(datum)
if not matching_data:
# No dump for this tensor.
return cli_shared.error(
"Tensor \"%s\" did not generate any dumps." % parsed.tensor_name)
elif len(matching_data) == 1:
# There is only one dump for this tensor.
if parsed.number <= 0:
return cli_shared.format_tensor(
matching_data[0].get_tensor(),
matching_data[0].watch_key,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=highlight_options)
else:
return cli_shared.error(
"Invalid number (%d) for tensor %s, which generated one dump." %
(parsed.number, parsed.tensor_name))
else:
# There are more than one dumps for this tensor.
if parsed.number < 0:
lines = [
"Tensor \"%s\" generated %d dumps:" % (parsed.tensor_name,
len(matching_data))
]
for i, datum in enumerate(matching_data):
rel_time = (datum.timestamp - self._debug_dump.t0) / 1000.0
lines.append("#%d [%.3f ms] %s" % (i, rel_time, datum.watch_key))
lines.append("")
lines.append(
"Use the -n (--number) flag to specify which dump to print.")
lines.append("For example:")
lines.append(" print_tensor %s -n 0" % parsed.tensor_name)
return debugger_cli_common.RichTextLines(lines)
elif parsed.number >= len(matching_data):
return cli_shared.error(
"Specified number (%d) exceeds the number of available dumps "
"(%d) for tensor %s" %
(parsed.number, len(matching_data), parsed.tensor_name))
else:
return cli_shared.format_tensor(
matching_data[parsed.number].get_tensor(),
matching_data[parsed.number].watch_key + " (dump #%d)" %
parsed.number,
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=highlight_options)
