def testFormatAsSingleLineWithDisabledNode(self):
node2 = debugger_cli_common.MenuItem("write poem",
"write_poem",
enabled=False)
self.menu.append(node2)
output = self.menu.format_as_single_line(prefix="Menu: ",
divider=", ",
disabled_item_attrs="bold")
self.assertEqual(
["Menu: water flower, measure wavelength, write poem, "],
output.lines)
self.assertEqual((6, 18, [self.node1]), output.font_attr_segs[0][0])
self.assertEqual((20, 38, [self.node2]), output.font_attr_segs[0][1])
self.assertEqual((40, 50, ["bold"]), output.font_attr_segs[0][2])
def _run_info_handler(self, args, screen_info=None):
output = debugger_cli_common.RichTextLines([])
if self._run_call_count == 1:
output.extend(cli_shared.get_tfdbg_logo())
output.extend(self._run_info)
if (not self._is_run_start and
debugger_cli_common.MAIN_MENU_KEY in output.annotations):
menu = output.annotations[debugger_cli_common.MAIN_MENU_KEY]
if "list_tensors" not in menu.captions():
menu.insert(
0, debugger_cli_common.MenuItem("list_tensors", "list_tensors"))
return output
def _list_node_dumps(self, node_name):
"""List dumped tensor data from a node.
Args:
node_name: Name of the node of which the attributes are to be listed.
Returns:
A RichTextLines object.
"""
lines = []
font_attr_segs = {}
watch_keys = self._debug_dump.debug_watch_keys(node_name)
dump_count = 0
for watch_key in watch_keys:
debug_tensor_data = self._debug_dump.watch_key_to_data(watch_key)
for datum in debug_tensor_data:
line = " Slot %d @ %s @ %.3f ms" % (
datum.output_slot, datum.debug_op,
(datum.timestamp - self._debug_dump.t0) / 1000.0)
lines.append(line)
command = "pt %s:%d -n %d" % (node_name, datum.output_slot,
dump_count)
font_attr_segs[len(lines) - 1] = [
(2, len(line), debugger_cli_common.MenuItem(None, command))
]
dump_count += 1
output = debugger_cli_common.RichTextLines(
lines, font_attr_segs=font_attr_segs)
output_with_header = debugger_cli_common.RichTextLines(
["%d dumped tensor(s):" % dump_count, ""])
output_with_header.extend(output)
return output_with_header
def print_source(self, args, screen_info=None):
"""Print a Python source file with line-level profile information.
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.
"""
del screen_info
parsed = self._arg_parsers["print_source"].parse_args(args)
device_name_regex = (re.compile(parsed.device_name_filter)
if parsed.device_name_filter else None)
profile_data = []
data_generator = self._get_profile_data_generator()
device_count = len(self._run_metadata.step_stats.dev_stats)
for index in range(device_count):
device_stats = self._run_metadata.step_stats.dev_stats[index]
if device_name_regex and not device_name_regex.match(device_stats.device):
continue
profile_data.extend(data_generator(device_stats))
source_annotation = source_utils.annotate_source_against_profile(
profile_data,
os.path.expanduser(parsed.source_file_path),
node_name_filter=parsed.node_name_filter,
op_type_filter=parsed.op_type_filter)
if not source_annotation:
return debugger_cli_common.RichTextLines(
["The source file %s does not contain any profile information for "
"the previous Session run under the following "
"filters:" % parsed.source_file_path,
" --%s: %s" % (_DEVICE_NAME_FILTER_FLAG, parsed.device_name_filter),
" --%s: %s" % (_NODE_NAME_FILTER_FLAG, parsed.node_name_filter),
" --%s: %s" % (_OP_TYPE_FILTER_FLAG, parsed.op_type_filter)])
max_total_cost = 0
for line_index in source_annotation:
total_cost = self._get_total_cost(source_annotation[line_index],
parsed.cost_type)
max_total_cost = max(max_total_cost, total_cost)
source_lines, line_num_width = source_utils.load_source(
parsed.source_file_path)
cost_bar_max_length = 10
total_cost_head = parsed.cost_type
column_widths = {
"cost_bar": cost_bar_max_length + 3,
"total_cost": len(total_cost_head) + 3,
"num_nodes_execs": len(self._NUM_EXECS_SUB_HEAD) + 1,
"line_number": line_num_width,
}
head = RL(
" " * column_widths["cost_bar"] +
total_cost_head +
" " * (column_widths["total_cost"] - len(total_cost_head)) +
self._NUM_NODES_HEAD +
" " * (column_widths["num_nodes_execs"] - len(self._NUM_NODES_HEAD)),
font_attr=self._LINE_COST_ATTR)
head += RL(self._LINENO_HEAD, font_attr=self._LINE_NUM_ATTR)
sub_head = RL(
" " * (column_widths["cost_bar"] +
column_widths["total_cost"]) +
self._NUM_EXECS_SUB_HEAD +
" " * (column_widths["num_nodes_execs"] -
len(self._NUM_EXECS_SUB_HEAD)) +
" " * column_widths["line_number"],
font_attr=self._LINE_COST_ATTR)
sub_head += RL(self._SOURCE_HEAD, font_attr="bold")
lines = [head, sub_head]
output_annotations = {}
for i, line in enumerate(source_lines):
lineno = i + 1
if lineno in source_annotation:
annotation = source_annotation[lineno]
cost_bar = self._render_normalized_cost_bar(
self._get_total_cost(annotation, parsed.cost_type), max_total_cost,
cost_bar_max_length)
annotated_line = cost_bar
annotated_line += " " * (column_widths["cost_bar"] - len(cost_bar))
total_cost = RL(cli_shared.time_to_readable_str(
self._get_total_cost(annotation, parsed.cost_type),
force_time_unit=parsed.time_unit),
font_attr=self._LINE_COST_ATTR)
total_cost += " " * (column_widths["total_cost"] - len(total_cost))
annotated_line += total_cost
file_path_filter = re.escape(parsed.source_file_path) + "$"
command = "lp --file_path_filter %s --min_lineno %d --max_lineno %d" % (
file_path_filter, lineno, lineno + 1)
if parsed.device_name_filter:
command += " --%s %s" % (_DEVICE_NAME_FILTER_FLAG,
parsed.device_name_filter)
if parsed.node_name_filter:
command += " --%s %s" % (_NODE_NAME_FILTER_FLAG,
parsed.node_name_filter)
if parsed.op_type_filter:
command += " --%s %s" % (_OP_TYPE_FILTER_FLAG,
parsed.op_type_filter)
menu_item = debugger_cli_common.MenuItem(None, command)
num_nodes_execs = RL("%d(%d)" % (annotation.node_count,
annotation.node_exec_count),
font_attr=[self._LINE_COST_ATTR, menu_item])
num_nodes_execs += " " * (
column_widths["num_nodes_execs"] - len(num_nodes_execs))
annotated_line += num_nodes_execs
else:
annotated_line = RL(
" " * sum(column_widths[col_name] for col_name in column_widths
if col_name != "line_number"))
line_num_column = RL(" L%d" % (lineno), self._LINE_NUM_ATTR)
line_num_column += " " * (
column_widths["line_number"] - len(line_num_column))
annotated_line += line_num_column
annotated_line += line
lines.append(annotated_line)
if parsed.init_line == lineno:
output_annotations[
debugger_cli_common.INIT_SCROLL_POS_KEY] = len(lines) - 1
return debugger_cli_common.rich_text_lines_from_rich_line_list(
lines, annotations=output_annotations)
def _get_list_profile_lines(
self, device_name, device_index, device_count,
profile_datum_list, sort_by, sort_reverse, time_unit,
device_name_filter=None, node_name_filter=None, op_type_filter=None,
screen_cols=80):
"""Get `RichTextLines` object for list_profile command for a given device.
Args:
device_name: (string) Device name.
device_index: (int) Device index.
device_count: (int) Number of devices.
profile_datum_list: List of `ProfileDatum` objects.
sort_by: (string) Identifier of column to sort. Sort identifier
must match value of SORT_OPS_BY_OP_NAME, SORT_OPS_BY_OP_TYPE,
SORT_OPS_BY_EXEC_TIME, SORT_OPS_BY_MEMORY or SORT_OPS_BY_LINE.
sort_reverse: (bool) Whether to sort in descending instead of default
(ascending) order.
time_unit: time unit, must be in cli_shared.TIME_UNITS.
device_name_filter: Regular expression to filter by device name.
node_name_filter: Regular expression to filter by node name.
op_type_filter: Regular expression to filter by op type.
screen_cols: (int) Number of columns available on the screen (i.e.,
available screen width).
Returns:
`RichTextLines` object containing a table that displays profiling
information for each op.
"""
profile_data = ProfileDataTableView(profile_datum_list, time_unit=time_unit)
# Calculate total time early to calculate column widths.
total_op_time = sum(datum.op_time for datum in profile_datum_list)
total_exec_time = sum(datum.node_exec_stats.all_end_rel_micros
for datum in profile_datum_list)
device_total_row = [
"Device Total", "",
cli_shared.time_to_readable_str(total_op_time,
force_time_unit=time_unit),
cli_shared.time_to_readable_str(total_exec_time,
force_time_unit=time_unit)]
# Calculate column widths.
column_widths = [
len(column_name) for column_name in profile_data.column_names()]
for col in range(len(device_total_row)):
column_widths[col] = max(column_widths[col], len(device_total_row[col]))
for col in range(len(column_widths)):
for row in range(profile_data.row_count()):
column_widths[col] = max(
column_widths[col], len(profile_data.value(
row,
col,
device_name_filter=device_name_filter,
node_name_filter=node_name_filter,
op_type_filter=op_type_filter)))
column_widths[col] += 2 # add margin between columns
# Add device name.
output = [RL("-" * screen_cols)]
device_row = "Device %d of %d: %s" % (
device_index + 1, device_count, device_name)
output.append(RL(device_row))
output.append(RL())
# Add headers.
base_command = "list_profile"
row = RL()
for col in range(profile_data.column_count()):
column_name = profile_data.column_names()[col]
sort_id = profile_data.column_sort_id(col)
command = "%s -s %s" % (base_command, sort_id)
if sort_by == sort_id and not sort_reverse:
command += " -r"
head_menu_item = debugger_cli_common.MenuItem(None, command)
row += RL(column_name, font_attr=[head_menu_item, "bold"])
row += RL(" " * (column_widths[col] - len(column_name)))
output.append(row)
# Add data rows.
for row in range(profile_data.row_count()):
new_row = RL()
for col in range(profile_data.column_count()):
new_cell = profile_data.value(
row,
col,
device_name_filter=device_name_filter,
node_name_filter=node_name_filter,
op_type_filter=op_type_filter)
new_row += new_cell
new_row += RL(" " * (column_widths[col] - len(new_cell)))
output.append(new_row)
# Add stat totals.
row_str = ""
for width, row in zip(column_widths, device_total_row):
row_str += ("{:<%d}" % width).format(row)
output.append(RL())
output.append(RL(row_str))
return debugger_cli_common.rich_text_lines_from_rich_line_list(output)
def get_run_start_intro(run_call_count,
fetches,
feed_dict,
tensor_filters,
is_callable_runner=False):
"""Generate formatted intro for run-start UI.
Args:
run_call_count: (int) Run call counter.
fetches: Fetches of the `Session.run()` call. See doc of `Session.run()`
for more details.
feed_dict: Feeds to the `Session.run()` call. See doc of `Session.run()`
for more details.
tensor_filters: (dict) A dict from tensor-filter name to tensor-filter
callable.
is_callable_runner: (bool) whether a runner returned by
Session.make_callable is being run.
Returns:
(RichTextLines) Formatted intro message about the `Session.run()` call.
"""
fetch_lines = _get_fetch_names(fetches)
if not feed_dict:
feed_dict_lines = [debugger_cli_common.RichLine(" (Empty)")]
else:
feed_dict_lines = []
for feed_key in feed_dict:
feed_key_name = get_graph_element_name(feed_key)
feed_dict_line = debugger_cli_common.RichLine(" ")
feed_dict_line += debugger_cli_common.RichLine(
feed_key_name,
debugger_cli_common.MenuItem(None, "pf '%s'" % feed_key_name))
# Surround the name string with quotes, because feed_key_name may contain
# spaces in some cases, e.g., SparseTensors.
feed_dict_lines.append(feed_dict_line)
feed_dict_lines = debugger_cli_common.rich_text_lines_from_rich_line_list(
feed_dict_lines)
out = debugger_cli_common.RichTextLines(_HORIZONTAL_BAR)
if is_callable_runner:
out.append("Running a runner returned by Session.make_callable()")
else:
out.append("Session.run() call #%d:" % run_call_count)
out.append("")
out.append("Fetch(es):")
out.extend(
debugger_cli_common.RichTextLines(
[" " + line for line in fetch_lines]))
out.append("")
out.append("Feed dict:")
out.extend(feed_dict_lines)
out.append(_HORIZONTAL_BAR)
out.append("")
out.append("Select one of the following commands to proceed ---->")
out.extend(
_recommend_command("run",
"Execute the run() call with debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -n",
"Execute the run() call without debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -t <T>",
"Execute run() calls (T - 1) times without debugging, then "
"execute run() once more with debugging and drop back to the CLI"))
out.extend(
_recommend_command(
"run -f <filter_name>",
"Keep executing run() calls until a dumped tensor passes a given, "
"registered filter (conditional breakpoint mode)"))
more_lines = [" Registered filter(s):"]
if tensor_filters:
filter_names = []
for filter_name in tensor_filters:
filter_names.append(filter_name)
command_menu_node = debugger_cli_common.MenuItem(
"", "run -f %s" % filter_name)
more_lines.append(
RL(" * ") + RL(filter_name, command_menu_node))
else:
more_lines.append(" (None)")
out.extend(
debugger_cli_common.rich_text_lines_from_rich_line_list(more_lines))
out.extend(
_recommend_command(
"invoke_stepper",
"Use the node-stepper interface, which allows you to interactively "
"step through nodes involved in the graph run() call and "
"inspect/modify their values",
create_link=True))
out.append("")
out.append_rich_line(
RL("For more details, see ") +
RL("help.", debugger_cli_common.MenuItem("", "help")) + ".")
out.append("")
# Make main menu for the run-start intro.
menu = debugger_cli_common.Menu()
menu.append(debugger_cli_common.MenuItem("run", "run"))
menu.append(
debugger_cli_common.MenuItem("invoke_stepper", "invoke_stepper"))
menu.append(debugger_cli_common.MenuItem("exit", "exit"))
out.annotations[debugger_cli_common.MAIN_MENU_KEY] = menu
return out
def testCommandTypeConstructorSucceeds(self):
menu_node = debugger_cli_common.MenuItem("water flower", "water_flower")
self.assertEqual("water flower", menu_node.caption)
self.assertEqual("water_flower", menu_node.content)
def get_run_start_intro(run_call_count,
fetches,
feed_dict,
tensor_filters):
"""Generate formatted intro for run-start UI.
Args:
run_call_count: (int) Run call counter.
fetches: Fetches of the `Session.run()` call. See doc of `Session.run()`
for more details.
feed_dict: Feeds to the `Session.run()` call. See doc of `Session.run()`
for more details.
tensor_filters: (dict) A dict from tensor-filter name to tensor-filter
callable.
Returns:
(RichTextLines) Formatted intro message about the `Session.run()` call.
"""
fetch_lines = _get_fetch_names(fetches)
if not feed_dict:
feed_dict_lines = ["(Empty)"]
else:
feed_dict_lines = []
for feed_key in feed_dict:
if isinstance(feed_key, six.string_types):
feed_dict_lines.append(feed_key)
else:
feed_dict_lines.append(feed_key.name)
intro_lines = [
"======================================",
"Session.run() call #%d:" % run_call_count,
"", "Fetch(es):"
]
intro_lines.extend([" " + line for line in fetch_lines])
intro_lines.extend(["", "Feed dict(s):"])
intro_lines.extend([" " + line for line in feed_dict_lines])
intro_lines.extend([
"======================================", "",
"Select one of the following commands to proceed ---->"
])
out = debugger_cli_common.RichTextLines(intro_lines)
out.extend(
_recommend_command(
"run",
"Execute the run() call with debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -n",
"Execute the run() call without debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -t <T>",
"Execute run() calls (T - 1) times without debugging, then "
"execute run() once more with debugging and drop back to the CLI"))
out.extend(
_recommend_command(
"run -f <filter_name>",
"Keep executing run() calls until a dumped tensor passes a given, "
"registered filter (conditional breakpoint mode)"))
more_lines = [" Registered filter(s):"]
if tensor_filters:
filter_names = []
for filter_name in tensor_filters:
filter_names.append(filter_name)
command_menu_node = debugger_cli_common.MenuItem(
"", "run -f %s" % filter_name)
more_lines.append(RL(" * ") + RL(filter_name, command_menu_node))
else:
more_lines.append(" (None)")
out.extend(
debugger_cli_common.rich_text_lines_from_rich_line_list(more_lines))
out.extend(
_recommend_command(
"invoke_stepper",
"Use the node-stepper interface, which allows you to interactively "
"step through nodes involved in the graph run() call and "
"inspect/modify their values", create_link=True))
out.append("")
out.append_rich_line(RL("For more details, see ") +
RL("help.", debugger_cli_common.MenuItem("", "help")) +
".")
out.append("")
# Make main menu for the run-start intro.
menu = debugger_cli_common.Menu()
menu.append(debugger_cli_common.MenuItem("run", "run"))
menu.append(debugger_cli_common.MenuItem(
"invoke_stepper", "invoke_stepper"))
menu.append(debugger_cli_common.MenuItem("exit", "exit"))
out.annotations[debugger_cli_common.MAIN_MENU_KEY] = menu
return out
def _add_main_menu(output,
node_name=None,
enable_list_tensors=True,
enable_node_info=True,
enable_print_tensor=True,
enable_list_inputs=True,
enable_list_outputs=True):
"""Generate main menu for the screen output from a command.
Args:
output: (debugger_cli_common.RichTextLines) the output object to modify.
node_name: (str or None) name of the node involved (if any). If None,
the menu items node_info, list_inputs and list_outputs will be
automatically disabled, overriding the values of arguments
enable_node_info, enable_list_inputs and enable_list_outputs.
enable_list_tensors: (bool) whether the list_tensor menu item will be
enabled.
enable_node_info: (bool) whether the node_info item will be enabled.
enable_print_tensor: (bool) whether the print_tensor item will be enabled.
enable_list_inputs: (bool) whether the item list_inputs will be enabled.
enable_list_outputs: (bool) whether the item list_outputs will be enabled.
"""
menu = debugger_cli_common.Menu()
menu.append(
debugger_cli_common.MenuItem("list_tensors",
"list_tensors",
enabled=enable_list_tensors))
if node_name:
menu.append(
debugger_cli_common.MenuItem("node_info",
"node_info -a -d %s" % node_name,
enabled=enable_node_info))
menu.append(
debugger_cli_common.MenuItem("print_tensor",
"print_tensor %s" % node_name,
enabled=enable_print_tensor))
menu.append(
debugger_cli_common.MenuItem("list_inputs",
"list_inputs -c -r %s" % node_name,
enabled=enable_list_inputs))
menu.append(
debugger_cli_common.MenuItem("list_outputs",
"list_outputs -c -r %s" % node_name,
enabled=enable_list_outputs))
else:
menu.append(
debugger_cli_common.MenuItem("node_info", None, enabled=False))
menu.append(
debugger_cli_common.MenuItem("print_tensor", None, enabled=False))
menu.append(
debugger_cli_common.MenuItem("list_inputs", None, enabled=False))
menu.append(
debugger_cli_common.MenuItem("list_outputs", None, enabled=False))
menu.append(debugger_cli_common.MenuItem("run_info", "run_info"))
menu.append(debugger_cli_common.MenuItem("help", "help"))
output.annotations[debugger_cli_common.MAIN_MENU_KEY] = menu
def list_tensors(self, args, screen_info=None):
"""Command handler for list_tensors.
List tensors dumped during debugged Session.run() call.
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 annotations of substrings for dumped tensor names, to
# facilitate on-screen highlighting/selection of node names.
_ = screen_info
parsed = self._arg_parsers["list_tensors"].parse_args(args)
output = []
font_attr_segs = {}
filter_strs = []
if parsed.op_type_filter:
op_type_regex = re.compile(parsed.op_type_filter)
filter_strs.append("Op type regex filter: \"%s\"" %
parsed.op_type_filter)
else:
op_type_regex = None
if parsed.node_name_filter:
node_name_regex = re.compile(parsed.node_name_filter)
filter_strs.append("Node name regex filter: \"%s\"" %
parsed.node_name_filter)
else:
node_name_regex = None
filter_output = debugger_cli_common.RichTextLines(filter_strs)
if parsed.tensor_filter:
try:
filter_callable = self.get_tensor_filter(parsed.tensor_filter)
except ValueError:
output = cli_shared.error(
"There is no tensor filter named \"%s\"." %
parsed.tensor_filter)
_add_main_menu(output,
node_name=None,
enable_list_tensors=False)
return output
data_to_show = self._debug_dump.find(filter_callable)
else:
data_to_show = self._debug_dump.dumped_tensor_data
# TODO(cais): Implement filter by lambda on tensor value.
dump_count = 0
for dump in data_to_show:
if node_name_regex and not node_name_regex.match(dump.node_name):
continue
if op_type_regex:
op_type = self._debug_dump.node_op_type(dump.node_name)
if not op_type_regex.match(op_type):
continue
rel_time = (dump.timestamp - self._debug_dump.t0) / 1000.0
dumped_tensor_name = "%s:%d" % (dump.node_name, dump.output_slot)
output.append("[%.3f ms] %s" % (rel_time, dumped_tensor_name))
font_attr_segs[len(output) - 1] = [
(len(output[-1]) - len(dumped_tensor_name), len(output[-1]),
debugger_cli_common.MenuItem("",
"pt %s" % dumped_tensor_name))
]
dump_count += 1
filter_output.append("")
filter_output.extend(
debugger_cli_common.RichTextLines(output,
font_attr_segs=font_attr_segs))
output = filter_output
if parsed.tensor_filter:
output.prepend([
"%d dumped tensor(s) passing filter \"%s\":" %
(dump_count, parsed.tensor_filter)
])
else:
output.prepend(["%d dumped tensor(s):" % dump_count])
_add_main_menu(output, node_name=None, enable_list_tensors=False)
return output
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 _dfs_from_node(self,
lines,
attr_segs,
node_name,
tracker,
max_depth,
depth,
unfinished,
include_control=False,
show_op_type=False,
command_template=None):
"""Perform depth-first search (DFS) traversal of a node's input tree.
It recursively tracks the inputs (or output recipients) of the node called
node_name, and append these inputs (or output recipients) to a list of text
lines (lines) with proper indentation that reflects the recursion depth,
together with some formatting attributes (to attr_segs). The formatting
attributes can include command shortcuts, for example.
Args:
lines: Text lines to append to, as a list of str.
attr_segs: (dict) Attribute segments dictionary to append to.
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.
command_template: (str) Template for command shortcut of the node 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()
line = hang + ctrl_str + op_type_str + inp
lines.append(line)
if command_template:
attr_segs[len(lines) - 1] = [
(len(line) - len(inp), len(line),
debugger_cli_common.MenuItem(None,
command_template % 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,
attr_segs,
inp_node_name,
tracker,
max_depth,
depth + 1,
unfinished,
include_control=include_control,
show_op_type=show_op_type,
command_template=command_template)
def _get_list_profile_lines(self, device_name, device_index, device_count,
profile_datum_list, sort_by, sort_reverse):
"""Get `RichTextLines` object for list_profile command for a given device.
Args:
device_name: (string) Device name.
device_index: (int) Device index.
device_count: (int) Number of devices.
profile_datum_list: List of `ProfileDatum` objects.
sort_by: (string) Identifier of column to sort. Sort identifier
must match value of SORT_OPS_BY_OP_NAME, SORT_OPS_BY_EXEC_TIME,
SORT_OPS_BY_MEMORY or SORT_OPS_BY_LINE.
sort_reverse: (bool) Whether to sort in descending instead of default
(ascending) order.
Returns:
`RichTextLines` object containing a table that displays profiling
information for each op.
"""
profile_data = ProfileDataTableView(profile_datum_list)
# Calculate total time early to calculate column widths.
total_op_time = sum(datum.op_time for datum in profile_datum_list)
total_exec_time = sum(datum.node_exec_stats.all_end_rel_micros
for datum in profile_datum_list)
device_total_row = [
"Device Total",
cli_shared.time_to_readable_str(total_op_time),
cli_shared.time_to_readable_str(total_exec_time)
]
# Calculate column widths.
column_widths = [
len(column_name) for column_name in profile_data.column_names()
]
for col in range(len(device_total_row)):
column_widths[col] = max(column_widths[col],
len(device_total_row[col]))
for col in range(len(column_widths)):
for row in range(profile_data.row_count()):
column_widths[col] = max(
column_widths[col], len(str(profile_data.value(row, col))))
column_widths[col] += 2 # add margin between columns
# Add device name.
output = debugger_cli_common.RichTextLines(["-" * 80])
device_row = "Device %d of %d: %s" % (device_index + 1, device_count,
device_name)
output.extend(debugger_cli_common.RichTextLines([device_row, ""]))
# Add headers.
base_command = "list_profile"
attr_segs = {0: []}
row = ""
for col in range(profile_data.column_count()):
column_name = profile_data.column_names()[col]
sort_id = profile_data.column_sort_id(col)
command = "%s -s %s" % (base_command, sort_id)
if sort_by == sort_id and not sort_reverse:
command += " -r"
curr_row = ("{:<%d}" % column_widths[col]).format(column_name)
prev_len = len(row)
row += curr_row
attr_segs[0].append(
(prev_len, prev_len + len(column_name),
[debugger_cli_common.MenuItem(None, command), "bold"]))
output.extend(
debugger_cli_common.RichTextLines([row], font_attr_segs=attr_segs))
# Add data rows.
for row in range(profile_data.row_count()):
row_str = ""
for col in range(profile_data.column_count()):
row_str += ("{:<%d}" % column_widths[col]).format(
profile_data.value(row, col))
output.extend(debugger_cli_common.RichTextLines([row_str]))
# Add stat totals.
row_str = ""
for col in range(len(device_total_row)):
row_str += ("{:<%d}" % column_widths[col]).format(
device_total_row[col])
output.extend(debugger_cli_common.RichTextLines(""))
output.extend(debugger_cli_common.RichTextLines(row_str))
return output
