def testShapeError(self):
tf_error = errors.OpError(None, self.var_a.initializer, "foo description", None)
error_intro = cli_shared.get_error_intro(tf_error)
self.assertEqual("!!! An error occurred during the run !!!", error_intro.lines[1])
self.assertEqual([(0, len(error_intro.lines[1]), "blink")], error_intro.font_attr_segs[1])
self.assertEqual(2, error_intro.lines[4].index("ni -a -d -t a/Assign"))
self.assertEqual(2, error_intro.font_attr_segs[4][0][0])
self.assertEqual(22, error_intro.font_attr_segs[4][0][1])
self.assertEqual("ni -a -d -t a/Assign", error_intro.font_attr_segs[4][0][2][0].content)
self.assertEqual("bold", error_intro.font_attr_segs[4][0][2][1])
self.assertEqual(2, error_intro.lines[6].index("li -r a/Assign"))
self.assertEqual(2, error_intro.font_attr_segs[6][0][0])
self.assertEqual(16, error_intro.font_attr_segs[6][0][1])
self.assertEqual("li -r a/Assign", error_intro.font_attr_segs[6][0][2][0].content)
self.assertEqual("bold", error_intro.font_attr_segs[6][0][2][1])
self.assertEqual(2, error_intro.lines[8].index("lt"))
self.assertEqual(2, error_intro.font_attr_segs[8][0][0])
self.assertEqual(4, error_intro.font_attr_segs[8][0][1])
self.assertEqual("lt", error_intro.font_attr_segs[8][0][2][0].content)
self.assertEqual("bold", error_intro.font_attr_segs[8][0][2][1])
self.assertStartsWith(error_intro.lines[11], "Op name:")
self.assertTrue(error_intro.lines[11].endswith("a/Assign"))
self.assertStartsWith(error_intro.lines[12], "Error type:")
self.assertTrue(error_intro.lines[12].endswith(str(type(tf_error))))
self.assertEqual("Details:", error_intro.lines[14])
self.assertStartsWith(error_intro.lines[15], "foo description")
def testShapeError(self):
tf_error = errors.OpError(None, self.var_a.initializer,
"foo description", None)
error_intro = cli_shared.get_error_intro(tf_error)
self.assertEqual("!!! An error occurred during the run !!!",
error_intro.lines[1])
self.assertEqual([(0, len(error_intro.lines[1]), "blink")],
error_intro.font_attr_segs[1])
self.assertEqual(2, error_intro.lines[4].index("ni a/Assign"))
self.assertEqual([(2, 13, "bold")], error_intro.font_attr_segs[4])
self.assertEqual(2, error_intro.lines[6].index("li -r a/Assign"))
self.assertEqual([(2, 16, "bold")], error_intro.font_attr_segs[6])
self.assertEqual(2, error_intro.lines[8].index("lt"))
self.assertEqual([(2, 4, "bold")], error_intro.font_attr_segs[8])
self.assertStartsWith(error_intro.lines[11], "Op name:")
self.assertTrue(error_intro.lines[11].endswith("a/Assign"))
self.assertStartsWith(error_intro.lines[12], "Error type:")
self.assertTrue(error_intro.lines[12].endswith(str(type(tf_error))))
self.assertEqual("Details:", error_intro.lines[14])
self.assertStartsWith(error_intro.lines[15], "foo description")
def _prep_cli_for_run_end(self, debug_dump, tf_error, passed_filter):
"""Prepare (but not launch) CLI for run-end, with debug dump from the run.
Args:
debug_dump: (debug_data.DebugDumpDir) The debug dump directory from this
run.
tf_error: (None or OpError) OpError that happened during the run() call
(if any).
passed_filter: (None or str) Name of the tensor filter that just passed
and caused the preparation of this run-end CLI (if any).
"""
if tf_error:
help_intro = cli_shared.get_error_intro(tf_error)
self._init_command = "help"
self._title_color = "red_on_white"
else:
help_intro = None
self._init_command = "lt"
self._title_color = "black_on_white"
if passed_filter is not None:
# Some dumped tensor(s) from this run passed the filter.
self._init_command = "lt -f %s" % passed_filter
self._title_color = "red_on_white"
self._run_cli = analyzer_cli.create_analyzer_ui(
debug_dump,
self._tensor_filters,
ui_type=self._ui_type,
on_ui_exit=self._remove_dump_root)
# Get names of all dumped tensors.
dumped_tensor_names = []
for datum in debug_dump.dumped_tensor_data:
dumped_tensor_names.append("%s:%d" %
(datum.node_name, datum.output_slot))
# Tab completions for command "print_tensors".
self._run_cli.register_tab_comp_context(["print_tensor", "pt"],
dumped_tensor_names)
# Tab completion for commands "node_info", "list_inputs" and
# "list_outputs". The list comprehension is used below because nodes()
# output can be unicodes and they need to be converted to strs.
self._run_cli.register_tab_comp_context(
["node_info", "ni", "list_inputs", "li", "list_outputs", "lo"],
[str(node_name) for node_name in debug_dump.nodes()])
# TODO(cais): Reduce API surface area for aliases vis-a-vis tab
# completion contexts and registered command handlers.
self._title = "run-end: " + self._run_description
if help_intro:
self._run_cli.set_help_intro(help_intro)
def _prep_cli_for_run_end(self, debug_dump, tf_error, passed_filter):
"""Prepare (but not launch) CLI for run-end, with debug dump from the run.
Args:
debug_dump: (debug_data.DebugDumpDir) The debug dump directory from this
run.
tf_error: (None or OpError) OpError that happened during the run() call
(if any).
passed_filter: (None or str) Name of the tensor filter that just passed
and caused the preparation of this run-end CLI (if any).
"""
if tf_error:
help_intro = cli_shared.get_error_intro(tf_error)
self._init_command = "help"
self._title_color = "red_on_white"
else:
help_intro = None
self._init_command = "lt"
self._title_color = "black_on_white"
if passed_filter is not None:
# Some dumped tensor(s) from this run passed the filter.
self._init_command = "lt -f %s" % passed_filter
self._title_color = "red_on_white"
self._run_cli = analyzer_cli.create_analyzer_ui(
debug_dump, self._tensor_filters, ui_type=self._ui_type,
on_ui_exit=self._remove_dump_root)
# Get names of all dumped tensors.
dumped_tensor_names = []
for datum in debug_dump.dumped_tensor_data:
dumped_tensor_names.append("%s:%d" %
(datum.node_name, datum.output_slot))
# Tab completions for command "print_tensors".
self._run_cli.register_tab_comp_context(["print_tensor", "pt"],
dumped_tensor_names)
# Tab completion for commands "node_info", "list_inputs" and
# "list_outputs". The list comprehension is used below because nodes()
# output can be unicodes and they need to be converted to strs.
self._run_cli.register_tab_comp_context(
["node_info", "ni", "list_inputs", "li", "list_outputs", "lo"],
[str(node_name) for node_name in debug_dump.nodes()])
# TODO(cais): Reduce API surface area for aliases vis-a-vis tab
# completion contexts and registered command handlers.
self._title = "run-end: " + self._run_description
if help_intro:
self._run_cli.set_help_intro(help_intro)
def on_run_end(self, request):
"""Overrides on-run-end callback.
Actions taken:
1) Load the debug dump.
2) Bring up the Analyzer CLI.
Args:
request: An instance of OnSessionInitRequest.
Returns:
An instance of OnSessionInitResponse.
"""
if request.performed_action == framework.OnRunStartAction.DEBUG_RUN:
partition_graphs = None
if request.run_metadata and request.run_metadata.partition_graphs:
partition_graphs = request.run_metadata.partition_graphs
elif request.client_graph_def:
partition_graphs = [request.client_graph_def]
debug_dump = debug_data.DebugDumpDir(self._dump_root, partition_graphs=partition_graphs)
if request.tf_error:
help_intro = cli_shared.get_error_intro(request.tf_error)
init_command = "help"
title_color = "red_on_white"
else:
help_intro = None
init_command = "lt"
title_color = "black_on_white"
if self._run_till_filter_pass:
if not debug_dump.find(self._tensor_filters[self._run_till_filter_pass], first_n=1):
# No dumped tensor passes the filter in this run. Clean up the dump
# directory and move on.
shutil.rmtree(self._dump_root)
return framework.OnRunEndResponse()
else:
# Some dumped tensor(s) from this run passed the filter.
init_command = "lt -f %s" % self._run_till_filter_pass
title_color = "red_on_white"
self._run_till_filter_pass = None
analyzer = analyzer_cli.DebugAnalyzer(debug_dump)
# Supply all the available tensor filters.
for filter_name in self._tensor_filters:
analyzer.add_tensor_filter(filter_name, self._tensor_filters[filter_name])
run_end_cli = curses_ui.CursesUI()
run_end_cli.register_command_handler(
"list_tensors", analyzer.list_tensors, analyzer.get_help("list_tensors"), prefix_aliases=["lt"]
)
run_end_cli.register_command_handler(
"node_info", analyzer.node_info, analyzer.get_help("node_info"), prefix_aliases=["ni"]
)
run_end_cli.register_command_handler(
"list_inputs", analyzer.list_inputs, analyzer.get_help("list_inputs"), prefix_aliases=["li"]
)
run_end_cli.register_command_handler(
"list_outputs", analyzer.list_outputs, analyzer.get_help("list_outputs"), prefix_aliases=["lo"]
)
run_end_cli.register_command_handler(
"print_tensor", analyzer.print_tensor, analyzer.get_help("print_tensor"), prefix_aliases=["pt"]
)
run_end_cli.register_command_handler(
"run",
self._run_end_run_command_handler,
"Helper command for incorrectly entered run command at the run-end " "prompt.",
prefix_aliases=["r"],
)
# Get names of all dumped tensors.
dumped_tensor_names = []
for datum in debug_dump.dumped_tensor_data:
dumped_tensor_names.append("%s:%d" % (datum.node_name, datum.output_slot))
# Tab completions for command "print_tensors".
run_end_cli.register_tab_comp_context(["print_tensor", "pt"], dumped_tensor_names)
# Tab completion for commands "node_info", "list_inputs" and
# "list_outputs". The list comprehension is used below because nodes()
# output can be unicodes and they need to be converted to strs.
run_end_cli.register_tab_comp_context(
["node_info", "ni", "list_inputs", "li", "list_outputs", "lo"],
[str(node_name) for node_name in debug_dump.nodes()],
)
# TODO(cais): Reduce API surface area for aliases vis-a-vis tab
# completion contexts and registered command handlers.
title = "run-end: " + self._run_description
if help_intro:
run_end_cli.set_help_intro(help_intro)
run_end_cli.run_ui(init_command=init_command, title=title, title_color=title_color)
# Clean up the dump directory.
shutil.rmtree(self._dump_root)
else:
print("No debug information to show following a non-debug run() call.")
# Return placeholder response that currently holds no additional
# information.
return framework.OnRunEndResponse()
def testGetErrorIntroForNoOpName(self):
tf_error = errors.OpError(None, None, "Fake OpError", -1)
error_intro = cli_shared.get_error_intro(tf_error)
self.assertIn("Cannot determine the name of the op", error_intro.lines[3])
def testGetErrorIntroForNoOpName(self):
tf_error = errors.OpError(None, None, "Fake OpError", -1)
error_intro = cli_shared.get_error_intro(tf_error)
self.assertIn("Cannot determine the name of the op",
error_intro.lines[3])
def on_run_end(self, request):
"""Overrides on-run-end callback.
Actions taken:
1) Load the debug dump.
2) Bring up the Analyzer CLI.
Args:
request: An instance of OnSessionInitRequest.
Returns:
An instance of OnSessionInitResponse.
"""
if request.performed_action == framework.OnRunStartAction.DEBUG_RUN:
partition_graphs = None
if request.run_metadata and request.run_metadata.partition_graphs:
partition_graphs = request.run_metadata.partition_graphs
elif request.client_graph_def:
partition_graphs = [request.client_graph_def]
debug_dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=partition_graphs)
if request.tf_error:
help_intro = cli_shared.get_error_intro(request.tf_error)
init_command = "help"
title_color = "red_on_white"
else:
help_intro = None
init_command = "lt"
title_color = "black_on_white"
if self._run_till_filter_pass:
if not debug_dump.find(
self._tensor_filters[self._run_till_filter_pass],
first_n=1):
# No dumped tensor passes the filter in this run. Clean up the dump
# directory and move on.
shutil.rmtree(self._dump_root)
return framework.OnRunEndResponse()
else:
# Some dumped tensor(s) from this run passed the filter.
init_command = "lt -f %s" % self._run_till_filter_pass
title_color = "red_on_white"
self._run_till_filter_pass = None
analyzer = analyzer_cli.DebugAnalyzer(debug_dump)
# Supply all the available tensor filters.
for filter_name in self._tensor_filters:
analyzer.add_tensor_filter(filter_name,
self._tensor_filters[filter_name])
run_end_cli = curses_ui.CursesUI()
run_end_cli.register_command_handler(
"list_tensors",
analyzer.list_tensors,
analyzer.get_help("list_tensors"),
prefix_aliases=["lt"])
run_end_cli.register_command_handler(
"node_info",
analyzer.node_info,
analyzer.get_help("node_info"),
prefix_aliases=["ni"])
run_end_cli.register_command_handler(
"list_inputs",
analyzer.list_inputs,
analyzer.get_help("list_inputs"),
prefix_aliases=["li"])
run_end_cli.register_command_handler(
"list_outputs",
analyzer.list_outputs,
analyzer.get_help("list_outputs"),
prefix_aliases=["lo"])
run_end_cli.register_command_handler(
"print_tensor",
analyzer.print_tensor,
analyzer.get_help("print_tensor"),
prefix_aliases=["pt"])
run_end_cli.register_command_handler(
"run",
self._run_end_run_command_handler,
"Helper command for incorrectly entered run command at the run-end "
"prompt.",
prefix_aliases=["r"])
# Get names of all dumped tensors.
dumped_tensor_names = []
for datum in debug_dump.dumped_tensor_data:
dumped_tensor_names.append(
"%s:%d" % (datum.node_name, datum.output_slot))
# Tab completions for command "print_tensors".
run_end_cli.register_tab_comp_context(["print_tensor", "pt"],
dumped_tensor_names)
# Tab completion for commands "node_info", "list_inputs" and
# "list_outputs". The list comprehension is used below because nodes()
# output can be unicodes and they need to be converted to strs.
run_end_cli.register_tab_comp_context(
["node_info", "ni", "list_inputs", "li", "list_outputs", "lo"],
[str(node_name) for node_name in debug_dump.nodes()])
# TODO(cais): Reduce API surface area for aliases vis-a-vis tab
# completion contexts and registered command handlers.
title = "run-end: " + self._run_description
if help_intro:
run_end_cli.set_help_intro(help_intro)
run_end_cli.run_ui(init_command=init_command,
title=title,
title_color=title_color)
# Clean up the dump directory.
shutil.rmtree(self._dump_root)
else:
print(
"No debug information to show following a non-debug run() call."
)
# Return placeholder response that currently holds no additional
# information.
return framework.OnRunEndResponse()