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[tfdbg] Add device_name to GraphExecutionTrace proto; Refactor test.

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- The device_name information will be important for debugging
  distributed execution (e.g., MirroredStrategy on multiple devices)
- Refactor out a base class for the unit test for the dumping callback.
- The base class will be used in the to-be-added tests for tfdbg v2 +
  DistributionStrategy.

PiperOrigin-RevId: 276332541
Change-Id: Ifa47b3c86fb2706624fecdcc4eac027ce6c9172a
This commit is contained in:
Shanqing Cai 2019-10-23 12:54:02 -07:00 committed by TensorFlower Gardener
parent b07081a4df
commit 2e84f72d2d
7 changed files with 259 additions and 183 deletions
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10
tensorflow/core/kernels/debug_ops.h
View File

@ -395,7 +395,10 @@ class DebugNumericSummaryOp : public BaseDebugOp {
class DebugIdentityV2Op : public OpKernel {
public:
explicit DebugIdentityV2Op(OpKernelConstruction* context)
: OpKernel(context), output_slot_(-1), tensor_debug_mode_(0) {
: OpKernel(context),
device_name_(context->device()->name()),
output_slot_(-1),
tensor_debug_mode_(0) {
std::vector<string> debug_urls;
OP_REQUIRES_OK(context, context->GetAttr("debug_urls", &debug_urls));
for (const string& debug_url : debug_urls) {
@ -420,8 +423,8 @@ class DebugIdentityV2Op : public OpKernel {
for (const string& dump_root : dump_roots_) {
tfdbg::DebugEventsWriter* debug_events_writer =
tfdbg::DebugEventsWriter::GetDebugEventsWriter(dump_root);
debug_events_writer->WriteGraphExecutionTrace(tfdbg_context_id_, op_name_,
output_slot_,
debug_events_writer->WriteGraphExecutionTrace(
tfdbg_context_id_, device_name_, op_name_, output_slot_,
tensor_debug_mode_, tensor);
}
context->set_output(0, tensor);
@ -430,6 +433,7 @@ class DebugIdentityV2Op : public OpKernel {
private:
std::vector<string> dump_roots_;
string tfdbg_context_id_;
string device_name_;
string op_name_;
int32 output_slot_;
int32 tensor_debug_mode_;

3
tensorflow/core/protobuf/debug_event.proto
View File

@ -252,4 +252,7 @@ message GraphExecutionTrace {
// This tensor may summarize the value of a single intermediate op of the
// graph, or those of multiple intermediate tensors.
TensorProto tensor_proto = 5;
// Name of the device that the op belongs to.
string device_name = 6;
}

2
tensorflow/core/util/debug_events_writer.cc
View File

@ -261,6 +261,7 @@ void DebugEventsWriter::WriteGraphExecutionTrace(
}
void DebugEventsWriter::WriteGraphExecutionTrace(const string& tfdbg_context_id,
const string& device_name,
const string& op_name,
int32 output_slot,
int32 tensor_debug_mode,
@ -276,6 +277,7 @@ void DebugEventsWriter::WriteGraphExecutionTrace(const string& tfdbg_context_id,
if (tensor_debug_mode > 0) {
trace->set_tensor_debug_mode(TensorDebugMode(tensor_debug_mode));
}
trace->set_device_name(device_name);
tensor_value.AsProtoTensorContent(trace->mutable_tensor_proto());
WriteGraphExecutionTrace(trace.release());
}

1
tensorflow/core/util/debug_events_writer.h
View File

@ -155,6 +155,7 @@ class DebugEventsWriter {
// that this trace is concerned with. The sematics of this tensor value
// depends on the value of `tensor_debug_mode`.
void WriteGraphExecutionTrace(const string& tfdbg_context_id,
const string& device_name,
const string& op_name, int32 output_slot,
int32 tensor_debug_mode,
const Tensor& tensor_value);

14
tensorflow/python/debug/BUILD
View File

@ -32,6 +32,7 @@ py_library(
":debug_graphs",
":debug_utils",
":dumping_callback",
":dumping_callback_test_lib",
":grpc_debug_server",
":grpc_debug_test_server",
":hooks",
@ -93,6 +94,18 @@ py_library(
],
)
py_library(
name = "dumping_callback_test_lib",
srcs = ["lib/dumping_callback_test_lib.py"],
srcs_version = "PY2AND3",
deps = [
":check_numerics_callback",
":debug_events_reader",
":dumping_callback",
"//third_party/py/numpy",
],
)
py_library(
name = "common",
srcs = ["lib/common.py"],
@ -697,6 +710,7 @@ cuda_py_test(
":debug_events_reader",
":debug_events_writer",
":dumping_callback",
":dumping_callback_test_lib",
"//third_party/py/numpy",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:math_ops",

189
tensorflow/python/debug/lib/dumping_callback_test.py
View File

@ -21,7 +21,6 @@ from __future__ import print_function
import collections
import os
import shutil
import socket
import tempfile
import threading
@ -31,6 +30,7 @@ import numpy as np
from tensorflow.core.protobuf import debug_event_pb2
from tensorflow.python.debug.lib import debug_events_reader
from tensorflow.python.debug.lib import dumping_callback
from tensorflow.python.debug.lib import dumping_callback_test_lib
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
@ -38,7 +38,6 @@ from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import test_util
from tensorflow.python.framework import versions
from tensorflow.python.keras import models
from tensorflow.python.keras.applications import mobilenet_v2
from tensorflow.python.keras.layers import core
@ -62,7 +61,8 @@ def _create_simple_recurrent_keras_model(input_shape):
return model
class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
class TracingCallbackTest(
dumping_callback_test_lib.DumpingCallbackTestBase, parameterized.TestCase):
def setUp(self):
super(TracingCallbackTest, self).setUp()
@ -74,175 +74,6 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
dumping_callback.disable_dumping()
super(TracingCallbackTest, self).tearDown()
def _readAndCheckMetadataFile(self):
"""Read and check the .metadata debug-events file."""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
metadata_iter = reader.metadata_iterator()
metadata = next(metadata_iter).debug_metadata
self.assertEqual(metadata.tensorflow_version, versions.__version__)
self.assertTrue(metadata.file_version.startswith("debug.Event"))
def _readAndCheckSourceFilesAndStackFrames(self):
"""Read and verify the .source_files & .stack_frames debug-event files.
Returns:
A dict mapping stack frame IDs to stack frames (FileLineCol).
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
# Check the content of the .source_files file.
source_files_iter = reader.source_files_iterator()
source_file_paths = []
prev_wall_time = 1
for debug_event in source_files_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
source_file = debug_event.source_file
self.assertEqual(source_file.host_name, socket.gethostname())
self.assertTrue(source_file.file_path)
if source_file.lines:
self.assertTrue(os.path.isfile(source_file.file_path))
source_file_paths.append(source_file.file_path)
# Assert the file paths are unique.
self.assertEqual(len(source_file_paths), len(set(source_file_paths)))
# Check the content of the .stack_frames file.
stack_frame_by_id = dict() # A map from ID to stack frame.
stack_frames_iter = reader.stack_frames_iterator()
prev_wall_time = 0
for debug_event in stack_frames_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
stack_frame_with_id = debug_event.stack_frame_with_id
stack_frame_id = stack_frame_with_id.id
file_line_col = stack_frame_with_id.file_line_col
self.assertTrue(stack_frame_id)
self.assertNotIn(stack_frame_id, stack_frame_by_id,
"Duplicate stack frame ID: %s" % id)
stack_frame_by_id[stack_frame_id] = (file_line_col.file_index,
file_line_col.line,
file_line_col.func)
self.assertGreaterEqual(file_line_col.file_index, 0)
self.assertLess(file_line_col.file_index, len(source_file_paths))
self.assertTrue(file_line_col.line) # Line numbers are 1-based.
self.assertTrue(file_line_col.func)
# Assert the stack frames are unique.
self.assertEqual(
len(stack_frame_by_id.values()), len(set(stack_frame_by_id.values())))
return stack_frame_by_id
def _readAndCheckGraphsFile(self, stack_frame_by_id):
"""Read and verify the content of the .graphs debug-event file.
Args:
stack_frame_by_id: A dict mapping unique string IDs to stack frames.
It is used by this method to look up stack frames.
Returns:
context_ids: IDs of op creation contexts (e.g., TensorFlow graphs), as a
`list` of `str`s.
op_types: Types of the ops that are created, as a `list` of `str`s with
the same length as `context_ids`.
op_name_to_op_type: A `dict` mapping op name to op type.
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
graphs_iter = reader.graphs_iterator()
prev_wall_time = 0
op_types = []
op_name_to_op_type = dict()
context_ids = set()
for debug_event in graphs_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
graph_op_creation = debug_event.graph_op_creation
self.assertTrue(graph_op_creation.op_type)
op_types.append(graph_op_creation.op_type)
self.assertTrue(graph_op_creation.op_name)
op_name_to_op_type[graph_op_creation.op_name] = graph_op_creation.op_type
self.assertTrue(graph_op_creation.graph_id)
context_ids.add(graph_op_creation.graph_id)
self.assertTrue(graph_op_creation.code_location)
for stack_frame_id in graph_op_creation.code_location.stack_frame_ids:
self.assertIn(stack_frame_id, stack_frame_by_id)
return context_ids, op_types, op_name_to_op_type
def _readAndCheckExecutionFile(self):
"""Read and verify the content of the .execution debug-event file.
Returns:
executed_op_types: Types of ops that are created, as a `list` of `str`.
input_tensor_ids: Input tensor IDs for each of the ops executed, as a
`list` of `list` of `int`s, with the same length as `executed_op_types`.
output_tensor_ids: Output tensor IDs for each of the ops executed, as a
`list` of `list` of `int`s, with the same length as `executed_op_types`.
tensor_debug_modes: Tensor debug modes used to instrument each of ops
executed.
tensor_values: A `list` of `list` of `np.ndarray`s, representing the
tensor values. Each item of the outer `list` corresponds to one
execution event. Each item of the inner `list` corresponds to one
output tensor slot of the executed op or Function.
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
execution_iter = reader.execution_iterator()
prev_wall_time = 1
executed_op_types = []
input_tensor_ids = []
output_tensor_ids = []
tensor_debug_modes = []
tensor_values = []
for debug_event in execution_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
execution = debug_event.execution
executed_op_types.append(execution.op_type)
input_tensor_ids.append(execution.input_tensor_ids)
output_tensor_ids.append(execution.output_tensor_ids)
tensor_debug_modes.append(execution.tensor_debug_mode)
tensor_values.append([
tensor_util.MakeNdarray(tensor_proto)
for tensor_proto in execution.tensor_protos
])
# TODO(cais): When tensor debug modes other than NO_TENSOR is supported,
# return tensor_values as well.
return (executed_op_types, input_tensor_ids, output_tensor_ids,
tensor_debug_modes, tensor_values)
def _readAndCheckGraphExecutionTracesFile(self, context_ids):
"""Read & verify the content of the .graph_execution_trace debug-event file.
Args:
context_ids: Op-creation context IDs from _readAndCheckGraphsFile().
Returns:
op_names: Names of the ops that are executed, as a `list` of `str`s.
output_slots: Output slots, as a `list` of `int`s, with the same length as
`op_names`. In other words, for an executed op with N output tensors,
there will be N entries in this `list` and in `op_names`, at
corresponding indices.
tensor_values: Tensor values or their concise summaries, depending on
TensorDebugMode.
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
graph_execution_traces_iter = reader.graph_execution_traces_iterator()
op_names = []
output_slots = []
tensor_values = []
for debug_event in graph_execution_traces_iter:
self.assertGreaterEqual(debug_event.wall_time, 0)
graph_execution_trace = debug_event.graph_execution_trace
op_names.append(graph_execution_trace.op_name)
# All the ops in the graph have only one output.
self.assertTrue(graph_execution_trace.tfdbg_context_id)
self.assertIn(graph_execution_trace.tfdbg_context_id, context_ids)
output_slots.append(graph_execution_trace.output_slot)
try:
tensor_values.append(
tensor_util.MakeNdarray(graph_execution_trace.tensor_proto))
except KeyError:
# Certain dtypes are not convertible to numpy arrays.
tensor_values.append(None)
return op_names, output_slots, tensor_values
def testInvalidTensorDebugModeCausesError(self):
with self.assertRaisesRegexp(
ValueError,
@ -398,7 +229,7 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
self.assertIn("Log", op_types)
self.assertIn("Sin", op_types)
(op_names, _,
(op_names, _, _,
tensor_values) = self._readAndCheckGraphExecutionTracesFile(context_ids)
executed_op_types = [op_name_to_op_type[op_name] for op_name in op_names]
self.assertEqual(executed_op_types, ["AddV2", "Log", "AddV2", "Sin"])
@ -476,7 +307,7 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
if tensor_debug_mode == "FULL_TENSOR":
self.assertAllClose(tensor_values, [[8.0]])
(op_names, output_slots,
(op_names, _, output_slots,
tensor_values) = self._readAndCheckGraphExecutionTracesFile(context_ids)
executed_op_types = [op_name_to_op_type[op_name] for op_name in op_names]
# The Less op should have been executed 5 times.
@ -582,7 +413,7 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
context_ids, _, _ = self._readAndCheckGraphsFile(stack_frame_by_id)
_, _, _, _, tensor_values = self._readAndCheckExecutionFile()
self.assertEqual(tensor_values, [[]])
(_, _,
(_, _, _,
tensor_values) = self._readAndCheckGraphExecutionTracesFile(context_ids)
self.assertLen(tensor_values, 2)
for tensor_value in tensor_values:
@ -663,7 +494,7 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
(context_ids, _,
op_name_to_op_type) = self._readAndCheckGraphsFile(stack_frame_by_id)
(op_names, output_slots,
(op_names, _, output_slots,
tensor_values) = self._readAndCheckGraphExecutionTracesFile(context_ids)
executed_op_types = [op_name_to_op_type[op_name] for op_name in op_names]
self.assertEqual(executed_op_types.count("Mul"), 1 + num_threads)
@ -720,7 +551,7 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
if tensor_debug_mode == "NO_TENSOR":
self.assertFalse(value_list)
(op_names, _,
(op_names, _, _,
tensor_values) = self._readAndCheckGraphExecutionTracesFile(context_ids)
executed_op_types = [op_name_to_op_type[op_name] for op_name in op_names]
# These are the ops that we can safely assume to have been executed during
@ -786,7 +617,7 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
for value_list in tensor_values:
self.assertFalse(value_list)
(op_names, _,
(op_names, _, _,
tensor_values) = self._readAndCheckGraphExecutionTracesFile(context_ids)
executed_op_types = [op_name_to_op_type[op_name] for op_name in op_names]
# These are the ops that we can safely assume to have been executed during
@ -855,7 +686,7 @@ class TracingCallbackTest(test_util.TensorFlowTestCase, parameterized.TestCase):
executed_op_types, _, _, _, _ = self._readAndCheckExecutionFile()
self.assertTrue(executed_op_types)
(op_names, _,
(op_names, _, _,
tensor_values) = self._readAndCheckGraphExecutionTracesFile(context_ids)
executed_op_types = [op_name_to_op_type[op_name] for op_name in op_names]
# These are the ops that we can safely assume to have been executed during

221
tensorflow/python/debug/lib/dumping_callback_test_lib.py Normal file
View File

@ -0,0 +1,221 @@
# Copyright 2019 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.
# ==============================================================================
"""Shared library for testing tfdbg v2 dumping callback."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import socket
import tempfile
from tensorflow.python.debug.lib import check_numerics_callback
from tensorflow.python.debug.lib import debug_events_reader
from tensorflow.python.debug.lib import dumping_callback
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import test_util
from tensorflow.python.framework import versions
class DumpingCallbackTestBase(test_util.TensorFlowTestCase):
"""Base test-case class for tfdbg v2 callbacks."""
def setUp(self):
super(DumpingCallbackTestBase, self).setUp()
self.dump_root = tempfile.mkdtemp()
def tearDown(self):
if os.path.isdir(self.dump_root):
shutil.rmtree(self.dump_root, ignore_errors=True)
check_numerics_callback.disable_check_numerics()
dumping_callback.disable_dumping()
super(DumpingCallbackTestBase, self).tearDown()
def _readAndCheckMetadataFile(self):
"""Read and check the .metadata debug-events file."""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
metadata_iter = reader.metadata_iterator()
metadata = next(metadata_iter).debug_metadata
self.assertEqual(metadata.tensorflow_version, versions.__version__)
self.assertTrue(metadata.file_version.startswith("debug.Event"))
def _readAndCheckSourceFilesAndStackFrames(self):
"""Read and verify the .source_files & .stack_frames debug-event files.
Returns:
A dict mapping stack frame IDs to stack frames (FileLineCol).
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
# Check the content of the .source_files file.
source_files_iter = reader.source_files_iterator()
source_file_paths = []
prev_wall_time = 1
for debug_event in source_files_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
source_file = debug_event.source_file
self.assertEqual(source_file.host_name, socket.gethostname())
self.assertTrue(source_file.file_path)
if source_file.lines:
self.assertTrue(os.path.isfile(source_file.file_path))
source_file_paths.append(source_file.file_path)
# Assert the file paths are unique.
self.assertEqual(len(source_file_paths), len(set(source_file_paths)))
# Check the content of the .stack_frames file.
stack_frame_by_id = dict() # A map from ID to stack frame.
stack_frames_iter = reader.stack_frames_iterator()
prev_wall_time = 0
for debug_event in stack_frames_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
stack_frame_with_id = debug_event.stack_frame_with_id
stack_frame_id = stack_frame_with_id.id
file_line_col = stack_frame_with_id.file_line_col
self.assertTrue(stack_frame_id)
self.assertNotIn(stack_frame_id, stack_frame_by_id,
"Duplicate stack frame ID: %s" % id)
stack_frame_by_id[stack_frame_id] = (file_line_col.file_index,
file_line_col.line,
file_line_col.func)
self.assertGreaterEqual(file_line_col.file_index, 0)
self.assertLess(file_line_col.file_index, len(source_file_paths))
self.assertTrue(file_line_col.line) # Line numbers are 1-based.
self.assertTrue(file_line_col.func)
# Assert the stack frames are unique.
self.assertEqual(
len(stack_frame_by_id.values()), len(set(stack_frame_by_id.values())))
return stack_frame_by_id
def _readAndCheckGraphsFile(self, stack_frame_by_id):
"""Read and verify the content of the .graphs debug-event file.
Args:
stack_frame_by_id: A dict mapping unique string IDs to stack frames.
It is used by this method to look up stack frames.
Returns:
context_ids: IDs of op creation contexts (e.g., TensorFlow graphs), as a
`list` of `str`s.
op_types: Types of the ops that are created, as a `list` of `str`s with
the same length as `context_ids`.
op_name_to_op_type: A `dict` mapping op name to op type.
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
graphs_iter = reader.graphs_iterator()
prev_wall_time = 0
op_types = []
op_name_to_op_type = dict()
context_ids = set()
for debug_event in graphs_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
graph_op_creation = debug_event.graph_op_creation
self.assertTrue(graph_op_creation.op_type)
op_types.append(graph_op_creation.op_type)
self.assertTrue(graph_op_creation.op_name)
op_name_to_op_type[graph_op_creation.op_name] = graph_op_creation.op_type
self.assertTrue(graph_op_creation.graph_id)
context_ids.add(graph_op_creation.graph_id)
self.assertTrue(graph_op_creation.code_location)
for stack_frame_id in graph_op_creation.code_location.stack_frame_ids:
self.assertIn(stack_frame_id, stack_frame_by_id)
return context_ids, op_types, op_name_to_op_type
def _readAndCheckExecutionFile(self):
"""Read and verify the content of the .execution debug-event file.
Returns:
executed_op_types: Types of ops that are created, as a `list` of `str`.
input_tensor_ids: Input tensor IDs for each of the ops executed, as a
`list` of `list` of `int`s, with the same length as `executed_op_types`.
output_tensor_ids: Output tensor IDs for each of the ops executed, as a
`list` of `list` of `int`s, with the same length as `executed_op_types`.
tensor_debug_modes: Tensor debug modes used to instrument each of ops
executed.
tensor_values: A `list` of `list` of `np.ndarray`s, representing the
tensor values. Each item of the outer `list` corresponds to one
execution event. Each item of the inner `list` corresponds to one
output tensor slot of the executed op or Function.
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
execution_iter = reader.execution_iterator()
prev_wall_time = 1
executed_op_types = []
input_tensor_ids = []
output_tensor_ids = []
tensor_debug_modes = []
tensor_values = []
for debug_event in execution_iter:
self.assertGreaterEqual(debug_event.wall_time, prev_wall_time)
prev_wall_time = debug_event.wall_time
execution = debug_event.execution
executed_op_types.append(execution.op_type)
input_tensor_ids.append(execution.input_tensor_ids)
output_tensor_ids.append(execution.output_tensor_ids)
tensor_debug_modes.append(execution.tensor_debug_mode)
tensor_values.append([
tensor_util.MakeNdarray(tensor_proto)
for tensor_proto in execution.tensor_protos
])
# TODO(cais): When tensor debug modes other than NO_TENSOR is supported,
# return tensor_values as well.
return (executed_op_types, input_tensor_ids, output_tensor_ids,
tensor_debug_modes, tensor_values)
def _readAndCheckGraphExecutionTracesFile(self, context_ids):
"""Read & verify the content of the .graph_execution_trace debug-event file.
Args:
context_ids: Op-creation context IDs from _readAndCheckGraphsFile().
Returns:
op_names: Names of the ops that are executed, as a `list` of `str`s.
device_names: Names of the devices that the ops belong to, respectively.
A `list` of `str`s of the same length as `op_name`s.
output_slots: Output slots, as a `list` of `int`s, with the same length as
`op_names`. In other words, for an executed op with N output tensors,
there will be N entries in this `list` and in `op_names`, at
corresponding indices.
tensor_values: Tensor values or their concise summaries, depending on
TensorDebugMode.
"""
reader = debug_events_reader.DebugEventsDir(self.dump_root)
graph_execution_traces_iter = reader.graph_execution_traces_iterator()
op_names = []
device_names = []
output_slots = []
tensor_values = []
for debug_event in graph_execution_traces_iter:
self.assertGreaterEqual(debug_event.wall_time, 0)
graph_execution_trace = debug_event.graph_execution_trace
op_names.append(graph_execution_trace.op_name)
self.assertTrue(graph_execution_trace.device_name)
device_names.append(graph_execution_trace.device_name)
# All the ops in the graph have only one output.
self.assertTrue(graph_execution_trace.tfdbg_context_id)
self.assertIn(graph_execution_trace.tfdbg_context_id, context_ids)
output_slots.append(graph_execution_trace.output_slot)
dtype = dtypes.DType(graph_execution_trace.tensor_proto.dtype)
if dtype.is_numpy_compatible: # pylint:disable=protected-access
tensor_values.append(
tensor_util.MakeNdarray(graph_execution_trace.tensor_proto))
else:
tensor_values.append(None)
return op_names, device_names, output_slots, tensor_values