def testDumpingDebugHookWithStatefulLegacyWatchFnWorks(self):
watch_fn_state = {"run_counter": 0}
def counting_watch_fn(fetches, feed_dict):
del fetches, feed_dict
watch_fn_state["run_counter"] += 1
if watch_fn_state["run_counter"] % 2 == 1:
# If odd-index run (1-based), watch everything.
return "DebugIdentity", r".*", r".*"
else:
# If even-index run, watch nothing.
return "DebugIdentity", r"$^", r"$^"
dumping_hook = hooks.DumpingDebugHook(
self.session_root, watch_fn=counting_watch_fn, log_usage=False)
mon_sess = monitored_session._HookedSession(self.sess, [dumping_hook])
for _ in range(4):
mon_sess.run(self.inc_v)
dump_dirs = glob.glob(os.path.join(self.session_root, "run_*"))
dump_dirs = sorted(
dump_dirs, key=lambda x: int(os.path.basename(x).split("_")[1]))
self.assertEqual(4, len(dump_dirs))
for i, dump_dir in enumerate(dump_dirs):
self._assert_correct_run_subdir_naming(os.path.basename(dump_dir))
dump = debug_data.DebugDumpDir(dump_dir)
if i % 2 == 0:
self.assertAllClose([10.0 + 1.0 * i],
dump.get_tensors("v", 0, "DebugIdentity"))
else:
self.assertEqual(0, dump.size)
self.assertEqual(repr(self.inc_v), dump.run_fetches_info)
self.assertEqual(repr(None), dump.run_feed_keys_info)
def testDebugDumpDir_invalidFileNamingPattern(self):
# File name with too few underscores should lead to an exception.
open(os.path.join(self._dump_root, "node1_DebugIdentity_1234"), "wb")
with self.assertRaisesRegexp(ValueError,
"does not conform to the naming pattern"):
debug_data.DebugDumpDir(self._dump_root)
def _session_run_for_graph_structure_lookup(self):
with session.Session() as sess:
u_name = "testDumpGraphStructureLookup/u"
v_name = "testDumpGraphStructureLookup/v"
w_name = "testDumpGraphStructureLookup/w"
u_init = constant_op.constant([2.0, 4.0])
u = variables.Variable(u_init, name=u_name)
v = math_ops.add(u, u, name=v_name)
w = math_ops.add(v, v, name=w_name)
u.initializer.run()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls=self._debug_urls())
run_metadata = config_pb2.RunMetadata()
sess.run(w, options=run_options, run_metadata=run_metadata)
self.assertEqual(self._expected_partition_graph_count,
len(run_metadata.partition_graphs))
dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=run_metadata.partition_graphs)
return u_name, v_name, w_name, dump
def testDuplicateNodeNamesInGraphDefOfSingleDeviceRaisesException(self):
self._makeDataDirWithMultipleDevicesAndDuplicateNodeNames()
graph_cpu_0 = graph_pb2.GraphDef()
node = graph_cpu_0.node.add()
node.name = "node_foo_1"
node.op = "FooOp"
node.device = "/job:localhost/replica:0/task:0/cpu:0"
graph_gpu_0 = graph_pb2.GraphDef()
node = graph_gpu_0.node.add()
node.name = "node_foo_1"
node.op = "FooOp"
node.device = "/job:localhost/replica:0/task:0/device:GPU:0"
graph_gpu_1 = graph_pb2.GraphDef()
node = graph_gpu_1.node.add()
node.name = "node_foo_1"
node.op = "FooOp"
node.device = "/job:localhost/replica:0/task:0/device:GPU:1"
node = graph_gpu_1.node.add() # Here is the duplicate.
node.name = "node_foo_1"
node.op = "FooOp"
node.device = "/job:localhost/replica:0/task:0/device:GPU:1"
with self.assertRaisesRegex(ValueError,
r"Duplicate node name on device "):
debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=[graph_cpu_0, graph_gpu_0, graph_gpu_1])
def testGrpcDebugWrapperSessionWithWatchFnWorks(self):
def watch_fn(feeds, fetch_keys):
del feeds, fetch_keys
return ["DebugIdentity", "DebugNumericSummary"], r".*/read", None
u = variables.Variable(2.1, name="u")
v = variables.Variable(20.0, name="v")
w = math_ops.multiply(u, v, name="w")
sess = session.Session(
config=session_debug_testlib.no_rewrite_session_config())
sess.run(u.initializer)
sess.run(v.initializer)
sess = grpc_wrapper.GrpcDebugWrapperSession(sess,
"localhost:%d" %
self._server_port,
watch_fn=watch_fn)
w_result = sess.run(w)
self.assertAllClose(42.0, w_result)
dump = debug_data.DebugDumpDir(self._dump_root)
self.assertEqual(4, dump.size)
self.assertAllClose([2.1],
dump.get_tensors("u/read", 0, "DebugIdentity"))
self.assertEqual(
14, len(dump.get_tensors("u/read", 0, "DebugNumericSummary")[0]))
self.assertAllClose([20.0],
dump.get_tensors("v/read", 0, "DebugIdentity"))
self.assertEqual(
14, len(dump.get_tensors("v/read", 0, "DebugNumericSummary")[0]))
def testAdditionalHooks(self):
checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
# First, save out the current model to a checkpoint:
self._prepareCheckpoint(checkpoint_path)
# Next, determine the metric to evaluate:
value_op, update_op = metric_ops.streaming_accuracy(
self._predictions, self._labels)
dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir')
dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False)
try:
# Run the evaluation and verify the results:
accuracy_value = evaluation.evaluate_once('',
checkpoint_path,
log_dir,
eval_op=update_op,
final_op=value_op,
hooks=[dumping_hook])
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
dump = debug_data.DebugDumpDir(
glob.glob(os.path.join(dumping_root, 'run_*'))[0])
# Here we simply assert that the dumped data has been loaded and is
# non-empty. We do not care about the detailed model-internal tensors or
# their values.
self.assertTrue(dump.dumped_tensor_data)
finally:
if os.path.isdir(dumping_root):
shutil.rmtree(dumping_root)
def testTrainWithSessionWrapper(self):
"""Test that slim.learning.train can take `session_wrapper` args.
One of the applications of `session_wrapper` is the wrappers of TensorFlow
Debugger (tfdbg), which intercept methods calls to `tf.Session` (e.g., run)
to achieve debugging. `DumpingDebugWrapperSession` is used here for testing
purpose.
"""
dump_root = tempfile.mkdtemp()
def dumping_wrapper(sess): # pylint: disable=invalid-name
return dumping_wrapper_lib.DumpingDebugWrapperSession(sess, dump_root)
with ops.Graph().as_default():
random_seed.set_random_seed(0)
tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)
tf_predictions = LogisticClassifier(tf_inputs)
loss_ops.log_loss(tf_predictions, tf_labels)
total_loss = loss_ops.get_total_loss()
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
train_op = learning.create_train_op(total_loss, optimizer)
loss = learning.train(
train_op, None, number_of_steps=1, session_wrapper=dumping_wrapper)
self.assertIsNotNone(loss)
run_root = glob.glob(os.path.join(dump_root, 'run_*'))[-1]
dump = debug_data.DebugDumpDir(run_root)
self.assertAllEqual(0,
dump.get_tensors('global_step', 0, 'DebugIdentity')[0])
def createAndRunGraphWithWhileLoop(self):
"""Create and run a TensorFlow Graph with a while loop to generate dumps."""
self.dump_root = self.get_temp_dir()
self.curr_file_path = os.path.abspath(
tf_inspect.getfile(tf_inspect.currentframe()))
# Run a simple TF graph to generate some debug dumps that can be used in
# source annotation.
with session.Session() as sess:
loop_body = lambda i: math_ops.add(i, 2)
self.traceback_first_line = line_number_above()
loop_cond = lambda i: math_ops.less(i, 16)
i = constant_op.constant(10, name="i")
loop = control_flow_ops.while_loop(loop_cond, loop_body, [i])
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_urls=["file://%s" % self.dump_root])
run_metadata = config_pb2.RunMetadata()
sess.run(loop, options=run_options, run_metadata=run_metadata)
self.dump = debug_data.DebugDumpDir(
self.dump_root, partition_graphs=run_metadata.partition_graphs)
self.dump.set_python_graph(sess.graph)
def testOutputSlotWithoutOutgoingEdgeCanBeWatched(self):
"""Test watching output slots not attached to any outgoing edges."""
with session.Session() as sess:
u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
u = constant_op.constant(u_init_val, shape=[2, 2], name="u")
# Create a control edge from a node with an output: From u to z.
# Node u will get executed only because of the control edge. The output
# tensor u:0 is not attached to any outgoing edge in the graph. This test
# checks that the debugger can watch such a tensor.
with ops.control_dependencies([u]):
z = control_flow_ops.no_op(name="z")
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls=self._debug_urls())
run_metadata = config_pb2.RunMetadata()
sess.run(z, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
# Assert that the DebugIdentity watch on u works properly.
self.assertEqual(1, len(dump.dumped_tensor_data))
datum = dump.dumped_tensor_data[0]
self.assertEqual("u", datum.node_name)
self.assertEqual(0, datum.output_slot)
self.assertEqual("DebugIdentity", datum.debug_op)
self.assertAllClose([[5.0, 3.0], [-1.0, 0.0]], datum.get_tensor())
def testDumpingWithWatchFnWithNonDefaultDebugOpsWorks(self):
"""Use a watch_fn that specifies non-default debug ops."""
def watch_fn(fetches, feeds):
del fetches, feeds
return framework.WatchOptions(
debug_ops=["DebugIdentity", "DebugNumericSummary"],
node_name_regex_whitelist=r"^v.*",
op_type_regex_whitelist=r".*",
tensor_dtype_regex_whitelist=".*_ref")
sess = dumping_wrapper.DumpingDebugWrapperSession(
self.sess,
session_root=self.session_root,
watch_fn=watch_fn,
log_usage=False)
sess.run(self.inc_v)
dump_dirs = glob.glob(os.path.join(self.session_root, "run_*"))
self.assertEqual(1, len(dump_dirs))
dump = debug_data.DebugDumpDir(dump_dirs[0])
self.assertAllClose([10.0], dump.get_tensors("v", 0, "DebugIdentity"))
self.assertEqual(
14, len(dump.get_tensors("v", 0, "DebugNumericSummary")[0]))
dumped_nodes = [dump.node_name for dump in dump.dumped_tensor_data]
self.assertNotIn("inc_v", dumped_nodes)
self.assertNotIn("delta", dumped_nodes)
def testDebugDumpDir_usesGfileGlob(self):
if platform.system() == "Windows":
self.skipTest("gfile.Glob is not used on Windows.")
self._makeDataDirWithMultipleDevicesAndDuplicateNodeNames()
def fake_gfile_glob(glob_pattern):
del glob_pattern
return []
with test.mock.patch.object(gfile,
"Glob",
side_effect=fake_gfile_glob,
autospec=True) as fake:
debug_data.DebugDumpDir(self._dump_root)
expected_calls = [
test.mock.call(
os.path.join(self._dump_root,
(debug_data.METADATA_FILE_PREFIX +
debug_data.CORE_METADATA_TAG + "*"))),
test.mock.call(
os.path.join(self._dump_root,
(debug_data.METADATA_FILE_PREFIX +
debug_data.FETCHES_INFO_FILE_TAG + "*"))),
test.mock.call(
os.path.join(self._dump_root,
(debug_data.METADATA_FILE_PREFIX +
debug_data.FEED_KEYS_INFO_FILE_TAG + "*"))),
test.mock.call(
os.path.join(self._dump_root,
(debug_data.METADATA_FILE_PREFIX +
debug_data.DEVICE_TAG + "*")))
]
fake.assert_has_calls(expected_calls, any_order=True)
def testDebugNumericSummaryOnUninitializedTensorGivesCorrectResult(self):
with session.Session() as sess:
a = variables.Variable([42],
dtype=np.float32,
name="numeric_summary_uninit/a")
run_metadata = config_pb2.RunMetadata()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_ops=["DebugNumericSummary"],
debug_urls=self._debug_urls())
sess.run(a.initializer,
options=run_options,
run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
self.assertTrue(dump.loaded_partition_graphs())
# DebugNumericSummary output should reflect the uninitialized state of
# the watched tensor.
numeric_summary = dump.get_tensors("numeric_summary_uninit/a", 0,
"DebugNumericSummary")[0]
self.assertAllClose([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
numeric_summary[0:8])
self.assertTrue(np.isinf(numeric_summary[8]))
self.assertGreater(numeric_summary[8], 0.0)
self.assertTrue(np.isinf(numeric_summary[9]))
self.assertLess(numeric_summary[9], 0.0)
self.assertTrue(np.isnan(numeric_summary[10]))
self.assertTrue(np.isnan(numeric_summary[11]))
def testDebugNumericSummaryOnInitializedTensorGivesCorrectResult(self):
with session.Session() as sess:
a = variables.Variable([
np.nan, np.nan, 0.0, 0.0, 0.0, -1.0, -3.0, 3.0, 7.0, -np.inf,
-np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.nan, np.nan
],
dtype=np.float32,
name="numeric_summary/a")
b = variables.Variable([0.0] * 18,
dtype=np.float32,
name="numeric_summary/b")
c = math_ops.add(a, b, name="numeric_summary/c")
sess.run(variables.global_variables_initializer())
run_metadata = config_pb2.RunMetadata()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_ops=["DebugNumericSummary"],
debug_urls=self._debug_urls())
sess.run(c, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
self.assertTrue(dump.loaded_partition_graphs())
self.assertAllClose([[
1.0, 18.0, 2.0, 2.0, 3.0, 2.0, 5.0, 4.0, -3.0, 7.0, 0.85714286,
8.97959184
]],
dump.get_tensors("numeric_summary/a/read", 0,
"DebugNumericSummary"))
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.
"""
self._is_run_start = False
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]
if request.tf_error and not os.path.isdir(self._dump_root):
# It is possible that the dump root may not exist due to errors that
# have occurred prior to graph execution (e.g., invalid device
# assignments), in which case we will just raise the exception as the
# unwrapped Session does.
raise request.tf_error
debug_dump = debug_data.DebugDumpDir(
self._dump_root, partition_graphs=partition_graphs)
debug_dump.set_python_graph(self._sess.graph)
passed_filter = None
if self._active_tensor_filter:
if not debug_dump.find(
self._tensor_filters[self._active_tensor_filter], first_n=1):
# No dumped tensor passes the filter in this run. Clean up the dump
# directory and move on.
self._remove_dump_root()
return framework.OnRunEndResponse()
else:
# Some dumped tensor(s) from this run passed the filter.
passed_filter = self._active_tensor_filter
self._active_tensor_filter = None
self._prep_cli_for_run_end(debug_dump, request.tf_error, passed_filter)
self._run_start_response = self._launch_cli()
# Clean up the dump generated by this run.
self._remove_dump_root()
else:
# No debug information to show following a non-debug run() call.
self._run_start_response = None
# Return placeholder response that currently holds no additional
# information.
return framework.OnRunEndResponse()
def _load_dumped_intermediate_tensors(self, dump_path, target_name):
dump_dir = debug_data.DebugDumpDir(dump_path, validate=False)
for dump in dump_dir.dumped_tensor_data:
if (dump.tensor_name not in self._ref_tensor_names
and dump.tensor_name not in self._tensor_handles
and dump.tensor_name not in self._override_tensors
and dump.tensor_name != target_name):
self._dumped_intermediate_tensors[dump.tensor_name] = dump
def testGraphStructureLookupWithoutPartitionGraphsDoesNotErrorOut(self):
_, _, _, dump = self._session_run_for_graph_structure_lookup()
# Now load the dump again, without the partition graphs, so we can check
# errors are not raised because the partition graphs are loaded from the
# dump directory.
dump = debug_data.DebugDumpDir(self._dump_root, validate=False)
self.assertTrue(dump.loaded_partition_graphs())
def testDebugDumpDir_invalidFileNamingPattern(self):
# File name with too few underscores should lead to an exception.
device_dir = os.path.join(
self._dump_root, debug_data.METADATA_FILE_PREFIX +
debug_data.DEVICE_TAG + ",job_localhost,replica_0,task_0,cpu_0")
os.makedirs(device_dir)
open(os.path.join(device_dir, "node1_DebugIdentity_1234"), "wb")
with self.assertRaisesRegex(ValueError,
"does not conform to the naming pattern"):
debug_data.DebugDumpDir(self._dump_root)
def testDumpUninitializedVariable(self):
op_namespace = "testDumpUninitializedVariable"
with session.Session() as sess:
u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
s_init_val = b"str1"
u_name = "%s/u" % op_namespace
s_name = "%s/s" % op_namespace
u_init = constant_op.constant(u_init_val, shape=[2, 2])
u = variables.Variable(u_init, name=u_name)
s_init = constant_op.constant(s_init_val)
s = variables.Variable(s_init, name=s_name)
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_urls = self._debug_urls()
# Add debug tensor watch for u.
debug_utils.add_debug_tensor_watch(run_options,
"%s" % u_name,
0,
debug_urls=debug_urls)
debug_utils.add_debug_tensor_watch(run_options,
"%s" % s_name,
0,
debug_urls=debug_urls)
run_metadata = config_pb2.RunMetadata()
# Initialize u and s.
sess.run(variables.global_variables_initializer(),
options=run_options,
run_metadata=run_metadata)
# Verify the dump file for the uninitialized value of u.
dump = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
self.assertEqual(2, dump.size)
self.assertEqual(self._expected_partition_graph_count,
len(run_metadata.partition_graphs))
# Verify that the variable is properly initialized by the run() call.
u_vals = dump.get_tensors(u_name, 0, "DebugIdentity")
s_vals = dump.get_tensors(s_name, 0, "DebugIdentity")
self.assertEqual(1, len(u_vals))
self.assertIsNone(u_vals[0])
self.assertEqual(1, len(s_vals))
self.assertIsNone(s_vals[0])
# Call run() again, to check that u is initialized properly.
self.assertAllClose(u_init_val, sess.run(u))
self.assertEqual(s_init_val, sess.run(s))
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.
"""
self._is_run_start = False
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)
debug_dump.set_python_graph(self._sess.graph)
passed_filter = None
if self._active_tensor_filter:
if not debug_dump.find(
self._tensor_filters[self._active_tensor_filter],
first_n=1):
# No dumped tensor passes the filter in this run. Clean up the dump
# directory and move on.
self._remove_dump_root()
return framework.OnRunEndResponse()
else:
# Some dumped tensor(s) from this run passed the filter.
passed_filter = self._active_tensor_filter
self._active_tensor_filter = None
self._prep_cli_for_run_end(debug_dump, request.tf_error,
passed_filter)
self._run_start_response = self._launch_cli()
# Clean up the dump generated by this run.
self._remove_dump_root()
else:
# No debug information to show following a non-debug run() call.
self._run_start_response = None
# Return placeholder response that currently holds no additional
# information.
return framework.OnRunEndResponse()
def _generate_dump_from_simple_addition_graph(self):
with session.Session() as sess:
u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
v_init_val = np.array([[2.0], [-1.0]])
# Use node names with overlapping namespace (i.e., parent directory) to
# test concurrent, non-racing directory creation.
u_name = "u"
v_name = "v"
w_name = "w"
u_init = constant_op.constant(u_init_val, shape=[2, 2])
u = variables.Variable(u_init, name=u_name)
v_init = constant_op.constant(v_init_val, shape=[2, 1])
v = variables.Variable(v_init, name=v_name)
w = math_ops.matmul(u, v, name=w_name)
u.initializer.run()
v.initializer.run()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_urls = "file://%s" % self._dump_root
# Add debug tensor watch for u.
debug_utils.add_debug_tensor_watch(run_options,
"%s/read" % u_name,
0,
debug_urls=debug_urls)
# Add debug tensor watch for v.
debug_utils.add_debug_tensor_watch(run_options,
"%s/read" % v_name,
0,
debug_urls=debug_urls)
run_metadata = config_pb2.RunMetadata()
# Invoke Session.run().
sess.run(w, options=run_options, run_metadata=run_metadata)
self.assertEqual(self._expected_partition_graph_count,
len(run_metadata.partition_graphs))
dump = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
simple_add_results = collections.namedtuple("SimpleAddResults", [
"u_init_val", "v_init_val", "u", "v", "w", "u_name", "v_name",
"w_name", "dump"
])
return simple_add_results(u_init_val, v_init_val, u, v, w, u_name,
v_name, w_name, dump)
def testWatchingVariableUpdateOpsSeesUpdatedValues(self):
"""Watch output slots on Variable-updating ops, with no emitted edges."""
with session.Session() as sess:
u_init = constant_op.constant(10.0)
u = variables.Variable(u_init, name="gdo/u")
v_init = constant_op.constant(20.0)
v = variables.Variable(v_init, name="gdo/v")
w = math_ops.multiply(u, v, name="gdo/w")
# gdo stands for GradientDescentOptimizer.
train_op = gradient_descent.GradientDescentOptimizer(
learning_rate=0.1).minimize(w, name="gdo/train")
u.initializer.run()
v.initializer.run()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_ops=["DebugIdentity"],
debug_urls=self._debug_urls())
run_metadata = config_pb2.RunMetadata()
sess.run(train_op, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
update_u_data = dump.watch_key_to_data(
"gdo/train/update_gdo/u/ApplyGradientDescent:0:DebugIdentity")
self.assertEqual(1, len(update_u_data))
# Gradient descent on u: w = u * v, so dw / du = v.
# Updated value of u should be:
# 10.0 - learning_rate * v = 10.0 - 0.1 * 20.0 = 8.0
self.assertAllClose(8.0, update_u_data[0].get_tensor())
update_v_data = dump.watch_key_to_data(
"gdo/train/update_gdo/v/ApplyGradientDescent:0:DebugIdentity")
self.assertEqual(1, len(update_v_data))
# Gradient descent on u: w = u * v, so dw / dv = u.
# Updated value of u should be:
# 20.0 - learning_rate * u = 20.0 - 0.1 * 10.0 = 19.0
self.assertAllClose(19.0, update_v_data[0].get_tensor())
# Verify that the Variables u and v are updated properly.
self.assertAllClose(8.0, sess.run(u))
self.assertAllClose(19.0, sess.run(v))
def testDumpingOnASingleRunWorksWithRelativePathForDebugDumpDir(self):
sess = dumping_wrapper.DumpingDebugWrapperSession(
self.sess, session_root=self.session_root, log_usage=False)
sess.run(self.inc_v)
dump_dirs = glob.glob(os.path.join(self.session_root, "run_*"))
cwd = os.getcwd()
try:
os.chdir(self.session_root)
dump = debug_data.DebugDumpDir(
os.path.relpath(dump_dirs[0], self.session_root))
self.assertAllClose([10.0], dump.get_tensors("v", 0, "DebugIdentity"))
finally:
os.chdir(cwd)
def testGradientsValuesFromDumpWorks(self):
y = math_ops.add(self.w, -1.0, name="y")
z = math_ops.square(y, name="z")
grad_debugger = debug_gradients.GradientsDebugger()
with grad_debugger.watch_gradients_by_tensors(self.sess.graph,
[self.w, self.u, y]):
train_op = gradient_descent.GradientDescentOptimizer(0.1).minimize(
z)
self.sess.run(variables.global_variables_initializer())
run_options = config_pb2.RunOptions(output_partition_graphs=True)
dump_dir = tempfile.mkdtemp()
debug_url = "file://" + dump_dir
debug_utils.watch_graph(run_options,
self.sess.graph,
debug_urls=debug_url)
run_metadata = config_pb2.RunMetadata()
self.assertAllClose(2.0, self.sess.run(self.u))
self.sess.run(train_op, options=run_options, run_metadata=run_metadata)
self.assertAllClose(-1.0, self.sess.run(self.u))
dump = debug_data.DebugDumpDir(
dump_dir, partition_graphs=run_metadata.partition_graphs)
dump.set_python_graph(self.sess.graph)
y_grad_values = debug_gradients.gradient_values_from_dump(
grad_debugger, y, dump)
self.assertEqual(1, len(y_grad_values))
self.assertAllClose(10.0, y_grad_values[0])
w_grad_values = debug_gradients.gradient_values_from_dump(
grad_debugger, self.w, dump)
self.assertEqual(1, len(w_grad_values))
self.assertAllClose(10.0, w_grad_values[0])
u_grad_values = debug_gradients.gradient_values_from_dump(
grad_debugger, self.u, dump)
self.assertEqual(1, len(u_grad_values))
self.assertAllClose(30.0, u_grad_values[0])
with self.assertRaisesRegexp(
LookupError,
r"This GradientsDebugger has not received any gradient tensor for "
r"x-tensor v:0"):
debug_gradients.gradient_values_from_dump(grad_debugger, self.v,
dump)
# Cleanup.
shutil.rmtree(dump_dir)
def testDumpingDebugHookWithoutWatchFnWorks(self):
dumping_hook = hooks.DumpingDebugHook(self.session_root, log_usage=False)
mon_sess = monitored_session._HookedSession(self.sess, [dumping_hook])
mon_sess.run(self.inc_v)
dump_dirs = glob.glob(os.path.join(self.session_root, "run_*"))
self.assertEqual(1, len(dump_dirs))
self._assert_correct_run_subdir_naming(os.path.basename(dump_dirs[0]))
dump = debug_data.DebugDumpDir(dump_dirs[0])
self.assertAllClose([10.0], dump.get_tensors("v", 0, "DebugIdentity"))
self.assertEqual(repr(self.inc_v), dump.run_fetches_info)
self.assertEqual(repr(None), dump.run_feed_keys_info)
def testLookUpNodePythonTracebackWorks(self):
with session.Session() as sess:
u_init = constant_op.constant(10.0)
u = variables.Variable(u_init, name="traceback/u")
v_init = constant_op.constant(20.0)
v = variables.Variable(v_init, name="traceback/v")
w = math_ops.multiply(u, v, name="traceback/w")
sess.run(variables.global_variables_initializer())
run_metadata = config_pb2.RunMetadata()
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_urls=self._debug_urls())
sess.run(w, options=run_options, run_metadata=run_metadata)
dump = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
# Prior to setting the Python graph, attempts to do traceback lookup
# should lead to exceptions.
with self.assertRaisesRegexp(
LookupError,
"Python graph is not available for traceback lookup"):
dump.node_traceback("traceback/w")
dump.set_python_graph(sess.graph)
# After setting the Python graph, attempts to look up nonexistent nodes
# should lead to exceptions.
with self.assertRaisesRegexp(
KeyError, r"Cannot find node \"foo\" in Python graph"):
dump.node_traceback("foo")
# Lookup should work with node name input.
traceback = dump.node_traceback("traceback/w")
self.assertIsInstance(traceback, list)
self.assertGreater(len(traceback), 0)
for trace in traceback:
self.assertIsInstance(trace, tuple)
# Lookup should also work with tensor name input.
traceback = dump.node_traceback("traceback/w:0")
self.assertIsInstance(traceback, list)
self.assertGreater(len(traceback), 0)
for trace in traceback:
self.assertIsInstance(trace, tuple)
def testDumpingOnASingleRunWorks(self):
sess = dumping_wrapper.DumpingDebugWrapperSession(
self.sess, session_root=self.session_root, log_usage=False)
sess.run(self.inc_v)
dump_dirs = glob.glob(os.path.join(self.session_root, "run_*"))
self.assertEqual(1, len(dump_dirs))
self._assert_correct_run_subdir_naming(os.path.basename(dump_dirs[0]))
dump = debug_data.DebugDumpDir(dump_dirs[0])
self.assertAllClose([10.0], dump.get_tensors("v", 0, "DebugIdentity"))
self.assertEqual(repr(self.inc_v), dump.run_fetches_info)
self.assertEqual(repr(None), dump.run_feed_keys_info)
def testMultiGPUSessionRun(self):
local_devices = device_lib.list_local_devices()
gpu_device_names = []
for device in local_devices:
if device.device_type == "GPU":
gpu_device_names.append(device.name)
gpu_device_names = sorted(gpu_device_names)
if len(gpu_device_names) < 2:
self.skipTest(
"This test requires at least 2 GPUs, but only %d is available."
% len(gpu_device_names))
with session.Session() as sess:
v = variables.Variable([10.0, 15.0],
dtype=dtypes.float32,
name="v")
with ops.device(gpu_device_names[0]):
u0 = math_ops.add(v, v, name="u0")
with ops.device(gpu_device_names[1]):
u1 = math_ops.multiply(v, v, name="u1")
w = math_ops.subtract(u1, u0, name="w")
sess.run(v.initializer)
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_urls="file://" + self._dump_root)
run_metadata = config_pb2.RunMetadata()
self.assertAllClose([80.0, 195.0],
sess.run(w,
options=run_options,
run_metadata=run_metadata))
debug_dump_dir = debug_data.DebugDumpDir(
self._dump_root,
partition_graphs=run_metadata.partition_graphs)
self.assertEqual(3, len(debug_dump_dir.devices()))
self.assertAllClose([10.0, 15.0],
debug_dump_dir.get_tensors(
"v", 0, "DebugIdentity")[0])
self.assertAllClose([20.0, 30.0],
debug_dump_dir.get_tensors(
"u0", 0, "DebugIdentity")[0])
self.assertAllClose([100.0, 225.0],
debug_dump_dir.get_tensors(
"u1", 0, "DebugIdentity")[0])
def _compareOriginalAndReconstructedGraphDefs(self,
sess,
fetches,
feed_dict=None,
expected_output=None):
run_options = config_pb2.RunOptions(output_partition_graphs=True)
run_metadata = config_pb2.RunMetadata()
output = sess.run(fetches,
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
if expected_output is not None:
self.assertAllClose(expected_output, output)
non_debug_graph_defs = run_metadata.partition_graphs
debug_utils.watch_graph(run_options,
sess.graph,
debug_urls=self._debug_url)
run_metadata = config_pb2.RunMetadata()
output = sess.run(fetches,
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
if expected_output is not None:
self.assertAllClose(expected_output, output)
dump = debug_data.DebugDumpDir(
self._dump_dir,
partition_graphs=run_metadata.partition_graphs,
validate=True)
reconstructed = dump.reconstructed_non_debug_partition_graphs()
self.assertEqual(len(non_debug_graph_defs), len(reconstructed))
for i, non_debug_graph_def in enumerate(non_debug_graph_defs):
device_name = debug_graphs._infer_device_name(non_debug_graph_def)
test_util.assert_equal_graph_def(
self._graphDefWithoutBlacklistedNodes(
reconstructed[device_name]),
self._graphDefWithoutBlacklistedNodes(non_debug_graph_def))
# Test debug_graphs.reconstruct_non_debug_graph_def.
reconstructed_again = (
debug_graphs.reconstruct_non_debug_graph_def(
run_metadata.partition_graphs[i]))
test_util.assert_equal_graph_def(
self._graphDefWithoutBlacklistedNodes(reconstructed_again),
self._graphDefWithoutBlacklistedNodes(non_debug_graph_def))
def createAndRunGraphHelper(self):
"""Create and run a TensorFlow Graph to generate debug dumps.
This is intentionally done in separate method, to make it easier to test
the stack-top mode of source annotation.
"""
self.dump_root = self.get_temp_dir()
self.curr_file_path = os.path.abspath(
tf_inspect.getfile(tf_inspect.currentframe()))
# Run a simple TF graph to generate some debug dumps that can be used in
# source annotation.
with session.Session() as sess:
self.u_init = constant_op.constant(np.array([[5.0, 3.0],
[-1.0, 0.0]]),
shape=[2, 2],
name="u_init")
self.u_init_line_number = line_number_above()
self.u = variables.Variable(self.u_init, name="u")
self.u_line_number = line_number_above()
self.v_init = constant_op.constant(np.array([[2.0], [-1.0]]),
shape=[2, 1],
name="v_init")
self.v_init_line_number = line_number_above()
self.v = variables.Variable(self.v_init, name="v")
self.v_line_number = line_number_above()
self.w = math_ops.matmul(self.u, self.v, name="w")
self.w_line_number = line_number_above()
self.evaluate(self.u.initializer)
self.evaluate(self.v.initializer)
run_options = config_pb2.RunOptions(output_partition_graphs=True)
debug_utils.watch_graph(run_options,
sess.graph,
debug_urls=["file://%s" % self.dump_root])
run_metadata = config_pb2.RunMetadata()
sess.run(self.w, options=run_options, run_metadata=run_metadata)
self.dump = debug_data.DebugDumpDir(
self.dump_root, partition_graphs=run_metadata.partition_graphs)
self.dump.set_python_graph(sess.graph)
def testDumpingOnMultipleRunsWorks(self):
sess = dumping_wrapper.DumpingDebugWrapperSession(
self.sess, session_root=self.session_root, log_usage=False)
for _ in range(3):
sess.run(self.inc_v)
dump_dirs = glob.glob(os.path.join(self.session_root, "run_*"))
dump_dirs = sorted(
dump_dirs, key=lambda x: int(os.path.basename(x).split("_")[1]))
self.assertEqual(3, len(dump_dirs))
for i, dump_dir in enumerate(dump_dirs):
self._assert_correct_run_subdir_naming(os.path.basename(dump_dir))
dump = debug_data.DebugDumpDir(dump_dir)
self.assertAllClose([10.0 + 1.0 * i],
dump.get_tensors("v", 0, "DebugIdentity"))
self.assertEqual(repr(self.inc_v), dump.run_fetches_info)
self.assertEqual(repr(None), dump.run_feed_keys_info)
