def test_profile(self):
profiler.start()
with traceme.TraceMe('three_times_five'):
three = constant_op.constant(3)
five = constant_op.constant(5)
product = three * five
self.assertAllEqual(15, product)
with self.assertRaises(profiler.ProfilerAlreadyRunningError):
profiler.start()
profile_result = profiler.stop()
profile_pb = trace_events_pb2.Trace()
profile_pb.ParseFromString(profile_result)
devices = frozenset(device.name
for device in profile_pb.devices.values())
self.assertIn('/host:CPU', devices)
if not test_util.IsBuiltWithROCm() and config.list_physical_devices(
'GPU'):
# device tracing is not yet supported on the ROCm platform
self.assertIn('/device:GPU:0', devices)
events = frozenset(event.name for event in profile_pb.trace_events)
self.assertIn('three_times_five', events)
self.assertIn('Mul', events)
with self.assertRaises(profiler.ProfilerNotRunningError):
profiler.stop()
def benchmark_keras_model_functional_fit_graph_mode_with_profiler(self):
profiler.start()
with context.graph_mode():
model = make_keras_model(initializer="glorot_uniform")
self._benchmark_keras_model_fit(model)
result = profiler.stop()
assert result is not None
def trace_on(graph=True, profiler=False): # pylint: disable=redefined-outer-name
"""Enables execution trace.
Args:
graph: whether to collect graphs used in execution
profiler: whether to enable profiler.
Returns:
None
"""
if not context.context().executing_eagerly():
logging.warn("Must enable trace in eager mode.")
return
global _current_trace_context
with _current_trace_context_lock:
if _current_trace_context:
logging.warn("Trace already enabled")
return
if graph and not profiler:
context.context().enable_graph_collection()
if profiler:
context.context().enable_run_metadata()
_profiler.start()
_current_trace_context = _TraceContext(graph=graph, profiler=profiler)
def benchmark_keras_model_functional_fit_graph_mode_with_profiler(self):
profiler.start()
with context.graph_mode():
model = make_keras_model(initializer="glorot_uniform")
self._benchmark_keras_model_fit(model)
result = profiler.stop()
assert result is not None
def benchmark_keras_model_functional_fit_run_model_eagerly_with_profiler(
self):
profiler.start()
model = make_keras_model(initializer="glorot_uniform")
self._benchmark_keras_model_fit(model, run_eagerly=True)
result = profiler.stop()
assert result is not None
def main():
profile = None
if "--run-cProfile" in sys.argv:
import cProfile
import pstats
profile_file = "cProfile.log"
print("Running cProfile, writing output to '{}'".format(profile_file), file=sys.stderr)
profile = cProfile.Profile()
profile.enable()
parser = create_argparser()
if len(sys.argv) < 2:
parser.error("Too few arguments, run '{} --help' for more information.".format(lidbox.__name__))
# TODO when a subcommand is used incorrectly, get usage strings for its subparser instead of the root parser
args = parser.parse_args()
tf_profiler = None
if args.run_tf_profiler:
from tensorflow.python.eager import profiler as tf_profiler
tf_profile_file = os.path.abspath(os.path.join("tf_profile", str(int(time.time()))))
print("Running TensorFlow profiler, writing output to '{}'".format(tf_profile_file), file=sys.stderr)
tf_profiler.start()
ret = 1
try:
# Initialize a Command object from the class specified in args.cmd_class and remove the class from args
command = args.__dict__.pop("cmd_class")(args)
ret = command.run()
finally:
if tf_profiler:
tf_profiler_result = tf_profiler.stop()
tf_profiler.save(tf_profile_file, tf_profiler_result)
if profile:
profile.disable()
with open(profile_file, "w") as out_f:
pstats.Stats(profile, stream=out_f).sort_stats("tottime").print_stats()
if ret:
sys.exit(ret)
def benchmark_keras_model_functional_fit_run_model_eagerly_with_profiler(
self):
profiler.start()
model = make_keras_model(initializer="glorot_uniform")
self._benchmark_keras_model_fit(model, run_eagerly=True)
result = profiler.stop()
assert result is not None
def _start_profiler():
from tensorflow.python.eager import profiler # pylint: disable=g-import-not-at-top
try:
logging.info('Starting profiler')
profiler.start()
except Exception: # pylint: disable=W0703
logging.error('Profiler failed to start due to error:\n %s',
traceback.format_exc())
def test_profile_exceptions(self):
logdir = self.get_temp_dir()
profiler.start(logdir)
with self.assertRaises(errors.AlreadyExistsError):
profiler.start(logdir)
profiler.stop()
with self.assertRaises(errors.UnavailableError):
profiler.stop()
def _start_profiler():
from tensorflow.python.eager import profiler # pylint: disable=g-import-not-at-top
try:
profiler.start()
logging.info('Started TensorFlow profiler')
except Exception: # pylint: disable=broad-except
logging.error('TensorFlow profiler failed to start due to error:\n %s',
traceback.format_exc())
def test_profile(self):
profiler.start()
three = constant_op.constant(3)
five = constant_op.constant(5)
product = three * five
self.assertAllEqual(15, product)
profile_result = profiler.stop()
profile_pb = trace_events_pb2.Trace()
profile_pb.ParseFromString(profile_result)
profile_pb_str = '%s' % profile_pb
self.assertTrue('Mul' in profile_pb_str)
def test_profile(self):
profiler.start()
three = constant_op.constant(3)
five = constant_op.constant(5)
product = three * five
self.assertAllEqual(15, product)
profile_result = profiler.stop()
profile_pb = trace_events_pb2.Trace()
profile_pb.ParseFromString(profile_result)
profile_pb_str = '%s' % profile_pb
self.assertTrue('Mul' in profile_pb_str)
def test_save_profile(self):
logdir = self.get_temp_dir()
profiler.start(logdir)
with traceme.TraceMe('three_times_five'):
three = constant_op.constant(3)
five = constant_op.constant(5)
product = three * five
self.assertAllEqual(15, product)
profiler.stop()
file_list = gfile.ListDirectory(logdir)
self.assertEqual(len(file_list), 2)
for file_name in gfile.ListDirectory(logdir):
if gfile.IsDirectory(os.path.join(logdir, file_name)):
self.assertEqual(file_name, 'plugins')
else:
self.assertTrue(file_name.endswith('.profile-empty'))
profile_dir = os.path.join(logdir, 'plugins', 'profile')
run = gfile.ListDirectory(profile_dir)[0]
hostname = socket.gethostname()
overview_page = os.path.join(profile_dir, run,
hostname + '.overview_page.pb')
self.assertTrue(gfile.Exists(overview_page))
input_pipeline = os.path.join(profile_dir, run,
hostname + '.input_pipeline.pb')
self.assertTrue(gfile.Exists(input_pipeline))
tensorflow_stats = os.path.join(profile_dir, run,
hostname + '.tensorflow_stats.pb')
self.assertTrue(gfile.Exists(tensorflow_stats))
trace_file = os.path.join(profile_dir, run, hostname + '.trace')
self.assertTrue(gfile.Exists(trace_file))
with gfile.Open(trace_file, 'rb') as f:
profile_pb = trace_events_pb2.Trace()
profile_pb.ParseFromString(f.read())
devices = frozenset(device.name
for device in profile_pb.devices.values())
self.assertIn('/host:CPU', devices)
if config.list_physical_devices('GPU'):
self.assertIn('/device:GPU:0', devices)
events = frozenset(event.name for event in profile_pb.trace_events)
self.assertIn('three_times_five', events)
self.assertIn('Mul:Mul', events)
def trace_on(graph=True, profiler=False): # pylint: disable=redefined-outer-name
"""Starts a trace to record computation graphs and profiling information.
Must be invoked in eager mode.
When enabled, TensorFlow runtime will collection information that can later be
exported and consumed by TensorBoard. The trace is activated across the entire
TensorFlow runtime and affects all threads of execution.
To stop the trace and export the collected information, use
`tf.summary.trace_export`. To stop the trace without exporting, use
`tf.summary.trace_off`.
Args:
graph: If True, enables collection of executed graphs. It includes ones from
tf.function invocation and ones from the legacy graph mode. The default
is True.
profiler: If True, enables the advanced profiler. Enabling profiler
implicitly enables the graph collection. The profiler may incur a high
memory overhead. The default is False.
"""
if ops.inside_function():
logging.warn("Cannot enable trace inside a tf.function.")
return
if not context.executing_eagerly():
logging.warn("Must enable trace in eager mode.")
return
global _current_trace_context
with _current_trace_context_lock:
if _current_trace_context:
logging.warn("Trace already enabled")
return
if graph and not profiler:
context.context().enable_graph_collection()
if profiler:
context.context().enable_run_metadata()
_profiler.start()
_current_trace_context = _TraceContext(graph=graph, profiler=profiler)
def trace_on(graph=True, profiler=False): # pylint: disable=redefined-outer-name
"""Starts a trace to record computation graphs and profiling information.
Must be invoked in eager mode.
When enabled, TensorFlow runtime will collection information that can later be
exported and consumed by TensorBoard. The trace is activated across the entire
TensorFlow runtime and affects all threads of execution.
To stop the trace and export the collected information, use
`tf.summary.trace_export`. To stop the trace without exporting, use
`tf.summary.trace_off`.
Args:
graph: If True, enables collection of executed graphs. It includes ones from
tf.function invocation and ones from the legacy graph mode. The default
is True.
profiler: If True, enables the advanced profiler. Enabling profiler
implicitly enables the graph collection. The profiler may incur a high
memory overhead. The default is False.
"""
if ops.inside_function():
logging.warn("Cannot enable trace inside a tf.function.")
return
if not context.context().executing_eagerly():
logging.warn("Must enable trace in eager mode.")
return
global _current_trace_context
with _current_trace_context_lock:
if _current_trace_context:
logging.warn("Trace already enabled")
return
if graph and not profiler:
context.context().enable_graph_collection()
if profiler:
context.context().enable_run_metadata()
_profiler.start()
_current_trace_context = _TraceContext(graph=graph, profiler=profiler)
def test_profile(self):
profiler.start()
three = constant_op.constant(3)
five = constant_op.constant(5)
product = three * five
self.assertAllEqual(15, product)
with self.assertRaises(profiler.ProfilerAlreadyRunningError):
profiler.start()
profile_result = profiler.stop()
profile_pb = trace_events_pb2.Trace()
profile_pb.ParseFromString(profile_result)
devices = frozenset(device.name
for device in profile_pb.devices.values())
self.assertIn('/host:CPU', devices)
if config.list_physical_devices('GPU'):
self.assertIn('/device:GPU:0', devices)
events = frozenset(event.name for event in profile_pb.trace_events)
self.assertIn('Mul:Mul', events)
with self.assertRaises(profiler.ProfilerNotRunningError):
profiler.stop()
def custom_training(waveGlow, hparams, optimizer, checkpoint,
manager_checkpoint):
step = tf.cast(checkpoint.step, tf.int64)
for epoch in tf.range(1):
tf.summary.text(name='epoch',
data='Start epoch {}'.format(epoch.numpy()) +\
'at ' + datetime.now().strftime("%Y%m%d-%H%M%S"),
step=step)
for int_step, (step,
x_train) in zip(range(50),
training_dataset.enumerate(start=step)):
if int_step == 2:
profiler.start()
train_step(step=step,
x_train=x_train,
waveGlow=waveGlow,
hparams=hparams,
optimizer=optimizer)
if tf.equal(step % hparams['save_model_every'], 0):
save_path = manager_checkpoint.save()
tf.summary.text(name='save_checkpoint',
data="Saved checkpoint in" + save_path,
step=step)
if tf.equal(step % hparams['save_audio_every'], 0):
utils.eval_step(eval_dataset=validation_dataset,
waveGlow=waveGlow,
hparams=hparams,
step=step)
if int_step == 50:
profiler_result = profiler.stop()
profiler.save(hparams['log_dir'], profiler_result)
break
checkpoint.step.assign_add(1)
def on_batch_end(self, batch, logs=None):
"""Writes scalar summaries for metrics on every training batch.
Performs profiling if current batch is in profiler_batches.
"""
# Don't output batch_size and batch number as TensorBoard summaries
logs = logs or {}
self._samples_seen += logs.get('size', 1)
samples_seen_since = self._samples_seen - self._samples_seen_at_last_write
if self.update_freq != 'epoch' and samples_seen_since >= self.update_freq:
batch_logs = {('batch_' + k): v
for k, v in logs.items()
if k not in ['batch', 'size', 'num_steps']}
self._write_custom_summaries(self._total_batches_seen, batch_logs)
self._samples_seen_at_last_write = self._samples_seen
self._total_batches_seen += 1
if self._is_profiling:
profiler.save(self.log_dir, profiler.stop())
self._is_profiling = False
elif (not self._is_profiling
and self._total_batches_seen == self._profile_batch - 1):
profiler.start()
self._is_profiling = True
def on_batch_end(self, batch, logs=None):
"""Writes scalar summaries for metrics on every training batch.
Performs profiling if current batch is in profiler_batches.
"""
# Don't output batch_size and batch number as TensorBoard summaries
logs = logs or {}
self._samples_seen += logs.get('size', 1)
samples_seen_since = self._samples_seen - self._samples_seen_at_last_write
if self.update_freq != 'epoch' and samples_seen_since >= self.update_freq:
batch_logs = {('batch_' + k): v
for k, v in logs.items()
if k not in ['batch', 'size', 'num_steps']}
self._write_custom_summaries(self._total_batches_seen, batch_logs)
self._samples_seen_at_last_write = self._samples_seen
self._total_batches_seen += 1
if self._is_profiling:
profiler.save(self.log_dir, profiler.stop())
self._is_profiling = False
elif (not self._is_profiling and
self._total_batches_seen == self._profile_batch - 1):
profiler.start()
self._is_profiling = True
def run_profile(net,
train=False,
n_steps=150,
do_profile=True,
reps=1,
dtype="float32",
**kwargs):
"""
Run profiler on a benchmark network.
Parameters
----------
net : `~nengo.Network`
The nengo Network to be profiled.
train : bool
If True, profile the `.Simulator.fit` function. Otherwise, profile the
`.Simulator.run` function.
n_steps : int
The number of timesteps to run the simulation.
do_profile : bool
Whether or not to run profiling
reps : int
Repeat the run this many times (only profile data from the last
run will be kept).
dtype : str
Simulation dtype (e.g. "float32")
Returns
-------
exec_time : float
Time (in seconds) taken to run the benchmark, taking the minimum over
``reps``.
Notes
-----
kwargs will be passed on to `.Simulator`
"""
exec_time = 1e10
n_batches = 1
with net:
nengo_dl.configure_settings(inference_only=not train, dtype=dtype)
with nengo_dl.Simulator(net, **kwargs) as sim:
if hasattr(net, "inp"):
x = {
net.inp:
np.random.randn(sim.minibatch_size * n_batches, n_steps,
net.inp.size_out)
}
elif hasattr(net, "inp_a"):
x = {
net.inp_a:
np.random.randn(sim.minibatch_size * n_batches, n_steps,
net.inp_a.size_out),
net.inp_b:
np.random.randn(sim.minibatch_size * n_batches, n_steps,
net.inp_b.size_out),
}
else:
x = None
if train:
y = {
net.p:
np.random.randn(sim.minibatch_size * n_batches, n_steps,
net.p.size_in)
}
sim.compile(tf.optimizers.SGD(0.001), loss=tf.losses.mse)
# run once to eliminate startup overhead
start = timeit.default_timer()
sim.fit(x, y, epochs=1, n_steps=n_steps)
print("Warmup time:", timeit.default_timer() - start)
for _ in range(reps):
if do_profile:
profiler.start()
start = timeit.default_timer()
sim.fit(x, y, epochs=1, n_steps=n_steps)
exec_time = min(timeit.default_timer() - start, exec_time)
if do_profile:
profiler.save("profile", profiler.stop())
else:
# run once to eliminate startup overhead
start = timeit.default_timer()
sim.predict(x, n_steps=n_steps)
print("Warmup time:", timeit.default_timer() - start)
for _ in range(reps):
if do_profile:
profiler.start()
start = timeit.default_timer()
sim.predict(x, n_steps=n_steps)
exec_time = min(timeit.default_timer() - start, exec_time)
if do_profile:
profiler.save("profile", profiler.stop())
exec_time /= n_batches
print("Execution time:", exec_time)
return exec_time
def run(flags_obj):
"""Run ResNet ImageNet training and eval loop using native Keras APIs.
Args:
flags_obj: An object containing parsed flag values.
Raises:
ValueError: If fp16 is passed as it is not currently supported.
Returns:
Dictionary of training and eval stats.
"""
# TODO(tobyboyd): Remove eager flag when tf 1.0 testing ends.
# Eager is default in tf 2.0 and should not be toggled
if keras_common.is_v2_0():
keras_common.set_config_v2()
else:
config = keras_common.get_config_proto_v1()
if flags_obj.enable_eager:
tf.compat.v1.enable_eager_execution(config=config)
else:
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
# Execute flag override logic for better model performance
if flags_obj.tf_gpu_thread_mode:
keras_common.set_gpu_thread_mode_and_count(flags_obj)
dtype = flags_core.get_tf_dtype(flags_obj)
if dtype == 'float16':
policy = tf.keras.mixed_precision.experimental.Policy('infer_float32_vars')
tf.keras.mixed_precision.experimental.set_policy(policy)
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
tf.keras.backend.set_image_data_format(data_format)
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_obj.num_gpus,
num_workers=distribution_utils.configure_cluster())
strategy_scope = keras_common.get_strategy_scope(strategy)
# pylint: disable=protected-access
if flags_obj.use_synthetic_data:
distribution_utils.set_up_synthetic_data()
input_fn = keras_common.get_synth_input_fn(
height=imagenet_main.DEFAULT_IMAGE_SIZE,
width=imagenet_main.DEFAULT_IMAGE_SIZE,
num_channels=imagenet_main.NUM_CHANNELS,
num_classes=imagenet_main.NUM_CLASSES,
dtype=dtype)
else:
distribution_utils.undo_set_up_synthetic_data()
input_fn = imagenet_main.input_fn
train_input_dataset = input_fn(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=parse_record_keras,
datasets_num_private_threads=flags_obj.datasets_num_private_threads,
dtype=dtype)
eval_input_dataset = None
if not flags_obj.skip_eval:
eval_input_dataset = input_fn(
is_training=False,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=parse_record_keras,
dtype=dtype)
with strategy_scope:
optimizer = keras_common.get_optimizer()
if dtype == 'float16':
# TODO(reedwm): Remove manually wrapping optimizer once mixed precision
# can be enabled with a single line of code.
optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer, loss_scale=flags_core.get_loss_scale(flags_obj))
if flags_obj.use_trivial_model:
model = trivial_model.trivial_model(imagenet_main.NUM_CLASSES)
else:
model = resnet_model.resnet50(num_classes=imagenet_main.NUM_CLASSES,
dtype=dtype)
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=['sparse_categorical_accuracy'])
time_callback, tensorboard_callback, lr_callback = keras_common.get_callbacks(
learning_rate_schedule, imagenet_main.NUM_IMAGES['train'])
train_steps = imagenet_main.NUM_IMAGES['train'] // flags_obj.batch_size
train_epochs = flags_obj.train_epochs
if flags_obj.train_steps:
train_steps = min(flags_obj.train_steps, train_steps)
train_epochs = 1
num_eval_steps = (imagenet_main.NUM_IMAGES['validation'] //
flags_obj.batch_size)
validation_data = eval_input_dataset
if flags_obj.skip_eval:
# Only build the training graph. This reduces memory usage introduced by
# control flow ops in layers that have different implementations for
# training and inference (e.g., batch norm).
tf.keras.backend.set_learning_phase(1)
num_eval_steps = None
validation_data = None
callbacks = [time_callback, lr_callback]
if flags_obj.enable_tensorboard:
callbacks.append(tensorboard_callback)
if flags_obj.enable_e2e_xprof:
profiler.start()
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=validation_data,
validation_freq=flags_obj.epochs_between_evals,
verbose=2)
if flags_obj.enable_e2e_xprof:
results = profiler.stop()
profiler.save(flags_obj.model_dir, results)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
stats = keras_common.build_stats(history, eval_output, time_callback)
return stats
def on_batch_begin(self, batch, logs=None):
if batch == self.start_step:
profiler.start()
tf.compat.v1.logging.info('Profiler started at Step %s', self.start_step)
def on_batch_begin(self, batch, logs=None):
if batch == self.start_step:
profiler.start()
tf.compat.v1.logging.info('Profiler started at Step %s', self.start_step)
def on_train_begin(self, logs=None):
if self._profile_batch == 1:
profiler.start()
self._is_profiling = True
def on_train_begin(self, logs=None):
if self._profile_batch == 1:
profiler.start()
self._is_profiling = True
def on_batch_begin(self, batch, logs=None):
if batch == self.start_step_in_epoch and self.should_start:
self.should_start = False
profiler.start()
tf.compat.v1.logging.info('Profiler started at Step %s',
self.start_step)
value_grad = tf.random.normal(value.shape, dtype=value.dtype)
v = tf.reduce_sum(value * value_grad)
grad, = tf.gradients(v, feature)
return grad, value_grad
@tf.function
def get_gradient_gt(feature, coordinate, value_grad):
value = resampler.resampler(feature, coordinate)
v = tf.reduce_sum(value * value_grad)
grad, = tf.gradients(v, feature)
return grad
mask, value = resample_image(feature, coordinate, tfnative=TFNATIVE)
gt = resampler.resampler(feature, coordinate)
grad, value_grad = get_gradient(feature, coordinate, TFNATIVE)
value_grad = tf.where(mask[..., None], value_grad, 0)
grad_gt = get_gradient_gt(feature, coordinate, value_grad)
profiler.start()
for i in range(3):
# feature_grad, _ = get_gradient(feature, coordinate, tfnative=TFNATIVE)
# print(feature_grad.shape)
mask, value = resample_image(feature, coordinate, tfnative=TFNATIVE)
profiler_result = profiler.stop()
profiler.save('./profile.log', profiler_result)
