def test_find_layer(self):
model_def = "mnist_functional_api.mnist_functional_api.custom_model"
model = _create_model_instance(model_def)
layer_num = {
tf.keras.layers.Conv2D: 2,
tf.keras.layers.Dropout: 1,
tf.keras.layers.Embedding: 0,
}
for layer_class in layer_num:
layers = find_layer(model, layer_class)
self.assertEqual(layer_num[layer_class], len(layers))
def test_find_layer_nested(self):
model_def = "resnet50_subclass.resnet50_subclass.CustomModel"
model = _create_model_instance(model_def)
layer_num = {
tf.keras.layers.Conv2D: 53,
tf.keras.layers.Activation: 50,
tf.keras.layers.Embedding: 0,
}
for layer_class in layer_num:
layers = find_layer(model, layer_class)
self.assertEqual(layer_num[layer_class], len(layers))
def _test_correctness(self, optimizer_class, X, Y, seed, **opt_kwargs):
"""Test the correctness of specific TensorFlow optimizer."""
_model_file = get_module_file_path(
os.path.dirname(os.path.realpath(__file__)),
"embedding_test_module.KerasEmbeddingModel",
)
model_module = load_module(_model_file).__dict__
# train model with TensorFlow optimizer
dim = 4
weights = self._random_init_model_weight([(4, dim), (4, dim), (72, 1),
(1, )], seed)
loss_fn = model_module["loss"]
model1 = model_module["KerasEmbeddingModel"](4, dim, weights)
opt1 = optimizer_class(**opt_kwargs)
_train(model1, opt1, X, Y, loss_fn, random_seed=seed)
model2 = model_module["EdlEmbeddingModel"](dim, weights[2:])
opt2 = optimizer_class(**opt_kwargs)
layer_names = [layer.name for layer in find_layer(model2, Embedding)]
# create Parameters object and initialize embedding vectors
params = Parameters()
for layer_name, embed_value in zip(layer_names, weights[:2]):
embed_table = EmbeddingTable(layer_name, dim)
embed_table.set(range(len(embed_value)), embed_value)
params.embedding_params[layer_name] = embed_table
_train_edl_embedding_with_optimizer_wrapper(model2,
opt2,
X,
Y,
loss_fn,
params,
random_seed=seed)
# compare trained parameters
wrong_msg = (
"The updated parameters of Optimizer Wrapper and TensorFlow "
"optimizer %s differ." % opt1.get_config()["name"])
for layer1, layer2 in zip(model1.layers, model2.layers):
if "embedding" in layer2.name:
w1 = layer1.weights[0].numpy()
w2 = params.get_embedding_param(layer2.name, range(4))
self.assertTrue(np.isclose(w1, w2).all(), msg=wrong_msg)
else:
for w1, w2 in zip(layer1.weights, layer2.weights):
self.assertTrue(np.isclose(w1.numpy(), w2.numpy()).all(),
msg=wrong_msg)
def main():
args = parse_master_args()
logger = get_logger("master", level=args.log_level.upper())
# Master addr
master_ip = os.getenv("MY_POD_IP", "localhost")
master_addr = "%s:%d" % (master_ip, args.port)
# Start TensorBoard service if requested
if args.tensorboard_log_dir:
logger.info(
"Starting TensorBoard service with log directory %s",
args.tensorboard_log_dir,
)
# Start TensorBoard CLI
tb_service = TensorboardService(args.tensorboard_log_dir, master_ip)
tb_service.start()
else:
tb_service = None
# Start task queue
logger.debug(
"Starting task queue with training data directory %s, "
"evaluation data directory %s, "
"and prediction data directory %s",
args.training_data_dir,
args.evaluation_data_dir,
args.prediction_data_dir,
)
records_per_task = args.minibatch_size * args.num_minibatches_per_task
task_d = _make_task_dispatcher(
args.training_data_dir,
args.evaluation_data_dir,
args.prediction_data_dir,
records_per_task,
args.num_epochs,
)
model_module = load_module(
get_module_file_path(args.model_zoo, args.model_def)
).__dict__
model_inst = load_model_from_module(
args.model_def, model_module, args.model_params
)
optimizer = model_module[args.optimizer]()
if all(
(
args.training_data_dir,
args.evaluation_data_dir,
args.evaluation_throttle_secs or args.evaluation_steps,
)
):
job_type = JobType.TRAINING_WITH_EVALUATION
elif all(
(
args.evaluation_data_dir,
not args.training_data_dir,
not args.prediction_data_dir,
)
):
job_type = JobType.EVALUATION_ONLY
elif all(
(
args.prediction_data_dir,
not args.evaluation_data_dir,
not args.training_data_dir,
)
):
job_type = JobType.PREDICTION_ONLY
else:
job_type = JobType.TRAINING_ONLY
# Initialize checkpoint service
if args.checkpoint_steps or job_type == JobType.TRAINING_WITH_EVALUATION:
logger.info("Starting checkpoint service")
checkpoint_service = CheckpointService(
args.checkpoint_dir,
args.checkpoint_steps,
args.keep_checkpoint_max,
job_type == JobType.TRAINING_WITH_EVALUATION,
)
else:
checkpoint_service = None
# Initialize evaluation service
evaluation_service = None
if (
job_type == JobType.TRAINING_WITH_EVALUATION
or job_type == JobType.EVALUATION_ONLY
):
logger.info(
"Starting evaluation service with throttle seconds %d "
" and evaluation steps %d",
args.evaluation_throttle_secs,
args.evaluation_steps,
)
evaluation_service = EvaluationService(
checkpoint_service,
tb_service,
task_d,
args.evaluation_start_delay_secs,
args.evaluation_throttle_secs,
args.evaluation_steps,
job_type == JobType.EVALUATION_ONLY,
)
evaluation_service.start()
task_d.set_evaluation_service(evaluation_service)
embedding_service_endpoint = None
embedding_dims = {}
# Search for embedding layers in the model,
# if found, initialize embedding service
layers = find_layer(model_inst, Embedding)
if layers:
embedding_service = EmbeddingService()
embedding_service_endpoint = embedding_service.start_embedding_service(
job_name=args.job_name,
image_name=args.worker_image,
namespace=args.namespace,
resource_request=args.master_resource_request,
resource_limit=args.master_resource_limit,
pod_priority=args.worker_pod_priority,
volume=args.volume,
image_pull_policy=args.image_pull_policy,
restart_policy=args.restart_policy,
cluster_spec=args.cluster_spec,
)
logger.info(
"Embedding service start succeeded. The endpoint is %s."
% str(embedding_service_endpoint)
)
embedding_dims = dict(
[(layer.name, layer.output_dim) for layer in layers]
)
# The master service
logger.info("Starting master service")
server = grpc.server(
futures.ThreadPoolExecutor(max_workers=64),
options=[
("grpc.max_send_message_length", GRPC.MAX_SEND_MESSAGE_LENGTH),
(
"grpc.max_receive_message_length",
GRPC.MAX_RECEIVE_MESSAGE_LENGTH,
),
],
)
master_servicer = MasterServicer(
args.grads_to_wait,
args.minibatch_size,
optimizer,
task_d,
init_var=model_inst.trainable_variables if model_inst.built else [],
embedding_dims=embedding_dims,
checkpoint_filename_for_init=args.checkpoint_filename_for_init,
checkpoint_service=checkpoint_service,
evaluation_service=evaluation_service,
embedding_service_endpoint=embedding_service_endpoint,
lr_staleness_modulation=args.lr_staleness_modulation,
use_async=args.use_async,
)
elasticdl_pb2_grpc.add_MasterServicer_to_server(master_servicer, server)
server.add_insecure_port("[::]:{}".format(args.port))
server.start()
logger.info("Server started at port: %d", args.port)
worker_manager = None
if args.num_workers:
assert args.worker_image, "Worker image cannot be empty"
worker_command = ["python"]
worker_args = [
"-m",
"elasticdl.python.worker.main",
"--master_addr",
master_addr,
"--job_type",
job_type,
"--embedding_service_endpoint",
str(embedding_service_endpoint),
]
worker_args.extend(build_arguments_from_parsed_result(args))
env_dict = parse_envs(args.envs)
env = []
for key in env_dict:
env.append(V1EnvVar(name=key, value=env_dict[key]))
worker_manager = WorkerManager(
task_d,
job_name=args.job_name,
image_name=args.worker_image,
command=worker_command,
args=worker_args,
namespace=args.namespace,
num_workers=args.num_workers,
worker_resource_request=args.worker_resource_request,
worker_resource_limit=args.worker_resource_limit,
pod_priority=args.worker_pod_priority,
volume=args.volume,
image_pull_policy=args.image_pull_policy,
restart_policy=args.restart_policy,
cluster_spec=args.cluster_spec,
envs=env,
)
worker_manager.update_status(WorkerManagerStatus.PENDING)
logger.info("Launching %d workers", args.num_workers)
worker_manager.start_workers()
worker_manager.update_status(WorkerManagerStatus.RUNNING)
# Start TensorBoard k8s Service if requested
if tb_service:
TensorBoardClient(
job_name=args.job_name,
image_name=args.worker_image,
namespace=args.namespace,
).start_tensorboard_service()
try:
while True:
if task_d.finished():
if worker_manager:
worker_manager.update_status(WorkerManagerStatus.FINISHED)
if args.output:
master_servicer.save_latest_checkpoint(args.output)
break
time.sleep(30)
except KeyboardInterrupt:
logger.warning("Server stopping")
if evaluation_service:
logger.info("Stopping evaluation service")
evaluation_service.stop()
logger.info("Stopping RPC server")
server.stop(0)
# Keep TensorBoard running when all the tasks are finished
if tb_service:
logger.info(
"All tasks finished. Keeping TensorBoard service running..."
)
while True:
if tb_service.is_active():
time.sleep(10)
else:
logger.warning(
"Unable to keep TensorBoard running. "
"It has already terminated"
)
break
logger.info("Master stopped")
def _train_edl_embedding_with_optimizer_wrapper(model, opt_keras, X, Y,
loss_fn, params, random_seed):
"""Train model with optimizer wrapper."""
tf.random.set_seed(random_seed)
opt_wrapper = OptimizerWrapper(
opt_keras,
lookup_embedding_func=params.get_embedding_param,
update_embedding_func=params.set_embedding_param,
)
embed_layers = find_layer(model, Embedding)
# initialize slot params
params.create_slot_params(opt_wrapper.allowed_slot_names,
opt_wrapper.slot_initial_value)
# initialize ElasticDL embedding layer
for layer in embed_layers:
layer.set_lookup_embedding_func(params.get_embedding_param)
# training process
for train_iter, (features, labels) in enumerate(zip(X, Y)):
with tf.GradientTape() as tape:
for layer in embed_layers:
layer.set_tape(tape)
outputs = model.call(features)
loss = loss_fn(outputs, labels)
# Need to get non-embedding variables inside for loop because model
# creates variables after the first time `model.call` is called
if not train_iter:
non_embed_vars = get_non_embedding_trainable_vars(
model, embed_layers)
embed_items = []
for layer in embed_layers:
embed_items.extend([(bet, layer.name, ids)
for bet, ids in layer.embedding_and_ids])
grads = tape.gradient(
loss, non_embed_vars + [var for var, _, _ in embed_items])
# TODO: do not need to merge gradient from the same embedding layer
# after `optimizer_wrapper` support grads_and_vars with duplicated
# layer name
non_embed_vars_n = len(non_embed_vars)
non_embed_grads = grads[:non_embed_vars_n]
embed_grads_dict = {}
for (_, layer_name, ids), grad in zip(embed_items,
grads[non_embed_vars_n:]):
if layer_name in embed_grads_dict:
merged_grads = embed_grads_dict[layer_name]
embed_grads_dict[layer_name] = tf.IndexedSlices(
tf.concat([merged_grads.values, grad.values], axis=0),
tf.concat([merged_grads.indices, ids], axis=0),
)
else:
embed_grads_dict[layer_name] = tf.IndexedSlices(
grad.values, ids)
opt_wrapper.apply_gradients(
list(zip(non_embed_grads, non_embed_vars)) +
[(grad, layer_name)
for layer_name, grad in embed_grads_dict.items()])
for layer in embed_layers:
layer.reset()
