def test_on_train_end(self):
worker = MockWorker()
task_data_service = TaskDataService(worker,
JobType.TRAINING_WITH_EVALUATION)
dataset = tf.data.Dataset.from_tensor_slices(
np.array([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]]))
task_data_service._pending_train_end_callback_task = (
"",
0,
1,
elasticdl_pb2.TRAIN_END_CALLBACK,
)
task_data_service.get_dataset_by_task = mock.Mock(return_value=dataset)
with tempfile.TemporaryDirectory() as temp_dir_name:
checkpoint_dir = os.path.join(temp_dir_name, "checkpoint")
model = custom_model_with_embedding_layer()
save_checkpoint_without_embedding(model, checkpoint_dir)
model_handler = ModelHandler.get_model_handler(
distribution_strategy=DistributionStrategy.PARAMETER_SERVER,
checkpoint_dir=checkpoint_dir,
)
saved_model_exporter = SavedModelExporter(task_data_service,
dataset_fn,
model_handler)
saved_model_path = os.path.join(temp_dir_name, "test_exporter")
params = {"batch_size": 10, "saved_model_path": saved_model_path}
saved_model_exporter.set_params(params)
saved_model_exporter.set_model(model)
saved_model_exporter.on_train_end()
self.assertTrue(os.path.exists(saved_model_path))
self.assertTrue(
os.path.exists(os.path.join(saved_model_path,
"saved_model.pb")))
def __init__(
self,
worker_id,
job_type,
minibatch_size,
model_zoo,
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
channel=None,
embedding_service_endpoint=None,
model_def=None,
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
get_model_steps=1,
):
"""
Arguments:
model_file: A module to define the model
channel: grpc channel
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
"""
self._worker_id = worker_id
self._job_type = job_type
self._minibatch_size = minibatch_size
(
self._model,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=model_zoo,
model_def=model_def,
dataset_fn=dataset_fn,
loss=loss,
optimizer=optimizer,
eval_metrics_fn=eval_metrics_fn,
model_params=model_params,
prediction_outputs_processor=prediction_outputs_processor,
)
self._init_embedding_layer()
self._var_created = self._model.built
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._embedding_service_endpoint = embedding_service_endpoint
self._max_minibatch_retry_num = max_minibatch_retry_num
self._model_version = -1
self._task_data_service = TaskDataService(
self, self._job_type == JobType.TRAINING_WITH_EVALUATION
)
self._get_model_steps = get_model_steps
def _init_task_data_service(self, args):
self._task_data_service = TaskDataService(
self._data_shard_service,
custom_data_reader=self._custom_data_reader,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
data_origin=args.training_data,
)
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
)
self._collective_communicator = (
CollectiveCommunicator() if self._distribution_strategy
== DistributionStrategy.ALLREDUCE else None)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._model_versions_from_ps = [-1 for _ in range(self._ps_num)]
self._task_data_service = TaskDataService(
self,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
)
if self._dataset_fn is None:
if hasattr(self._task_data_service.data_reader,
"default_dataset_fn"):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn())
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn")
self._get_model_steps = args.get_model_steps
if self._get_model_steps > 1:
self._opt = self._opt_fn()
self._non_embed_grads = {}
self._evaluation_result = {}
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
)
self._embedding_service_endpoint = eval(
args.embedding_service_endpoint)
self._distribution_strategy = args.distribution_strategy
self._collective_communicator = (
CollectiveCommunicator() if self._distribution_strategy
== DistributionStrategy.ALLREDUCE else None)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._task_data_service = TaskDataService(
self,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
)
self._get_model_steps = args.get_model_steps
if self._get_model_steps > 1:
self._opt = self._opt_fn()
self._non_embed_grads = None
self._evaluation_result = {}
class Worker(object):
"""ElasticDL worker"""
def __init__(
self,
args,
channel=None,
ps_channels=None,
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
max_allreduce_retry_num=DEFAULT_MAX_ALLREDUCE_RETRY_NUM,
set_parallelism=False,
):
"""
Arguments:
channel: The channel for the gRPC master service.
ps_channels: The PS channels for PS service
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
max_allreduce_retry_num: The maximum number of retries for
allreduce operation if allreduce-based distributed
training strategy is used.
"""
self._args = args
self.logger = get_logger("Worker", level=args.log_level.upper())
if set_parallelism:
# Explicitly setting the parallelism will avoid multi-process hangs
# Maybe due to an unknown bug in Tensorflow?
# Must called before TensorFlow is initialized.
# Not set_parallelism by default to make unittests happy.
num_threads = os.cpu_count()
tf.config.threading.set_inter_op_parallelism_threads(num_threads)
tf.config.threading.set_intra_op_parallelism_threads(num_threads)
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._use_multi_ps = False
self._ps_vars = {}
if isinstance(ps_channels, list):
if len(ps_channels) > 0:
self._use_multi_ps = True
self._ps_stubs = [
elasticdl_pb2_grpc.PserverStub(c) for c in ps_channels
]
self._var_to_ps = {}
self._ps_num = len(self._ps_stubs)
else:
self._ps_num = 0
self._distribution_strategy = args.distribution_strategy
if (
self._distribution_strategy
== DistributionStrategy.PARAMETER_SERVER
and self._use_multi_ps is False
):
raise ValueError(
"PS channels are not set up under parameter server strategy"
)
self._max_minibatch_retry_num = max_minibatch_retry_num
self._max_allreduce_retry_num = max_allreduce_retry_num
self._init_from_args(args)
self._timing = Timing(args.log_level.upper() == "DEBUG", self.logger)
self._log_loss_count = 0
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
self._log_loss_steps = args.log_loss_steps
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
self._callbacks_list,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
callbacks=args.callbacks,
)
self._collective_communicator = (
CollectiveCommunicator(
service_name=args.collective_communicator_service_name
)
if self._distribution_strategy == DistributionStrategy.ALLREDUCE
else None
)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir
)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
if self._distribution_strategy != DistributionStrategy.ALLREDUCE:
self._model_versions_from_ps = [-1 for _ in range(self._ps_num)]
self._task_data_service = TaskDataService(
self,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
data_reader_params=get_dict_from_params_str(
args.data_reader_params
),
data_origin=args.training_data,
)
if self._dataset_fn is None:
if hasattr(
self._task_data_service.data_reader, "default_dataset_fn"
):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn()
)
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn"
)
self._get_model_steps = args.get_model_steps
self._opt = self._opt_fn()
self._model.optimizer = self._opt
self._non_embed_grads = {}
self._evaluation_result = {}
saved_model_exporter = SavedModelExporter(
self._task_data_service, self._dataset_fn, self._model_handler
)
# Place default callbacks at the head to execute them firstly
self._callbacks_list.callbacks.insert(0, saved_model_exporter)
self._callbacks_list.set_model(model_inst)
set_callback_parameters(
self._callbacks_list,
batch_size=args.minibatch_size,
saved_model_path=args.output,
checkpoint_path=args.checkpoint_dir,
)
# TODO: Multiple tests are currently using this function to initialize
# self._model, where the initialization should be done via constructor.
def set_model(self, model_inst):
"""Set model instance to worker."""
self._model = model_inst
self._train_eagerly = False
self._init_embeddings()
self._var_created = self._model.built
self._non_embed_vars = {}
if self._var_created:
for var in get_non_embedding_trainable_vars(
self._model, self._embedding_layers
):
self._non_embed_vars[var.name] = var
if self._use_multi_ps:
self.init_ps_var_partition()
def _init_embedding_layer(self):
"""
Init elasticdl.layers.embedding layer list and assign worker to them
"""
self._embedding_layers = find_layer(self._model, Embedding)
if self._use_multi_ps:
for layer in self._embedding_layers:
layer.set_lookup_embedding_func(self.pull_embedding_vectors)
def _init_embedding_column(self):
self._embedding_columns = []
for layer in self._model.layers:
if isinstance(layer, tf.keras.layers.DenseFeatures):
for column in layer._feature_columns:
if isinstance(column, feature_column.EmbeddingColumn):
self._embedding_columns.append(column)
self.logger.info(
"Initialize ElasticDL EmbeddingColumn:{}".format(
column.name
)
)
if self._use_multi_ps:
for column in self._embedding_columns:
column.set_lookup_embedding_func(self.pull_embedding_vectors)
def _check_name_conflict_of_embedding_layer_and_column(self):
if not self._embedding_layers or not self._embedding_columns:
return
embedding_layer_name_set = set(
[layer.name for layer in self._embedding_layers]
)
embedding_column_name_set = set(
[column.name for column in self._embedding_columns]
)
conflict_name_set = embedding_column_name_set.union(
embedding_layer_name_set
)
if conflict_name_set:
raise Exception(
"Name conflict between embedding layer and column: {}".format(
conflict_name_set
)
)
def _init_embeddings(self):
self._init_embedding_layer()
self._init_embedding_column()
self._check_name_conflict_of_embedding_layer_and_column()
if self._use_multi_ps:
self.report_embedding_info()
self._need_embedding_layer_check = (
True
if self._embedding_layers or self._embedding_columns
else False
)
def _set_tape_for_embedding(self, tape):
for layer in self._embedding_layers:
layer.set_tape(tape)
for column in self._embedding_columns:
column.set_tape(tape)
def _reset_embedding(self):
for layer in self._embedding_layers:
layer.reset()
for column in self._embedding_columns:
column.reset()
def _update_local_model(self):
if not self._non_embed_grads:
return
# Take care of the order of grads and vars if worker modifies
# `_non_embed_vars` during training.
self._opt.apply_gradients(
zip(self._non_embed_grads, self._non_embed_vars.values())
)
self._non_embed_grads = None
def get_task(self, task_type=None):
"""
get task from master
"""
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = self._worker_id
if task_type is not None:
req.task_type = task_type
try:
res = self._stub.get_task(req)
except Exception:
# Master may have stopped GRPC service when there are no more
# tasks. This will result in a GRPC call exception.
self.logger.info(
"Cannot connect to master, assuming no more tasks"
)
res = elasticdl_pb2.Task()
return res
def get_model(self):
self._timing.start_record_time("get_model")
if self._distribution_strategy != DistributionStrategy.ALLREDUCE:
variable_future_and_id_pairs = []
if self._use_multi_ps:
self.init_ps_var_partition()
for ps_id, stub in enumerate(self._ps_stubs):
if ps_id not in self._ps_vars:
continue
# async grpc call
req = elasticdl_pb2.PullDenseParametersRequest()
req.version = self._model_versions_from_ps[ps_id]
var_future = stub.pull_dense_parameters.future(req)
variable_future_and_id_pairs.append((var_future, ps_id))
for var_future, ps_id in variable_future_and_id_pairs:
res = var_future.result()
if not res.initialized:
# push variable to ps for initialization
self.report_variable_to_ps(ps_id)
req = elasticdl_pb2.PullDenseParametersRequest()
req.version = self._model_versions_from_ps[ps_id]
res = self._ps_stubs[ps_id].pull_dense_parameters(req)
if not res.initialized:
# TODO: support PS fault-tolerance
raise RuntimeError(
"PS pod %d cannot be initialized" % ps_id
)
for name, pb in res.dense_parameters.items():
self._non_embed_vars[name].assign(pb_to_ndarray(pb))
self._model_versions_from_ps[ps_id] = res.version
self._model_version = max(self._model_versions_from_ps)
self._timing.end_record_time("get_model")
def pull_embedding_vectors(self, layer_name, embedding_ids):
"""Pulls and returns embedding vectors ordered by the embedding ids."""
ps_ids = {}
ps_ids_index = {}
for idx, embedding_id in enumerate(embedding_ids):
ps_id = int_to_id(embedding_id, self._ps_num)
ps_ids.setdefault(ps_id, []).append(embedding_id)
ps_ids_index.setdefault(ps_id, []).append(idx)
embeddings = []
index = []
pb_future_and_id_pairs = []
for ps_id, embedding_ids in ps_ids.items():
req = elasticdl_pb2.PullEmbeddingVectorRequest()
req.name = layer_name
req.ids.extend(embedding_ids)
pb_future = self._ps_stubs[ps_id].pull_embedding_vectors.future(
req
)
pb_future_and_id_pairs.append((pb_future, ps_id))
for pb_future, ps_id in pb_future_and_id_pairs:
pb = pb_future.result()
embeddings.append(pb_to_ndarray(pb))
index.extend(ps_ids_index[ps_id])
embeddings = np.concatenate(embeddings)
# adjust the order of embedding vectors
new_embeddings = np.empty_like(embeddings)
new_embeddings[index] = embeddings
return new_embeddings
def report_task_result(self, task_id, err_msg, exec_counters=None):
"""
report task result to master
"""
report = elasticdl_pb2.ReportTaskResultRequest()
report.task_id = task_id
report.err_message = err_msg
if isinstance(exec_counters, dict):
report.exec_counters.update(exec_counters)
return self._stub.report_task_result(report)
def init_ps_var_partition(self):
ps_vars = {}
for v in self._non_embed_vars.values():
if v.name not in self._var_to_ps:
self._var_to_ps[v.name] = string_to_id(v.name, self._ps_num)
ps_id = self._var_to_ps[v.name]
if ps_id not in ps_vars:
ps_vars[ps_id] = [v]
else:
ps_vars[ps_id].append(v)
self._ps_vars = ps_vars
def report_embedding_info(self):
model = elasticdl_pb2.Model()
if self._embedding_layers:
embedding_infos = model.embedding_table_infos
for layer in self._embedding_layers:
embedding_info = embedding_infos.add()
embedding_info.name = layer.embedding_weight_name
embedding_info.dim = layer.output_dim
embedding_info.initializer = layer.embeddings_initializer
# set to float32
embedding_info.dtype = dtype_numpy_to_tensor(
np.dtype("float32")
)
if self._embedding_columns:
embedding_infos = model.embedding_table_infos
for column in self._embedding_columns:
embedding_info = embedding_infos.add()
embedding_info.name = column.embedding_weight_name
embedding_info.dim = column.dimension
# TODO(brightcoder01): The initializer in embedding column is
# a variable initializer function. For embedding layer, it's a
# tf.keras.initializers. Keep aligned between these two.
embedding_info.initializer = "uniform"
# set to float32
embedding_info.dtype = dtype_numpy_to_tensor(
np.dtype("float32")
)
for ps_id in range(self._ps_num):
self._ps_stubs[ps_id].push_embedding_table_infos(model)
def report_variable_to_ps(self, ps_id):
model = elasticdl_pb2.Model()
model.version = self._model_versions_from_ps[ps_id]
if ps_id in self._ps_vars:
vars = self._ps_vars[ps_id]
for var in vars:
serialize_ndarray(
var.numpy(), model.dense_parameters[var.name]
)
self._ps_stubs[ps_id].push_model(model)
def report_variable(self):
# TODO: call `push_model` in parallel
for ps_id in range(self._ps_num):
self.report_variable_to_ps(ps_id)
def _collect_edl_embedding_name_values(self):
"""
Collect the information of ElasticDL customized
embeddings such as EDL embedding layer and EDL embedding column.
Return:
An array of key-value pair.
Key is embedding names, layer name for embedding layer
and column name for embedding column.
Value is the EmbeddingAndIds tuple.
"""
embedding_name_values = []
for layer in self._embedding_layers:
embedding_name_values.append(
(layer.embedding_weight_name, layer.embedding_and_ids)
)
for column in self._embedding_columns:
embedding_name_values.append(
(column.embedding_weight_name, column.embedding_and_ids)
)
return embedding_name_values
def report_gradient_to_ps(self, grads):
self._timing.start_record_time("report_gradient")
reqs = [
elasticdl_pb2.PushGradientsRequest() for i in range(self._ps_num)
]
ps_grads = {}
non_embed_vars_n = len(self._non_embed_vars)
for g, v in zip(
grads[:non_embed_vars_n], self._non_embed_vars.values()
):
ps_id = self._var_to_ps[v.name]
if ps_id not in ps_grads:
ps_grads[ps_id] = {v.name: g}
else:
if v.name not in ps_grads[ps_id]:
ps_grads[ps_id][v.name] = g
else:
if isinstance(g, tf.IndexedSlices):
ps_grads[ps_id][v.name] = merge_indexed_slices(
ps_grads[ps_id][v.name], g
)
else:
ps_grads[ps_id][v.name] += g
for ps_id, pair in ps_grads.items():
for name, g in pair.items():
if isinstance(g, tf.IndexedSlices):
v, i = deduplicate_indexed_slices(g.values, g.indices)
ps_grads[ps_id][name] = tf.IndexedSlices(v, i)
for ps_id in ps_grads:
req = reqs[ps_id]
for name, g in ps_grads[ps_id].items():
# Keras embedding layer has a dense parameter,
# but an indexed slices type gradient
if isinstance(g, tf.IndexedSlices):
serialize_indexed_slices(
Tensor(None, g.values.numpy(), g.indices.numpy()),
req.gradients.embedding_tables[name],
)
else:
serialize_ndarray(
g.numpy(), req.gradients.dense_parameters[name]
)
edl_embedding_name_values = self._collect_edl_embedding_name_values()
if edl_embedding_name_values:
edl_embedding_grads = grads[non_embed_vars_n:]
bet_number = 0
for name, embedding_and_ids in edl_embedding_name_values:
bet_number += len(embedding_and_ids)
if len(edl_embedding_grads) != bet_number:
raise ValueError(
"elasticdl.layers.embedding related gradient number %d "
"does not match the number of its output tensor %d."
% (len(edl_embedding_grads), bet_number)
)
grad_accum_iter = 0
for name, embedding_and_ids in edl_embedding_name_values:
g_values = None
g_indices = None
for _, ids in embedding_and_ids:
grad = edl_embedding_grads[grad_accum_iter]
grad_accum_iter += 1
# ElasticDL embedding layer with Sparse Gradients
if isinstance(grad, tf.IndexedSlices):
grad = grad.values
if g_values is not None:
g_values = tf.concat([g_values, grad], axis=0)
g_indices = tf.concat([g_indices, ids], axis=0)
else:
g_values = grad
g_indices = ids
# Sum up the values of the duplicated indices in the
# gradients. It can reduce the gradient payload of the
# dense embedding.
g_values, g_indices = deduplicate_indexed_slices(
values=g_values, indices=g_indices
)
results = scatter_embedding_vector(
g_values.numpy(), g_indices.numpy(), self._ps_num
)
for ps_id in results:
req = reqs[ps_id]
gv, gi = results[ps_id]
serialize_indexed_slices(
Tensor(None, gv, gi),
req.gradients.embedding_tables[name],
)
report_futures = []
for ps_id in range(self._ps_num):
req = reqs[ps_id]
req.gradients.version = self._model_versions_from_ps[ps_id]
req.learning_rate = K.get_value(self._model.optimizer.lr)
report_future = self._ps_stubs[ps_id].push_gradients.future(req)
report_futures.append(report_future)
accepted = False
max_version = -1
for report_future in report_futures:
res = report_future.result()
if res.accepted:
accepted = True
if res.version > max_version:
max_version = res.version
self._timing.end_record_time("report_gradient")
return accepted, max_version
def report_gradient_locally(self, grads):
if self._embedding_layers or self._embedding_columns:
raise ValueError(
"ElasticDL embedding layer is not supported when"
"reporting gradients locally"
)
self._non_embed_grads = grads[: len(self._non_embed_vars)]
return True, None
def report_gradient(self, grads):
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
self.report_gradient_locally(grads)
self._update_local_model()
return True, None
else:
if self._use_multi_ps:
return self.report_gradient_to_ps(grads)
raise RuntimeError("Only support report gradients to PS")
def report_evaluation_metrics(self, model_outputs, labels):
"""
report evaluation metrics to ps.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for name, output in model_outputs.items():
output = np.concatenate(output)
serialize_ndarray(output, req.model_outputs[name])
labels = np.concatenate(labels)
serialize_ndarray(labels, req.labels)
req.worker_id = self._worker_id
self._stub.report_evaluation_metrics(req)
def report_prediction_outputs(self, predictions):
if self._prediction_outputs_processor:
self._prediction_outputs_processor.process(
predictions, self._worker_id
)
else:
self.logger.warning(
"prediction_outputs_processor is not "
"defined in the model definition. Prediction outputs "
"are not processed."
)
return True
def _run_model_call_before_training(self, features):
"""Call `self._model.call` before training for two things:
* Create variables and report to ps if not created.
* Check whether there is an embedding layer that is called
more than once during one forward-pass.
"""
if self._embedding_layers:
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
_ = self._model.call(features)
else:
_ = self._model.call(features)
self._non_embed_vars = {}
for var in get_non_embedding_trainable_vars(
self._model, self._embedding_layers
):
self._non_embed_vars[var.name] = var
if not self._var_created:
if self._use_multi_ps:
self.init_ps_var_partition()
else:
self.report_variable()
self._var_created = True
if self._need_embedding_layer_check:
self._train_eagerly = False
for layer in self._embedding_layers:
if len(layer.embedding_and_ids) > 1:
self._train_eagerly = True
self.logger.warning(
"ElasticDL embedding layer %s is called more than "
"once, this will make the training process unable "
"to accelerate with tf.function." % (layer.name)
)
self._need_embedding_layer_check = False
self._reset_embedding()
def get_trainable_items(self):
"""
return all trainable variables list, including batch embedding
tensor (BET) if exists. take care to keep the same order as in
self.report_gradient()
"""
bets = []
if self._embedding_layers:
for layer in self._embedding_layers:
bets.extend(
[
batch_embedding
for (batch_embedding, _) in layer.embedding_and_ids
]
)
if self._embedding_columns:
for column in self._embedding_columns:
bets.extend(
[
batch_embedding
for (batch_embedding, _) in column.embedding_and_ids
]
)
return list(self._non_embed_vars.values()) + bets
def training_process(self, features, labels):
"""
training for models with elasticdl.layers.embedding does not
support tf.function decorator
"""
if self._train_eagerly:
return self.training_process_eagerly(features, labels)
else:
return self.training_process_with_acceleration(features, labels)
@tf.function
def training_process_with_acceleration(self, features, labels):
return self.training_process_eagerly(features, labels)
def training_process_eagerly(self, features, labels):
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
outputs = self._model.call(features, training=True)
loss = self._loss(labels, outputs)
# Add regularization loss if any
if self._model.losses:
loss += tf.math.add_n(self._model.losses)
grads = tape.gradient(loss, self.get_trainable_items())
return loss, grads
@tf.function
def forward_process(self, features):
"""Calculates model outputs in non-training mode."""
outputs = self._model.call(features, training=False)
return outputs
def _get_local_model_params(self):
return [v for v in self._non_embed_vars.values()]
@staticmethod
def _get_rank_of_broadcast_src_worker():
return 0
def _broadcast_model_params(self):
status = self._collective_communicator.barrier()
if status == CollectiveCommunicatorStatus.FAILED:
self.logger.warning("Failed to perform barrier operation")
return False
broadcast_root_worker_rank = self._get_rank_of_broadcast_src_worker()
model_params = self._get_local_model_params()
status = self._collective_communicator.tf_broadcast(
model_params, broadcast_root_worker_rank
)
if status == CollectiveCommunicatorStatus.FAILED:
self.logger.warning("Failed to broadcast model parameters")
return False
return True
def _calculate_grads_and_report_with_allreduce(self, grads):
status, averaged_grads = self._collective_communicator.tf_allreduce(
grads
)
accepted = False
if status == CollectiveCommunicatorStatus.SUCCEEDED:
accepted, _ = self.report_gradient(averaged_grads)
if not accepted:
self.logger.warning("Failed to report the averaged gradients")
return accepted
def _collect_gradients_with_allreduce_robust(self, grads):
accepted = self._calculate_grads_and_report_with_allreduce(grads)
if not accepted:
start_time = time.time()
while not self._collective_communicator.is_initialized():
if (
time.time() - start_time
< DEFAULT_COMMUNICATOR_REINITIALIZING_TIMEOUT
):
self.logger.info(
"(Re-)initializing the collective communicator..."
)
time.sleep(3)
else:
self.logger.warning(
"Failed to (re-)initializing the "
"collective communicator"
)
return False
succeeded = self._broadcast_model_params()
if succeeded:
return self._calculate_grads_and_report_with_allreduce(grads)
else:
self.logger.warning("Failed to broadcast model parameters")
return False
else:
return True
def _collect_gradients_without_allreduce(self, grads):
accepted, min_model_version = self.report_gradient(grads)
if accepted and self._get_model_steps > 1:
non_embed_vars_n = len(self._non_embed_vars)
self._non_embed_grads = grads[:non_embed_vars_n]
self._reset_embedding()
return accepted, min_model_version
def _run_training_task(self, features, labels):
loss, grads = self.training_process(features, labels)
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
# TODO: Delay certain amount of time before retrying
for _ in range(self._max_allreduce_retry_num + 1):
accepted = self._collect_gradients_with_allreduce_robust(grads)
if accepted:
return accepted, None, loss
else:
self.logger.warning(
"Failed to perform allreduce operation on"
"the gradients. Retrying..."
)
else:
return (*self._collect_gradients_without_allreduce(grads), loss)
def _collect_evaluation_result(self, outputs, labels):
key = MetricsDictKey.MODEL_OUTPUT
if key not in self._evaluation_result:
outputs = {k: [v.numpy()] for k, v in outputs.items()}
self._evaluation_result[key] = outputs
else:
for k, v in outputs.items():
self._evaluation_result[key][k].append(v.numpy())
key = MetricsDictKey.LABEL
if key not in self._evaluation_result:
self._evaluation_result[key] = [labels.numpy()]
else:
self._evaluation_result[key].append(labels.numpy())
def _run_evaluation_task(self, features, labels):
outputs = self.forward_process(features)
if not isinstance(outputs, dict):
outputs = {MetricsDictKey.MODEL_OUTPUT: outputs}
self._collect_evaluation_result(outputs, labels)
def _run_prediction_task(self, features):
predictions = self.forward_process(features)
return self.report_prediction_outputs(predictions)
def _process_minibatch(
self,
task_type,
features,
labels,
min_model_version,
train_with_local_model=False,
):
if self._need_embedding_layer_check or not self._var_created:
self._run_model_call_before_training(features)
self._timing.start_record_time("batch_process")
for _ in range(self._max_minibatch_retry_num):
if task_type == elasticdl_pb2.EVALUATION:
self._run_evaluation_task(features, labels)
break
elif task_type == elasticdl_pb2.TRAINING:
# TODO: optimize the logic to avoid unnecessary
# get_model call.
if not train_with_local_model:
self.get_model()
self._callbacks_list.on_train_batch_begin(self._model_version)
*accepted, min_model_version, loss = self._run_training_task(
features, labels
)
if (
self._model_version
>= self._log_loss_count * self._log_loss_steps
):
self.logger.info(
"Loss = {}, steps = {}".format(
loss.numpy(), self._model_version
)
)
self._log_loss_count = (
int(self._model_version / self._log_loss_steps) + 1
)
if accepted:
break
elif task_type == elasticdl_pb2.PREDICTION:
if self._model_version != min_model_version:
self.get_model()
accepted = self._run_prediction_task(features)
if accepted:
break
else:
raise RuntimeError("Unrecognized task type, %s" % task_type)
else:
# Worker got stuck, fail the task.
# TODO: stop the worker if it fails to make any
# progress for some time.
raise RuntimeError("Worker got stuck")
self._timing.end_record_time("batch_process")
return min_model_version
def _process_eval_task(self, task):
"""
Check if there are evaluation tasks and process the tasks if any.
Return:
A python bool indicating whether worker processed some evaluation
tasks.
"""
self.logger.info("the evaluation task_id: %d" % task.task_id)
gen = self._task_data_service.get_dataset_gen(task)
if not gen:
return None
def create_dataset():
eval_dataset = tf.data.Dataset.from_generator(
gen, self._task_data_service.data_reader.records_output_types
)
eval_dataset = self._dataset_fn(
eval_dataset,
Mode.EVALUATION,
self._task_data_service.data_reader.metadata,
)
eval_dataset = eval_dataset.batch(self._minibatch_size).prefetch(1)
return eval_dataset
with tf.device("/device:cpu:0"):
eval_dataset = create_dataset()
model_version = task.model_version
task_id = task.task_id
err_msg = ""
for dataset_batch in eval_dataset:
data_err_msg = self._process_minibatch_and_report(
dataset_batch, elasticdl_pb2.EVALUATION, model_version
)
if data_err_msg:
err_msg = data_err_msg
break
del eval_dataset
self.report_evaluation_metrics(
model_outputs=self._evaluation_result[MetricsDictKey.MODEL_OUTPUT],
labels=self._evaluation_result[MetricsDictKey.LABEL],
)
self.report_task_result(task_id, err_msg)
self._evaluation_result = {}
def _process_train_end_callback_task_if_needed(self):
train_end_task = self._task_data_service.get_train_end_callback_task()
if train_end_task:
self._callbacks_list.on_train_end()
self._task_data_service.clear_train_end_callback_task()
self.report_task_result(task_id=train_end_task.task_id, err_msg="")
def _process_minibatch_and_report(
self,
dataset_batch,
task_type,
model_version,
train_with_local_model=False,
):
err_msg = ""
try:
if self._job_type == JobType.PREDICTION_ONLY:
features = dataset_batch
labels = None
else:
features = dataset_batch[0]
labels = dataset_batch[1]
self._process_minibatch(
task_type,
features,
labels,
model_version,
train_with_local_model,
)
except RuntimeError as err:
err_msg = str(err)
traceback.print_exc()
except Exception as ex:
err_msg = str(ex)
traceback.print_exc()
raise ex
return err_msg
def _train_and_evaluate(self):
"""
Train and evaluate the model on the worker
"""
# The worker needs to get model from PS if
# `train_with_local_model=False`. This happens when:
# processing first minibatch
# any evaluation task has been executed just before this minibatch
# last minibatch is training task and failed
# local_update_count >= worker._get_model_steps
# Otherwise, worker trains with local model, i.e.
# `train_with_local_model=True`
train_with_local_model = False
# Initialize `local_update_count=get_model_steps` in order to set
# `train_with_local_model` to False inside for-loop for the first
# minibatch.
local_update_count = self._get_model_steps
last_training_minibatch_failed = False
evaluation_task_executed = False
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
self._process_train_end_callback_task_if_needed()
break
dataset = self._dataset_fn(
dataset,
Mode.TRAINING,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
self._timing.start_record_time("task_process")
for dataset_batch in dataset:
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
# Give the worker a chance to process an evaluation task
# during training if the task exists
evaluation_task_executed = (
True
if self._evaluate_only()
else evaluation_task_executed
)
task = self._task_data_service.get_current_task()
if (
evaluation_task_executed
or last_training_minibatch_failed
or local_update_count >= self._get_model_steps
):
local_update_count = 0
train_with_local_model = False
else:
train_with_local_model = True
err_msg = self._process_minibatch_and_report(
dataset_batch,
task.type,
task.model_version,
train_with_local_model,
)
local_update_count += 1
if err_msg:
last_training_minibatch_failed = True
else:
last_training_minibatch_failed = False
if local_update_count < self._get_model_steps:
self._update_local_model()
if self._task_data_service.report_record_done(
self._minibatch_size, err_msg
):
self._timing.end_record_time("task_process")
self._timing.report_timing(reset=True)
self._timing.start_record_time("task_process")
del dataset
# New evaluation tasks may be created after this worker's
# training tasks are done, as other workers' may still
# have pending training tasks.
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
evaluation_task_executed = self._evaluate_only()
self._process_train_end_callback_task_if_needed()
def _evaluate_only(self):
"""
Only evaluate the model on the worker.
"""
evaluation_task_executed = False
# should not get model before finishing some training tasks, because
# variables of subclass models are not created.
is_model_got = False
while True:
task = self.get_task(elasticdl_pb2.EVALUATION)
# no evaluation task in eval_todo of master
if not task.shard_name:
break
# get the latest model before processing eval tasks
if not is_model_got:
self.get_model()
is_model_got = True
self._process_eval_task(task)
evaluation_task_executed = True
return evaluation_task_executed
def _predict_only(self):
"""
Only predict outputs of the model with data in tasks on the worker.
"""
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
break
dataset = self._dataset_fn(
dataset,
Mode.PREDICTION,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
for dataset_batch in dataset:
task = self._task_data_service.get_current_task()
err_msg = self._process_minibatch_and_report(
dataset_batch, task.type, task.model_version
)
self._task_data_service.report_record_done(
self._minibatch_size, err_msg
)
del dataset
def run(self):
"""
Fetches task from master with and performs training, evaluation
or prediction.
"""
if self._job_type == JobType.PREDICTION_ONLY:
self._predict_only()
elif self._job_type == JobType.EVALUATION_ONLY:
self._evaluate_only()
else:
self._train_and_evaluate()
class Worker(object):
"""ElasticDL worker"""
def __init__(
self,
worker_id,
job_type,
minibatch_size,
model_zoo,
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
channel=None,
embedding_service_endpoint=None,
model_def=None,
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
):
"""
Arguments:
model_file: A module to define the model
channel: grpc channel
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
"""
self._worker_id = worker_id
self._job_type = job_type
self._minibatch_size = minibatch_size
(
self._model,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=model_zoo,
model_def=model_def,
dataset_fn=dataset_fn,
loss=loss,
optimizer=optimizer,
eval_metrics_fn=eval_metrics_fn,
model_params=model_params,
prediction_outputs_processor=prediction_outputs_processor,
)
self._init_embedding_layer()
self._var_created = self._model.built
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._embedding_service_endpoint = embedding_service_endpoint
self._max_minibatch_retry_num = max_minibatch_retry_num
self._model_version = -1
self._task_data_service = TaskDataService(
self, self._job_type == JobType.TRAINING_WITH_EVALUATION)
def _init_embedding_layer(self):
"""
Init elasticdl.layers.embedding layer list and assign worker to them
"""
self._embedding_layers = find_layer(self._model, Embedding)
for layer in self._embedding_layers:
layer.set_lookup_func(self.lookup_embedding)
if self._embedding_layers:
# TODO check that Redis IP/PORT is set
pass
def _set_tape_for_embedding(self, tape):
for layer in self._embedding_layers:
layer.set_tape(tape)
def _reset_embedding(self):
for layer in self._embedding_layers:
layer.reset()
def get_task(self):
"""
get task from master
"""
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = self._worker_id
return self._stub.GetTask(req)
def get_model(self, version, method):
"""
get model from master, and update model_version
"""
req = elasticdl_pb2.GetModelRequest()
req.version = version
req.method = method
model = self._stub.GetModel(req)
for var in self._model.trainable_variables:
# Assumes all trainable variables exist in model.param.
var.assign(tensor_to_ndarray(model.param[var.name]))
self._model_version = model.version
def lookup_embedding(self,
ids,
layer_name,
initializer="uniform",
embedding_table_dim=128):
keys = [Embedding.get_key([layer_name, id]) for id in ids]
ES_lookup_embedding = EmbeddingService.lookup_embedding
embedding_vectors, unknown_keys_index = ES_lookup_embedding(
keys=keys,
embedding_service_endpoint=self._embedding_service_endpoint,
)
if unknown_keys_index:
# Initialize unknown_keys' embedding vectors and write into Redis.
unknown_keys = [keys[index] for index in unknown_keys_index]
initializer = tf.keras.initializers.get(initializer)
embedding_vector_init = [
initializer(shape=[1, embedding_table_dim]).numpy()
for _ in unknown_keys
]
embedding_vector_init = np.concatenate(embedding_vector_init,
axis=0)
EmbeddingService.update_embedding(
keys=unknown_keys,
embedding_vectors=embedding_vector_init,
embedding_service_endpoint=self._embedding_service_endpoint,
set_if_not_exist=True,
)
# Lookup unknown_keys' embedding vectors
embedding_vectors_new, unknown_keys_idx_new = ES_lookup_embedding(
keys=unknown_keys,
embedding_service_endpoint=self._embedding_service_endpoint,
)
if unknown_keys_idx_new:
raise Exception("Update embedding vector: %s failed." % str(
[unknown_keys[index] for index in unknown_keys_idx_new]))
for key_index, vector in zip(unknown_keys_index,
embedding_vectors_new):
embedding_vectors[key_index] = vector
embedding_vectors = np.concatenate(embedding_vectors, axis=0)
return embedding_vectors.reshape((len(keys), embedding_table_dim))
def report_task_result(self, task_id, err_msg):
"""
report task result to master
"""
report = elasticdl_pb2.ReportTaskResultRequest()
report.task_id = task_id
report.err_message = err_msg
return self._stub.ReportTaskResult(report)
def report_variable(self):
"""
report variable to ps.
"""
req = elasticdl_pb2.ReportVariableRequest()
for v in self._model.trainable_variables:
req.variable[v.name].CopyFrom(ndarray_to_tensor(v.numpy()))
self._stub.ReportVariable(req)
def report_gradient(self, grads):
"""
report gradient to ps, return (accepted, model_version) from rpc call.
"""
req = elasticdl_pb2.ReportGradientRequest()
origin_vars = self._model.trainable_variables
origin_var_n = len(origin_vars)
# should keep the same order as self.get_trainable_items()
for g, v in zip(grads[:origin_var_n], origin_vars):
if isinstance(g, tf.IndexedSlices):
req.gradient[v.name].CopyFrom(
ndarray_to_tensor(g.values.numpy(),
tuple(g.indices.numpy())))
else:
req.gradient[v.name].CopyFrom(ndarray_to_tensor(g.numpy()))
# deal with gradients of ElasticDL embedding layer
# should keep the same order as self.get_trainable_items()
if self._embedding_layers:
grads_edlembedding = grads[origin_var_n:]
bet_number = 0
for layer in self._embedding_layers:
bet_number += len(layer.bet_ids_pair)
if len(grads_edlembedding) != bet_number:
raise ValueError(
"elasticdl.layers.embedding related gradient number %d "
"does not match the number of its output tensor %d." %
(len(grads_edlembedding), bet_number))
it = 0
for layer in self._embedding_layers:
g_values = None
g_indices = None
for bet, ids in layer.bet_ids_pair:
grad = grads_edlembedding[it]
it += 1
# ElasticDL embedding layer with Sparse Gradients
if isinstance(grad, tf.IndexedSlices):
grad = grad.values
if g_values is not None:
g_values = tf.concat([g_values, grad], axis=0)
g_indices = tf.concat([g_indices, ids], axis=0)
else:
g_values = grad
g_indices = ids
req.gradient[layer.name].CopyFrom(
ndarray_to_tensor(g_values.numpy(),
tuple(g_indices.numpy())))
req.model_version = self._model_version
res = self._stub.ReportGradient(req)
return res.accepted, res.model_version
def report_evaluation_metrics(self, evaluation_metrics):
"""
report evaluation metrics to ps, return (accepted, model_version)
from rpc call.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for k, v in evaluation_metrics.items():
v_np = v.numpy()
# If scalar, convert to numpy 1D array with size 1
if not v_np.shape:
v_np = v_np.reshape(1)
req.evaluation_metrics[k].CopyFrom(ndarray_to_tensor(v_np))
req.model_version = self._model_version
res = self._stub.ReportEvaluationMetrics(req)
return res.accepted, res.model_version
def report_prediction_outputs(self, predictions):
if self._prediction_outputs_processor:
self._prediction_outputs_processor.process(predictions,
self._worker_id)
else:
logger.warning(
"prediction_outputs_processor is not "
"defined in the model definition. Prediction outputs "
"are not processed.")
return True
def _create_variable_and_report(self, features):
# Use model.call to create variables, then report to ps
_ = self._model.call(features)
self.report_variable()
self._var_created = True
def get_trainable_items(self):
"""
return all trainable variables list, including batch embedding
tensor (BET) if exists. take care to keep the same order as in
self.report_gradient()
"""
bets = []
if self._embedding_layers:
for layer in self._embedding_layers:
bets.extend([i for (i, _) in layer.bet_ids_pair])
return self._model.trainable_variables + bets
def training_process(self, features, labels):
"""
training for models with elasticdl.layers.embedding does not
support tf.function decorator
"""
if self._embedding_layers:
return self.training_process_eagerly(features, labels)
else:
return self.training_process_with_acceleration(features, labels)
@tf.function
def training_process_with_acceleration(self, features, labels):
return self.training_process_eagerly(features, labels)
def training_process_eagerly(self, features, labels):
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
outputs = self._model.call(features, training=True)
loss = self._loss(outputs, labels)
# Add regularization loss if any
if self._model.losses:
loss += tf.math.add_n(self._model.losses)
grads = tape.gradient(loss, self.get_trainable_items())
return loss, grads
@tf.function
def evaluation_process(self, features, labels):
outputs = self._model.call(features, training=False)
evaluation_metrics = self._eval_metrics_fn(outputs, labels)
return evaluation_metrics
@tf.function
def predict_process(self, features):
outputs = self._model.call(features, training=False)
return outputs
def _run_training_task(self, features, labels):
loss, grads = self.training_process(features, labels)
accepted, min_model_version = self.report_gradient(grads)
self._reset_embedding()
return accepted, min_model_version, loss
def _run_evaluation_task(self, features, labels):
evaluation_metrics = self.evaluation_process(features, labels)
accepted, _ = self.report_evaluation_metrics(evaluation_metrics)
return accepted
def _run_prediction_task(self, features):
predictions = self.predict_process(features)
return self.report_prediction_outputs(predictions)
def _process_minibatch(self, task_type, features, labels,
min_model_version):
if not self._var_created:
self._create_variable_and_report(features)
for _ in range(self._max_minibatch_retry_num):
if task_type == elasticdl_pb2.EVALUATION:
if min_model_version == -1:
if self._model_version < 0:
self.get_model(0, elasticdl_pb2.MINIMUM)
elif self._model_version != min_model_version:
self.get_model(min_model_version, elasticdl_pb2.FIXED)
accepted = self._run_evaluation_task(features, labels)
if accepted:
break
elif task_type == elasticdl_pb2.TRAINING:
# TODO: optimize the logic to avoid unnecessary
# get_model call.
self.get_model(
max(self._model_version, min_model_version),
elasticdl_pb2.MINIMUM,
)
accepted, min_model_version, loss = self._run_training_task(
features, labels)
if accepted:
logger.info("Loss is %f" % loss.numpy())
break
elif task_type == elasticdl_pb2.PREDICTION:
if self._model_version != min_model_version:
self.get_model(min_model_version, elasticdl_pb2.FIXED)
accepted = self._run_prediction_task(features)
if accepted:
break
else:
raise RuntimeError("Unrecognized task type, %s" % task_type)
else:
# Worker got stuck, fail the task.
# TODO: stop the worker if it fails to make any
# progress for some time.
raise RuntimeError("Worker got stuck")
return min_model_version
def _process_eval_task_if_needed(self):
"""
Check if there are evaluation tasks and process the tasks if any.
"""
eval_info = self._task_data_service.get_evaluation_dataset()
if not eval_info:
return
(eval_dataset, model_version, task_id) = eval_info
eval_dataset = self._dataset_fn(eval_dataset, Mode.EVALUATION)
eval_dataset = eval_dataset.batch(self._minibatch_size).prefetch(1)
err_msg = ""
for dataset_batch in eval_dataset:
data_err_msg = self._process_minibatch_and_report(
dataset_batch, elasticdl_pb2.EVALUATION, model_version)
if data_err_msg:
err_msg = data_err_msg
break
del eval_dataset
self.report_task_result(task_id, err_msg)
def _process_minibatch_and_report(self, dataset_batch, task_type,
model_version):
err_msg = ""
try:
if self._job_type == JobType.PREDICTION_ONLY:
features = dataset_batch
labels = None
else:
features = dataset_batch[0]
labels = dataset_batch[1]
self._process_minibatch(task_type, features, labels, model_version)
except RuntimeError as err:
err_msg = str(err)
traceback.print_exc()
except Exception as ex:
err_msg = str(ex)
traceback.print_exc()
raise ex
return err_msg
def run(self):
"""
Fetches task from master with and performs training or evaluation.
"""
if self._job_type == JobType.PREDICTION_ONLY:
mode = Mode.PREDICTION
elif self._job_type == JobType.EVALUATION_ONLY:
mode = Mode.EVALUATION
else:
mode = Mode.TRAINING
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
break
dataset = self._dataset_fn(dataset, mode)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
for dataset_batch in dataset:
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
self._process_eval_task_if_needed()
task = self._task_data_service.get_current_task()
err_msg = self._process_minibatch_and_report(
dataset_batch, task.type, task.model_version)
self._task_data_service.report_record_done(
self._minibatch_size, err_msg)
del dataset
# New evaluation tasks may be created after this worker's
# training tasks are done, as other workers' may still
# have pending training tasks.
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
self._process_eval_task_if_needed()
def __init__(
self,
worker_id,
job_type,
minibatch_size,
model_zoo,
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
channel=None,
embedding_service_endpoint=None,
model_def=None,
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
get_model_steps=1,
):
"""
Arguments:
worker_id: The worker ID.
job_type: The job type.
minibatch_size: The size of the minibatch used for each iteration.
model_zoo: The directory that contains user-defined model files
or a specific model file.
dataset_fn: The name of the dataset function defined in the
model file.
loss: The name of the loss function defined in the model file.
optimizer: The name of the optimizer defined in the model file.
eval_metrics_fn: The name of the evaluation metrics function
defined in the model file.
channel: The channel for the gRPC master service.
embedding_service_endpoint: The endpoint to the embedding service.
model_def: The import path to the model definition
function/class in the model zoo, e.g.
"cifar10_subclass.CustomModel".
model_params: The dictionary of model parameters in a string that
will be used to instantiate the model,
e.g. "param1=1,param2=2".
prediction_outputs_processor: The name of the prediction output
processor class defined in the model file.
get_model_steps: Worker will perform `get_model` from the
parameter server every this many steps.
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
"""
self._worker_id = worker_id
self._job_type = job_type
self._minibatch_size = minibatch_size
(
self._model,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=model_zoo,
model_def=model_def,
dataset_fn=dataset_fn,
loss=loss,
optimizer=optimizer,
eval_metrics_fn=eval_metrics_fn,
model_params=model_params,
prediction_outputs_processor=prediction_outputs_processor,
)
self._init_embedding_layer()
self._var_created = self._model.built
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._embedding_service_endpoint = embedding_service_endpoint
self._max_minibatch_retry_num = max_minibatch_retry_num
self._model_version = -1
self._task_data_service = TaskDataService(
self, self._job_type == JobType.TRAINING_WITH_EVALUATION)
self._get_model_steps = get_model_steps
class Worker(object):
"""ElasticDL worker"""
def __init__(
self,
args,
master_client=None,
ps_client=None,
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
set_parallelism=False,
):
"""
Arguments:
channel: The channel for the gRPC master service.
ps_channels: The PS channels for PS service
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
max_allreduce_retry_num: The maximum number of retries for
allreduce operation if allreduce-based distributed
training strategy is used.
"""
self._args = args
self.logger = get_logger("Worker", level=args.log_level.upper())
if set_parallelism:
# Explicitly setting the parallelism will avoid multi-process hangs
# Maybe due to an unknown bug in Tensorflow?
# Must called before TensorFlow is initialized.
# Not set_parallelism by default to make unittests happy.
num_threads = os.cpu_count()
tf.config.threading.set_inter_op_parallelism_threads(num_threads)
tf.config.threading.set_intra_op_parallelism_threads(num_threads)
self._mc = master_client
self._ps_client = ps_client
self._distribution_strategy = args.distribution_strategy
if (self._distribution_strategy ==
DistributionStrategy.PARAMETER_SERVER):
if self._ps_client is None:
raise ValueError("PS channels are not set up under "
"parameter server strategy")
else:
self._model_versions_from_ps = [
-1 for _ in range(self._ps_client.ps_num)
]
self._max_minibatch_retry_num = max_minibatch_retry_num
self._init_from_args(args)
self._timing = Timing(args.log_level.upper() == "DEBUG", self.logger)
self._log_loss_count = 0
self._var_created = False
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
self._log_loss_steps = args.log_loss_steps
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
self._callbacks_list,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
callbacks=args.callbacks,
)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._task_data_service = TaskDataService(
self._mc,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
custom_data_reader=self._custom_data_reader,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
data_origin=args.training_data,
)
if self._dataset_fn is None:
if hasattr(self._task_data_service.data_reader,
"default_dataset_fn"):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn())
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn")
self._get_model_steps = args.get_model_steps
self._opt = self._opt_fn()
self._model.optimizer = self._opt
self._non_embed_grads = {}
self._evaluation_result = {}
saved_model_exporter = SavedModelExporter(self._task_data_service,
self._dataset_fn,
self._model_handler)
# Place default callbacks at the head to execute them firstly
self._callbacks_list.callbacks.insert(0, saved_model_exporter)
self._callbacks_list.set_model(model_inst)
set_callback_parameters(
self._callbacks_list,
batch_size=args.minibatch_size,
saved_model_path=args.output,
checkpoint_path=args.checkpoint_dir,
)
self._allreduce_trainer = None
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
master_addr = args.master_addr.split(":")[0]
self._allreduce_trainer = AllReduceTrainer(self._mc, master_addr,
self._model, self._loss,
self._opt)
# TODO: Multiple tests are currently using this function to initialize
# self._model, where the initialization should be done via constructor.
def set_model(self, model_inst):
"""Set model instance to worker."""
self._model = model_inst
self._train_eagerly = False
self._init_embeddings()
def _init_embedding_layer(self):
"""
Init elasticdl.layers.embedding layer list and assign worker to them
"""
self._embedding_layers = find_layer(self._model, Embedding)
if (self._distribution_strategy ==
DistributionStrategy.PARAMETER_SERVER):
for layer in self._embedding_layers:
layer.set_lookup_embedding_func(
self._ps_client.pull_embedding_vectors)
def _init_embedding_column(self):
self._embedding_columns = []
for layer in self._model.layers:
if isinstance(layer, tf.keras.layers.DenseFeatures):
for column in layer._feature_columns:
if isinstance(column, feature_column.EmbeddingColumn):
self._embedding_columns.append(column)
self.logger.info(
"Initialize ElasticDL EmbeddingColumn:{}".format(
column.name))
if (self._distribution_strategy ==
DistributionStrategy.PARAMETER_SERVER):
for column in self._embedding_columns:
column.set_lookup_embedding_func(
self._ps_client.pull_embedding_vectors)
def _check_name_conflict_of_embedding_layer_and_column(self):
if not self._embedding_layers or not self._embedding_columns:
return
embedding_layer_name_set = set(
[layer.name for layer in self._embedding_layers])
embedding_column_name_set = set(
[column.name for column in self._embedding_columns])
conflict_name_set = embedding_column_name_set.union(
embedding_layer_name_set)
if conflict_name_set:
raise Exception(
"Name conflict between embedding layer and column: {}".format(
conflict_name_set))
def _init_embeddings(self):
self._init_embedding_layer()
self._init_embedding_column()
self._check_name_conflict_of_embedding_layer_and_column()
if (self._distribution_strategy ==
DistributionStrategy.PARAMETER_SERVER):
self.report_embedding_info()
self._need_embedding_layer_check = (True if self._embedding_layers or
self._embedding_columns else False)
def _set_tape_for_embedding(self, tape):
for layer in self._embedding_layers:
layer.set_tape(tape)
for column in self._embedding_columns:
column.set_tape(tape)
def _reset_embedding(self):
for layer in self._embedding_layers:
layer.reset()
for column in self._embedding_columns:
column.reset()
def _update_local_model(self):
if not self._non_embed_grads:
return
# Take care of the order of grads and vars if worker modifies
# `_non_embed_vars` during training.
self._opt.apply_gradients(
zip(self._non_embed_grads, self._non_embed_vars.values()))
self._non_embed_grads = None
def get_model(self):
self._timing.start_record_time("get_model")
if (self._distribution_strategy ==
DistributionStrategy.PARAMETER_SERVER):
# 1. Worker tries to pull dense parameters from the PS, maybe one
# or more PS instances are uninitialized.
dense_params, uninit_ps = self._ps_client.pull_dense_parameters(
[i for i in range(self._ps_client.ps_num)],
self._model_versions_from_ps,
)
# 2. Worker pushes local dense parameters to these PS instances
# to initialize their partition of parameters.
if len(uninit_ps) > 0:
for ps_id in uninit_ps:
# push variable to ps for initialization
parameters = [
Tensor(name, self._non_embed_vars[name].numpy(), None)
for name in self._ps_client.ps_to_parameter[ps_id]
]
self._ps_client.push_dense_parameters(
parameters, ps_id, self._model_versions_from_ps[ps_id])
ps_params, uninit = self._ps_client.pull_dense_parameters(
uninit_ps, self._model_versions_from_ps)
if len(uninit) > 0:
# TODO: support PS fault-tolerance
raise RuntimeError("PS initialization failed")
dense_params.update(ps_params)
# 3. Assign parameters to local model
for k, v in dense_params.items():
self._non_embed_vars[k].assign(v)
self._model_version = max(self._model_versions_from_ps)
self._timing.end_record_time("get_model")
def report_embedding_info(self):
# TODO(qijun): only support float32
infos = []
if self._embedding_layers:
for layer in self._embedding_layers:
infos.append(
EmbeddingTableInfo(
layer.embedding_weight_name,
layer.output_dim,
layer.embeddings_initializer,
dtype_numpy_to_tensor(np.dtype("float32")),
))
if self._embedding_columns:
for column in self._embedding_columns:
# TODO(brightcoder01): The initializer in embedding column is
# a variable initializer function. For embedding layer, it's a
# tf.keras.initializers. Keep aligned between these two.
infos.append(
EmbeddingTableInfo(
column.embedding_weight_name,
column.dimension,
Initializer.UNIFORM,
dtype_numpy_to_tensor(np.dtype("float32")),
))
self._ps_client.push_embedding_table_infos(infos)
def _collect_edl_embedding_name_values(self):
"""
Collect the information of ElasticDL customized
embeddings such as EDL embedding layer and EDL embedding column.
Return:
An array of key-value pair.
Key is embedding names, layer name for embedding layer
and column name for embedding column.
Value is the EmbeddingAndIds tuple.
"""
embedding_name_values = []
for layer in self._embedding_layers:
embedding_name_values.append(
(layer.embedding_weight_name, layer.embedding_and_ids))
for column in self._embedding_columns:
embedding_name_values.append(
(column.embedding_weight_name, column.embedding_and_ids))
return embedding_name_values
def report_gradient(self, gradients):
self._timing.start_record_time("report_gradient")
grads = []
for i, v in enumerate(self._non_embed_vars.values()):
if isinstance(gradients[i], tf.IndexedSlices):
grad = Tensor(
v.name,
gradients[i].values.numpy(),
gradients[i].indices.numpy(),
)
else:
grad = Tensor(v.name, gradients[i].numpy(), None)
grads.append(grad)
edl_grads = []
edl_embedding_name_values = self._collect_edl_embedding_name_values()
if edl_embedding_name_values:
non_embed_vars_n = len(self._non_embed_vars)
edl_embedding_grads = gradients[non_embed_vars_n:]
bet_number = 0
for name, embedding_and_ids in edl_embedding_name_values:
for i in range(bet_number):
grad = Tensor(
name,
edl_embedding_grads[i + bet_number].values.numpy(),
edl_embedding_grads[i + bet_number].indices.numpy(),
)
edl_grads.append(grad)
bet_number += len(embedding_and_ids)
if len(edl_embedding_grads) != bet_number:
raise ValueError(
"elasticdl.layers.embedding related gradient number %d "
"does not match the number of its output tensor %d." %
(len(edl_embedding_grads), bet_number))
learning_rate = K.get_value(self._model.optimizer.lr)
accepted, max_version = self._ps_client.push_gradients(
grads,
edl_grads,
learning_rate,
self._model_versions_from_ps,
)
self._timing.end_record_time("report_gradient")
return accepted, max_version
def report_prediction_outputs(self, predictions):
if self._prediction_outputs_processor:
self._prediction_outputs_processor.process(predictions,
self._worker_id)
else:
self.logger.warning(
"prediction_outputs_processor is not "
"defined in the model definition. Prediction outputs "
"are not processed.")
return True
def _run_model_call_before_training(self, features):
"""Call `self._model.call` before training for two things:
* Create variables and report to ps if not created.
* Check whether there is an embedding layer that is called
more than once during one forward-pass.
"""
if self._embedding_layers:
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
_ = self._model.call(features)
else:
_ = self._model.call(features)
self._non_embed_vars = {}
for var in get_non_embedding_trainable_vars(self._model,
self._embedding_layers):
self._non_embed_vars[var.name] = var
self._var_created = True
if (self._distribution_strategy ==
DistributionStrategy.PARAMETER_SERVER):
self._ps_client.partition_dense_parameters(
self._non_embed_vars.keys())
if self._need_embedding_layer_check:
self._train_eagerly = False
for layer in self._embedding_layers:
if len(layer.embedding_and_ids) > 1:
self._train_eagerly = True
self.logger.warning(
"ElasticDL embedding layer %s is called more than "
"once, this will make the training process unable "
"to accelerate with tf.function." % (layer.name))
self._need_embedding_layer_check = False
self._reset_embedding()
def get_trainable_items(self):
"""
return all trainable variables list, including batch embedding
tensor (BET) if exists. take care to keep the same order as in
self.report_gradient()
"""
bets = []
if self._embedding_layers:
for layer in self._embedding_layers:
bets.extend([
batch_embedding
for (batch_embedding, _) in layer.embedding_and_ids
])
if self._embedding_columns:
for column in self._embedding_columns:
bets.extend([
batch_embedding
for (batch_embedding, _) in column.embedding_and_ids
])
return list(self._non_embed_vars.values()) + bets
def training_process(self, features, labels):
"""
training for models with elasticdl.layers.embedding does not
support tf.function decorator
"""
if self._train_eagerly:
return self.training_process_eagerly(features, labels)
else:
return self.training_process_with_acceleration(features, labels)
@tf.function
def training_process_with_acceleration(self, features, labels):
return self.training_process_eagerly(features, labels)
def training_process_eagerly(self, features, labels):
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
outputs = self._model.call(features, training=True)
loss = self._loss(labels, outputs)
# Add regularization loss if any
if self._model.losses:
loss += tf.math.add_n(self._model.losses)
grads = tape.gradient(loss, self.get_trainable_items())
return loss, grads
@tf.function
def forward_process(self, features):
"""Calculates model outputs in non-training mode."""
outputs = self._model.call(features, training=False)
return outputs
def _get_local_model_params(self):
return [v for v in self._non_embed_vars.values()]
def _collect_gradients_with_ps(self, grads):
accepted, min_model_version = self.report_gradient(grads)
if accepted and self._get_model_steps > 1:
non_embed_vars_n = len(self._non_embed_vars)
self._non_embed_grads = grads[:non_embed_vars_n]
self._reset_embedding()
return accepted, min_model_version
def _run_training_task(self, features, labels):
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
(
version,
loss,
) = self._allreduce_trainer.training_process_with_fault_tolerance(
features, labels)
self._model_version = version
return True, version, loss
else:
loss, grads = self.training_process(features, labels)
return (*self._collect_gradients_with_ps(grads), loss)
def _collect_evaluation_result(self, outputs, labels):
key = MetricsDictKey.MODEL_OUTPUT
if key not in self._evaluation_result:
outputs = {k: [v.numpy()] for k, v in outputs.items()}
self._evaluation_result[key] = outputs
else:
for k, v in outputs.items():
self._evaluation_result[key][k].append(v.numpy())
key = MetricsDictKey.LABEL
if key not in self._evaluation_result:
self._evaluation_result[key] = [labels.numpy()]
else:
self._evaluation_result[key].append(labels.numpy())
def _run_evaluation_task(self, features, labels):
outputs = self.forward_process(features)
if not isinstance(outputs, dict):
outputs = {MetricsDictKey.MODEL_OUTPUT: outputs}
self._collect_evaluation_result(outputs, labels)
def _run_prediction_task(self, features):
predictions = self.forward_process(features)
return self.report_prediction_outputs(predictions)
def _process_minibatch(
self,
task_type,
features,
labels,
min_model_version,
train_with_local_model=False,
):
if self._need_embedding_layer_check or not self._var_created:
self._run_model_call_before_training(features)
self._timing.start_record_time("batch_process")
for _ in range(self._max_minibatch_retry_num):
if task_type == elasticdl_pb2.EVALUATION:
self._run_evaluation_task(features, labels)
break
elif task_type == elasticdl_pb2.TRAINING:
# TODO: optimize the logic to avoid unnecessary
# get_model call.
if not train_with_local_model:
self.get_model()
self._callbacks_list.on_train_batch_begin(self._model_version)
*accepted, min_model_version, loss = self._run_training_task(
features, labels)
if (self._model_version >=
self._log_loss_count * self._log_loss_steps):
self.logger.info("Loss = {}, steps = {}".format(
loss.numpy(), self._model_version))
self._log_loss_count = (
int(self._model_version / self._log_loss_steps) + 1)
if accepted:
break
elif task_type == elasticdl_pb2.PREDICTION:
if self._model_version != min_model_version:
self.get_model()
accepted = self._run_prediction_task(features)
if accepted:
break
else:
raise RuntimeError("Unrecognized task type, %s" % task_type)
else:
# Worker got stuck, fail the task.
# TODO: stop the worker if it fails to make any
# progress for some time.
raise RuntimeError("Worker got stuck")
self._timing.end_record_time("batch_process")
return min_model_version
def _process_eval_task(self, task):
"""
Check if there are evaluation tasks and process the tasks if any.
Return:
A python bool indicating whether worker processed some evaluation
tasks.
"""
self.logger.info("the evaluation task_id: %d" % task.task_id)
gen = self._task_data_service.get_dataset_gen(task)
if not gen:
return None
def create_dataset():
eval_dataset = tf.data.Dataset.from_generator(
gen, self._task_data_service.data_reader.records_output_types)
eval_dataset = self._dataset_fn(
eval_dataset,
Mode.EVALUATION,
self._task_data_service.data_reader.metadata,
)
eval_dataset = eval_dataset.batch(self._minibatch_size).prefetch(1)
return eval_dataset
with tf.device("/device:cpu:0"):
eval_dataset = create_dataset()
model_version = task.model_version
task_id = task.task_id
err_msg = ""
for dataset_batch in eval_dataset:
data_err_msg = self._process_minibatch_and_report(
dataset_batch, elasticdl_pb2.EVALUATION, model_version)
if data_err_msg:
err_msg = data_err_msg
break
del eval_dataset
self._mc.report_evaluation_metrics(
model_outputs=self._evaluation_result[MetricsDictKey.MODEL_OUTPUT],
labels=self._evaluation_result[MetricsDictKey.LABEL],
)
self._mc.report_task_result(task_id, err_msg)
self._evaluation_result = {}
def _process_train_end_callback_task_if_needed(self):
train_end_task = self._task_data_service.get_train_end_callback_task()
if train_end_task:
self._callbacks_list.on_train_end()
self._task_data_service.clear_train_end_callback_task()
self._mc.report_task_result(task_id=train_end_task.task_id,
err_msg="")
def _process_minibatch_and_report(
self,
dataset_batch,
task_type,
model_version,
train_with_local_model=False,
):
err_msg = ""
try:
if self._job_type == JobType.PREDICTION_ONLY:
features = dataset_batch
labels = None
else:
features = dataset_batch[0]
labels = dataset_batch[1]
self._process_minibatch(
task_type,
features,
labels,
model_version,
train_with_local_model,
)
except RuntimeError as err:
err_msg = str(err)
traceback.print_exc()
except Exception as ex:
err_msg = str(ex)
traceback.print_exc()
raise ex
return err_msg
def _train_and_evaluate(self):
"""
Train and evaluate the model on the worker
"""
# The worker needs to get model from PS if
# `train_with_local_model=False`. This happens when:
# processing first minibatch
# any evaluation task has been executed just before this minibatch
# last minibatch is training task and failed
# local_update_count >= worker._get_model_steps
# Otherwise, worker trains with local model, i.e.
# `train_with_local_model=True`
train_with_local_model = False
# Initialize `local_update_count=get_model_steps` in order to set
# `train_with_local_model` to False inside for-loop for the first
# minibatch.
local_update_count = self._get_model_steps
last_training_minibatch_failed = False
evaluation_task_executed = False
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
self._process_train_end_callback_task_if_needed()
break
dataset = self._dataset_fn(
dataset,
Mode.TRAINING,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
if self._allreduce_trainer:
self._allreduce_trainer.init_horovod_if_needed()
self._timing.start_record_time("task_process")
for dataset_batch in dataset:
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
# Give the worker a chance to process an evaluation task
# during training if the task exists
evaluation_task_executed = (True if self._evaluate_only()
else evaluation_task_executed)
task = self._task_data_service.get_current_task()
if (evaluation_task_executed or last_training_minibatch_failed
or local_update_count >= self._get_model_steps):
local_update_count = 0
train_with_local_model = False
else:
train_with_local_model = True
err_msg = self._process_minibatch_and_report(
dataset_batch,
task.type,
task.model_version,
train_with_local_model,
)
local_update_count += 1
if err_msg:
last_training_minibatch_failed = True
else:
last_training_minibatch_failed = False
if local_update_count < self._get_model_steps:
self._update_local_model()
if self._task_data_service.report_record_done(
self._minibatch_size, err_msg):
self._timing.end_record_time("task_process")
self._timing.report_timing(reset=True)
self._timing.start_record_time("task_process")
if (self._allreduce_trainer and self._model_version %
DEFAULT_STEPS_TO_CHECK_RENDEZVOUS == 0):
self._allreduce_trainer.init_horovod_if_needed()
del dataset
# New evaluation tasks may be created after this worker's
# training tasks are done, as other workers' may still
# have pending training tasks.
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
evaluation_task_executed = self._evaluate_only()
self._process_train_end_callback_task_if_needed()
def _evaluate_only(self):
"""
Only evaluate the model on the worker.
"""
evaluation_task_executed = False
# should not get model before finishing some training tasks, because
# variables of subclass models are not created.
is_model_got = False
while True:
task = self._mc.get_task(elasticdl_pb2.EVALUATION)
# no evaluation task in eval_todo of master
if not task.shard_name:
break
# get the latest model before processing eval tasks
if not is_model_got:
self.get_model()
is_model_got = True
self._process_eval_task(task)
evaluation_task_executed = True
return evaluation_task_executed
def _predict_only(self):
"""
Only predict outputs of the model with data in tasks on the worker.
"""
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
break
dataset = self._dataset_fn(
dataset,
Mode.PREDICTION,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
for dataset_batch in dataset:
task = self._task_data_service.get_current_task()
err_msg = self._process_minibatch_and_report(
dataset_batch, task.type, task.model_version)
self._task_data_service.report_record_done(
self._minibatch_size, err_msg)
del dataset
def run(self):
"""
Fetches task from master with and performs training, evaluation
or prediction.
"""
if self._job_type == JobType.PREDICTION_ONLY:
self._predict_only()
elif self._job_type == JobType.EVALUATION_ONLY:
self._evaluate_only()
else:
self._train_and_evaluate()
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
self._log_loss_steps = args.log_loss_steps
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
self._callbacks_list,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
callbacks=args.callbacks,
)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._task_data_service = TaskDataService(
self._mc,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
custom_data_reader=self._custom_data_reader,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
data_origin=args.training_data,
)
if self._dataset_fn is None:
if hasattr(self._task_data_service.data_reader,
"default_dataset_fn"):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn())
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn")
self._get_model_steps = args.get_model_steps
self._opt = self._opt_fn()
self._model.optimizer = self._opt
self._non_embed_grads = {}
self._evaluation_result = {}
saved_model_exporter = SavedModelExporter(self._task_data_service,
self._dataset_fn,
self._model_handler)
# Place default callbacks at the head to execute them firstly
self._callbacks_list.callbacks.insert(0, saved_model_exporter)
self._callbacks_list.set_model(model_inst)
set_callback_parameters(
self._callbacks_list,
batch_size=args.minibatch_size,
saved_model_path=args.output,
checkpoint_path=args.checkpoint_dir,
)
self._allreduce_trainer = None
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
master_addr = args.master_addr.split(":")[0]
self._allreduce_trainer = AllReduceTrainer(self._mc, master_addr,
self._model, self._loss,
self._opt)
class Worker(object):
"""ElasticDL worker"""
def __init__(
self,
worker_id,
job_type,
minibatch_size,
model_zoo,
dataset_fn="dataset_fn",
loss="loss",
optimizer="optimizer",
eval_metrics_fn="eval_metrics_fn",
channel=None,
embedding_service_endpoint=None,
model_def=None,
model_params="",
prediction_outputs_processor="PredictionOutputsProcessor",
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
get_model_steps=1,
):
"""
Arguments:
worker_id: The worker ID.
job_type: The job type.
minibatch_size: The size of the minibatch used for each iteration.
model_zoo: The directory that contains user-defined model files
or a specific model file.
dataset_fn: The name of the dataset function defined in the
model file.
loss: The name of the loss function defined in the model file.
optimizer: The name of the optimizer defined in the model file.
eval_metrics_fn: The name of the evaluation metrics function
defined in the model file.
channel: The channel for the gRPC master service.
embedding_service_endpoint: The endpoint to the embedding service.
model_def: The import path to the model definition
function/class in the model zoo, e.g.
"cifar10_subclass.CustomModel".
model_params: The dictionary of model parameters in a string that
will be used to instantiate the model,
e.g. "param1=1,param2=2".
prediction_outputs_processor: The name of the prediction output
processor class defined in the model file.
get_model_steps: Worker will perform `get_model` from the
parameter server every this many steps.
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
"""
self._worker_id = worker_id
self._job_type = job_type
self._minibatch_size = minibatch_size
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=model_zoo,
model_def=model_def,
dataset_fn=dataset_fn,
loss=loss,
optimizer=optimizer,
eval_metrics_fn=eval_metrics_fn,
model_params=model_params,
prediction_outputs_processor=prediction_outputs_processor,
)
self._embedding_service_endpoint = embedding_service_endpoint
self.set_model(model_inst)
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._max_minibatch_retry_num = max_minibatch_retry_num
self._model_version = -1
self._task_data_service = TaskDataService(
self, self._job_type == JobType.TRAINING_WITH_EVALUATION)
self._get_model_steps = get_model_steps
# TODO: Multiple tests are currently using this function to initialize
# self._model, where the initialization should be done via constructor.
def set_model(self, model_inst):
"""Set model instance to worker."""
self._model = model_inst
self._train_eagerly = False
self._init_embedding_layer()
self._var_created = self._model.built
self._non_embed_vars = []
if self._var_created:
self._non_embed_vars = get_non_embedding_trainable_vars(
self._model, self._embedding_layers)
def _init_embedding_layer(self):
"""
Init elasticdl.layers.embedding layer list and assign worker to them
"""
self._embedding_layers = find_layer(self._model, Embedding)
for layer in self._embedding_layers:
layer.set_endpoint(self._embedding_service_endpoint)
self._need_embedding_layer_check = (True if self._embedding_layers else
False)
def _set_tape_for_embedding(self, tape):
for layer in self._embedding_layers:
layer.set_tape(tape)
def _reset_embedding(self):
for layer in self._embedding_layers:
layer.reset()
def get_task(self):
"""
get task from master
"""
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = self._worker_id
return self._stub.GetTask(req)
def get_model(self, version, method):
"""
get model from master, and update model_version
"""
req = elasticdl_pb2.GetModelRequest()
req.version = version
req.method = method
model = self._stub.GetModel(req)
for var in self._non_embed_vars:
# Assumes all trainable variables exist in model.param.
var.assign(tensor_to_ndarray(model.param[var.name]))
self._model_version = model.version
def report_task_result(self, task_id, err_msg):
"""
report task result to master
"""
report = elasticdl_pb2.ReportTaskResultRequest()
report.task_id = task_id
report.err_message = err_msg
return self._stub.ReportTaskResult(report)
def report_variable(self):
"""
report variable to ps.
"""
req = elasticdl_pb2.ReportVariableRequest()
for v in self._non_embed_vars:
req.variable[v.name].CopyFrom(ndarray_to_tensor(v.numpy()))
self._stub.ReportVariable(req)
def report_gradient(self, grads):
"""
report gradient to ps, return (accepted, model_version) from rpc call.
"""
req = elasticdl_pb2.ReportGradientRequest()
non_embed_vars_n = len(self._non_embed_vars)
# The first `non_embed_vars_n` items in `grads` are gradients for
# `self._non_embed_vars`
for g, v in zip(grads[:non_embed_vars_n], self._non_embed_vars):
if isinstance(g, tf.IndexedSlices):
req.gradient[v.name].CopyFrom(
ndarray_to_tensor(g.values.numpy(),
tuple(g.indices.numpy())))
else:
req.gradient[v.name].CopyFrom(ndarray_to_tensor(g.numpy()))
# Accumulate gradients of ElasticDL embedding layer
if self._embedding_layers:
# The `edl_embedding_grads` are gradients for bets in
# `self._embedding_layers`
edl_embedding_grads = grads[non_embed_vars_n:]
# Check that the number of bet equal to the number of gradients.
# Please note that every embedding layer may have more than one
# `bet_id_pair`.
bet_number = 0
for layer in self._embedding_layers:
bet_number += len(layer.embedding_and_ids)
if len(edl_embedding_grads) != bet_number:
raise ValueError(
"elasticdl.layers.embedding related gradient number %d "
"does not match the number of its output tensor %d." %
(len(edl_embedding_grads), bet_number))
grad_accum_iter = 0
for layer in self._embedding_layers:
g_values = None
g_indices = None
for _, ids in layer.embedding_and_ids:
grad = edl_embedding_grads[grad_accum_iter]
grad_accum_iter += 1
# ElasticDL embedding layer with Sparse Gradients
if isinstance(grad, tf.IndexedSlices):
grad = grad.values
if g_values is not None:
g_values = tf.concat([g_values, grad], axis=0)
g_indices = tf.concat([g_indices, ids], axis=0)
else:
g_values = grad
g_indices = ids
req.gradient[layer.name].CopyFrom(
ndarray_to_tensor(g_values.numpy(),
tuple(g_indices.numpy())))
req.model_version = self._model_version
res = self._stub.ReportGradient(req)
return res.accepted, res.model_version
def report_evaluation_metrics(self, evaluation_metrics):
"""
report evaluation metrics to ps, return (accepted, model_version)
from rpc call.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for k, v in evaluation_metrics.items():
v_np = v.numpy()
# If scalar, convert to numpy 1D array with size 1
if not v_np.shape:
v_np = v_np.reshape(1)
req.evaluation_metrics[k].CopyFrom(ndarray_to_tensor(v_np))
req.model_version = self._model_version
res = self._stub.ReportEvaluationMetrics(req)
return res.accepted, res.model_version
def report_prediction_outputs(self, predictions):
if self._prediction_outputs_processor:
self._prediction_outputs_processor.process(predictions,
self._worker_id)
else:
logger.warning(
"prediction_outputs_processor is not "
"defined in the model definition. Prediction outputs "
"are not processed.")
return True
def _run_model_call_before_training(self, features):
"""Call `self._model.call` before training for two things:
* Create variables and report to ps if not created.
* Check whether there is an embedding layer that is called
more than once during one forward-pass.
"""
if self._embedding_layers:
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
_ = self._model.call(features)
else:
_ = self._model.call(features)
self._non_embed_vars = get_non_embedding_trainable_vars(
self._model, self._embedding_layers)
if not self._var_created:
self.report_variable()
self._var_created = True
if self._need_embedding_layer_check:
self._train_eagerly = False
for layer in self._embedding_layers:
if len(layer.embedding_and_ids) > 1:
self._train_eagerly = True
logger.warning(
"ElasticDL embedding layer %s is called more than "
"once, this will make the training process unable "
"to accelerate with tf.function." % (layer.name))
self._need_embedding_layer_check = False
self._reset_embedding()
def get_trainable_items(self):
"""
return all trainable variables list, including batch embedding
tensor (BET) if exists. take care to keep the same order as in
self.report_gradient()
"""
bets = []
if self._embedding_layers:
for layer in self._embedding_layers:
bets.extend([
batch_embedding
for (batch_embedding, _) in layer.embedding_and_ids
])
return self._non_embed_vars + bets
def training_process(self, features, labels):
"""
training for models with elasticdl.layers.embedding does not
support tf.function decorator
"""
if self._train_eagerly:
return self.training_process_eagerly(features, labels)
else:
return self.training_process_with_acceleration(features, labels)
@tf.function
def training_process_with_acceleration(self, features, labels):
return self.training_process_eagerly(features, labels)
def training_process_eagerly(self, features, labels):
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
outputs = self._model.call(features, training=True)
loss = self._loss(outputs, labels)
# Add regularization loss if any
if self._model.losses:
loss += tf.math.add_n(self._model.losses)
grads = tape.gradient(loss, self.get_trainable_items())
return loss, grads
@tf.function
def evaluation_process(self, features, labels):
outputs = self._model.call(features, training=False)
evaluation_metrics = self._eval_metrics_fn(outputs, labels)
return evaluation_metrics
@tf.function
def predict_process(self, features):
outputs = self._model.call(features, training=False)
return outputs
def _run_training_task(self, features, labels):
loss, grads = self.training_process(features, labels)
accepted, min_model_version = self.report_gradient(grads)
self._reset_embedding()
return accepted, min_model_version, loss
def _run_evaluation_task(self, features, labels):
evaluation_metrics = self.evaluation_process(features, labels)
accepted, _ = self.report_evaluation_metrics(evaluation_metrics)
return accepted
def _run_prediction_task(self, features):
predictions = self.predict_process(features)
return self.report_prediction_outputs(predictions)
def _process_minibatch(self, task_type, features, labels,
min_model_version):
if self._need_embedding_layer_check or not self._var_created:
self._run_model_call_before_training(features)
for _ in range(self._max_minibatch_retry_num):
if task_type == elasticdl_pb2.EVALUATION:
if min_model_version == -1:
if self._model_version < 0:
self.get_model(0, elasticdl_pb2.MINIMUM)
elif self._model_version != min_model_version:
self.get_model(min_model_version, elasticdl_pb2.FIXED)
accepted = self._run_evaluation_task(features, labels)
if accepted:
break
elif task_type == elasticdl_pb2.TRAINING:
# TODO: optimize the logic to avoid unnecessary
# get_model call.
self.get_model(
max(self._model_version, min_model_version),
elasticdl_pb2.MINIMUM,
)
accepted, min_model_version, loss = self._run_training_task(
features, labels)
if accepted:
logger.info("Loss is %f" % loss.numpy())
break
elif task_type == elasticdl_pb2.PREDICTION:
if self._model_version != min_model_version:
self.get_model(min_model_version, elasticdl_pb2.FIXED)
accepted = self._run_prediction_task(features)
if accepted:
break
else:
raise RuntimeError("Unrecognized task type, %s" % task_type)
else:
# Worker got stuck, fail the task.
# TODO: stop the worker if it fails to make any
# progress for some time.
raise RuntimeError("Worker got stuck")
return min_model_version
def _process_eval_task_if_needed(self):
"""
Check if there are evaluation tasks and process the tasks if any.
"""
eval_info = self._task_data_service.get_evaluation_dataset()
if not eval_info:
return
(eval_dataset, model_version, task_id) = eval_info
eval_dataset = self._dataset_fn(eval_dataset, Mode.EVALUATION)
eval_dataset = eval_dataset.batch(self._minibatch_size).prefetch(1)
err_msg = ""
for dataset_batch in eval_dataset:
data_err_msg = self._process_minibatch_and_report(
dataset_batch, elasticdl_pb2.EVALUATION, model_version)
if data_err_msg:
err_msg = data_err_msg
break
del eval_dataset
self.report_task_result(task_id, err_msg)
def _process_minibatch_and_report(self, dataset_batch, task_type,
model_version):
err_msg = ""
try:
if self._job_type == JobType.PREDICTION_ONLY:
features = dataset_batch
labels = None
else:
features = dataset_batch[0]
labels = dataset_batch[1]
self._process_minibatch(task_type, features, labels, model_version)
except RuntimeError as err:
err_msg = str(err)
traceback.print_exc()
except Exception as ex:
err_msg = str(ex)
traceback.print_exc()
raise ex
return err_msg
def run(self):
"""
Fetches task from master with and performs training or evaluation.
"""
if self._job_type == JobType.PREDICTION_ONLY:
mode = Mode.PREDICTION
elif self._job_type == JobType.EVALUATION_ONLY:
mode = Mode.EVALUATION
else:
mode = Mode.TRAINING
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
break
dataset = self._dataset_fn(dataset, mode)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
for dataset_batch in dataset:
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
self._process_eval_task_if_needed()
task = self._task_data_service.get_current_task()
err_msg = self._process_minibatch_and_report(
dataset_batch, task.type, task.model_version)
self._task_data_service.report_record_done(
self._minibatch_size, err_msg)
del dataset
# New evaluation tasks may be created after this worker's
# training tasks are done, as other workers' may still
# have pending training tasks.
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
self._process_eval_task_if_needed()
class Worker(object):
"""ElasticDL worker"""
def __init__(
self,
args,
master_client=None,
ps_client=None,
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
set_parallelism=False,
):
"""
Arguments:
channel: The channel for the gRPC master service.
ps_channels: The PS channels for PS service
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
max_allreduce_retry_num: The maximum number of retries for
allreduce operation if allreduce-based distributed
training strategy is used.
"""
self._args = args
self.logger = get_logger("Worker", level=args.log_level.upper())
if set_parallelism:
# Explicitly setting the parallelism will avoid multi-process hangs
# Maybe due to an unknown bug in Tensorflow?
# Must called before TensorFlow is initialized.
# Not set_parallelism by default to make unittests happy.
num_threads = os.cpu_count()
tf.config.threading.set_inter_op_parallelism_threads(num_threads)
tf.config.threading.set_intra_op_parallelism_threads(num_threads)
self._mc = master_client
self._ps_client = ps_client
self._distribution_strategy = args.distribution_strategy
self._max_minibatch_retry_num = max_minibatch_retry_num
self._timing = Timing(args.log_level.upper() == "DEBUG", self.logger)
self._log_loss_count = 0
self._var_created = False
self._init_from_args(args)
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
self._log_loss_steps = args.log_loss_steps
(
model_inst,
self._dataset_fn,
loss,
opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
self._custom_data_reader,
self._callbacks_list,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
prediction_outputs_processor=args.prediction_outputs_processor,
custom_data_reader=args.custom_data_reader,
callbacks=args.callbacks,
)
model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir
)
model_inst = model_handler.get_model_to_train(model_inst)
model_inst.optimizer = opt_fn()
model_inst.loss = loss
self._model_version = -1
self._task_data_service = TaskDataService(
self._mc,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
custom_data_reader=self._custom_data_reader,
data_reader_params=get_dict_from_params_str(
args.data_reader_params
),
data_origin=args.training_data,
)
if self._dataset_fn is None:
if hasattr(
self._task_data_service.data_reader, "default_dataset_fn"
):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn()
)
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn"
)
self._get_model_steps = args.get_model_steps
saved_model_exporter = SavedModelExporter(
self._task_data_service, self._dataset_fn, model_handler
)
# Place default callbacks at the head to execute them firstly
self._callbacks_list.callbacks.insert(0, saved_model_exporter)
self._callbacks_list.set_model(model_inst)
set_callback_parameters(
self._callbacks_list,
batch_size=args.minibatch_size,
saved_model_path=args.output,
checkpoint_path=args.checkpoint_dir,
)
self._saved_model_path = args.output
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
master_addr = args.master_addr.split(":")[0]
self._trainer = AllReduceTrainer(self._mc, master_addr, model_inst)
elif (
self._distribution_strategy
== DistributionStrategy.PARAMETER_SERVER
):
self._trainer = ParameterServerTrainer(
model_inst, self._ps_client, self._timing, args
)
def _process_minibatch(
self,
task_type,
features,
labels,
min_model_version,
train_with_local_model=False,
):
self._trainer.init_variables_if_need(features, labels)
self._timing.start_record_time("batch_process")
for _ in range(self._max_minibatch_retry_num):
if task_type == elasticdl_pb2.EVALUATION:
self._trainer.evaluate_minibatch(features, labels)
break
elif task_type == elasticdl_pb2.TRAINING:
# TODO: optimize the logic to avoid unnecessary
# get_model call.
self._callbacks_list.on_train_batch_begin(self._model_version)
(
*accepted,
min_model_version,
loss,
) = self._trainer.train_minibatch(
features, labels, train_with_local_model
)
self._model_version = self._trainer.get_model_version()
if (
self._model_version
>= self._log_loss_count * self._log_loss_steps
):
self.logger.info(
"Loss = {}, steps = {}".format(
loss.numpy(), self._model_version
)
)
self._log_loss_count = (
int(self._model_version / self._log_loss_steps) + 1
)
if accepted:
break
elif task_type == elasticdl_pb2.PREDICTION:
accepted = self._trainer.predict_minibatch(features)
if accepted:
break
else:
raise RuntimeError("Unrecognized task type, %s" % task_type)
else:
# Worker got stuck, fail the task.
# TODO: stop the worker if it fails to make any
# progress for some time.
raise RuntimeError("Worker got stuck")
self._timing.end_record_time("batch_process")
return min_model_version
def _process_eval_task(self, task):
"""
Check if there are evaluation tasks and process the tasks if any.
Return:
A python bool indicating whether worker processed some evaluation
tasks.
"""
self.logger.info("the evaluation task_id: %d" % task.task_id)
gen = self._task_data_service.get_dataset_gen(task)
if not gen:
return None
def create_dataset():
eval_dataset = tf.data.Dataset.from_generator(
gen, self._task_data_service.data_reader.records_output_types
)
eval_dataset = self._dataset_fn(
eval_dataset,
Mode.EVALUATION,
self._task_data_service.data_reader.metadata,
)
eval_dataset = eval_dataset.batch(self._minibatch_size).prefetch(1)
return eval_dataset
with tf.device("/device:cpu:0"):
eval_dataset = create_dataset()
model_version = task.model_version
task_id = task.task_id
err_msg = ""
for dataset_batch in eval_dataset:
data_err_msg = self._process_minibatch_and_report(
dataset_batch, elasticdl_pb2.EVALUATION, model_version
)
if data_err_msg:
err_msg = data_err_msg
break
del eval_dataset
evaluation_result = self._trainer.get_evaluation_result()
self._mc.report_evaluation_metrics(
model_outputs=evaluation_result[MetricsDictKey.MODEL_OUTPUT],
labels=evaluation_result[MetricsDictKey.LABEL],
)
self._mc.report_task_result(task_id, err_msg)
self._trainer.reset_evaluation_result()
def _process_train_end_callback_task_if_needed(self):
train_end_task = self._task_data_service.get_train_end_callback_task()
if train_end_task:
self._callbacks_list.on_train_end()
self._task_data_service.clear_train_end_callback_task()
self._mc.report_task_result(
task_id=train_end_task.task_id, err_msg=""
)
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
self._trainer.export_saved_model(self._saved_model_path)
def _process_minibatch_and_report(
self,
dataset_batch,
task_type,
model_version,
train_with_local_model=False,
):
err_msg = ""
try:
if self._job_type == JobType.PREDICTION_ONLY:
features = dataset_batch
labels = None
else:
features = dataset_batch[0]
labels = dataset_batch[1]
self._process_minibatch(
task_type,
features,
labels,
model_version,
train_with_local_model,
)
except RuntimeError as err:
err_msg = str(err)
traceback.print_exc()
except Exception as ex:
err_msg = str(ex)
traceback.print_exc()
raise ex
return err_msg
def _train_and_evaluate(self):
"""
Train and evaluate the model on the worker
"""
# The worker needs to get model from PS if
# `train_with_local_model=False`. This happens when:
# processing first minibatch
# any evaluation task has been executed just before this minibatch
# last minibatch is training task and failed
# local_update_count >= worker._get_model_steps
# Otherwise, worker trains with local model, i.e.
# `train_with_local_model=True`
train_with_local_model = False
# Initialize `local_update_count=get_model_steps` in order to set
# `train_with_local_model` to False inside for-loop for the first
# minibatch.
local_update_count = self._get_model_steps
last_training_minibatch_failed = False
evaluation_task_executed = False
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
self._process_train_end_callback_task_if_needed()
break
dataset = self._dataset_fn(
dataset,
Mode.TRAINING,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
self._timing.start_record_time("task_process")
for dataset_batch in dataset:
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
# Give the worker a chance to process an evaluation task
# during training if the task exists
evaluation_task_executed = (
True
if self._evaluate_only()
else evaluation_task_executed
)
task = self._task_data_service.get_current_task()
if (
evaluation_task_executed
or last_training_minibatch_failed
or local_update_count >= self._get_model_steps
):
local_update_count = 0
train_with_local_model = False
else:
train_with_local_model = True
err_msg = self._process_minibatch_and_report(
dataset_batch,
task.type,
task.model_version,
train_with_local_model,
)
local_update_count += 1
if err_msg:
last_training_minibatch_failed = True
else:
last_training_minibatch_failed = False
if local_update_count < self._get_model_steps:
self._update_local_model()
if self._task_data_service.report_record_done(
self._minibatch_size, err_msg
):
self._timing.end_record_time("task_process")
self._timing.report_timing(reset=True)
self._timing.start_record_time("task_process")
del dataset
# New evaluation tasks may be created after this worker's
# training tasks are done, as other workers' may still
# have pending training tasks.
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
evaluation_task_executed = self._evaluate_only()
self._process_train_end_callback_task_if_needed()
def _evaluate_only(self):
"""
Only evaluate the model on the worker.
"""
evaluation_task_executed = False
# should not get model before finishing some training tasks, because
# variables of subclass models are not created.
while True:
task = self._mc.get_task(elasticdl_pb2.EVALUATION)
# no evaluation task in eval_todo of master
if not task.shard_name:
break
self._process_eval_task(task)
evaluation_task_executed = True
return evaluation_task_executed
def _predict_only(self):
"""
Only predict outputs of the model with data in tasks on the worker.
"""
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
break
dataset = self._dataset_fn(
dataset,
Mode.PREDICTION,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
for dataset_batch in dataset:
task = self._task_data_service.get_current_task()
err_msg = self._process_minibatch_and_report(
dataset_batch, task.type, task.model_version
)
self._task_data_service.report_record_done(
self._minibatch_size, err_msg
)
del dataset
def run(self):
"""
Fetches task from master with and performs training, evaluation
or prediction.
"""
if self._job_type == JobType.PREDICTION_ONLY:
self._predict_only()
elif self._job_type == JobType.EVALUATION_ONLY:
self._evaluate_only()
else:
self._train_and_evaluate()
class Worker(object):
"""ElasticDL worker"""
def __init__(
self,
args,
channel=None,
ps_channels=None,
max_minibatch_retry_num=DEFAULT_MAX_MINIBATCH_RETRY_NUM,
max_allreduce_retry_num=DEFAULT_MAX_ALLREDUCE_RETRY_NUM,
):
"""
Arguments:
channel: The channel for the gRPC master service.
ps_channels: The PS channels for PS service
max_minibatch_retry_num: The maximum number of a minibatch retry
as its results (e.g. gradients) are not accepted by master.
max_allreduce_retry_num: The maximum number of retries for
allreduce operation if allreduce-based distributed
training strategy is used.
"""
self._args = args
if channel is None:
self._stub = None
else:
self._stub = elasticdl_pb2_grpc.MasterStub(channel)
self._use_multi_ps = False
self._ps_vars = {}
if isinstance(ps_channels, list):
if len(ps_channels) > 0:
self._use_multi_ps = True
self._ps_stubs = [
elasticdl_pb2_grpc.PserverStub(c) for c in ps_channels
]
self._var_to_ps = {}
self._distribution_strategy = args.distribution_strategy
if (self._distribution_strategy
== DistributionStrategy.PARAMETER_SERVER
and self._use_multi_ps is False):
raise ValueError(
"PS channels are not set up under parameter server strategy")
self._max_minibatch_retry_num = max_minibatch_retry_num
self._max_allreduce_retry_num = max_allreduce_retry_num
self._init_from_args(args)
def _init_from_args(self, args):
"""
Please refer to elastic/python/common/args.py for more
details about arguments of a worker.
"""
self._worker_id = args.worker_id
self._job_type = args.job_type
self._minibatch_size = args.minibatch_size
(
model_inst,
self._dataset_fn,
self._loss,
self._opt_fn,
self._eval_metrics_fn,
self._prediction_outputs_processor,
) = get_model_spec(
model_zoo=args.model_zoo,
model_def=args.model_def,
dataset_fn=args.dataset_fn,
loss=args.loss,
optimizer=args.optimizer,
eval_metrics_fn=args.eval_metrics_fn,
model_params=args.model_params,
prediction_outputs_processor=args.prediction_outputs_processor,
)
self._collective_communicator = (
CollectiveCommunicator() if self._distribution_strategy
== DistributionStrategy.ALLREDUCE else None)
self._model_handler = ModelHandler.get_model_handler(
self._distribution_strategy, checkpoint_dir=args.checkpoint_dir)
model_inst = self._model_handler.get_model_to_train(model_inst)
self.set_model(model_inst)
self._model_version = -1
self._task_data_service = TaskDataService(
self,
self._job_type == JobType.TRAINING_WITH_EVALUATION,
data_reader_params=get_dict_from_params_str(
args.data_reader_params),
)
if self._dataset_fn is None:
if hasattr(self._task_data_service.data_reader,
"default_dataset_fn"):
self._dataset_fn = (
self._task_data_service.data_reader.default_dataset_fn())
else:
raise ValueError(
"dataset_fn is required if the data_reader used does "
"not provide default implementation of dataset_fn")
self._get_model_steps = args.get_model_steps
if self._get_model_steps > 1:
self._opt = self._opt_fn()
self._non_embed_grads = {}
self._evaluation_result = {}
# TODO: Multiple tests are currently using this function to initialize
# self._model, where the initialization should be done via constructor.
def set_model(self, model_inst):
"""Set model instance to worker."""
self._model = model_inst
self._train_eagerly = False
self._init_embedding_layer()
self._var_created = self._model.built
self._non_embed_vars = {}
if self._var_created:
for var in get_non_embedding_trainable_vars(
self._model, self._embedding_layers):
self._non_embed_vars[var.name] = var
if self._use_multi_ps:
self.init_ps_var_partition()
def _init_embedding_layer(self):
"""
Init elasticdl.layers.embedding layer list and assign worker to them
"""
self._embedding_layers = find_layer(self._model, Embedding)
if self._use_multi_ps:
self.report_embedding_info()
for layer in self._embedding_layers:
layer.set_lookup_embedding_func(self.pull_embedding_vector)
self._need_embedding_layer_check = (True if self._embedding_layers else
False)
def _set_tape_for_embedding(self, tape):
for layer in self._embedding_layers:
layer.set_tape(tape)
def _reset_embedding(self):
for layer in self._embedding_layers:
layer.reset()
def _update_local_model(self):
if not self._non_embed_grads:
return
# Take care of the order of grads and vars if worker modifies
# `_non_embed_vars` during training.
self._opt.apply_gradients(
zip(self._non_embed_grads, self._non_embed_vars.values()))
self._non_embed_grads = None
def get_task(self, task_type=None):
"""
get task from master
"""
req = elasticdl_pb2.GetTaskRequest()
req.worker_id = self._worker_id
if task_type is not None:
req.task_type = task_type
return self._stub.get_task(req)
def get_model(self):
model_version = -1
variable_future_and_id_pairs = []
req = empty_pb2.Empty()
if self._use_multi_ps:
self.init_ps_var_partition()
for ps_id, stub in enumerate(self._ps_stubs):
if ps_id not in self._ps_vars:
continue
# async grpc call
var_future = stub.pull_variable.future(req)
variable_future_and_id_pairs.append((var_future, ps_id))
for var_future, ps_id in variable_future_and_id_pairs:
res = var_future.result()
if not res.model_init_status:
# push variable to ps for initialization
self.report_variable_to_ps(ps_id)
res = self._ps_stubs[ps_id].pull_variable(req)
if not res.model_init_status:
# TODO: support PS fault-tolerance
raise RuntimeError("PS pod %d cannot be initialized" %
ps_id)
for tensor_pb in res.model.param:
tensor = Tensor.from_tensor_pb(tensor_pb)
self._non_embed_vars[tensor.name].assign(tensor.to_ndarray())
model_version = max(model_version, res.model.version)
self._model_version = model_version
def pull_embedding_vector(self, layer_name, embedding_ids):
"""Pulls and returns embedding vectors ordered by the embedding ids."""
ps_ids = {}
ps_ids_index = {}
for idx, embedding_id in enumerate(embedding_ids):
ps_id = int_to_id(embedding_id, len(self._ps_stubs))
ps_ids.setdefault(ps_id, []).append(embedding_id)
ps_ids_index.setdefault(ps_id, []).append(idx)
embeddings = []
index = []
pb_future_and_id_pairs = []
for ps_id, embedding_ids in ps_ids.items():
req = elasticdl_pb2.PullEmbeddingVectorRequest()
req.name = layer_name
req.ids.extend(embedding_ids)
pb_future = self._ps_stubs[ps_id].pull_embedding_vector.future(req)
pb_future_and_id_pairs.append((pb_future, ps_id))
for pb_future, ps_id in pb_future_and_id_pairs:
pb = pb_future.result()
embeddings.append(tensor_pb_to_ndarray(pb))
index.extend(ps_ids_index[ps_id])
embeddings = np.concatenate(embeddings)
# adjust the order of embedding vectors
new_embeddings = np.empty_like(embeddings)
new_embeddings[index] = embeddings
return new_embeddings
def report_task_result(self, task_id, err_msg, exec_counters=None):
"""
report task result to master
"""
report = elasticdl_pb2.ReportTaskResultRequest()
report.task_id = task_id
report.err_message = err_msg
if isinstance(exec_counters, dict):
report.exec_counters.update(exec_counters)
return self._stub.report_task_result(report)
def init_ps_var_partition(self):
ps_vars = {}
for v in self._non_embed_vars.values():
if v.name not in self._var_to_ps:
self._var_to_ps[v.name] = string_to_id(v.name,
len(self._ps_stubs))
ps_id = self._var_to_ps[v.name]
if ps_id not in ps_vars:
ps_vars[ps_id] = [v]
else:
ps_vars[ps_id].append(v)
self._ps_vars = ps_vars
def report_embedding_info(self):
model = elasticdl_pb2.Model()
if self._embedding_layers:
embedding_infos = model.embedding_table_info
for layer in self._embedding_layers:
embedding_info = embedding_infos.add()
embedding_info.name = layer.name
embedding_info.dim = layer.output_dim
embedding_info.initializer = layer.embeddings_initializer
for ps_id in range(len(self._ps_stubs)):
self._ps_stubs[ps_id].push_embedding_info(model)
def report_variable_to_ps(self, ps_id):
model = elasticdl_pb2.Model()
if ps_id in self._ps_vars:
vars = self._ps_vars[ps_id]
for var in vars:
emplace_tensor_pb_from_ndarray(model.param,
var.numpy(),
name=var.name)
self._ps_stubs[ps_id].push_model(model)
def report_variable(self):
# TODO: call `push_model` in parallel
for ps_id in range(len(self._ps_stubs)):
self.report_variable_to_ps(ps_id)
def report_gradient_to_ps(self, grads):
reqs = [
elasticdl_pb2.PushGradientRequest()
for i in range(len(self._ps_stubs))
]
ps_grads = {}
non_embed_vars_n = len(self._non_embed_vars)
for g, v in zip(grads[:non_embed_vars_n],
self._non_embed_vars.values()):
ps_id = self._var_to_ps[v.name]
if ps_id not in ps_grads:
ps_grads[ps_id] = [(g, v.name)]
else:
ps_grads[ps_id].append((g, v.name))
for ps_id in ps_grads:
req = reqs[ps_id]
for g, name in ps_grads[ps_id]:
emplace_tensor_pb_from_ndarray(req.gradients, g, name=name)
if self._embedding_layers:
edl_embedding_grads = grads[non_embed_vars_n:]
bet_number = 0
for layer in self._embedding_layers:
bet_number += len(layer.embedding_and_ids)
if len(edl_embedding_grads) != bet_number:
raise ValueError(
"elasticdl.layers.embedding related gradient number %d "
"does not match the number of its output tensor %d." %
(len(edl_embedding_grads), bet_number))
grad_accum_iter = 0
for layer in self._embedding_layers:
g_values = None
g_indices = None
for _, ids in layer.embedding_and_ids:
grad = edl_embedding_grads[grad_accum_iter]
grad_accum_iter += 1
# ElasticDL embedding layer with Sparse Gradients
if isinstance(grad, tf.IndexedSlices):
grad = grad.values
if g_values is not None:
g_values = tf.concat([g_values, grad], axis=0)
g_indices = tf.concat([g_indices, ids], axis=0)
else:
g_values = grad
g_indices = ids
results = scatter_embedding_vector(g_values.numpy(),
g_indices.numpy(),
len(self._ps_stubs))
for ps_id in results:
req = reqs[ps_id]
gv, gi = results[ps_id]
emplace_tensor_pb_from_ndarray(req.gradients,
values=gv,
indices=gi,
name=layer.name)
report_futures = []
for ps_id in range(len(self._ps_stubs)):
req = reqs[ps_id]
req.model_version = self._model_version
report_future = self._ps_stubs[ps_id].push_gradient.future(req)
report_futures.append(report_future)
accepted = False
max_version = -1
for report_future in report_futures:
res = report_future.result()
if res.accepted:
accepted = True
if res.model_version > max_version:
max_version = res.model_version
return accepted, max_version
def report_gradient_locally(self, grads):
if self._embedding_layers:
raise ValueError("ElasticDL embedding layer is not supported when"
"reporting gradients locally")
for g, v in zip(grads[:len(self._non_embed_vars)],
self._non_embed_vars.values()):
self._non_embed_grads[v.name] = g
return True, None
def report_gradient(self, grads):
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
return self.report_gradient_locally(grads)
else:
if self._use_multi_ps:
return self.report_gradient_to_ps(grads)
raise RuntimeError("Only support report gradients to PS")
def report_evaluation_metrics(self, model_outputs, labels):
"""
report evaluation metrics to ps.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for name, output in model_outputs.items():
output = np.concatenate(output)
emplace_tensor_pb_from_ndarray(req.model_outputs,
output,
name=name)
labels = np.concatenate(labels)
tensor = Tensor(values=labels)
serialize_tensor(tensor, req.labels)
self._stub.report_evaluation_metrics(req)
def report_prediction_outputs(self, predictions):
if self._prediction_outputs_processor:
self._prediction_outputs_processor.process(predictions,
self._worker_id)
else:
logger.warning(
"prediction_outputs_processor is not "
"defined in the model definition. Prediction outputs "
"are not processed.")
return True
def _run_model_call_before_training(self, features):
"""Call `self._model.call` before training for two things:
* Create variables and report to ps if not created.
* Check whether there is an embedding layer that is called
more than once during one forward-pass.
"""
if self._embedding_layers:
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
_ = self._model.call(features)
else:
_ = self._model.call(features)
self._non_embed_vars = {}
for var in get_non_embedding_trainable_vars(self._model,
self._embedding_layers):
self._non_embed_vars[var.name] = var
if not self._var_created:
if self._use_multi_ps:
self.init_ps_var_partition()
else:
self.report_variable()
self._var_created = True
if self._need_embedding_layer_check:
self._train_eagerly = False
for layer in self._embedding_layers:
if len(layer.embedding_and_ids) > 1:
self._train_eagerly = True
logger.warning(
"ElasticDL embedding layer %s is called more than "
"once, this will make the training process unable "
"to accelerate with tf.function." % (layer.name))
self._need_embedding_layer_check = False
self._reset_embedding()
def get_trainable_items(self):
"""
return all trainable variables list, including batch embedding
tensor (BET) if exists. take care to keep the same order as in
self.report_gradient()
"""
bets = []
if self._embedding_layers:
for layer in self._embedding_layers:
bets.extend([
batch_embedding
for (batch_embedding, _) in layer.embedding_and_ids
])
return list(self._non_embed_vars.values()) + bets
def training_process(self, features, labels):
"""
training for models with elasticdl.layers.embedding does not
support tf.function decorator
"""
if self._train_eagerly:
return self.training_process_eagerly(features, labels)
else:
return self.training_process_with_acceleration(features, labels)
@tf.function
def training_process_with_acceleration(self, features, labels):
return self.training_process_eagerly(features, labels)
def training_process_eagerly(self, features, labels):
with tf.GradientTape() as tape:
self._set_tape_for_embedding(tape)
outputs = self._model.call(features, training=True)
loss = self._loss(labels, outputs)
# Add regularization loss if any
if self._model.losses:
loss += tf.math.add_n(self._model.losses)
grads = tape.gradient(loss, self.get_trainable_items())
return loss, grads
@tf.function
def forward_process(self, features):
"""Calculates model outputs in non-training mode."""
outputs = self._model.call(features, training=False)
return outputs
def _update_local_model_params(self, model_params):
self._non_embed_vars = model_params
def _get_local_model_params(self):
return self._non_embed_vars
# TODO: Implement master gRPC service to select a worker
# to be used for broadcast model parameters from.
@staticmethod
def _get_ip_of_broadcast_root_worker():
return 1
@staticmethod
def _get_ip_of_this_worker():
hostname = socket.gethostname()
# Use localhost for testing locally on a laptop or inside a container
if ".local" in hostname or "." not in hostname:
hostname = "localhost"
return socket.gethostbyname(hostname)
def _broadcast_model_params(self):
status = self._collective_communicator.barrier()
if status == CollectiveCommunicatorStatus.FAILED:
logger.warning("Failed to perform barrier operation")
return False
broadcast_root_worker_ip = self._get_ip_of_broadcast_root_worker()
this_worker_ip = self._get_ip_of_this_worker()
is_broadcast_root_worker = this_worker_ip == broadcast_root_worker_ip
model_params = (self._get_local_model_params()
if is_broadcast_root_worker else None)
status, model_params = self._collective_communicator.broadcast(
model_params,
broadcast_root_worker_ip,
)
if status == CollectiveCommunicatorStatus.FAILED:
logger.warning("Failed to broadcast model parameters")
return False
if not is_broadcast_root_worker and model_params is not None:
self._update_local_model_params(model_params)
status = self._collective_communicator.barrier()
if status == CollectiveCommunicatorStatus.FAILED:
logger.warning("Failed to perform barrier operation")
return False
return True
def _calculate_grads_and_report_with_allreduce(self, grads):
status, averaged_grads = self._collective_communicator.allreduce(grads)
accepted = False
if status == CollectiveCommunicatorStatus.SUCCEEDED:
accepted, _ = self.report_gradient(averaged_grads)
if not accepted:
logger.warning("Failed to report the averaged gradients")
return accepted
def _collect_gradients_with_allreduce_robust(self, grads):
accepted = self._calculate_grads_and_report_with_allreduce(grads)
if not accepted:
if self._collective_communicator.has_new_worker_joining():
succeeded = self._broadcast_model_params()
if succeeded:
return self._calculate_grads_and_report_with_allreduce(
grads)
else:
return False
else:
logger.warning(
"No new worker joining. Broadcast operation skipped")
return False
else:
return True
def _collect_gradients_without_allreduce(self, grads):
accepted, min_model_version = self.report_gradient(grads)
if accepted and self._get_model_steps > 1:
non_embed_vars_n = len(self._non_embed_vars)
self._non_embed_grads = grads[:non_embed_vars_n]
self._reset_embedding()
return accepted, min_model_version
def _run_training_task(self, features, labels):
loss, grads = self.training_process(features, labels)
if self._distribution_strategy == DistributionStrategy.ALLREDUCE:
# TODO: Delay certain amount of time before retrying
for _ in range(self._max_allreduce_retry_num + 1):
accepted = self._collect_gradients_with_allreduce_robust(grads)
if accepted:
return accepted, None, loss
else:
logger.warning("Failed to perform allreduce operation on"
"the gradients. Retrying...")
else:
return (*self._collect_gradients_without_allreduce(grads), loss)
def _collect_evaluation_result(self, outputs, labels):
key = MetricsDictKey.MODEL_OUTPUT
if key not in self._evaluation_result:
outputs = {k: [v.numpy()] for k, v in outputs.items()}
self._evaluation_result[key] = outputs
else:
for k, v in outputs.items():
self._evaluation_result[key][k].append(v.numpy())
key = MetricsDictKey.LABEL
if key not in self._evaluation_result:
self._evaluation_result[key] = [labels.numpy()]
else:
self._evaluation_result[key].append(labels.numpy())
def _run_evaluation_task(self, features, labels):
outputs = self.forward_process(features)
if not isinstance(outputs, dict):
outputs = {MetricsDictKey.MODEL_OUTPUT: outputs}
self._collect_evaluation_result(outputs, labels)
def _run_prediction_task(self, features):
predictions = self.forward_process(features)
return self.report_prediction_outputs(predictions)
def _process_minibatch(
self,
task_type,
features,
labels,
min_model_version,
train_with_local_model=False,
):
if self._need_embedding_layer_check or not self._var_created:
self._run_model_call_before_training(features)
for _ in range(self._max_minibatch_retry_num):
if task_type == elasticdl_pb2.EVALUATION:
self._run_evaluation_task(features, labels)
break
elif task_type == elasticdl_pb2.TRAINING:
# TODO: optimize the logic to avoid unnecessary
# get_model call.
if not train_with_local_model:
self.get_model()
*accepted, min_model_version, loss = self._run_training_task(
features, labels)
if accepted:
logger.info("Loss is {}".format(loss.numpy()))
break
elif task_type == elasticdl_pb2.PREDICTION:
if self._model_version != min_model_version:
self.get_model()
accepted = self._run_prediction_task(features)
if accepted:
break
else:
raise RuntimeError("Unrecognized task type, %s" % task_type)
else:
# Worker got stuck, fail the task.
# TODO: stop the worker if it fails to make any
# progress for some time.
raise RuntimeError("Worker got stuck")
return min_model_version
def _process_eval_task(self, task):
"""
Check if there are evaluation tasks and process the tasks if any.
Return:
A python bool indicating whether worker processed some evaluation
tasks.
"""
logger.info("the evaluation task_id: %d" % task.task_id)
eval_info = self._task_data_service.get_validation_dataset(task)
if not eval_info:
return
(eval_dataset, model_version, task_id) = eval_info
eval_dataset = self._dataset_fn(
eval_dataset,
Mode.EVALUATION,
self._task_data_service.data_reader.metadata,
)
eval_dataset = eval_dataset.batch(self._minibatch_size).prefetch(1)
err_msg = ""
for dataset_batch in eval_dataset:
data_err_msg = self._process_minibatch_and_report(
dataset_batch, elasticdl_pb2.EVALUATION, model_version)
if data_err_msg:
err_msg = data_err_msg
break
del eval_dataset
self.report_evaluation_metrics(
model_outputs=self._evaluation_result[MetricsDictKey.MODEL_OUTPUT],
labels=self._evaluation_result[MetricsDictKey.LABEL],
)
self.report_task_result(task_id, err_msg)
self._evaluation_result = {}
def _process_save_model_task_if_needed(self):
(
task,
dataset,
) = self._task_data_service.get_save_model_task_and_dataset()
if task is not None and dataset is not None:
dataset = self._dataset_fn(
dataset,
Mode.PREDICTION,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size)
saved_model_path = task.extended_config.get(
SaveModelConfig.SAVED_MODEL_PATH)
saved_model_path = os.path.join(saved_model_path,
str(int(time.time())))
logger.info(
"The path to export model is {}".format(saved_model_path))
model = self._model_handler.get_model_to_export(
self._model, dataset)
tf.saved_model.save(model, saved_model_path)
self.report_task_result(task_id=task.task_id, err_msg="")
def _process_minibatch_and_report(
self,
dataset_batch,
task_type,
model_version,
train_with_local_model=False,
):
err_msg = ""
try:
if self._job_type == JobType.PREDICTION_ONLY:
features = dataset_batch
labels = None
else:
features = dataset_batch[0]
labels = dataset_batch[1]
self._process_minibatch(
task_type,
features,
labels,
model_version,
train_with_local_model,
)
except RuntimeError as err:
err_msg = str(err)
traceback.print_exc()
except Exception as ex:
err_msg = str(ex)
traceback.print_exc()
raise ex
return err_msg
def _train_and_evaluate(self):
"""
Train and evaluate the model on the worker
"""
# The worker needs to get model from PS if
# `train_with_local_model=False`. This happens when:
# processing first minibatch
# any evaluation task has been executed just before this minibatch
# last minibatch is training task and failed
# local_update_count >= worker._get_model_steps
# Otherwise, worker trains with local model, i.e.
# `train_with_local_model=True`
train_with_local_model = False
# Initialize `local_update_count=get_model_steps` in order to set
# `train_with_local_model` to False inside for-loop for the first
# minibatch.
local_update_count = self._get_model_steps
last_training_minibatch_failed = False
evaluation_task_executed = False
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
break
dataset = self._dataset_fn(
dataset,
Mode.TRAINING,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
for dataset_batch in dataset:
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
# Give the worker a chance to process an evaluation task
# during training if the task exists
evaluation_task_executed = (True if self._evaluate_only()
else evaluation_task_executed)
task = self._task_data_service.get_current_task()
if (evaluation_task_executed or last_training_minibatch_failed
or local_update_count >= self._get_model_steps):
local_update_count = 0
train_with_local_model = False
else:
train_with_local_model = True
err_msg = self._process_minibatch_and_report(
dataset_batch,
task.type,
task.model_version,
train_with_local_model,
)
local_update_count += 1
if err_msg:
last_training_minibatch_failed = True
else:
last_training_minibatch_failed = False
if local_update_count < self._get_model_steps:
self._update_local_model()
self._task_data_service.report_record_done(
self._minibatch_size, err_msg)
del dataset
# New evaluation tasks may be created after this worker's
# training tasks are done, as other workers' may still
# have pending training tasks.
if self._job_type == JobType.TRAINING_WITH_EVALUATION:
evaluation_task_executed = self._evaluate_only()
self._process_save_model_task_if_needed()
def _evaluate_only(self):
"""
Only evaluate the model on the worker.
"""
evaluation_task_executed = False
# should not get model before finishing some training tasks, because
# variables of subclass models are not created.
is_model_got = False
while True:
task = self.get_task(elasticdl_pb2.EVALUATION)
# no evaluation task in eval_todo of master
if not task.shard_name:
break
# get the latest model before processing eval tasks
if not is_model_got:
self.get_model()
is_model_got = True
self._process_eval_task(task)
evaluation_task_executed = True
return evaluation_task_executed
def _predict_only(self):
"""
Only predict outputs of the model with data in tasks on the worker.
"""
while True:
dataset = self._task_data_service.get_dataset()
if not dataset:
break
dataset = self._dataset_fn(
dataset,
Mode.PREDICTION,
self._task_data_service.data_reader.metadata,
)
dataset = dataset.batch(self._minibatch_size).prefetch(1)
for dataset_batch in dataset:
task = self._task_data_service.get_current_task()
err_msg = self._process_minibatch_and_report(
dataset_batch, task.type, task.model_version)
self._task_data_service.report_record_done(
self._minibatch_size, err_msg)
del dataset
def run(self):
"""
Fetches task from master with and performs training, evaluation
or prediction.
"""
if self._job_type == JobType.PREDICTION_ONLY:
self._predict_only()
elif self._job_type == JobType.EVALUATION_ONLY:
self._evaluate_only()
else:
self._train_and_evaluate()
