def test_delete_variables(self):
params = Parameters()
embed_layers = ["test_1", "test_2"]
slot_names = ["m", "v"]
dim = 8
for layer in embed_layers:
params.embedding_params[layer] = EmbeddingTable(layer, dim)
for slot in slot_names:
slot_key = get_slot_table_name(layer, slot)
params.embedding_params[slot_key] = EmbeddingTable(
slot_key, dim, "0.0", True)
opt = Adam()
opt_wrapper = OptimizerWrapper(opt, None, params.get_embedding_param,
params.set_embedding_param)
opt_wrapper._init_thread_local()
for name in embed_layers:
opt_wrapper._tls._unique_ids_all_layers[name] = np.ndarray(
[2], np.int32)
opt_wrapper._create_embedding_variable(
name, np.ndarray([2, dim], np.float32))
opt_wrapper._get_slot_and_set_to_optimizer(name)
self.assertTrue(len(opt._weights) == 4)
self.assertTrue(len(opt._slots) == 2)
for slot_dict in opt._slots.values():
self.assertTrue(len(slot_dict) == 2)
opt_wrapper._delete_slots_and_weights_in_optimizer()
self.assertTrue(len(opt._weights) == 0)
self.assertTrue(len(opt._slots) == 0)
def wrap_optimizer(self):
self._optimizer = OptimizerWrapper(
self._optimizer,
self._use_async,
self._parameters.get_embedding_param,
self._parameters.set_embedding_param,
)
def test_set_slot_to_optimizer(self):
embed_name = "test_emb"
indices = np.ndarray([2], dtype=np.int32)
embed_values = np.ndarray([2, 2], dtype=np.float32)
slot_values = {
"m": np.ndarray([2, 2], dtype=np.float32),
"v": np.ndarray([2, 2], dtype=np.float32),
}
params = Parameters()
params.embedding_params[embed_name] = EmbeddingTable(embed_name, 8)
for slot in ["m", "v"]:
slot_table_name = get_slot_table_name(embed_name, slot)
params.embedding_params[slot_table_name] = EmbeddingTable(
slot_table_name, 2, "0.0", True)
opt = Adam()
opt_wrapper = OptimizerWrapper(opt, None, params.get_embedding_param)
opt_wrapper._init_thread_local()
opt_wrapper._tls._unique_ids_all_layers[embed_name] = indices
opt_wrapper._create_embedding_variable(embed_name, embed_values)
opt_wrapper._get_slot_and_set_to_optimizer(embed_name)
self.assertEqual(len(opt._slots), 1)
opt_slots = list(opt._slots.values())[0]
self.assertEqual(sorted(opt_slots.keys()), ["m", "v"])
for name in ["m", "v"]:
self.assertTrue(
np.allclose(opt_slots[name].numpy(), slot_values[name]))
def test_update_embedding_param(self):
params = Parameters()
for name in ["test_1", "test_2"]:
params.embedding_params[name] = EmbeddingTable(name, 8)
slot_key = get_slot_table_name(name, "momentum")
params.embedding_params[slot_key] = EmbeddingTable(
slot_key, 8, "0.0", True)
indices = {
"test_1": np.array([1, 5]),
"test_2": np.array([10]),
}
embed_vars = {
"test_1": tf.Variable(np.random.rand(2, 8).astype(np.float32)),
"test_2": tf.Variable(np.random.rand(1, 8).astype(np.float32)),
}
slot_vars = {
"test_1": {
"momentum":
tf.Variable(np.random.rand(2, 8).astype(np.float32))
},
"test_2": {
"momentum":
tf.Variable(np.random.rand(1, 8).astype(np.float32))
},
}
opt = SGD(momentum=0.1)
opt_wrapper = OptimizerWrapper(opt, None, None,
params.set_embedding_param)
opt_wrapper._tls._unique_ids_all_layers = indices
opt_wrapper._tls._embed_variables = embed_vars
opt_wrapper._tls._slot_variables = slot_vars
opt_wrapper._update_embedding_param()
for name in ["test_1", "test_2"]:
self.assertTrue(
np.allclose(
embed_vars[name].numpy(),
params.get_embedding_param(name, indices[name]),
))
slot = "momentum"
slot_table_name = get_slot_table_name(name, slot)
self.assertTrue(
np.allclose(
slot_vars[name][slot].numpy(),
params.get_embedding_param(slot_table_name, indices[name]),
))
def _train_edl_embedding_with_optimizer_wrapper(model, opt_keras, X, Y,
loss_fn, params, random_seed):
"""Train model with optimizer wrapper."""
tf.random.set_seed(random_seed)
opt_wrapper = OptimizerWrapper(
opt_keras,
lookup_embedding_func=params.get_embedding_param,
update_embedding_func=params.set_embedding_param,
)
embed_layers = find_layer(model, Embedding)
# initialize slot params
params.create_slot_params(opt_wrapper.allowed_slot_names,
opt_wrapper.slot_initial_value)
# initialize ElasticDL embedding layer
for layer in embed_layers:
layer.set_lookup_embedding_func(params.get_embedding_param)
# training process
for train_iter, (features, labels) in enumerate(zip(X, Y)):
with tf.GradientTape() as tape:
for layer in embed_layers:
layer.set_tape(tape)
outputs = model.call(features)
loss = loss_fn(outputs, labels)
# Need to get non-embedding variables inside for loop because model
# creates variables after the first time `model.call` is called
if not train_iter:
non_embed_vars = get_non_embedding_trainable_vars(
model, embed_layers)
embed_items = []
for layer in embed_layers:
embed_items.extend([(bet, layer.name, ids)
for bet, ids in layer.embedding_and_ids])
grads = tape.gradient(
loss, non_embed_vars + [var for var, _, _ in embed_items])
# TODO: do not need to merge gradient from the same embedding layer
# after `optimizer_wrapper` support grads_and_vars with duplicated
# layer name
non_embed_vars_n = len(non_embed_vars)
non_embed_grads = grads[:non_embed_vars_n]
embed_grads_dict = {}
for (_, layer_name, ids), grad in zip(embed_items,
grads[non_embed_vars_n:]):
if layer_name in embed_grads_dict:
merged_grads = embed_grads_dict[layer_name]
embed_grads_dict[layer_name] = tf.IndexedSlices(
tf.concat([merged_grads.values, grad.values], axis=0),
tf.concat([merged_grads.indices, ids], axis=0),
)
else:
embed_grads_dict[layer_name] = tf.IndexedSlices(
grad.values, ids)
opt_wrapper.apply_gradients(
list(zip(non_embed_grads, non_embed_vars)) +
[(grad, layer_name)
for layer_name, grad in embed_grads_dict.items()])
for layer in embed_layers:
layer.reset()
def _test_async_correctness(
self,
grads_and_vars_batches,
embed_values,
expected_non_embed_values,
expected_embed_values=None,
):
"""Checks the correctness of async OptimizerWrapper. This function
creates many threads and these threads call
`OptimizerWrapper.apply_gradients` simultaneously.
Args:
grads_and_vars_batches: A python list of `grads_and_vars`. Every
thread takes a `grads_and_vars` and calls `apply_gradients`.
embed_values: A python dictionary of
`(layer_name, embedding table)`.
expected_non_embed_values: A python list of expected non-embdding
values after applying gradients.
expected_embed_values: A python dictionary of expected embedding
values after applying gradients. None means no need to check
embedding values.
"""
thread_num = len(grads_and_vars_batches)
input_dims = {}
embed_var_n = len(embed_values)
params = Parameters()
for layer, values in embed_values.items():
embed_dim = values.shape[1]
input_dims[layer] = values.shape[0]
embed_table = EmbeddingTable(layer, embed_dim)
embed_table.set(range(input_dims[layer]), values)
params.embedding_params[layer] = embed_table
opt = SGD(0.1)
opt_wrapper = OptimizerWrapper(
opt,
True,
lookup_embedding_func=params.get_embedding_param,
update_embedding_func=params.set_embedding_param,
)
# call optimizer_wrapper.apply_gradients asynchronously
def _apply_gradients(opt_wrapper, grads_and_vars):
# sleep 1s to wait that all threads are in this method call
time.sleep(1)
opt_wrapper.apply_gradients(grads_and_vars)
executor = ThreadPoolExecutor(max_workers=thread_num)
tasks = [
executor.submit(_apply_gradients, opt_wrapper, grads_and_vars)
for grads_and_vars in grads_and_vars_batches
]
_ = [task.result() for task in tasks]
# check updated results of non-embedding variables
non_embed_vars = [
var for grad, var in grads_and_vars_batches[0][:-embed_var_n]
]
for var, expected_value in zip(non_embed_vars,
expected_non_embed_values):
self.assertTrue(np.isclose(var.numpy(), expected_value).all())
# `expected_embed_values=None` means that no need to check
# embedding table
if not expected_embed_values:
return
# check updated results of embedding table
for layer, expected_values in expected_embed_values.items():
value = params.get_embedding_param(layer, range(input_dims[layer]))
self.assertTrue(
any([
np.isclose(value, expected).all()
for expected in expected_values
]))
def _compare_slot_names(self, opt, expected):
tmp = OptimizerWrapper(opt)
self.assertTrue(sorted(tmp.allowed_slot_names) == sorted(expected))
class PserverServicer(elasticdl_pb2_grpc.PserverServicer):
"""PS service implementation"""
def __init__(
self,
parameters,
grads_to_wait,
optimizer,
lr_staleness_modulation=False,
sync_version_tolerance=0,
use_async=False,
evaluation_steps=0,
master_channel=None,
checkpoint_saver=None,
ps_id=None,
num_ps_pods=None,
):
if master_channel is None:
self._master_stub = None
else:
self._master_stub = elasticdl_pb2_grpc.MasterStub(master_channel)
self._parameters = parameters
self._grads_to_wait = grads_to_wait
self._optimizer = optimizer
self._lr_staleness_modulation = lr_staleness_modulation
self._sync_version_tolerance = sync_version_tolerance
self._use_async = use_async
self._eval_steps = evaluation_steps
self._checkpoint_saver = checkpoint_saver
self._ps_id = ps_id
self._num_ps_pods = num_ps_pods
self._version_lock = threading.Lock()
self._lock = threading.Lock()
self._use_wrap_opt = False
self._grads_n = 0
self._grads_buffer = {}
def pull_dense_parameters(self, request, _):
"""
Response with all non-embedding parameters if initialized.
"""
res = elasticdl_pb2.PullDenseParametersResponse()
if not self._parameters.initialized:
res.initialized = False
return res
# Only sync-SGD needs lock
# TODO: use a read-write lock to support multiple concurrent reads
if not self._use_async:
self._lock.acquire()
res.version = self._parameters.version
# No need to send variables if the requester has the latest version.
if self._parameters.version > request.version:
for name, var in self._parameters.non_embedding_params.items():
serialize_ndarray(var.numpy(), res.dense_parameters[name])
if not self._use_async:
self._lock.release()
res.initialized = True
return res
def pull_embedding_vectors(self, request, _):
result = tensor_pb2.TensorProto()
if not request.ids:
return result
embedding_vectors = self._parameters.get_embedding_param(
request.name, request.ids)
serialize_ndarray(embedding_vectors, result)
return result
def push_model(self, request, _):
with self._lock:
accepted = self._parameters.init_from_model_pb(request)
if accepted and self._parameters.has_embedding_params():
self.wrap_optimizer_and_set_slot()
return empty_pb2.Empty()
def push_embedding_table_infos(self, request, _):
with self._lock:
self._parameters.init_embedding_params(
request.embedding_table_infos)
self.wrap_optimizer_and_set_slot()
return empty_pb2.Empty()
def push_gradients(self, request, _):
res = elasticdl_pb2.PushGradientsResponse()
if self._use_async:
grad_vars = []
for name, pb in request.gradients.dense_parameters.items():
grad = pb_to_ndarray(pb)
self._parameters.check_grad(Tensor(name, grad, None))
grad = tf.constant(grad)
var = self._parameters.get_non_embedding_param(name)
grad_vars.append((grad, var))
for name, pb in request.gradients.embedding_tables.items():
grad = pb_to_indexed_slices(pb)
self._parameters.check_grad(
Tensor(name, grad.values, grad.indices))
if name in self._parameters.non_embedding_params:
var = self._parameters.get_non_embedding_param(name)
grad_vars.append((grad, var))
else:
grad_vars.append((grad, name))
learning_rate = request.learning_rate
# TODO: if request.learning_rate == 0.0, modulate learning_rate
# in self._optimizer with staleness
if self._lr_staleness_modulation and learning_rate > 0.0:
staleness = max(
1, self._parameters.version - request.gradients.version)
# Modulate learning rate by staleness
learning_rate /= staleness
self._set_optimizer_learning_rate(learning_rate)
self._optimizer.apply_gradients(grad_vars)
with self._version_lock:
self._parameters.version += 1
self._save_params_to_checkpoint_if_needed()
version = self._parameters.version
self._report_version_if_needed(version)
res.accepted = True
res.version = self._parameters.version
return res
else:
if (request.gradients.version <
self._parameters.version - self._sync_version_tolerance):
res.accepted = False
res.version = self._parameters.version
return res
with self._lock:
for name, pb in request.gradients.dense_parameters.items():
grad = pb_to_ndarray(pb)
self._parameters.check_grad(Tensor(name, grad, None))
if name in self._grads_buffer:
self._grads_buffer[name] = (self._grads_buffer[name] +
grad)
else:
self._grads_buffer[name] = grad
for name, pb in request.gradients.embedding_tables.items():
grad = pb_to_indexed_slices(pb)
self._parameters.check_grad(
Tensor(name, grad.values, grad.indices))
if name in self._grads_buffer:
self._grads_buffer[name] = merge_indexed_slices(
self._grads_buffer[name], grad)
else:
self._grads_buffer[name] = grad
self._grads_n += 1
res.accepted = True
updated_version = False
version = self._parameters.version
if self._grads_n == self._grads_to_wait:
grad_vars = []
for name, grad in self._grads_buffer.items():
# Dense gradients are averaged,
# while sparse gradients are summed
if not isinstance(grad, tf.IndexedSlices):
grad = grad / self._grads_to_wait
grad = tf.constant(grad)
var = self._parameters.get_non_embedding_param(name)
if var is None:
grad_vars.append((grad, name))
else:
grad_vars.append((grad, var))
self._set_optimizer_learning_rate(request.learning_rate)
self._optimizer.apply_gradients(grad_vars)
self._grads_n = 0
self._grads_buffer.clear()
self._parameters.version += 1
self._save_params_to_checkpoint_if_needed()
version = self._parameters.version
updated_version = True
if updated_version:
self._report_version_if_needed(version)
res.version = version
return res
def wrap_optimizer(self):
self._optimizer = OptimizerWrapper(
self._optimizer,
self._use_async,
self._parameters.get_embedding_param,
self._parameters.set_embedding_param,
)
def _report_version_if_needed(self, version):
if self._eval_steps and version % self._eval_steps == 0:
self._report_version(version)
def _report_version(self, version):
req = elasticdl_pb2.ReportVersionRequest()
req.model_version = version
self._master_stub.report_version(req)
def wrap_optimizer_and_set_slot(self):
if not self._use_wrap_opt:
self.wrap_optimizer()
self._parameters.create_slot_params(
self._optimizer.allowed_slot_names,
self._optimizer.slot_initial_value,
)
self._use_wrap_opt = True
def _save_params_to_checkpoint_if_needed(self):
"""Save a checkpoint of parameters to a protobuf file"""
if (self._checkpoint_saver and
self._parameters.version % self._checkpoint_saver._steps == 0):
model_pb = self._parameters.to_model_pb()
logger.info("Save checkpoint for version %s" % model_pb.version)
self._checkpoint_saver.save(
model_pb.version,
model_pb,
is_eval_checkpoint=False,
shard_index=self._ps_id,
shard_num=self._num_ps_pods,
)
def _set_optimizer_learning_rate(self, learning_rate):
if learning_rate == 0.0:
return
if self._use_wrap_opt:
self._optimizer.set_learning_rate(learning_rate)
else:
K.set_value(self._optimizer.lr, K.get_value(learning_rate))
