def setUp(self):
self.params = Parameters()
self.model_pb = Model()
self.infos_pb = self.model_pb.embedding_table_infos
self.tensors_pb = self.model_pb.dense_parameters
self.embedding_tables_pb = self.model_pb.embedding_tables
self.embedding_table_name = "embedding_1"
self.embedding_dim = 10
embedding_pb = self.infos_pb.add()
embedding_pb.name = self.embedding_table_name
embedding_pb.dim = self.embedding_dim
embedding_pb.initializer = "uniform"
arr1 = np.random.uniform(size=(3, 4))
serialize_ndarray(arr1, self.tensors_pb["x"])
arr2 = np.random.uniform(size=(4, 5))
serialize_ndarray(arr2, self.tensors_pb["y"])
embedding_vectors = np.random.uniform(size=(2, 10))
embedding_indices = np.array([0, 8])
serialize_indexed_slices(
Tensor(None, embedding_vectors, embedding_indices),
self.embedding_tables_pb[self.embedding_table_name],
)
def __init__(self, args):
self.logger = get_logger("PS", level=args.log_level.upper())
self.grads_to_wait = args.grads_to_wait
self.lr_staleness_modulation = args.lr_staleness_modulation
self.sync_version_tolerance = args.sync_version_tolerance
self.use_async = args.use_async
self.port = args.port
model_module = load_module(
get_module_file_path(args.model_zoo, args.model_def)).__dict__
self.optimizer = model_module[args.optimizer]()
self._set_lr_scheduler(model_module, args.learning_rate_scheduler)
self.ps_id = args.ps_id
self.num_ps_pods = args.num_ps_pods
self.num_workers = args.num_workers
# Create Parameters instance
self.parameters = Parameters()
if args.master_addr is None:
raise ValueError("master_addr is missing for parameter servers")
self.master_channel = build_channel(args.master_addr)
self.evaluation_steps = args.evaluation_steps
self.master_name = get_master_pod_name(args.job_name)
self.namespace = args.namespace
self._init_checkpoint_saver(args)
self._restore_params_from_checkpoint(args.checkpoint_dir_for_init)
self._debug_info_needed = args.log_level.upper() == "DEBUG"
def testSaveLoadCheckpoint(self):
init_var = m["custom_model"]().trainable_variables
with tempfile.TemporaryDirectory() as tempdir:
ckpt_dir = os.path.join(tempdir, "testSaveLoadCheckpoint")
os.makedirs(ckpt_dir)
checkpoint_saver = CheckpointSaver(ckpt_dir, 3, 5, False)
self.assertTrue(checkpoint_saver.is_enabled())
params = Parameters()
for var in init_var:
params.non_embedding_params[var.name] = var
model_pb = params.to_model_pb()
checkpoint_saver.save(0, model_pb, False)
ckpt_version_dir = os.path.join(ckpt_dir, "version-0")
restore_params = CheckpointSaver.restore_params_from_checkpoint(
ckpt_version_dir, 0, 1)
self.assertEqual(restore_params.version, params.version)
for var_name in params.non_embedding_params:
self.assertTrue(
np.array_equal(
params.non_embedding_params[var_name].numpy(),
restore_params.non_embedding_params[var_name].numpy(),
))
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_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 setUp(self):
self.params = Parameters()
self.model_pb = Model()
self.tensors_pb = self.model_pb.param
self.embeddings_pb = self.model_pb.embedding_table_info
arr1 = np.random.uniform(size=(3, 4))
tensor1_pb = Tensor(arr1, name="x").to_tensor_pb()
arr2 = np.random.uniform(size=(4, 5))
tensor2_pb = Tensor(arr2, name="y").to_tensor_pb()
self.tensors_pb.extend([tensor1_pb, tensor2_pb])
self.embedding_table_name = "embedding_1"
self.embedding_dim = 10
embedding_pb = EmbeddingTableInfo()
embedding_pb.name = self.embedding_table_name
embedding_pb.dim = self.embedding_dim
embedding_pb.initializer = "uniform"
embedding_vectors = np.random.uniform(size=(2, 10))
embedding_indices = np.array([0, 8])
embedding_tensor = Tensor(
embedding_vectors,
indices=embedding_indices,
name=self.embedding_table_name,
)
embedding_tensor_pb = embedding_tensor.to_tensor_pb()
self.tensors_pb.append(embedding_tensor_pb)
self.embeddings_pb.append(embedding_pb)
def save_checkpoint_without_embedding(model, checkpoint_dir, version=100):
checkpoint_saver = CheckpointSaver(checkpoint_dir, 0, 0, False)
params = Parameters()
for var in model.trainable_variables:
params.non_embedding_params[var.name] = var
params.version = version
model_pb = params.to_model_pb()
checkpoint_saver.save(version, model_pb, False)
def _test_correctness(self, optimizer_class, X, Y, seed, **opt_kwargs):
"""Test the correctness of specific TensorFlow optimizer."""
_model_file = get_module_file_path(
os.path.dirname(os.path.realpath(__file__)),
"embedding_test_module.KerasEmbeddingModel",
)
model_module = load_module(_model_file).__dict__
# train model with TensorFlow optimizer
dim = 4
weights = self._random_init_model_weight([(4, dim), (4, dim), (72, 1),
(1, )], seed)
loss_fn = model_module["loss"]
model1 = model_module["KerasEmbeddingModel"](4, dim, weights)
opt1 = optimizer_class(**opt_kwargs)
_train(model1, opt1, X, Y, loss_fn, random_seed=seed)
model2 = model_module["EdlEmbeddingModel"](dim, weights[2:])
opt2 = optimizer_class(**opt_kwargs)
embedding_weight_names = [
layer.embedding_weight_name
for layer in find_layer(model2, Embedding)
]
# create Parameters object and initialize embedding vectors
params = Parameters()
for weight_name, embed_value in zip(embedding_weight_names,
weights[:2]):
embed_table = EmbeddingTable(weight_name, dim)
embed_table.set(range(len(embed_value)), embed_value)
params.embedding_params[weight_name] = embed_table
_train_edl_embedding_with_optimizer_wrapper(model2,
opt2,
X,
Y,
loss_fn,
params,
random_seed=seed)
# compare trained parameters
wrong_msg = (
"The updated parameters of Optimizer Wrapper and TensorFlow "
"optimizer %s differ." % opt1.get_config()["name"])
for layer1, layer2 in zip(model1.layers, model2.layers):
if "embedding" in layer2.name:
w1 = layer1.weights[0].numpy()
w2 = params.get_embedding_param(layer2.embedding_weight_name,
range(4))
self.assertTrue(np.isclose(w1, w2).all(), msg=wrong_msg)
else:
for w1, w2 in zip(layer1.weights, layer2.weights):
self.assertTrue(np.isclose(w1.numpy(), w2.numpy()).all(),
msg=wrong_msg)
def restore_params_from_checkpoint(checkpoint_dir, shard_index, shard_num):
"""Restore a shard parameters from the checkpoint directory.
If shard_num=1, a entire model parameters will be restored.
Args:
checkpoint_dir: a directory with checkpoint files.
shard_index: Model shard index, e.g. the PS instance index
using ParameterServerStrategy with multiple PS instances.
shard_num: The total number of model shards, e.g. the total PS
instancecount using ParameterServerStrategy with multiple
PS instances.
Return:
parameters: A Parameter object which contains model version,
non-embedding parameters and embedding tables for the
PS instance with ps_id.
"""
variable_shard_files = os.listdir(checkpoint_dir)
non_embedding_vars = {}
embedding_tables = {}
version = None
for shard_file in variable_shard_files:
shard_file_path = os.path.join(checkpoint_dir, shard_file)
model_pb = elasticdl_pb2.Model()
model_pb = load_pb_from_file(model_pb, shard_file_path)
if version is None:
version = model_pb.version
elif version != model_pb.version:
raise ValueError(
"The versions in model shards are not consistent"
)
for embedding_info_pb in model_pb.embedding_table_infos:
embedding_table = create_embedding_table(embedding_info_pb)
embedding_tables.setdefault(
embedding_table.name, embedding_table
)
(
shard_non_embedding_vars,
shard_embedding_table_values,
) = _get_params_shard_from_pb(model_pb, shard_index, shard_num)
non_embedding_vars.update(shard_non_embedding_vars)
for name, pair in shard_embedding_table_values.items():
embedding_tables[name].set(pair[0], pair[1])
parameters = Parameters()
parameters.non_embedding_params.update(non_embedding_vars)
parameters.embedding_params.update(embedding_tables)
parameters.version = version
return parameters
def test_export_to_model_pb(self):
self.params.init_from_model_pb(self.model_pb)
self.params.version = 15
model_pb = self.params.to_model_pb()
params = Parameters()
params.init_from_model_pb(model_pb)
self.assertEqual(params.version, self.params.version)
self.assertEqual(
params.non_embedding_params.keys(),
self.params.non_embedding_params.keys(),
)
self.assertEqual(
params.embedding_params["embedding_1"].get([0]).tolist(),
self.params.embedding_params["embedding_1"].get([0]).tolist(),
)
def _mock_model_parameters(self, model):
params = Parameters()
for weight in model.trainable_variables:
if "embedding" in weight.name:
embedding_table = EmbeddingTable(
name=weight.name,
dim=weight.shape[1],
initializer="RandomUniform",
)
embedding_table.set(np.arange(weight.shape[0]),
np.ones(weight.shape))
params.embedding_params[weight.name] = embedding_table
else:
params.non_embedding_params[weight.name] = tf.ones(
weight.shape)
params.version = 100
return params
def __init__(self, args):
self.logger = get_logger("PS", level=args.log_level.upper())
self.grads_to_wait = args.grads_to_wait
self.lr_staleness_modulation = args.lr_staleness_modulation
self.use_async = args.use_async
self.port = args.port
model_module = load_module(
get_module_file_path(args.model_zoo, args.model_def)
).__dict__
self.optimizer = model_module[args.optimizer]()
# Create Parameters instance
self.parameters = Parameters()
def test_save_parameters_to_checkpoint_file(self):
with tempfile.TemporaryDirectory() as tempdir:
checkpoint_saver = CheckpointSaver(
checkpoint_dir=os.path.join(tempdir, "ckpt/"),
checkpoint_steps=5,
keep_checkpoint_max=3,
include_evaluation=False,
)
pserver_servicer = PserverServicer(
parameters=Parameters(),
grads_to_wait=0,
optimizer="optimizer",
checkpoint_saver=checkpoint_saver,
ps_id=0,
num_ps_pods=1,
)
model_params = {
"v0": tf.Variable([[1, 1, 1], [1, 1, 1]]),
"v1": tf.Variable([[2, 2, 2], [2, 2, 2]]),
}
server_params = pserver_servicer._parameters
for var_name, var_value in model_params.items():
server_params.non_embedding_params[var_name] = var_value
embedding_table = EmbeddingTable(
name="embedding_0", dim=3, initializer="random_uniform"
)
server_params.embedding_params["embedding_0"] = embedding_table
server_params.set_embedding_param(
name="embedding_0",
indices=np.array([0, 1]),
values=np.array([[1, 1, 1], [2, 2, 2]]),
)
for i in range(100):
pserver_servicer._parameters.version += 1
pserver_servicer._save_params_to_checkpoint_if_needed()
self.assertEqual(len(os.listdir(checkpoint_saver._directory)), 3)
self.assertEqual(
sorted(os.listdir(checkpoint_saver._directory)),
["version-100", "version-90", "version-95"],
)
self.assertEqual(
os.listdir(checkpoint_saver._directory + "/version-100"),
["variables-0-of-1.ckpt"],
)
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 __init__(self, args):
self.logger = get_logger("PS", level=args.log_level.upper())
self.grads_to_wait = args.grads_to_wait
self.lr_staleness_modulation = args.lr_staleness_modulation
self.use_async = args.use_async
self.port = args.port
model_module = load_module(
get_module_file_path(args.model_zoo, args.model_def)).__dict__
self.optimizer = model_module[args.optimizer]()
self.ps_id = args.ps_id
self.num_ps_pods = args.num_ps_pods
# Create Parameters instance
self.parameters = Parameters()
if args.master_addr is None:
raise ValueError("master_addr is missing for parameter servers")
self.master_channel = build_channel(args.master_addr)
self.evaluation_steps = args.evaluation_steps
self.master_name = get_master_pod_name(args.job_name)
self.namespace = args.namespace
self._init_checkpoint_service(args)
def test_restore_parameters_from_checkpoint(self):
checkpoint_dir = "elasticdl/python/tests/testdata/ps_ckpt"
checkpoint_saver = CheckpointSaver(checkpoint_dir, 0, 0, False)
params = Parameters()
table = EmbeddingTable("embedding", 2, "random_uniform")
table.set([0, 1, 2, 3], np.ones((4, 2), dtype=np.float32))
params.embedding_params["embedding"] = table
params.non_embedding_params["dense/kernel:0"] = tf.Variable(
[[1.0], [1.0]]
)
params.non_embedding_params["dense/bias:0"] = tf.Variable([1.0])
params.version = 100
model_pb = params.to_model_pb()
checkpoint_saver.save(100, model_pb, False)
checkpoint_dir_for_init = checkpoint_dir + "/version-100"
args = PserverArgs(
ps_id=0,
num_ps_pods=2,
model_zoo=_test_model_zoo_path,
model_def="test_module.custom_model",
checkpoint_dir_for_init=checkpoint_dir_for_init,
)
pserver_0 = ParameterServer(args)
embedding_table = pserver_0.parameters.embedding_params["embedding"]
self.assertEqual(
list(embedding_table.embedding_vectors.keys()), [0, 2]
)
self.assertEqual(
list(pserver_0.parameters.non_embedding_params.keys()),
["dense/kernel:0"],
)
self.assertTrue(
np.array_equal(
pserver_0.parameters.non_embedding_params[
"dense/kernel:0"
].numpy(),
np.array([[1], [1]], dtype=int),
)
)
self.assertEqual(pserver_0.parameters.version, 100)
args = PserverArgs(
ps_id=1,
num_ps_pods=2,
model_zoo=_test_model_zoo_path,
model_def="test_module.custom_model",
checkpoint_dir_for_init=checkpoint_dir_for_init,
)
pserver_1 = ParameterServer(args)
embedding_table = pserver_1.parameters.embedding_params["embedding"]
self.assertEqual(
list(embedding_table.embedding_vectors.keys()), [1, 3]
)
self.assertEqual(
list(pserver_1.parameters.non_embedding_params.keys()),
["dense/bias:0"],
)
self.assertTrue(
np.array_equal(
pserver_1.parameters.non_embedding_params[
"dense/bias:0"
].numpy(),
np.array([1], dtype=int),
)
)
self.assertEqual(pserver_1.parameters.version, 100)
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
]))
class ParametersTest(unittest.TestCase):
def setUp(self):
self.params = Parameters()
self.model_pb = Model()
self.infos_pb = self.model_pb.embedding_table_infos
self.tensors_pb = self.model_pb.dense_parameters
self.embedding_tables_pb = self.model_pb.embedding_tables
self.embedding_table_name = "embedding_1"
self.embedding_dim = 10
embedding_pb = self.infos_pb.add()
embedding_pb.name = self.embedding_table_name
embedding_pb.dim = self.embedding_dim
embedding_pb.initializer = "uniform"
arr1 = np.random.uniform(size=(3, 4))
serialize_ndarray(arr1, self.tensors_pb["x"])
arr2 = np.random.uniform(size=(4, 5))
serialize_ndarray(arr2, self.tensors_pb["y"])
embedding_vectors = np.random.uniform(size=(2, 10))
embedding_indices = np.array([0, 8])
serialize_indexed_slices(
Tensor(None, embedding_vectors, embedding_indices),
self.embedding_tables_pb[self.embedding_table_name],
)
def _test_get_embedding_param(self, slot_names=[], slot_init_value={}):
indices = [0, 3, 7]
res = self.params.get_embedding_param(
self.embedding_table_name, indices
)
self.assertTupleEqual(res.shape, (3, 10))
for slot in slot_names:
res = self.params.get_embedding_param(
get_slot_table_name(self.embedding_table_name, slot), indices
)
self.assertTrue(((res - slot_init_value[slot]) < 0.0001).all())
res = self.params.get_embedding_param(self.embedding_table_name, [])
self.assertIsNone(res)
with self.assertRaises(ValueError):
self.params.get_embedding_param("tom", indices)
def test_init_from_model_pb(self):
self.params.reset()
self.params.init_from_model_pb(self.model_pb)
res = self.params.non_embedding_params
self.assertTrue("x" in res)
self.assertTrue("y" in res)
self.assertTrue(res["x"].trainable)
self.assertTupleEqual(tuple(res["y"].shape.as_list()), (4, 5))
self._test_get_embedding_param()
def test_non_embedding_params(self):
self.params.reset()
res = self.params.non_embedding_params
self.assertFalse(any(res))
variables = {
"x": tf.Variable(1, name="x"),
"y": tf.Variable(2, name="y"),
}
self.params.non_embedding_params = variables
self.assertTrue("x" in self.params.non_embedding_params)
self.assertTrue("y" in self.params.non_embedding_params)
def test_get_embedding_param(self):
self.params.reset()
self.params.init_embedding_params(self.infos_pb)
self._test_get_embedding_param()
def test_set_embedding_param(self):
self.params.reset()
self.params.init_embedding_params(self.infos_pb)
indices = [100, 34, 8]
x = len(indices)
values = np.random.uniform(size=x * self.embedding_dim).reshape(
(x, self.embedding_dim)
)
self.params.set_embedding_param(
self.embedding_table_name, indices, values
)
row0 = self.params.get_embedding_param(
self.embedding_table_name, [100]
)
row1 = self.params.get_embedding_param(self.embedding_table_name, [34])
row2 = self.params.get_embedding_param(self.embedding_table_name, [8])
rows = [row0, row1, row2]
rows = np.concatenate(rows)
np.testing.assert_array_equal(rows, values)
with self.assertRaises(ValueError):
self.params.set_embedding_param("tom", [0, 1, 2], values)
def test_check_grad(self):
self.params.reset()
self.params.init_from_model_pb(self.model_pb)
grad0 = Tensor("z", None, None)
with self.assertRaisesRegex(ValueError, "Name error"):
self.params.check_grad(grad0)
grad1 = Tensor("x", np.random.uniform(size=(3, 5)), None)
with self.assertRaisesRegex(ValueError, "Non embedding param error"):
self.params.check_grad(grad1)
grad2 = Tensor(
name="embedding_1",
values=np.random.uniform(size=(3, 11)),
indices=np.array([1, 2, 3]),
)
with self.assertRaisesRegex(
ValueError, "ElasticDL embedding param error"
):
self.params.check_grad(grad2)
grad3 = Tensor(
name="x",
values=np.random.uniform(size=(4, 4)),
indices=np.array([1, 2, 3, 4]),
)
with self.assertRaisesRegex(ValueError, "Keras embedding param error"):
self.params.check_grad(grad3)
def test_create_slot_params(self):
# At first, no embedding table are in the parameters
self.assertFalse(self.params.has_embedding_params())
# create embedding tables in the parameters
self.params.init_embedding_params(self.infos_pb)
self.assertTrue(self.params.has_embedding_params())
slot_names = ["accumulator", "linear"]
slot_init_value = {slot_names[0]: 3.5, slot_names[1]: 0.0}
self.params.create_slot_params(slot_names, slot_init_value)
self._test_get_embedding_param(slot_names, slot_init_value)
def test_export_to_model_pb(self):
self.params.init_from_model_pb(self.model_pb)
self.params.version = 15
model_pb = self.params.to_model_pb()
params = Parameters()
params.init_from_model_pb(model_pb)
self.assertEqual(params.version, self.params.version)
self.assertEqual(
params.non_embedding_params.keys(),
self.params.non_embedding_params.keys(),
)
self.assertEqual(
params.embedding_params["embedding_1"].get([0]).tolist(),
self.params.embedding_params["embedding_1"].get([0]).tolist(),
)
def setUp(self):
init_var = m["custom_model"]().trainable_variables
self.params = Parameters()
for var in init_var:
self.params.non_embedding_params[var.name] = var
class ParameterServer(object):
def __init__(self, args):
self.logger = get_logger("PS", level=args.log_level.upper())
self.grads_to_wait = args.grads_to_wait
self.lr_staleness_modulation = args.lr_staleness_modulation
self.sync_version_tolerance = args.sync_version_tolerance
self.use_async = args.use_async
self.port = args.port
model_module = load_module(
get_module_file_path(args.model_zoo, args.model_def)).__dict__
self.optimizer = model_module[args.optimizer]()
self._set_lr_scheduler(model_module, args.learning_rate_scheduler)
self.ps_id = args.ps_id
self.num_ps_pods = args.num_ps_pods
self.num_workers = args.num_workers
# Create Parameters instance
self.parameters = Parameters()
if args.master_addr is None:
raise ValueError("master_addr is missing for parameter servers")
self.master_channel = build_channel(args.master_addr)
self.evaluation_steps = args.evaluation_steps
self.master_name = get_master_pod_name(args.job_name)
self.namespace = args.namespace
self._init_checkpoint_saver(args)
self._restore_params_from_checkpoint(args.checkpoint_dir_for_init)
self._debug_info_needed = args.log_level.upper() == "DEBUG"
def _set_lr_scheduler(self, model_module, learning_rate_scheduler_arg):
if learning_rate_scheduler_arg in model_module:
self.lr_scheduler = add_lr_scheduler_to_optimizer(
self.optimizer, model_module[learning_rate_scheduler_arg])
else:
self.lr_scheduler = None
def _restore_params_from_checkpoint(self, checkpoint_dir_for_init):
"""Restore parameters from a checkpint directory for the PS instance
"""
if not checkpoint_dir_for_init:
self.logger.info("checkpoint directory for init is None")
return
if not CheckpointSaver.check_checkpoint_valid(checkpoint_dir_for_init):
raise ValueError("Invalid checkpoint directory")
self.parameters = CheckpointSaver.restore_params_from_checkpoint(
checkpoint_dir_for_init, self.ps_id, self.num_ps_pods)
self.parameters.init_status = True
self.logger.info("The version of restored parameters is %d" %
self.parameters.version)
def _init_checkpoint_saver(self, args):
if all([args.checkpoint_dir, args.checkpoint_steps]):
self.checkpoint_saver = CheckpointSaver(
args.checkpoint_dir,
args.checkpoint_steps,
args.keep_checkpoint_max,
include_evaluation=False,
)
else:
self.checkpoint_saver = None
self.logger.warning(
"Invalid checkpoint config and no model will be saved")
def prepare(self):
max_workers = min(self.num_workers, 64)
self.logger.info("The max threads in PS servers is %d" % max_workers)
server = grpc.server(
futures.ThreadPoolExecutor(max_workers=max_workers),
options=[
("grpc.max_send_message_length", GRPC.MAX_SEND_MESSAGE_LENGTH),
(
"grpc.max_receive_message_length",
GRPC.MAX_RECEIVE_MESSAGE_LENGTH,
),
],
)
pserver_servicer = PserverServicer(
self.parameters,
self.grads_to_wait,
self.optimizer,
self.lr_scheduler,
lr_staleness_modulation=self.lr_staleness_modulation,
sync_version_tolerance=self.sync_version_tolerance,
use_async=self.use_async,
evaluation_steps=self.evaluation_steps,
master_channel=self.master_channel,
checkpoint_saver=self.checkpoint_saver,
ps_id=self.ps_id,
num_ps_pods=self.num_ps_pods,
)
elasticdl_pb2_grpc.add_PserverServicer_to_server(
pserver_servicer, server)
server.add_insecure_port("[::]:{}".format(self.port))
server.start()
self.server = server
self.logger.info("RPC Server started at port: %d", self.port)
def run(self):
config.load_incluster_config()
api = client.CoreV1Api()
try:
while True:
time.sleep(30)
master_pod = api.read_namespaced_pod(namespace=self.namespace,
name=self.master_name)
if master_pod.status.phase == PodStatus.SUCCEEDED:
self.logger.info("Master pod is Succeeded")
break
elif master_pod.status.phase == PodStatus.FAILED:
self.logger.info("Master pod is Failed")
break
elif (master_pod.status.phase == PodStatus.RUNNING
and master_pod.metadata.labels["status"]
== PodStatus.FINISHED):
self.logger.info(
"Task is finished, "
"master pod is still running tensorboard service")
break
if self._debug_info_needed:
self.logger.debug("Parameters info:\n%s" %
self.parameters.debug_info())
except KeyboardInterrupt:
self.logger.warning("Server stopping")
self.server.stop(0)
self.logger.info("RPC server stopped")