def test_pslib_1(self):
"""Test cases for pslib."""
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
os.environ["POD_IP"] = "127.0.0.1"
os.environ["PADDLE_PORT"] = "36001"
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
os.environ["PADDLE_TRAINER_ID"] = "0"
role_maker = GeneralRoleMaker()
#role_maker.generate_role()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
#fleet.init(role_maker)
train_program = fluid.Program()
startup_program = fluid.Program()
scope = fluid.Scope()
with fluid.program_guard(train_program, startup_program):
show = fluid.layers.data(name="show", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
emb = fluid.layers.embedding(input=show, size=[1, 1], \
is_sparse=True, is_distributed=True, \
param_attr=fluid.ParamAttr(name="embedding"))
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow = fluid.layers.data_norm(input=bow, epsilon=1e-4, name="norm")
fc = fluid.layers.fc(input=bow, size=1, act=None)
label = fluid.layers.data(name="click", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(
adam,
strategy={
"embedding": {
"sparse_accessor_class": "DownpourSparseValueAccessor"
}
})
adam.minimize([cost], [scope])
fleet.run_server()
except:
print("do not support pslib test, skip")
return
try:
# worker should call these methods instead of server
# the following is only for test when with_pslib=off
def test_func():
"""
it is only a test function
"""
return True
fleet._role_maker.is_first_worker = test_func
fleet._role_maker._barrier_worker = test_func
fleet.save_model("./model_000")
fleet.save_one_table(0, "./model_001")
fleet.save_one_table(0, "./model_002", prefix="hahaha")
fleet.load_model("./model_0003")
fleet.load_one_table(0, "./model_004")
except:
print("do not support pslib test, skip")
return
def test_pslib_1(self):
"""Test cases for pslib."""
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
from paddle.fluid.incubate.fleet.parameter_server.pslib import \
fleet_embedding, _prepare_params, _fleet_embedding, \
_fleet_embedding_v2, FLEET_GLOBAL_DICT
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
try:
import netifaces
except:
print("warning: no netifaces, skip test_pslib_1")
return
os.environ["POD_IP"] = "127.0.0.1"
os.environ["PADDLE_PORT"] = "36001"
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
os.environ["PADDLE_TRAINER_ID"] = "0"
role_maker = GeneralRoleMaker()
role_maker.generate_role()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
fleet.init(role_maker)
train_program = fluid.Program()
startup_program = fluid.Program()
scope = fluid.Scope()
global FLEET_GLOBAL_DICT
with fluid.program_guard(train_program, startup_program):
show = fluid.layers.data(name="show", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
click = fluid.layers.data(name="click", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
with fleet_embedding(click_name=click.name):
emb = fluid.layers.embedding(input=show, size=[1, 1], \
is_sparse=True, is_distributed=True, \
param_attr=fluid.ParamAttr(name="embedding"))
emb = fluid.layers.data_norm(input=emb,
name="a",
epsilon=1e-4,
param_attr={
"batch_size": 1e4,
"batch_sum_default": 0.0,
"batch_square": 1e4
})
fc = fluid.layers.fc(input=emb, size=1, act=None)
label = fluid.layers.data(name="click", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(
adam,
strategy={
"embedding": {
"sparse_accessor_class": "DownpourSparseValueAccessor"
}
})
adam.minimize([cost], [scope])
except:
print("do not support pslib test, skip")
return
FLEET_GLOBAL_DICT["cur_accessor"] = "DownpourCtrAccessor"
try:
_prepare_params(input=show, size=[1, 1])
except:
print("catch expected exception of param_attr=None")
try:
_prepare_params(input=show,
size=[1, 1],
param_attr=fluid.ParamAttr())
except:
print("catch expected exception of name=None")
try:
tmp = fluid.ParamAttr(name="embedding")
_prepare_params(input=show, size=1, param_attr=tmp)
except:
print("catch expected exception of size not list")
try:
tmp = fluid.ParamAttr(name="embedding")
_prepare_params(input=show, size=[-1, 12], param_attr=tmp)
except:
print("catch expected exception of size not equal")
try:
tmp = fluid.ParamAttr(name="embedding")
_prepare_params(input=show,
size=[-1, 1],
param_attr=tmp,
is_sparse=False)
except:
print("catch expected exception of is_sparse=False")
try:
tmp = fluid.ParamAttr(name="embedding")
_prepare_params(input=show, size=[-1, 1], param_attr=tmp, \
is_sparse=True, is_distributed=False)
except:
print("catch expected exception of is_distributed=False")
try:
_prepare_params(input=show, size=[-1, 1], \
param_attr=fluid.ParamAttr(name="embedding"), \
is_sparse=True, is_distributed=True, dtype="abc")
except:
print("catch expected exception of unknown dtype")
try:
FLEET_GLOBAL_DICT["emb_to_accessor"]["embedding"] = "unknown"
tmp = fluid.ParamAttr(name="embedding")
_prepare_params(input=show, size=[-1, 1], param_attr=tmp)
except:
print("catch expected exception of unknown accessor")
FLEET_GLOBAL_DICT["cur_accessor"] = "DownpourCtrAccessor"
try:
_fleet_embedding(input=show, size=[-1, 1], is_sparse=True, \
is_distributed=True, dtype="float32", \
param_attr=fluid.ParamAttr(name="embedding"))
except:
print("catch expected exception of unknown accessor")
try:
_fleet_embedding_v2(input=show, size=[-1, 1], is_sparse=True, \
is_distributed=True, dtype="float32", \
param_attr=fluid.ParamAttr(name="embedding"))
except:
print("catch expected exception of unknown accessor")
adam1 = fluid.optimizer.Adam(learning_rate=0.000005)
adam1 = fleet.distributed_optimizer(
adam1,
strategy={
"embedding": {
"sparse_accessor_class": "DownpourSparseValueAccessor"
}
})
try:
pre = FLEET_GLOBAL_DICT["emb_to_table"]
FLEET_GLOBAL_DICT["emb_to_table"] = {}
adam1.minimize([cost], [scope])
except:
FLEET_GLOBAL_DICT["emb_to_table"] = pre
print("catch expected exception of empty emb_to_table")
try:
pre = FLEET_GLOBAL_DICT["emb_to_table"]
FLEET_GLOBAL_DICT["emb_to_table"] = {}
FLEET_GLOBAL_DICT["emb_to_table"]["emb1"] = 0
adam1.minimize([cost], [scope])
except:
FLEET_GLOBAL_DICT["emb_to_table"] = pre
print("catch expected exception of error emb_to_table")
try:
adam2 = fluid.optimizer.Adam(learning_rate=0.000005)
adam2 = fleet.distributed_optimizer(adam2)
adam2.supported_embedding_types = []
adam2.minimize([cost], [scope])
except:
print("catch expected exception of embedding_types")
try:
adam3 = fluid.optimizer.Adam(learning_rate=0.000005)
adam3 = fleet.distributed_optimizer(
adam3,
strategy={
"embedding": {
"sparse_accessor_class": "DownpourSparseValueAccessor",
"sparse_embedx_dim": 999
}
})
adam3.minimize([cost], [scope])
except:
print("catch expected exception of embedx_dim error")
try:
adam4 = fluid.optimizer.Adam(learning_rate=0.000005)
adam4 = fleet.distributed_optimizer(
adam4,
strategy={
"embedding": {
"sparse_accessor_class": "DownpourCtrAccessor",
"sparse_embedx_dim": 999
}
})
adam4.minimize([cost], [scope])
except:
print("catch expected exception of embedx_dim error")
train_program1 = fluid.Program()
startup_program1 = fluid.Program()
FLEET_GLOBAL_DICT["emb_to_accessor"] = {}
with fluid.program_guard(train_program1, startup_program1):
show = fluid.layers.data(name="show", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
with fleet_embedding(click_name=click.name):
emb = fluid.layers.embedding(input=show, size=[1, 1], \
is_sparse=True, is_distributed=True, \
param_attr=fluid.ParamAttr(name="embedding"))
with fleet_embedding(click_name=click.name):
emb1 = fluid.embedding(input=show, size=[1, 1], \
is_sparse=True, is_distributed=True, \
param_attr=fluid.ParamAttr(name="embedding"))
fleet.save_model("./tmodel_000")
fleet.save_one_table(0, "./tmodel_001")
fleet.save_one_table(0, "./tmodel_002", prefix="thahaha")
fleet.load_model("./tmodel_0003")
fleet.load_one_table(0, "./tmodel_004")