def run_main_with_place(self, places, use_compiled_program=True):
with fluid.scope_guard(fluid.Scope()):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input_data, loss, loader = self.build_network(places)
fetch_list = [input_data]
exe = fluid.Executor(places[0])
exe.run(fluid.default_startup_program())
dev_cnt = len(places)
if dev_cnt > 1:
self.assertTrue(use_compiled_program)
main_program = fluid.default_main_program()
if use_compiled_program:
main_program = fluid.CompiledProgram(
main_program).with_data_parallel(
loss_name=loss.name, places=places)
max_batch_num = min(self.break_num,
int(self.batch_num / dev_cnt))
if loader.iterable:
early_break = False
for epoch_id in six.moves.range(self.epoch_num):
early_break = False
self.clear_visited()
batch_id = 0
for data in loader():
if batch_id >= self.break_num:
early_break = True
break
self.assertInputData(
batch_id, data, dev_cnt, check_visited=False)
fetch_val, = exe.run(program=main_program,
feed=data,
fetch_list=fetch_list)
self.assertInputData(batch_id, fetch_val, dev_cnt)
batch_id += 1
if dev_cnt == 1:
self.assertEqual(batch_id, max_batch_num)
else:
self.assertLessEqual(batch_id, max_batch_num)
if early_break:
loader._reset()
else:
for epoch_id in six.moves.range(self.epoch_num):
batch_id = 0
self.clear_visited()
loader.start()
try:
while True:
if batch_id >= self.break_num:
loader.reset()
break
fetch_val, = exe.run(program=main_program,
fetch_list=fetch_list)
self.assertInputData(batch_id, fetch_val,
dev_cnt)
batch_id += 1
except fluid.core.EOFException:
loader.reset()
if dev_cnt == 1:
self.assertEqual(batch_id, max_batch_num)
else:
self.assertLessEqual(batch_id, max_batch_num)
def run_func_with_guard(self, func):
with fluid.program_guard(fluid.Program(), fluid.Program()):
with fluid.unique_name.guard():
with fluid.scope_guard(fluid.Scope()):
func()
def test_pslib_2(self):
"""Test cases for pslib."""
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
from paddle.fluid.incubate.fleet.base.role_maker import RoleMakerBase
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"
os.environ["PADDLE_TRAINERS_NUM"] = "1"
place = fluid.CPUPlace()
exe = fluid.Executor(place)
try:
fleet.init(None)
except:
print("no mpi4py, skip test_pslib_2")
return
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="float32", lod_level=1, append_batch_size=False)
fc = fluid.layers.fc(input=show, 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)
adam.minimize([cost], [scope])
fleet.run_server()
except:
print("do not support pslib test, skip")
return
os.environ["TRAINING_ROLE"] = "wrong"
try:
role1 = GeneralRoleMaker(path="./test_gloo_1")
role1.generate_role()
except:
print("catch expected error of wrong TRAINING_ROLE")
os.environ["TRAINING_ROLE"] = "PSERVER"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
role2 = GeneralRoleMaker(path="./test_gloo_2")
role2._finalize()
role2._all_gather(1)
role2._all_gather(1)
role2._barrier_server()
role2._all_gather(1)
role3 = GeneralRoleMaker(path="./test_gloo_3")
role3._worker_gather(1)
role3._worker_gather(1)
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
role4 = GeneralRoleMaker(path="./test_gloo_4")
role4._worker_gather(1)
role4._get_rank()
role4._get_size()
role4._all_comm.init()
role5 = GeneralRoleMaker(path="./test_gloo_5")
role5.get_local_endpoint()
role5.get_local_endpoint()
role6 = GeneralRoleMaker(path="./test_gloo_6")
role6.get_trainer_endpoints()
role6.get_trainer_endpoints()
role7 = GeneralRoleMaker(path="./test_gloo_7")
role7.get_pserver_endpoints()
role7.get_pserver_endpoints()
role8 = GeneralRoleMaker(path="./test_gloo_8")
role8.is_worker()
role8.is_worker()
role9 = GeneralRoleMaker(path="./test_gloo_9")
role9.is_server()
role9.is_server()
role10 = GeneralRoleMaker(path="./test_gloo_10")
role10.is_first_worker()
role10.is_first_worker()
role11 = GeneralRoleMaker(path="./test_gloo_11")
role11.worker_index()
role11.worker_index()
role12 = GeneralRoleMaker(path="./test_gloo_12")
role12.server_index()
role12.server_index()
role13 = GeneralRoleMaker(path="./test_gloo_13")
role13.worker_num()
role13.worker_num()
role14 = GeneralRoleMaker(path="./test_gloo_14")
role14.server_num()
role14.server_num()
role15 = GeneralRoleMaker(path="./test_gloo_15")
role15._barrier_worker()
role15._barrier_worker()
role16 = GeneralRoleMaker(path="./test_gloo_16")
role16._barrier_all()
role16._barrier_all()
role17 = GeneralRoleMaker(path="./test_gloo_17")
role17._barrier_server()
role17._barrier_server()
role18 = GeneralRoleMaker(path="./test_gloo_18")
role18._worker_num()
role18._worker_num()
role19 = GeneralRoleMaker(path="./test_gloo_19")
role19._server_num()
role19._server_num()
role20 = GeneralRoleMaker(path="./test_gloo_20")
a = [1]
b = [0]
role20._all_reduce(a, b)
role21 = GeneralRoleMaker(path="./test_gloo_21")
role21.all_reduce_worker([], [])
role21.all_reduce_worker([], [])
role21.barrier_worker()
role21.barrier_all()
role22 = GeneralRoleMaker(path="./test_gloo_22")
role22._get_rank()
role22._get_rank()
os.environ["PADDLE_PSERVER_ID"] = "0"
role23 = GeneralRoleMaker(path="./test_gloo_23")
role23._get_size()
role23._get_size()
with open("test_fleet_gloo_role_maker_1.txt", "w") as f:
data = "1 1 1 1\n"
f.write(data)
dataset = paddle.distributed.InMemoryDataset()
dataset.set_filelist(["test_fleet_gloo_role_maker_1.txt"])
dataset._set_use_var([show, label])
dataset.load_into_memory()
dataset.get_memory_data_size(fleet)
dataset.get_shuffle_data_size(fleet)
os.remove("./test_fleet_gloo_role_maker_1.txt")
class TmpClass():
"""
dummy tmp class
"""
def __init__(self):
pass
def all_reduce_worker(self, input, output):
"""
dummy all reduce worker
Args:
input(None): fake input
output(None): fale output
"""
pass
def barrier_worker(self):
"""
dummy barrier worker
"""
pass
from paddle.fluid.incubate.fleet.base.fleet_base import Fleet
class TmpFleet(Fleet):
"""
dummy tmp fleet
"""
def __init__(self):
super(TmpFleet, self).__init__()
self._role_maker = None
def init_worker(self):
"""
dummy init worker
"""
pass
def init_server(self, model_dir=None):
"""
dummy init server
Args:
model_dir(None): fake model_dir
"""
pass
def run_server(self):
"""
dummy run server
"""
pass
def stop_worker(self):
"""
dummy stop worker
"""
pass
def distributed_optimizer(self, optimizer, strategy=None):
"""
dummy distributed optimizer
Args:
optimizer(None): fake optimizer
strategy(None): fake strategy
"""
pass
def save_inference_model(self):
"""
dummy save inference model
"""
pass
def save_persistables(self):
"""
dummy save persistables
"""
pass
os.environ["TRAINING_ROLE"] = "TRAINER"
tmp = TmpFleet()
tmp._role_maker = TmpClass()
tmp.all_reduce_worker([], [])
tmp.barrier_worker()
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
tmp = RoleMakerBase()
tmp.all_gather(1)
tmp.all_reduce_worker([], [])
tmp.barrier_worker()
tmp.barrier_all()
from paddle.fluid.incubate.fleet.base.role_maker import \
MPISymetricRoleMaker
tmp1 = MPISymetricRoleMaker()
tmp1.all_gather(1)
tmp1.all_gather(1)
tmp2 = MPISymetricRoleMaker()
tmp2.all_reduce_worker([], [])
tmp3 = MPISymetricRoleMaker()
tmp3.barrier_worker()
tmp3.barrier_worker()
tmp4 = MPISymetricRoleMaker()
tmp4.barrier_all()
tmp4.barrier_all()
def build_network(self, context):
context["model"] = {}
if len(context["env"]["phase"]) > 1:
warnings.warn("Cluster Train Only Support One Phase.",
category=UserWarning,
stacklevel=2)
model_dict = context["env"]["phase"][0]
train_program = fluid.Program()
startup_program = fluid.Program()
scope = fluid.Scope()
dataset_name = model_dict["dataset_name"]
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
with fluid.scope_guard(scope):
context["model"][model_dict["name"]] = {}
model_path = envs.os_path_adapter(
envs.workspace_adapter(model_dict["model"]))
model = envs.lazy_instance_by_fliename(
model_path, "Model")(context["env"])
model._data_var = model.input_data(
dataset_name=model_dict["dataset_name"])
if envs.get_global_env("dataset." + dataset_name +
".type") == "DataLoader":
model._init_dataloader(is_infer=False)
model.net(model._data_var, False)
optimizer = model.optimizer()
optimizer = context["fleet"].distributed_optimizer(
optimizer)
optimizer.minimize([model._cost], [fluid.global_scope()])
context["model"][
model_dict["name"]]["main_program"] = train_program
context["model"][model_dict["name"]][
"startup_program"] = startup_program
context["model"][model_dict["name"]]["scope"] = scope
context["model"][model_dict["name"]]["model"] = model
context["model"][model_dict["name"]][
"default_main_program"] = train_program.clone()
context["model"][
model_dict["name"]]["compile_program"] = None
if context["fleet"].is_server():
self._server(context)
else:
context["dataset"] = {}
for phase in context["env"]["phase"]:
type = envs.get_global_env("dataset." + dataset["name"] +
".type")
if type == "DataLoader":
data_loader = DataLoader(context)
data_loader.get_dataloader(
context, dataset_name, context["model"][
model_dict["name"]]["model"]._data_loader)
elif type == "QueueDataset":
dataset_class = QueueDataset(context)
context["dataset"][
dataset["name"]] = dataset_class.create_dataset(
dataset["name"], context)
context["status"] = "startup_pass"
def freeze_graph(self,
use_cuda,
seed,
activation_quant_type,
bias_correction=False,
weight_quant_type='abs_max',
for_ci=True,
quant_skip_pattern='skip_quant'):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
loss = conv_net(img, label, quant_skip_pattern)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type,
skip_pattern=quant_skip_pattern)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
if not for_ci:
marked_nodes = set()
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
quantized_test_program = test_graph.to_program()
iters = 5
batch_size = 8
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name +
activation_quant_type + '_' +
weight_quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
quantized_test_program)
# Testing
with fluid.scope_guard(scope):
test_loss1, w_quant = exe.run(program=quantized_test_program,
feed=feeder.feed(test_data),
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, bias_correction=bias_correction, \
weight_quantize_type=weight_quant_type)
freeze_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
server_program = test_graph.to_program()
with fluid.scope_guard(scope):
test_loss2, = exe.run(program=server_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
if not for_ci:
print(
'{}: {}'.format('test_loss1' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss1))
print(
'{}: {}'.format('test_loss2' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
if not for_ci:
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
convert_int8_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + activation_quant_type
+ '_' + weight_quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model(
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
if not for_ci:
print('{}: {}'.format('w_8bit' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model(
'mobile_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
mobile_program)
def quan(self, config_file):
if not fluid.core.is_compiled_with_cuda():
return
class_dim = 10
image_shape = [1, 28, 28]
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
image = fluid.layers.data(
name='image', shape=image_shape, dtype='float32')
image.stop_gradient = False
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
out = MobileNet(name='quan').net(input=image,
class_dim=class_dim)
print("out: {}".format(out.name))
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
val_program = train_program.clone(for_test=False)
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5))
scope = fluid.Scope()
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(startup_program, scope=scope)
val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
val_feed_list = [('img', image.name), ('label', label.name)]
val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5',
acc_top5.name)]
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
train_feed_list = [('img', image.name), ('label', label.name)]
train_fetch_list = [('loss', avg_cost.name)]
com_pass = Compressor(
place,
scope,
train_program,
train_reader=train_reader,
train_feed_list=train_feed_list,
train_fetch_list=train_fetch_list,
eval_program=val_program,
eval_reader=val_reader,
eval_feed_list=val_feed_list,
eval_fetch_list=val_fetch_list,
train_optimizer=optimizer)
com_pass.config(config_file)
eval_graph = com_pass.run()
def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w):
""" inference function """
epoch_model_path_list = []
epoch_model_name_list = []
for file in os.listdir(model_dir):
file_path = os.path.join(model_dir, file)
# hard code for epoch model folder
if os.path.isdir(file_path) and is_number(file):
epoch_model_path_list.append(file_path)
epoch_model_name_list.append(file)
if len(epoch_model_path_list) == 0:
return
print("Save model len {}".format(len(epoch_model_path_list)))
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
emb_size = args.emb_size
batch_size = args.batch_size
result_dict = collections.OrderedDict()
with fluid.scope_guard(fluid.Scope()):
main_program = fluid.Program()
with fluid.program_guard(main_program):
values, pred = infer_network(vocab_size, emb_size)
for epoch, model_path in enumerate(epoch_model_path_list):
print("Begin infer model: {}".format(model_path))
copy_program = main_program.clone()
fluid.io.load_vars(
executor=exe, dirname=model_path, predicate=_load_emb)
accum_num = 0
accum_num_sum = 0.0
t0 = time.time()
step_id = 0
for data in test_reader():
step_id += 1
b_size = len([dat[0] for dat in data])
wa = np.array([dat[0] for dat in data]).astype(
"int64").reshape(b_size)
wb = np.array([dat[1] for dat in data]).astype(
"int64").reshape(b_size)
wc = np.array([dat[2] for dat in data]).astype(
"int64").reshape(b_size)
label = [dat[3] for dat in data]
input_word = [dat[4] for dat in data]
para = exe.run(copy_program,
feed={
"analogy_a": wa,
"analogy_b": wb,
"analogy_c": wc,
"all_label": np.arange(vocab_size)
.reshape(vocab_size).astype("int64"),
},
fetch_list=[pred.name, values],
return_numpy=False)
pre = np.array(para[0])
val = np.array(para[1])
for ii in range(len(label)):
top4 = pre[ii]
accum_num_sum += 1
for idx in top4:
if int(idx) in input_word[ii]:
continue
if int(idx) == int(label[ii][0]):
accum_num += 1
break
if step_id % 1 == 0:
print("step:%d %d " % (step_id, accum_num))
print("model: {} \t acc: {} ".format(
model_path, 1.0 * accum_num / accum_num_sum))
epoch_acc = 1.0 * accum_num / accum_num_sum
result_dict[epoch] = epoch_acc
def quantize_program(self,
use_cuda,
seed,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
label = fluid.layers.data(name='label',
shape=[1],
dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.0001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
# 1 Define program
train_program = fluid.Program()
startup_program = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(train_program, startup_program, False)
build_program(test_program, startup_program, True)
test_program = test_program.clone(for_test=True)
if not for_ci:
train_graph = IrGraph(core.Graph(train_program.desc),
for_test=False)
train_graph.draw('.', 'train_program_1')
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
test_graph.draw('.', 'test_program_1')
# 2 Apply quantization
qt = QuantizeTranspilerV2(
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
qt.apply(train_program, startup_program, is_test=False)
qt.apply(test_program, startup_program, is_test=True)
# 3 Train
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup_program)
if not for_ci:
train_graph = IrGraph(core.Graph(train_program.desc),
for_test=False)
train_graph.draw('.', 'train_program_2')
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
test_graph.draw('.', 'test_program_2')
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(train_program).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.dataset.mnist.train(),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for idx in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci and idx % 20 == 0:
print('{}: {}'.format('loss', np.mean(loss_v)))
print('{}: {}'.format('loss', np.mean(loss_v)))
# 4 Convert
qt.convert(test_program, scope)
if not for_ci:
with fluid.scope_guard(scope):
fluid.io.save_inference_model('./infer_model',
['image', 'label'], [loss],
exe,
test_program,
clip_extra=True)
def f3_data_reader(box):
pass
# Initialization
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
startup = fluid.Program()
# Feed configure
# if you want to shuffle "reader=paddle.reader.shuffle(dataReader(), buf_size)"
# load infer model
f1_scope = fluid.Scope()
f2_scope = fluid.Scope()
se_scope = fluid.Scope()
with fluid.scope_guard(f1_scope):
[infer_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(model_path, exe)
with fluid.scope_guard(f2_scope):
[infer_program2, feed_target_names2,
fetch_targets2] = fluid.io.load_inference_model(model_path2, exe)
f1_data_list = f1_data_reader(img_file_path)
# Start infer
exe.run(startup)
s = time.time()
def test_batch_number_with_different_length_files(self):
for p in self.get_all_places():
with fluid.scope_guard(fluid.Scope()):
self.check_batch_number(place=p, randomize_batch_num=True)
def _test(self,
place,
use_tensor=True,
use_fluid_api=True,
use_global_beta_pow=False,
flatten_param_grads=False):
paddle.enable_static()
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
SEED = 2021
paddle.seed(SEED)
np.random.seed(SEED)
a_np = np.random.random(size=(2, 2)).astype('float32')
b_np = np.random.random(size=(2, 2)).astype('float32')
label_np = np.random.randint(2, size=(2, 1)).astype('int64')
weight_attr1 = paddle.ParamAttr(
name="weight1",
initializer=fluid.initializer.Constant(value=1.0),
trainable=True)
weight_attr2 = paddle.ParamAttr(
name="weight2",
initializer=fluid.initializer.Constant(value=2.0),
trainable=True)
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
with paddle.static.program_guard(main_prog, startup_prog):
with paddle.utils.unique_name.guard():
a = paddle.static.data(name="a", shape=[2, 2], dtype='float32')
b = paddle.static.data(name="b", shape=[2, 2], dtype='float32')
label = paddle.static.data(name="label",
shape=[2, 1],
dtype='int64')
sum = paddle.add(a, b)
z = paddle.pow(sum, 2.0)
fc_1 = fluid.layers.fc(input=z,
size=2,
param_attr=weight_attr1)
prediction = fluid.layers.fc(input=fc_1,
size=2,
param_attr=weight_attr2,
act='softmax')
cost = fluid.layers.cross_entropy(input=prediction,
label=label)
loss = fluid.layers.reduce_mean(cost)
beta1_init = 0.9
beta2_init = 0.999
epsilon_init = 1e-8
if use_tensor:
beta1 = fluid.layers.create_global_var(
shape=[1],
value=float(beta1_init),
dtype='float32',
persistable=True,
name="beta1")
beta2 = fluid.layers.create_global_var(
shape=[1],
value=float(beta2_init),
dtype='float32',
persistable=True,
name="beta2")
epsilon = fluid.layers.create_global_var(
shape=[1],
value=float(epsilon_init),
dtype='float32',
persistable=True,
name="epsilon")
if use_fluid_api:
adam = fluid.optimizer.Adam(
learning_rate=0.01,
beta1=beta1,
beta2=beta2,
epsilon=epsilon,
use_global_beta_pow=use_global_beta_pow,
flatten_param_grads=flatten_param_grads,
align_size=256,
grad_clip=clip)
else:
adam = paddle.optimizer.Adam(learning_rate=0.01,
beta1=beta1,
beta2=beta2,
epsilon=epsilon,
grad_clip=clip)
else:
if use_fluid_api:
adam = fluid.optimizer.Adam(
learning_rate=0.01,
beta1=beta1_init,
beta2=beta2_init,
epsilon=epsilon_init,
use_global_beta_pow=use_global_beta_pow,
flatten_param_grads=flatten_param_grads,
align_size=256,
grad_clip=clip)
else:
adam = fluid.optimizer.Adam(learning_rate=0.01,
beta1=beta1_init,
beta2=beta2_init,
epsilon=epsilon_init,
grad_clip=clip)
adam.minimize(loss)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe = paddle.static.Executor(place)
exe.run(startup_prog)
print("Start run on {}".format(place))
for epoch in range(10):
pred_res, loss_res = exe.run(main_prog,
feed={
"a": a_np,
"b": b_np,
"label": label_np
},
fetch_list=[prediction, loss])
print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
epoch, pred_res[0], loss_res))
paddle.disable_static()
return pred_res, loss_res
def quantization_scale(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(name='image',
shape=[1, 28, 28],
dtype='float32')
label = fluid.layers.data(name='label',
shape=[1],
dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.0001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
add_quant_dequant_pass = AddQuantDequantPass(scope=scope, place=place)
add_quant_dequant_pass.apply(main_graph)
add_quant_dequant_pass.apply(test_graph)
scale_training_pass = OutScaleForTrainingPass(scope=scope, place=place)
scale_training_pass.apply(main_graph)
dev_name = '_gpu' if use_cuda else '_cpu'
if not for_ci:
marked_nodes = set()
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main_scale' + dev_name, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_scale' + dev_name, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
build_strategy.fuse_all_reduce_ops = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name, loss_v))
scale_inference_pass = OutScaleForInferencePass(scope=scope)
scale_inference_pass.apply(test_graph)
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
freeze_pass.apply(test_graph)
server_program = test_graph.to_program()
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'quant_scale' + dev_name, marked_nodes)
with open('quant_scale_model' + dev_name + '.txt', 'w') as f:
f.write(str(server_program))
with fluid.scope_guard(scope):
fluid.io.save_inference_model('quant_scale_model' + dev_name,
['image', 'label'], [loss], exe,
server_program)
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()
#print("init rolemaker")
#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="float32", lod_level=1, append_batch_size=False)
fc = fluid.layers.fc(input=show, 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)
adam.minimize([cost], [scope])
fleet.run_server()
except:
print("do not support pslib test, skip")
return
fleet.clear_one_table(0)
from paddle.fluid.incubate.fleet.base.role_maker import \
MPISymetricRoleMaker
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2])
except:
print("catch expected error of not inited")
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2], "min")
except:
print("catch expected error of not inited")
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2], "max")
except:
print("catch expected error of not inited")
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2], "unknown")
except:
print("catch expected error of unknown type")
def test_main(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
with fluid.scope_guard(fluid.Scope()):
self.run_network()
def set_program(self, avg_cost, strategy):
with fluid.scope_guard(fluid.Scope()):
optimizer = fluid.optimizer.SGD(0.1)
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(avg_cost)
def test_prune(self):
main_program = fluid.Program()
startup_program = fluid.Program()
# X X O X O
# conv1-->conv2-->sum1-->conv3-->conv4-->sum2-->conv5-->conv6
# | ^ | ^
# |____________| |____________________|
#
# X: prune output channels
# O: prune input channels
with fluid.program_guard(main_program, startup_program):
input = fluid.data(name="image", shape=[None, 3, 16, 16])
conv1 = conv_bn_layer(input, 8, 3, "conv1")
conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
sum1 = conv1 + conv2
conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
sum2 = conv4 + sum1
conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
conv7 = fluid.layers.conv2d_transpose(input=conv6,
num_filters=16,
filter_size=2,
stride=2)
shapes = {}
for param in main_program.global_block().all_parameters():
shapes[param.name] = param.shape
place = fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
exe.run(startup_program, scope=scope)
pruner = Pruner()
main_program, _, _ = pruner.prune(
main_program,
scope,
params=["conv4_weights", "conv2d_transpose_0.w_0"],
ratios=[0.5, 0.6],
place=place,
lazy=False,
only_graph=False,
param_backup=None,
param_shape_backup=None)
shapes = {
"conv1_weights": (4, 3, 3, 3),
"conv2_weights": (4, 4, 3, 3),
"conv3_weights": (8, 4, 3, 3),
"conv4_weights": (4, 8, 3, 3),
"conv5_weights": (8, 4, 3, 3),
"conv6_weights": (8, 8, 3, 3),
"conv2d_transpose_0.w_0": (8, 16, 2, 2),
}
for param in main_program.global_block().all_parameters():
if param.name in shapes:
print("param: {}; param shape: {}".format(
param.name, param.shape))
self.assertTrue(param.shape == shapes[param.name])
def evaluate():
place = fluid.CUDAPlace(0) if cfg.use_cuda else fluid.CPUPlace()
inference_scope = fluid.Scope()
test_files = [
os.path.join(cfg.evaluate_file_path, x)
for x in os.listdir(cfg.evaluate_file_path)
]
dataset = CriteoDataset()
test_reader = paddle.batch(dataset.test(test_files),
batch_size=cfg.batch_size)
startup_program = fluid.framework.Program()
test_program = fluid.framework.Program()
model = DNN()
model_path = os.path.join(cfg.save_path,
model.name + "_epoch_" + str(cfg.test_epoch),
"checkpoint")
with fluid.framework.program_guard(test_program, startup_program):
with fluid.unique_name.guard():
inputs = model.input_data()
loss, auc_var = model.net(inputs)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=inputs, place=place)
fluid.load(fluid.default_main_program(), model_path, exe)
auc_states_names = [
'_generated_var_0', '_generated_var_1', '_generated_var_2',
'_generated_var_3'
]
for var in auc_states_names:
set_zero(var, scope=inference_scope, place=place)
run_index = 0
infer_auc = 0
L = []
for batch_id, data_test in enumerate(test_reader()):
loss_val, auc_val = exe.run(test_program,
feed=feeder.feed(data_test),
fetch_list=[loss, auc_var])
run_index += 1
infer_auc = auc_val
L.append(loss_val / cfg.batch_size)
if batch_id % cfg.log_interval == 0:
logger.info("TEST --> batch: {} loss: {} auc: {}".format(
batch_id, loss_val / cfg.batch_size, auc_val))
infer_loss = np.mean(L)
infer_result = {}
infer_result['loss'] = infer_loss
infer_result['auc'] = infer_auc
if not os.path.isdir(cfg.log_dir):
os.makedirs(cfg.log_dir)
log_path = os.path.join(cfg.log_dir,
model.name + '_infer_result.log')
logger.info(str(infer_result))
with open(log_path, 'w+') as f:
f.write(str(infer_result))
logger.info("Done.")
return infer_result
def test_concat(self):
main_program = fluid.Program()
startup_program = fluid.Program()
# X
# conv1 conv2-->concat conv3-->sum-->out
# | ^ | ^
# |____________| |____________________|
#
with fluid.program_guard(main_program, startup_program):
input = fluid.data(name="image", shape=[None, 3, 16, 16])
conv1 = conv_bn_layer(input, 8, 3, "conv1")
conv2 = conv_bn_layer(input, 8, 3, "conv2", sync_bn=True)
tmp = fluid.layers.concat([conv1, conv2], axis=1)
conv3 = conv_bn_layer(input, 16, 3, "conv3", bias=None)
out = conv3 + tmp
shapes = {}
for param in main_program.global_block().all_parameters():
shapes[param.name] = param.shape
place = fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
exe.run(startup_program, scope=scope)
pruner = Pruner()
# test backward search of concat
pruned_program, _, _ = pruner.prune(main_program,
scope,
params=["conv3_weights"],
ratios=[0.5],
place=place,
lazy=False,
only_graph=True,
param_backup=None,
param_shape_backup=None)
shapes = {
"conv3_weights": (8, 3, 3, 3),
"conv2_weights": (4, 3, 3, 3),
"conv1_weights": (4, 3, 3, 3)
}
for param in pruned_program.global_block().all_parameters():
if "weights" in param.name and "conv2d" in param.name:
self.assertTrue(shapes[param.name] == param.shape)
# test forward search of concat
pruned_program, _, _ = pruner.prune(
main_program,
scope,
params=["conv1_weights", "conv2_weights"],
ratios=[0.5, 0.5],
place=place,
lazy=False,
only_graph=False,
param_backup=None,
param_shape_backup=None)
shapes = {
"conv1_weights": (4, 3, 3, 3),
"conv1_bn_scale": (4, ),
"conv1_bn_variance": (4, ),
"conv1_bn_mean": (4, ),
"conv1_bn_offset": (4, ),
"conv2_weights": (4, 3, 3, 3),
"sync_batch_norm_0.w_0": (4, ),
"sync_batch_norm_0.w_1": (4, ),
"conv2_bn_scale": (4, ),
"conv2_bn_offset": (4, ),
"conv3_weights": (8, 3, 3, 3),
"conv3_bn_mean": (8, ),
"conv3_bn_offset": (8, ),
"conv3_bn_scale": (8, ),
"conv3_bn_variance": (8, ),
"conv3_out.b_0": (8, ),
}
for param in pruned_program.global_block().all_parameters():
if "weights" in param.name and "conv2d" in param.name:
self.assertTrue(shapes[param.name] == param.shape)
def loss_scaling_check(self, use_cuda=True, scope=fluid.Scope()):
a = fluid.data(name="a", shape=[1024, 1024], dtype='float32')
b = fluid.data(name="b", shape=[512, 128], dtype='float32')
x = [a, b]
found_inf = fluid.data(name="found_inf", shape=[1], dtype='bool')
prev_loss_scaling = fluid.data(name="prev_loss_scaling",
shape=[1],
dtype='float32')
num_good_steps = fluid.data(name="num_good_steps",
shape=[1],
dtype='int32')
num_bad_steps = fluid.data(name="num_bad_steps",
shape=[1],
dtype='int32')
a_v = np.random.random([1024, 1024]).astype('float32')
b_v = np.random.random([512, 128]).astype('float32')
found_inf_v = np.array([False]).astype('bool')
prev_loss_scaling_v = np.array([2048]).astype('float32')
num_good_steps_v = np.array([999], dtype=np.int32)
num_bad_steps_v = np.array([1], dtype=np.int32)
incr_every_n_steps = 1000
decr_every_n_nan_or_inf = 2
incr_ratio = 2
decr_ratio = 0.8
result = amp_nn.update_loss_scaling(x,
found_inf,
prev_loss_scaling,
num_good_steps,
num_bad_steps,
incr_every_n_steps,
decr_every_n_nan_or_inf,
incr_ratio,
decr_ratio,
name="update_loss_scaling")
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
with fluid.scope_guard(scope):
exe.run(fluid.default_startup_program())
result_v = exe.run(feed={
'a': a_v,
'b': b_v,
'found_inf': found_inf_v,
'prev_loss_scaling': prev_loss_scaling_v,
'num_good_steps': num_good_steps_v,
'num_bad_steps': num_bad_steps_v
},
fetch_list=[
result, x, found_inf, prev_loss_scaling,
num_good_steps, num_bad_steps
])
assert np.array_equal(result_v[0], a_v)
assert np.array_equal(result_v[1], b_v)
assert np.array_equal(result_v[0], result_v[2])
assert np.array_equal(result_v[1], result_v[3])
assert np.array_equal(result_v[4], found_inf_v)
assert np.array_equal(result_v[5], prev_loss_scaling_v * incr_ratio)
assert np.array_equal(result_v[6], np.zeros_like(num_good_steps_v))
assert np.array_equal(result_v[7], np.zeros_like(num_bad_steps_v))
def mkldnn_based_freeze_graph(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
quant_perf=False,
for_ci=False):
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = self.build_program(main, startup, False, seed)
self.build_program(test_program, startup, True, seed)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
# Apply the QuantizationTransformPass
transform_pass = QuantizationTransformPass(
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
quantized_test_program = test_graph.to_program()
iters = 5
batch_size = 8
train_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(paddle.dataset.mnist.test(),
batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
# Training the model to get the weights value
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(binary,
feed=feeder.feed(data),
fetch_list=[loss])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
freeze_pass.apply(test_graph)
# Transform quantized graph for MKL-DNN INT8 inference
mkldnn_int8_pass = QuantInt8MkldnnPass(_scope=scope, _place=place)
mkldnn_int8_pass.apply(test_graph)
dev_name = '_cpu_'
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw(
'.', 'test_mkldnn' + dev_name + activation_quant_type + '_' +
weight_quant_type, marked_nodes)
mkldnn_program = test_graph.to_program()
# Check the transformation weights of conv2d and mul
conv_w_mkldnn = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
mul_w_mkldnn = np.array(scope.find_var('fc_0.w_0').get_tensor())
# Check if weights are still integer
self.assertFalse(self.isinteger(np.sum(conv_w_mkldnn)))
self.assertFalse(self.isinteger(np.sum(mul_w_mkldnn)))
# Check if the conv2d output and mul output are correctly linked to fake_dequantize's
# output
self.check_program(mkldnn_program)
if not for_ci:
print('{}: {}'.format(
'w_mkldnn' + dev_name + activation_quant_type + '_' +
weight_quant_type, np.sum(w_mkldnn)))
def build_network(self, context):
context["model"] = {}
for model_dict in context["phases"]:
context["model"][model_dict["name"]] = {}
train_program = fluid.Program()
startup_program = fluid.Program()
scope = fluid.Scope()
dataset_name = model_dict["dataset_name"]
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
with fluid.scope_guard(scope):
model_path = envs.os_path_adapter(
envs.workspace_adapter(model_dict["model"]))
model = envs.lazy_instance_by_fliename(
model_path, "Model")(context["env"])
model._data_var = model.input_data(
dataset_name=model_dict["dataset_name"])
if envs.get_global_env("dataset." + dataset_name +
".type") == "DataLoader":
model._init_dataloader(
is_infer=context["is_infer"])
data_loader = DataLoader(context)
data_loader.get_dataloader(context, dataset_name,
model._data_loader)
model.net(model._data_var, context["is_infer"])
finetuning_varnames = envs.get_global_env(
"runner." + context["runner_name"] +
".finetuning_aspect_varnames",
default_value=[])
if len(finetuning_varnames) == 0:
raise ValueError(
"nothing need to be fine tuning, you may use other traning mode"
)
if len(finetuning_varnames) != 1:
raise ValueError(
"fine tuning mode can only accept one varname now"
)
varname = finetuning_varnames[0]
finetuning_vars = train_program.global_block(
).vars[varname]
finetuning_vars.stop_gradient = True
optimizer = model.optimizer()
optimizer.minimize(model._cost)
context["model"][
model_dict["name"]]["main_program"] = train_program
context["model"][
model_dict["name"]]["startup_program"] = startup_program
context["model"][model_dict["name"]]["scope"] = scope
context["model"][model_dict["name"]]["model"] = model
context["model"][model_dict["name"]][
"default_main_program"] = train_program.clone()
context["model"][model_dict["name"]]["compiled_program"] = None
context["dataset"] = {}
for dataset in context["env"]["dataset"]:
type = envs.get_global_env("dataset." + dataset["name"] + ".type")
if type == "QueueDataset":
dataset_class = QueueDataset(context)
context["dataset"][
dataset["name"]] = dataset_class.create_dataset(
dataset["name"], context)
context["status"] = "startup_pass"
def run_main(self, use_legacy_py_reader, with_data_parallel, places,
use_double_buffer):
scope = fluid.Scope()
with fluid.scope_guard(scope):
startup_prog, main_prog, py_reader, loss = simple_fc_net(
places, use_legacy_py_reader, use_double_buffer)
reader = paddle.batch(random_reader, batch_size=BATCH_SIZE)
ps = places if use_double_buffer else fluid.cpu_places(len(places))
py_reader.set_sample_list_generator(
reader, places=ps if py_reader.iterable else None)
exe = fluid.Executor(place=places[0])
exe.run(startup_prog)
prog = fluid.CompiledProgram(main_prog)
if with_data_parallel:
prog = prog.with_data_parallel(loss_name=loss.name,
places=places)
step = 0
step_list = []
loss_list = []
start_t = time.time()
if not py_reader.iterable:
for _ in six.moves.range(EPOCH_NUM):
step = 0
py_reader.start()
while True:
try:
L, = exe.run(program=prog,
fetch_list=[loss],
use_program_cache=True)
loss_list.append(np.mean(L))
step += 1
except fluid.core.EOFException:
py_reader.reset()
break
step_list.append(step)
else:
for _ in six.moves.range(EPOCH_NUM):
step = 0
for d in py_reader():
assert len(d) == len(places), "{} != {}".format(
len(d), len(places))
for i, item in enumerate(d):
image = item['image']
label = item['label']
assert image.shape() == [BATCH_SIZE, 784]
assert label.shape() == [BATCH_SIZE, 1]
assert image._place()._equals(ps[i])
assert label._place()._equals(ps[i])
L, = exe.run(program=prog,
feed=d,
fetch_list=[loss],
use_program_cache=True)
loss_list.append(np.mean(L))
step += 1
step_list.append(step)
end_t = time.time()
ret = {
"time": end_t - start_t,
"step": step_list,
"loss": np.array(loss_list)
}
return ret
def build_network(self, context):
context["model"] = {}
for model_dict in context["phases"]:
context["model"][model_dict["name"]] = {}
train_program = fluid.Program()
startup_program = fluid.Program()
scope = fluid.Scope()
dataset_name = model_dict["dataset_name"]
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
with fluid.scope_guard(scope):
model_path = envs.os_path_adapter(
envs.workspace_adapter(model_dict["model"]))
model = envs.lazy_instance_by_fliename(
model_path, "Model")(context["env"])
if context["is_infer"]:
model._infer_data_var = model.input_data(
is_infer=context["is_infer"],
dataset_name=model_dict["dataset_name"])
else:
model._data_var = model.input_data(
dataset_name=model_dict["dataset_name"])
if envs.get_global_env("dataset." + dataset_name +
".type") == "DataLoader":
model._init_dataloader(
is_infer=context["is_infer"])
data_loader = DataLoader(context)
data_loader.get_dataloader(context, dataset_name,
model._data_loader)
if context["is_infer"]:
model.net(model._infer_data_var,
context["is_infer"])
else:
model.net(model._data_var, context["is_infer"])
optimizer = model.optimizer()
optimizer.minimize(model._cost)
context["model"][
model_dict["name"]]["main_program"] = train_program
context["model"][
model_dict["name"]]["startup_program"] = startup_program
context["model"][model_dict["name"]]["scope"] = scope
context["model"][model_dict["name"]]["model"] = model
context["model"][model_dict["name"]][
"default_main_program"] = train_program.clone()
context["model"][model_dict["name"]]["compiled_program"] = None
context["dataset"] = {}
for dataset in context["env"]["dataset"]:
type = envs.get_global_env("dataset." + dataset["name"] + ".type")
if type == "QueueDataset":
dataset_class = QueueDataset(context)
context["dataset"][
dataset["name"]] = dataset_class.create_dataset(
dataset["name"], context)
context["status"] = "startup_pass"
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"))
data = fluid.data(name='data', shape=[-1, 10], dtype='float32')
fc = layers.fc(data, size=10)
loss = layers.reduce_sum(fc)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
opt_out = sgd_optimizer.minimize(loss)
print_program(main_program, 'program.before')
grad_dict = {grad.name: grad for param, grad in opt_out[1]}
coalesce_program, grad_out_dict, grad_fused, fused_shape_var = create_coalesce_program(
grad_dict)
print_program(coalesce_program, 'program.coalesce')
scope = fluid.Scope()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# initialize parameters
exe.run(start_program, scope=scope)
# pre-allocate coalesce buffer in scope and get the runtime buffer size
# coalesce_program could be merged with startup program
shape_array = exe.run(coalesce_program,
fetch_list=[fused_shape_var.name],
scope=scope)
runtime_shape = shape_array[0]
# rewrite the main program by replacing all original gradients to sliced variables
# from gradient-fused buffer, and update the buffer size by runtime shape
def create_fake_model(program_config):
''' Create a Paddle model(in memory) according to the given config. '''
paddle.enable_static()
main_program_desc = core.ProgramDesc()
util_program = fluid.Program()
main_block_desc = main_program_desc.block(0)
var_desc = main_block_desc.var(cpt.to_bytes("feed"))
var_desc.set_type(core.VarDesc.VarType.FEED_MINIBATCH)
var_desc.set_persistable(True)
index = 0
for name, tensor_config in program_config.inputs.items():
var_desc = main_block_desc.var(cpt.to_bytes(name))
var_desc.set_type(core.VarDesc.VarType.LOD_TENSOR)
var_desc.set_dtype(convert_np_dtype_to_dtype_(tensor_config.dtype))
var_desc.set_shape(tensor_config.shape)
var_desc.set_need_check_feed(True)
if tensor_config.lod is not None:
var_desc.set_lod_level(len(tensor_config.lod))
op_desc = main_block_desc._prepend_op()
op_desc.set_type("feed")
op_desc.set_input('X', ["feed"])
op_desc.set_output('Out', [name])
op_desc._set_attr("col", index)
index = index + 1
save_var_map = {}
for name, tensor_config in program_config.weights.items():
var_desc = main_block_desc.var(cpt.to_bytes(name))
var_desc.set_type(core.VarDesc.VarType.LOD_TENSOR)
var_desc.set_dtype(convert_np_dtype_to_dtype_(tensor_config.dtype))
var_desc.set_shape(tensor_config.shape)
var_desc.set_persistable(True)
save_var_map[name] = util_program.global_block().create_parameter(
dtype=tensor_config.dtype,
shape=tensor_config.shape,
type=core.VarDesc.VarType.LOD_TENSOR,
name=name,
initializer=NumpyArrayInitializer(tensor_config.data))
in_vars = []
for name in sorted(save_var_map.keys()):
in_vars.append(save_var_map[name])
out_var = util_program.global_block().create_var(
type=core.VarDesc.VarType.RAW, name="out_var_0")
out_var.desc.set_persistable(True)
util_program.global_block().append_op(type='save_combine',
inputs={'X': in_vars},
outputs={'Y': out_var},
attrs={
'file_path': '',
'save_to_memory': True
})
for op_config in program_config.ops:
op_desc = main_block_desc.append_op()
op_desc.set_type(op_config.type)
for name, values in op_config.inputs.items():
op_desc.set_input(name, values)
for name, values in op_config.attrs.items():
op_desc._set_attr(name, values)
for name, values in op_config.outputs.items():
op_desc.set_output(name, values)
for v in values:
var_desc = main_block_desc.var(cpt.to_bytes(v))
var_desc.set_type(core.VarDesc.VarType.LOD_TENSOR)
var_desc.set_dtype(convert_np_dtype_to_dtype_(np.float32))
if op_config.outputs_dtype is not None and v in op_config.outputs_dtype.keys(
):
var_desc.set_dtype(
convert_np_dtype_to_dtype_(op_config.outputs_dtype[v]))
op_desc.infer_var_type(main_block_desc)
op_desc.infer_shape(main_block_desc)
op_desc.check_attrs()
for index, name in enumerate(program_config.outputs):
var_desc = main_block_desc.var(cpt.to_bytes("fetch"))
var_desc.set_type(core.VarDesc.VarType.FETCH_LIST)
var_desc.set_need_check_feed(True)
op_desc = main_block_desc.append_op()
op_desc.set_type("fetch")
op_desc.set_input('X', [name])
op_desc.set_output('Out', ["fetch"])
op_desc._set_attr("col", index)
main_program_desc._set_version()
paddle.fluid.core.save_op_version_info(main_program_desc)
model = main_program_desc.serialize_to_string()
util_program._sync_with_cpp()
place = fluid.CPUPlace()
executor = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
executor.run(util_program)
params = scope.find_var("out_var_0").get_bytes()
return model, params
def infer_step(args, vocab_size, test_reader, use_cuda, i2w):
""" inference function """
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
emb_size = args.emb_size
batch_size = args.batch_size
with fluid.scope_guard(fluid.Scope()):
main_program = fluid.Program()
with fluid.program_guard(main_program):
values, pred = net.infer_network(vocab_size, emb_size)
for epoch in range(start_index, last_index + 1):
for batchid in range(args.start_batch, args.end_batch):
copy_program = main_program.clone()
model_path = model_dir + "/pass-" + str(epoch) + (
'/batch-' + str(batchid * args.print_step))
fluid.load(copy_program, model_path, exe)
accum_num = 0
accum_num_sum = 0.0
t0 = time.time()
step_id = 0
for data in test_reader():
step_id += 1
b_size = len([dat[0] for dat in data])
wa = np.array([dat[0] for dat in data
]).astype("int64").reshape(b_size)
wb = np.array([dat[1] for dat in data
]).astype("int64").reshape(b_size)
wc = np.array([dat[2] for dat in data
]).astype("int64").reshape(b_size)
label = [dat[3] for dat in data]
input_word = [dat[4] for dat in data]
para = exe.run(
copy_program,
feed={
"analogy_a":
wa,
"analogy_b":
wb,
"analogy_c":
wc,
"all_label":
np.arange(vocab_size).reshape(vocab_size),
},
fetch_list=[pred.name, values],
return_numpy=False)
pre = np.array(para[0])
val = np.array(para[1])
for ii in range(len(label)):
top4 = pre[ii]
accum_num_sum += 1
for idx in top4:
if int(idx) in input_word[ii]:
continue
if int(idx) == int(label[ii][0]):
accum_num += 1
break
if step_id % 1 == 0:
print("step:%d %d " % (step_id, accum_num))
print("epoch:%d \t acc:%.3f " %
(epoch, 1.0 * accum_num / accum_num_sum))
t1 = time.time()
# Author: Acer Zhang
# Datetime:2020/5/10 14:19
# Copyright belongs to the author.
# Please indicate the source for reprinting.
import paddle.fluid as fluid
import numpy as np
import PIL.Image as Image
img_path = r"D:\DLExample\easy07_visual_feature_map\data\1.jpg"
save_model_path = "./model"
img1 = Image.open(img_path).convert('L')
img1 = np.array(img1).reshape(1, 1, 30, 15).astype(np.float32) # NCHW格式
img1 /= 255 # 归一化以提升训练效果
# 防止Notebook中出现冲突等问题，使用新的ScopeS、来保证程序的健壮性。
new_scope = fluid.Scope()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
with fluid.scope_guard(new_scope):
# 读取预测模型
infer_program, feed_name, fetch_list = fluid.io.load_inference_model(
save_model_path, exe)
outs = exe.run(program=infer_program,
feed={feed_name[0]: img1},
fetch_list=fetch_list)
print("概率分布:", np.round(outs[0][0], 2)) # 保留2位整数
print("推理结果:", np.argmax(outs[0][0])) # 获取概率最高的标签索引
exe.run(fluid.default_startup_program())
##开始训练，迭代500次
for i in range(500):
outs = exe.run(feed={
'x': train_data,
'y': y_true
},
fetch_list=[y_predict.name, avg_cost.name])
if i % 50 == 0:
print('iter={:.0f},cost={}'.format(i, outs[1][0]))
# 存储训练结果
params_dirname = "result"
fluid.io.save_inference_model(params_dirname, ['x'], [y_predict], exe)
# 开始预测
infer_exe = fluid.Executor(cpu)
inference_scope = fluid.Scope()
# 加载训练好的模型
with fluid.scope_guard(inference_scope):
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(params_dirname, infer_exe)
# 生成测试数据
test = np.array([[[9], [5], [2], [10]]]).astype('float32')
# 进行预测
results = infer_exe.run(inference_program,
feed={"x": test},
fetch_list=fetch_targets)
# 给出题目为 【9,5,2,10】 输出y=4*9+6*5+7*2+10*2的值
print("9a+5b+2c+10d={}".format(results[0][0]))
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
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()
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"))
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":
"DownpourCtrAccessor"
}
})
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")
fleet.confirm()
fleet.revert()
except:
print("do not support pslib test, skip")
return