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未验证 提交 4d4322a6 编写于 作者: 武 武毅 提交者： GitHub
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merge fluid dist tests (#8573) 

* merge fluid dist tests

* update cmake
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python/paddle/fluid/tests/CMakeLists.txt
浏览文件 @ 4d4322a6
......@@ -7,5 +7,4 @@ endforeach()
add_subdirectory(unittests)
add_subdirectory(book)
add_subdirectory(book_distribute)
add_subdirectory(book_memory_optimization)
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @ 4d4322a6
......@@ -19,9 +19,10 @@ import numpy
import unittest
import math
import sys
import os
def train(use_cuda, save_dirname):
def train(use_cuda, save_dirname, is_local):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
......@@ -32,7 +33,7 @@ def train(use_cuda, save_dirname):
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
......@@ -42,15 +43,16 @@ def train(use_cuda, save_dirname):
batch_size=BATCH_SIZE)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
def train_loop(main_program):
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_loss_value, = exe.run(fluid.default_main_program(),
avg_loss_value, = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
print(avg_loss_value)
......@@ -64,6 +66,35 @@ def train(use_cuda, save_dirname):
raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
avg_loss_value[0]))
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
......@@ -94,14 +125,14 @@ def infer(use_cuda, save_dirname=None):
print("infer results: ", results[0])
def main(use_cuda):
def main(use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "fit_a_line.inference.model"
train(use_cuda, save_dirname)
train(use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
......
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ 4d4322a6
......@@ -21,6 +21,7 @@ import math
import sys
import numpy
import unittest
import os
def resnet_cifar10(input, depth=32):
......@@ -92,7 +93,7 @@ def vgg16_bn_drop(input):
return fc2
def train(net_type, use_cuda, save_dirname):
def train(net_type, use_cuda, save_dirname, is_local):
classdim = 10
data_shape = [3, 32, 32]
......@@ -117,7 +118,7 @@ def train(net_type, use_cuda, save_dirname):
test_program = fluid.default_main_program().clone()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_cost)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
BATCH_SIZE = 128
PASS_NUM = 1
......@@ -133,12 +134,13 @@ def train(net_type, use_cuda, save_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe.run(fluid.default_startup_program())
def train_loop(main_program):
exe.run(fluid.default_startup_program())
loss = 0.0
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
exe.run(feed=feeder.feed(data))
exe.run(main_program, feed=feeder.feed(data))
if (batch_id % 10) == 0:
acc_list = []
......@@ -166,6 +168,35 @@ def train(net_type, use_cuda, save_dirname):
[predict], exe)
return
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
......@@ -196,14 +227,14 @@ def infer(use_cuda, save_dirname=None):
print("infer results: ", results[0])
def main(net_type, use_cuda):
def main(net_type, use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "image_classification_" + net_type + ".inference.model"
train(net_type, use_cuda, save_dirname)
train(net_type, use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
......
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ 4d4322a6
......@@ -22,6 +22,7 @@ from paddle.fluid.initializer import init_on_cpu
import contextlib
import time
import unittest
import os
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
......@@ -138,7 +139,7 @@ def create_random_lodtensor(lod, place, low, high):
return res
def train(use_cuda, save_dirname=None):
def train(use_cuda, save_dirname=None, is_local=True):
# define network topology
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
......@@ -178,7 +179,7 @@ def train(use_cuda, save_dirname=None):
decay_rate=0.5,
staircase=True),
global_step=global_step)
sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
# TODO(qiao)
# add dependency track and move this config before optimizer
......@@ -204,11 +205,14 @@ def train(use_cuda, save_dirname=None):
place=place)
exe = fluid.Executor(place)
def train_loop(main_program):
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(embedding_name).get_tensor()
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim), place)
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
start_time = time.time()
batch_id = 0
......@@ -216,7 +220,7 @@ def train(use_cuda, save_dirname=None):
chunk_evaluator.reset(exe)
for data in train_data():
cost, precision, recall, f1_score = exe.run(
fluid.default_main_program(),
main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost] + chunk_evaluator.metrics)
pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
......@@ -224,13 +228,14 @@ def train(use_cuda, save_dirname=None):
if batch_id % 10 == 0:
print("avg_cost:" + str(cost) + " precision:" + str(
precision) + " recall:" + str(recall) + " f1_score:" + str(
f1_score) + " pass_precision:" + str(
pass_precision) + " pass_recall:" + str(pass_recall)
+ " pass_f1_score:" + str(pass_f1_score))
precision) + " recall:" + str(recall) + " f1_score:" +
str(f1_score) + " pass_precision:" + str(
pass_precision) + " pass_recall:" + str(
pass_recall) + " pass_f1_score:" + str(
pass_f1_score))
if batch_id != 0:
print("second per batch: " + str((time.time() - start_time)
/ batch_id))
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
# Set the threshold low to speed up the CI test
if float(pass_precision) > 0.05:
if save_dirname is not None:
......@@ -244,6 +249,35 @@ def train(use_cuda, save_dirname=None):
batch_id = batch_id + 1
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
......@@ -308,14 +342,14 @@ def infer(use_cuda, save_dirname=None):
print("Inference Shape: ", np_data.shape)
def main(use_cuda):
def main(use_cuda, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
save_dirname = "label_semantic_roles.inference.model"
train(use_cuda, save_dirname)
train(use_cuda, save_dirname, is_local)
infer(use_cuda, save_dirname)
......
python/paddle/fluid/tests/book/test_machine_translation.py
浏览文件 @ 4d4322a6
......@@ -20,6 +20,7 @@ import paddle.fluid.framework as framework
import paddle.fluid.layers as pd
from paddle.fluid.executor import Executor
import unittest
import os
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
......@@ -168,7 +169,7 @@ def to_lodtensor(data, place):
return res
def train_main(use_cuda, is_sparse):
def train_main(use_cuda, is_sparse, is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
......@@ -181,7 +182,7 @@ def train_main(use_cuda, is_sparse):
avg_cost = pd.mean(cost)
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimizer.minimize(avg_cost)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
......@@ -190,6 +191,7 @@ def train_main(use_cuda, is_sparse):
exe = Executor(place)
def train_loop(main_program):
exe.run(framework.default_startup_program())
batch_id = 0
......@@ -198,7 +200,7 @@ def train_main(use_cuda, is_sparse):
word_data = to_lodtensor(map(lambda x: x[0], data), place)
trg_word = to_lodtensor(map(lambda x: x[1], data), place)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(framework.default_main_program(),
outs = exe.run(main_program,
feed={
'src_word_id': word_data,
'target_language_word': trg_word,
......@@ -212,6 +214,35 @@ def train_main(use_cuda, is_sparse):
break
batch_id += 1
if is_local:
train_loop(framework.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def decode_main(use_cuda, is_sparse):
if use_cuda and not fluid.core.is_compiled_with_cuda():
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ 4d4322a6
......@@ -20,27 +20,7 @@ import numpy
import unittest
import math
import sys
def parse_arg():
parser = argparse.ArgumentParser()
parser.add_argument(
"nn_type",
help="The neural network type, in ['mlp', 'conv']",
type=str,
choices=['mlp', 'conv'])
parser.add_argument(
"--parallel",
help='Run in parallel or not',
default=False,
action="store_true")
parser.add_argument(
"--use_cuda",
help="Run the program by using CUDA",
default=False,
action="store_true")
return parser.parse_args()
import os
BATCH_SIZE = 64
......@@ -83,7 +63,8 @@ def train(nn_type,
parallel,
save_dirname=None,
model_filename=None,
params_filename=None):
params_filename=None,
is_local=True):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
......@@ -114,12 +95,11 @@ def train(nn_type,
test_program = fluid.default_main_program().clone()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_loss)
optimize_ops, params_grads = optimizer.minimize(avg_loss)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -129,16 +109,20 @@ def train(nn_type,
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
def train_loop(main_program):
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch, fetch nothing
exe.run(feed=feeder.feed(data))
exe.run(main_program, feed=feeder.feed(data))
if (batch_id + 1) % 10 == 0:
acc_set = []
avg_loss_set = []
for test_data in test_reader():
acc_np, avg_loss_np = exe.run(program=test_program,
acc_np, avg_loss_np = exe.run(
program=test_program,
feed=feeder.feed(test_data),
fetch_list=[acc, avg_loss])
acc_set.append(float(acc_np))
......@@ -146,7 +130,8 @@ def train(nn_type,
# get test acc and loss
acc_val = numpy.array(acc_set).mean()
avg_loss_val = numpy.array(avg_loss_set).mean()
if float(acc_val) > 0.2: # Smaller value to increase CI speed
if float(acc_val
) > 0.2: # Smaller value to increase CI speed
if save_dirname is not None:
fluid.io.save_inference_model(
save_dirname, ["img"], [prediction],
......@@ -163,6 +148,36 @@ def train(nn_type,
sys.exit("got NaN loss, training failed.")
raise AssertionError("Loss of recognize digits is too large")
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
pserver_endpoints = os.getenv("PSERVERS")
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda,
save_dirname=None,
......@@ -208,6 +223,7 @@ def main(use_cuda, parallel, nn_type, combine):
model_filename = "__model_combined__"
params_filename = "__params_combined__"
# call train() with is_local argument to run distributed train
train(
nn_type=nn_type,
use_cuda=use_cuda,
......
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @ 4d4322a6
......@@ -14,6 +14,7 @@
import math
import sys
import os
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
......@@ -152,19 +153,18 @@ def model():
return scale_infer, avg_cost
def train(use_cuda, save_dirname):
def train(use_cuda, save_dirname, is_local=True):
scale_infer, avg_cost = model()
# test program
test_program = fluid.default_main_program().clone()
sgd_optimizer = SGDOptimizer(learning_rate=0.2)
opts = sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -212,18 +212,22 @@ def train(use_cuda, save_dirname):
feed_tensors[key] = tensor
return feed_tensors
def train_loop(main_program):
exe.run(framework.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch
outs = exe.run(program=fluid.default_main_program(),
outs = exe.run(program=main_program,
feed=func_feed(feeding, data),
fetch_list=[avg_cost])
out = np.array(outs[0])
if (batch_id + 1) % 10 == 0:
avg_cost_set = []
for test_data in test_reader():
avg_cost_np = exe.run(program=test_program,
avg_cost_np = exe.run(
program=test_program,
feed=func_feed(feeding, test_data),
fetch_list=[avg_cost])
avg_cost_set.append(avg_cost_np[0])
......@@ -243,6 +247,35 @@ def train(use_cuda, save_dirname):
if math.isnan(float(out[0])):
sys.exit("got NaN loss, training failed.")
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
......
python/paddle/fluid/tests/book/test_understand_sentiment.py
浏览文件 @ 4d4322a6
......@@ -20,6 +20,7 @@ import contextlib
import math
import numpy as np
import sys
import os
def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
......@@ -132,7 +133,12 @@ def create_random_lodtensor(lod, place, low, high):
return res
def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
def train(word_dict,
net_method,
use_cuda,
parallel=False,
save_dirname=None,
is_local=True):
BATCH_SIZE = 128
PASS_NUM = 5
dict_dim = len(word_dict)
......@@ -164,7 +170,7 @@ def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
assert save_dirname is None
adagrad = fluid.optimizer.Adagrad(learning_rate=0.002)
adagrad.minimize(cost)
optimize_ops, params_grads = adagrad.minimize(cost)
train_data = paddle.batch(
paddle.reader.shuffle(
......@@ -174,11 +180,12 @@ def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
def train_loop(main_program):
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
for data in train_data():
cost_val, acc_val = exe.run(fluid.default_main_program(),
cost_val, acc_val = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
......@@ -192,6 +199,35 @@ def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
raise AssertionError("Cost is too large for {0}".format(
net_method.__name__))
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(word_dict, use_cuda, save_dirname=None):
if save_dirname is None:
......
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @ 4d4322a6
......@@ -30,7 +30,7 @@ def create_random_lodtensor(lod, place, low, high):
return res
def train(use_cuda, is_sparse, is_parallel, save_dirname):
def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
PASS_NUM = 100
EMBED_SIZE = 32
HIDDEN_SIZE = 256
......@@ -101,7 +101,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname):
avg_cost = fluid.layers.mean(pd())
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
......@@ -112,11 +112,12 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname):
feed_list=[first_word, second_word, third_word, forth_word, next_word],
place=place)
def train_loop(main_program):
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_cost_np = exe.run(fluid.default_main_program(),
avg_cost_np = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
if avg_cost_np[0] < 5.0:
......@@ -130,6 +131,35 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname):
raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver_endpoints,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
train_loop(t.get_trainer_program())
def infer(use_cuda, save_dirname=None):
if save_dirname is None:
......
python/paddle/fluid/tests/book_distribute/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ ec338326
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/fluid/tests/book_distribute/notest_dist_fit_a_line.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import os
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
else:
trainer_prog = t.get_trainer_program()
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_loss_value = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost])
print("loss:" + str(avg_loss_value))
if avg_loss_value[0] < 10.0:
exit(0)
exit(1)
python/paddle/fluid/tests/book_distribute/notest_dist_image_classification.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.v2 as paddle
import paddle.fluid as fluid
import os
import sys
TRAINERS = 5
BATCH_SIZE = 128
PASS_NUM = 100
def resnet_cifar10(input, depth=32):
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=False)
return fluid.layers.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0, None)
else:
return input
def basicblock(input, ch_in, ch_out, stride):
tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None)
short = shortcut(input, ch_in, ch_out, stride)
return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
def layer_warp(block_func, input, ch_in, ch_out, count, stride):
tmp = block_func(input, ch_in, ch_out, stride)
for i in range(1, count):
tmp = block_func(tmp, ch_out, ch_out, 1)
return tmp
assert (depth - 2) % 6 == 0
n = (depth - 2) / 6
conv1 = conv_bn_layer(
input=input, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
pool = fluid.layers.pool2d(
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
return pool
def vgg16_bn_drop(input):
def conv_block(input, num_filter, groups, dropouts):
return fluid.nets.img_conv_group(
input=input,
pool_size=2,
pool_stride=2,
conv_num_filter=[num_filter] * groups,
conv_filter_size=3,
conv_act='relu',
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type='max')
conv1 = conv_block(input, 64, 2, [0.3, 0])
conv2 = conv_block(conv1, 128, 2, [0.4, 0])
conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=512, act=None)
bn = fluid.layers.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
return fc2
classdim = 10
data_shape = [3, 32, 32]
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
net_type = "vgg"
if len(sys.argv) >= 2:
net_type = sys.argv[1]
if net_type == "vgg":
print("training vgg net")
net = vgg16_bn_drop(images)
elif net_type == "resnet":
print("training resnet")
net = resnet_cifar10(images, 32)
else:
raise ValueError("%s network is not supported" % net_type)
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=TRAINERS)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
for data in train_reader():
loss, acc = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("pass_id:" + str(pass_id) + "loss:" + str(loss) + " pass_acc:"
+ str(pass_acc))
# this model is slow, so if we can train two mini batches,
# we think it works properly.
print("trainer run end")
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
exit(1)
python/paddle/fluid/tests/book_distribute/notest_dist_label_semantic_roles.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import paddle.v2 as paddle
import paddle.v2.dataset.conll05 as conll05
import paddle.fluid as fluid
import time
import os
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)
mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
mix_hidden_lr = 1e-3
IS_SPARSE = True
PASS_NUM = 10
BATCH_SIZE = 20
embedding_name = 'emb'
def load_parameter(file_name, h, w):
with open(file_name, 'rb') as f:
f.read(16) # skip header.
return np.fromfile(f, dtype=np.float32).reshape(h, w)
def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
**ignored):
# 8 features
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[pred_len, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='vemb')
mark_embedding = fluid.layers.embedding(
input=mark,
size=[mark_dict_len, mark_dim],
dtype='float32',
is_sparse=IS_SPARSE)
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
fluid.layers.embedding(
size=[word_dict_len, word_dim],
input=x,
param_attr=fluid.ParamAttr(
name=embedding_name, trainable=False)) for x in word_input
]
emb_layers.append(predicate_embedding)
emb_layers.append(mark_embedding)
hidden_0_layers = [
fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
]
hidden_0 = fluid.layers.sums(input=hidden_0_layers)
lstm_0 = fluid.layers.dynamic_lstm(
input=hidden_0,
size=hidden_dim,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid')
# stack L-LSTM and R-LSTM with direct edges
input_tmp = [hidden_0, lstm_0]
for i in range(1, depth):
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim),
fluid.layers.fc(input=input_tmp[1], size=hidden_dim)
])
lstm = fluid.layers.dynamic_lstm(
input=mix_hidden,
size=hidden_dim,
candidate_activation='relu',
gate_activation='sigmoid',
cell_activation='sigmoid',
is_reverse=((i % 2) == 1))
input_tmp = [mix_hidden, lstm]
feature_out = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=label_dict_len),
fluid.layers.fc(input=input_tmp[1], size=label_dict_len)
])
return feature_out
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
# define network topology
word = fluid.layers.data(
name='word_data', shape=[1], dtype='int64', lod_level=1)
predicate = fluid.layers.data(
name='verb_data', shape=[1], dtype='int64', lod_level=1)
ctx_n2 = fluid.layers.data(
name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
ctx_n1 = fluid.layers.data(
name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
ctx_0 = fluid.layers.data(
name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
ctx_p1 = fluid.layers.data(
name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
ctx_p2 = fluid.layers.data(
name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
mark = fluid.layers.data(
name='mark_data', shape=[1], dtype='int64', lod_level=1)
feature_out = db_lstm(**locals())
target = fluid.layers.data(
name='target', shape=[1], dtype='int64', lod_level=1)
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
param_attr=fluid.ParamAttr(
name='crfw', learning_rate=mix_hidden_lr))
avg_cost = fluid.layers.mean(crf_cost)
# TODO(qiao)
# check other optimizers and check why out will be NAN
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
# TODO(qiao)
# add dependency track and move this config before optimizer
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
chunk_evaluator = fluid.evaluator.ChunkEvaluator(
input=crf_decode,
label=target,
chunk_scheme="IOB",
num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(
feed_list=[
word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
],
place=place)
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv(
"TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
start_time = time.time()
batch_id = 0
exe.run(fluid.default_startup_program())
embedding_param = fluid.global_scope().find_var(
embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
place)
for pass_id in xrange(PASS_NUM):
chunk_evaluator.reset(exe)
for data in train_data():
cost, precision, recall, f1_score = exe.run(
trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost] + chunk_evaluator.metrics)
pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
exe)
if batch_id % 10 == 0:
print("avg_cost:" + str(cost) + " precision:" + str(
precision) + " recall:" + str(recall) + " f1_score:" +
str(f1_score) + " pass_precision:" + str(
pass_precision) + " pass_recall:" + str(
pass_recall) + " pass_f1_score:" + str(
pass_f1_score))
if batch_id != 0:
print("second per batch: " + str((time.time(
) - start_time) / batch_id))
batch_id = batch_id + 1
if __name__ == '__main__':
main()
python/paddle/fluid/tests/book_distribute/notest_dist_word2vec.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import os
PASS_NUM = 100
EMBED_SIZE = 32
HIDDEN_SIZE = 256
N = 5
BATCH_SIZE = 32
IS_SPARSE = True
TRAINERS = 2
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
embed_first = fluid.layers.embedding(
input=first_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='shared_w')
embed_second = fluid.layers.embedding(
input=second_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='shared_w')
embed_third = fluid.layers.embedding(
input=third_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='shared_w')
embed_forth = fluid.layers.embedding(
input=forth_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr='shared_w')
concat_embed = fluid.layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=TRAINERS)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
feeder = fluid.DataFeeder(
feed_list=[first_word, second_word, third_word, forth_word, next_word],
place=place)
exe.run(fluid.default_startup_program())
trainer_prog = t.get_trainer_program()
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_cost_np = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost])
print("avg_cost_np", avg_cost_np)
if avg_cost_np[0] < 5.0:
exit(
0) # if avg cost less than 10.0, we think our code is good.
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
exit(1)
python/paddle/fluid/tests/book_distribute/notest_machine_translation.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.framework as framework
import paddle.fluid.layers as layers
from paddle.fluid.executor import Executor
import os
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
hidden_dim = 32
word_dim = 16
IS_SPARSE = True
batch_size = 10
max_length = 50
topk_size = 50
trg_dic_size = 10000
decoder_size = hidden_dim
def encoder_decoder():
# encoder
src_word_id = layers.data(
name="src_word_id", shape=[1], dtype='int64', lod_level=1)
src_embedding = layers.embedding(
input=src_word_id,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(name='vemb'))
fc1 = fluid.layers.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
lstm_hidden0, lstm_0 = layers.dynamic_lstm(input=fc1, size=hidden_dim * 4)
encoder_out = layers.sequence_last_step(input=lstm_hidden0)
# decoder
trg_language_word = layers.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = layers.embedding(
input=trg_language_word,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr=fluid.ParamAttr(name='vemb'))
rnn = fluid.layers.DynamicRNN()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
mem = rnn.memory(init=encoder_out)
fc1 = fluid.layers.fc(input=[current_word, mem],
size=decoder_size,
act='tanh')
out = fluid.layers.fc(input=fc1, size=target_dict_dim, act='softmax')
rnn.update_memory(mem, fc1)
rnn.output(out)
return rnn()
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
rnn_out = encoder_decoder()
label = layers.data(
name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
cost = layers.cross_entropy(input=rnn_out, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
place = core.CPUPlace()
exe = Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv(
"TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
exe.run(framework.default_startup_program())
batch_id = 0
for pass_id in xrange(2):
for data in train_data():
word_data = to_lodtensor(map(lambda x: x[0], data), place)
trg_word = to_lodtensor(map(lambda x: x[1], data), place)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(trainer_prog,
feed={
'src_word_id': word_data,
'target_language_word': trg_word,
'target_language_next_word': trg_word_next
},
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 3:
exit(0)
batch_id += 1
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
python/paddle/fluid/tests/book_distribute/notest_recognize_digits_conv_dist.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import os
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=images,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.Adam(learning_rate=0.01)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 50
PASS_NUM = 3
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
pserver_endpoints = os.getenv("PSERVERS") # all pserver endpoints
trainers = int(os.getenv("TRAINERS")) # total trainer count
current_endpoint = os.getenv("SERVER_ENDPOINT") # current pserver endpoint
training_role = os.getenv("TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
# TODO(typhoonzero): change trainer startup program to fetch parameters from pserver
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
batch_id = 0
for data in train_reader():
loss, acc = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
if batch_id % 100 == 0:
print("batch_id %d, loss: %f, acc: %f" %
(batch_id, loss, pass_acc))
batch_id += 1
pass_acc = accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
python/paddle/fluid/tests/book_distribute/notest_recognize_digits_mlp_dist.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
import os
BATCH_SIZE = 128
PASS_NUM = 100
images = fluid.layers.data(name='x', shape=[784], dtype='float32')
# TODO(aroraabhinav) Add regularization and error clipping after
# Issue 7432(https://github.com/PaddlePaddle/Paddle/issues/7432) is resolved.
hidden1 = fluid.layers.fc(input=images, size=128, act='relu')
hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
optimize_ops, params_grads = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE",
"TRAINER") # get the training role: trainer/pserver
t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
batch_id = 0
for data in train_reader():
loss, acc = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
if batch_id % 100 == 0:
print("batch_id %d, loss: %f, acc: %f" %
(batch_id, loss, pass_acc))
batch_id += 1
pass_acc = accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
python/paddle/fluid/tests/book_distribute/notest_recommender_system_dist.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import os
import paddle.v2 as paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers as layers
import paddle.fluid.nets as nets
from paddle.fluid.optimizer import SGDOptimizer
IS_SPARSE = True
BATCH_SIZE = 256
PASS_NUM = 100
def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
param_attr='user_table',
is_sparse=IS_SPARSE)
usr_fc = layers.fc(input=usr_emb, size=32)
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
is_sparse=IS_SPARSE)
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
param_attr='age_table')
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
is_sparse=IS_SPARSE)
usr_job_fc = layers.fc(input=usr_job_emb, size=16)
concat_embed = layers.concat(
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return usr_combined_features
def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
param_attr='movie_table',
is_sparse=IS_SPARSE)
mov_fc = layers.fc(input=mov_emb, size=32)
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(name='category_id', shape=[1], dtype='int64')
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
input=mov_categories_emb, pool_type="sum")
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(name='movie_title', shape=[1], dtype='int64')
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
input=mov_title_emb,
num_filters=32,
filter_size=3,
act="tanh",
pool_type="sum")
concat_embed = layers.concat(
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return mov_combined_features
def model():
usr_combined_features = get_usr_combined_features()
mov_combined_features = get_mov_combined_features()
# need cos sim
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
scale_infer = layers.scale(x=inference, scale=5.0)
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
return avg_cost
def func_feed(feeding, data, place):
feed_tensors = {}
for (key, idx) in feeding.iteritems():
tensor = core.LoDTensor()
if key != "category_id" and key != "movie_title":
if key == "score":
numpy_data = np.array(map(lambda x: x[idx], data)).astype(
"float32")
else:
numpy_data = np.array(map(lambda x: x[idx], data)).astype(
"int64")
else:
numpy_data = map(lambda x: np.array(x[idx]).astype("int64"), data)
lod_info = [len(item) for item in numpy_data]
offset = 0
lod = [offset]
for item in lod_info:
offset += item
lod.append(offset)
numpy_data = np.concatenate(numpy_data, axis=0)
tensor.set_lod([lod])
numpy_data = numpy_data.reshape([numpy_data.shape[0], 1])
tensor.set(numpy_data, place)
feed_tensors[key] = tensor
return feed_tensors
def main():
cost = model()
optimizer = SGDOptimizer(learning_rate=0.2)
optimize_ops, params_grads = optimizer.minimize(cost)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
exe.run(fluid.default_startup_program())
trainer_prog = t.get_trainer_program()
feeding = {
'user_id': 0,
'gender_id': 1,
'age_id': 2,
'job_id': 3,
'movie_id': 4,
'category_id': 5,
'movie_title': 6,
'score': 7
}
for pass_id in range(PASS_NUM):
for data in train_reader():
outs = exe.run(trainer_prog,
feed=func_feed(feeding, data, place),
fetch_list=[cost])
out = np.array(outs[0])
print("cost=" + str(out[0]))
if out[0] < 6.0:
print("Training complete. Average cost is less than 6.0.")
# if avg cost less than 6.0, we think our code is good.
exit(0)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
python/paddle/fluid/tests/book_distribute/notest_understand_sentiment_conv_dist.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import numpy as np
import paddle.v2 as paddle
import paddle.fluid as fluid
def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
hid_dim=32):
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
optimize_ops, params_grads = adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
BATCH_SIZE = 100
PASS_NUM = 5
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost, accuracy, acc_out, optimize_ops, params_grads = convolution_net(
data, label, input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv(
"TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
exe.run(fluid.default_startup_program())
trainer_prog = t.get_trainer_program()
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
cost_val, acc_val = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and pass_acc > 0.8:
exit(0)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
python/paddle/fluid/tests/book_distribute/notest_understand_sentiment_dynamic_lstm.py 已删除 100644 → 0
浏览文件 @ ec338326
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import os
import paddle.v2 as paddle
import paddle.fluid as fluid
def stacked_lstm_net(data,
label,
input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3):
assert stacked_num % 2 == 1
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
# add bias attr
# TODO(qijun) linear act
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = fluid.layers.fc(input=inputs, size=hid_dim)
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]
fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
prediction = fluid.layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
optimize_ops, params_grads = adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
BATCH_SIZE = 100
PASS_NUM = 5
word_dict = paddle.dataset.imdb.word_dict()
print "loaded word dict successfully"
dict_dim = len(word_dict)
class_dim = 2
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost, accuracy, acc_out, optimize_ops, params_grads = stacked_lstm_net(
data, label, input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv(
"TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
exe.run(fluid.default_startup_program())
trainer_prog = t.get_trainer_program()
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
cost_val, acc_val = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and acc_val > 0.8:
exit(0)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
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