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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into initialize

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Merge branch develop
wangkuiyi-patch-2
weixing02 7 years ago
parent 0586b9e5c9 47a4ec0672
commit 9f462e43eb
7 changed files with 1854 additions and 58 deletions
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626
benchmark/tensorflow/machine_translation.py
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benchmark/tensorflow/mnist.py
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# 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 absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import time
import numpy as np
import tensorflow as tf
import paddle.v2 as paddle
DTYPE = tf.float32
def parse_args():
parser = argparse.ArgumentParser("mnist model benchmark.")
parser.add_argument(
'--batch_size', type=int, default=128, help='The minibatch size.')
parser.add_argument(
'--iterations', type=int, default=35, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=5, help='The number of passes.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
args = parser.parse_args()
return args
def run_benchmark(args):
def weight_variable(dtype, shape):
initial = tf.truncated_normal(shape, stddev=0.1, dtype=dtype)
return tf.Variable(initial)
def bias_variable(dtype, shape):
initial = tf.constant(0.1, shape=shape, dtype=dtype)
return tf.Variable(initial)
device = '/cpu:0' if args.device == 'CPU' else '/device:GPU:0'
with tf.device(device):
images = tf.placeholder(DTYPE, shape=(None, 28, 28, 1))
labels = tf.placeholder(tf.int64, shape=(None, ))
# conv1, relu, pool1
conv1_weights = weight_variable(DTYPE, [5, 5, 1, 20])
conv1_bias = bias_variable(DTYPE, [20])
conv1 = tf.nn.conv2d(
images, conv1_weights, strides=[1, 1, 1, 1], padding="VALID")
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias))
pool1 = tf.nn.max_pool(
relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
# conv2, relu, pool2
conv2_weights = weight_variable(DTYPE, [5, 5, 20, 50])
conv2_bias = bias_variable(DTYPE, [50])
conv2 = tf.nn.conv2d(
pool1, conv2_weights, strides=[1, 1, 1, 1], padding="VALID")
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias))
pool2 = tf.nn.max_pool(
relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
# FC
pool_shape = pool2.get_shape().as_list()
hidden_dim = reduce(lambda a, b: a * b, pool_shape[1:], 1)
reshape = tf.reshape(pool2, shape=(tf.shape(pool2)[0], hidden_dim))
fc_weights = weight_variable(DTYPE, [hidden_dim, 10])
fc_bias = bias_variable(DTYPE, [10])
logits = tf.matmul(reshape, fc_weights) + fc_bias
# Get prediction
prediction = tf.nn.softmax(logits)
# Loss
one_hot_labels = tf.one_hot(labels, depth=10)
cost = -tf.reduce_sum(tf.log(prediction) * one_hot_labels, [1])
avg_cost = tf.reduce_mean(cost)
# Get accuracy
correct = tf.equal(tf.argmax(prediction, 1), labels)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
# metrics, g_accuracy
with tf.variable_scope("reset_metrics_accuracy_scope") as scope:
g_accuracy = tf.metrics.accuracy(
labels, tf.argmax(
prediction, axis=1))
vars = tf.contrib.framework.get_variables(
scope, collection=tf.GraphKeys.LOCAL_VARIABLES)
g_accuracy_reset_op = tf.variables_initializer(vars)
# Optimizer
opt = tf.train.AdamOptimizer(
learning_rate=0.001, beta1=0.9, beta2=0.999)
train_op = opt.minimize(avg_cost)
# train_op = tf.train.AdamOptimizer(1e-4).minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=args.batch_size)
def eval_test():
sess.run(g_accuracy_reset_op)
for batch_id, data in enumerate(test_reader()):
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape([1, 28, 28]), axes=[1,2,0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype("int64")
loss, acc, g_acc = sess.run(
[avg_cost, accuracy, g_accuracy],
feed_dict={images: images_data,
labels: labels_data})
return g_acc[1]
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
for pass_id in range(args.pass_num):
sess.run(g_accuracy_reset_op)
pass_start = time.time()
for batch_id, data in enumerate(train_reader()):
images_data = np.array(
map(lambda x: np.transpose(x[0].reshape([1, 28, 28]), axes=[1,2,0]), data)).astype("float32")
labels_data = np.array(map(lambda x: x[1], data)).astype(
"int64")
start = time.time()
_, loss, acc, g_acc = sess.run(
[train_op, avg_cost, accuracy, g_accuracy],
feed_dict={images: images_data,
labels: labels_data})
end = time.time()
print("pass=%d, batch=%d, loss=%f, error=%f, elapse=%f" %
(pass_id, batch_id, loss, 1 - acc, (end - start) / 1000))
pass_end = time.time()
test_avg_acc = eval_test()
print(
"pass=%d, training_avg_accuracy=%f, test_avg_acc=%f, elapse=%f"
% (pass_id, g_acc[1], test_avg_acc,
(pass_end - pass_start) / 1000))
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
run_benchmark(args)

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benchmark/tensorflow/resnet.py
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benchmark/tensorflow/stacked_dynamic_lstm.py
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# 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 absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import time
import tensorflow as tf
import paddle.v2 as paddle
def parse_args():
parser = argparse.ArgumentParser("LSTM model benchmark.")
parser.add_argument(
'--batch_size',
type=int,
default=32,
help='The sequence number of a batch data. (default: %(default)d)')
parser.add_argument(
'--stacked_num',
type=int,
default=5,
help='Number of lstm layers to stack. (default: %(default)d)')
parser.add_argument(
'--embedding_dim',
type=int,
default=512,
help='Dimension of embedding table. (default: %(default)d)')
parser.add_argument(
'--hidden_dim',
type=int,
default=512,
help='Hidden size of lstm unit. (default: %(default)d)')
parser.add_argument(
'--pass_num',
type=int,
default=10,
help='Epoch number to train. (default: %(default)d)')
parser.add_argument(
'--learning_rate',
type=float,
default=0.0002,
help='Learning rate used to train. (default: %(default)f)')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
args = parser.parse_args()
return args
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
def dynamic_lstm_model(dict_size,
embedding_dim,
hidden_dim,
stacked_num,
class_num=2,
is_train=True):
word_idx = tf.placeholder(tf.int64, shape=[None, None])
sequence_length = tf.placeholder(tf.int64, shape=[None, ])
embedding_weights = tf.get_variable('word_embeddings',
[dict_size, embedding_dim])
embedding = tf.nn.embedding_lookup(embedding_weights, word_idx)
lstm_cell = tf.nn.rnn_cell.LSTMCell(
num_units=hidden_dim, use_peepholes=False)
stacked_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * stacked_num)
# final_state [LSTMTuple(c, h), LSTMTuple(c, h) ...] total stacked_num LSTMTuples
_, final_state = tf.nn.dynamic_rnn(
cell=stacked_cell,
inputs=embedding,
dtype=tf.float32,
sequence_length=sequence_length)
w = tf.Variable(
tf.truncated_normal([hidden_dim, class_num]), dtype=tf.float32)
bias = tf.Variable(
tf.constant(
value=0.0, shape=[class_num], dtype=tf.float32))
prediction = tf.matmul(final_state[-1][1], w) + bias
if not is_train:
return (word_idx, sequence_length), tf.nn.softmax(prediction)
label = tf.placeholder(tf.int64, shape=[None, ])
loss = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(label, 2), logits=prediction)
avg_loss = tf.reduce_mean(loss)
correct_count = tf.equal(tf.argmax(prediction, 1), label)
acc = tf.reduce_mean(tf.cast(correct_count, tf.float32))
with tf.variable_scope("reset_metrics_accuracy_scope") as scope:
g_acc = tf.metrics.accuracy(label, tf.argmax(prediction, axis=1))
vars = tf.contrib.framework.get_variables(
scope, collection=tf.GraphKeys.LOCAL_VARIABLES)
reset_op = tf.variables_initializer(vars)
return (word_idx, sequence_length, label), avg_loss, acc, g_acc, reset_op
def padding_data(data, padding_size, value):
data = data + [value] * padding_size
return data[:padding_size]
def train(args):
word_dict = paddle.dataset.imdb.word_dict()
dict_size = len(word_dict)
feeding_list, avg_loss, acc, g_acc, reset_op = dynamic_lstm_model(
dict_size, args.embedding_dim, args.hidden_dim, args.stacked_num)
adam_optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
train_op = adam_optimizer.minimize(avg_loss)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.test(word_dict), buf_size=25000),
batch_size=args.batch_size)
def do_validation(sess):
sess.run(reset_op)
for batch_id, data in enumerate(test_reader()):
word_idx = map(lambda x: x[0], data)
sequence_length = np.array(
[len(seq) for seq in word_idx]).astype('int64')
maxlen = np.max(sequence_length)
word_idx = [padding_data(seq, maxlen, 0) for seq in word_idx]
word_idx = np.array(word_idx).astype('int64')
label = np.array(map(lambda x: x[1], data)).astype('int64')
_, loss, fetch_acc, fetch_g_acc = sess.run(
[train_op, avg_loss, acc, g_acc],
feed_dict={
feeding_list[0]: word_idx,
feeding_list[1]: sequence_length,
feeding_list[2]: label
})
return fetch_g_acc[1]
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_l)
sess.run(init_g)
for pass_id in xrange(args.pass_num):
# clear accuracy local variable
sess.run(reset_op)
pass_start_time = time.time()
words_seen = 0
for batch_id, data in enumerate(train_reader()):
word_idx = map(lambda x: x[0], data)
sequence_length = np.array(
[len(seq) for seq in word_idx]).astype('int64')
words_seen += np.sum(sequence_length)
maxlen = np.max(sequence_length)
word_idx = [padding_data(seq, maxlen, 0) for seq in word_idx]
word_idx = np.array(word_idx).astype('int64')
label = np.array(map(lambda x: x[1], data)).astype('int64')
_, loss, fetch_acc, fetch_g_acc = sess.run(
[train_op, avg_loss, acc, g_acc],
feed_dict={
feeding_list[0]: word_idx,
feeding_list[1]: sequence_length,
feeding_list[2]: label
})
print("pass_id=%d, batch_id=%d, loss: %f, acc: %f, avg_acc: %f"
% (pass_id, batch_id, loss, fetch_acc, fetch_g_acc[1]))
pass_end_time = time.time()
time_consumed = pass_end_time - pass_start_time
words_per_sec = words_seen / time_consumed
test_acc = do_validation(sess)
print("pass_id=%d, test_acc: %f, words/s: %f, sec/pass: %f" %
(pass_id, test_acc, words_per_sec, time_consumed))
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
if args.infer_only:
pass
else:
train(args)

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benchmark/tensorflow/vgg.py
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paddle/fluid/platform/call_once.h
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/* Copyright (c) 2016 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. */
#pragma once
#include <mutex>
namespace paddle {
namespace platform {
/*
The current implementation of std::call_once has a bug described in
https://stackoverflow.com/questions/41717579/stdcall-once-hangs-on-second-call-after-callable-threw-on-first-call.
This is likely caused by a deeper bug of pthread_once, which is discussed in
https://patchwork.ozlabs.org/patch/482350/
This wrap is a hack to avoid this bug.
*/
template <typename Callable, typename... Args>
inline void call_once(std::once_flag& flag, Callable&& f, Args&&... args) {
bool good = true;
std::exception ex;
try {
std::call_once(flag,
[&](Args&&... args) {
try {
f(args...);
} catch (const std::exception& e) {
ex = e;
good = false;
} catch (...) {
ex = std::runtime_error("excption caught in call_once");
good = false;
}
},
args...);
} catch (std::system_error& x) {
throw std::runtime_error("call once failed");
}
if (!good) {
throw std::exception(ex);
}
}
} // namespace platform
} // namespace paddle

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paddle/fluid/platform/dynload/nccl.h
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#include <mutex> // NOLINT
#include "paddle/fluid/platform/call_once.h"
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
namespace paddle {

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