Resolve conflict

.travis.yml

@@ -8,10 +8,13 @@ os:
 env:
   - JOB=DOCS
   - JOB=BUILD_AND_TEST
+  - JOB=PRE_COMMIT
 matrix:
   exclude:
     - os: osx
-      env: JOB=DOCS  # Only generate documentation in linux
+      env: JOB=DOCS  # Only generate documentation in linux.
+    - os: osx
+      env: JOB=PRE_COMMIT # Only check pre-commit hook in linux
 
 addons:
   apt:
@@ -39,18 +42,23 @@ addons:
       - lcov
       - graphviz
       - swig
+      - clang-format-3.8
 before_install:
   - |
     if [ ${JOB} == "BUILD_AND_TEST" ]; then
-      if ! git diff --name-only $TRAVIS_COMMIT_RANGE | grep -qvE '(\.md$)|(\.rst$)|(\.jpg$)|(\.png$)'
+      local change_list=`git diff --name-only $TRAVIS_COMMIT_RANGE`
+      if [ $? -eq 0 ]; then  # if git diff return no zero, then rerun unit test.
+        if ! echo ${change_list} | grep -qvE '(\.md$)|(\.rst$)|(\.jpg$)|(\.png$)'
         then
           echo "Only markdown docs were updated, stopping build process."
           exit
         fi
       fi
+    fi
   - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
   - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
-  - pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme
+  - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
+  - pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme pre-commit
 script:
   - paddle/scripts/travis/main.sh
 notifications:

WORKSPACE

 # External dependency to Google protobuf.
 http_archive(
-    name = "protobuf",
-    url = "http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
-    sha256 = "0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
-    strip_prefix = "protobuf-3.1.0",
-)
+    name="protobuf",
+    url="http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
+    sha256="0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
+    strip_prefix="protobuf-3.1.0", )
 
 # External dependency to gtest 1.7.0.  This method comes from
 # https://www.bazel.io/versions/master/docs/tutorial/cpp.html.

benchmark/tensorflow/rnn/run_multi.sh

@@ -25,4 +25,3 @@ test 4 2 256 512
 test 4 2 512 128 
 test 4 2 512 256 
 test 4 2 512 512 
-

demo/gan/data/download_cifar.sh

@@ -15,4 +15,3 @@ set -e
 wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
 tar zxf cifar-10-python.tar.gz
 rm cifar-10-python.tar.gz
-

demo/gan/data/get_mnist_data.sh

@@ -15,5 +15,3 @@ do
         gunzip ${fname}.gz
     fi
 done
-
-

demo/gan/gan_conf.py

@@ -14,10 +14,9 @@
 from paddle.trainer_config_helpers import *
 
 mode = get_config_arg("mode", str, "generator")
-assert mode in set(["generator",
-                    "discriminator",
-                    "generator_training",
-                    "discriminator_training"])
+assert mode in set([
+    "generator", "discriminator", "generator_training", "discriminator_training"
+])
 
 is_generator_training = mode == "generator_training"
 is_discriminator_training = mode == "discriminator_training"
@@ -38,8 +37,8 @@ sample_dim = 2
 settings(
     batch_size=128,
     learning_rate=1e-4,
-    learning_method=AdamOptimizer(beta1=0.5)
-)
+    learning_method=AdamOptimizer(beta1=0.5))
+
 
 def discriminator(sample):
     """
@@ -50,71 +49,88 @@ def discriminator(sample):
     of the sample is from real data.
     """
     param_attr = ParamAttr(is_static=is_generator_training)
-    bias_attr = ParamAttr(is_static=is_generator_training,
-                          initial_mean=1.0,
-                          initial_std=0)
+    bias_attr = ParamAttr(
+        is_static=is_generator_training, initial_mean=1.0, initial_std=0)
 
-    hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim,
+    hidden = fc_layer(
+        input=sample,
+        name="dis_hidden",
+        size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=ReluActivation())
 
-    hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
+    hidden2 = fc_layer(
+        input=hidden,
+        name="dis_hidden2",
+        size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
-    hidden_bn = batch_norm_layer(hidden2, 
+    hidden_bn = batch_norm_layer(
+        hidden2,
         act=ReluActivation(),
         name="dis_hidden_bn",
         bias_attr=bias_attr,
-                     param_attr=ParamAttr(is_static=is_generator_training,
-                           initial_mean=1.0,
+        param_attr=ParamAttr(
+            is_static=is_generator_training, initial_mean=1.0,
             initial_std=0.02),
         use_global_stats=False)
 
-    return fc_layer(input=hidden_bn, name="dis_prob", size=2,
+    return fc_layer(
+        input=hidden_bn,
+        name="dis_prob",
+        size=2,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=SoftmaxActivation())
 
+
 def generator(noise):
     """
     generator generates a sample given noise
     """
     param_attr = ParamAttr(is_static=is_discriminator_training)
-    bias_attr = ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=1.0,
-                           initial_std=0)
+    bias_attr = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=1.0, initial_std=0)
 
-    hidden = fc_layer(input=noise,
+    hidden = fc_layer(
+        input=noise,
         name="gen_layer_hidden",
         size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=ReluActivation())
 
-    hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
+    hidden2 = fc_layer(
+        input=hidden,
+        name="gen_hidden2",
+        size=hidden_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
-    hidden_bn = batch_norm_layer(hidden2, 
+    hidden_bn = batch_norm_layer(
+        hidden2,
         act=ReluActivation(),
         name="gen_layer_hidden_bn",
         bias_attr=bias_attr,
-                     param_attr=ParamAttr(is_static=is_discriminator_training,
+        param_attr=ParamAttr(
+            is_static=is_discriminator_training,
             initial_mean=1.0,
             initial_std=0.02),
         use_global_stats=False)
 
-    return fc_layer(input=hidden_bn,
+    return fc_layer(
+        input=hidden_bn,
         name="gen_layer1",
         size=sample_dim,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
+
 if is_generator_training:
     noise = data_layer(name="noise", size=noise_dim)
     sample = generator(noise)
@@ -126,7 +142,8 @@ if is_generator_training or is_discriminator_training:
     label = data_layer(name="label", size=1)
     prob = discriminator(sample)
     cost = cross_entropy(input=prob, label=label)
-    classification_error_evaluator(input=prob, label=label, name=mode+'_error')
+    classification_error_evaluator(
+        input=prob, label=label, name=mode + '_error')
     outputs(cost)
 
 if is_generator:

demo/gan/gan_conf_image.py

@@ -15,10 +15,9 @@ from paddle.trainer_config_helpers import *
 
 mode = get_config_arg("mode", str, "generator")
 dataSource = get_config_arg("data", str, "mnist")
-assert mode in set(["generator",
-                    "discriminator",
-                    "generator_training",
-                    "discriminator_training"])
+assert mode in set([
+    "generator", "discriminator", "generator_training", "discriminator_training"
+])
 
 is_generator_training = mode == "generator_training"
 is_discriminator_training = mode == "discriminator_training"
@@ -41,19 +40,28 @@ if dataSource == "mnist":
 else:
     sample_dim = 32
     c_dim = 3
-s2, s4 = int(sample_dim/2), int(sample_dim/4), 
-s8, s16 = int(sample_dim/8), int(sample_dim/16)
+s2, s4 = int(sample_dim / 2), int(sample_dim / 4),
+s8, s16 = int(sample_dim / 8), int(sample_dim / 16)
 
 settings(
     batch_size=128,
     learning_rate=2e-4,
-    learning_method=AdamOptimizer(beta1=0.5)
-)
-
-def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, 
-                 param_attr, bias_attr, param_attr_bn, bn, trans=False, 
+    learning_method=AdamOptimizer(beta1=0.5))
+
+
+def conv_bn(input,
+            channels,
+            imgSize,
+            num_filters,
+            output_x,
+            stride,
+            name,
+            param_attr,
+            bias_attr,
+            param_attr_bn,
+            bn,
+            trans=False,
             act=ReluActivation()):
-    
     """
     conv_bn is a utility function that constructs a convolution/deconv layer 
     with an optional batch_norm layer
@@ -76,7 +84,7 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
         filter_size = tmp
         padding = 0
 
-    print (imgSize, output_x, stride, filter_size, padding)
+    print(imgSize, output_x, stride, filter_size, padding)
 
     if trans:
         nameApx = "_conv"
@@ -84,16 +92,27 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
         nameApx = "_convt"
 
     if bn:
-        conv = img_conv_layer(input, filter_size=filter_size, 
+        conv = img_conv_layer(
+            input,
+            filter_size=filter_size,
             num_filters=num_filters,
-                   name=name + nameApx, num_channels=channels,
-                   act=LinearActivation(), groups=1, stride=stride, 
-                   padding=padding, bias_attr=bias_attr,
-                   param_attr=param_attr, shared_biases=True, layer_attr=None,
-                   filter_size_y=None, stride_y=None, padding_y=None, 
+            name=name + nameApx,
+            num_channels=channels,
+            act=LinearActivation(),
+            groups=1,
+            stride=stride,
+            padding=padding,
+            bias_attr=bias_attr,
+            param_attr=param_attr,
+            shared_biases=True,
+            layer_attr=None,
+            filter_size_y=None,
+            stride_y=None,
+            padding_y=None,
             trans=trans)
 
-        conv_bn = batch_norm_layer(conv, 
+        conv_bn = batch_norm_layer(
+            conv,
             act=act,
             name=name + nameApx + "_bn",
             bias_attr=bias_attr,
@@ -102,49 +121,60 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
 
         return conv_bn
     else:
-        conv = img_conv_layer(input, filter_size=filter_size, 
+        conv = img_conv_layer(
+            input,
+            filter_size=filter_size,
             num_filters=num_filters,
-                   name=name + nameApx, num_channels=channels,
-                   act=act, groups=1, stride=stride, 
-                   padding=padding, bias_attr=bias_attr,
-                   param_attr=param_attr, shared_biases=True, layer_attr=None,
-                   filter_size_y=None, stride_y=None, padding_y=None,
+            name=name + nameApx,
+            num_channels=channels,
+            act=act,
+            groups=1,
+            stride=stride,
+            padding=padding,
+            bias_attr=bias_attr,
+            param_attr=param_attr,
+            shared_biases=True,
+            layer_attr=None,
+            filter_size_y=None,
+            stride_y=None,
+            padding_y=None,
             trans=trans)
         return conv
 
+
 def generator(noise):
     """
     generator generates a sample given noise
     """
-    param_attr = ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=0.0,
-                           initial_std=0.02)
-    bias_attr = ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=0.0,
-                           initial_std=0.0)
-    
-    param_attr_bn=ParamAttr(is_static=is_discriminator_training,
-                           initial_mean=1.0,
-                           initial_std=0.02)
-    
-    h1 = fc_layer(input=noise,
+    param_attr = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.02)
+    bias_attr = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=0.0, initial_std=0.0)
+
+    param_attr_bn = ParamAttr(
+        is_static=is_discriminator_training, initial_mean=1.0, initial_std=0.02)
+
+    h1 = fc_layer(
+        input=noise,
         name="gen_layer_h1",
         size=s8 * s8 * gf_dim * 4,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=LinearActivation())
 
-    h1_bn = batch_norm_layer(h1, 
+    h1_bn = batch_norm_layer(
+        h1,
         act=ReluActivation(),
         name="gen_layer_h1_bn",
         bias_attr=bias_attr,
         param_attr=param_attr_bn,
         use_global_stats=False)
 
-    h2_bn = conv_bn(h1_bn, 
-                    channels=gf_dim*4, 
+    h2_bn = conv_bn(
+        h1_bn,
+        channels=gf_dim * 4,
         output_x=s8,
-                    num_filters=gf_dim*2, 
+        num_filters=gf_dim * 2,
         imgSize=s4,
         stride=2,
         name="gen_layer_h2",
@@ -154,8 +184,9 @@ def generator(noise):
         bn=True,
         trans=True)
 
-    h3_bn = conv_bn(h2_bn, 
-                    channels=gf_dim*2, 
+    h3_bn = conv_bn(
+        h2_bn,
+        channels=gf_dim * 2,
         output_x=s4,
         num_filters=gf_dim,
         imgSize=s2,
@@ -167,8 +198,8 @@ def generator(noise):
         bn=True,
         trans=True)
 
-    
-    return conv_bn(h3_bn,
+    return conv_bn(
+        h3_bn,
         channels=gf_dim,
         output_x=s2,
         num_filters=c_dim,
@@ -191,18 +222,16 @@ def discriminator(sample):
     of the sample is from generator and dimension 1 is the probabblity
     of the sample is from real data.
     """
-    param_attr = ParamAttr(is_static=is_generator_training,
-                           initial_mean=0.0,
-                           initial_std=0.02)
-    bias_attr = ParamAttr(is_static=is_generator_training,
-                          initial_mean=0.0,
-                          initial_std=0.0)
-    
-    param_attr_bn=ParamAttr(is_static=is_generator_training,
-                           initial_mean=1.0,
-                           initial_std=0.02)
-    
-    h0 = conv_bn(sample, 
+    param_attr = ParamAttr(
+        is_static=is_generator_training, initial_mean=0.0, initial_std=0.02)
+    bias_attr = ParamAttr(
+        is_static=is_generator_training, initial_mean=0.0, initial_std=0.0)
+
+    param_attr_bn = ParamAttr(
+        is_static=is_generator_training, initial_mean=1.0, initial_std=0.02)
+
+    h0 = conv_bn(
+        sample,
         channels=c_dim,
         imgSize=sample_dim,
         num_filters=df_dim,
@@ -214,10 +243,11 @@ def discriminator(sample):
         param_attr_bn=param_attr_bn,
         bn=False)
 
-    h1_bn = conv_bn(h0, 
+    h1_bn = conv_bn(
+        h0,
         channels=df_dim,
         imgSize=s2,
-                 num_filters=df_dim*2, 
+        num_filters=df_dim * 2,
         output_x=s4,
         stride=2,
         name="dis_h1",
@@ -226,10 +256,11 @@ def discriminator(sample):
         param_attr_bn=param_attr_bn,
         bn=True)
 
-    h2_bn = conv_bn(h1_bn, 
-                 channels=df_dim*2,
+    h2_bn = conv_bn(
+        h1_bn,
+        channels=df_dim * 2,
         imgSize=s4,
-                 num_filters=df_dim*4, 
+        num_filters=df_dim * 4,
         output_x=s8,
         stride=2,
         name="dis_h2",
@@ -238,25 +269,28 @@ def discriminator(sample):
         param_attr_bn=param_attr_bn,
         bn=True)
 
-    return fc_layer(input=h2_bn, name="dis_prob", size=2,
+    return fc_layer(
+        input=h2_bn,
+        name="dis_prob",
+        size=2,
         bias_attr=bias_attr,
         param_attr=param_attr,
         act=SoftmaxActivation())
 
 
-
 if is_generator_training:
     noise = data_layer(name="noise", size=noise_dim)
     sample = generator(noise)
 
 if is_discriminator_training:
-    sample = data_layer(name="sample", size=sample_dim * sample_dim*c_dim)
+    sample = data_layer(name="sample", size=sample_dim * sample_dim * c_dim)
 
 if is_generator_training or is_discriminator_training:
     label = data_layer(name="label", size=1)
     prob = discriminator(sample)
     cost = cross_entropy(input=prob, label=label)
-    classification_error_evaluator(input=prob, label=label, name=mode+'_error')
+    classification_error_evaluator(
+        input=prob, label=label, name=mode + '_error')
     outputs(cost)
 
 if is_generator:

demo/gan/gan_trainer.py

@@ -16,7 +16,7 @@ import argparse
 import random
 import numpy
 import cPickle
-import sys,os
+import sys, os
 from PIL import Image
 
 from paddle.trainer.config_parser import parse_config
@@ -24,6 +24,7 @@ from paddle.trainer.config_parser import logger
 import py_paddle.swig_paddle as api
 import matplotlib.pyplot as plt
 
+
 def plot2DScatter(data, outputfile):
     '''
     Plot the data as a 2D scatter plot and save to outputfile
@@ -41,9 +42,11 @@ def plot2DScatter(data, outputfile):
     plt.scatter(x, y)
     plt.savefig(outputfile, bbox_inches='tight')
 
+
 def CHECK_EQ(a, b):
     assert a == b, "a=%s, b=%s" % (a, b)
 
+
 def copy_shared_parameters(src, dst):
     '''
     copy the parameters from src to dst
@@ -52,11 +55,9 @@ def copy_shared_parameters(src, dst):
     :param dst: the destination of the parameters
     :type dst: GradientMachine
     '''
-    src_params = [src.getParameter(i)
-               for i in xrange(src.getParameterSize())]
+    src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
     src_params = dict([(p.getName(), p) for p in src_params])
 
-
     for i in xrange(dst.getParameterSize()):
         dst_param = dst.getParameter(i)
         src_param = src_params.get(dst_param.getName(), None)
@@ -68,14 +69,16 @@ def copy_shared_parameters(src, dst):
         dst_value.copyFrom(src_value)
         dst_param.setValueUpdated()
 
+
 def print_parameters(src):
-    src_params = [src.getParameter(i)
-               for i in xrange(src.getParameterSize())]
+    src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
 
     print "***************"
     for p in src_params:
         print "Name is %s" % p.getName()
-        print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray()
+        print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray(
+        )
+
 
 def load_mnist_data(imageFile):
     f = open(imageFile, "rb")
@@ -87,32 +90,35 @@ def load_mnist_data(imageFile):
     else:
         n = 10000
 
-    data = numpy.fromfile(f, 'ubyte', count=n*28*28).reshape((n, 28*28))
+    data = numpy.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
     data = data / 255.0 * 2.0 - 1.0
 
     f.close()
     return data.astype('float32')
 
+
 def load_cifar_data(cifar_path):
     batch_size = 10000
-    data = numpy.zeros((5*batch_size, 32*32*3), dtype = "float32")
+    data = numpy.zeros((5 * batch_size, 32 * 32 * 3), dtype="float32")
     for i in range(1, 6):
         file = cifar_path + "/data_batch_" + str(i)
         fo = open(file, 'rb')
         dict = cPickle.load(fo)
         fo.close()
-        data[(i - 1)*batch_size:(i*batch_size), :] = dict["data"]
+        data[(i - 1) * batch_size:(i * batch_size), :] = dict["data"]
 
     data = data / 255.0 * 2.0 - 1.0
     return data
 
+
 # synthesize 2-D uniform data
 def load_uniform_data():
     data = numpy.random.rand(1000000, 2).astype('float32')
     return data
 
+
 def merge(images, size):
-    if images.shape[1] == 28*28:
+    if images.shape[1] == 28 * 28:
         h, w, c = 28, 28, 1
     else:
         h, w, c = 32, 32, 3
@@ -124,6 +130,7 @@ def merge(images, size):
           ((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
     return img.astype('uint8')
 
+
 def save_images(images, path):
     merged_img = merge(images, [8, 8])
     if merged_img.shape[2] == 1:
@@ -132,13 +139,16 @@ def save_images(images, path):
         im = Image.fromarray(merged_img, mode="RGB")
     im.save(path)
 
+
 def get_real_samples(batch_size, data_np):
-    return data_np[numpy.random.choice(data_np.shape[0], batch_size, 
-                                       replace=False),:]
+    return data_np[numpy.random.choice(
+        data_np.shape[0], batch_size, replace=False), :]
+
 
 def get_noise(batch_size, noise_dim):
     return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
 
+
 def get_fake_samples(generator_machine, batch_size, noise):
     gen_inputs = api.Arguments.createArguments(1)
     gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
@@ -147,12 +157,14 @@ def get_fake_samples(generator_machine, batch_size, noise):
     fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
     return fake_samples
 
+
 def get_training_loss(training_machine, inputs):
     outputs = api.Arguments.createArguments(0)
     training_machine.forward(inputs, outputs, api.PASS_TEST)
     loss = outputs.getSlotValue(0).copyToNumpyMat()
     return numpy.mean(loss)
 
+
 def prepare_discriminator_data_batch_pos(batch_size, data_np):
     real_samples = get_real_samples(batch_size, data_np)
     labels = numpy.ones(batch_size, dtype='int32')
@@ -161,6 +173,7 @@ def prepare_discriminator_data_batch_pos(batch_size, data_np):
     inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
     return inputs
 
+
 def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
     fake_samples = get_fake_samples(generator_machine, batch_size, noise)
     labels = numpy.zeros(batch_size, dtype='int32')
@@ -169,6 +182,7 @@ def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
     inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
     return inputs
 
+
 def prepare_generator_data_batch(batch_size, noise):
     label = numpy.ones(batch_size, dtype='int32')
     inputs = api.Arguments.createArguments(2)
@@ -193,10 +207,9 @@ def get_layer_size(model_conf, layer_name):
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
-    parser.add_argument("--use_gpu", default="1", 
-                        help="1 means use gpu for training")
-    parser.add_argument("--gpu_id", default="0", 
-                        help="the gpu_id parameter")
+    parser.add_argument(
+        "--use_gpu", default="1", help="1 means use gpu for training")
+    parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
     args = parser.parse_args()
     data_source = args.data_source
     use_gpu = args.use_gpu
@@ -209,8 +222,9 @@ def main():
     if not os.path.exists("./%s_params/" % data_source):
         os.makedirs("./%s_params/" % data_source)
 
-    api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10', '--log_period=100', 
-                   '--gpu_id=' + args.gpu_id, '--save_dir=' + "./%s_params/" % data_source)
+    api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
+                   '--log_period=100', '--gpu_id=' + args.gpu_id,
+                   '--save_dir=' + "./%s_params/" % data_source)
 
     if data_source == "uniform":
         conf = "gan_conf.py"
@@ -220,7 +234,8 @@ def main():
         num_iter = 1000
 
     gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source)
-    dis_conf = parse_config(conf, "mode=discriminator_training,data=" + data_source)
+    dis_conf = parse_config(conf,
+                            "mode=discriminator_training,data=" + data_source)
     generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
     batch_size = dis_conf.opt_config.batch_size
     noise_dim = get_layer_size(gen_conf.model_config, "noise")
@@ -245,11 +260,9 @@ def main():
     generator_machine = api.GradientMachine.createFromConfigProto(
         generator_conf.model_config)
 
-    dis_trainer = api.Trainer.create(
-        dis_conf, dis_training_machine)
+    dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
 
-    gen_trainer = api.Trainer.create(
-        gen_conf, gen_training_machine)
+    gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
 
     dis_trainer.startTrain()
     gen_trainer.startTrain()
@@ -272,21 +285,23 @@ def main():
             noise = get_noise(batch_size, noise_dim)
             data_batch_dis_pos = prepare_discriminator_data_batch_pos(
                 batch_size, data_np)
-            dis_loss_pos = get_training_loss(dis_training_machine, data_batch_dis_pos)
+            dis_loss_pos = get_training_loss(dis_training_machine,
+                                             data_batch_dis_pos)
 
             data_batch_dis_neg = prepare_discriminator_data_batch_neg(
                 generator_machine, batch_size, noise)
-            dis_loss_neg = get_training_loss(dis_training_machine, data_batch_dis_neg)            
+            dis_loss_neg = get_training_loss(dis_training_machine,
+                                             data_batch_dis_neg)
 
             dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
 
             # Do forward pass in generator to get the gen_loss
-            data_batch_gen = prepare_generator_data_batch(
-                    batch_size, noise)
+            data_batch_gen = prepare_generator_data_batch(batch_size, noise)
             gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
 
             if i % 100 == 0:
-                print "d_pos_loss is %s     d_neg_loss is %s" % (dis_loss_pos, dis_loss_neg) 
+                print "d_pos_loss is %s     d_neg_loss is %s" % (dis_loss_pos,
+                                                                 dis_loss_neg)
                 print "d_loss is %s    g_loss is %s" % (dis_loss, gen_loss)
 
             # Decide which network to train based on the training history
@@ -300,7 +315,8 @@ def main():
                     curr_strike = 1
                 dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
                 dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
-                copy_shared_parameters(dis_training_machine, gen_training_machine)
+                copy_shared_parameters(dis_training_machine,
+                                       gen_training_machine)
 
             else:
                 if curr_train == "gen":
@@ -311,7 +327,8 @@ def main():
                 gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
                 # TODO: add API for paddle to allow true parameter sharing between different GradientMachines 
                 # so that we do not need to copy shared parameters. 
-                copy_shared_parameters(gen_training_machine, dis_training_machine)
+                copy_shared_parameters(gen_training_machine,
+                                       dis_training_machine)
                 copy_shared_parameters(gen_training_machine, generator_machine)
 
         dis_trainer.finishTrainPass()
@@ -319,11 +336,14 @@ def main():
         # At the end of each pass, save the generated samples/images
         fake_samples = get_fake_samples(generator_machine, batch_size, noise)
         if data_source == "uniform":
-            plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
+            plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" %
+                          (data_source, train_pass))
         else:
-            save_images(fake_samples, "./%s_samples/train_pass%s.png" % (data_source, train_pass))
+            save_images(fake_samples, "./%s_samples/train_pass%s.png" %
+                        (data_source, train_pass))
     dis_trainer.finishTrain()
     gen_trainer.finishTrain()
 
+
 if __name__ == '__main__':
     main()

demo/quick_start/trainer_config.resnet-lstm.py

@@ -13,7 +13,6 @@
 # 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.
-
 """
 This configuration is a demonstration of how to implement the stacked LSTM
 with residual connections, i.e. an LSTM layer takes the sum of the hidden states
@@ -46,7 +45,8 @@ is_predict = get_config_arg('is_predict', bool, False)
 trn = 'data/train.list' if not is_predict else None
 tst = 'data/test.list' if not is_predict else 'data/pred.list'
 process = 'process' if not is_predict else 'process_predict'
-define_py_data_sources2(train_list=trn,
+define_py_data_sources2(
+    train_list=trn,
     test_list=tst,
     module="dataprovider_emb",
     obj=process,
@@ -58,10 +58,9 @@ settings(
     learning_rate=2e-3,
     learning_method=AdamOptimizer(),
     regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25
-)
+    gradient_clipping_threshold=25)
 
-bias_attr = ParamAttr(initial_std=0.,l2_rate=0.)
+bias_attr = ParamAttr(initial_std=0., l2_rate=0.)
 
 data = data_layer(name="word", size=len(word_dict))
 emb = embedding_layer(input=data, size=128)
@@ -73,17 +72,15 @@ for i in range(3):
     # The input to the current layer is the sum of the hidden state
     # and input of the previous layer.
     current_input = addto_layer(input=[previous_input, previous_hidden_state])
-    hidden_state = simple_lstm(input=current_input, size=128,
-                               lstm_cell_attr=ExtraAttr(drop_rate=0.1))
+    hidden_state = simple_lstm(
+        input=current_input, size=128, lstm_cell_attr=ExtraAttr(drop_rate=0.1))
     previous_input, previous_hidden_state = current_input, hidden_state
 
 lstm = previous_hidden_state
 
 lstm_last = pooling_layer(input=lstm, pooling_type=MaxPooling())
-output = fc_layer(input=lstm_last, size=2,
-                  bias_attr=bias_attr,
-                  act=SoftmaxActivation())
-
+output = fc_layer(
+    input=lstm_last, size=2, bias_attr=bias_attr, act=SoftmaxActivation())
 
 if is_predict:
     maxid = maxid_layer(output)

demo/semantic_role_labeling/data/extract_dict_feature.py

@@ -69,7 +69,6 @@ def extract_dict_features(pair_file, feature_file):
             feature_out.write(feature_str + '\n')
 
 
-
 if __name__ == '__main__':
 
     usage = '-p pair_file -f feature_file'

demo/semantic_role_labeling/data/extract_pairs.py

@@ -66,7 +66,7 @@ def transform_labels(sentences, labels):
         else:
             verb_list = []
             for x in labels[i][0]:
-                if x !='-':
+                if x != '-':
                     verb_list.append(x)
 
             for j in xrange(1, len(labels[i])):
@@ -93,7 +93,7 @@ def transform_labels(sentences, labels):
                         is_in_bracket = True
                     else:
                         print 'error:', ll
-                sen_lab_pair.append((sentences[i], verb_list[j-1], label_seq))
+                sen_lab_pair.append((sentences[i], verb_list[j - 1], label_seq))
     return sen_lab_pair
 
 
@@ -103,7 +103,7 @@ def write_file(sen_lab_pair, output_file):
             sentence = x[0]
             label_seq = ' '.join(x[2])
             assert len(sentence.split()) == len(x[2])
-            fout.write(sentence + '\t' + x[1]+'\t' +label_seq + '\n')
+            fout.write(sentence + '\t' + x[1] + '\t' + label_seq + '\n')
 
 
 if __name__ == '__main__':

demo/semantic_role_labeling/dataprovider.py

@@ -30,8 +30,7 @@ def hook(settings, word_dict, label_dict, predicate_dict, **kwargs):
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(predicate_dict)),
-        integer_value_sequence(2),
+        integer_value_sequence(len(predicate_dict)), integer_value_sequence(2),
         integer_value_sequence(len(label_dict))
     ]
 
@@ -40,8 +39,12 @@ def get_batch_size(yeild_data):
     return len(yeild_data[0])
 
 
-@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, 
-          can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
+@provider(
+    init_hook=hook,
+    should_shuffle=True,
+    calc_batch_size=get_batch_size,
+    can_over_batch_size=False,
+    cache=CacheType.CACHE_PASS_IN_MEM)
 def process(settings, file_name):
     with open(file_name, 'r') as fdata:
         for line in fdata:

demo/semantic_role_labeling/db_lstm.py

@@ -20,7 +20,7 @@ from paddle.trainer_config_helpers import *
 #file paths
 word_dict_file = './data/wordDict.txt'
 label_dict_file = './data/targetDict.txt'
-predicate_file= './data/verbDict.txt'
+predicate_file = './data/verbDict.txt'
 train_list_file = './data/train.list'
 test_list_file = './data/test.list'
 
@@ -47,7 +47,6 @@ if not is_predict:
             w = line.strip()
             predicate_dict[w] = i
 
-
     if is_test:
         train_list_file = None
 
@@ -57,9 +56,11 @@ if not is_predict:
         test_list=test_list_file,
         module='dataprovider',
         obj='process',
-        args={'word_dict': word_dict,
+        args={
+            'word_dict': word_dict,
             'label_dict': label_dict,
-              'predicate_dict': predicate_dict })
+            'predicate_dict': predicate_dict
+        })
 
     word_dict_len = len(word_dict)
     label_dict_len = len(label_dict)
@@ -77,24 +78,16 @@ mark_dim = 5
 hidden_dim = 512
 depth = 8
 
-
-
 ########################### Optimizer #######################################
 
-
 settings(
     batch_size=150,
     learning_method=MomentumOptimizer(momentum=0),
     learning_rate=2e-2,
     regularization=L2Regularization(8e-4),
     is_async=False,
-    model_average=ModelAverage(average_window=0.5,
-                               max_average_window=10000),
-                               
-)
-
-
-
+    model_average=ModelAverage(
+        average_window=0.5, max_average_window=10000), )
 
 ####################################### network ##############################
 #8 features and 1 target
@@ -108,22 +101,28 @@ ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len)
 ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len)
 mark = data_layer(name='mark_data', size=mark_dict_len)
 
-
 if not is_predict:
     target = data_layer(name='target', size=label_dict_len)
 
-
-default_std=1/math.sqrt(hidden_dim)/3.0
+default_std = 1 / math.sqrt(hidden_dim) / 3.0
 
 emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.)
 std_0 = ParameterAttribute(initial_std=0.)
 std_default = ParameterAttribute(initial_std=default_std)
 
-predicate_embedding = embedding_layer(size=word_dim, input=predicate, param_attr=ParameterAttribute(name='vemb',initial_std=default_std))
-mark_embedding = embedding_layer(name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
-
-word_input=[word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
-emb_layers = [embedding_layer(size=word_dim, input=x, param_attr=emb_para) for x in word_input]
+predicate_embedding = embedding_layer(
+    size=word_dim,
+    input=predicate,
+    param_attr=ParameterAttribute(
+        name='vemb', initial_std=default_std))
+mark_embedding = embedding_layer(
+    name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
+
+word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
+emb_layers = [
+    embedding_layer(
+        size=word_dim, input=x, param_attr=emb_para) for x in word_input
+]
 emb_layers.append(predicate_embedding)
 emb_layers.append(mark_embedding)
 
@@ -131,14 +130,18 @@ hidden_0 = mixed_layer(
     name='hidden0',
     size=hidden_dim,
     bias_attr=std_default,
-    input=[ full_matrix_projection(input=emb, param_attr=std_default ) for emb in emb_layers ])
-
+    input=[
+        full_matrix_projection(
+            input=emb, param_attr=std_default) for emb in emb_layers
+    ])
 
 mix_hidden_lr = 1e-3
 lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0)
-hidden_para_attr = ParameterAttribute(initial_std=default_std, learning_rate=mix_hidden_lr)
+hidden_para_attr = ParameterAttribute(
+    initial_std=default_std, learning_rate=mix_hidden_lr)
 
-lstm_0 = lstmemory(name='lstm0',
+lstm_0 = lstmemory(
+    name='lstm0',
     input=hidden_0,
     act=ReluActivation(),
     gate_act=SigmoidActivation(),
@@ -149,66 +152,67 @@ lstm_0 = lstmemory(name='lstm0',
 #stack L-LSTM and R-LSTM with direct edges
 input_tmp = [hidden_0, lstm_0]
 
-
 for i in range(1, depth):
 
-    mix_hidden = mixed_layer(name='hidden'+str(i),
+    mix_hidden = mixed_layer(
+        name='hidden' + str(i),
         size=hidden_dim,
         bias_attr=std_default,
-                             input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
-                                    full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
-                                   ]
-                             )
-
-    lstm = lstmemory(name='lstm'+str(i),
+        input=[
+            full_matrix_projection(
+                input=input_tmp[0], param_attr=hidden_para_attr),
+            full_matrix_projection(
+                input=input_tmp[1], param_attr=lstm_para_attr)
+        ])
+
+    lstm = lstmemory(
+        name='lstm' + str(i),
         input=mix_hidden,
         act=ReluActivation(),
         gate_act=SigmoidActivation(),
         state_act=SigmoidActivation(),
-                     reverse=((i % 2)==1),
+        reverse=((i % 2) == 1),
         bias_attr=std_0,
         param_attr=lstm_para_attr)
 
     input_tmp = [mix_hidden, lstm]
 
-feature_out = mixed_layer(name='output',
+feature_out = mixed_layer(
+    name='output',
     size=label_dict_len,
     bias_attr=std_default,
-                          input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
-                                 full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
-                                ],
-                          )
-
-
+    input=[
+        full_matrix_projection(
+            input=input_tmp[0], param_attr=hidden_para_attr),
+        full_matrix_projection(
+            input=input_tmp[1], param_attr=lstm_para_attr)
+    ], )
 
 if not is_predict:
-    crf_l = crf_layer( name = 'crf',
-                       size = label_dict_len,
-                       input = feature_out, 
-                       label = target,
-                       param_attr=ParameterAttribute(name='crfw',initial_std=default_std, learning_rate=mix_hidden_lr)
-
-                      )
-
-    
-    crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
-                                   size = label_dict_len,
-                                   input = feature_out,
-                                   label = target,
-                                   param_attr=ParameterAttribute(name='crfw')
-                                       )
+    crf_l = crf_layer(
+        name='crf',
+        size=label_dict_len,
+        input=feature_out,
+        label=target,
+        param_attr=ParameterAttribute(
+            name='crfw', initial_std=default_std, learning_rate=mix_hidden_lr))
 
+    crf_dec_l = crf_decoding_layer(
+        name='crf_dec_l',
+        size=label_dict_len,
+        input=feature_out,
+        label=target,
+        param_attr=ParameterAttribute(name='crfw'))
 
     eval = sum_evaluator(input=crf_dec_l)
 
     outputs(crf_l)
 
 else:
-    crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
-                                   size = label_dict_len,
-                                   input = feature_out,
-                                   param_attr=ParameterAttribute(name='crfw')
-                                       )
+    crf_dec_l = crf_decoding_layer(
+        name='crf_dec_l',
+        size=label_dict_len,
+        input=feature_out,
+        param_attr=ParameterAttribute(name='crfw'))
 
     outputs(crf_dec_l)
-

demo/semantic_role_labeling/predict.py

@@ -26,7 +26,8 @@ UNK_IDX = 0
 
 
 class Prediction():
-    def __init__(self, train_conf, dict_file, model_dir, label_file, predicate_dict_file):
+    def __init__(self, train_conf, dict_file, model_dir, label_file,
+                 predicate_dict_file):
         """
         train_conf: trainer configure.
         dict_file: word dictionary file name.
@@ -35,7 +36,7 @@ class Prediction():
 
         self.dict = {}
         self.labels = {}
-        self.predicate_dict={}
+        self.predicate_dict = {}
         self.labels_reverse = {}
         self.load_dict_label(dict_file, label_file, predicate_dict_file)
 
@@ -44,24 +45,17 @@ class Prediction():
         len_pred = len(self.predicate_dict)
 
         conf = parse_config(
-            train_conf,
-            'dict_len=' + str(len_dict) + 
-            ',label_len=' + str(len_label) +
-            ',pred_len=' + str(len_pred) +
-            ',is_predict=True')
+            train_conf, 'dict_len=' + str(len_dict) + ',label_len=' +
+            str(len_label) + ',pred_len=' + str(len_pred) + ',is_predict=True')
         self.network = swig_paddle.GradientMachine.createFromConfigProto(
             conf.model_config)
         self.network.loadParameters(model_dir)
 
         slots = [
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict),
-            integer_value_sequence(len_dict), 
-            integer_value_sequence(len_pred),
-            integer_value_sequence(2)
+            integer_value_sequence(len_dict), integer_value_sequence(len_dict),
+            integer_value_sequence(len_dict), integer_value_sequence(len_dict),
+            integer_value_sequence(len_dict), integer_value_sequence(len_dict),
+            integer_value_sequence(len_pred), integer_value_sequence(2)
         ]
         self.converter = DataProviderConverter(slots)
 
@@ -78,6 +72,7 @@ class Prediction():
 
         for line_count, line in enumerate(open(predicate_dict_file, 'r')):
             self.predicate_dict[line.strip()] = line_count
+
     def get_data(self, data_file):
         """
         Get input data of paddle format.
@@ -90,7 +85,8 @@ class Prediction():
                 sen_len = len(words)
 
                 word_slot = [self.dict.get(w, UNK_IDX) for w in words]
-                predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)] * sen_len
+                predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)
+                                  ] * sen_len
                 ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len
                 ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len
                 ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len
@@ -123,7 +119,8 @@ class Prediction():
 
 
 def option_parser():
-    usage = ("python predict.py -c config -w model_dir " 
+    usage = (
+        "python predict.py -c config -w model_dir "
         "-d word dictionary -l label_file -i input_file  -p pred_dict_file")
     parser = OptionParser(usage="usage: %s [options]" % usage)
     parser.add_option(
@@ -187,8 +184,9 @@ def main():
     output_file = options.output_file
 
     swig_paddle.initPaddle("--use_gpu=0")
-    predict = Prediction(train_conf, dict_file, model_path, label_file, predict_dict_file)
-    predict.predict(data_file,output_file)
+    predict = Prediction(train_conf, dict_file, model_path, label_file,
+                         predict_dict_file)
+    predict.predict(data_file, output_file)
 
 
 if __name__ == '__main__':

demo/sentiment/predict.py

@@ -71,9 +71,7 @@ class SentimentPrediction():
         transform word into integer index according to the dictionary.
         """
         words = data.strip().split()
-        word_slot = [
-            self.word_dict[w] for w in words if w in self.word_dict
-        ]
+        word_slot = [self.word_dict[w] for w in words if w in self.word_dict]
         return word_slot
 
     def batch_predict(self, data_batch):
@@ -85,8 +83,8 @@ class SentimentPrediction():
             if self.label is None:
                 print("predicting label is %d" % (lab[0]))
             else:
-                print("predicting label is %s" %
-                      (self.label[lab[0]]))
+                print("predicting label is %s" % (self.label[lab[0]]))
+
 
 def option_parser():
     usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
@@ -143,9 +141,10 @@ def main():
         batch.append([predict.get_index(line)])
         if len(batch) == batch_size:
             predict.batch_predict(batch)
-            batch=[]
+            batch = []
     if len(batch) > 0:
         predict.batch_predict(batch)
 
+
 if __name__ == '__main__':
     main()

doc/api/data_provider/pydataprovider2_en.rst

-..  _api_pydataprovider:
+..  _api_pydataprovider2_en:
 
 PyDataProvider2
 ===============
@@ -104,6 +104,8 @@ And PaddlePadle will do all of the rest things\:
 
 Is this cool?
 
+..  _api_pydataprovider2_en_sequential_model:
+
 DataProvider for the sequential model
 -------------------------------------
 A sequence model takes sequences as its input. A sequence is made up of several

doc/api/predict/swig_py_paddle_en.rst

@@ -23,7 +23,7 @@ python's :code:`help()` function. Let's walk through the above python script:
 
 * At the beginning, use :code:`swig_paddle.initPaddle()` to initialize
   PaddlePaddle with command line arguments, for more about command line arguments
-  see `Command Line Arguments <../cmd_argument/detail_introduction.html>`_.
+  see :ref:`cmd_detail_introduction_en` .
 * Parse the configuration file that is used in training with :code:`parse_config()`.
   Because data to predict with always have no label, and output of prediction work
   normally is the output layer rather than the cost layer, so you should modify
@@ -36,7 +36,7 @@ python's :code:`help()` function. Let's walk through the above python script:
     - Note: As swig_paddle can only accept C++ matrices, we offer a utility
       class DataProviderConverter that can accept the same input data with
       PyDataProvider2, for more information please refer to document
-      of `PyDataProvider2 <../data_provider/pydataprovider2.html>`_.
+      of :ref:`api_pydataprovider2_en` .
 * Do the prediction with :code:`forwardTest()`, which takes the converted
   input data and outputs the activations of the output layer.
 

doc/api/trainer_config_helpers/layers.rst

+..  _api_trainer_config_helpers_layers:
+
 ======
 Layers
 ======

doc/getstarted/basic_usage/index_en.rst

@@ -99,11 +99,3 @@ In PaddlePaddle, training is just to get a collection of model parameters, which
 Although starts from a random guess, you can see that value of ``w`` changes quickly towards 2 and ``b`` changes quickly towards 0.3. In the end, the predicted line is almost identical with real answer.
 
 There, you have recovered the underlying pattern between ``X`` and ``Y`` only from observed data.
-
-
-5. Where to Go from Here
--------------------------
-
-- `Install and Build <../build_and_install/index.html>`_
-- `Tutorials <../demo/quick_start/index_en.html>`_
-- `Example and Demo <../demo/index.html>`_

doc/getstarted/build_and_install/build_from_source_en.md

@@ -14,6 +14,13 @@ cd paddle
 git submodule update --init --recursive
 ```
 
+If you already have a local PaddlePaddle repo and have not initialized the submodule, your local submodule folder will be empty. You can simply run the last line of the above codes in your PaddlePaddle home directory to initialize your submodule folder.
+
+If you have already initialized your submodule and you would like to sync with the upstream submodule repo, you can run the following command
+```
+git submodule update --remote
+```
+
 ## <span id="requirements">Requirements</span>
 
 To compile the source code, your computer must be equipped with the following dependencies.

doc/getstarted/build_and_install/docker_install_en.rst

@@ -122,9 +122,9 @@ The general development workflow with Docker and Bazel is as follows:
       git clone --recursive https://github.com/paddlepaddle/paddle
 
 
-2. Build a development Docker image `paddle:dev` from the source code.
-   This image contains all the development tools and dependencies of
-   PaddlePaddle.
+2. Build a development Docker image :code:`paddle:dev` from the source
+   code.  This image contains all the development tools and
+   dependencies of PaddlePaddle.
 
 
    .. code-block:: bash
@@ -140,13 +140,21 @@ The general development workflow with Docker and Bazel is as follows:
    .. code-block:: bash
 
       docker run       \
-       -d # run the container in background mode \
-       --name paddle # we can run a nginx container to serve documents \
-       -p 2022:22    # so we can SSH into this container \
-       -v $PWD:/paddle # mount the source code \
-       -v $HOME/.cache/bazel:/root/.cache/bazel # mount Bazel cache \
+       -d              \
+       --name paddle   \
+       -p 2022:22      \
+       -v $PWD:/paddle \
+       -v $HOME/.cache/bazel:/root/.cache/bazel \
        paddle:dev
 
+   where :code:`-d` makes the container running in background,
+   :code:`--name paddle` allows us to run a nginx container to serve
+   documents in this container, :code:`-p 2022:22` allows us to SSH
+   into this container, :code:`-v $PWD:/paddle` shares the source code
+   on the host with the container, :code:`-v
+   $HOME/.cache/bazel:/root/.cache/bazel` shares Bazel cache on the
+   host with the container.
+
 4. SSH into the container:
 
    .. code-block:: bash

doc/howto/cmd_parameter/detail_introduction_en.md

+```eval_rst
+..  _cmd_detail_introduction_en:
+```
+
 # Detail Description
 
 ## Common

doc/howto/deep_model/rnn/rnn_en.rst

@@ -30,7 +30,7 @@ Then at the :code:`process` function, each :code:`yield` function will return th
     yield src_ids, trg_ids, trg_ids_next
 
 
-For more details description of how to write a data provider, please refer to `PyDataProvider2 <../../ui/data_provider/index.html>`_. The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
+For more details description of how to write a data provider, please refer to :ref:`api_pydataprovider2_en` . The full data provider file is located at :code:`demo/seqToseq/dataprovider.py`.
 
 ===============================================
 Configure Recurrent Neural Network Architecture
@@ -106,7 +106,7 @@ We will use the sequence to sequence model with attention as an example to demon
 
 In this model, the source sequence :math:`S = \{s_1, \dots, s_T\}` is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network :math:`H_S = \{H_1, \dots, H_T\}` is called *encoder vector* The decoder is a gated recurrent neural network. When decoding each token :math:`y_t`, the gated recurrent neural network generates a set of weights :math:`W_S^t = \{W_1^t, \dots, W_T^t\}`, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token :math:`y_t`.
 
-The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to `Layers <../../ui/api/trainer_config_helpers/layers_index.html>`_  for more details.
+The encoder part of the model is listed below. It calls :code:`grumemory` to represent gated recurrent neural network. It is the recommended way of using recurrent neural network if the network architecture is simple, because it is faster than :code:`recurrent_group`. We have implemented most of the commonly used recurrent neural network architectures, you can refer to :ref:`api_trainer_config_helpers_layers` for more details.
 
 We also project the encoder vector to :code:`decoder_size` dimensional space, get the first instance of the backward recurrent network, and project it to :code:`decoder_size` dimensional space:
 
@@ -246,6 +246,6 @@ The code is listed below:
     outputs(beam_gen)
 
 
-Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to `Semantic Role Labeling Demo <../../demo/semantic_role_labeling/index.html>`_ for more details.
+Notice that this generation technique is only useful for decoder like generation process. If you are working on sequence tagging tasks, please refer to :ref:`semantic_role_labeling_en` for more details.
 
 The full configuration file is located at :code:`demo/seqToseq/seqToseq_net.py`.

doc/howto/optimization/gpu_profiling_en.rst

@@ -51,7 +51,7 @@ In this tutorial, we will focus on nvprof and nvvp.
 :code:`test_GpuProfiler` from :code:`paddle/math/tests` directory will be used to evaluate
 above profilers. 
 
-.. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+.. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
    :language: c++
    :lines: 111-124
    :linenos:
@@ -77,7 +77,7 @@ As a simple example, consider the following:
 
 1. Add :code:`REGISTER_TIMER_INFO` and :code:`printAllStatus` functions (see the emphasize-lines).
 
-    .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 111-124
         :emphasize-lines: 8-10,13
@@ -124,7 +124,7 @@ To use this command line profiler **nvprof**, you can simply issue the following
 
 1. Add :code:`REGISTER_GPU_PROFILER` function (see the emphasize-lines).
 
-    .. literalinclude:: ../../paddle/math/tests/test_GpuProfiler.cpp
+    .. literalinclude:: ../../../paddle/math/tests/test_GpuProfiler.cpp
         :language: c++
         :lines: 111-124
         :emphasize-lines: 6-7

doc/tutorials/embedding_model/index_en.md

@@ -93,7 +93,7 @@ where `train.sh` is almost the same as `demo/seqToseq/translation/train.sh`, the
 - `--init_model_path`: path of the initialization model, here is `data/paraphrase_model`
 - `--load_missing_parameter_strategy`: operations when model file is missing, here use a normal distibution to initialize the other parameters except for the embedding layer
 
-For users who want to understand the dataset format, model architecture and training procedure in detail, please refer to [Text generation Tutorial](../text_generation/text_generation.md).
+For users who want to understand the dataset format, model architecture and training procedure in detail, please refer to [Text generation Tutorial](../text_generation/index_en.md).
 
 ## Optional Function ##
 ###  Embedding Parameters Observation

doc/tutorials/quick_start/index_en.md

@@ -12,7 +12,7 @@ This tutorial will teach the basics of deep learning (DL), including how to impl
 
 To get started, please install PaddlePaddle on your computer. Throughout this tutorial, you will learn by implementing different DL models for text classification.
 
-To install PaddlePaddle, please follow the instructions here: <a href = "../../build/index.html" >Build and Install</a>.
+To install PaddlePaddle, please follow the instructions here: <a href = "../../getstarted/build_and_install/index_en.html" >Build and Install</a>.
 
 ## Overview
 For the first step, you will use PaddlePaddle to build a **text classification** system. For example, suppose you run an e-commence  website, and you want to analyze the sentiment of user reviews to evaluate product quality.
@@ -156,14 +156,14 @@ define_py_data_sources2(train_list='data/train.list',
                         obj="process",
                         args={"dictionary": word_dict})
 ```
-You can refer to the following link for more detailed examples and data formats: <a href = "../../ui/data_provider/pydataprovider2.html">PyDataProvider2</a>.
+You can refer to the following link for more detailed examples and data formats: <a href = "../../api/data_provider/pydataprovider2_en.html">PyDataProvider2</a>.
 
 ## Network Architecture
 You will describe four kinds of network architectures in this section.
 <center> ![](./PipelineNetwork_en.jpg) </center>
 
 First, you will build a logistic regression model. Later, you will also get chance to build other more powerful network architectures.
-For more detailed documentation, you could refer to: <a href = "../../ui/api/trainer_config_helpers/layers_index.html">Layer documentation</a>。All configuration files are in `demo/quick_start` directory.
+For more detailed documentation, you could refer to: <a href = "../../api/trainer_config_helpers/layers.html">layer documentation</a>. All configuration files are in `demo/quick_start` directory.
 
 ### Logistic Regression
 The architecture is illustrated in the following picture:
@@ -366,7 +366,7 @@ You can use single layer LSTM model with Dropout for our text classification pro
 <br>
 
 ## Optimization Algorithm
-<a href = "../../ui/api/trainer_config_helpers/optimizers.html">Optimization algorithms</a> include Momentum, RMSProp, AdaDelta, AdaGrad, Adam, and Adamax. You can use Adam optimization method here, with L2 regularization and gradient clipping, because Adam has been proved to work very well for training recurrent neural network.
+<a href = "../../api/trainer_config_helpers/optimizers.html">Optimization algorithms</a> include Momentum, RMSProp, AdaDelta, AdaGrad, Adam, and Adamax. You can use Adam optimization method here, with L2 regularization and gradient clipping, because Adam has been proved to work very well for training recurrent neural network.
 
 ```python
 settings(batch_size=128,
@@ -391,7 +391,8 @@ paddle train \
 --use_gpu=false
 ```
 
-If you want to install the remote training platform, which enables distributed training on clusters, follow the instructions here: <a href = "../../cluster/index.html">Platform</a> documentation. We do not provide examples on how to train on clusters. Please refer to other demos or platform training documentation for mode details on training on clusters.
+We do not provide examples on how to train on clusters here. If you want to train on clusters, please follow the <a href = "../../howto/cluster/cluster_train_en.html">distributed training</a> documentation or other demos for more details.
+
 ## Inference
 You can use the trained model to perform prediction on the dataset with no labels. You can also evaluate the model on dataset with labels to obtain its test accuracy.
 <center> ![](./PipelineTest_en.png) </center>
@@ -406,7 +407,7 @@ paddle train \
 --init_model_path=./output/pass-0000x
 ```
 
-We will give an example of performing prediction using Recurrent model on a dataset with no labels. You can refer to: <a href = "../../ui/predict/swig_py_paddle_en.html">Python Prediction API</a> tutorial，or other <a href = "../../demo/index.html">demo</a> for the prediction process using Python. You can also use the following script for inference or evaluation.
+We will give an example of performing prediction using Recurrent model on a dataset with no labels. You can refer to <a href = "../../api/predict/swig_py_paddle_en.html">Python Prediction API</a> tutorial，or other <a href = "../../tutorials/index_en.html">demo</a> for the prediction process using Python. You can also use the following script for inference or evaluation.
 
 inference script (predict.sh)：
 
@@ -508,7 +509,7 @@ The scripts of data downloading, network configurations, and training scrips are
 * \--config_args：Other configuration arguments.
 * \--init_model_path：The path of the initial model parameter.
 
-By default, the trainer will save model every pass. You can also specify `saving_period_by_batches` to set the frequency of batch saving. You can use `show_parameter_stats_period` to print the statistics of the parameters, which are very useful for tuning parameters. Other command line arguments can be found in <a href = "../../ui/index.html#command-line-argument">command line argument documentation</a>。
+By default, the trainer will save model every pass. You can also specify `saving_period_by_batches` to set the frequency of batch saving. You can use `show_parameter_stats_period` to print the statistics of the parameters, which are very useful for tuning parameters. Other command line arguments can be found in <a href = "../../howto/cmd_parameter/index_en.html">command line argument documentation</a>。
 
 ### Log
 

doc/tutorials/rec/ml_regression_en.rst

@@ -264,7 +264,7 @@ In this :code:`dataprovider.py`, we should set\:
 * use_seq\: Whether this :code:`dataprovider.py` in sequence mode or not.
 * process\: Return each sample of data to :code:`paddle`.
 
-The data provider details document see :ref:`api_pydataprovider`.
+The data provider details document see :ref:`api_pydataprovider2_en`.
 
 Train
 `````

doc/tutorials/semantic_role_labeling/index_en.md

+```eval_rst
+..  _semantic_role_labeling_en:
+```
+
 # Semantic Role labeling Tutorial #
 
 Semantic role labeling (SRL) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence. SRL is useful as an intermediate step in a wide range of natural language processing tasks, such as information extraction. automatic document categorization and question answering.  An instance is as following [1]:

doc/tutorials/semantic_role_labeling/semantic_role_labeling_cn.md

-# 语义角色标注教程 #
-
-语义角色标注（Semantic role labeling, SRL）是浅语义解析的一种形式，其目的是在给定的输入句子中发现每个谓词的谓词参数结构。 SRL作为很多自然语言处理任务中的中间步骤是很有用的，如信息提取、文档自动分类和问答。 实例如下 [1]:
-
- [ <sub>A0</sub> 他 ] [ <sub>AM-MOD</sub> 将 ][ <sub>AM-NEG</sub> 不会 ] [ <sub>V</sub> 接受] [ <sub>A1</sub> 任何东西 ] 从 [<sub>A2</sub> 那些他写的东西中 ]。
-
-- V: 动词
-- A0: 接受者
-- A1: 接受的东西
-- A2: 从……接受
-- A3: 属性
-- AM-MOD: 情态动词 
-- AM-NEG: 否定
-
-给定动词“接受”，句子中的大部分将会扮演某些语义角色。这里，标签方案来自 Penn Proposition Bank。
-
-到目前为止，大多数成功的SRL系统是建立在某种形式的解析结果之上的，其中在语法结构上使用了预先定义的特征模板。 本教程将介绍使用深度双向长短期记忆（DB-LSTM）模型[2]的端到端系统来解决SRL任务，这在很大程度上优于先前的最先进的系统。 这个系统将SRL任务视为序列标记问题。
-
-## 数据描述
-相关论文[2]采用 CoNLL-2005＆2012 共享任务中设置的数据进行训练和测试。根据数据许可证，演示采用 CoNLL-2005 的测试数据集，可以在网站上找到。
-
-用户只需执行以下命令就可以下载并处理原始数据：
-
-```bash
-cd data
-./get_data.sh
-```
-`data `目录会出现如下几个新的文件：
-```bash
-conll05st-release：the test data set of CoNll-2005 shared task 
-test.wsj.words：the Wall Street Journal data sentences
-test.wsj.props:  the propositional arguments
-feature: the extracted features from data set
-```
-
-## 训练
-### DB-LSTM
-请参阅情绪分析的演示以了解有关长期短期记忆单元的更多信息。
-
-与在 Sentiment Analysis 演示中使用的 Bidirectional-LSTM 不同，DB-LSTM 采用另一种方法来堆叠LSTM层。首先，标准LSTM以正向处理该序列。该 LSTM 层的输入和输出作为下一个 LSTM 层的输入，并被反向处理。这两个标准 LSTM 层组成一对 LSTM。然后我们堆叠一对对的 LSTM 层后得到深度 LSTM 模型。
-
-下图展示了时间扩展的2层 DB-LSTM 网络。
-<center>
-![pic](./network_arch.png)
-</center>
-
-### 特征
-两个输入特性在这个管道中起着至关重要的作用：predicate（pred）和argument（arguments）。 还采用了两个其他特征：谓词上下文（ctx-p）和区域标记（mr）。 因为单个谓词不能精确地描述谓词信息，特别是当相同的词在句子中出现多于一次时。 使用谓词上下文，可以在很大程度上消除歧义。类似地，如果它位于谓词上下文区域中，则使用区域标记 m<sub>r</sub> = 1 来表示参数位置，反之则 m<sub>r</sub> = 0。这四个简单的特征是我们的SRL系统所需要的。上下文大小设置为1的一个样本的特征如下[2]所示：
-<center>
-![pic](./feature.jpg)
-</center>
-
-在这个示例中，相应的标记句子是：
-
-[ <sub>A1</sub> A record date ] has [ <sub>AM-NEG</sub> n't ] been [ <sub>V</sub> set ] . 
-
-在演示中, 我们采用上面的特征模板, 包括：  `argument`, `predicate`, `ctx-p (p=-1,0,1)`, `mark` 并使用 `B/I/O` 方案来标记每个参数。这些特征和标签存储在 `feature` 文件中, 用`\t`分割。
-
-### 数据提供
-
-`dataprovider.py` 是一个包装数据的 Python 文件。 函数 `hook()` 定义了网络的数据槽。六个特征和标签都是索引槽。
-```
-def hook(settings, word_dict, label_dict, **kwargs):
-    settings.word_dict = word_dict
-    settings.label_dict = label_dict
-    #all inputs are integral and sequential type
-    settings.slots = [
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(predicate_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(len(word_dict)),
-        integer_value_sequence(2),
-        integer_value_sequence(len(label_dict))]
-```
-相应的数据迭代器如下：
-```
-@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
-          can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
-def process(settings, file_name):
-    with open(file_name, 'r') as fdata:
-        for line in fdata:
-            sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2,  mark, label = \
-                line.strip().split('\t')
-
-            words = sentence.split()
-            sen_len = len(words)
-            word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
-
-            predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
-            ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
-            ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
-            ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
-            ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
-            ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
-
-            marks = mark.split()
-            mark_slot = [int(w) for w in marks]
-
-            label_list = label.split()
-            label_slot = [settings.label_dict.get(w) for w in label_list]
-            yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
-                  ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
-```
-函数 `process` 产出有8个特征和标签的9个表。
-
-### 神经网络配置
-
-`db_lstm.py` 是在训练过程中加载字典并定义数据提供程序模块和网络架构的神经网络配置文件。
-
-九个 `data_layer` 从数据提供程序加载实例。八个特征分别转换为嵌入，并由`mixed_layer`混合。 深度双向LSTM层提取softmax层的特征。目标函数是标签的交叉熵。
-
-### 训练 
-训练的脚本是 `train.sh`，用户只需执行:
-```bash
-  ./train.sh
-```
-`train.sh` 中的内容：
-```
-paddle train \
-  --config=./db_lstm.py \
-  --use_gpu=0 \
-  --log_period=5000 \
-  --trainer_count=1 \
-  --show_parameter_stats_period=5000 \
-  --save_dir=./output \
-  --num_passes=10000 \
-  --average_test_period=10000000 \
-  --init_model_path=./data \
-  --load_missing_parameter_strategy=rand \
-  --test_all_data_in_one_period=1 \
-2>&1 | tee 'train.log'
-```
-
--  \--config=./db_lstm.py : 网络配置文件
--  \--use_gpu=false: 使用 CPU 训练（如果已安装 PaddlePaddle GPU版本并想使用 GPU 训练可以设置为true，目前 crf_layer 不支持 GPU）
--  \--log_period=500: 每20批(batch)输出日志
--  \--trainer_count=1: 设置线程数（或 GPU 数）
--  \--show_parameter_stats_period=5000: 每100批显示参数统计
--  \--save_dir=./output: 模型输出路径
--  \--num_passes=10000: 设置通过数，一次通过意味着PaddlePaddle训练数据集中的所有样本一次
--  \--average_test_period=10000000:  每个 average_test_period 批次对平均参数进行测试
--  \--init_model_path=./data: 参数初始化路径
--  \--load_missing_parameter_strategy=rand: 随机初始不存在的参数
--  \--test_all_data_in_one_period=1: 在一个周期内测试所有数据
-
-
-训练后，模型将保存在目录`output`中。 我们的训练曲线如下：
-<center>
-![pic](./curve.jpg)
-</center>
-
-### 测试
-测试脚本是 `test.sh`, 执行:
-```bash
-  ./test.sh
-```
-`tesh.sh` 的主要部分：
-```
-paddle train \
-  --config=./db_lstm.py \
-  --model_list=$model_list \
-  --job=test \
-  --config_args=is_test=1 \
-```
-
-  - \--config=./db_lstm.py: 网络配置文件
-  - \--model_list=$model_list.list: 模型列表文件
-  - \--job=test: 指示测试任务
-  - \--config_args=is_test=1: 指示测试任务的标记
-  - \--test_all_data_in_one_period=1: 在一个周期内测试所有数据
-  
-
-### 预测
-预测脚本是 `predict.sh`，用户只需执行：
-```bash
-  ./predict.sh
-  
-```
-在`predict.sh`中，用户应该提供网络配置文件，模型路径，标签文件，字典文件，特征文件。
-```
-python predict.py 
-     -c $config_file \
-     -w $best_model_path \
-     -l $label_file \
-     -p $predicate_dict_file  \
-     -d $dict_file \
-     -i $input_file \
-     -o $output_file
-```
-
-`predict.py` 是主要的可执行python脚本，其中包括函数：加载模型，加载数据，数据预测。网络模型将输出标签的概率分布。 在演示中，我们使用最大概率的标签作为结果。用户还可以根据概率分布矩阵实现集束搜索或维特比解码。
-
-预测后，结果保存在 `predict.res` 中。
-
-## 引用
-[1] Martha Palmer, Dan Gildea, and Paul Kingsbury. The Proposition Bank: An Annotated Corpus of Semantic Roles , Computational Linguistics, 31(1), 2005. 
-
-[2] Zhou, Jie, and Wei Xu. "End-to-end learning of semantic role labeling using recurrent neural networks." Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2015.

doc_cn/cluster/k8s/start_paddle.py

@@ -19,7 +19,6 @@ import socket
 import os
 import argparse
 
-
 # configuration for cluster
 API = "/api/v1/namespaces/"
 JOBSELECTOR = "labelSelector=job-name="
@@ -145,8 +144,8 @@ def startPaddle(idMap={}, train_args_dict=None):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(prog="start_paddle.py",
-                                     description='simple tool for k8s')
+    parser = argparse.ArgumentParser(
+        prog="start_paddle.py", description='simple tool for k8s')
     args, train_args_list = parser.parse_known_args()
     train_args = refine_unknown_args(train_args_list)
     train_args_dict = dict(zip(train_args[:-1:2], train_args[1::2]))

paddle/api/GradientMachine.cpp

@@ -15,8 +15,8 @@ limitations under the License. */
 #include "PaddleAPI.h"
 #include "PaddleAPIPrivate.h"
 
-#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
 #include "Internal.h"
+#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
 
 std::vector<int> GradientMachine::defaultParamTypes = {
     PARAMETER_VALUE, PARAMETER_GRADIENT, PARAMETER_MOMENTUM};

paddle/api/Internal.h

@@ -16,14 +16,13 @@ limitations under the License. */
 
 #include "PaddleAPI.h"
 
-#include <vector>
 #include <algorithm>
+#include <vector>
 
 template <typename T1, typename T2>
 void staticCastVector(std::vector<T2>* dest, const std::vector<T1>& src) {
   dest->resize(src.size());
-  std::transform(src.begin(),
-                 src.end(),
-                 dest->begin(),
-                 [](T1 t) { return static_cast<T2>(t); });
+  std::transform(src.begin(), src.end(), dest->begin(), [](T1 t) {
+    return static_cast<T2>(t);
+  });
 }

paddle/api/Matrix.cpp

@@ -12,12 +12,12 @@ 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. */
 
-#include "PaddleAPI.h"
 #include "paddle/math/Matrix.h"
-#include "paddle/math/SparseMatrix.h"
-#include "paddle/math/CpuSparseMatrix.h"
-#include <iostream>
 #include <cstring>
+#include <iostream>
+#include "PaddleAPI.h"
+#include "paddle/math/CpuSparseMatrix.h"
+#include "paddle/math/SparseMatrix.h"
 
 struct MatrixPrivate {
   std::shared_ptr<paddle::Matrix> mat;

paddle/api/PaddleAPI.h

@@ -16,8 +16,8 @@ limitations under the License. */
 
 #include <stddef.h>
 #include <stdint.h>
-#include <string>
 #include <stdexcept>
+#include <string>
 #include <vector>
 #include "paddle/utils/GlobalConstants.h"
 #include "paddle/utils/TypeDefs.h"

paddle/api/Parameter.cpp

@@ -12,8 +12,8 @@ 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. */
 
-#include "PaddleAPI.h"
 #include "paddle/parameter/Parameter.h"
+#include "PaddleAPI.h"
 
 struct ParameterPrivate {
   std::shared_ptr<paddle::Parameter> sharedPtr;

paddle/api/ParameterOptimizer.cpp

@@ -12,11 +12,11 @@ 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. */
 
-#include "PaddleAPI.h"
-#include "PaddleAPIPrivate.h"
 #include "paddle/parameter/ParameterOptimizer.h"
-#include "Internal.h"
 #include <algorithm>
+#include "Internal.h"
+#include "PaddleAPI.h"
+#include "PaddleAPIPrivate.h"
 
 struct ParameterOptimizerPrivate {
   std::unique_ptr<paddle::ParameterOptimizer> optimizer;
@@ -36,10 +36,7 @@ struct ParameterTraverseCallbackPrivate {
              size_t sparseId) {
     std::vector<paddle::VectorPtr> real_vecs;
     real_vecs.resize(vecs.size());
-    std::transform(vecs.begin(),
-                   vecs.end(),
-                   real_vecs.begin(),
-                   [](Vector* v) {
+    std::transform(vecs.begin(), vecs.end(), real_vecs.begin(), [](Vector* v) {
       if (v) {
         return *(paddle::VectorPtr*)(v->getSharedPtr());
       } else {

paddle/api/SequenceGenerator.cpp

@@ -12,14 +12,14 @@ 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. */
 
+#include <algorithm>
+#include <iterator>
+#include <sstream>
+#include <vector>
 #include "PaddleAPI.h"
 #include "paddle/gserver/gradientmachines/GradientMachine.h"
 #include "paddle/parameter/Argument.h"
 #include "paddle/utils/Flags.h"
-#include <vector>
-#include <sstream>
-#include <algorithm>
-#include <iterator>
 
 // used to represent partial sequence
 struct Path {

paddle/api/Trainer.cpp

@@ -16,12 +16,12 @@ limitations under the License. */
 #include "PaddleAPIPrivate.h"
 
 #include <stdlib.h>
-#include <memory>
 #include <atomic>
+#include <memory>
 
+#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
 #include "paddle/trainer/ParamUtil.h"
 #include "paddle/trainer/Trainer.h"
-#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
 #include "paddle/trainer/TrainerInternal.h"
 #include "paddle/utils/Flags.h"
 

paddle/api/Util.cpp

@@ -14,16 +14,16 @@ limitations under the License. */
 
 #include "PaddleAPI.h"
 
-#include "paddle/utils/Util.h"
-#include "paddle/utils/PythonUtil.h"
-#include "paddle/utils/Flags.h"
-#include "paddle/utils/Excepts.h"
 #include "paddle/parameter/Parameter.h"
+#include "paddle/utils/Excepts.h"
+#include "paddle/utils/Flags.h"
+#include "paddle/utils/PythonUtil.h"
+#include "paddle/utils/Util.h"
 
 #include <fenv.h>
+#include <algorithm>
 #include <iostream>
 #include <iterator>
-#include <algorithm>
 
 void initPaddle(int argc, char** argv) {
   paddle::initMain(argc, argv);

paddle/api/test/testMatrix.py

@@ -104,7 +104,8 @@ class TestMatrix(unittest.TestCase):
     def test_numpy(self):
         numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
         m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat)
-        self.assertEqual((int(m.getHeight()), int(m.getWidth())), numpy_mat.shape)
+        self.assertEqual((int(m.getHeight()), int(m.getWidth())),
+                         numpy_mat.shape)
         self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
         for a, e in zip(m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
             self.assertAlmostEqual(a, e)

paddle/api/test/testVector.py

@@ -29,7 +29,7 @@ class TestIVector(unittest.TestCase):
 
         m = swig_paddle.IVector.createZero(10)
         self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
-        self.assertEqual(m.getData(), [0]*10)
+        self.assertEqual(m.getData(), [0] * 10)
 
     def test_create(self):
         m = swig_paddle.IVector.create(range(10), False)
@@ -88,7 +88,7 @@ class TestVector(unittest.TestCase):
 
         v = swig_paddle.Vector.createZero(10)
         self.assertEqual(v.isGpu(), swig_paddle.isUsingGpu())
-        self.assertEqual(v.getData(), [0]*10)
+        self.assertEqual(v.getData(), [0] * 10)
 
     def testCreate(self):
         v = swig_paddle.Vector.create([x / 100.0 for x in xrange(100)], False)
@@ -104,7 +104,6 @@ class TestVector(unittest.TestCase):
         for i in xrange(len(v)):
             self.assertTrue(util.doubleEqual(vdata[i], i / 100.0))
 
-
     def testCpuNumpy(self):
         numpy_arr = np.array([1.2, 2.3, 3.4, 4.5], dtype="float32")
         vec = swig_paddle.Vector.createCpuVectorFromNumpy(numpy_arr, copy=False)
@@ -137,7 +136,6 @@ class TestVector(unittest.TestCase):
         for n, v in zip(numpy_arr, vecData):
             self.assertTrue(util.doubleEqual(n, v))
 
-
     def testCopyFromNumpy(self):
         vec = swig_paddle.Vector.createZero(1, False)
         arr = np.array([1.3, 3.2, 2.4], dtype="float32")

paddle/cuda/include/hl_base.h

@@ -223,9 +223,9 @@ typedef struct {
 
 #ifdef __NVCC__
 
-#include "paddle/utils/Logging.h"
-#include "hl_cuda.h"
 #include "cuda_runtime.h"
+#include "hl_cuda.h"
+#include "paddle/utils/Logging.h"
 
 extern __thread bool g_sync_flag;
 extern __thread cudaStream_t default_stream;

paddle/cuda/include/hl_dso_loader.h

@@ -16,8 +16,8 @@ limitations under the License. */
 #define HL_DSO_LOADER_H_
 
 #include <dlfcn.h>
-#include <string>
 #include <memory>
+#include <string>
 #include "hl_base.h"
 
 /**

paddle/cuda/include/hl_gpu.h

@@ -15,28 +15,28 @@ limitations under the License. */
 #ifndef HL_GPU_H_
 #define HL_GPU_H_
 
+#include "hl_aggregate.h"
 #include "hl_base.h"
+#include "hl_cnn.h"
 #include "hl_cuda.h"
 #include "hl_cuda_cublas.h"
 #include "hl_cuda_cudnn.h"
-#include "hl_matrix.h"
-#include "hl_aggregate.h"
-#include "hl_cnn.h"
-#include "hl_sparse.h"
 #include "hl_lstm.h"
+#include "hl_matrix.h"
 #include "hl_sequence.h"
+#include "hl_sparse.h"
 #include "hl_warpctc_wrap.h"
 
 #ifdef HPPL_STUB_FUNC
-#include "stub/hl_cuda_stub.h"
-#include "stub/hl_cuda_cublas_stub.h"
-#include "stub/hl_cuda_cudnn_stub.h"
-#include "stub/hl_matrix_stub.h"
 #include "stub/hl_aggregate_stub.h"
 #include "stub/hl_cnn_stub.h"
-#include "stub/hl_sparse_stub.h"
+#include "stub/hl_cuda_cublas_stub.h"
+#include "stub/hl_cuda_cudnn_stub.h"
+#include "stub/hl_cuda_stub.h"
 #include "stub/hl_lstm_stub.h"
+#include "stub/hl_matrix_stub.h"
 #include "stub/hl_sequence_stub.h"
+#include "stub/hl_sparse_stub.h"
 #endif
 
 #endif /* HL_GPU_H_ */

paddle/cuda/include/hl_time.h

@@ -14,7 +14,7 @@ limitations under the License. */
 
 #ifndef HL_TIME_H_
 #define HL_TIME_H_
-
+#include <cstdint>
 /**
  * @brief   High resolution timer.
  *

paddle/cuda/src/hl_cuda_cublas.cc

@@ -12,12 +12,12 @@ 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. */
 
+#include "hl_cuda_cublas.h"
 #include <sys/time.h>
 #include <mutex>
 #include "hl_cuda.h"
-#include "hl_cuda_cublas.h"
-#include "hl_thread.ph"
 #include "hl_dso_loader.h"
+#include "hl_thread.ph"
 #include "paddle/utils/Logging.h"
 
 namespace dynload {

paddle/cuda/src/hl_cuda_cudnn.cc

@@ -12,14 +12,14 @@ 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. */
 
+#include "hl_cuda_cudnn.h"
 #include <cudnn.h>
 #include <mutex>
-#include "hl_cuda_cudnn.h"
 #include "hl_cuda_cudnn.ph"
-#include "hl_thread.ph"
 #include "hl_dso_loader.h"
-#include "paddle/utils/Logging.h"
+#include "hl_thread.ph"
 #include "paddle/utils/CommandLineParser.h"
+#include "paddle/utils/Logging.h"
 
 P_DEFINE_int32(cudnn_conv_workspace_limit_in_mb,
                4096,

paddle/cuda/src/hl_cuda_device.cc

@@ -12,13 +12,13 @@ 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. */
 
+#include "hl_cuda.h"
 #include <cuda_profiler_api.h>
 #include <string.h>
 #include <sys/syscall.h>
 #include <sys/time.h>
 #include <unistd.h>
 #include <mutex>
-#include "hl_cuda.h"
 #include "hl_cuda.ph"
 #include "hl_dso_loader.h"
 #include "hl_thread.ph"

paddle/cuda/src/hl_cudart_wrap.cc

@@ -14,8 +14,8 @@ limitations under the License. */
 
 #ifdef PADDLE_USE_DSO
 
-#include <mutex>
 #include <cuda_runtime.h>
+#include <mutex>
 #include "hl_dso_loader.h"
 
 /**

paddle/cuda/src/hl_time.cc

@@ -12,10 +12,11 @@ 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. */
 
-#include <chrono>
+#include "hl_time.h"
 #include <stdlib.h>
+#include <chrono>
+#include <cstdint>
 #include <iostream>
-#include "hl_time.h"
 
 using std::chrono::high_resolution_clock;
 

paddle/cuda/src/hl_warpctc_wrap.cc

@@ -12,8 +12,8 @@ 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. */
 
-#include <mutex>
 #include "hl_warpctc_wrap.h"
+#include <mutex>
 #include "hl_dso_loader.h"
 #include "paddle/utils/Logging.h"
 

paddle/gserver/activations/ActivationFunction.cpp

@@ -15,13 +15,13 @@ limitations under the License. */
 #include "ActivationFunction.h"
 
 #include <algorithm>
-#include <memory>
 #include <iostream>
-#include <type_traits>
+#include <memory>
 #include <string>
 #include <thread>
-#include "paddle/utils/ClassRegistrar.h"
+#include <type_traits>
 #include "paddle/parameter/Argument.h"
+#include "paddle/utils/ClassRegistrar.h"
 
 #include "paddle/utils/Logging.h"
 

paddle/gserver/dataproviders/DataProvider.cpp

@@ -14,12 +14,12 @@ limitations under the License. */
 
 #include "DataProvider.h"
 
-#include "paddle/utils/Util.h"
-#include "paddle/utils/StringUtil.h"
-#include "paddle/utils/Logging.h"
-#include <algorithm>
 #include <unistd.h>
+#include <algorithm>
 #include "ProtoDataProvider.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/StringUtil.h"
+#include "paddle/utils/Util.h"
 
 namespace paddle {
 

paddle/gserver/dataproviders/DataProvider.h

@@ -14,28 +14,28 @@ limitations under the License. */
 
 #pragma once
 
-#include <vector>
-#include <memory>
-#include <mutex>
-#include <iostream>
-#include <fstream>
 #include <stdint.h>
-#include <string.h>
-#include <stdlib.h>
 #include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <mutex>
+#include <vector>
 
+#include "DataConfig.pb.h"
+#include "paddle/math/Matrix.h"
+#include "paddle/math/SparseMatrix.h"
+#include "paddle/math/Vector.h"
+#include "paddle/parameter/Argument.h"
+#include "paddle/utils/ClassRegistrar.h"
+#include "paddle/utils/Locks.h"
 #include "paddle/utils/Logging.h"
 #include "paddle/utils/Queue.h"
-#include "paddle/utils/Locks.h"
 #include "paddle/utils/ThreadLocal.h"
 #include "paddle/utils/TypeDefs.h"
-#include "paddle/math/Matrix.h"
-#include "paddle/math/SparseMatrix.h"
 #include "paddle/utils/Util.h"
-#include "paddle/math/Vector.h"
-#include "DataConfig.pb.h"
-#include "paddle/utils/ClassRegistrar.h"
-#include "paddle/parameter/Argument.h"
 
 namespace paddle {
 /**

paddle/gserver/dataproviders/MultiDataProvider.cpp

@@ -12,10 +12,10 @@ 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. */
 
-#include "paddle/utils/Util.h"
 #include "MultiDataProvider.h"
-#include "paddle/utils/Logging.h"
 #include <algorithm>
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Util.h"
 
 namespace paddle {
 

paddle/gserver/dataproviders/ProtoDataProvider.cpp

@@ -13,14 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "ProtoDataProvider.h"
-#include "paddle/utils/Util.h"
-#include "paddle/utils/StringUtil.h"
 #include <algorithm>
 #include <fstream>
 #include <istream>
+#include "paddle/utils/StringUtil.h"
+#include "paddle/utils/Util.h"
 
-#include "paddle/utils/Logging.h"
 #include "DataProviderGroup.h"
+#include "paddle/utils/Logging.h"
 
 P_DEFINE_double(memory_threshold_on_load_data,
                 1.0,
@@ -562,14 +562,14 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size,
         auto mat = cpuArguments[slot].value;
         mat->resize(size, dim);
         if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
-              ->copyFrom(dataPos.data(),
+          std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
+              dataPos.data(),
               slots_[slot].indices.data(),
               slots_[slot].sparseNonValueData.data(),
               HPPL_STREAM_1);
         } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
-              ->copyFrom(dataPos.data(),
+          std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
+              dataPos.data(),
               slots_[slot].indices.data(),
               slots_[slot].sparseNonValueData.data());
         } else {
@@ -598,14 +598,14 @@ int64_t ProtoDataProvider::getNextBatchInternal(int64_t size,
         auto mat = cpuArguments[slot].value;
         mat->resize(size, dim);
         if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
-              ->copyFrom(dataPos.data(),
+          std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
+              dataPos.data(),
               slots_[slot].indices.data(),
               slots_[slot].sparseFloatValueData.data(),
               HPPL_STREAM_1);
         } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
-          std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
-              ->copyFrom(dataPos.data(),
+          std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
+              dataPos.data(),
               slots_[slot].indices.data(),
               slots_[slot].sparseFloatValueData.data());
         } else {

paddle/gserver/dataproviders/ProtoDataProvider.h

@@ -16,8 +16,8 @@ limitations under the License. */
 
 #include <vector>
 
-#include "paddle/utils/Stat.h"
 #include "DataFormat.pb.h"
+#include "paddle/utils/Stat.h"
 
 #include "DataProvider.h"
 #include "ProtoReader.h"

paddle/gserver/dataproviders/ProtoReader.h

@@ -16,10 +16,10 @@ limitations under the License. */
 
 #include <memory>
 
-#include <google/protobuf/message_lite.h>
 #include <google/protobuf/io/coded_stream.h>
-#include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/io/gzip_stream.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+#include <google/protobuf/message_lite.h>
 
 namespace paddle {
 

paddle/gserver/dataproviders/PyDataProvider.cpp

@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "PyDataProvider.h"
-#include "paddle/utils/PythonUtil.h"
 #include <fenv.h>
-#include "paddle/utils/Util.h"
 #include "paddle/utils/Excepts.h"
+#include "paddle/utils/PythonUtil.h"
+#include "paddle/utils/Util.h"
 
 namespace paddle {
 
@@ -316,14 +316,14 @@ void PyDataProvider::handleSparseNonValueSlot(
   auto mat = cpuArguments[slotIndex].value;
   mat->resize(slot.sampleNum, dim, slot.sampleNum, NO_VALUE, SPARSE_CSR);
   if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
-    std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
-        ->copyFrom(slot.sampleSequenceIdVec.data(),
+    std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
+        slot.sampleSequenceIdVec.data(),
         slot.indices.data(),
         slot.sparseNonValueData.data(),
         HPPL_STREAM_1);
   } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
-    std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
-        ->copyFrom(slot.sampleSequenceIdVec.data(),
+    std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
+        slot.sampleSequenceIdVec.data(),
         slot.indices.data(),
         slot.sparseNonValueData.data());
   } else {
@@ -347,14 +347,14 @@ void PyDataProvider::handleSparseValueSlot(
   auto mat = cpuArguments[slotIndex].value;
   mat->resize(slot.sampleNum, dim, slot.sampleNum, FLOAT_VALUE, SPARSE_CSR);
   if (std::dynamic_pointer_cast<GpuSparseMatrix>(mat)) {
-    std::dynamic_pointer_cast<GpuSparseMatrix>(mat)
-        ->copyFrom(slot.sampleSequenceIdVec.data(),
+    std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
+        slot.sampleSequenceIdVec.data(),
         slot.indices.data(),
         slot.sparseFloatValueData.data(),
         HPPL_STREAM_DEFAULT);
   } else if (std::dynamic_pointer_cast<CpuSparseMatrix>(mat)) {
-    std::dynamic_pointer_cast<CpuSparseMatrix>(mat)
-        ->copyFrom(slot.sampleSequenceIdVec.data(),
+    std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
+        slot.sampleSequenceIdVec.data(),
         slot.indices.data(),
         slot.sparseFloatValueData.data());
   } else {

paddle/gserver/dataproviders/PyDataProvider2.cpp

@@ -15,18 +15,18 @@ limitations under the License. */
 #ifndef PADDLE_NO_PYTHON
 
 #include <Python.h>
+#include <numpy/numpyconfig.h>
 #include <stdio.h>
 #include <stdlib.h>
-#include <unordered_set>
 #include <list>
-#include <numpy/numpyconfig.h>
+#include <unordered_set>
 #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
 #include <numpy/ndarrayobject.h>
 
 #include "DataProvider.h"
 
-#include "paddle/utils/PythonUtil.h"
 #include "paddle/utils/Locks.h"
+#include "paddle/utils/PythonUtil.h"
 #include "paddle/utils/Stat.h"
 
 namespace paddle {
@@ -400,8 +400,7 @@ private:
 
       if (this->loadThread_) {  // wait poolActualSize < poolSize;
         std::unique_lock<std::mutex> l(mtx_);
-        pushCV_.wait(l,
-                     [this, additionalBatchSize] {
+        pushCV_.wait(l, [this, additionalBatchSize] {
           return this->poolActualSize_ < poolSize_;
         });
       }
@@ -529,10 +528,8 @@ public:
                         // but, loading from cache, cache object should ensure
                         // data pool ready.
       std::unique_lock<std::mutex> l(mtx_);
-      pullCV_.wait(l,
-                   [this, &size] {
-                     return this->poolActualSize_ >=
-                                std::max(size, this->minPoolSize_) ||
+      pullCV_.wait(l, [this, &size] {
+        return this->poolActualSize_ >= std::max(size, this->minPoolSize_) ||
                callingContexts_.empty();
       });
 

paddle/gserver/evaluators/Evaluator.cpp

@@ -12,8 +12,8 @@ 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. */
 
-#include "paddle/utils/Stat.h"
 #include "paddle/gserver/evaluators/Evaluator.h"
+#include "paddle/utils/Stat.h"
 
 #include "paddle/gserver/gradientmachines/NeuralNetwork.h"
 
@@ -842,9 +842,9 @@ void PnpairEvaluator::calc(std::vector<PredictionResult>& predictArray) {
   auto start = predictArray.begin();
   while (start != predictArray.end()) {
     auto end = std::find_if(
-        start + 1,
-        predictArray.end(),
-        [=](const PredictionResult& x) { return x.queryid != start->queryid; });
+        start + 1, predictArray.end(), [=](const PredictionResult& x) {
+          return x.queryid != start->queryid;
+        });
     CHECK(end != start);
     stat(start - predictArray.begin(),
          end - predictArray.begin(),

paddle/gserver/evaluators/Evaluator.h

@@ -14,11 +14,11 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/pserver/ParameterClient2.h"
-#include "paddle/utils/ClassRegistrar.h"
+#include <fstream>
 #include "ModelConfig.pb.h"
 #include "paddle/parameter/Argument.h"
-#include <fstream>
+#include "paddle/pserver/ParameterClient2.h"
+#include "paddle/utils/ClassRegistrar.h"
 
 namespace paddle {
 

paddle/gserver/gradientmachines/GradientMachine.cpp

@@ -14,16 +14,16 @@ limitations under the License. */
 
 #include "GradientMachine.h"
 
-#include "paddle/utils/Logging.h"
 #include <fstream>
+#include "paddle/utils/Logging.h"
 
-#include "hl_gpu.h"
-#include "NeuralNetwork.h"
-#include "ParallelNeuralNetwork.h"
+#include "GradientMachineMode.h"
 #include "MultiGradientMachine.h"
-#include "NeuralNetwork.h"
 #include "MultiNetwork.h"
-#include "GradientMachineMode.h"
+#include "NeuralNetwork.h"
+#include "NeuralNetwork.h"
+#include "ParallelNeuralNetwork.h"
+#include "hl_gpu.h"
 
 namespace paddle {
 

paddle/gserver/gradientmachines/GradientMachine.h

@@ -17,15 +17,15 @@ limitations under the License. */
 #include <iostream>
 #include <vector>
 
-#include "paddle/math/Matrix.h"
-#include "paddle/parameter/Parameter.h"
-#include "paddle/parameter/ParameterUpdaterBase.h"
-#include "paddle/utils/Thread.h"
-#include "TrainerConfig.pb.h"
 #include "ModelConfig.pb.h"
+#include "TrainerConfig.pb.h"
 #include "paddle/gserver/dataproviders/DataProvider.h"
 #include "paddle/gserver/evaluators/Evaluator.h"
 #include "paddle/gserver/layers/Layer.h"
+#include "paddle/math/Matrix.h"
+#include "paddle/parameter/Parameter.h"
+#include "paddle/parameter/ParameterUpdaterBase.h"
+#include "paddle/utils/Thread.h"
 
 namespace paddle {
 /**

paddle/gserver/gradientmachines/MultiGradientMachine.h

@@ -18,9 +18,9 @@ limitations under the License. */
 
 #include "GradientMachine.h"
 
-#include "paddle/utils/Queue.h"
-#include "paddle/utils/Locks.h"
 #include "hl_gpu.h"
+#include "paddle/utils/Locks.h"
+#include "paddle/utils/Queue.h"
 
 namespace paddle {
 

paddle/gserver/gradientmachines/MultiNetwork.cpp

@@ -12,9 +12,9 @@ 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. */
 
+#include <algorithm>
 #include "paddle/utils/Stat.h"
 #include "paddle/utils/Util.h"
-#include <algorithm>
 
 #include "MultiNetwork.h"
 

paddle/gserver/gradientmachines/NeuralNetwork.cpp

@@ -14,15 +14,15 @@ limitations under the License. */
 
 #include "paddle/utils/Util.h"
 
-#include "paddle/utils/Logging.h"
 #include "paddle/utils/CustomStackTrace.h"
+#include "paddle/utils/Logging.h"
 
-#include "paddle/utils/Stat.h"
-#include "hl_gpu.h"
+#include "MultiNetwork.h"
 #include "NeuralNetwork.h"
 #include "RecurrentGradientMachine.h"
-#include "MultiNetwork.h"
+#include "hl_gpu.h"
 #include "paddle/gserver/layers/AgentLayer.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 void parameterInitNN(int paramId,

paddle/gserver/gradientmachines/NeuralNetwork.h

@@ -14,18 +14,18 @@ limitations under the License. */
 
 #pragma once
 
-#include <memory>
-#include <map>
 #include <functional>
+#include <map>
+#include <memory>
 
-#include "paddle/utils/ClassRegistrar.h"
-#include "paddle/parameter/Parameter.h"
 #include "ModelConfig.pb.h"
+#include "paddle/gserver/dataproviders/DataProvider.h"
 #include "paddle/gserver/gradientmachines/GradientMachine.h"
 #include "paddle/gserver/layers/CostLayer.h"
 #include "paddle/gserver/layers/DataLayer.h"
-#include "paddle/gserver/dataproviders/DataProvider.h"
 #include "paddle/gserver/layers/Layer.h"
+#include "paddle/parameter/Parameter.h"
+#include "paddle/utils/ClassRegistrar.h"
 
 namespace paddle {
 /*
@@ -57,11 +57,10 @@ void parameterInitNN(int paramId,
 
 class NeuralNetwork : public GradientMachine {
 public:
-  virtual void init(
-      const ModelConfig& config,
+  virtual void init(const ModelConfig& config,
                     ParamInitCallback callback = nullptr,
-      const std::vector<ParameterType>&
-          parameterTypes = std::vector<ParameterType>{PARAMETER_VALUE,
+                    const std::vector<ParameterType>& parameterTypes =
+                        std::vector<ParameterType>{PARAMETER_VALUE,
                                                    PARAMETER_GRADIENT,
                                                    PARAMETER_MOMENTUM},
                     bool useGpu = FLAGS_use_gpu);

paddle/gserver/gradientmachines/ParallelNeuralNetwork.h

@@ -37,11 +37,10 @@ public:
                         NeuralNetwork *rootNetwork = nullptr)
       : NeuralNetwork(subModelName, rootNetwork) {}
 
-  virtual void init(
-      const ModelConfig &config,
+  virtual void init(const ModelConfig &config,
                     ParamInitCallback callback = nullptr,
-      const std::vector<ParameterType>
-          &parameterTypes = std::vector<ParameterType>{PARAMETER_VALUE,
+                    const std::vector<ParameterType> &parameterTypes =
+                        std::vector<ParameterType>{PARAMETER_VALUE,
                                                    PARAMETER_GRADIENT,
                                                    PARAMETER_MOMENTUM},
                     bool useGpu = FLAGS_use_gpu);

paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp

@@ -12,17 +12,17 @@ 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. */
 
-#include "paddle/utils/Stat.h"
-#include "paddle/utils/Util.h"
-#include "paddle/utils/Flags.h"
+#include "RecurrentGradientMachine.h"
+#include <dlfcn.h>
 #include <algorithm>
+#include <cmath>
 #include <functional>
-#include <dlfcn.h>
 #include <limits>
-#include <cmath>
-#include "RecurrentGradientMachine.h"
 #include "NeuralNetwork.h"
 #include "paddle/gserver/layers/AgentLayer.h"
+#include "paddle/utils/Flags.h"
+#include "paddle/utils/Stat.h"
+#include "paddle/utils/Util.h"
 
 P_DEFINE_string(diy_beam_search_prob_so, "", "the diy beam search cost so");
 
@@ -78,7 +78,8 @@ static inline SymbolType loadDiySymbol(const char* symbolName) {
   return reinterpret_cast<SymbolType>(sym);
 }
 
-static InitFunction __init__diy_prob_method([] {
+static InitFunction __init__diy_prob_method(
+    [] {
       std::string soName = FLAGS_diy_beam_search_prob_so;
       if (!soName.empty()) {
         gDiyProbHandle = dlopen(soName.c_str(), RTLD_LAZY);
@@ -86,12 +87,13 @@ static InitFunction __init__diy_prob_method([] {
         atexit(exit_diy_prob);
         gDiyProbMethod =
             loadDiySymbol<decltype(gDiyProbMethod)>(DIY_CALC_PROB_SYMBOL_NAME);
-    gDiyProbStart =
-        loadDiySymbol<decltype(gDiyProbStart)>(DIY_START_CALC_PROB_SYMBOL_NAME);
-    gDiyProbStop =
-        loadDiySymbol<decltype(gDiyProbStop)>(DIY_FINISH_CALC_PROB_SYMBOL_NAME);
+        gDiyProbStart = loadDiySymbol<decltype(gDiyProbStart)>(
+            DIY_START_CALC_PROB_SYMBOL_NAME);
+        gDiyProbStop = loadDiySymbol<decltype(gDiyProbStop)>(
+            DIY_FINISH_CALC_PROB_SYMBOL_NAME);
       }
-}, std::numeric_limits<int>::max());
+    },
+    std::numeric_limits<int>::max());
 
 class BeamSearchControlCallbacks {
 public:
@@ -1281,10 +1283,9 @@ void RecurrentGradientMachine::beamSearch(size_t batchSize) {
       std::vector<std::vector<int>*> prefixes;
       prefixes.resize(paths.size());
       std::transform(
-          paths.begin(),
-          paths.end(),
-          prefixes.begin(),
-          [](const Path& p) { return const_cast<std::vector<int>*>(&p.ids); });
+          paths.begin(), paths.end(), prefixes.begin(), [](const Path& p) {
+            return const_cast<std::vector<int>*>(&p.ids);
+          });
       beamSearchCtrlCallbacks_->beamSearchCandidateAdjust(
           prefixes, frames_[machineCur].get(), i);
     }

paddle/gserver/gradientmachines/RecurrentGradientMachine.h

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <functional>
 #include "GradientMachine.h"
 #include "NeuralNetwork.h"
-#include <functional>
 
 #include "paddle/utils/Locks.h"
 

paddle/gserver/layers/BatchNormBaseLayer.cpp

@@ -12,10 +12,10 @@ 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. */
 
-#include "paddle/utils/Stat.h"
-#include "Layer.h"
 #include "BatchNormBaseLayer.h"
 #include "BatchNormalizationLayer.h"
+#include "Layer.h"
+#include "paddle/utils/Stat.h"
 #ifndef PADDLE_ONLY_CPU
 #include "CudnnBatchNormLayer.h"
 #endif

paddle/gserver/layers/BatchNormBaseLayer.h

@@ -14,8 +14,8 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/utils/Stat.h"
 #include "Layer.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 

paddle/gserver/layers/BatchNormalizationLayer.h

@@ -14,8 +14,8 @@ limitations under the License. */
 
 #pragma once
 
-#include "Layer.h"
 #include "BatchNormBaseLayer.h"
+#include "Layer.h"
 
 namespace paddle {
 

paddle/gserver/layers/ConcatenateLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
-#include "paddle/utils/Stat.h"
 #include "Layer.h"
 #include "Projection.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 

paddle/gserver/layers/ContextProjection.cpp

@@ -12,8 +12,8 @@ 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. */
 
-#include "paddle/utils/Stat.h"
 #include "ContextProjection.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 

paddle/gserver/layers/ConvBaseLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
-#include "paddle/utils/Logging.h"
 #include "ConvBaseLayer.h"
 #include "paddle/math/MathUtils.h"
+#include "paddle/utils/Logging.h"
 namespace paddle {
 
 bool ConvBaseLayer::init(const LayerMap& layerMap,

paddle/gserver/layers/ConvOperator.cpp

@@ -12,9 +12,9 @@ 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. */
 
-#include "paddle/math/Matrix.h"
-#include "paddle/math/MathUtils.h"
 #include "Operator.h"
+#include "paddle/math/MathUtils.h"
+#include "paddle/math/Matrix.h"
 
 namespace paddle {
 

paddle/gserver/layers/ConvProjection.cpp

@@ -12,8 +12,8 @@ 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. */
 
-#include "paddle/utils/Stat.h"
 #include "ConvProjection.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 

paddle/gserver/layers/ConvShiftLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
-#include "paddle/utils/Logging.h"
 #include "Layer.h"
 #include "paddle/math/Matrix.h"
+#include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
 
 namespace paddle {

paddle/gserver/layers/ConvexCombinationLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
-#include "paddle/utils/Logging.h"
 #include "Layer.h"
 #include "paddle/math/Matrix.h"
+#include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
 
 namespace paddle {

paddle/gserver/layers/CosSimVecMatLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
-#include "paddle/utils/Logging.h"
 #include "Layer.h"
 #include "paddle/math/Matrix.h"
+#include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
 
 namespace paddle {

paddle/gserver/layers/CostLayer.cpp

@@ -12,11 +12,11 @@ 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. */
 
-#include <memory>
+#include "CostLayer.h"
 #include <algorithm>
-#include "paddle/utils/Logging.h"
 #include <cmath>
-#include "CostLayer.h"
+#include <memory>
+#include "paddle/utils/Logging.h"
 
 #include "paddle/math/SparseMatrix.h"
 

paddle/gserver/layers/CudnnBatchNormLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
-#include "paddle/utils/Stat.h"
-#include "Layer.h"
 #include "CudnnBatchNormLayer.h"
+#include "Layer.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 

paddle/gserver/layers/CudnnBatchNormLayer.h

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/utils/Stat.h"
-#include "Layer.h"
 #include "BatchNormBaseLayer.h"
+#include "Layer.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 

paddle/gserver/layers/CudnnConvLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
+#include "CudnnConvLayer.h"
 #include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
-#include "CudnnConvLayer.h"
 
 namespace paddle {
 

paddle/gserver/layers/CudnnConvLayer.h

@@ -14,10 +14,10 @@ limitations under the License. */
 
 #pragma once
 
+#include <vector>
 #include "ConvBaseLayer.h"
-#include "paddle/math/Matrix.h"
 #include "Projection.h"
-#include <vector>
+#include "paddle/math/Matrix.h"
 
 namespace paddle {
 

paddle/gserver/layers/CudnnPoolLayer.cpp

@@ -12,10 +12,10 @@ 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. */
 
+#include "CudnnPoolLayer.h"
+#include "paddle/math/Matrix.h"
 #include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
-#include "paddle/math/Matrix.h"
-#include "CudnnPoolLayer.h"
 
 namespace paddle {
 

paddle/gserver/layers/EosIdCheckLayer.cpp

@@ -12,8 +12,8 @@ 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. */
 
-#include "paddle/utils/Logging.h"
 #include "Layer.h"
+#include "paddle/utils/Logging.h"
 
 namespace paddle {
 /**

paddle/gserver/layers/ExpandConvBaseLayer.h

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <vector>
 #include "ConvBaseLayer.h"
 #include "paddle/math/Matrix.h"
-#include <vector>
 
 namespace paddle {
 

paddle/gserver/layers/ExpandConvLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
+#include "ExpandConvLayer.h"
 #include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
-#include "ExpandConvLayer.h"
 
 namespace paddle {
 

paddle/gserver/layers/ExpandConvLayer.h

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/math/Matrix.h"
 #include <vector>
 #include "ExpandConvBaseLayer.h"
+#include "paddle/math/Matrix.h"
 
 namespace paddle {
 

paddle/gserver/layers/ExpandConvTransLayer.cpp

@@ -12,9 +12,9 @@ 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. */
 
+#include "ExpandConvTransLayer.h"
 #include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
-#include "ExpandConvTransLayer.h"
 
 /* The implementation of the convTransLayer is basically a swap of forward and
  * backward of the original convLayer.

paddle/gserver/layers/ExpandConvTransLayer.h

@@ -14,9 +14,9 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/math/Matrix.h"
 #include <vector>
 #include "ExpandConvBaseLayer.h"
+#include "paddle/math/Matrix.h"
 
 namespace paddle {