Merge branch 'develop' into enable_drop_in_average_and_max_layer

demo/mnist/.gitignore

@@ -4,3 +4,4 @@ mnist_vgg_model
 plot.png
 train.log
 *pyc
+.ipynb_checkpoints

demo/mnist/api_train.py

+"""
+A very basic example for how to use current Raw SWIG API to train mnist network.
+
+Current implementation uses Raw SWIG, which means the API call is directly \
+passed to C++ side of Paddle.
+
+The user api could be simpler and carefully designed.
+"""
+import py_paddle.swig_paddle as api
+from py_paddle import DataProviderConverter
+import paddle.trainer.PyDataProvider2 as dp
+import numpy as np
+import random
+from mnist_util import read_from_mnist
+from paddle.trainer_config_helpers import *
+
+
+def optimizer_config():
+    settings(
+        learning_rate=1e-4,
+        learning_method=AdamOptimizer(),
+        batch_size=1000,
+        model_average=ModelAverage(average_window=0.5),
+        regularization=L2Regularization(rate=0.5))
+
+
+def network_config():
+    imgs = data_layer(name='pixel', size=784)
+    hidden1 = fc_layer(input=imgs, size=200)
+    hidden2 = fc_layer(input=hidden1, size=200)
+    inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation())
+    cost = classification_cost(
+        input=inference, label=data_layer(
+            name='label', size=10))
+    outputs(cost)
+
+
+def init_parameter(network):
+    assert isinstance(network, api.GradientMachine)
+    for each_param in network.getParameters():
+        assert isinstance(each_param, api.Parameter)
+        array_size = len(each_param)
+        array = np.random.uniform(-1.0, 1.0, array_size).astype('float32')
+        each_param.getBuf(api.PARAMETER_VALUE).copyFromNumpyArray(array)
+
+
+def generator_to_batch(generator, batch_size):
+    ret_val = list()
+    for each_item in generator:
+        ret_val.append(each_item)
+        if len(ret_val) == batch_size:
+            yield ret_val
+            ret_val = list()
+    if len(ret_val) != 0:
+        yield ret_val
+
+
+class BatchPool(object):
+    def __init__(self, generator, batch_size):
+        self.data = list(generator)
+        self.batch_size = batch_size
+
+    def __call__(self):
+        random.shuffle(self.data)
+        for offset in xrange(0, len(self.data), self.batch_size):
+            limit = min(offset + self.batch_size, len(self.data))
+            yield self.data[offset:limit]
+
+
+def input_order_converter(generator):
+    for each_item in generator:
+        yield each_item['pixel'], each_item['label']
+
+
+def main():
+    api.initPaddle("-use_gpu=false", "-trainer_count=4")  # use 4 cpu cores
+
+    # get enable_types for each optimizer.
+    # enable_types = [value, gradient, momentum, etc]
+    # For each optimizer(SGD, Adam), GradientMachine should enable different
+    # buffers.
+    opt_config_proto = parse_optimizer_config(optimizer_config)
+    opt_config = api.OptimizationConfig.createFromProto(opt_config_proto)
+    _temp_optimizer_ = api.ParameterOptimizer.create(opt_config)
+    enable_types = _temp_optimizer_.getParameterTypes()
+
+    # Create Simple Gradient Machine.
+    model_config = parse_network_config(network_config)
+    m = api.GradientMachine.createFromConfigProto(
+        model_config, api.CREATE_MODE_NORMAL, enable_types)
+
+    # This type check is not useful. Only enable type hint in IDE.
+    # Such as PyCharm
+    assert isinstance(m, api.GradientMachine)
+
+    # Initialize Parameter by numpy.
+    init_parameter(network=m)
+
+    # Create Local Updater. Local means not run in cluster.
+    # For a cluster training, here we can change to createRemoteUpdater
+    # in future.
+    updater = api.ParameterUpdater.createLocalUpdater(opt_config)
+    assert isinstance(updater, api.ParameterUpdater)
+
+    # Initialize ParameterUpdater.
+    updater.init(m)
+
+    # DataProvider Converter is a utility convert Python Object to Paddle C++
+    # Input. The input format is as same as Paddle's DataProvider.
+    converter = DataProviderConverter(
+        input_types=[dp.dense_vector(784), dp.integer_value(10)])
+
+    train_file = './data/raw_data/train'
+    test_file = './data/raw_data/t10k'
+
+    # start gradient machine.
+    # the gradient machine must be started before invoke forward/backward.
+    # not just for training, but also for inference.
+    m.start()
+
+    # evaluator can print error rate, etc. It is a C++ class.
+    batch_evaluator = m.makeEvaluator()
+    test_evaluator = m.makeEvaluator()
+
+    # Get Train Data.
+    # TrainData will stored in a data pool. Currently implementation is not care
+    # about memory, speed. Just a very naive implementation.
+    train_data_generator = input_order_converter(read_from_mnist(train_file))
+    train_data = BatchPool(train_data_generator, 512)
+
+    # outArgs is Neural Network forward result. Here is not useful, just passed
+    # to gradient_machine.forward
+    outArgs = api.Arguments.createArguments(0)
+
+    for pass_id in xrange(2):  # we train 2 passes.
+        updater.startPass()
+
+        for batch_id, data_batch in enumerate(train_data()):
+            # data_batch is input images.
+            # here, for online learning, we could get data_batch from network.
+
+            # Start update one batch.
+            pass_type = updater.startBatch(len(data_batch))
+
+            # Start BatchEvaluator.
+            # batch_evaluator can be used between start/finish.
+            batch_evaluator.start()
+
+            # forwardBackward is a shortcut for forward and backward.
+            # It is sometimes faster than invoke forward/backward separately,
+            # because in GradientMachine, it may be async.
+            m.forwardBackward(converter(data_batch), outArgs, pass_type)
+
+            for each_param in m.getParameters():
+                updater.update(each_param)
+
+            # Get cost. We use numpy to calculate total cost for this batch.
+            cost_vec = outArgs.getSlotValue(0)
+            cost_vec = cost_vec.copyToNumpyMat()
+            cost = cost_vec.sum() / len(data_batch)
+
+            # Make evaluator works.
+            m.eval(batch_evaluator)
+
+            # Print logs.
+            print 'Pass id', pass_id, 'Batch id', batch_id, 'with cost=', \
+                cost, batch_evaluator
+
+            batch_evaluator.finish()
+            # Finish batch.
+            #  * will clear gradient.
+            #  * ensure all values should be updated.
+            updater.finishBatch(cost)
+
+        # testing stage. use test data set to test current network.
+        updater.apply()
+        test_evaluator.start()
+        test_data_generator = input_order_converter(read_from_mnist(test_file))
+        for data_batch in generator_to_batch(test_data_generator, 512):
+            # in testing stage, only forward is needed.
+            m.forward(converter(data_batch), outArgs, api.PASS_TEST)
+            m.eval(test_evaluator)
+
+        # print error rate for test data set
+        print 'Pass', pass_id, ' test evaluator: ', test_evaluator
+        test_evaluator.finish()
+        updater.restore()
+
+        updater.catchUpWith()
+        params = m.getParameters()
+        for each_param in params:
+            assert isinstance(each_param, api.Parameter)
+            value = each_param.getBuf(api.PARAMETER_VALUE)
+            value = value.copyToNumpyArray()
+
+            # Here, we could save parameter to every where you want
+            print each_param.getName(), value
+
+        updater.finishPass()
+
+    m.finish()
+
+
+if __name__ == '__main__':
+    main()

demo/mnist/mnist_provider.py

 from paddle.trainer.PyDataProvider2 import *
-import numpy
+from mnist_util import read_from_mnist
 
 
 # Define a py data provider
@@ -8,27 +8,5 @@ import numpy
                  'label': integer_value(10)},
     cache=CacheType.CACHE_PASS_IN_MEM)
 def process(settings, filename):  # settings is not used currently.
-    imgf = filename + "-images-idx3-ubyte"
-    labelf = filename + "-labels-idx1-ubyte"
-    f = open(imgf, "rb")
-    l = open(labelf, "rb")
-
-    f.read(16)
-    l.read(8)
-
-    # Define number of samples for train/test
-    if "train" in filename:
-        n = 60000
-    else:
-        n = 10000
-
-    images = numpy.fromfile(
-        f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
-    images = images / 255.0 * 2.0 - 1.0
-    labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
-
-    for i in xrange(n):
-        yield {"pixel": images[i, :], 'label': labels[i]}
-
-    f.close()
-    l.close()
+    for each in read_from_mnist(filename):
+        yield each

demo/mnist/mnist_util.py

+import numpy
+
+__all__ = ['read_from_mnist']
+
+
+def read_from_mnist(filename):
+    imgf = filename + "-images-idx3-ubyte"
+    labelf = filename + "-labels-idx1-ubyte"
+    f = open(imgf, "rb")
+    l = open(labelf, "rb")
+
+    f.read(16)
+    l.read(8)
+
+    # Define number of samples for train/test
+    if "train" in filename:
+        n = 60000
+    else:
+        n = 10000
+
+    images = numpy.fromfile(
+        f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
+    images = images / 255.0 * 2.0 - 1.0
+    labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
+
+    for i in xrange(n):
+        yield {"pixel": images[i, :], 'label': labels[i]}
+
+    f.close()
+    l.close()

demo/quick_start/api_predict.sh

@@ -17,7 +17,7 @@ set -e
 #Note the default model is pass-00002, you shold make sure the model path
 #exists or change the mode path.
 #only test on trainer_config.lr.py
-model=output/pass-00001/
+model=output/model/pass-00001/
 config=trainer_config.lr.py
 label=data/labels.list
 dict=data/dict.txt

demo/quick_start/cluster/cluster_train.sh

+#!/bin/bash
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+set -e
+
+# Should run pserver.sh before run this script.
+bin_dir=$(cd `dirname $0`; pwd)
+home_dir=$(cd "${bin_dir}/.."; pwd)
+source "$bin_dir/env.sh"
+
+model_dir="$bin_dir/output"
+log_file="$bin_dir/train.log"
+
+pushd "$home_dir"
+cfg=trainer_config.lr.py
+paddle train \
+  --config=$cfg \
+  --save_dir=${model_dir} \
+  --trainer_count=4 \
+  --local=0 \
+  --log_period=100 \
+  --num_passes=15 \
+  --use_gpu=false \
+  --show_parameter_stats_period=100 \
+  --test_all_data_in_one_period=1 \
+  --num_gradient_servers=1 \
+  --nics=`get_nics` \
+  --port=7164 \
+  --ports_num=1 \
+  --pservers="127.0.0.1" \
+  --comment="paddle_trainer" \
+  2>&1 | tee "$log_file"
+popd

demo/quick_start/cluster/env.sh

+#!/bin/bash
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+set -e
+
+function get_nics() {
+  machine=`uname -s`
+  local nics=""
+  if [ "$machine" == "Linux" ]; then
+    nics="lo"
+  elif [ "$machine" == "Darwin" ]; then
+    nics="lo0"
+  else
+    nics="unsupport"
+  fi
+  echo $nics
+}

demo/quick_start/cluster/pserver.sh

+#!/bin/bash
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+set -e
+bin_dir=$(cd `dirname $0`; pwd)
+source "$bin_dir/env.sh"
+
+paddle pserver \
+  --nics=`get_nics` \
+  --port=7164 \
+  --ports_num=1 \
+  --ports_num_for_sparse=1 \
+  --num_gradient_servers=1 \
+  --comment="paddle_pserver" \
+  2>&1 | tee 'pserver.log'

doc/faq/index_cn.rst

@@ -72,7 +72,7 @@ PaddlePaddle支持非常多的优化算法(Optimizer)，不同的优化算法需
 减少数据载入的耗时
 ++++++++++++++++++
 
-使用 :code:`pydataprovider`时，可以减少缓存池的大小，同时设置内存缓存功能，即可以极大的加速数据载入流程。
+使用\ :code:`pydataprovider`\ 时，可以减少缓存池的大小，同时设置内存缓存功能，即可以极大的加速数据载入流程。
 :code:`DataProvider` 缓存池的减小，和之前减小通过减小缓存池来减小内存占用的原理一致。
 
 ..  literalinclude:: src/reduce_min_pool_size.py

doc/tutorials/gan/index_en.md

+# Generative Adversarial Networks (GAN) 
+
+This demo implements GAN training described in the original [GAN paper](https://arxiv.org/abs/1406.2661) and deep convolutional generative adversarial networks [DCGAN paper](https://arxiv.org/abs/1511.06434).
+
+The high-level structure of GAN is shown in Figure. 1 below. It is composed of two major parts: a generator and a discriminator, both of which are based on neural networks. The generator takes in some kind of noise with a known distribution and transforms it into an image. The discriminator takes in an image and determines whether it is artificially generated by the generator or a real image. So the generator and the discriminator are in a competitive game in which generator is trying to generate image to look as real as possible to fool the discriminator, while the discriminator is trying to distinguish between real and fake images. 
+
+<p align="center">
+    <img src="./gan.png" width="500" height="300"> 
+</p>
+<p align="center">
+    Figure 1. GAN-Model-Structure
+    <a href="https://ishmaelbelghazi.github.io/ALI/">figure credit</a>
+</p>
+
+The generator and discriminator take turn to be trained using SGD. The objective function of the generator is for its generated images being classified as real by the discriminator, and the objective function of the discriminator is to correctly classify real and fake images. When the GAN model is trained to converge to the equilibrium state, the generator will transform the given noise distribution to the distribution of real images, and the discriminator will not be able to distinguish between real and fake images at all. 
+
+## Implementation of GAN Model Structure
+Since GAN model involves multiple neural networks, it requires to use paddle python API. So the code walk-through below can also partially serve as an introduction to the usage of Paddle Python API.
+
+There are three networks defined in gan_conf.py, namely **generator_training**, **discriminator_training** and **generator**. The relationship to the model structure we defined above is that **discriminator_training** is the discriminator, **generator** is the generator, and the **generator_training** combined the generator and discriminator since training generator would require the discriminator to provide loss function. This relationship is described in the following code:
+```python
+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)
+
+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')
+    outputs(cost)
+
+if is_generator:
+    noise = data_layer(name="noise", size=noise_dim)
+    outputs(generator(noise))
+```
+
+In order to train the networks defined in gan_conf.py, one first needs to initialize a Paddle environment, parse the config, create GradientMachine from the config and create trainer from GradientMachine as done in the code chunk below:
+```python
+import py_paddle.swig_paddle as api
+# init paddle environment
+api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
+               '--log_period=100', '--gpu_id=' + args.gpu_id,
+               '--save_dir=' + "./%s_params/" % data_source)
+
+# Parse config
+gen_conf = parse_config(conf, "mode=generator_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)
+
+# Create GradientMachine
+dis_training_machine = api.GradientMachine.createFromConfigProto(
+dis_conf.model_config)
+gen_training_machine = api.GradientMachine.createFromConfigProto(
+gen_conf.model_config)
+generator_machine = api.GradientMachine.createFromConfigProto(
+generator_conf.model_config)
+
+# Create trainer
+dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
+gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
+```
+
+In order to balance the strength between generator and discriminator, we schedule to train whichever one is performing worse by comparing their loss function value. The loss function value can be calculated by a forward pass through the GradientMachine.
+```python
+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)
+```
+
+After training one network, one needs to sync the new parameters to the other networks. The code below demonstrates one example of such use case:
+```python
+# Train the gen_training
+gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
+
+# Copy the parameters from gen_training to dis_training and generator
+copy_shared_parameters(gen_training_machine,
+dis_training_machine)
+copy_shared_parameters(gen_training_machine, generator_machine)
+```
+
+
+## A Toy Example 
+With the infrastructure explained above, we can now walk you through a toy example of generating two dimensional uniform distribution using 10 dimensional Gaussian noise. 
+
+The Gaussian noises are generated using the code below:
+```python
+def get_noise(batch_size, noise_dim):
+    return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
+```
+
+The real samples (2-D uniform) are generated using the code below:
+```python
+# synthesize 2-D uniform data in gan_trainer.py:114
+def load_uniform_data():
+    data = numpy.random.rand(1000000, 2).astype('float32')
+    return data
+```
+
+The generator and discriminator network are built using fully-connected layer and batch_norm layer, and are defined in gan_conf.py. 
+
+To train the GAN model, one can use the command below. The flag -d specifies the training data (cifar, mnist or uniform) and flag --useGpu specifies whether to use gpu for training (0 is cpu, 1 is gpu).  
+```bash
+$python gan_trainer.py -d uniform --useGpu 1
+```
+The generated samples can be found in ./uniform_samples/ and one example is shown below as Figure 2. One can see that it roughly recovers the 2D uniform distribution. 
+
+<p align="center">
+    <img src="./uniform_sample.png" width="300" height="300"> 
+</p>
+<p align="center">
+    Figure 2. Uniform Sample
+</p>
+
+## MNIST Example
+### Data preparation
+To download the MNIST data, one can use the following commands:
+```bash
+$cd data/
+$./get_mnist_data.sh
+```
+
+### Model description
+Following the DC-Gan paper (https://arxiv.org/abs/1511.06434), we use convolution/convolution-transpose layer in the discriminator/generator network to better deal with images. The details of the network structures are defined in gan_conf_image.py. 
+
+### Training the model
+To train the GAN model on mnist data, one can use the following command:
+```bash
+$python gan_trainer.py -d mnist --useGpu 1
+```
+The generated sample images can be found at ./mnist_samples/ and one example is shown below as Figure 3. 
+<p align="center">
+    <img src="./mnist_sample.png" width="300" height="300"> 
+</p>
+<p align="center">
+    Figure 3. MNIST Sample
+</p>

paddle/api/CMakeLists.txt

 set(API_SOURCES
     Arguments.cpp
     ConfigParser.cpp
+    Evaluator.cpp
     GradientMachine.cpp
     Matrix.cpp
     Parameter.cpp
     ParameterOptimizer.cpp
+    ParameterUpdater.cpp
     SequenceGenerator.cpp
     Trainer.cpp
     Util.cpp
@@ -63,6 +65,15 @@ install(DIRECTORY ${PROJ_ROOT}/paddle/dist/
 
 add_custom_target(python_api_wheel ALL DEPENDS
   ${PROJ_ROOT}/paddle/dist/.timestamp)
+add_dependencies(python_api_wheel python_swig_sources
+  paddle_parameter
+  paddle_math
+  paddle_utils
+  paddle_gserver
+  paddle_pserver
+  paddle_trainer
+  paddle_api
+  paddle_cuda)
 
 if(WITH_TESTING)
     add_subdirectory(test)

paddle/utils/DisableCopy.h → paddle/api/Evaluator.cpp

@@ -11,13 +11,19 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
+#include <sstream>
+#include "PaddleAPI.h"
+#include "PaddleAPIPrivate.h"
 
-#pragma once
+Evaluator::Evaluator() : m(new EvaluatorPrivate()) {}
+Evaluator::~Evaluator() { delete m; }
 
-/**
- * Disable copy macro.
- */
-#define DISABLE_COPY(CLASS_NAME)                \
-  CLASS_NAME(CLASS_NAME &&) = delete;           \
-  CLASS_NAME(const CLASS_NAME &other) = delete; \
-  CLASS_NAME &operator=(const CLASS_NAME &other) = delete
+void Evaluator::start() { m->rawPtr->start(); }
+
+void Evaluator::finish() { m->rawPtr->finish(); }
+
+std::string Evaluator::toString() {
+  std::ostringstream sout;
+  m->rawPtr->printStats(sout);
+  return sout.str();
+}

paddle/api/GradientMachine.cpp

@@ -64,6 +64,18 @@ GradientMachine* GradientMachine::createByModelConfig(
   return GradientMachine::createFromPaddleModelPtr(confPtr, mode, types);
 }
 
+void GradientMachine::start() { m->machine->start(); }
+
+void GradientMachine::finish() { m->machine->finish(); }
+
+void GradientMachine::onPassEnd() { m->machine->onPassEnd(); }
+
+void GradientMachine::prefetch(const Arguments& inArgs) {
+  auto& in =
+      m->cast<std::vector<paddle::Argument>>(inArgs.getInternalArgumentsPtr());
+  m->machine->prefetch(in);
+}
+
 void GradientMachine::forward(const Arguments& inArgs,
                               Arguments* outArgs,
                               PassType passType) {
@@ -158,3 +170,13 @@ SequenceGenerator* GradientMachine::asSequenceGenerator(
   r->setBeamSize(beam_size);
   return r;
 }
+
+Evaluator* GradientMachine::makeEvaluator() {
+  auto ev = new Evaluator();
+  ev->m->rawPtr = m->machine->makeEvaluator();
+  return ev;
+}
+
+void GradientMachine::eval(Evaluator* evaluator) {
+  m->machine->eval(evaluator->m->rawPtr);
+}

paddle/api/Paddle.swig

@@ -96,7 +96,9 @@ namespace std {
 %rename(__getitem__) Vector::get;
 %rename(__setitem__) Vector::set;
 %rename(__len__) Vector::getSize;
+%rename(__len__) Parameter::getSize;
 %rename(__call__) ParameterTraverseCallback::apply;
+%rename(__repr__) Evaluator::toString;
 
 %apply (float* INPLACE_ARRAY2, int DIM1, int DIM2) { 
   (float* data, int dim1, int dim2) 
@@ -167,6 +169,7 @@ namespace std {
 %newobject GradientMachine::asSequenceGenerator;
 %newobject GradientMachine::getParameter;
 %newobject GradientMachine::getLayerOutput;
+%newobject GradientMachine::makeEvaluator;
 %newobject TrainerConfig::createFromTrainerConfigFile;
 %newobject TrainerConfig::getModelConfig;
 %newobject TrainerConfig::getOptimizationConfig;
@@ -174,6 +177,7 @@ namespace std {
 %newobject Parameter::getConfig;
 %newobject ParameterOptimizer::create;
 %newobject ParameterOptimizer::needSpecialTraversal;
+%newobject ParameterUpdater::createLocalUpdater;
 
 %feature("director") UpdateCallback;
 %feature("autodoc", 1); // To generate method stub, for code hint in ide
@@ -193,4 +197,4 @@ namespace std {
 %ignore OptimizationConfigPrivate;
 %ignore ParameterTraverseCallbackPrivate;
 %include "utils/GlobalConstants.h"
-%include "api/PaddleAPI.h"
\ No newline at end of file
+%include "api/PaddleAPI.h"

paddle/api/PaddleAPI.h

@@ -20,15 +20,11 @@ limitations under the License. */
 #include <string>
 #include <vector>
 #include "paddle/utils/GlobalConstants.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 /// Import PaddlePaddle's enumeration into global namespace.
 using namespace paddle::enumeration_wrapper;  // NOLINT
 
-#define DISABLE_COPY_AND_ASSIGN(classname) \
-  classname(const classname& other);       \
-  classname& operator=(const classname& other)
-
 /**
  * @brief Initialize paddle.
  *
@@ -102,7 +98,7 @@ const size_t NO_SPARSE_ID = -1UL;
 struct MatrixPrivate;
 class Matrix {
   Matrix();  // User Cannot Create Matrix.
-  DISABLE_COPY_AND_ASSIGN(Matrix);
+  DISABLE_COPY(Matrix);
   static Matrix* createByPaddleMatrixPtr(void* sharedPtr);
 
 public:
@@ -242,7 +238,7 @@ private:
 
 struct VectorPrivate;
 class Vector {
-  DISABLE_COPY_AND_ASSIGN(Vector);
+  DISABLE_COPY(Vector);
   Vector();
   static Vector* createByPaddleVectorPtr(void* ptr);
 
@@ -322,7 +318,7 @@ private:
 struct IVectorPrivate;
 class IVector {
   IVector();
-  DISABLE_COPY_AND_ASSIGN(IVector);
+  DISABLE_COPY(IVector);
   static IVector* createByPaddleVectorPtr(void* ptr);
 
 public:
@@ -402,7 +398,7 @@ struct ArgumentsPrivate;
 class Arguments {
 private:
   Arguments();  // Internal Create.
-  DISABLE_COPY_AND_ASSIGN(Arguments);
+  DISABLE_COPY(Arguments);
 
 public:
   /**
@@ -472,7 +468,7 @@ enum GradientMatchineCreateMode {
 
 struct ParameterConfigPrivate;
 class ParameterConfig {
-  DISABLE_COPY_AND_ASSIGN(ParameterConfig);
+  DISABLE_COPY(ParameterConfig);
   ParameterConfig();
 
   /**
@@ -502,7 +498,7 @@ private:
 
 struct OptimizationConfigPrivate;
 class OptimizationConfig {
-  DISABLE_COPY_AND_ASSIGN(OptimizationConfig);
+  DISABLE_COPY(OptimizationConfig);
   OptimizationConfig();
 
 public:
@@ -519,6 +515,7 @@ private:
 
   friend class TrainerConfig;
   friend class ParameterOptimizer;
+  friend class ParameterUpdater;
   friend class Trainer;
 };
 
@@ -526,7 +523,7 @@ struct ParameterPrivate;
 class Parameter {
 private:
   Parameter();
-  DISABLE_COPY_AND_ASSIGN(Parameter);
+  DISABLE_COPY(Parameter);
 
 public:
   virtual ~Parameter();
@@ -549,6 +546,8 @@ public:
   ParameterConfig* getConfig();
   void setValueUpdated();
 
+  size_t getSize() const;
+
 private:
   static Parameter* createFromRawPtr(void* ptr);
   static Parameter* createFromSharedPtr(void* ptr);
@@ -557,6 +556,7 @@ private:
   ParameterPrivate* m;
   friend class UpdateCallbackWrapper;
   friend class GradientMachine;
+  friend class ParameterUpdater;
 };
 
 struct ModelConfigPrivate;
@@ -568,7 +568,7 @@ struct ModelConfigPrivate;
 class ModelConfig {
 private:
   ModelConfig();
-  DISABLE_COPY_AND_ASSIGN(ModelConfig);
+  DISABLE_COPY(ModelConfig);
 
 public:
   virtual ~ModelConfig();
@@ -589,7 +589,7 @@ struct TrainerConfigPrivate;
 class TrainerConfig {
 private:
   TrainerConfig();
-  DISABLE_COPY_AND_ASSIGN(TrainerConfig);
+  DISABLE_COPY(TrainerConfig);
 
 public:
   virtual ~TrainerConfig();
@@ -629,7 +629,7 @@ public:
 
 struct ParameterTraverseCallbackPrivate;
 class ParameterTraverseCallback {
-  DISABLE_COPY_AND_ASSIGN(ParameterTraverseCallback);
+  DISABLE_COPY(ParameterTraverseCallback);
   ParameterTraverseCallback();
 
 public:
@@ -651,7 +651,7 @@ private:
  */
 struct ParameterOptimizerPrivate;
 class ParameterOptimizer {
-  DISABLE_COPY_AND_ASSIGN(ParameterOptimizer);
+  DISABLE_COPY(ParameterOptimizer);
   ParameterOptimizer();
 
 public:
@@ -683,12 +683,12 @@ private:
 };
 
 class SequenceGenerator;
-
+class Evaluator;
 struct GradientMachinePrivate;
 class GradientMachine {
 private:
   GradientMachine();
-  DISABLE_COPY_AND_ASSIGN(GradientMachine);
+  DISABLE_COPY(GradientMachine);
 
 public:
   virtual ~GradientMachine();
@@ -714,6 +714,23 @@ public:
       GradientMatchineCreateMode mode = CREATE_MODE_NORMAL,
       const std::vector<int>& parameterTypes = defaultParamTypes);
 
+  /**
+   * @brief finish
+   */
+  void finish();
+
+  void start();
+
+  /**
+   * Prefetch row ids of sparse parameter.
+   */
+  void prefetch(const Arguments& inArgs);
+
+  /**
+   * Do some thing when train pass ended.
+   */
+  void onPassEnd();
+
   /**
    * The forward stage of GradientMachine.
    *
@@ -761,6 +778,10 @@ public:
       size_t max_length = 100UL,
       size_t beam_size = -1UL);
 
+  Evaluator* makeEvaluator();
+
+  void eval(Evaluator* evaluator);
+
 private:
   GradientMachinePrivate* m;
 
@@ -772,6 +793,109 @@ private:
   // Not to use c++ 11 init-list, so we use static var as function default arg.
   static std::vector<int> defaultParamTypes;
   friend class Trainer;
+  friend class ParameterUpdater;
+};
+
+struct ParameterUpdaterPrivate;
+class ParameterUpdater {
+private:
+  ParameterUpdater();
+
+public:
+  static ParameterUpdater* createLocalUpdater(OptimizationConfig* config);
+  ~ParameterUpdater();
+
+  /**
+   * @brief initialize Parameter Updater by GradientMachine.
+   * @param gm
+   */
+  void init(const GradientMachine& gm);
+
+  /**
+   * @brief begin of a training/testing of one pass.
+   */
+  void startPass();
+
+  /**
+   * @brief end of a traning/testing of one pass.
+   */
+  void finishPass();
+
+  /**
+   * @brief begin of a training/testing of one batch.
+   * @param data batch's size
+   * @return PassType, mostly will be training.
+   */
+  PassType startBatch(size_t batchSize);
+
+  /**
+   * @brief end of a traning/testing of one batch
+   * @param cost current batch cost.
+   */
+  void finishBatch(float cost);
+
+  /**
+   * @brief update a parameter (by local optimizer or by cluster pserver)
+   * @param param
+   */
+  void update(Parameter* param);
+
+  /**
+   * @brief restore the average parameter.
+   * @note It is only used in AverageOptimizer. Restore will get the current
+   * PARAMETER_VALUE back.
+   */
+  void restore();
+
+  /**
+   * @brief apply. Store the average parameter.
+   * @note It is only used in AverageOptimizer. Apply will store the current
+   * PARAMETER_VALUE to buffer, calcaualte current Average Parameter, and save
+   * it to PARAMETER_VALUE.
+   */
+  void apply();
+
+  /**
+   * @brief catchUpWith The Regularization will be delayed in many situations(
+   * pserver, local sparse). Catch Up means catch the regularization up, apply
+   * regularization to all params.
+   */
+  void catchUpWith();
+
+private:
+  ParameterUpdaterPrivate* m;
+};
+
+struct EvaluatorPrivate;
+class Evaluator {
+private:
+  Evaluator();
+  DISABLE_COPY(Evaluator);
+
+public:
+  ~Evaluator();
+
+  /**
+   * @brief begin an evaluate stage.
+   */
+  void start();
+
+  /**
+   * @brief end an evaluate stage.
+   */
+  void finish();
+
+  /**
+   * @brief toString will get a evaluate result.
+   *
+   * __repr__ method in python
+   */
+  std::string toString();
+
+private:
+  EvaluatorPrivate* m;
+
+  friend class GradientMachine;
 };
 
 struct TrainerPrivate;
@@ -780,7 +904,7 @@ private:
   TrainerPrivate* m;
   Trainer();
   Trainer(TrainerConfig* optConfig, GradientMachine* gm);
-  DISABLE_COPY_AND_ASSIGN(Trainer);
+  DISABLE_COPY(Trainer);
 
 public:
   virtual ~Trainer();
@@ -846,7 +970,7 @@ public:
 
 struct SequenceGeneratorPrivate;
 class SequenceGenerator {
-  DISABLE_COPY_AND_ASSIGN(SequenceGenerator);
+  DISABLE_COPY(SequenceGenerator);
   SequenceGenerator();
 
 public:

paddle/api/PaddleAPIPrivate.h

@@ -11,12 +11,14 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
-
+#pragma once
+#include <memory>
+#include "PaddleAPI.h"
+#include "paddle/gserver/evaluators/Evaluator.h"
 #include "paddle/gserver/gradientmachines/GradientMachine.h"
+#include "paddle/parameter/ParameterUpdaterBase.h"
 #include "paddle/trainer/TrainerConfigHelper.h"
 
-#pragma once
-
 struct GradientMachinePrivate {
   std::shared_ptr<paddle::GradientMachine> machine;
 
@@ -65,3 +67,31 @@ struct ArgumentsPrivate {
     return *(std::shared_ptr<T>*)(rawPtr);
   }
 };
+
+struct ParameterUpdaterPrivate {
+  std::unique_ptr<paddle::ParameterUpdater> updater;
+};
+
+struct ParameterPrivate {
+  std::shared_ptr<paddle::Parameter> sharedPtr;
+  paddle::Parameter* rawPtr;  // rawPtr only used in ParameterUpdater,
+                              // in other situation sharedPtr should
+                              // contains value.
+
+  ParameterPrivate() : sharedPtr(nullptr), rawPtr(nullptr) {}
+
+  paddle::Parameter* getPtr() {
+    if (sharedPtr) {
+      return sharedPtr.get();
+    } else {
+      return rawPtr;
+    }
+  }
+};
+
+struct EvaluatorPrivate {
+  paddle::Evaluator* rawPtr;
+
+  EvaluatorPrivate() : rawPtr(nullptr) {}
+  ~EvaluatorPrivate() { delete rawPtr; }
+};

paddle/api/Parameter.cpp

@@ -14,21 +14,7 @@ limitations under the License. */
 
 #include "paddle/parameter/Parameter.h"
 #include "PaddleAPI.h"
-
-struct ParameterPrivate {
-  std::shared_ptr<paddle::Parameter> sharedPtr;
-  paddle::Parameter* rawPtr;
-
-  ParameterPrivate() : sharedPtr(nullptr), rawPtr(nullptr) {}
-
-  paddle::Parameter* getPtr() {
-    if (sharedPtr) {
-      return sharedPtr.get();
-    } else {
-      return rawPtr;
-    }
-  }
-};
+#include "PaddleAPIPrivate.h"
 
 Parameter::Parameter() : m(new ParameterPrivate()) {}
 
@@ -70,3 +56,5 @@ ParameterConfig* Parameter::getConfig() {
 size_t Parameter::getID() const { return m->getPtr()->getID(); }
 
 void Parameter::setValueUpdated() { m->getPtr()->setValueUpdated(); }
+
+size_t Parameter::getSize() const { return m->getPtr()->getSize(); }

paddle/api/ParameterUpdater.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "PaddleAPI.h"
+
+#include "PaddleAPIPrivate.h"
+#include "paddle/trainer/ThreadParameterUpdater.h"
+
+ParameterUpdater::ParameterUpdater() : m(new ParameterUpdaterPrivate()) {}
+
+ParameterUpdater *ParameterUpdater::createLocalUpdater(
+    OptimizationConfig *config) {
+  auto param = new ParameterUpdater();
+  param->m->updater.reset(new paddle::SgdThreadUpdater(config->m->getConfig()));
+  return param;
+}
+
+ParameterUpdater::~ParameterUpdater() { delete m; }
+
+void ParameterUpdater::init(const GradientMachine &gm) {
+  m->updater->init(gm.m->machine->getNonStaticParameters());
+}
+
+void ParameterUpdater::startPass() { m->updater->startPass(); }
+
+void ParameterUpdater::finishPass() { m->updater->finishPass(); }
+
+PassType ParameterUpdater::startBatch(size_t batchSize) {
+  return m->updater->startBatch((int64_t)batchSize);
+}
+
+void ParameterUpdater::finishBatch(float cost) {
+  m->updater->finishBatch(cost);
+}
+
+void ParameterUpdater::update(Parameter *param) {
+  auto paddleParam = param->m->getPtr();
+  m->updater->update(paddleParam);
+}
+
+void ParameterUpdater::restore() { m->updater->restore(); }
+
+void ParameterUpdater::apply() { m->updater->apply(); }
+
+void ParameterUpdater::catchUpWith() { m->updater->catchUpWith(); }

paddle/api/Vector.cpp

@@ -253,7 +253,7 @@ void Vector::copyToNumpyArray(float** view_m_data, int* dim1) {
   *view_m_data = new float[*dim1];
   if (auto cpuVec = dynamic_cast<paddle::CpuVector*>(m->vec.get())) {
     std::memcpy(*view_m_data, cpuVec->getData(), sizeof(float) * (*dim1));
-  } else if (auto gpuVec = dynamic_cast<paddle::CpuVector*>(m->vec.get())) {
+  } else if (auto gpuVec = dynamic_cast<paddle::GpuVector*>(m->vec.get())) {
     hl_memcpy_device2host(
         *view_m_data, gpuVec->getData(), sizeof(float) * (*dim1));
   } else {

paddle/api/paddle_ld_flags.py

@@ -141,9 +141,12 @@ try:
 
         def c_flag(self):
             if self.with_coverage:
-                return ["-fprofile-arcs", "-ftest-coverage", "-O0", "-g"]
+                return [
+                    "-fprofile-arcs", "-ftest-coverage", "-O0", "-g",
+                    "-std=c++11"
+                ]
             else:
-                return None
+                return ["-std=c++11"]
 except ImportError:
 
     class PaddleLDFlag(object):

paddle/cuda/include/hl_base.h

@@ -16,7 +16,31 @@ limitations under the License. */
 #define HL_BASE_H_
 
 #include <cstddef>
-#include "paddle/utils/TypeDefs.h"
+
+#ifdef PADDLE_TYPE_DOUBLE
+#define HL_FLOAT_MAX 3.40282347e+38F
+#define HL_FLOAT_MIN 1.17549435e-38F
+using real = double;
+#else
+#define HL_FLOAT_MAX 1.7976931348623157e+308
+#define HL_FLOAT_MIN 2.2250738585072014e-308
+using real = float;
+#endif
+
+/**
+ * The maximum input value for exp, used to avoid overflow problem.
+ * currently only used for tanh function.
+ */
+#define EXP_MAX_INPUT 40.0
+
+/**
+ * @brief DIVUP(x, y) is similar to ceil(x / y).
+ * @note  For CUDA, DIVUP will be used to specify
+ *        the size of blockDim.
+ */
+#ifndef DIVUP
+#define DIVUP(x, y) (((x) + (y)-1) / (y))
+#endif
 
 /**
  * HPPL is an internal high performance parallel computing library
@@ -181,46 +205,6 @@ typedef struct {
   size_t nnz;
 } _hl_sparse_matrix_s, *hl_sparse_matrix_s;
 
-#ifndef PADDLE_TYPE_DOUBLE
-/**
- * HPPL data type: real (float or double)
- *
- * if real == float
- *
- * HL_FLOAT_MAX: 3.40282347e+38F
- *
- * HL_FLOAT_MIN: 1.17549435e-38F
- */
-#define HL_FLOAT_MAX 3.40282347e+38F
-/**
- * if real == double
- *
- * HL_FLOAT_MAX: 1.7976931348623157e+308
- *
- * HL_FLOAT_MIN: 2.2250738585072014e-308
- */
-#define HL_FLOAT_MIN 1.17549435e-38F
-#else
-#define HL_FLOAT_MAX 1.7976931348623157e+308
-#define HL_FLOAT_MIN 2.2250738585072014e-308
-#endif
-
-/**
- * The maximum input value for exp, used to avoid overflow problem.
- *
- * Currently only used for tanh function.
- */
-#define EXP_MAX_INPUT 40.0
-
-/**
- * @brief DIVUP(x, y) is similar to ceil(x / y).
- * @note  For CUDA, DIVUP will be used to specify
- *        the size of blockDim.
- */
-#ifndef DIVUP
-#define DIVUP(x, y) (((x) + (y)-1) / (y))
-#endif
-
 #ifdef __NVCC__
 
 #include "cuda_runtime.h"

paddle/gserver/dataproviders/DataProvider.h

@@ -34,8 +34,8 @@ limitations under the License. */
 #include "paddle/utils/Logging.h"
 #include "paddle/utils/Queue.h"
 #include "paddle/utils/ThreadLocal.h"
-#include "paddle/utils/TypeDefs.h"
 #include "paddle/utils/Util.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 /**

paddle/gserver/layers/GruCompute.h

@@ -16,7 +16,7 @@ limitations under the License. */
 
 #include "ModelConfig.pb.h"
 #include "hl_gpu.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/gserver/layers/LstmCompute.h

@@ -16,7 +16,7 @@ limitations under the License. */
 
 #include "ModelConfig.pb.h"
 #include "hl_gpu.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/gserver/layers/MultinomialSampler.h

@@ -16,7 +16,7 @@ limitations under the License. */
 
 #include <memory>
 #include <random>
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/math/BaseMatrix.h

@@ -16,7 +16,7 @@ limitations under the License. */
 #include <stdint.h>
 #include <cstddef>
 #include "TensorExpression.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/math/Matrix.h

@@ -27,7 +27,7 @@ limitations under the License. */
 #include "MemoryHandle.h"
 #include "Vector.h"
 #include "paddle/utils/ThreadLocal.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/math/TensorExpression.h

@@ -17,7 +17,7 @@ limitations under the License. */
 #include <cstddef>
 #include "hl_tensor_ops.h"
 #include "paddle/utils/Logging.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/math/Vector.h

@@ -22,7 +22,7 @@ limitations under the License. */
 #include "BaseMatrix.h"
 #include "MemoryHandle.h"
 #include "paddle/utils/Thread.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/parameter/ParallelParameter.h

@@ -28,7 +28,7 @@ limitations under the License. */
 #include "paddle/parameter/ParameterUpdateFunctions.h"
 #include "paddle/utils/Flags.h"
 #include "paddle/utils/Locks.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 #include "ParameterConfig.pb.h"
 

paddle/parameter/Parameter.h

@@ -29,8 +29,8 @@ limitations under the License. */
 #include "paddle/utils/GlobalConstants.h"
 #include "paddle/utils/Locks.h"
 #include "paddle/utils/ThreadLocal.h"
-#include "paddle/utils/TypeDefs.h"
 #include "paddle/utils/Util.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/parameter/ParameterUpdateFunctions.h

@@ -15,7 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/math/Vector.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/pserver/BaseClient.h

@@ -18,7 +18,7 @@ limitations under the License. */
 #include "paddle/math/Matrix.h"
 #include "paddle/pserver/ProtoServer.h"
 #include "paddle/utils/Queue.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 namespace paddle {
 

paddle/pserver/ParameterClient2.h

@@ -26,8 +26,8 @@ limitations under the License. */
 #include "paddle/utils/Flags.h"
 #include "paddle/utils/Locks.h"
 #include "paddle/utils/Queue.h"
-#include "paddle/utils/TypeDefs.h"
 #include "paddle/utils/Util.h"
+#include "paddle/utils/common.h"
 
 #include "ParameterService.pb.h"
 

paddle/pserver/ParameterServer2.h

@@ -32,7 +32,7 @@ limitations under the License. */
 #include "paddle/utils/Locks.h"
 #include "paddle/utils/Stat.h"
 #include "paddle/utils/ThreadLocal.h"
-#include "paddle/utils/TypeDefs.h"
+#include "paddle/utils/common.h"
 
 #include "ParameterService.pb.h"
 

paddle/py_paddle/dataprovider_converter.py

@@ -15,6 +15,7 @@
 import paddle.trainer.PyDataProvider2 as dp2
 import collections
 import swig_paddle
+import numpy
 
 __all__ = ['DataProviderConverter']
 
@@ -35,18 +36,18 @@ class IScanner(object):
 class DenseScanner(IScanner):
     def __init__(self, input_type, pos):
         IScanner.__init__(self, input_type, pos)
-        self.__mat__ = []
-        self.__height__ = 0
+        self.__mat__ = None
 
     def scan(self, dat):
-        self.__mat__.extend(dat)
-        self.__height__ += 1
+        if self.__mat__ is None:
+            self.__mat__ = numpy.array([dat], dtype='float32')
+        else:
+            self.__mat__ = numpy.append(self.__mat__, [dat], axis=0)
 
     def finish_scan(self, argument):
         assert isinstance(argument, swig_paddle.Arguments)
         assert isinstance(self.input_type, dp2.InputType)
-        m = swig_paddle.Matrix.createDense(self.__mat__, self.__height__,
-                                           self.input_type.dim, False)
+        m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False)
         argument.setSlotValue(self.pos, m)
 
 

paddle/setup.py.in

@@ -30,8 +30,10 @@ is_lin = (system == 'linux')
 # The extra links will passed from COMAKE
 #   because generate paddle LDFLAGS is too complicated to do in setup.py
 #   it just read COMAKE generated LDFLAGS.
+extra_comps = []
 extra_links = []
 obj = api.paddle_ld_flags.PaddleLDFlag()
+extra_comps = obj.c_flag()
 ldflags = obj.ldflag_str()
 if ldflags is not None:
   extra_links.extend(ldflags.split(" "))
@@ -51,20 +53,15 @@ elif is_osx == True:
 
 include_dirs = [np.get_include(), "../"]    # include numpy and paddle.
 
-extra_c = obj.c_flag()
-
-attr=dict()
-if extra_c is not None:
-  attr["extra_compile_args"] = extra_c
-
 setup(name="py_paddle",
   version="@PADDLE_VERSION@",
   ext_modules=[
     Extension('py_paddle._swig_paddle',      # Build SWIG Extension.
        ['Paddle_wrap.cxx'],
+       language = "c++",
        include_dirs = include_dirs,
        extra_link_args = extra_links,
-       **attr
+       extra_compile_args = extra_comps
     )
   ],
   packages=['py_paddle'],

paddle/trainer/ThreadParameterUpdater.h

@@ -33,8 +33,8 @@ namespace paddle {
    because at the current moment, the merging on CPU is happening on the
    main thread, and the its parameter size can be much larger than the one GPU.
    Thus, for GPU, the parameter updates happens in updateImpl() function, which
-   is called by gradient machines as a callback function as a callback function
-   supplied to backward() and forwardBackward().
+   is called by gradient machines as a callback function supplied to backward()
+   and forwardBackward().
    For CPU, the parameter updates happens in separate threads maintained by this
    class.
  */

paddle/utils/CpuId.h

@@ -11,7 +11,7 @@ limitations under the License. */
 
 #pragma once
 
-#include "DisableCopy.h"
+#include "common.h"
 
 namespace paddle {
 

paddle/utils/Locks.h

@@ -19,7 +19,7 @@ limitations under the License. */
 #include <condition_variable>
 #include <mutex>
 
-#include "DisableCopy.h"
+#include "common.h"
 
 namespace paddle {
 

paddle/utils/Util.h

@@ -26,12 +26,11 @@ limitations under the License. */
 #include <unordered_map>
 #include <vector>
 
-#include "DisableCopy.h"
 #include "Logging.h"
 #include "TrainerConfig.pb.h"
+#include "common.h"
 
 #include "Flags.h"
-#include "TypeDefs.h"
 #include "hl_gpu.h"
 
 /**

paddle/utils/Version.h

@@ -15,7 +15,7 @@ limitations under the License. */
 #pragma once
 #include <stddef.h>
 #include <iostream>
-#include "TypeDefs.h"
+#include "common.h"
 
 namespace paddle {
 

paddle/utils/TypeDefs.h → paddle/utils/common.h

@@ -14,13 +14,20 @@ limitations under the License. */
 
 #pragma once
 
+/**
+ * Disable copy macro.
+ */
+#define DISABLE_COPY(class_name)                \
+  class_name(class_name &&) = delete;           \
+  class_name(const class_name &other) = delete; \
+  class_name &operator=(const class_name &other) = delete
+
 namespace paddle {
+
 #ifdef PADDLE_TYPE_DOUBLE
-typedef double real;
+using real = double;
 #else
-typedef float real;
+using real = float;
 #endif
 
 }  // namespace paddle
-
-using paddle::real;

python/paddle/trainer/config_parser.py

@@ -3419,8 +3419,35 @@ def register_parse_config_hook(f):
     _parse_config_hooks.add(f)
 
 
-def parse_config(config_file, config_arg_str):
+def update_g_config():
     '''
+    Update g_config after execute config_file or config_functions.
+    '''
+    for k, v in settings.iteritems():
+        if v is None:
+            continue
+        g_config.opt_config.__setattr__(k, v)
+
+    for k, v in trainer_settings.iteritems():
+        if v is None:
+            continue
+        g_config.__setattr__(k, v)
+
+    for name in g_config.model_config.input_layer_names:
+        assert name in g_layer_map, \
+            'input name "%s" does not correspond to a layer name' % name
+        assert (g_layer_map[name].type == "data" or g_layer_map[name].type == "data_trim"), \
+            'The type of input layer "%s" is not "data"' % name
+    for name in g_config.model_config.output_layer_names:
+        assert name in g_layer_map, \
+            'input name "%s" does not correspond to a layer name' % name
+    return g_config
+
+
+def parse_config(trainer_config, config_arg_str):
+    '''
+    @param trainer_config: can be a string of config file name or a function name
+    with config logic
     @param config_arg_str: a string of the form var1=val1,var2=val2. It will be
     passed to config script as a dictionary CONFIG_ARGS
     '''
@@ -3454,46 +3481,21 @@ def parse_config(config_file, config_arg_str):
     g_root_submodel.is_recurrent_layer_group = False
     g_current_submodel = g_root_submodel
 
-    # for paddle on spark, need support non-file config.
-    # you can use parse_config like below:
-    #
-    # from paddle.trainer.config_parser import parse_config
-    # def configs():
-    #    #your paddle config code, which is same as config file.
-    #
-    # config = parse_config(configs, "is_predict=1")
-    # # then you get config proto object.
-    if hasattr(config_file, '__call__'):
-        config_file.func_globals.update(
+    if hasattr(trainer_config, '__call__'):
+        trainer_config.func_globals.update(
             make_config_environment("", config_args))
-        config_file()
+        trainer_config()
     else:
-        execfile(config_file, make_config_environment(config_file, config_args))
-    for k, v in settings.iteritems():
-        if v is None:
-            continue
-        g_config.opt_config.__setattr__(k, v)
-
-    for k, v in trainer_settings.iteritems():
-        if v is None:
-            continue
-        g_config.__setattr__(k, v)
+        execfile(trainer_config,
+                 make_config_environment(trainer_config, config_args))
 
-    for name in g_config.model_config.input_layer_names:
-        assert name in g_layer_map, \
-            'input name "%s" does not correspond to a layer name' % name
-        assert (g_layer_map[name].type == "data" or g_layer_map[name].type == "data_trim"), \
-            'The type of input layer "%s" is not "data"' % name
-    for name in g_config.model_config.output_layer_names:
-        assert name in g_layer_map, \
-            'input name "%s" does not correspond to a layer name' % name
-    return g_config
+    return update_g_config()
 
 
-def parse_config_and_serialize(config_file, config_arg_str):
+def parse_config_and_serialize(trainer_config, config_arg_str):
     try:
-        config = parse_config(config_file, config_arg_str)
-        # logger.info(config)
+        config = parse_config(trainer_config, config_arg_str)
+        #logger.info(config)
         return config.SerializeToString()
     except:
         traceback.print_exc()

python/paddle/trainer_config_helpers/__init__.py

@@ -20,6 +20,6 @@ from layers import *
 from networks import *
 from optimizers import *
 from attrs import *
-
+from config_parser_utils import *
 # This will enable operator overload for LayerOutput
-import math as layer_math
+import layer_math

python/paddle/trainer_config_helpers/attrs.py

@@ -19,34 +19,34 @@ __all__ = [
 
 
 def convert_and_compare(x, Type):
-    """                                                                                                                                                                                                
-    Convert x to be the same type as Type and then convert back to                                                                                                                                      
-    check whether there is a loss of information                                                                                                                                                        
-    :param x: object to be checked                                                                                                                                                                      
-    :param Type: target type to check x over                                                                                                                                                           
-    
+    """
+    Convert x to be the same type as Type and then convert back to
+    check whether there is a loss of information
+    :param x: object to be checked
+    :param Type: target type to check x over
+
     """
     return type(x)(Type(x)) == x
 
 
 def is_compatible_with(x, Type):
-    """                                                                                                                                                                                                
-    Check if x has a type compatible with Type                                                                                                                                                         
-    :param x: object to be checked                                                                                                                                                                     
-    :param Type: target type to check x over                                                                                                                                                           
-    
+    """
+    Check if x has a type compatible with Type
+    :param x: object to be checked
+    :param Type: target type to check x over
+
     """
     if type(x) == Type:
         return True
     try:
         if float == Type or int == Type:
-            # avoid those types that can be converted to float/int but not very                                                                                                                            
-            # meaningful and  could potentially lead to error                                                                                                                                              
-            # i.e., str and bool typed value should not be used for initializing float/int variable                                                                                                        
+            # avoid those types that can be converted to float/int but not very
+            # meaningful and  could potentially lead to error
+            # i.e., str and bool typed value should not be used for initializing float/int variable
             if not isinstance(x, str) and not isinstance(x, bool):
                 return convert_and_compare(x, Type)
         elif bool == Type:
-            # should not use string type to initialize bool variable                                                                                                                                   
+            # should not use string type to initialize bool variable
             if not isinstance(x, str):
                 return convert_and_compare(x, Type)
         else:
@@ -88,6 +88,10 @@ class ParameterAttribute(object):
     :type learning_rate: float or None
     :param momentum: The parameter momentum. None means use global value.
     :type momentum: float or None
+    :param gradient_clipping_threshold: gradient clipping threshold. If gradient
+                                        value larger than some value, will be
+                                        clipped.
+    :type gradient_clipping_threshold: float
     :param sparse_update: Enable sparse update for this parameter. It will
                           enable both local and remote sparse update.
     :type sparse_update: bool
@@ -104,6 +108,7 @@ class ParameterAttribute(object):
                  l2_rate=None,
                  learning_rate=None,
                  momentum=None,
+                 gradient_clipping_threshold=None,
                  sparse_update=False):
         # initialize strategy.
         if is_static:
@@ -152,6 +157,11 @@ class ParameterAttribute(object):
             self.attr['sparse_update'] = True
             self.attr['sparse_remote_update'] = True
 
+        if gradient_clipping_threshold is not None and \
+                is_compatible_with(gradient_clipping_threshold, float):
+            self.attr['gradient_clipping_threshold'] = \
+                gradient_clipping_threshold
+
     def set_default_parameter_name(self, name):
         """
         Set default parameter name. If parameter not set, then will use default

python/paddle/trainer_config_helpers/config_parser.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.trainer.config_parser as config_parser
+'''
+This file is a wrapper of formal config_parser. The main idea of this file is to 
+separete different config logic into different function, such as network configuration
+ and optimizer configuration.
+'''
+
+__all__ = [
+    "parse_trainer_config", "parse_network_config", "parse_optimizer_config"
+]
+
+
+def parse_trainer_config(trainer_conf, config_arg_str):
+    return config_parser.parse_config(trainer_conf, config_arg_str)
+
+
+def parse_network_config(network_conf):
+    config = config_parser.parse_config(network_conf, '')
+    return config.model_config
+
+
+def parse_optimizer_config(optimizer_conf):
+    config = config_parser.parse_config(optimizer_conf, '')
+    return config.opt_config

python/paddle/trainer_config_helpers/config_parser_utils.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.trainer.config_parser as config_parser
+'''
+This file is a wrapper of formal config_parser. The main idea of this file is to 
+separete different config logic into different function, such as network configuration
+ and optimizer configuration.
+'''
+
+__all__ = [
+    "parse_trainer_config", "parse_network_config", "parse_optimizer_config"
+]
+
+
+def parse_trainer_config(trainer_conf, config_arg_str):
+    return config_parser.parse_config(trainer_conf, config_arg_str)
+
+
+def parse_network_config(network_conf, config_arg_str=''):
+    config = config_parser.parse_config(network_conf, config_arg_str)
+    return config.model_config
+
+
+def parse_optimizer_config(optimizer_conf, config_arg_str=''):
+    config = config_parser.parse_config(optimizer_conf, config_arg_str)
+    return config.opt_config