Merge branch 'develop' of github.com:baidu/Paddle into feature/mnist_train_api

.travis.yml

@@ -56,7 +56,7 @@ before_install:
   - 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
   - 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
+  - pip install wheel protobuf sphinx recommonmark virtualenv numpy sphinx_rtd_theme pre-commit requests==2.9.2 LinkChecker
 script:
   - paddle/scripts/travis/main.sh
 notifications:

cmake/util.cmake

@@ -96,6 +96,7 @@ function(link_paddle_exe TARGET_NAME)
     target_circle_link_libraries(${TARGET_NAME}
         ARCHIVE_START
         paddle_gserver
+        paddle_function
         ${METRIC_LIBS}
         ARCHIVE_END
         paddle_pserver
@@ -106,6 +107,7 @@ function(link_paddle_exe TARGET_NAME)
         paddle_parameter
         paddle_proto
         paddle_cuda
+        paddle_test_main
         ${METRIC_LIBS}
         ${PROTOBUF_LIBRARY}
         ${LIBGLOG_LIBRARY}

demo/semantic_role_labeling/dataprovider.py

@@ -43,7 +43,7 @@ def get_batch_size(yeild_data):
     init_hook=hook,
     should_shuffle=True,
     calc_batch_size=get_batch_size,
-    can_over_batch_size=False,
+    can_over_batch_size=True,
     cache=CacheType.CACHE_PASS_IN_MEM)
 def process(settings, file_name):
     with open(file_name, 'r') as fdata:

doc/getstarted/build_and_install/docker_install_en.rst

@@ -39,12 +39,20 @@ The general development workflow with Docker and Bazel is as follows:
    code.  This image contains all the development tools and
    dependencies of PaddlePaddle.
 
-
    .. code-block:: bash
 
       cd paddle
       docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
 
+   Sometimes docker build might suffer from a slow network connection to the official Ubuntu apt-source servers. In such case, we can specify an apt-source mirror server that is geologically nearer to us. In the following example, we specified an apt-source server that responds fast in China.You can specify the UBUNTU MIRROR with :code:`--build-arg UBUNTU_MIRROR` like the example below.
+
+   .. code-block:: bash
+
+      docker build \
+       --build-arg UBUNTU_MIRROR="http://mirrors.163.com" \
+       -t paddle:dev \
+       -f paddle/scripts/docker/Dockerfile .
+
 
 3. Run the image as a container and mounting local source code
    directory into the container.  This allows us to change the code on

doc/howto/deep_model/rnn/rnn_cn.md

+RNN 配置
+=================
+
+本教程将指导你如何在 PaddlePaddle 中配置循环神经网络（RNN）。PaddlePaddle 高度支持灵活和高效的循环神经网络配置。 在本教程中，您将了解如何：
+
+-   准备用来学习循环神经网络的序列数据。
+-   配置循环神经网络架构。
+-   使用学习完成的循环神经网络模型生成序列。
+
+我们将使用 vanilla 循环神经网络和 sequence to sequence 模型来指导你完成这些步骤。sequence to sequence 模型的代码可以在`demo / seqToseq`找到。
+
+准备序列数据
+---------------------
+
+PaddlePaddle 不需要对序列数据进行任何预处理，例如填充。唯一需要做的是将相应类型设置为输入。例如，以下代码段定义了三个输入。 它们都是序列，它们的大小是`src_dict`，`trg_dict`和`trg_dict`：
+
+``` sourceCode
+settings.input_types = [
+  integer_value_sequence(len(settings.src_dict)),
+  integer_value_sequence(len(settings.trg_dict)),
+  integer_value_sequence(len(settings.trg_dict))]
+```
+
+在`process`函数中，每个`yield`函数将返回三个整数列表。每个整数列表被视为一个整数序列：
+
+``` sourceCode
+yield src_ids, trg_ids, trg_ids_next
+```
+
+有关如何编写数据提供程序的更多细节描述，请参考 [PyDataProvider2](../../ui/data_provider/index.html)。完整的数据提供文件在 `demo/seqToseq/dataprovider.py`。
+
+配置循环神经网络架构
+-----------------------------------------------
+
+### 简单门控循环神经网络(Gated Recurrent Neural Network)
+
+循环神经网络在每个时间步骤顺序地处理序列。下面列出了 LSTM 的架构的示例。
+
+![image](../../../tutorials/sentiment_analysis/bi_lstm.jpg)
+
+一般来说，循环网络从 *t* = 1 到 *t* = *T* 或者反向地从 *t* = *T* 到 *t* = 1 执行以下操作。
+
+*x*<sub>*t* + 1</sub> = *f*<sub>*x*</sub>(*x*<sub>*t*</sub>),*y*<sub>*t*</sub> = *f*<sub>*y*</sub>(*x*<sub>*t*</sub>)
+
+其中 *f*<sub>*x*</sub>(.) 称为**单步函数**（即单时间步执行的函数，step function），而 *f*<sub>*y*</sub>(.) 称为**输出函数**。在 vanilla 循环神经网络中，单步函数和输出函数都非常简单。然而，PaddlePaddle 可以通过修改这两个函数来实现复杂的网络配置。我们将使用 sequence to sequence 模型演示如何配置复杂的循环神经网络模型。在本节中，我们将使用简单的 vanilla 循环神经网络作为使用`recurrent_group`配置简单循环神经网络的例子。 注意，如果你只需要使用简单的RNN，GRU或LSTM，那么推荐使用`grumemory`和`lstmemory`，因为它们的计算效率比`recurrent_group`更高。
+
+对于 vanilla RNN，在每个时间步长，**单步函数**为：
+
+*x*<sub>*t* + 1</sub> = *W*<sub>*x*</sub>*x*<sub>*t*</sub> + *W*<sub>*i*</sub>*I*<sub>*t*</sub> + *b*
+
+其中 *x*<sub>*t*</sub> 是RNN状态，并且 *I*<sub>*t*</sub> 是输入，*W*<sub>*x*</sub> 和 *W*<sub>*i*</sub> 分别是RNN状态和输入的变换矩阵。*b* 是偏差。它的**输出函数**只需要*x*<sub>*t*</sub>作为输出。
+
+`recurrent_group`是构建循环神经网络的最重要的工具。 它定义了**单步函数**，**输出函数**和循环神经网络的输入。注意，这个函数的`step`参数需要实现`step function`（单步函数）和`output function`（输出函数）：
+
+
+``` sourceCode
+def simple_rnn(input,
+               size=None,
+               name=None,
+               reverse=False,
+               rnn_bias_attr=None,
+               act=None,
+               rnn_layer_attr=None):
+    def __rnn_step__(ipt):
+       out_mem = memory(name=name, size=size)
+       rnn_out = mixed_layer(input = [full_matrix_projection(ipt),
+                                      full_matrix_projection(out_mem)],
+                             name = name,
+                             bias_attr = rnn_bias_attr,
+                             act = act,
+                             layer_attr = rnn_layer_attr,
+                             size = size)
+       return rnn_out
+    return recurrent_group(name='%s_recurrent_group' % name,
+                           step=__rnn_step__,
+                           reverse=reverse,
+                           input=input)
+```
+
+PaddlePaddle 使用“Memory”（记忆模块）实现单步函数。**Memory**是在PaddlePaddle中构造循环神经网络时最重要的概念。 Memory是在单步函数中循环使用的状态，例如*x*<sub>*t* + 1</sub> = *f*<sub>*x*</sub>(*x*<sub>*t*</sub>)。 一个Memory包含**输出**和**输入**。当前时间步处的Memory的输出作为下一时间步Memory的输入。Memory也可以具有**boot layer(引导层)**，其输出被用作Memory的初始值。 在我们的例子中，门控循环单元的输出被用作输出Memory。请注意，`rnn_out`层的名称与`out_mem`的名称相同。这意味着`rnn_out` (*x*<sub>*t* + 1</sub>)的输出被用作`out_mem`Memory的**输出**。
+
+Memory也可以是序列。在这种情况下，在每个时间步中，我们有一个序列作为循环神经网络的状态。这在构造非常复杂的循环神经网络时是有用的。 其他高级功能包括定义多个Memory，以及使用子序列来定义分级循环神经网络架构。
+
+我们在函数的结尾返回`rnn_out`。 这意味着 `rnn_out` 层的输出被用作门控循环神经网络的**输出**函数。
+
+### Sequence to Sequence Model with Attention
+
+我们将使用 sequence to sequence model with attention 作为例子演示如何配置复杂的循环神经网络模型。该模型的说明如下图所示。
+
+![image](../../../tutorials/text_generation/encoder-decoder-attention-model.png)
+
+在这个模型中，源序列 *S* = {*s*<sub>1</sub>, …, *s*<sub>*T*</sub>} 用双向门控循环神经网络编码。双向门控循环神经网络的隐藏状态 *H*<sub>*S*</sub> = {*H*<sub>1</sub>, …, *H*<sub>*T*</sub>} 被称为 *编码向量*。解码器是门控循环神经网络。当解读每一个*y*<sub>*t*</sub>时, 这个门控循环神经网络生成一系列权重 *W*<sub>*S*</sub><sup>*t*</sup> = {*W*<sub>1</sub><sup>*t*</sup>, …, *W*<sub>*T*</sub><sup>*t*</sup>}, 用于计算编码向量的加权和。加权和用来生成*y*<sub>*t*</sub>。
+
+模型的编码器部分如下所示。它叫做`grumemory`来表示门控循环神经网络。如果网络架构简单，那么推荐使用循环神经网络的方法，因为它比 `recurrent_group` 更快。我们已经实现了大多数常用的循环神经网络架构，可以参考 [Layers](../../ui/api/trainer_config_helpers/layers_index.html) 了解更多细节。
+
+我们还将编码向量投射到 `decoder_size` 维空间。这通过获得反向循环网络的第一个实例，并将其投射到 `decoder_size` 维空间完成：
+
+``` sourceCode
+# 定义源语句的数据层
+src_word_id = data_layer(name='source_language_word', size=source_dict_dim)
+# 计算每个词的词向量
+src_embedding = embedding_layer(
+    input=src_word_id,
+    size=word_vector_dim,
+    param_attr=ParamAttr(name='_source_language_embedding'))
+# 应用前向循环神经网络
+src_forward = grumemory(input=src_embedding, size=encoder_size)
+# 应用反向递归神经网络（reverse=True表示反向循环神经网络）
+src_backward = grumemory(input=src_embedding,
+                          size=encoder_size,
+                          reverse=True)
+# 将循环神经网络的前向和反向部分混合在一起
+encoded_vector = concat_layer(input=[src_forward, src_backward])
+
+# 投射编码向量到 decoder_size
+encoder_proj = mixed_layer(input = [full_matrix_projection(encoded_vector)],
+                           size = decoder_size)
+
+# 计算反向RNN的第一个实例
+backward_first = first_seq(input=src_backward)
+
+# 投射反向RNN的第一个实例到 decoder size
+decoder_boot = mixed_layer(input=[full_matrix_projection(backward_first)], size=decoder_size, act=TanhActivation())
+```
+
+解码器使用 `recurrent_group` 来定义循环神经网络。单步函数和输出函数在 `gru_decoder_with_attention` 中定义：
+
+``` sourceCode
+group_inputs=[StaticInput(input=encoded_vector,is_seq=True),
+              StaticInput(input=encoded_proj,is_seq=True)]
+trg_embedding = embedding_layer(
+    input=data_layer(name='target_language_word',
+                     size=target_dict_dim),
+    size=word_vector_dim,
+    param_attr=ParamAttr(name='_target_language_embedding'))
+group_inputs.append(trg_embedding)
+
+# 对于配备有注意力机制的解码器，在训练中，
+# 目标向量（groudtruth）是数据输入，
+# 而源序列的编码向量可以被无边界的memory访问
+# StaticInput 意味着不同时间步的输入都是相同的值，
+# 否则它以一个序列输入，不同时间步的输入是不同的。
+# 所有输入序列应该有相同的长度。
+decoder = recurrent_group(name=decoder_group_name,
+                          step=gru_decoder_with_attention,
+                          input=group_inputs)
+```
+
+单步函数的实现如下所示。首先，它定义解码网络的**Memory**。然后定义 attention，门控循环单元单步函数和输出函数：
+
+``` sourceCode
+def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
+    # 定义解码器的Memory
+    # Memory的输出定义在 gru_step 内
+    # 注意 gru_step 应该与它的Memory名字相同
+    decoder_mem = memory(name='gru_decoder',
+                         size=decoder_size,
+                         boot_layer=decoder_boot)
+    # 计算 attention 加权编码向量
+    context = simple_attention(encoded_sequence=enc_vec,
+                               encoded_proj=enc_proj,
+                               decoder_state=decoder_mem)
+    # 混合当前词向量和attention加权编码向量
+    decoder_inputs = mixed_layer(inputs = [full_matrix_projection(context),
+                                           full_matrix_projection(current_word)],
+                                 size = decoder_size * 3)
+    # 定义门控循环单元循环神经网络单步函数
+    gru_step = gru_step_layer(name='gru_decoder',
+                              input=decoder_inputs,
+                              output_mem=decoder_mem,
+                              size=decoder_size)
+    # 定义输出函数
+    out = mixed_layer(input=[full_matrix_projection(input=gru_step)],
+                      size=target_dict_dim,
+                      bias_attr=True,
+                      act=SoftmaxActivation())
+    return out
+```
+
+生成序列
+-----------------
+
+训练模型后，我们可以使用它来生成序列。通常的做法是使用**beam search** 生成序列。以下代码片段定义 beam search 算法。注意，`beam_search` 函数假设 `step` 的输出函数返回的是下一个时刻输出词的 softmax 归一化概率向量。我们对模型进行了以下更改。
+
+-   使用 `GeneratedInput` 来表示 trg\_embedding。 `GeneratedInput` 将上一时间步所生成的词的向量来作为当前时间步的输入。
+-   使用 `beam_search` 函数。这个函数需要设置：
+    -   `bos_id`: 开始标记。每个句子都以开始标记开头。
+    -   `eos_id`: 结束标记。每个句子都以结束标记结尾。
+    -   `beam_size`: beam search 算法中的beam大小。
+    -   `max_length`: 生成序列的最大长度。
+-   使用 `seqtext_printer_evaluator` 根据索引矩阵和字典打印文本。这个函数需要设置：
+    -   `id_input`: 数据的整数ID，用于标识生成的文件中的相应输出。
+    -   `dict_file`: 用于将词ID转换为词的字典文件。
+    -   `result_file`: 生成结果文件的路径。
+
+代码如下：
+
+``` sourceCode
+group_inputs=[StaticInput(input=encoded_vector,is_seq=True),
+              StaticInput(input=encoded_proj,is_seq=True)]
+# 在生成时，解码器基于编码源序列和最后生成的目标词预测下一目标词。
+# 编码源序列（编码器输出）必须由只读Memory的 StaticInput 指定。
+# 这里， GeneratedInputs 自动获取上一个生成的词，并在最开始初始化为起始词，如 <s>。
+trg_embedding = GeneratedInput(
+    size=target_dict_dim,
+    embedding_name='_target_language_embedding',
+    embedding_size=word_vector_dim)
+group_inputs.append(trg_embedding)
+beam_gen = beam_search(name=decoder_group_name,
+                       step=gru_decoder_with_attention,
+                       input=group_inputs,
+                       bos_id=0, # Beginnning token.
+                       eos_id=1, # End of sentence token.
+                       beam_size=beam_size,
+                       max_length=max_length)
+
+seqtext_printer_evaluator(input=beam_gen,
+                          id_input=data_layer(name="sent_id", size=1),
+                          dict_file=trg_dict_path,
+                          result_file=gen_trans_file)
+outputs(beam_gen)
+```
+
+注意，这种生成技术只用于类似解码器的生成过程。如果你正在处理序列标记任务，请参阅 [Semantic Role Labeling Demo](../../demo/semantic_role_labeling/index.html) 了解更多详细信息。
+
+完整的配置文件在`demo/seqToseq/seqToseq_net.py`。

doc/howto/deep_model/rnn_config_cn.rst

+RNN 配置
+========
+
+本教程将指导你如何在 PaddlePaddle
+中配置循环神经网络（RNN）。PaddlePaddle
+高度支持灵活和高效的循环神经网络配置。 在本教程中，您将了解如何：
+
+-  准备用来学习循环神经网络的序列数据。
+-  配置循环神经网络架构。
+-  使用学习完成的循环神经网络模型生成序列。
+
+我们将使用 vanilla 循环神经网络和 sequence to sequence
+模型来指导你完成这些步骤。sequence to sequence
+模型的代码可以在\ ``demo / seqToseq``\ 找到。
+
+准备序列数据
+------------
+
+PaddlePaddle
+不需要对序列数据进行任何预处理，例如填充。唯一需要做的是将相应类型设置为输入。例如，以下代码段定义了三个输入。
+它们都是序列，它们的大小是\ ``src_dict``\ ，\ ``trg_dict``\ 和\ ``trg_dict``\ ：
+
+.. code:: sourcecode
+
+    settings.input_types = [
+      integer_value_sequence(len(settings.src_dict)),
+      integer_value_sequence(len(settings.trg_dict)),
+      integer_value_sequence(len(settings.trg_dict))]
+
+在\ ``process``\ 函数中，每个\ ``yield``\ 函数将返回三个整数列表。每个整数列表被视为一个整数序列：
+
+.. code:: sourcecode
+
+    yield src_ids, trg_ids, trg_ids_next
+
+有关如何编写数据提供程序的更多细节描述，请参考
+`PyDataProvider2 <../../ui/data_provider/index.html>`__\ 。完整的数据提供文件在
+``demo/seqToseq/dataprovider.py``\ 。
+
+配置循环神经网络架构
+--------------------
+
+简单门控循环神经网络(Gated Recurrent Neural Network)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+循环神经网络在每个时间步骤顺序地处理序列。下面列出了 LSTM 的架构的示例。
+
+.. figure:: ../../../tutorials/sentiment_analysis/bi_lstm.jpg
+   :alt: image
+
+   image
+
+一般来说，循环网络从 *t* = 1 到 *t* = *T* 或者反向地从 *t* = *T* 到 *t*
+= 1 执行以下操作。
+
+*x*\ \ *t* + 1 = *f*\ \ *x*\ (*x*\ \ *t*\ ),\ *y*\ \ *t*\  = *f*\ \ *y*\ (*x*\ \ *t*\ )
+
+其中 *f*\ \ *x*\ (.) 称为\ **单步函数**\ （即单时间步执行的函数，step
+function），而 *f*\ \ *y*\ (.) 称为\ **输出函数**\ 。在 vanilla
+循环神经网络中，单步函数和输出函数都非常简单。然而，PaddlePaddle
+可以通过修改这两个函数来实现复杂的网络配置。我们将使用 sequence to
+sequence
+模型演示如何配置复杂的循环神经网络模型。在本节中，我们将使用简单的
+vanilla
+循环神经网络作为使用\ ``recurrent_group``\ 配置简单循环神经网络的例子。
+注意，如果你只需要使用简单的RNN，GRU或LSTM，那么推荐使用\ ``grumemory``\ 和\ ``lstmemory``\ ，因为它们的计算效率比\ ``recurrent_group``\ 更高。
+
+对于 vanilla RNN，在每个时间步长，\ **单步函数**\ 为：
+
+*x*\ \ *t* + 1 = *W*\ \ *x*\ \ *x*\ \ *t*\  + *W*\ \ *i*\ \ *I*\ \ *t*\  + *b*
+
+其中 *x*\ \ *t*\  是RNN状态，并且 *I*\ \ *t*\  是输入，\ *W*\ \ *x*\  和
+*W*\ \ *i*\  分别是RNN状态和输入的变换矩阵。\ *b*
+是偏差。它的\ **输出函数**\ 只需要\ *x*\ \ *t*\ 作为输出。
+
+``recurrent_group``\ 是构建循环神经网络的最重要的工具。
+它定义了\ **单步函数**\ ，\ **输出函数**\ 和循环神经网络的输入。注意，这个函数的\ ``step``\ 参数需要实现\ ``step function``\ （单步函数）和\ ``output function``\ （输出函数）：
+
+.. code:: sourcecode
+
+    def simple_rnn(input,
+                   size=None,
+                   name=None,
+                   reverse=False,
+                   rnn_bias_attr=None,
+                   act=None,
+                   rnn_layer_attr=None):
+        def __rnn_step__(ipt):
+           out_mem = memory(name=name, size=size)
+           rnn_out = mixed_layer(input = [full_matrix_projection(ipt),
+                                          full_matrix_projection(out_mem)],
+                                 name = name,
+                                 bias_attr = rnn_bias_attr,
+                                 act = act,
+                                 layer_attr = rnn_layer_attr,
+                                 size = size)
+           return rnn_out
+        return recurrent_group(name='%s_recurrent_group' % name,
+                               step=__rnn_step__,
+                               reverse=reverse,
+                               input=input)
+
+PaddlePaddle
+使用“Memory”（记忆模块）实现单步函数。\ **Memory**\ 是在PaddlePaddle中构造循环神经网络时最重要的概念。
+Memory是在单步函数中循环使用的状态，例如\ *x*\ \ *t* + 1 = *f*\ \ *x*\ (*x*\ \ *t*\ )。
+一个Memory包含\ **输出**\ 和\ **输入**\ 。当前时间步处的Memory的输出作为下一时间步Memory的输入。Memory也可以具有\ **boot
+layer(引导层)**\ ，其输出被用作Memory的初始值。
+在我们的例子中，门控循环单元的输出被用作输出Memory。请注意，\ ``rnn_out``\ 层的名称与\ ``out_mem``\ 的名称相同。这意味着\ ``rnn_out``
+(*x*\ \ *t* + 1)的输出被用作\ ``out_mem``\ Memory的\ **输出**\ 。
+
+Memory也可以是序列。在这种情况下，在每个时间步中，我们有一个序列作为循环神经网络的状态。这在构造非常复杂的循环神经网络时是有用的。
+其他高级功能包括定义多个Memory，以及使用子序列来定义分级循环神经网络架构。
+
+我们在函数的结尾返回\ ``rnn_out``\ 。 这意味着 ``rnn_out``
+层的输出被用作门控循环神经网络的\ **输出**\ 函数。
+
+Sequence to Sequence Model with Attention
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+我们将使用 sequence to sequence model with attention
+作为例子演示如何配置复杂的循环神经网络模型。该模型的说明如下图所示。
+
+.. figure:: ../../../tutorials/text_generation/encoder-decoder-attention-model.png
+   :alt: image
+
+   image
+
+在这个模型中，源序列 *S* = {*s*\ 1, …, \ *s*\ \ *T*\ }
+用双向门控循环神经网络编码。双向门控循环神经网络的隐藏状态
+*H*\ \ *S*\  = {*H*\ 1, …, \ *H*\ \ *T*\ } 被称为
+*编码向量*\ 。解码器是门控循环神经网络。当解读每一个\ *y*\ \ *t*\ 时,
+这个门控循环神经网络生成一系列权重
+*W*\ \ *S*\ \ *t*\  = {*W*\ 1\ *t*\ , …, \ *W*\ \ *T*\ \ *t*\ },
+用于计算编码向量的加权和。加权和用来生成\ *y*\ \ *t*\ 。
+
+模型的编码器部分如下所示。它叫做\ ``grumemory``\ 来表示门控循环神经网络。如果网络架构简单，那么推荐使用循环神经网络的方法，因为它比
+``recurrent_group``
+更快。我们已经实现了大多数常用的循环神经网络架构，可以参考
+`Layers <../../ui/api/trainer_config_helpers/layers_index.html>`__
+了解更多细节。
+
+我们还将编码向量投射到 ``decoder_size``
+维空间。这通过获得反向循环网络的第一个实例，并将其投射到
+``decoder_size`` 维空间完成：
+
+.. code:: sourcecode
+
+    # 定义源语句的数据层
+    src_word_id = data_layer(name='source_language_word', size=source_dict_dim)
+    # 计算每个词的词向量
+    src_embedding = embedding_layer(
+        input=src_word_id,
+        size=word_vector_dim,
+        param_attr=ParamAttr(name='_source_language_embedding'))
+    # 应用前向循环神经网络
+    src_forward = grumemory(input=src_embedding, size=encoder_size)
+    # 应用反向递归神经网络（reverse=True表示反向循环神经网络）
+    src_backward = grumemory(input=src_embedding,
+                              size=encoder_size,
+                              reverse=True)
+    # 将循环神经网络的前向和反向部分混合在一起
+    encoded_vector = concat_layer(input=[src_forward, src_backward])
+
+    # 投射编码向量到 decoder_size
+    encoder_proj = mixed_layer(input = [full_matrix_projection(encoded_vector)],
+                               size = decoder_size)
+
+    # 计算反向RNN的第一个实例
+    backward_first = first_seq(input=src_backward)
+
+    # 投射反向RNN的第一个实例到 decoder size
+    decoder_boot = mixed_layer(input=[full_matrix_projection(backward_first)], size=decoder_size, act=TanhActivation())
+
+解码器使用 ``recurrent_group`` 来定义循环神经网络。单步函数和输出函数在
+``gru_decoder_with_attention`` 中定义：
+
+.. code:: sourcecode
+
+    group_inputs=[StaticInput(input=encoded_vector,is_seq=True),
+                  StaticInput(input=encoded_proj,is_seq=True)]
+    trg_embedding = embedding_layer(
+        input=data_layer(name='target_language_word',
+                         size=target_dict_dim),
+        size=word_vector_dim,
+        param_attr=ParamAttr(name='_target_language_embedding'))
+    group_inputs.append(trg_embedding)
+
+    # 对于配备有注意力机制的解码器，在训练中，
+    # 目标向量（groudtruth）是数据输入，
+    # 而源序列的编码向量可以被无边界的memory访问
+    # StaticInput 意味着不同时间步的输入都是相同的值，
+    # 否则它以一个序列输入，不同时间步的输入是不同的。
+    # 所有输入序列应该有相同的长度。
+    decoder = recurrent_group(name=decoder_group_name,
+                              step=gru_decoder_with_attention,
+                              input=group_inputs)
+
+单步函数的实现如下所示。首先，它定义解码网络的\ **Memory**\ 。然后定义
+attention，门控循环单元单步函数和输出函数：
+
+.. code:: sourcecode
+
+    def gru_decoder_with_attention(enc_vec, enc_proj, current_word):
+        # 定义解码器的Memory
+        # Memory的输出定义在 gru_step 内
+        # 注意 gru_step 应该与它的Memory名字相同
+        decoder_mem = memory(name='gru_decoder',
+                             size=decoder_size,
+                             boot_layer=decoder_boot)
+        # 计算 attention 加权编码向量
+        context = simple_attention(encoded_sequence=enc_vec,
+                                   encoded_proj=enc_proj,
+                                   decoder_state=decoder_mem)
+        # 混合当前词向量和attention加权编码向量
+        decoder_inputs = mixed_layer(inputs = [full_matrix_projection(context),
+                                               full_matrix_projection(current_word)],
+                                     size = decoder_size * 3)
+        # 定义门控循环单元循环神经网络单步函数
+        gru_step = gru_step_layer(name='gru_decoder',
+                                  input=decoder_inputs,
+                                  output_mem=decoder_mem,
+                                  size=decoder_size)
+        # 定义输出函数
+        out = mixed_layer(input=[full_matrix_projection(input=gru_step)],
+                          size=target_dict_dim,
+                          bias_attr=True,
+                          act=SoftmaxActivation())
+        return out
+
+生成序列
+--------
+
+训练模型后，我们可以使用它来生成序列。通常的做法是使用\ **beam search**
+生成序列。以下代码片段定义 beam search 算法。注意，\ ``beam_search``
+函数假设 ``step`` 的输出函数返回的是下一个时刻输出词的 softmax
+归一化概率向量。我们对模型进行了以下更改。
+
+-  使用 ``GeneratedInput`` 来表示 trg\_embedding。 ``GeneratedInput``
+   将上一时间步所生成的词的向量来作为当前时间步的输入。
+-  使用 ``beam_search`` 函数。这个函数需要设置：
+
+   -  ``bos_id``: 开始标记。每个句子都以开始标记开头。
+   -  ``eos_id``: 结束标记。每个句子都以结束标记结尾。
+   -  ``beam_size``: beam search 算法中的beam大小。
+   -  ``max_length``: 生成序列的最大长度。
+
+-  使用 ``seqtext_printer_evaluator``
+   根据索引矩阵和字典打印文本。这个函数需要设置：
+
+   -  ``id_input``: 数据的整数ID，用于标识生成的文件中的相应输出。
+   -  ``dict_file``: 用于将词ID转换为词的字典文件。
+   -  ``result_file``: 生成结果文件的路径。
+
+代码如下：
+
+.. code:: sourcecode
+
+    group_inputs=[StaticInput(input=encoded_vector,is_seq=True),
+                  StaticInput(input=encoded_proj,is_seq=True)]
+    # 在生成时，解码器基于编码源序列和最后生成的目标词预测下一目标词。
+    # 编码源序列（编码器输出）必须由只读Memory的 StaticInput 指定。
+    # 这里， GeneratedInputs 自动获取上一个生成的词，并在最开始初始化为起始词，如 <s>。
+    trg_embedding = GeneratedInput(
+        size=target_dict_dim,
+        embedding_name='_target_language_embedding',
+        embedding_size=word_vector_dim)
+    group_inputs.append(trg_embedding)
+    beam_gen = beam_search(name=decoder_group_name,
+                           step=gru_decoder_with_attention,
+                           input=group_inputs,
+                           bos_id=0, # Beginnning token.
+                           eos_id=1, # End of sentence token.
+                           beam_size=beam_size,
+                           max_length=max_length)
+
+    seqtext_printer_evaluator(input=beam_gen,
+                              id_input=data_layer(name="sent_id", size=1),
+                              dict_file=trg_dict_path,
+                              result_file=gen_trans_file)
+    outputs(beam_gen)
+
+注意，这种生成技术只用于类似解码器的生成过程。如果你正在处理序列标记任务，请参阅
+`Semantic Role Labeling
+Demo <../../demo/semantic_role_labeling/index.html>`__
+了解更多详细信息。
+
+完整的配置文件在\ ``demo/seqToseq/seqToseq_net.py``\ 。

paddle/CMakeLists.txt

 add_subdirectory(cuda)
+add_subdirectory(function)
 add_subdirectory(utils)
 add_subdirectory(math)
 add_subdirectory(parameter)

paddle/api/CMakeLists.txt

@@ -48,6 +48,7 @@ add_custom_command(OUTPUT ${PROJ_ROOT}/paddle/dist/.timestamp
     WORKING_DIRECTORY ${PROJ_ROOT}/paddle
     DEPENDS python_swig_sources
             paddle_parameter
+            paddle_function
             paddle_math
             paddle_utils
             paddle_gserver

paddle/api/paddle_ld_flags.py

@@ -30,8 +30,8 @@ try:
         whole_end = ""
 
     LIB_DIRS = [
-        "math", 'utils', 'parameter', "gserver", "api", "cuda", "pserver",
-        "trainer"
+        "math", 'function', 'utils', 'parameter', "gserver", "api", "cuda",
+        "pserver", "trainer"
     ]
     PARENT_LIB_DIRS = ['proto']
 
@@ -75,6 +75,7 @@ try:
             libs = [
                 whole_start,
                 "-lpaddle_gserver",
+                "-lpaddle_function",
                 whole_end,
                 "-lpaddle_pserver",
                 "-lpaddle_trainer_lib",

paddle/cuda/include/hl_cnn.h

@@ -240,62 +240,6 @@ extern void hl_avgpool_backward(const int frameCnt,
                                 real* backGrad,
                                 const int outStride);
 
-/**
- * @brief   Cross-map-respose normalize forward.
- *
- * @param[in]   frameCnt    batch size of input image.
- * @param[in]   in          input data.
- * @param[in]   scale       buffer.
- * @param[out]  out         output data.
- * @param[in]   channels    number of channel.
- * @param[in]   height      image height.
- * @param[in]   width       image width.
- * @param[in]   sizeX       size.
- * @param[in]   alpha       scale.
- * @param[in]   beta        scale.
- *
- */
-extern void hl_CMRNorm_forward(size_t frameCnt,
-                               const real* in,
-                               real* scale,
-                               real* out,
-                               size_t channels,
-                               size_t height,
-                               size_t width,
-                               size_t sizeX,
-                               real alpha,
-                               real beta);
-
-/**
- * @brief   Cross-map-respose normalize backward.
- *
- * @param[in]   frameCnt    batch size of input image.
- * @param[in]   inV         input data.
- * @param[in]   scale       buffer.
- * @param[out]  outV        output value.
- * @param[out]  outDiff     output grad.
- * @param[out]  inDiff      input grad.
- * @param[in]   channels    number of channel.
- * @param[in]   height      image height.
- * @param[in]   width       image width.
- * @param[in]   sizeX       size.
- * @param[in]   alpha       scale.
- * @param[in]   beta        scale.
- *
- */
-extern void hl_CMRNorm_backward(size_t frameCnt,
-                                const real* inV,
-                                const real* scale,
-                                const real* outV,
-                                const real* outDiff,
-                                real* inDiff,
-                                size_t channels,
-                                size_t height,
-                                size_t width,
-                                size_t sizeX,
-                                real alpha,
-                                real beta);
-
 /**
  * @brief   Bilinear interpolation forward.
  *

paddle/cuda/include/stub/hl_cnn_stub.h

@@ -117,30 +117,6 @@ inline void hl_avgpool_backward(const int frameCnt,
                                 real* backGrad,
                                 const int outStride) {}
 
-inline void hl_CMRNorm_forward(size_t frameCnt,
-                               const real* in,
-                               real* scale,
-                               real* out,
-                               size_t channels,
-                               size_t height,
-                               size_t width,
-                               size_t sizeX,
-                               real alpha,
-                               real beta) {}
-
-inline void hl_CMRNorm_backward(size_t frameCnt,
-                                const real* inV,
-                                const real* scale,
-                                const real* outV,
-                                const real* outDiff,
-                                real* inDiff,
-                                size_t channels,
-                                size_t height,
-                                size_t width,
-                                size_t sizeX,
-                                real alpha,
-                                real beta) {}
-
 inline void hl_bilinear_forward(const real* inData,
                                 const size_t inImgH,
                                 const size_t inImgW,

paddle/cuda/src/hl_cuda_cnn.cu

@@ -381,164 +381,6 @@ void hl_avgpool_backward(const int frameCnt, const real* outGrad,
   CHECK_SYNC("hl_avgpool_backward failed");
 }
 
-__global__ void KeCMRNormFillScale(size_t nthreads, const real* in,
-                                   real* scale, size_t channels,
-                                   size_t height, size_t width, size_t size,
-                                   real alpha) {
-  size_t index = threadIdx.x + blockIdx.x * blockDim.x;
-  if (index < nthreads) {
-    // find out the local offset
-    size_t w = index % width;
-    size_t h = (index / width) % height;
-    size_t n = index / width / height;
-    size_t offset = (n * channels * height + h) * width + w;
-    size_t step = height * width;
-    in += offset;
-    scale += offset;
-    size_t head = 0;
-    size_t pre_pad = (size - 1) / 2;
-    size_t post_pad = size - pre_pad - 1;
-    real accum_scale = 0;
-    // fill the scale at [n, :, h, w]
-    // accumulate values
-    while (head < post_pad) {
-      accum_scale += in[head * step] * in[head * step];
-      ++head;
-    }
-    // until we reach size, nothing needs to be subtracted
-    while (head < size) {
-      accum_scale += in[head * step] * in[head * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha;
-      ++head;
-    }
-    // both add and subtract
-    while (head < channels) {
-      accum_scale += in[head * step] * in[head * step];
-      accum_scale -= in[(head - size) * step] * in[(head - size) * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha;
-      ++head;
-    }
-    // subtract only
-    while (head < channels + post_pad) {
-      accum_scale -= in[(head - size) * step] * in[(head - size) * step];
-      scale[(head - post_pad) * step] = 1. + accum_scale * alpha;
-      ++head;
-    }
-  }
-}
-
- __global__ void KeCMRNormOutput(size_t nthreads, const real* in,
-                                 const real* scale, real negative_beta,
-                                 real* out) {
-  size_t index = threadIdx.x + blockIdx.x * blockDim.x;
-  if (index < nthreads) {
-    out[index] = in[index] * pow(scale[index], negative_beta);
-  }
-}
-
-void hl_CMRNorm_forward(size_t frameCnt, const real* in, real* scale,
-                        real* out, size_t channels,
-                        size_t height, size_t width, size_t sizeX,
-                        real alpha, real beta) {
-  size_t threadsNum = frameCnt * height * width;
-  size_t blocksX = (threadsNum + 1024 - 1) / 1024;
-  size_t blocksY = 1;
-  dim3 threads(1024, 1);
-  dim3 grid(blocksX, blocksY);
-
-  KeCMRNormFillScale<<<grid, threads, 0, STREAM_DEFAULT>>>
-      (threadsNum, in, scale, channels, height, width, sizeX, alpha);
-
-  threadsNum = frameCnt * height * width *channels;
-  blocksX = (threadsNum + 1024 -1) / 1024;
-  dim3 threads2(1024, 1);
-  dim3 grid2(blocksX, blocksY);
-  KeCMRNormOutput<<<grid2, threads2, 0, STREAM_DEFAULT>>>
-           (threadsNum, in, scale, beta, out);
-  CHECK_SYNC("hl_CMRNorm_forward");
-}
-
-__global__ void KeCMRNormDiff(size_t nthreads, const real* bottom_data,
-                              const real* top_data, const real* scale,
-                              const real* top_diff, size_t channels,
-                              size_t height, size_t width, size_t size,
-                              real negative_beta, real cache_ratio,
-                              real* bottom_diff ) {
-  int index = threadIdx.x + blockIdx.x * blockDim.x;
-  if (index < nthreads) {
-    // find out the local offset
-    size_t w = index % width;
-    size_t h = (index / width) % height;
-    size_t n = index / width / height;
-    size_t offset = (n * channels * height + h) * width + w;
-    size_t step = height * width;
-    bottom_data += offset;
-    top_data += offset;
-    scale += offset;
-    top_diff += offset;
-    bottom_diff += offset;
-    int head = 0;
-    int pre_pad = size - (size + 1) / 2;
-    int post_pad = size - pre_pad - 1;
-    real accum_ratio = 0;
-    // accumulate values
-    while (head < post_pad) {
-      accum_ratio += top_diff[head * step] *
-        top_data[head * step] / scale[head * step];
-      ++head;
-    }
-    // until we reach size, nothing needs to be subtracted
-    while (head < size) {
-      accum_ratio += top_diff[head * step] *
-        top_data[head * step] / scale[head * step];
-      bottom_diff[(head - post_pad) * step] +=
-        top_diff[(head - post_pad) * step] *
-        pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-        bottom_data[(head - post_pad) * step] * accum_ratio;
-      ++head;
-    }
-    // both add and subtract
-    while (head < channels) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
-      accum_ratio -= top_diff[(head - size) * step] *
-          top_data[(head - size) * step] / scale[(head - size) * step];
-      bottom_diff[(head - post_pad) * step] +=
-        top_diff[(head - post_pad) * step] *
-        pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-        bottom_data[(head - post_pad) * step] * accum_ratio;
-      ++head;
-    }
-    // subtract only
-    while (head < channels + post_pad) {
-      accum_ratio -= top_diff[(head - size) * step] *
-          top_data[(head - size) * step] / scale[(head - size) * step];
-      bottom_diff[(head - post_pad) * step] +=
-        top_diff[(head - post_pad) * step] *
-        pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-        bottom_data[(head - post_pad) * step] * accum_ratio;
-      ++head;
-    }
-  }
-}
-
-void hl_CMRNorm_backward(size_t frameCnt, const real* inV,
-                         const real* scale,
-                         const real* outV, const real* outDiff,
-                         real *inDiff, size_t channels,
-                         size_t height, size_t width, size_t sizeX,
-                         real alpha, real beta) {
-  size_t threadsNum = frameCnt * height * width;
-  size_t blocksX = (threadsNum + 1024 - 1) / 1024;
-  size_t blocksY = 1;
-  dim3 threads(1024, 1);
-  dim3 grid(blocksX, blocksY);
-  KeCMRNormDiff <<<grid, threads, 0, STREAM_DEFAULT>>>
-           (threadsNum, inV, outV, scale, outDiff, channels,
-           height, width, sizeX, alpha, beta, inDiff);
-  CHECK_SYNC("hl_CMRNorm_backward");
-}
-
 __global__ void KeBilinearInterpFw(const real* in,
                                    const size_t inImgH,
                                    const size_t inImgW,

paddle/function/CMakeLists.txt

+file(GLOB h_files . *_op.h)
+file(GLOB cpp_files . *_op.cpp)
+
+list(APPEND h_files Function.h)
+list(APPEND cpp_files Function.cpp)
+
+if(WITH_GPU)
+    file(GLOB cu_files . *_op_gpu.cu)
+    cuda_compile(cu_objs ${cu_files})
+endif()
+
+add_library(paddle_function STATIC ${cpp_files} ${cu_objs})
+
+add_library(paddle_test_main STATIC TestMain.cpp)
+
+if(WITH_GPU)
+    # TODO:
+    # file(GLOB test_files . *_op_test.cpp)
+    # add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files})
+    add_simple_unittest(cross_map_normal_op_test)
+endif()
+
+add_style_check_target(paddle_function ${h_files})
+add_style_check_target(paddle_function ${cpp_files})
+if(WITH_GPU)
+    add_style_check_target(paddle_function ${cu_files})
+endif()

paddle/function/Function.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 "Function.h"
+
+namespace paddle {
+
+template <>
+size_t FuncConfig::get<size_t>(const std::string& key) const {
+  auto it = valueMap_.find(key);
+  CHECK(it != valueMap_.end()) << "Cannot find value: '" << key << "'";
+  return it->second.s;
+}
+
+template <>
+real FuncConfig::get<real>(const std::string& key) const {
+  auto it = valueMap_.find(key);
+  CHECK(it != valueMap_.end()) << "Cannot find value: '" << key << "'";
+  return it->second.r;
+}
+
+template <>
+FuncConfig& FuncConfig::set<size_t>(const std::string& key, size_t v) {
+  CHECK(valueMap_.count(key) == 0) << "Duplicated value: " << key;
+  valueMap_[key].s = v;
+  return *this;
+}
+
+template <>
+FuncConfig& FuncConfig::set<real>(const std::string& key, real v) {
+  CHECK(valueMap_.count(key) == 0) << "Duplicated value: " << key;
+  valueMap_[key].r = v;
+  return *this;
+}
+
+ClassRegistrar<FunctionBase> FunctionBase::funcRegistrar_;
+
+}  // namespace paddle

paddle/function/Function.h

+/* 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. */
+
+#pragma once
+
+#include <map>
+#include <vector>
+#include "paddle/math/Matrix.h"
+#include "paddle/utils/ClassRegistrar.h"
+
+namespace paddle {
+
+enum DeviceType {
+  DEVICE_TYPE_UNSPECIFIED = 0,
+  DEVICE_TYPE_CPU = 1,
+  DEVICE_TYPE_GPU = 2,
+};
+
+template <DeviceType Device>
+struct MatrixT;
+
+template <>
+struct MatrixT<DEVICE_TYPE_CPU> {
+  using type = CpuMatrix;
+};
+
+template <>
+struct MatrixT<DEVICE_TYPE_GPU> {
+  using type = GpuMatrix;
+};
+
+typedef std::vector<size_t> Dims;
+
+class Tensor {
+public:
+  Tensor(real* data, const Dims& dim) : buf_(data), dims_(dim) {}
+
+  real* getData() const { return buf_; }
+
+  real* buf_;
+  Dims dims_;
+};
+
+typedef std::vector<Tensor> Arguments;
+
+class FuncConfig {
+public:
+  union value {
+    size_t s;
+    real r;
+  };
+
+  template <typename T>
+  T get(const std::string& key) const;
+
+  template <typename T>
+  FuncConfig& set(const std::string& key, T v);
+
+protected:
+  std::map<std::string, value> valueMap_;
+};
+
+class FunctionBase {
+public:
+  virtual ~FunctionBase() {}
+
+  virtual void init(const FuncConfig& config) {}
+
+  virtual void calc(const Arguments& inputs,
+                    const Arguments& outputs,
+                    const Arguments& inouts) {}
+
+  static ClassRegistrar<FunctionBase> funcRegistrar_;
+};
+
+#define FUNC_NAME(typeName, deviceName) #typeName "-" #deviceName
+
+#define REGISTER_TYPED_FUNC(typeName, deviceName, className)   \
+  static InitFunction __reg_type_##typeName##deviceName([]() { \
+    FunctionBase::funcRegistrar_                               \
+        .registerClass<className<DEVICE_TYPE_##deviceName>>(   \
+            FUNC_NAME(typeName, deviceName));                  \
+  })
+
+}  // namespace paddle

paddle/function/FunctionTest.h

+/* 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 "Function.h"
+#include "paddle/math/Vector.h"
+#include "paddle/math/tests/TensorCheck.h"
+
+namespace paddle {
+
+class FunctionCompare {
+public:
+  FunctionCompare(const std::string& name, const FuncConfig& config)
+      : cpu(FunctionBase::funcRegistrar_.createByType(name + "-CPU")),
+        gpu(FunctionBase::funcRegistrar_.createByType(name + "-GPU")) {
+    cpu->init(config);
+    gpu->init(config);
+  }
+
+  void cmpWithArg(const Arguments& inputs,
+                  const Arguments& outputs,
+                  const Arguments& inouts) {
+    // init cpu and gpu arguments
+    auto initArgs = [=](
+        Arguments& cpuArgs, Arguments& gpuArgs, const Arguments& inArgs) {
+      for (auto arg : inArgs) {
+        size_t size = sizeof(real);
+        for (auto dim : arg.dims_) {
+          size *= dim;
+        }
+        cpuMemory.emplace_back(std::make_shared<CpuMemoryHandle>(size));
+        gpuMemory.emplace_back(std::make_shared<GpuMemoryHandle>(size));
+        cpuArgs.emplace_back(
+            Tensor((real*)cpuMemory.back()->getBuf(), arg.dims_));
+        gpuArgs.emplace_back(
+            Tensor((real*)gpuMemory.back()->getBuf(), arg.dims_));
+
+        // will use an api to refactor this code.
+        CpuVector cpuVector(size / sizeof(real),
+                            (real*)cpuArgs.back().getData());
+        GpuVector gpuVector(size / sizeof(real),
+                            (real*)gpuArgs.back().getData());
+        cpuVector.uniform(0.001, 1);
+        gpuVector.copyFrom(cpuVector);
+      }
+    };
+    initArgs(cpuInputs, gpuInputs, inputs);
+    initArgs(cpuOutputs, gpuOutputs, outputs);
+    initArgs(cpuInouts, gpuInouts, inouts);
+
+    // function calculate
+    cpu->calc(cpuInputs, cpuOutputs, cpuInouts);
+    gpu->calc(gpuInputs, gpuOutputs, gpuInouts);
+
+    // check outputs and inouts
+    auto checkArgs = [=](const Arguments& cpuArgs, const Arguments& gpuArgs) {
+      for (size_t i = 0; i < cpuArgs.size(); i++) {
+        auto cpu = cpuArgs[i];
+        auto gpu = gpuArgs[i];
+        size_t size = 1;
+        for (auto dim : cpu.dims_) {
+          size *= dim;
+        }
+        CpuVector cpuVector(size, (real*)cpu.getData());
+        GpuVector gpuVector(size, (real*)gpu.getData());
+
+        autotest::TensorCheckErr(cpuVector, gpuVector);
+      }
+    };
+    checkArgs(cpuOutputs, gpuOutputs);
+    checkArgs(cpuInouts, gpuInouts);
+  }
+
+protected:
+  std::shared_ptr<FunctionBase> cpu;
+  std::shared_ptr<FunctionBase> gpu;
+  std::vector<CpuMemHandlePtr> cpuMemory;
+  std::vector<GpuMemHandlePtr> gpuMemory;
+  Arguments cpuInputs;
+  Arguments cpuOutputs;
+  Arguments cpuInouts;
+  Arguments gpuInputs;
+  Arguments gpuOutputs;
+  Arguments gpuInouts;
+};
+
+}  // namespace paddle
+
+using paddle::FunctionCompare;
+using paddle::FuncConfig;
+using paddle::Dims;
+using paddle::Tensor;

paddle/function/TestMain.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 <gtest/gtest.h>
+#include "paddle/utils/Util.h"
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  paddle::initMain(argc, argv);
+  return RUN_ALL_TESTS();
+}

paddle/function/cross_map_normal_op.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 "cross_map_normal_op.h"
+#include "paddle/math/Vector.h"
+
+namespace paddle {
+
+template <>
+void CrossMapNormal<DEVICE_TYPE_CPU>(real* outputs,
+                                     real* denoms,
+                                     const real* inputs,
+                                     size_t numSamples,
+                                     size_t channels,
+                                     size_t height,
+                                     size_t width,
+                                     size_t size,
+                                     real scale,
+                                     real pow) {
+  size_t oneImage = height * width;
+  size_t oneSample = channels * oneImage;
+
+  CpuVector outputsV(numSamples * oneSample, outputs);
+  CpuVector inputsV(numSamples * oneSample, const_cast<real*>(inputs));
+  CpuVector denomsV(numSamples * oneSample, denoms);
+
+  // f(x) = x * ( 1 + scale * SUM((x)^2) )^(-pow)
+  // x represents inputs
+  // f(x) represents outputs
+  // denoms save the intermediate result for backward
+  denomsV = denomsV.constant(1.0);
+  const int start = -((int)size - 1) / 2;
+  const int end = (int)size + start;
+  for (size_t i = 0; i < numSamples; i++) {
+    real* oneDenom = denoms + i * oneSample;
+    real* oneInput = const_cast<real*>(inputs) + i * oneSample;
+    for (int c = 0; c < (int)channels; c++) {
+      CpuVector denom(oneImage, oneDenom + c * oneImage);
+      for (int s = start; s < end; s++) {
+        if (c + s >= 0 && c + s < (int)channels) {
+          CpuVector input(oneImage, oneInput + (c + s) * oneImage);
+          denom += input.square() * scale;
+        }
+      }
+    }
+  }
+
+  outputsV = inputsV * denomsV.pow(-pow);
+}
+
+template <>
+void CrossMapNormalGrad<DEVICE_TYPE_CPU>(real* inputsGrad,
+                                         const real* inputsValue,
+                                         const real* outputsValue,
+                                         const real* outputsGrad,
+                                         const real* denoms,
+                                         size_t numSamples,
+                                         size_t channels,
+                                         size_t height,
+                                         size_t width,
+                                         size_t size,
+                                         real scale,
+                                         real pow) {
+  size_t oneSample = channels * height * width;
+  std::function<CpuVector(real*, size_t)> oneImage = [=](real* data,
+                                                         size_t offset) {
+    return CpuVector(height * width, data + offset);
+  };
+
+  const int start = -((int)size) / 2;
+  const int end = (int)size + start;
+  const real ratio = -(real)2 * scale * pow;
+  for (size_t i = 0; i < numSamples; i++) {
+    size_t sOffset = i * oneSample;
+    real* oneInputGrad = inputsGrad + sOffset;
+    real* oneInputValue = const_cast<real*>(inputsValue) + sOffset;
+    real* oneDenom = const_cast<real*>(denoms) + sOffset;
+    real* oneOutputGrad = const_cast<real*>(outputsGrad) + sOffset;
+    real* oneOutputValue = const_cast<real*>(outputsValue) + sOffset;
+
+    for (int c = 0; c < (int)channels; c++) {
+      size_t cOffset = c * height * width;
+      CpuVector inputGrad = oneImage(oneInputGrad, cOffset);
+      CpuVector inputValue = oneImage(oneInputValue, cOffset);
+      CpuVector denom = oneImage(oneDenom, cOffset);
+      CpuVector outputGrad = oneImage(oneOutputGrad, cOffset);
+
+      inputGrad = inputGrad + denom.pow(-pow) * outputGrad;
+      for (int s = start; s < end; s++) {
+        if (c + s >= 0 && c + s < (int)channels) {
+          size_t offset = (c + s) * height * width;
+          CpuVector output = oneImage(oneOutputValue, offset);
+          CpuVector outputGrad = oneImage(oneOutputGrad, offset);
+          CpuVector denom = oneImage(oneDenom, offset);
+
+          inputGrad += ((outputGrad * output * ratio) / denom) * inputValue;
+        }
+      }
+    }
+  }
+}
+
+/**
+ * \param inputs[0] input value.
+ * \param outputs[0] output value.
+ * \param outputs[1] denoms.
+ */
+template <DeviceType Device>
+class CrossMapNormalFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    size_ = config.get<size_t>("size");
+    scale_ = config.get<real>("scale");
+    pow_ = config.get<real>("pow");
+  }
+
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(1, inputs.size());
+    CHECK_EQ(2, outputs.size());
+    CHECK_EQ(0, inouts.size());
+
+    CHECK_EQ(inputs[0].dims_.size(), 4);
+    for (size_t i = 0; i < inputs[0].dims_.size(); i++) {
+      CHECK_EQ(inputs[0].dims_[i], outputs[0].dims_[i]);
+      CHECK_EQ(inputs[0].dims_[i], outputs[1].dims_[i]);
+    }
+
+    size_t samples = inputs[0].dims_[0];
+    size_t channels = inputs[0].dims_[1];
+    size_t height = inputs[0].dims_[2];
+    size_t width = inputs[0].dims_[3];
+
+    CrossMapNormal<Device>(outputs[0].getData(),
+                           outputs[1].getData(),
+                           inputs[0].getData(),
+                           samples,
+                           channels,
+                           height,
+                           width,
+                           size_,
+                           scale_,
+                           pow_);
+  }
+
+private:
+  size_t size_;
+  real scale_;
+  real pow_;
+};
+
+/**
+ * \param inputs[0] input value.
+ * \param inputs[1] output value.
+ * \param inputs[2] output grad.
+ * \param inputs[3] denoms.
+ * \param outputs[0] input grad.
+ */
+template <DeviceType Device>
+class CrossMapNormalGradFunc : public FunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    size_ = config.get<size_t>("size");
+    scale_ = config.get<real>("scale");
+    pow_ = config.get<real>("pow");
+  }
+
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(4, inputs.size());
+    CHECK_EQ(1, outputs.size());
+    CHECK_EQ(0, inouts.size());
+
+    CHECK_EQ(inputs[0].dims_.size(), 4);
+    for (size_t i = 0; i < inputs[0].dims_.size(); i++) {
+      CHECK_EQ(inputs[0].dims_[i], inputs[1].dims_[i]);
+      CHECK_EQ(inputs[0].dims_[i], inputs[2].dims_[i]);
+      CHECK_EQ(inputs[0].dims_[i], inputs[3].dims_[i]);
+      CHECK_EQ(inputs[0].dims_[i], outputs[0].dims_[i]);
+    }
+
+    size_t samples = inputs[0].dims_[0];
+    size_t channels = inputs[0].dims_[1];
+    size_t height = inputs[0].dims_[2];
+    size_t width = inputs[0].dims_[3];
+
+    CrossMapNormalGrad<Device>(outputs[0].getData(),
+                               inputs[0].getData(),
+                               inputs[1].getData(),
+                               inputs[2].getData(),
+                               inputs[3].getData(),
+                               samples,
+                               channels,
+                               height,
+                               width,
+                               size_,
+                               scale_,
+                               pow_);
+  }
+
+private:
+  size_t size_;
+  real scale_;
+  real pow_;
+};
+
+REGISTER_TYPED_FUNC(CrossMapNormal, CPU, CrossMapNormalFunc);
+REGISTER_TYPED_FUNC(CrossMapNormalGrad, CPU, CrossMapNormalGradFunc);
+#ifndef PADDLE_ONLY_CPU
+REGISTER_TYPED_FUNC(CrossMapNormal, GPU, CrossMapNormalFunc);
+REGISTER_TYPED_FUNC(CrossMapNormalGrad, GPU, CrossMapNormalGradFunc);
+#endif
+
+}  // namespace paddle

paddle/function/cross_map_normal_op.h

+/* 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. */
+
+#pragma once
+
+#include "Function.h"
+
+namespace paddle {
+
+/**
+ * \brief   Cross map respose normalize forward.
+ *          The data structure of image data is NCHW.
+ *
+ * \param[out]  outputs     output data.
+ * \param[in]   denoms      denoms buffer.
+ * \param[in]   inputs      input data.
+ * \param[in]   numSamples  batch size of input image.
+ * \param[in]   channels    number of channel.
+ * \param[in]   height      image height.
+ * \param[in]   width       image width.
+ * \param[in]   size        size.
+ * \param[in]   scale       scale.
+ * \param[in]   pow         scale.
+ *
+ */
+template <DeviceType Device>
+void CrossMapNormal(real* outputs,
+                    real* denoms,
+                    const real* inputs,
+                    size_t numSamples,
+                    size_t channels,
+                    size_t height,
+                    size_t width,
+                    size_t size,
+                    real scale,
+                    real pow);
+
+/**
+ * \brief   Cross map respose normalize backward.
+ *          The data structure of image data is NCHW.
+ *
+ * \param[out]  inputsGrad      input grad.
+ * \param[in]   inputsValue     input value.
+ * \param[out]  outputsValue    output value.
+ * \param[out]  outputsGrad     output grad.
+ * \param[in]   denoms          denoms buffer.
+ * \param[in]   numSamples      batch size of input image.
+ * \param[in]   channels        number of channel.
+ * \param[in]   height          image height.
+ * \param[in]   width           image width.
+ * \param[in]   size            size.
+ * \param[in]   scale           scale.
+ * \param[in]   pow             scale.
+ *
+ */
+template <DeviceType Device>
+void CrossMapNormalGrad(real* inputsGrad,
+                        const real* inputsValue,
+                        const real* outputsValue,
+                        const real* outputsGrad,
+                        const real* denoms,
+                        size_t numSamples,
+                        size_t channels,
+                        size_t height,
+                        size_t width,
+                        size_t size,
+                        real scale,
+                        real pow);
+
+}  // namespace paddle

paddle/function/cross_map_normal_op_gpu.cu

+/* 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 "hl_base.h"
+#include "cross_map_normal_op.h"
+
+namespace paddle {
+
+__global__ void KeCMRNormFillScale(size_t imageSize, const real* in,
+                                   real* scale, size_t channels,
+                                   size_t height, size_t width, size_t size,
+                                   real alpha) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < imageSize) {
+    const int w = idx % width;
+    const int h = (idx / width) % height;
+    const int n = idx / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+
+    in += offset;
+    scale += offset;
+    const int step = height * width;
+    const int pre_pad = (size - 1) / 2;
+    const int post_pad = size - pre_pad - 1;
+
+    real accum = 0;
+    int index = 0;
+    while (index < channels + post_pad) {
+      if (index < channels) {
+        accum += in[index * step] * in[index * step];
+      }
+      if (index >= size) {
+        accum -= in[(index - size) * step] * in[(index - size) * step];
+      }
+      if (index >= post_pad) {
+        scale[(index - post_pad) * step] = 1. + accum * alpha;
+      }
+      ++index;
+    }
+  }
+}
+
+__global__ void KeCMRNormOutput(size_t inputSize, const real* in,
+                                const real* scale, real negative_beta,
+                                real* out) {
+  const int index = threadIdx.x + blockIdx.x * blockDim.x;
+  if (index < inputSize) {
+    out[index] = in[index] * pow(scale[index], negative_beta);
+  }
+}
+
+template <>
+void CrossMapNormal<DEVICE_TYPE_GPU>(real* outputs,
+                                     real* denoms,
+                                     const real* inputs,
+                                     size_t numSamples,
+                                     size_t channels,
+                                     size_t height,
+                                     size_t width,
+                                     size_t size,
+                                     real scale,
+                                     real pow) {
+  size_t imageSize = numSamples * height * width;
+  int blockSize = 1024;
+  int gridSize = (imageSize + 1024 - 1) / 1024;
+  KeCMRNormFillScale<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
+    (imageSize, inputs, denoms, channels, height, width, size, scale);
+
+  size_t inputSize = numSamples * height * width *channels;
+  blockSize = 1024;
+  gridSize = (inputSize + 1024 - 1) / 1024;
+  KeCMRNormOutput<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
+    (inputSize, inputs, denoms, -pow, outputs);
+
+  CHECK_SYNC("CrossMapNormal");
+}
+
+__global__ void KeCMRNormDiff(size_t imageSize, const real* bottom_data,
+                              const real* top_data, const real* scale,
+                              const real* top_diff, size_t channels,
+                              size_t height, size_t width, size_t size,
+                              real negative_beta, real cache_ratio,
+                              real* bottom_diff ) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < imageSize) {
+    const int w = idx % width;
+    const int h = (idx / width) % height;
+    const int n = idx / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+    bottom_data += offset;
+    top_data += offset;
+    scale += offset;
+    top_diff += offset;
+    bottom_diff += offset;
+
+    const int step = height * width;
+    const int pre_pad = size - (size + 1) / 2;
+    const int post_pad = size - pre_pad - 1;
+
+    int index = 0;
+    real accum = 0;
+    while (index < channels + post_pad) {
+      if (index < channels) {
+        accum += top_diff[index * step] * top_data[index * step] /
+          scale[index * step];
+      }
+      if (index >= size) {
+        accum -= top_diff[(index - size) * step] *
+          top_data[(index - size) * step] / scale[(index - size) * step];
+      }
+      if (index >= post_pad) {
+        bottom_diff[(index - post_pad) * step] +=
+          top_diff[(index - post_pad) * step] *
+          pow(scale[(index - post_pad) * step], negative_beta) - cache_ratio *
+          bottom_data[(index - post_pad) * step] * accum;
+      }
+      ++index;
+    }
+  }
+}
+
+template <>
+void CrossMapNormalGrad<DEVICE_TYPE_GPU>(real* inputsGrad,
+                                         const real* inputsValue,
+                                         const real* outputsValue,
+                                         const real* outputsGrad,
+                                         const real* denoms,
+                                         size_t numSamples,
+                                         size_t channels,
+                                         size_t height,
+                                         size_t width,
+                                         size_t size,
+                                         real scale,
+                                         real pow) {
+  size_t imageSize = numSamples * height * width;
+
+  int blockSize = 1024;
+  int gridSize = (imageSize + 1024 - 1) / 1024;
+  KeCMRNormDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
+    (imageSize, inputsValue, outputsValue, denoms, outputsGrad, channels,
+      height, width, size, -pow, 2.0f * pow * scale, inputsGrad);
+  CHECK_SYNC("CrossMapNormalGrad");
+}
+
+}  // namespace paddle

paddle/function/cross_map_normal_op_test.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 <gtest/gtest.h>
+#include "FunctionTest.h"
+
+TEST(CrossMapNormal, real) {
+  for (size_t numSamples : {5, 32}) {
+    for (size_t channels : {1, 5, 32}) {
+      for (size_t imgSizeH : {5, 33, 100}) {
+        for (size_t imgSizeW : {5, 32, 96}) {
+          for (size_t size : {1, 2, 3, 5, 7}) {
+            VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
+                    << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
+                    << " size=" << size;
+
+            FunctionCompare compare("CrossMapNormal",
+                                    FuncConfig()
+                                        .set("size", size)
+                                        .set("scale", (real)1.5)
+                                        .set("pow", (real)0.5));
+            Dims dims{numSamples, channels, imgSizeH, imgSizeW};
+            compare.cmpWithArg({Tensor(nullptr, dims)},
+                               {Tensor(nullptr, dims), Tensor(nullptr, dims)},
+                               {});
+          }
+        }
+      }
+    }
+  }
+}
+
+TEST(CrossMapNormalGrad, real) {
+  for (size_t numSamples : {5, 32}) {
+    for (size_t channels : {1, 5, 32}) {
+      for (size_t imgSizeH : {5, 33, 100}) {
+        for (size_t imgSizeW : {5, 32, 96}) {
+          for (size_t size : {1, 2, 3, 5, 7}) {
+            VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
+                    << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
+                    << " size=" << size;
+
+            FunctionCompare compare("CrossMapNormalGrad",
+                                    FuncConfig()
+                                        .set("size", size)
+                                        .set("scale", (real)1.5)
+                                        .set("pow", (real)0.5));
+            Dims dims{numSamples, channels, imgSizeH, imgSizeW};
+            compare.cmpWithArg({Tensor(nullptr, dims),
+                                Tensor(nullptr, dims),
+                                Tensor(nullptr, dims),
+                                Tensor(nullptr, dims)},
+                               {Tensor(nullptr, dims)},
+                               {});
+          }
+        }
+      }
+    }
+  }
+}

paddle/gserver/CMakeLists.txt

@@ -27,16 +27,12 @@ if(NOT WITH_GPU)
     list(REMOVE_ITEM GSERVER_HEADER
         layers/CudnnConvLayer.h
         layers/CudnnPoolLayer.h
-        layers/CudnnBatchNormLayer.h
-        layers/NormProjectionLayer.h
-        layers/NormLayer.h)
+        layers/CudnnBatchNormLayer.h)
 
     list(REMOVE_ITEM GSERVER_SOURCES
         layers/CudnnConvLayer.cpp
         layers/CudnnPoolLayer.cpp
-        layers/CudnnBatchNormLayer.cpp
-        layers/NormProjectionLayer.cpp
-        layers/NormLayer.cpp)
+        layers/CudnnBatchNormLayer.cpp)
     compile_cu_as_cpp(layers/LstmCompute.cu)
     compile_cu_as_cpp(layers/GruCompute.cu)
 endif()

paddle/gserver/evaluators/Evaluator.cpp

@@ -78,7 +78,7 @@ public:
                                               useGpu(arguments[0].deviceId));
     errorMat->zeroMem();
     if (label != nullptr) {
-      errorMat->classificationError(output, label);
+      errorMat->classificationError(*output, *label);
     } else if (dynamic_cast<CpuSparseMatrix*>(multiBinaryLabel.get()) ||
                dynamic_cast<GpuSparseMatrix*>(multiBinaryLabel.get())) {
       errorMat->classificationErrorMulti(

paddle/gserver/layers/ContextProjection.cpp

@@ -90,8 +90,8 @@ void ContextProjection::forward() {
   REGISTER_TIMER_INFO("ContextProjectionForward", getName().c_str());
   bool isPadding = config_.trainable_padding();
   out_->value->contextProjectionForward(
-      in_->value,
-      state_ ? state_ : isPadding ? weight_->getW() : nullptr,
+      *(in_->value),
+      state_ ? state_.get() : isPadding ? weight_->getW().get() : nullptr,
       *startPositions,
       config_.context_length(),
       config_.context_start(),
@@ -128,8 +128,8 @@ void ContextProjection::backward(const UpdateCallback& callback) {
   bool isPadding = config_.trainable_padding();
   if (!out_->grad->useGpu()) {
     out_->grad->contextProjectionBackward(
-        in_->grad,
-        isPadding ? weight_->getWGrad() : nullptr,
+        in_->grad.get(),
+        isPadding ? weight_->getWGrad().get() : nullptr,
         *startPositions,
         config_.context_length(),
         config_.context_start(),
@@ -137,7 +137,7 @@ void ContextProjection::backward(const UpdateCallback& callback) {
         isPadding);
   } else {
     if (in_->grad) {
-      out_->grad->contextProjectionBackwardData(in_->grad,
+      out_->grad->contextProjectionBackwardData(*(in_->grad),
                                                 *startPositions,
                                                 config_.context_length(),
                                                 config_.context_start());
@@ -145,7 +145,7 @@ void ContextProjection::backward(const UpdateCallback& callback) {
 
     if (isPadding && weight_->getWGrad()) {
       out_->grad->contextProjectionBackwardWeight(
-          weight_->getWGrad(),
+          *(weight_->getWGrad()),
           *startPositions,
           config_.context_length(),
           config_.context_start(),

paddle/gserver/layers/ConvexCombinationLayer.cpp

@@ -113,7 +113,7 @@ void ConvexCombinationLayer::forward(PassType passType) {
     tmpRow0->setData(inV0->getData() + i * weightDim);
     tmpRow1->setData(outV->getData() + i * dataDim);
 
-    tmpRow1->mul(tmpRow0, tmpMtx0, 1, 0);
+    tmpRow1->mul(*tmpRow0, *tmpMtx0, 1, 0);
   }
 }
 
@@ -136,7 +136,7 @@ void ConvexCombinationLayer::backward(const UpdateCallback& callback) {
       tmpRow1->setData(outG->getData() + i * dataDim);
       tmpMtx0->setData(inV1->getData() + i * weightDim * dataDim);
 
-      tmpRow0->mul(tmpRow1, tmpMtx0->getTranspose(), 1, 1);
+      tmpRow0->mul(*tmpRow1, *(tmpMtx0->getTranspose()), 1, 1);
     }
   }
 
@@ -146,7 +146,7 @@ void ConvexCombinationLayer::backward(const UpdateCallback& callback) {
       tmpRow1->setData(outG->getData() + i * dataDim);
       tmpMtx0->setData(inG1->getData() + i * weightDim * dataDim);
 
-      tmpMtx0->mul(tmpRow0->getTranspose(), tmpRow1, 1, 1);
+      tmpMtx0->mul(*(tmpRow0->getTranspose()), *tmpRow1, 1, 1);
     }
   }
 }

paddle/gserver/layers/ExpandConvBaseLayer.cpp

@@ -150,7 +150,7 @@ void ExpandConvBaseLayer::expandFwdOnce(MatrixPtr image,
         Matrix::create(wgtData, subM, subK, false, useGpu_);  // mark transpose
     MatrixPtr B = Matrix::create(expInData, subK, subN, false, useGpu_);
     MatrixPtr C = Matrix::create(outData, subM, subN, false, useGpu_);
-    C->mul(A, B, 1, 1);
+    C->mul(*A, *B, 1, 1);
 
     A->clear();
     B->clear();
@@ -185,7 +185,7 @@ void ExpandConvBaseLayer::bpropActs(MatrixPtr out,
       MatrixPtr C = Matrix::create(expandInData, subK, subN, false, useGpu_);
       MatrixPtr B = Matrix::create(localGradData, subM, subN, false, useGpu_);
       MatrixPtr A = Matrix::create(wgtData, subM, subK, true, useGpu_);
-      C->mul(A, B);  // mul
+      C->mul(*A, *B);  // mul
 
       // clear the temporary matrix
       A->clear();
@@ -252,7 +252,7 @@ void ExpandConvBaseLayer::bpropWeights(MatrixPtr image,
       MatrixPtr A = Matrix::create(expandInData, subK, subN, true, useGpu_);
       MatrixPtr B = Matrix::create(gradData, subM, subN, false, useGpu_);
       MatrixPtr C = Matrix::create(wGradData, subM, subK, false, useGpu_);
-      C->mul(B, A, 1, 1);
+      C->mul(*B, *A, 1, 1);
 
       A->clear();
       B->clear();

paddle/gserver/layers/FullMatrixProjection.cpp

@@ -28,7 +28,7 @@ FullMatrixProjection::FullMatrixProjection(const ProjectionConfig& config,
 
 void FullMatrixProjection::forward() {
   REGISTER_TIMER_INFO("FwMulTimer", getName().c_str());
-  out_->value->mul(in_->value, weight_->getW(), 1, 1);
+  out_->value->mul(*(in_->value), *(weight_->getW()), 1, 1);
 }
 
 void FullMatrixProjection::backward(const UpdateCallback& callback) {
@@ -37,7 +37,8 @@ void FullMatrixProjection::backward(const UpdateCallback& callback) {
   /* Calculate the W-gradient for the current layer */
   if (weight_->getWGrad()) {
     REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
-    weight_->getWGrad()->mul(in_->value->getTranspose(), out_->grad, 1, 1);
+    weight_->getWGrad()->mul(
+        *(in_->value->getTranspose()), *(out_->grad), 1, 1);
   }
 
   // If callback does not change value, backward propagation error
@@ -47,7 +48,7 @@ void FullMatrixProjection::backward(const UpdateCallback& callback) {
   /* Calculate the input layers error */
   if (in_->grad) {
     REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
-    in_->grad->mul(out_->grad, weight_->getW()->getTranspose(), 1, 1);
+    in_->grad->mul(*(out_->grad), *(weight_->getW()->getTranspose()), 1, 1);
   }
 
   hl_set_sync_flag(syncFlag);

paddle/gserver/layers/FullyConnectedLayer.cpp

@@ -84,8 +84,8 @@ void FullyConnectedLayer::forward(PassType passType) {
     auto input = getInput(i);
     CHECK(input.value) << "The input of 'fc' layer must be matrix";
     REGISTER_TIMER_INFO("FwMulTimer", getName().c_str());
-    i == 0 ? outV->mul(input.value, weights_[i]->getW(), 1, 0)
-           : outV->mul(input.value, weights_[i]->getW(), 1, 1);
+    i == 0 ? outV->mul(*input.value, *weights_[i]->getW(), 1, 0)
+           : outV->mul(*input.value, *weights_[i]->getW(), 1, 1);
   }
 
   /* add the bias-vector */
@@ -123,7 +123,7 @@ void FullyConnectedLayer::backward(const UpdateCallback& callback) {
       MatrixPtr oGrad = getOutputGrad();
       {
         REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
-        weights_[i]->getWGrad()->mul(input_T, oGrad, 1, 1);
+        weights_[i]->getWGrad()->mul(*input_T, *oGrad, 1, 1);
       }
     }
 
@@ -136,7 +136,7 @@ void FullyConnectedLayer::backward(const UpdateCallback& callback) {
     if (NULL != preGrad) {
       MatrixPtr weights_T = weights_[i]->getW()->getTranspose();
       REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
-      preGrad->mul(getOutputGrad(), weights_T, 1, 1);
+      preGrad->mul(*getOutputGrad(), *weights_T, 1, 1);
     }
 
     hl_set_sync_flag(syncFlag);

paddle/gserver/layers/Layer.h

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <functional>
 #include <memory>
 #include "ModelConfig.pb.h"
+#include "paddle/function/Function.h"
 #include "paddle/math/CpuSparseMatrix.h"
 #include "paddle/parameter/Parameter.h"
 #include "paddle/utils/ClassRegistrar.h"
@@ -100,6 +101,11 @@ protected:
   /// Mark input grad in(true) or out(false) of backward function.
   std::vector<bool> markInBackward_;
 
+  /// Layer forward function
+  std::vector<std::shared_ptr<FunctionBase>> forward_;
+  /// Layer backward function
+  std::vector<std::shared_ptr<FunctionBase>> backward_;
+
 public:
   /**
     * Wait until all input value ready.
@@ -126,6 +132,26 @@ public:
   virtual void markAllInputGrad();
 
 protected:
+  /**
+   * Create layer function. Function is called in forward or backward.
+   * \param function, Layer::forward_ or Layer::backward_
+   * \param name, function name
+   * \param config, initialization configuration for the function
+   */
+  void createFunction(std::vector<std::shared_ptr<FunctionBase>>& function,
+                      const std::string& name,
+                      const FuncConfig& config) {
+    if (useGpu_) {
+      function.emplace_back(
+          FunctionBase::funcRegistrar_.createByType(name + "-GPU"));
+    } else {
+      function.emplace_back(
+          FunctionBase::funcRegistrar_.createByType(name + "-CPU"));
+    }
+    auto& func = function.back();
+    func->init(config);
+  }
+
   /**
    * Notify specified layer the output grad ready.
    * Called in the backward function.

paddle/gserver/layers/LinearChainCRF.cpp

@@ -59,7 +59,7 @@ real LinearChainCRF::forward(real* x, int* s, int length) {
   matX->rowMax(*maxX_);
   expX_->assign(*matX);
   // subtract max to avoid overflow or underflow
-  expX_->mul(maxX_, ones_, (real)-1, (real)1);
+  expX_->mul(*maxX_, *ones_, (real)-1, (real)1);
   expX_->exp2();
 
   real* a = a_->getData();

paddle/gserver/layers/LstmLayer.cpp

@@ -316,7 +316,7 @@ void LstmLayer::forwardSequence(int batchSize,
     }
     if (prevOutput_) {
       frameGate->setData(lstmValue.gateValue);
-      frameGate->mul(prevOutput_, weight_->getW(), 1, 1);
+      frameGate->mul(*prevOutput_, *weight_->getW(), 1, 1);
     }
   }
   AsyncGpuBlock asyncGpuBlock;
@@ -338,7 +338,7 @@ void LstmLayer::forwardSequence(int batchSize,
         frameOutput->setData(lstmValue.outputValue);
         nextFrame(reversed_, getSize());
         frameGate->setData(lstmValue.gateValue);
-        frameGate->mul(frameOutput, weight_->getW(), 1, 1);
+        frameGate->mul(*frameOutput, *weight_->getW(), 1, 1);
       }
     }
     if (n != numSequences - 1) {
@@ -348,7 +348,7 @@ void LstmLayer::forwardSequence(int batchSize,
       if (!reversed_) {
         if (!prevState_) lstmValue.prevStateValue = nullptr;
         if (prevOutput_) {
-          frameGate->mul(frameOutput, weight_->getW(), 1, 1);
+          frameGate->mul(*frameOutput, *weight_->getW(), 1, 1);
         }
       } else {
         lstmValue.prevStateValue = nullptr;
@@ -470,7 +470,7 @@ void LstmLayer::backwardSequence(int batchSize,
           frameGate->setData(lstmGrad.gateGrad);
           nextFrame(reversed_, getSize());
           frameOutput->setData(lstmGrad.outputGrad);
-          frameOutput->mul(frameGate, weightT, 1, 1);
+          frameOutput->mul(*frameGate, *weightT, 1, 1);
         } else {
           nextFrame(reversed_, getSize());
         }
@@ -479,14 +479,14 @@ void LstmLayer::backwardSequence(int batchSize,
       if (weight_->getWGrad()) {
         if (!reversed_) {
           weight_->getWGrad()->mul(
-              output_.value->subMatrix(start, length - 1)->getTranspose(),
-              gate_.grad->subMatrix(start + 1, length - 1),
+              *output_.value->subMatrix(start, length - 1)->getTranspose(),
+              *gate_.grad->subMatrix(start + 1, length - 1),
               1,
               1);
         } else {
           weight_->getWGrad()->mul(
-              output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
-              gate_.grad->subMatrix(start, length - 1),
+              *output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
+              *gate_.grad->subMatrix(start, length - 1),
               1,
               1);
         }
@@ -541,7 +541,7 @@ void LstmLayer::forwardBatch(int batchSize,
 
       if (n != 0) {
         MatrixPtr batch1 = batchValue_->getBatchValue(n - 1, batchSize);
-        gateValue->mul(batch1, weight_->getW(), 1, 1);
+        gateValue->mul(*batch1, *weight_->getW(), 1, 1);
       } else if (prevOutput_) {
         Matrix::resizeOrCreate(prevBatchOutput2_,
                                gateValue->getHeight(),
@@ -549,7 +549,7 @@ void LstmLayer::forwardBatch(int batchSize,
                                false,
                                useGpu_);
         batchValue_->prevOutput2Batch(*prevOutput_, *prevBatchOutput2_);
-        gateValue->mul(prevBatchOutput2_, weight_->getW(), 1, 1);
+        gateValue->mul(*prevBatchOutput2_, *weight_->getW(), 1, 1);
 
         batchValue_->prevOutput2Batch(*prevState_,
                                       *totalState_->subMatrix(0, numSequences));
@@ -672,16 +672,16 @@ void LstmLayer::backwardBatch(int batchSize,
 
       if (n != 0) {
         MatrixPtr tmp = batchGrad_->getBatchValue(n - 1, batchSize);
-        tmp->mul(gateGrad, weightT, 1, 1);
+        tmp->mul(*gateGrad, *weightT, 1, 1);
       }
 
       if (n != 0 && weight_->getWGrad()) {
         /* backward weight */
         MatrixPtr outputValue = batchValue_->getBatchValue(n - 1, batchSize);
-        weight_->getWGrad()->mul(outputValue->getTranspose(), gateGrad, 1, 1);
+        weight_->getWGrad()->mul(*outputValue->getTranspose(), *gateGrad, 1, 1);
       } else if (prevOutput_ && weight_->getWGrad()) {
         weight_->getWGrad()->mul(
-            prevBatchOutput2_->getTranspose(), gateGrad, 1, 1);
+            *prevBatchOutput2_->getTranspose(), *gateGrad, 1, 1);
       }
     }
   }

paddle/gserver/layers/MDLstmLayer.cpp

@@ -547,7 +547,7 @@ void MDLstmLayer::forwardOneSequence(int start, CoordIterator& coordIter) {
       if (coordIter.getPrePos(delays_, i, prePos)) {
         int preOffset = coordIter.offset(prePos);
         frameGate_[start + offset].value->mul(
-            frameOutput_[start + preOffset].value, weight_->getW(), 1.0, 1.0);
+            *frameOutput_[start + preOffset].value, *weight_->getW(), 1.0, 1.0);
       }
     }
     forwardGate2OutputSequence(start, coordIter);
@@ -747,11 +747,11 @@ void MDLstmLayer::backwardOneSequence(int start, CoordIterator& coordIter) {
       if (coordIter.getPrePos(delays_, i, prePos)) {
         int preOffset = coordIter.offset(prePos);
         frameOutput_[start + preOffset].grad->mul(
-            frameGate_[start + offset].grad, weightT, 1.0, 1.0);
+            *frameGate_[start + offset].grad, *weightT, 1.0, 1.0);
         if (weight_->getWGrad()) {
           weight_->getWGrad()->mul(
-              frameOutput_[start + preOffset].value->getTranspose(),
-              frameGate_[start + offset].grad,
+              *frameOutput_[start + preOffset].value->getTranspose(),
+              *frameGate_[start + offset].grad,
               1.0,
               1.0);
         }

paddle/gserver/layers/NormLayer.h

@@ -50,7 +50,7 @@ public:
 class ResponseNormLayer : public NormLayer {
 protected:
   size_t channels_, size_, outputX_, imgSize_, outputY_, imgSizeY_;
-  float scale_, pow_;
+  real scale_, pow_;
   MatrixPtr denoms_;
 
 public:

paddle/gserver/layers/NormProjectionLayer.cpp

@@ -45,6 +45,15 @@ bool CMRProjectionNormLayer::init(const LayerMap& layerMap,
   /* the size of inputs for norm-layer is 1 */
   CHECK_EQ(config_.inputs_size(), 1);
 
+  createFunction(
+      forward_,
+      "CrossMapNormal",
+      FuncConfig().set("size", size_).set("scale", scale_).set("pow", pow_));
+  createFunction(
+      backward_,
+      "CrossMapNormalGrad",
+      FuncConfig().set("size", size_).set("scale", scale_).set("pow", pow_));
+
   return true;
 }
 
@@ -54,7 +63,7 @@ void CMRProjectionNormLayer::forward(PassType passType) {
   /* malloc memory for the output_ if necessary */
   /* note: one sample correspond to one row */
   MatrixPtr input = inputLayers_[0]->getOutputValue();
-  int batchSize = input->getHeight();
+  size_t batchSize = input->getHeight();
   int size = getSize();
   resetOutput(batchSize, size);
 
@@ -62,10 +71,11 @@ void CMRProjectionNormLayer::forward(PassType passType) {
 
   Matrix::resizeOrCreate(denoms_, batchSize, size, /* trans */ false, useGpu_);
 
-  denoms_->zeroMem();
-
-  outV->crossMapNormalFwd(
-      *input, imgSizeH_, imgSizeW_, *denoms_, channels_, size_, scale_, pow_);
+  dims_ = {batchSize, channels_, imgSizeH_, imgSizeW_};
+  forward_[0]->calc(
+      {Tensor(input->getData(), dims_)},
+      {Tensor(outV->getData(), dims_), Tensor(denoms_->getData(), dims_)},
+      {});
 }
 
 void CMRProjectionNormLayer::backward(const UpdateCallback& callback) {
@@ -80,15 +90,11 @@ void CMRProjectionNormLayer::backward(const UpdateCallback& callback) {
   MatrixPtr localOutV = getOutputValue();
   MatrixPtr preOutV = inputLayers_[0]->getOutputValue();
 
-  preOutGrad->crossMapNormalBwd(*localGrad,
-                                *denoms_,
-                                *preOutV,
-                                *localOutV,
-                                channels_,
-                                imgSizeH_,
-                                imgSizeW_,
-                                size_,
-                                scale_,
-                                pow_);
+  backward_[0]->calc({Tensor(preOutV->getData(), dims_),
+                      Tensor(localOutV->getData(), dims_),
+                      Tensor(localGrad->getData(), dims_),
+                      Tensor(denoms_->getData(), dims_)},
+                     {Tensor(preOutGrad->getData(), dims_)},
+                     {});
 }
 }  // namespace paddle

paddle/gserver/layers/NormProjectionLayer.h

@@ -39,5 +39,8 @@ public:
   bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
   void forward(PassType passType);
   void backward(const UpdateCallback& callback = nullptr);
+
+protected:
+  Dims dims_;
 };
 }  // namespace paddle

paddle/gserver/layers/OuterProdLayer.cpp

@@ -96,7 +96,7 @@ void OuterProdLayer::forward(PassType passType) {
       tmpRow0->setData(inV0->getData() + i * dim0);
       tmpRow1->setData(inV1->getData() + i * dim1);
 
-      tmpMtx0->mul(tmpRow0->getTranspose(), tmpRow1);
+      tmpMtx0->mul(*tmpRow0->getTranspose(), *tmpRow1);
     }
   }
 }
@@ -121,7 +121,7 @@ void OuterProdLayer::backward(const UpdateCallback& callback) {
         tmpRow0->setData(inG0->getData() + i * dim0);
         tmpRow1->setData(inV1->getData() + i * dim1);
 
-        tmpRow0->mul(tmpRow1, tmpMtx0->getTranspose(), 1, 1);
+        tmpRow0->mul(*tmpRow1, *tmpMtx0->getTranspose(), 1, 1);
       }
     }
 
@@ -131,7 +131,7 @@ void OuterProdLayer::backward(const UpdateCallback& callback) {
         tmpRow0->setData(inV0->getData() + i * dim0);
         tmpRow1->setData(inG1->getData() + i * dim1);
 
-        tmpRow1->mul(tmpRow0, tmpMtx0, 1, 1);
+        tmpRow1->mul(*tmpRow0, *tmpMtx0, 1, 1);
       }
     }
   }

paddle/gserver/layers/PriorBox.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 "Layer.h"
+#include "paddle/math/BaseMatrix.h"
+#include "paddle/math/Matrix.h"
+
+namespace paddle {
+/**
+ * @brief A layer for generating priorbox locations and variances.
+ * - Input: Two and only two input layer are accepted. The input layer must be
+ *        be a data output layer and a convolution output layer.
+ * - Output: The priorbox locations and variances of the input data.
+ * Reference:
+ *    Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed,
+ *    Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector
+ */
+
+class PriorBoxLayer : public Layer {
+public:
+  explicit PriorBoxLayer(const LayerConfig& config) : Layer(config) {}
+  bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
+
+  void forward(PassType passType);
+  void backward(const UpdateCallback& callback) {}
+
+protected:
+  int numPriors_;
+  std::vector<int> minSize_;
+  std::vector<int> maxSize_;
+  std::vector<real> aspectRatio_;
+  std::vector<real> variance_;
+  MatrixPtr buffer_;
+};
+
+bool PriorBoxLayer::init(const LayerMap& layerMap,
+                         const ParameterMap& parameterMap) {
+  Layer::init(layerMap, parameterMap);
+  auto pbConf = config_.inputs(0).priorbox_conf();
+  std::copy(pbConf.min_size().begin(),
+            pbConf.min_size().end(),
+            std::back_inserter(minSize_));
+  std::copy(pbConf.max_size().begin(),
+            pbConf.max_size().end(),
+            std::back_inserter(maxSize_));
+  std::copy(pbConf.aspect_ratio().begin(),
+            pbConf.aspect_ratio().end(),
+            std::back_inserter(aspectRatio_));
+  std::copy(pbConf.variance().begin(),
+            pbConf.variance().end(),
+            std::back_inserter(variance_));
+  // flip
+  int inputRatioLength = aspectRatio_.size();
+  for (int index = 0; index < inputRatioLength; index++)
+    aspectRatio_.push_back(1 / aspectRatio_[index]);
+  aspectRatio_.push_back(1.);
+  numPriors_ = aspectRatio_.size();
+  if (maxSize_.size() > 0) numPriors_++;
+  return true;
+}
+
+void PriorBoxLayer::forward(PassType passType) {
+  Layer::forward(passType);
+  auto input = getInput(0);
+  int layerWidth = input.getFrameWidth();
+  int layerHeight = input.getFrameHeight();
+
+  auto image = getInput(1);
+  int imageWidth = image.getFrameWidth();
+  int imageHeight = image.getFrameHeight();
+
+  real stepW = static_cast<real>(imageWidth) / layerWidth;
+  real stepH = static_cast<real>(imageHeight) / layerHeight;
+  int dim = layerHeight * layerWidth * numPriors_ * 4;
+  reserveOutput(1, dim * 2);
+  // use a cpu buffer to compute
+  Matrix::resizeOrCreate(buffer_, 1, dim * 2, false, false);
+  auto* tmpPtr = buffer_->getData();
+
+  int idx = 0;
+  for (int h = 0; h < layerHeight; ++h) {
+    for (int w = 0; w < layerWidth; ++w) {
+      real centerX = (w + 0.5) * stepW;
+      real centerY = (h + 0.5) * stepH;
+      int minSize = 0;
+      for (size_t s = 0; s < minSize_.size(); s++) {
+        // first prior.
+        minSize = minSize_[s];
+        int boxWidth = minSize;
+        int boxHeight = minSize;
+        // xmin, ymin, xmax, ymax.
+        tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth;
+        tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight;
+        tmpPtr[idx++] = (centerX + boxWidth / 2.) / imageWidth;
+        tmpPtr[idx++] = (centerY + boxHeight / 2.) / imageHeight;
+        // set the variance.
+        for (int t = 0; t < 4; t++) tmpPtr[idx++] = variance_[t];
+
+        if (maxSize_.size() > 0) {
+          CHECK_EQ(minSize_.size(), maxSize_.size());
+          // second prior.
+          for (size_t s = 0; s < maxSize_.size(); s++) {
+            int maxSize = maxSize_[s];
+            boxWidth = boxHeight = sqrt(minSize * maxSize);
+            tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth;
+            tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight;
+            tmpPtr[idx++] = (centerX + boxWidth / 2.) / imageWidth;
+            tmpPtr[idx++] = (centerY + boxHeight / 2.) / imageHeight;
+            // set the variance.
+            for (int t = 0; t < 4; t++) tmpPtr[idx++] = variance_[t];
+          }
+        }
+      }
+      // rest of priors.
+      for (size_t r = 0; r < aspectRatio_.size(); r++) {
+        real ar = aspectRatio_[r];
+        if (fabs(ar - 1.) < 1e-6) continue;
+        real boxWidth = minSize * sqrt(ar);
+        real boxHeight = minSize / sqrt(ar);
+        tmpPtr[idx++] = (centerX - boxWidth / 2.) / imageWidth;
+        tmpPtr[idx++] = (centerY - boxHeight / 2.) / imageHeight;
+        tmpPtr[idx++] = (centerX + boxWidth / 2.) / imageWidth;
+        tmpPtr[idx++] = (centerY + boxHeight / 2.) / imageHeight;
+        // set the variance.
+        for (int t = 0; t < 4; t++) tmpPtr[idx++] = variance_[t];
+      }
+    }
+  }
+  // clip the prior's coordidate such that it is within [0, 1]
+  for (int d = 0; d < dim * 2; ++d)
+    if ((d % 8) < 4)
+      tmpPtr[d] = std::min(std::max(tmpPtr[d], (real)0.), (real)1.);
+  MatrixPtr outV = getOutputValue();
+  outV->copyFrom(buffer_->data_, dim * 2);
+}
+REGISTER_LAYER(priorbox, PriorBoxLayer);
+
+}  // namespace paddle

paddle/gserver/layers/RecurrentLayer.cpp

@@ -215,12 +215,12 @@ void RecurrentLayer::forwardSequence(int batchSize,
 void RecurrentLayer::forwardOneSequence(int start, int length) {
   if (!reversed_) {
     if (prevOutput_) {
-      frameOutput_[start].value->mul(prevOutput_, weight_->getW(), 1, 1);
+      frameOutput_[start].value->mul(*prevOutput_, *weight_->getW(), 1, 1);
     }
     activation_->forward(frameOutput_[start]);
     for (int i = 1; i < length; ++i) {
       frameOutput_[start + i].value->mul(
-          frameOutput_[start + i - 1].value, weight_->getW(), 1, 1);
+          *frameOutput_[start + i - 1].value, *weight_->getW(), 1, 1);
       activation_->forward(frameOutput_[start + i]);
     }
     if (prevOutput_) {
@@ -230,7 +230,7 @@ void RecurrentLayer::forwardOneSequence(int start, int length) {
     activation_->forward(frameOutput_[start + length - 1]);
     for (int i = length - 2; i >= 0; --i) {
       frameOutput_[start + i].value->mul(
-          frameOutput_[start + i + 1].value, weight_->getW(), 1, 1);
+          *frameOutput_[start + i + 1].value, *weight_->getW(), 1, 1);
       activation_->forward(frameOutput_[start + i]);
     }
   }
@@ -282,13 +282,13 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
     for (int i = length - 1; i > 0; --i) {
       activation_->backward(frameOutput_[start + i]);
       frameOutput_[start + i - 1].grad->mul(
-          frameOutput_[start + i].grad, weightT, 1, 1);
+          *frameOutput_[start + i].grad, *weightT, 1, 1);
     }
     activation_->backward(frameOutput_[start]);
     if (weight_->getWGrad()) {
       weight_->getWGrad()->mul(
-          output_.value->subMatrix(start, length - 1)->getTranspose(),
-          output_.grad->subMatrix(start + 1, length - 1),
+          *output_.value->subMatrix(start, length - 1)->getTranspose(),
+          *output_.grad->subMatrix(start + 1, length - 1),
           1,
           1);
     }
@@ -296,13 +296,13 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
     for (int i = 0; i < length - 1; ++i) {
       activation_->backward(frameOutput_[start + i]);
       frameOutput_[start + i + 1].grad->mul(
-          frameOutput_[start + i].grad, weightT, 1, 1);
+          *frameOutput_[start + i].grad, *weightT, 1, 1);
     }
     activation_->backward(frameOutput_[start + length - 1]);
     if (weight_->getWGrad()) {
       weight_->getWGrad()->mul(
-          output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
-          output_.grad->subMatrix(start, length - 1),
+          *output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
+          *output_.grad->subMatrix(start, length - 1),
           1,
           1);
     }
@@ -329,7 +329,7 @@ void RecurrentLayer::forwardBatch(int batchSize,
       if (n != 0) {
         MatrixPtr batch1 =
             batchValue_->getBatchValue(n - 1, batch2->getHeight());
-        batch2->mul(batch1, weight_->getW(), 1, 1);
+        batch2->mul(*batch1, *weight_->getW(), 1, 1);
       }
       Argument arg;
       arg.value = batch2;
@@ -367,14 +367,14 @@ void RecurrentLayer::backwardBatch(int batchSize,
 
       if (n != 0) {
         batch1 = batchGrad_->getBatchValue(n - 1, batch2->getHeight());
-        batch1->mul(batch2, weightT, 1, 1);
+        batch1->mul(*batch2, *weightT, 1, 1);
       }
 
       if (backwardByBatch && weight_->getWGrad()) {
         if (n != 0) {
           /* backward weight */
           batch1 = batchValue_->getBatchValue(n - 1, batch2->getHeight());
-          weight_->getWGrad()->mul(batch1->getTranspose(), batch2, 1, 1);
+          weight_->getWGrad()->mul(*batch1->getTranspose(), *batch2, 1, 1);
         }
       }
     }
@@ -389,14 +389,14 @@ void RecurrentLayer::backwardBatch(int batchSize,
       int len = starts[seq + 1] - starts[seq];
       if (!reversed_) {
         weight_->getWGrad()->mul(
-            output_.value->subMatrix(starts[seq], len - 1)->getTranspose(),
-            output_.grad->subMatrix(starts[seq] + 1, len - 1),
+            *output_.value->subMatrix(starts[seq], len - 1)->getTranspose(),
+            *output_.grad->subMatrix(starts[seq] + 1, len - 1),
             1,
             1);
       } else {
         weight_->getWGrad()->mul(
-            output_.value->subMatrix(starts[seq] + 1, len - 1)->getTranspose(),
-            output_.grad->subMatrix(starts[seq], len - 1),
+            *output_.value->subMatrix(starts[seq] + 1, len - 1)->getTranspose(),
+            *output_.grad->subMatrix(starts[seq], len - 1),
             1,
             1);
       }

paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp

@@ -155,20 +155,20 @@ void SelectiveFullyConnectedLayer::forward(PassType passType) {
       // manully compute the multiplication of
       // the input vector and the selected rows.
       REGISTER_TIMER("selective.plain");
-      interOutput_->mul(input, weight->getTranspose(), 1, scaleT);
+      interOutput_->mul(*input, *weight->getTranspose(), 1, scaleT);
     } else {
       // if the indecies is not sparse enough,
       // use full mul instead
       REGISTER_TIMER("selective.mul");
       if (fullOutput_) {
-        interOutput_->mul(input, weight->getTranspose(), 1, scaleT);
+        interOutput_->mul(*input, *weight->getTranspose(), 1, scaleT);
       } else {
         Matrix::resizeOrCreate(mmat_,
                                hsize,
                                wsize,
                                /*trans=*/false,
                                /*useGpu=*/useGpu_);
-        mmat_->mul(input, weight->getTranspose());
+        mmat_->mul(*input, *weight->getTranspose());
         interOutput_->add3(mmat_);
       }
     }
@@ -242,14 +242,14 @@ void SelectiveFullyConnectedLayer::backward(const UpdateCallback& callback) {
     MatrixPtr preGrad = getInputGrad(i);
     if (preGrad) {
       REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
-      preGrad->mul(interOutGrad_, weights_[i]->getW(), 1, 1);
+      preGrad->mul(*interOutGrad_, *weights_[i]->getW(), 1, 1);
     }
 
     MatrixPtr wGrad = weights_[i]->getWGrad();
     if (wGrad) {
       REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
       MatrixPtr input = getInputValue(i);
-      wGrad->mul(interOutGrad_->getTranspose(), input, 1, 1);
+      wGrad->mul(*interOutGrad_->getTranspose(), *input, 1, 1);
     }
 
     {

paddle/gserver/layers/TensorLayer.cpp

@@ -77,7 +77,7 @@ void TensorLayer::forward(PassType passType) {
     REGISTER_TIMER_INFO("TensorFwMulTimer", getName().c_str());
     for (size_t i = 0; i < getSize(); ++i) {
       MatrixPtr weights = weights_[i]->getW();
-      tmpMat->mul(input1, weights, 1, 0);
+      tmpMat->mul(*input1, *weights, 1, 0);
       outV->rowDotMul(i, *tmpMat, *input2);
     }
   }
@@ -112,7 +112,7 @@ void TensorLayer::backward(const UpdateCallback& callback) {
       if (weights_[i]->getWGrad()) {
         tmpMat->rowScale(i, *input1, *oGrad);
         MatrixPtr input1_T = tmpMat->getTranspose();
-        weights_[i]->getWGrad()->mul(input1_T, input2, 1, 1);
+        weights_[i]->getWGrad()->mul(*input1_T, *input2, 1, 1);
       }
     }
   }
@@ -130,11 +130,11 @@ void TensorLayer::backward(const UpdateCallback& callback) {
       if (NULL != preGrad1) { /* (grad * e2) * trans(W) */
         tmpMat->rowScale(i, *input2, *oGrad);
         MatrixPtr weights_T = weights->getTranspose();
-        preGrad1->mul(tmpMat, weights_T, 1, 1);
+        preGrad1->mul(*tmpMat, *weights_T, 1, 1);
       }
       if (NULL != preGrad2) { /* (grad * e1) * W */
         tmpMat->rowScale(i, *input1, *oGrad);
-        preGrad2->mul(tmpMat, weights, 1, 1);
+        preGrad2->mul(*tmpMat, *weights, 1, 1);
       }
     }
   }

paddle/gserver/layers/TransposedFullMatrixProjection.cpp

@@ -46,7 +46,7 @@ TransposedFullMatrixProjection::TransposedFullMatrixProjection(
 
 void TransposedFullMatrixProjection::forward() {
   REGISTER_TIMER_INFO("FwMulTimer", getName().c_str());
-  out_->value->mul(in_->value, weight_->getW()->getTranspose(), 1, 1);
+  out_->value->mul(*(in_->value), *(weight_->getW()->getTranspose()), 1, 1);
 }
 
 void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
@@ -55,7 +55,8 @@ void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
   /* Calculate the W-gradient for the current layer */
   if (weight_->getWGrad()) {
     REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
-    weight_->getWGrad()->mul(out_->grad->getTranspose(), in_->value, 1, 1);
+    weight_->getWGrad()->mul(
+        *(out_->grad->getTranspose()), *(in_->value), 1, 1);
   }
 
   // If callback does not change value, backprop error asynchronously so that
@@ -69,7 +70,7 @@ void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
   /* Calculate the input layers error */
   if (in_->grad) {
     REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
-    in_->grad->mul(out_->grad, weight_->getW(), 1, 1);
+    in_->grad->mul(*(out_->grad), *(weight_->getW()), 1, 1);
   }
 
   hl_set_sync_flag(syncFlag);

paddle/gserver/tests/CMakeLists.txt

@@ -34,6 +34,14 @@ add_unittest_without_exec(test_ConvTrans
 
 add_test(NAME test_ConvTrans
     COMMAND test_ConvTrans)
+################# test_PriorBox #######################
+add_unittest_without_exec(test_PriorBox
+    test_PriorBox.cpp
+    LayerGradUtil.cpp
+    TestUtil.cpp)
+
+add_test(NAME test_PriorBox
+    COMMAND test_PriorBox)
 ################# test_ConvUnify #######################
 add_unittest_without_exec(test_ConvUnify
     test_ConvUnify.cpp

paddle/gserver/tests/LayerGradUtil.cpp

@@ -303,13 +303,31 @@ void initDataLayer(TestConfig testConf,
   ICpuGpuVectorPtr sequenceStartPositions;
   ICpuGpuVectorPtr subSequenceStartPositions;
   IVectorPtr cpuSequenceDims;
-  for (size_t i = 0; i < testConf.inputDefs.size(); i++) {
+  for (size_t i = 0; i < testConf.inputDefs.size(); ++i) {
+    if (testConf.inputDefs[i].inputType != INPUT_SEQUENCE_LABEL) continue;
+
+    const std::vector<int>& labelSeqStartPositions =
+        testConf.inputDefs[i].labelSeqStartPositions;
+    if (labelSeqStartPositions.size() != 0) {
+      CHECK(!sequenceStartPositions);
+      CHECK_GE(labelSeqStartPositions.size(), 2);
+
+      sequenceStartPositions =
+          ICpuGpuVector::create(labelSeqStartPositions.size(), useGpu);
+      sequenceStartPositions->copyFrom(
+          labelSeqStartPositions.data(), labelSeqStartPositions.size(), useGpu);
+    }
+  }
+
+  for (size_t i = 0; i < testConf.inputDefs.size(); ++i) {
     LayerConfig config;
     config.set_name(testConf.inputDefs[i].name);
     config.set_type("data");
     config.set_size(testConf.inputDefs[i].dim);
     LayerPtr layer = LayerPtr(new DataLayer(config));
-    size_t numSequence = batchSize / 10 + 1;
+    size_t numSequence = sequenceStartPositions
+                             ? sequenceStartPositions->getSize() - 1
+                             : batchSize / 10 + 1;
 
     Argument data;
     auto fillData = [&](bool trans, int height, int width) {
@@ -336,9 +354,17 @@ void initDataLayer(TestConfig testConf,
         break;
       case INPUT_LABEL:
       case INPUT_SEQUENCE_LABEL:
-        data.ids = VectorT<int>::create(batchSize, useGpu);
-        // now rand number can be 0 to inputDefs[i].dim
-        data.ids->rand(testConf.inputDefs[i].dim);
+        if (testConf.inputDefs[i].labelInitValue.size() != 0) {
+          const std::vector<int>& labelInitValue =
+              testConf.inputDefs[i].labelInitValue;
+          CHECK_EQ(labelInitValue.size(), batchSize);
+          data.ids = VectorT<int>::create(batchSize, useGpu);
+          data.ids->copyFrom(labelInitValue.data(), batchSize);
+        } else {
+          data.ids = VectorT<int>::create(batchSize, useGpu);
+          // now rand number can be 0 to inputDefs[i].dim
+          data.ids->rand(testConf.inputDefs[i].dim);
+        }
         break;
       case INPUT_SPARSE_NON_VALUE_DATA:
         data.value = makeRandomSparseMatrix(

paddle/gserver/tests/LayerGradUtil.h

@@ -64,6 +64,9 @@ struct InputDef {
   size_t paraSize;
   ParaSparse sparse;
   bool isStatic;
+  std::vector<int> labelInitValue;
+  std::vector<int> labelSeqStartPositions;
+
   InputDef(InputType type, string nameIn, size_t dimIn, size_t sizeIn) {
     inputType = type;
     name = nameIn;
@@ -72,6 +75,23 @@ struct InputDef {
     sparse = {""};
     isStatic = false;
   }
+
+  InputDef(InputType type,
+           string nameIn,
+           size_t dimIn,
+           size_t sizeIn,
+           const std::vector<int>& labelInitValue,
+           const std::vector<int>& labelSeqStartPositions)
+      : labelInitValue(labelInitValue),
+        labelSeqStartPositions(labelSeqStartPositions) {
+    inputType = type;
+    name = nameIn;
+    dim = dimIn;
+    paraSize = sizeIn;
+    sparse = {""};
+    isStatic = false;
+  }
+
   InputDef(InputType type,
            string nameIn,
            size_t dimIn,

paddle/gserver/tests/test_ConvTrans.cpp

@@ -206,8 +206,8 @@ TEST(Layer, convTransLayerFwd2) {
                  /* filter_size */ 5,
                  result);
 
-  float resultData[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
-                        4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
+  real resultData[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
+                       4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
   result->setData(resultData);
   doOneConvtTest(/* imgSize */ 5,
                  /* output_x */ 2,
@@ -216,8 +216,8 @@ TEST(Layer, convTransLayerFwd2) {
                  /* filter_size */ 4,
                  result);
 
-  float resultData2[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
-                         4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
+  real resultData2[] = {1, 2, 2, 2, 1, 2, 4, 4, 4, 2, 2, 4, 4,
+                        4, 2, 2, 4, 4, 4, 2, 1, 2, 2, 2, 1};
   result->setData(resultData2);
   doOneConvtTest(/* imgSize */ 5,
                  /* output_x */ 2,
@@ -226,8 +226,8 @@ TEST(Layer, convTransLayerFwd2) {
                  /* filter_size */ 5,
                  result);
 
-  float resultData3[] = {1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 4,
-                         2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1};
+  real resultData3[] = {1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 4,
+                        2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1};
   result->setData(resultData3);
   doOneConvtTest(/* imgSize */ 5,
                  /* output_x */ 2,

paddle/gserver/tests/test_ConvUnify.cpp

@@ -106,8 +106,8 @@ TEST(Layer, convParaUnified) {
 #ifndef PADDLE_ONLY_CPU
   MatrixPtr input, resultCpu, resultGpu;
   input = Matrix::create(1, 4 * 4, false, false);
-  float inputData[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
-  float param[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 8, 7, 6, 5, 4, 3, 2, 1};
+  real inputData[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
+  real param[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 8, 7, 6, 5, 4, 3, 2, 1};
 
   input->setData(inputData);
 
@@ -137,26 +137,9 @@ TEST(Layer, convParaUnified) {
   checkMatrixEqual(resultCpu, resultGpu);
 
   input = Matrix::create(1, 3 * 3 * 2, false, false);
-  float inputData2[] = {1,
-                        2,
-                        3,
-                        4,
-                        5,
-                        6,
-                        7,
-                        8,
-                        9,
-
-                        10,
-                        11,
-                        12,
-                        13,
-                        14,
-                        15,
-                        16,
-                        17,
-                        18};
-  float param2[] = {1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1};
+  real inputData2[] = {
+      1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
+  real param2[] = {1, 2, 3, 4, 5, 6, 7, 8, 8, 7, 6, 5, 4, 3, 2, 1};
 
   input->setData(inputData2);
 
@@ -185,7 +168,7 @@ TEST(Layer, convParaUnified) {
                             true);
   checkMatrixEqual(resultCpu, resultGpu);
 
-  float param3[] = {1, 2, 3, 4, 4, 3, 2, 1};
+  real param3[] = {1, 2, 3, 4, 4, 3, 2, 1};
 
   resultCpu = doOneConvTest(/* imgSize */ 3,
                             /* output_x */ 2,

paddle/gserver/tests/test_LayerGrad.cpp

@@ -1021,11 +1021,10 @@ void testNormLayer(const string& normType, bool trans, bool useGpu) {
   testLayerGrad(config, "norm", 100, trans, useGpu);
 }
 
-#ifndef PADDLE_ONLY_CPU
 TEST(Layer, NormLayer) {
   testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ true);
+  testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ false);
 }
-#endif
 
 void setPoolConfig(TestConfig* config,
                    PoolConfig* pool,

paddle/gserver/tests/test_PriorBox.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 <gtest/gtest.h>
+#include <string>
+#include <vector>
+
+#include "LayerGradUtil.h"
+#include "TestUtil.h"
+
+using namespace paddle;  // NOLINT
+using namespace std;     // NOLINT
+
+// Do one forward pass of priorBox layer and check to see if its output
+// matches the given result
+void doOnePriorBoxTest(size_t feature_map_width,
+                       size_t feature_map_height,
+                       size_t image_width,
+                       size_t image_height,
+                       vector<int> min_size,
+                       vector<int> max_size,
+                       vector<real> aspect_ratio,
+                       vector<real> variance,
+                       bool use_gpu,
+                       MatrixPtr& result) {
+  // Setting up the priorbox layer
+  TestConfig configt;
+  configt.layerConfig.set_type("priorbox");
+
+  configt.inputDefs.push_back({INPUT_DATA, "featureMap", 1, 0});
+  LayerInputConfig* input = configt.layerConfig.add_inputs();
+  configt.inputDefs.push_back({INPUT_DATA, "image", 1, 0});
+  configt.layerConfig.add_inputs();
+  PriorBoxConfig* pb = input->mutable_priorbox_conf();
+  for (size_t i = 0; i < min_size.size(); i++) pb->add_min_size(min_size[i]);
+  for (size_t i = 0; i < max_size.size(); i++) pb->add_max_size(max_size[i]);
+  for (size_t i = 0; i < variance.size(); i++) pb->add_variance(variance[i]);
+  for (size_t i = 0; i < aspect_ratio.size(); i++)
+    pb->add_aspect_ratio(aspect_ratio[i]);
+
+  // data layer initialize
+  std::vector<DataLayerPtr> dataLayers;
+  LayerMap layerMap;
+  vector<Argument> datas;
+  initDataLayer(
+      configt, &dataLayers, &datas, &layerMap, "priorbox", 1, false, use_gpu);
+  dataLayers[0]->getOutput().setFrameHeight(feature_map_height);
+  dataLayers[0]->getOutput().setFrameWidth(feature_map_width);
+  dataLayers[1]->getOutput().setFrameHeight(image_height);
+  dataLayers[1]->getOutput().setFrameWidth(image_width);
+
+  // test layer initialize
+  std::vector<ParameterPtr> parameters;
+  LayerPtr priorboxLayer;
+  initTestLayer(configt, &layerMap, &parameters, &priorboxLayer);
+  priorboxLayer->forward(PASS_GC);
+  checkMatrixEqual(priorboxLayer->getOutputValue(), result);
+}
+
+TEST(Layer, priorBoxLayerFwd) {
+  vector<int> minSize;
+  vector<int> maxSize;
+  vector<real> aspectRatio;
+  vector<real> variance;
+  bool useGpu = false;
+
+  minSize.push_back(276);
+  maxSize.push_back(330);
+  variance.push_back(0.1);
+  variance.push_back(0.1);
+  variance.push_back(0.2);
+  variance.push_back(0.2);
+
+  // CPU case 1.
+  MatrixPtr result;
+  real resultData[] = {0.04,
+                       0.04,
+                       0.96,
+                       0.96,
+                       0.1,
+                       0.1,
+                       0.2,
+                       0.2,
+                       0,
+                       0,
+                       1,
+                       1,
+                       0.1,
+                       0.1,
+                       0.2,
+                       0.2};
+  result = Matrix::create(1, 2 * 8, false, useGpu);
+  result->setData(resultData);
+  doOnePriorBoxTest(/* feature_map_width */ 1,
+                    /* feature_map_height */ 1,
+                    /* image_width */ 300,
+                    /* image_height */ 300,
+                    minSize,
+                    maxSize,
+                    aspectRatio,
+                    variance,
+                    useGpu,
+                    result);
+  // CPU case 2.
+  variance[1] = 0.2;
+  variance[3] = 0.1;
+  maxSize.pop_back();
+  real resultData2[] = {0,     0,     0.595, 0.595, 0.1, 0.2, 0.2, 0.1,
+                        0.405, 0,     1,     0.595, 0.1, 0.2, 0.2, 0.1,
+                        0,     0.405, 0.595, 1,     0.1, 0.2, 0.2, 0.1,
+                        0.405, 0.405, 1,     1,     0.1, 0.2, 0.2, 0.1};
+  Matrix::resizeOrCreate(result, 1, 4 * 8, false, useGpu);
+  result->setData(resultData2);
+  doOnePriorBoxTest(/* feature_map_width */ 2,
+                    /* feature_map_height */ 2,
+                    /* image_width */ 400,
+                    /* image_height */ 400,
+                    minSize,
+                    maxSize,
+                    aspectRatio,
+                    variance,
+                    useGpu,
+                    result);
+  // CPU case 3.
+  aspectRatio.push_back(2);
+  real resultData3[] = {0.04,     0.04, 0.96, 0.96,       0.1,        0.2,
+                        0.2,      0.1,  0,    0.17473088, 1,          0.825269,
+                        0.1,      0.2,  0.2,  0.1,        0.17473088, 0,
+                        0.825269, 1,    0.1,  0.2,        0.2,        0.1};
+  Matrix::resizeOrCreate(result, 1, 3 * 8, false, useGpu);
+  result->setData(resultData3);
+  doOnePriorBoxTest(/* feature_map_width */ 1,
+                    /* feature_map_height */ 1,
+                    /* image_width */ 300,
+                    /* image_height */ 300,
+                    minSize,
+                    maxSize,
+                    aspectRatio,
+                    variance,
+                    useGpu,
+                    result);
+
+#ifndef PADDLE_ONLY_CPU
+  // reset the input parameters
+  variance[1] = 0.1;
+  variance[3] = 0.2;
+  maxSize.push_back(330);
+  aspectRatio.pop_back();
+  MatrixPtr resultGpu;
+  useGpu = true;
+  // GPU case 1.
+  resultGpu = Matrix::create(1, 2 * 8, false, useGpu);
+  resultGpu->copyFrom(resultData, 2 * 8);
+  doOnePriorBoxTest(/* feature_map_width */ 1,
+                    /* feature_map_height */ 1,
+                    /* image_width */ 300,
+                    /* image_height */ 300,
+                    minSize,
+                    maxSize,
+                    aspectRatio,
+                    variance,
+                    useGpu,
+                    resultGpu);
+  // GPU case 2.
+  variance[1] = 0.2;
+  variance[3] = 0.1;
+  maxSize.pop_back();
+  Matrix::resizeOrCreate(resultGpu, 1, 4 * 8, false, useGpu);
+  resultGpu->copyFrom(resultData2, 4 * 8);
+  doOnePriorBoxTest(/* feature_map_width */ 2,
+                    /* feature_map_height */ 2,
+                    /* image_width */ 400,
+                    /* image_height */ 400,
+                    minSize,
+                    maxSize,
+                    aspectRatio,
+                    variance,
+                    useGpu,
+                    resultGpu);
+  // GPU case 3.
+  aspectRatio.push_back(2);
+  Matrix::resizeOrCreate(resultGpu, 1, 3 * 8, false, useGpu);
+  resultGpu->copyFrom(resultData3, 3 * 8);
+  doOnePriorBoxTest(/* feature_map_width */ 1,
+                    /* feature_map_height */ 1,
+                    /* image_width */ 300,
+                    /* image_height */ 300,
+                    minSize,
+                    maxSize,
+                    aspectRatio,
+                    variance,
+                    useGpu,
+                    resultGpu);
+#endif
+}
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  initMain(argc, argv);
+  return RUN_ALL_TESTS();
+}

paddle/math/CpuSparseMatrix.cpp

@@ -163,15 +163,16 @@ MatrixPtr CpuSparseMatrix::getTranspose() {
 
 SparseValueType CpuSparseMatrix::getValueType() { return valueType_; }
 
-void CpuSparseMatrix::mul(MatrixPtr a, MatrixPtr b, real scaleAB, real scaleT) {
+void CpuSparseMatrix::mul(const Matrix& a,
+                          const Matrix& b,
+                          real scaleAB,
+                          real scaleT) {
   CHECK(!isTransposed()) << "Not supported";
+  const auto a_ptr = dynamic_cast<const CpuMatrix*>(&a);
+  const auto b_ptr = dynamic_cast<const CpuMatrix*>(&b);
 
-  if (dynamic_cast<CpuMatrix*>(a.get()) && dynamic_cast<CpuMatrix*>(b.get())) {
-    CpuMatrix::mul(dynamic_cast<CpuMatrix*>(a.get()),
-                   dynamic_cast<CpuMatrix*>(b.get()),
-                   this,
-                   scaleAB,
-                   scaleT);
+  if (a_ptr && b_ptr) {
+    CpuMatrix::mul((CpuMatrix*)a_ptr, (CpuMatrix*)b_ptr, this, scaleAB, scaleT);
   } else {
     LOG(FATAL) << "not supported";
   }

paddle/math/CpuSparseMatrix.h

@@ -203,7 +203,7 @@ public:
   /// mem MUST be alloced outside (memAlloc=false)
   void transpose(MatrixPtr matTrans, bool memAlloc);
 
-  void mul(MatrixPtr A, MatrixPtr B, real alpha, real beta);
+  void mul(const Matrix& A, const Matrix& B, real alpha, real beta);
 
   /**
    * @brief sparseMatrix += denseMatrix

paddle/math/Matrix.cpp

@@ -582,18 +582,16 @@ void GpuMatrix::mul(const GpuMatrix& a,
 }
 
 /* this = a*b */
-void GpuMatrix::mul(const MatrixPtr a, const MatrixPtr b) {
-  mul(a, b, 1.0, 0.0);
-}
+void GpuMatrix::mul(const Matrix& a, const Matrix& b) { mul(a, b, 1.0, 0.0); }
 
-void GpuMatrix::mul(const MatrixPtr a,
-                    const MatrixPtr b,
+void GpuMatrix::mul(const Matrix& a,
+                    const Matrix& b,
                     real scaleAB,
                     real scaleT) {
-  GpuMatrixPtr a_ptr = std::dynamic_pointer_cast<GpuMatrix>(a);
-  GpuMatrixPtr b_ptr = std::dynamic_pointer_cast<GpuMatrix>(b);
-  GpuSparseMatrixPtr a_ptr_s = std::dynamic_pointer_cast<GpuSparseMatrix>(a);
-  GpuSparseMatrixPtr b_ptr_s = std::dynamic_pointer_cast<GpuSparseMatrix>(b);
+  const auto a_ptr = dynamic_cast<const GpuMatrix*>(&a);
+  const auto b_ptr = dynamic_cast<const GpuMatrix*>(&b);
+  const auto a_ptr_s = dynamic_cast<const GpuSparseMatrix*>(&a);
+  const auto b_ptr_s = dynamic_cast<const GpuSparseMatrix*>(&b);
 
   if (a_ptr && b_ptr) {
     mul(*a_ptr, *b_ptr, scaleAB, scaleT);
@@ -766,20 +764,19 @@ void GpuMatrix::maxoutBackward(Matrix& a,
 }
 
 /*calulate the error of classification */
-void GpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) {
-  GpuMatrixPtr output_ptr = std::dynamic_pointer_cast<GpuMatrix>(output);
-  GpuIVectorPtr label_ptr = std::dynamic_pointer_cast<GpuIVector>(label);
-
+void GpuMatrix::classificationError(Matrix& output, IVector& label) {
+  auto output_ptr = dynamic_cast<const GpuMatrix*>(&output);
+  auto label_ptr = dynamic_cast<const GpuIVector*>(&label);
   CHECK(output_ptr && label_ptr) << "Invalid argument pointer";
 
   CHECK(height_ == output_ptr->height_ && width_ == 1)
       << "Matrix dimensions are not equal";
-  real* output_d = output_ptr->data_;
-  real* recResult_d = data_;
-  int* label_d = label_ptr->getData();
 
-  hl_matrix_classification_error(
-      output_d, label_d, recResult_d, height_, output_ptr->width_);
+  hl_matrix_classification_error((real*)output_ptr->data_,
+                                 (int*)label_ptr->getData(),
+                                 data_,
+                                 height_,
+                                 output_ptr->width_);
 }
 
 /* copy -log(output[i * width + label]) to this->data[i] */
@@ -1265,69 +1262,6 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad,
                       outGrad.getStride());
 }
 
-void GpuMatrix::crossMapNormalFwd(Matrix& input,
-                                  size_t imgSizeH,
-                                  size_t imgSizeW,
-                                  Matrix& denoms,
-                                  size_t channels,
-                                  size_t sizeX,
-                                  float scale,
-                                  float pow) {
-  size_t num = input.getHeight();
-  size_t height = imgSizeH;
-  size_t width = imgSizeW;
-
-  CHECK(height * width * channels == input.getWidth());
-  CHECK(denoms.getHeight() == input.getHeight() &&
-        denoms.getWidth() == input.getWidth() && input.getHeight() == height_ &&
-        input.getWidth() == width_);
-  hl_CMRNorm_forward(num,
-                     input.getData(),
-                     denoms.getData(),
-                     data_,
-                     channels,
-                     height,
-                     width,
-                     sizeX,
-                     scale,
-                     -pow);
-}
-
-void GpuMatrix::crossMapNormalBwd(Matrix& localGrad,
-                                  Matrix& denoms,
-                                  Matrix& preOutV,
-                                  Matrix& localOutV,
-                                  size_t channels,
-                                  size_t imgSizeH,
-                                  size_t imgSizeW,
-                                  size_t sizeX,
-                                  float scale,
-                                  float pow) {
-  size_t num = preOutV.getHeight();
-  size_t height = imgSizeH;
-  size_t width = imgSizeW;
-
-  CHECK(width * height * channels == preOutV.getWidth());
-  CHECK(denoms.getHeight() == preOutV.getHeight() &&
-        denoms.getWidth() == preOutV.getWidth() &&
-        preOutV.getHeight() == height_ && preOutV.getWidth() == width_);
-  CHECK(denoms.getHeight() == localGrad.getHeight() &&
-        denoms.getWidth() == localGrad.getWidth());
-
-  hl_CMRNorm_backward(num,
-                      preOutV.getData(),
-                      denoms.getData(),
-                      localOutV.getData(),
-                      localGrad.getData(),
-                      data_,
-                      channels,
-                      height,
-                      width,
-                      sizeX,
-                      -pow,
-                      2.0f * pow * scale);
-}
-
 void GpuMatrix::maxSequenceForward(Matrix& input,
                                    const IVector& sequence,
                                    IVector& index) {
@@ -1370,86 +1304,62 @@ void GpuMatrix::maxSequenceBackward(Matrix& outputGrad,
   hl_max_sequence_backward(outGrad, maxIndex, inputGrad, numSequences, dim);
 }
 
-void GpuMatrix::contextProjectionForward(MatrixPtr input,
-                                         MatrixPtr weight,
+void GpuMatrix::contextProjectionForward(Matrix& input,
+                                         Matrix* weight,
                                          const IVector& sequence,
                                          int contextLength,
                                          int contextStart,
                                          size_t beginPad,
                                          bool isPadding) {
-  CHECK(dynamic_cast<GpuMatrix*>(input.get()));
+  CHECK(dynamic_cast<GpuMatrix*>(&input));
   CHECK(dynamic_cast<const GpuIVector*>(&sequence));
-  if (weight) CHECK(dynamic_cast<GpuMatrix*>(weight.get()));
-
-  size_t numSequences = sequence.getSize() - 1;
-  int64_t inputDim = input->getWidth();
-  int64_t dim = getWidth();
-  CHECK_EQ(dim, inputDim * contextLength);
-
-  real* outData = getData();
-  real* inputData = input->getData();
-  const int* starts = sequence.getData();
+  if (weight) CHECK(dynamic_cast<GpuMatrix*>(weight));
+  CHECK_EQ(getWidth(), input.getWidth() * contextLength);
 
-  hl_context_projection_forward(inputData,
-                                starts,
+  hl_context_projection_forward(input.getData(),
+                                sequence.getData(),
                                 isPadding ? weight->getData() : NULL,
-                                outData,
-                                numSequences,
-                                inputDim,
+                                getData(),
+                                sequence.getSize() - 1,
+                                input.getWidth(),
                                 contextLength,
                                 contextStart,
                                 beginPad,
                                 isPadding);
 }
 
-void GpuMatrix::contextProjectionBackwardData(MatrixPtr inputGrad,
+void GpuMatrix::contextProjectionBackwardData(Matrix& inputGrad,
                                               const IVector& sequence,
                                               int contextLength,
                                               int contextStart) {
-  CHECK(dynamic_cast<GpuMatrix*>(inputGrad.get()));
+  CHECK(dynamic_cast<GpuMatrix*>(&inputGrad));
   CHECK(dynamic_cast<const GpuIVector*>(&sequence));
+  CHECK_EQ(getWidth(), inputGrad.getWidth() * contextLength);
 
-  size_t numSequences = sequence.getSize() - 1;
-  int64_t inputDim = inputGrad->getWidth();
-  int64_t dim = getWidth();
-  CHECK_EQ(dim, inputDim * contextLength);
-
-  real* outGrad = getData();
-  real* inGrad = inputGrad->getData();
-  const int* starts = sequence.getData();
-
-  hl_context_projection_backward_data(outGrad,
-                                      starts,
-                                      inGrad,
-                                      numSequences,
-                                      inputDim,
+  hl_context_projection_backward_data(getData(),
+                                      sequence.getData(),
+                                      inputGrad.getData(),
+                                      sequence.getSize() - 1,
+                                      inputGrad.getWidth(),
                                       contextLength,
                                       contextStart);
 }
 
-void GpuMatrix::contextProjectionBackwardWeight(MatrixPtr weightGrad,
+void GpuMatrix::contextProjectionBackwardWeight(Matrix& weightGrad,
                                                 const IVector& sequence,
                                                 int contextLength,
                                                 int contextStart,
                                                 int totalPad,
                                                 size_t beginPad) {
-  CHECK(dynamic_cast<GpuMatrix*>(weightGrad.get()));
+  CHECK(dynamic_cast<GpuMatrix*>(&weightGrad));
   CHECK(dynamic_cast<const GpuIVector*>(&sequence));
+  CHECK_EQ(getWidth(), weightGrad.getWidth() * contextLength);
 
-  size_t numSequences = sequence.getSize() - 1;
-  int64_t weightDim = weightGrad->getWidth();
-  int64_t dim = getWidth();
-  CHECK_EQ(dim, weightDim * contextLength);
-
-  real* outGrad = getData();
-  real* wtGrad = weightGrad->getData();
-  const int* starts = sequence.getData();
-
-  hl_context_projection_backward_weight(outGrad,
-                                        starts,
-                                        wtGrad,
-                                        numSequences,
-                                        weightDim,
+  hl_context_projection_backward_weight(getData(),
+                                        sequence.getData(),
+                                        weightGrad.getData(),
+                                        sequence.getSize() - 1,
+                                        weightGrad.getWidth(),
                                         totalPad,
                                         contextLength,
                                         contextStart,
@@ -2219,84 +2129,6 @@ void CpuMatrix::avgPoolBackward(Matrix& input,
   }
 }
 
-void CpuMatrix::crossMapNormalFwd(Matrix& input,
-                                  size_t imgSizeH,
-                                  size_t imgSizeW,
-                                  Matrix& denoms,
-                                  size_t channels,
-                                  size_t sizeX,
-                                  float scale,
-                                  float pow) {
-  size_t num = input.getHeight();
-  size_t height = imgSizeH;
-  size_t width = imgSizeW;
-  size_t numCols = input.getWidth();
-  CHECK(height * width * channels == input.getWidth());
-  CHECK(denoms.getHeight() == input.getHeight() &&
-        denoms.getWidth() == input.getWidth() && input.getHeight() == height_ &&
-        input.getWidth() == width_);
-  real* imgData = input.getData();
-  real* diffData = input.getData();
-  real* targetData = getData();
-  size_t halfSize = sizeX / 2;
-  size_t imgPixels = height * width;
-
-  // use integral vector to implement the sum in local window
-  real* integralData =
-      (real*)malloc((channels + sizeX + 1) * sizeof(real));  // NOLINT // TODO:
-  for (size_t i = 0; i <= halfSize; i++) {
-    integralData[i] = 0;
-  }
-  for (size_t i = 0; i < num; i++) {
-    real* targetPtr = targetData + i * numCols;
-    real* imgPtr = imgData + i * numCols;
-    real* diffPtr = diffData + i * numCols;
-    for (size_t m = 0; m < height; m++) {
-      for (size_t n = 0; n < width; n++) {
-        for (size_t c = 0; c < channels; c++) {
-          integralData[c + halfSize + 1] =
-              integralData[c + halfSize] + _square(*(diffPtr + c * imgPixels));
-        }
-        for (size_t k = channels + halfSize + 1; k <= channels + sizeX; k++) {
-          integralData[k] = integralData[channels + halfSize];
-        }
-        for (size_t k = 0; k < channels; k += 1) {
-          real a = integralData[k + sizeX] - integralData[k];
-          a = scale * a + 1;
-          targetPtr[k * imgPixels] = imgPtr[k * imgPixels] * _pow(a, -pow);
-        }
-        diffPtr++;
-        targetPtr++;
-        imgPtr++;
-      }
-    }
-  }
-  free(integralData);
-  integralData = NULL;
-}
-
-void CpuMatrix::crossMapNormalBwd(Matrix& localGrad,
-                                  Matrix& denoms,
-                                  Matrix& preOutV,
-                                  Matrix& localOutV,
-                                  size_t channels,
-                                  size_t imgSizeH,
-                                  size_t imgSizeW,
-                                  size_t size,
-                                  float scale,
-                                  float pow) {
-  LOG(FATAL) << "Not implemented";
-
-  CHECK(imgSizeH * imgSizeW * channels == preOutV.getWidth());
-  CHECK(denoms.getHeight() == preOutV.getHeight() &&
-        denoms.getWidth() == preOutV.getWidth() &&
-        preOutV.getHeight() == height_ && preOutV.getWidth() == width_);
-  CHECK(denoms.getHeight() == localGrad.getHeight() &&
-        denoms.getWidth() == localGrad.getWidth());
-
-  // NOLINT // TODO:
-}
-
 /**
  * Input: one or more sequences. Each sequence contains some instances.
  * Output: output size is the number of input sequences (NOT input instances).
@@ -2371,23 +2203,21 @@ void CpuMatrix::maxSequenceBackward(Matrix& outputGrad,
   }
 }
 
-void CpuMatrix::contextProjectionForward(MatrixPtr input,
-                                         MatrixPtr weight,
+void CpuMatrix::contextProjectionForward(Matrix& input,
+                                         Matrix* weight,
                                          const IVector& sequence,
                                          int contextLength,
                                          int contextStart,
                                          size_t beginPad,
                                          bool isPadding) {
-  CHECK(dynamic_cast<CpuMatrix*>(input.get()));
-  CHECK(dynamic_cast<const CpuIVector*>(&sequence));
-  if (weight) CHECK(dynamic_cast<CpuMatrix*>(weight.get()));
-
-  size_t numSequences = sequence.getSize() - 1;
-  int64_t inputDim = input->getWidth();
-  int64_t dim = getWidth();
-  CHECK_EQ(dim, inputDim * contextLength);
-  const int* starts = sequence.getData();
-
+  auto input_ptr = dynamic_cast<CpuMatrix*>(&input);
+  auto seq_ptr = dynamic_cast<const CpuIVector*>(&sequence);
+  CHECK(input_ptr && seq_ptr);
+  if (weight) CHECK(dynamic_cast<CpuMatrix*>(weight));
+  CHECK_EQ(getWidth(), input_ptr->getWidth() * contextLength);
+
+  const int* starts = seq_ptr->getData();
+  size_t numSequences = seq_ptr->getSize() - 1;
   for (size_t i = 0; i < numSequences; ++i) {
     for (int j = 0; j < contextLength; ++j) {
       int begin = starts[i] + contextStart + j;
@@ -2400,7 +2230,7 @@ void CpuMatrix::contextProjectionForward(MatrixPtr input,
         MatrixPtr mat = this->subMatrix(starts[i], padSize);
         if (isPadding) {
           MatrixPtr sub = weight->subMatrix(j, padSize);
-          mat->addAtOffset(*sub, j * inputDim);
+          mat->addAtOffset(*sub, j * input_ptr->getWidth());
         }
         dstBegin = starts[i] + padSize;
         begin = starts[i];
@@ -2412,41 +2242,36 @@ void CpuMatrix::contextProjectionForward(MatrixPtr input,
         if (isPadding) {
           MatrixPtr sub =
               weight->subMatrix(beginPad + contextStart + j - padSize, padSize);
-          mat->addAtOffset(*sub, j * inputDim);
+          mat->addAtOffset(*sub, j * input_ptr->getWidth());
         }
         dstEnd = starts[i + 1] - padSize;
         end = starts[i + 1];
       }
       if (end <= begin) continue;
-      MatrixPtr src = input->subMatrix(begin, end - begin);
+      MatrixPtr src = input_ptr->subMatrix(begin, end - begin);
       MatrixPtr dst = this->subMatrix(dstBegin, dstEnd - dstBegin);
-      dst->addAtOffset(*src, j * inputDim);
+      dst->addAtOffset(*src, j * input_ptr->getWidth());
     }
   }
 }
 
-void CpuMatrix::contextProjectionBackward(MatrixPtr inputGrad,
-                                          MatrixPtr weightGrad,
+void CpuMatrix::contextProjectionBackward(Matrix* inputGrad,
+                                          Matrix* weightGrad,
                                           const IVector& sequence,
                                           int contextLength,
                                           int contextStart,
                                           size_t beginPad,
                                           bool isPadding) {
-  if (inputGrad) CHECK(dynamic_cast<CpuMatrix*>(inputGrad.get()));
-  if (weightGrad) CHECK(dynamic_cast<CpuMatrix*>(weightGrad.get()));
+  if (inputGrad) CHECK(dynamic_cast<CpuMatrix*>(inputGrad));
+  if (weightGrad) CHECK(dynamic_cast<CpuMatrix*>(weightGrad));
   CHECK(dynamic_cast<const CpuIVector*>(&sequence));
 
-  int64_t inputDim = 0;
-  int64_t dim = getWidth();
-  size_t numSequences = sequence.getSize() - 1;
-  const int* starts = sequence.getData();
-  if (inputGrad) {
-    inputDim = inputGrad->getWidth();
-  } else {
-    inputDim = weightGrad->getWidth();
-  }
-  CHECK_EQ(dim, inputDim * contextLength);
+  int64_t inputDim = inputGrad ? inputGrad->getWidth()
+                               : weightGrad ? weightGrad->getWidth() : 0;
+  CHECK_EQ(getWidth(), inputDim * contextLength);
 
+  const int* starts = sequence.getData();
+  size_t numSequences = sequence.getSize() - 1;
   for (size_t i = 0; i < numSequences; ++i) {
     for (int j = 0; j < contextLength; ++j) {
       int begin = starts[i] + contextStart + j;
@@ -2630,29 +2455,22 @@ void CpuMatrix::sequenceAvgForward(Matrix& a,
 }
 
 /* this = scaleAB*(a*b) + scaleT*this*/
-void CpuMatrix::mul(const MatrixPtr a,
-                    const MatrixPtr b,
+void CpuMatrix::mul(const Matrix& a,
+                    const Matrix& b,
                     real scaleAB,
                     real scaleT) {
   CHECK(!isTransposed()) << "Not supported";
+  const auto a_ptr = dynamic_cast<const CpuMatrix*>(&a);
+  const auto b_ptr = dynamic_cast<const CpuMatrix*>(&b);
+  const auto a_ptr_s = dynamic_cast<const CpuSparseMatrix*>(&a);
+  const auto b_ptr_s = dynamic_cast<const CpuSparseMatrix*>(&b);
 
-  if (dynamic_cast<CpuMatrix*>(a.get()) && dynamic_cast<CpuMatrix*>(b.get())) {
-    mul(dynamic_cast<CpuMatrix*>(a.get()),
-        dynamic_cast<CpuMatrix*>(b.get()),
-        scaleAB,
-        scaleT);
-  } else if (dynamic_cast<CpuSparseMatrix*>(a.get()) &&
-             dynamic_cast<CpuMatrix*>(b.get())) {
-    mul(dynamic_cast<CpuSparseMatrix*>(a.get()),
-        dynamic_cast<CpuMatrix*>(b.get()),
-        scaleAB,
-        scaleT);
-  } else if (dynamic_cast<CpuMatrix*>(a.get()) &&
-             dynamic_cast<CpuSparseMatrix*>(b.get())) {
-    mul(dynamic_cast<CpuMatrix*>(a.get()),
-        dynamic_cast<CpuSparseMatrix*>(b.get()),
-        scaleAB,
-        scaleT);
+  if (a_ptr && b_ptr) {
+    mul((CpuMatrix*)a_ptr, (CpuMatrix*)b_ptr, scaleAB, scaleT);
+  } else if (a_ptr_s && b_ptr) {
+    mul((CpuSparseMatrix*)a_ptr_s, (CpuMatrix*)b_ptr, scaleAB, scaleT);
+  } else if (a_ptr && b_ptr_s) {
+    mul((CpuMatrix*)a_ptr, (CpuSparseMatrix*)b_ptr_s, scaleAB, scaleT);
   } else {
     LOG(FATAL) << "Not supported";
   }
@@ -3321,7 +3139,7 @@ void CpuMatrix::addColumnVector(const Matrix& b) {
 }
 
 /* this = a*b */
-void CpuMatrix::mul(const MatrixPtr a, const MatrixPtr b) {
+void CpuMatrix::mul(const Matrix& a, const Matrix& b) {
   return mul(a, b, 1.0, 0.0);
 }
 
@@ -3544,21 +3362,20 @@ void CpuMatrix::rowNormalizeL1(Matrix& out) {
 }
 
 /* calulate classification error */
-void CpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) {
-  CHECK(dynamic_cast<CpuMatrix*>(output.get()));
-  CHECK(dynamic_cast<CpuIVector*>(label.get()));
+void CpuMatrix::classificationError(Matrix& output, IVector& label) {
+  CHECK(dynamic_cast<const CpuMatrix*>(&output));
+  CHECK(dynamic_cast<const CpuIVector*>(&label));
 
-  size_t numSamples = getHeight();
-  size_t dim = output->getWidth();
-  CHECK_EQ(label->getSize(), numSamples);
-  CHECK_EQ(output->getHeight(), numSamples);
   CHECK_EQ(getWidth(), (size_t)1);
+  size_t numSamples = getHeight();
+  CHECK_EQ(label.getSize(), numSamples);
+  CHECK_EQ(output.getHeight(), numSamples);
 
-  real* out = output->getData();
-  real* result = getData();
-  int* lbl = label->getData();
-  real maxData;
-  int maxIndex;
+  size_t dim = output.getWidth();
+  real* out = output.getData();
+  int* lbl = label.getData();
+  real maxData = 0.0;
+  int maxIndex = -1;
   for (size_t i = 0; i < numSamples; ++i) {
     CHECK_GE(lbl[i], 0);
     CHECK_LT((size_t)lbl[i], dim);
@@ -3570,7 +3387,7 @@ void CpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) {
         maxData = out[i * dim + j];
       }
     }
-    result[i] = (maxIndex != lbl[i]);
+    getData()[i] = (maxIndex != lbl[i]);
   }
 }
 

paddle/math/Matrix.h

@@ -444,8 +444,8 @@ public:
    * this = scaleAB*(a*b) + scaleT*this
    * @endcode
    */
-  virtual void mul(const MatrixPtr a,
-                   const MatrixPtr b,
+  virtual void mul(const Matrix& a,
+                   const Matrix& b,
                    real scaleAB,
                    real scaleT) {
     LOG(FATAL) << "Not implemented";
@@ -643,7 +643,7 @@ public:
    *  this = a*b
    * @endcode
    */
-  virtual void mul(const MatrixPtr a, const MatrixPtr b) {
+  virtual void mul(const Matrix& a, const Matrix& b) {
     LOG(FATAL) << "Not implemented";
   }
 
@@ -835,7 +835,7 @@ public:
    *
    * output[i] = 0 if row i is correct.
    */
-  virtual void classificationError(MatrixPtr output, IVectorPtr label) {
+  virtual void classificationError(Matrix& output, IVector& label) {
     LOG(FATAL) << "Not implemented";
   }
 
@@ -952,31 +952,6 @@ public:
     LOG(FATAL) << "Not implemeted";
   }
 
-  /// normalize-operation.
-  virtual void crossMapNormalFwd(Matrix& input,
-                                 size_t imgSizeH,
-                                 size_t imgSizeW,
-                                 Matrix& denoms,
-                                 size_t channels,
-                                 size_t sizeX,
-                                 float scale,
-                                 float pow) {
-    LOG(FATAL) << "Not implemeted";
-  }
-
-  virtual void crossMapNormalBwd(Matrix& localGrad,
-                                 Matrix& denoms,
-                                 Matrix& preOutV,
-                                 Matrix& localOutV,
-                                 size_t channels,
-                                 size_t imgSizeH,
-                                 size_t imgSizeW,
-                                 size_t size,
-                                 float scale,
-                                 float pow) {
-    LOG(FATAL) << "Not implemeted";
-  }
-
   /**
    * Input: one or more sequences. Each sequence contains some instances.
    *
@@ -997,8 +972,8 @@ public:
     LOG(FATAL) << "Not implemeted";
   }
 
-  virtual void contextProjectionForward(MatrixPtr input,
-                                        MatrixPtr weight,
+  virtual void contextProjectionForward(Matrix& input,
+                                        Matrix* weight,
                                         const IVector& sequence,
                                         int contextLength,
                                         int contextStart,
@@ -1007,8 +982,8 @@ public:
     LOG(FATAL) << "Not implemeted";
   }
 
-  virtual void contextProjectionBackward(MatrixPtr inputGrad,
-                                         MatrixPtr weightGrad,
+  virtual void contextProjectionBackward(Matrix* inputGrad,
+                                         Matrix* weightGrad,
                                          const IVector& sequence,
                                          int contextLength,
                                          int contextStart,
@@ -1017,14 +992,14 @@ public:
     LOG(FATAL) << "Not implemeted";
   }
 
-  virtual void contextProjectionBackwardData(MatrixPtr inputGrad,
+  virtual void contextProjectionBackwardData(Matrix& inputGrad,
                                              const IVector& sequence,
                                              int contextLength,
                                              int contextStart) {
     LOG(FATAL) << "Not implemeted";
   }
 
-  virtual void contextProjectionBackwardWeight(MatrixPtr weightGrad,
+  virtual void contextProjectionBackwardWeight(Matrix& weightGrad,
                                                const IVector& sequence,
                                                int contextLength,
                                                int contextStart,
@@ -1272,14 +1247,14 @@ public:
    * this = scaleAB*(a*b) + scaleT*this
    * @endcode
    */
-  void mul(const MatrixPtr a, const MatrixPtr b, real scaleAB, real scaleT);
+  void mul(const Matrix& a, const Matrix& b, real scaleAB, real scaleT);
 
   /**
    * @code
    * this = a*b
    * @endcode
    */
-  void mul(const MatrixPtr a, const MatrixPtr b);
+  void mul(const Matrix& a, const Matrix& b);
 
   void mul(const GpuMatrix& a, const GpuMatrix& b, real scaleAB, real scaleT);
 
@@ -1373,7 +1348,7 @@ public:
   void check(std::ostream& os, Matrix& refMat, bool printDiff = true);
   void randomizeUniform();
 
-  void classificationError(MatrixPtr output, IVectorPtr label);
+  void classificationError(Matrix& output, IVector& label);
 
   void convExpand(Matrix& feature,
                   int feaImgHeight,
@@ -1459,26 +1434,6 @@ public:
                        size_t paddingH,
                        size_t paddingW);
 
-  void crossMapNormalFwd(Matrix& input,
-                         size_t imgSizeH,
-                         size_t imgSizeW,
-                         Matrix& denoms,
-                         size_t channels,
-                         size_t sizeX,
-                         float scale,
-                         float pow);
-
-  void crossMapNormalBwd(Matrix& localGrad,
-                         Matrix& denoms,
-                         Matrix& preOutV,
-                         Matrix& localOutV,
-                         size_t channels,
-                         size_t imgSizeH,
-                         size_t imgSizeW,
-                         size_t sizeX,
-                         float scale,
-                         float pow);
-
   void maxSequenceForward(Matrix& input,
                           const IVector& sequence,
                           IVector& index);
@@ -1487,20 +1442,20 @@ public:
                            const IVector& sequence,
                            IVector& index);
 
-  void contextProjectionForward(MatrixPtr input,
-                                MatrixPtr weight,
+  void contextProjectionForward(Matrix& input,
+                                Matrix* weight,
                                 const IVector& sequence,
                                 int contextLength,
                                 int contextStart,
                                 size_t beginPad,
                                 bool isPadding);
 
-  void contextProjectionBackwardData(MatrixPtr inputGrad,
+  void contextProjectionBackwardData(Matrix& inputGrad,
                                      const IVector& sequence,
                                      int contextLength,
                                      int contextStart);
 
-  void contextProjectionBackwardWeight(MatrixPtr weightGrad,
+  void contextProjectionBackwardWeight(Matrix& weightGrad,
                                        const IVector& sequence,
                                        int contextLength,
                                        int contextStart,
@@ -1685,26 +1640,6 @@ public:
                        size_t paddingH,
                        size_t paddingW);
 
-  void crossMapNormalFwd(Matrix& input,
-                         size_t imgSizeH,
-                         size_t imgSizeW,
-                         Matrix& denoms,
-                         size_t channels,
-                         size_t sizeX,
-                         float scale,
-                         float pow);
-
-  void crossMapNormalBwd(Matrix& localGrad,
-                         Matrix& denoms,
-                         Matrix& preOutV,
-                         Matrix& localOutV,
-                         size_t channels,
-                         size_t imgSizeH,
-                         size_t imgSizeW,
-                         size_t sizeX,
-                         float scale,
-                         float pow);
-
   void maxSequenceForward(Matrix& input,
                           const IVector& sequence,
                           IVector& index);
@@ -1713,16 +1648,16 @@ public:
                            const IVector& sequence,
                            IVector& index);
 
-  void contextProjectionForward(MatrixPtr input,
-                                MatrixPtr weight,
+  void contextProjectionForward(Matrix& input,
+                                Matrix* weight,
                                 const IVector& sequence,
                                 int contextLength,
                                 int contextStart,
                                 size_t beginPad,
                                 bool isPadding);
 
-  void contextProjectionBackward(MatrixPtr inputGrad,
-                                 MatrixPtr weightGrad,
+  void contextProjectionBackward(Matrix* inputGrad,
+                                 Matrix* weightGrad,
                                  const IVector& sequence,
                                  int contextLength,
                                  int contextStart,
@@ -1784,7 +1719,7 @@ public:
 
   void addColumnVector(const Matrix& b);
 
-  void mul(const MatrixPtr a, const MatrixPtr b, real scaleAB, real scaleT);
+  void mul(const Matrix& a, const Matrix& b, real scaleAB, real scaleT);
   void mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT);
 
   void mul(CpuMatrix* a, CpuSparseMatrix* b, real scaleAB, real scaleT);
@@ -1807,7 +1742,7 @@ public:
 
   virtual void mul(CpuSparseMatrix* a, CpuMatrix* b, real scaleAB, real scaleT);
 
-  void mul(const MatrixPtr a, const MatrixPtr b);
+  void mul(const Matrix& a, const Matrix& b);
 
   void rightMul(Matrix& b, real scaleAB, real scaleT);
   void rightMul(Matrix& b);
@@ -1881,7 +1816,7 @@ public:
 
   void randomizeUniform();
 
-  void classificationError(MatrixPtr output, IVectorPtr label);
+  void classificationError(Matrix& output, IVector& label);
 
   void addByBitCode(size_t numClasses, const IVector& codes, const Matrix& vec);
 

paddle/math/SparseMatrix.cpp

@@ -571,49 +571,48 @@ void GpuSparseMatrix::transpose(MatrixPtr matTrans, bool memAlloc) {
   hl_stream_synchronize(stream);
 }
 
-void GpuSparseMatrix::mul(const GpuMatrixPtr a,
-                          const GpuMatrixPtr b,
+void GpuSparseMatrix::mul(const GpuMatrix& a,
+                          const GpuMatrix& b,
                           real scaleAB,
                           real scaleT) {
-  CHECK(a->useGpu_ && b->useGpu_) << "type not match";
+  CHECK(a.useGpu_ && b.useGpu_) << "type not match";
   CHECK(!trans_) << "trans not supported";
-  real* A_d = a->getData();
-  real* B_d = b->getData();
+  real* A_d = (real*)a.getData();
+  real* B_d = (real*)b.getData();
   hl_sparse_matrix_s C_d = sMatrix_.get();
-  hl_trans_op_t a_trans = a->trans_ ? HPPL_OP_T : HPPL_OP_N;
-  hl_trans_op_t b_trans = b->trans_ ? HPPL_OP_T : HPPL_OP_N;
-
-  if (!a->trans_ && !b->trans_) {
-    CHECK(height_ == a->getHeight());
-    CHECK(width_ == b->getWidth());
-    CHECK(a->getWidth() == b->getHeight());
-  } else if (a->trans_ && !b->trans_) {
-    CHECK(height_ == a->getWidth());
-    CHECK(width_ == b->getWidth());
-    CHECK(a->getHeight() == b->getHeight());
-  } else if (!a->trans_ && b->trans_) {
-    CHECK(height_ == a->getHeight());
-    CHECK(width_ == b->getHeight());
-    CHECK(a->getWidth() == b->getWidth());
+  hl_trans_op_t a_trans = a.trans_ ? HPPL_OP_T : HPPL_OP_N;
+  hl_trans_op_t b_trans = b.trans_ ? HPPL_OP_T : HPPL_OP_N;
+
+  if (!a.trans_ && !b.trans_) {
+    CHECK(height_ == a.getHeight());
+    CHECK(width_ == b.getWidth());
+    CHECK(a.getWidth() == b.getHeight());
+  } else if (a.trans_ && !b.trans_) {
+    CHECK(height_ == a.getWidth());
+    CHECK(width_ == b.getWidth());
+    CHECK(a.getHeight() == b.getHeight());
+  } else if (!a.trans_ && b.trans_) {
+    CHECK(height_ == a.getHeight());
+    CHECK(width_ == b.getHeight());
+    CHECK(a.getWidth() == b.getWidth());
   } else {
     LOG(INFO) << "Not support";
   }
   int dimM = height_;
   int dimN = width_;
-  int dimK = !b->trans_ ? b->getHeight() : b->getWidth();
+  int dimK = !b.trans_ ? b.getHeight() : b.getWidth();
   hl_sparse_matrix_mul(
       A_d, a_trans, B_d, b_trans, C_d, dimM, dimN, dimK, scaleAB, scaleT);
 }
 
-void GpuSparseMatrix::mul(const MatrixPtr a,
-                          const MatrixPtr b,
+void GpuSparseMatrix::mul(const Matrix& a,
+                          const Matrix& b,
                           real scaleAB,
                           real scaleT) {
-  if (std::dynamic_pointer_cast<GpuMatrix>(a) &&
-      std::dynamic_pointer_cast<GpuMatrix>(b)) {
-    GpuMatrixPtr a_ptr = std::dynamic_pointer_cast<GpuMatrix>(a);
-    GpuMatrixPtr b_ptr = std::dynamic_pointer_cast<GpuMatrix>(b);
-    mul(a_ptr, b_ptr, scaleAB, scaleT);
+  const auto a_ptr = dynamic_cast<const GpuMatrix*>(&a);
+  const auto b_ptr = dynamic_cast<const GpuMatrix*>(&b);
+  if (a_ptr && b_ptr) {
+    mul(*a_ptr, *b_ptr, scaleAB, scaleT);
   } else {
     LOG(FATAL) << "not supported";
   }

paddle/math/SparseMatrix.h

@@ -104,10 +104,7 @@ public:
                  size_t newNnz,
                  SparseValueType valueType);
 
-  void mul(const GpuMatrixPtr a,
-           const GpuMatrixPtr b,
-           real scaleAB,
-           real scaleT);
+  void mul(const GpuMatrix& a, const GpuMatrix& b, real scaleAB, real scaleT);
   /// B = A , B.trans = !A.trans
   MatrixPtr getTranspose();
 
@@ -218,7 +215,7 @@ protected:
   void copyRow(int offsets, size_t colNum, const sparse_float_value_t* row);
 
 public:
-  void mul(const MatrixPtr a, const MatrixPtr b, real scaleAB, real scaleT);
+  void mul(const Matrix& a, const Matrix& b, real scaleAB, real scaleT);
 
   void copyFrom(CpuSparseMatrix& src, hl_stream_t stream);
   void copyFrom(GpuSparseMatrix& src, hl_stream_t stream);

paddle/math/tests/test_SparseMatrix.cpp

@@ -33,8 +33,8 @@ TEST(Matrix, CopyCpuMatrixToSparseMatrix) {
       ret2(new CpuMatrix(HEIGHT, WIDTH_TEST));
   ret1->zeroMem();
   ret2->zeroMem();
-  ret1->mul(testMatrix, mulCpuMatrix, 1.0, 1.0);
-  ret2->mul(testCpuMatrix, mulCpuMatrix, 1.0, 1.0);
+  ret1->mul(*testMatrix, *mulCpuMatrix, 1.0, 1.0);
+  ret2->mul(*testCpuMatrix, *mulCpuMatrix, 1.0, 1.0);
   checkMatrixEqual(ret1, ret2);
 }
 
@@ -147,9 +147,9 @@ void test_sparse_matrix_mul(MatrixPara paraA,
   hl_stream_synchronize(stream);
 
   /*matrix mul*/
-  cpuMatrixC->mul(cpuMatrixA, cpuMatrixB, 1.0, 1.0);
-  gpuMatrixC->mul(gpuMatrixA, gpuMatrixB, 1.0, 1.0);
-  cpuDenseC->mul(cpuDenseA, cpuDenseB, 1.0, 1.0);
+  cpuMatrixC->mul(*cpuMatrixA, *cpuMatrixB, 1.0, 1.0);
+  gpuMatrixC->mul(*gpuMatrixA, *gpuMatrixB, 1.0, 1.0);
+  cpuDenseC->mul(*cpuDenseA, *cpuDenseB, 1.0, 1.0);
 
   gpuMatrixC_d2h->copyFrom(*gpuMatrixC, stream);
   hl_stream_synchronize(stream);
@@ -224,8 +224,8 @@ TEST(Matrix, CopySparseMatrixToGpuSparseMatrix) {
   MatrixPtr ret2(new GpuMatrix(HEIGHT, WIDTH_TEST));
   ret1->zeroMem();
   ret2->zeroMem();
-  ret1->mul(testMatrix, mulCpuMatrix, 1.0, 1.0);
-  ret2->mul(testGpuMatrix, mulGpuMatrix, 1.0, 1.0);
+  ret1->mul(*testMatrix, *mulCpuMatrix, 1.0, 1.0);
+  ret2->mul(*testGpuMatrix, *mulGpuMatrix, 1.0, 1.0);
   checkMatrixEqual(ret1, ret2);
 }
 

paddle/math/tests/test_matrixCompare.cpp

@@ -65,16 +65,16 @@ void testMatrixProjectionForward(int contextStart,
 
   // calculate
   int beginPad = std::max(0, -contextStart);
-  cpuOutput->contextProjectionForward(cpuInput,
-                                      cpuWeight,
+  cpuOutput->contextProjectionForward(*cpuInput,
+                                      cpuWeight.get(),
                                       *cpuSequence,
                                       contextLength,
                                       contextStart,
                                       beginPad,
                                       padding);
 
-  gpuOutput->contextProjectionForward(gpuInput,
-                                      gpuWeight,
+  gpuOutput->contextProjectionForward(*gpuInput,
+                                      gpuWeight.get(),
                                       *gpuSequence,
                                       contextLength,
                                       contextStart,
@@ -120,17 +120,17 @@ void testMatrixProjectionBackward(int contextStart,
 
   // calculate
   int beginPad = std::max(0, -contextStart);
-  cpuOutputGrad->contextProjectionBackward(cpuInputGrad,
-                                           cpuWeightGrad,
+  cpuOutputGrad->contextProjectionBackward(cpuInputGrad.get(),
+                                           cpuWeightGrad.get(),
                                            *cpuSequence,
                                            contextLength,
                                            contextStart,
                                            beginPad,
                                            padding);
   gpuOutputGrad->contextProjectionBackwardData(
-      gpuInputGrad, *gpuSequence, contextLength, contextStart);
+      *gpuInputGrad, *gpuSequence, contextLength, contextStart);
   if (padding) {
-    gpuOutputGrad->contextProjectionBackwardWeight(gpuWeightGrad,
+    gpuOutputGrad->contextProjectionBackwardWeight(*gpuWeightGrad,
                                                    *gpuSequence,
                                                    contextLength,
                                                    contextStart,
@@ -318,7 +318,7 @@ void testMatrixInverse(int height) {
   cpu->randomizeUniform();
   MatrixPtr cpuT = cpu->getTranspose();
   MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, height);
-  outputCheck->mul(cpu, cpuT);
+  outputCheck->mul(*cpu, *cpuT);
   cpu->setDiag(1.0);
   cpu->add(*outputCheck);
 
@@ -328,7 +328,7 @@ void testMatrixInverse(int height) {
 
   TensorCheckErr(*cpuI, *gpuI);
 
-  outputCheck->mul(cpu, cpuI);
+  outputCheck->mul(*cpu, *cpuI);
   cpu->setDiag(1.0);
   TensorCheckErr(*cpu, *outputCheck);
 }
@@ -509,8 +509,8 @@ void testMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) {
   gpuB->copyFrom(*cpuB);
   gpuC->copyFrom(*cpuC);
 
-  cpuC->mul(cpuA, cpuB, alpha, beta);
-  gpuC->mul(gpuA, gpuB, alpha, beta);
+  cpuC->mul(*cpuA, *cpuB, alpha, beta);
+  gpuC->mul(*gpuA, *gpuB, alpha, beta);
 
   TensorCheckErr(*cpuC, *gpuC);
 }
@@ -581,8 +581,8 @@ void testSubMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) {
   MatrixPtr subCpuC = cpuC->subMatrix(startM, endM, startN, endN);
   MatrixPtr subGpuC = gpuC->subMatrix(startM, endM, startN, endN);
 
-  subCpuC->mul(subCpuA, subCpuB, alpha, beta);
-  subGpuC->mul(subGpuA, subGpuB, alpha, beta);
+  subCpuC->mul(*subCpuA, *subCpuB, alpha, beta);
+  subGpuC->mul(*subGpuA, *subGpuB, alpha, beta);
 
   TensorCheckErr(*cpuC, *gpuC);
 }
@@ -939,8 +939,8 @@ void testClassificationError(int numSamples, int dim) {
   gpuOutput->copyFrom(*cpuOutput);
   gpuLabel->copyFrom(*cpuLabel);
 
-  cpuError->classificationError(cpuOutput, cpuLabel);
-  gpuError->classificationError(gpuOutput, gpuLabel);
+  cpuError->classificationError(*cpuOutput, *cpuLabel);
+  gpuError->classificationError(*gpuOutput, *gpuLabel);
 
   TensorCheckEqual(*cpuError, *gpuError);
 }

paddle/math/tests/test_sparseMatrixCompare.cpp

@@ -102,8 +102,8 @@ void testSpMatrixMul(int M, int N, int K, real rate) {
   gpuC->copyFrom(*cpuC, stream);
   hl_stream_synchronize(stream);
 
-  cpuC->mul(cpuA, cpuB->getTranspose(), 1, 1);
-  gpuC->mul(gpuA, gpuB->getTranspose(), 1, 1);
+  cpuC->mul(*cpuA, *cpuB->getTranspose(), 1, 1);
+  gpuC->mul(*gpuA, *gpuB->getTranspose(), 1, 1);
 
   MatrixPtr outputCheck(new CpuSparseMatrix(M, N, nnz));
   outputCheck->copyFrom(*gpuC, stream);

paddle/scripts/docker/Dockerfile

@@ -2,6 +2,8 @@ FROM ubuntu:14.04
 MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
 
 ARG DEBIAN_FRONTEND=noninteractive
+ARG UBUNTU_MIRROR
+RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
 RUN apt-get update \
     && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \
     libgoogle-glog-dev libgflags-dev libgtest-dev \

paddle/scripts/docker/Dockerfile.gpu

@@ -2,6 +2,8 @@ FROM nvidia/cuda:7.5-cudnn5-devel-ubuntu14.04
 MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
 
 ARG DEBIAN_FRONTEND=noninteractive
+ARG UBUNTU_MIRROR
+RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
 RUN apt-get update \
     && apt-get install -y cmake libprotobuf-dev protobuf-compiler git \
     libgoogle-glog-dev libgflags-dev libgtest-dev \

paddle/scripts/travis/docs.sh

@@ -7,6 +7,10 @@ source ./common.sh
 cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=ON
 make paddle_docs paddle_docs_cn
 
+# check websites for broken links
+linkchecker doc/en/html/index.html
+linkchecker doc/cn/html/index.html
+
 # Parse Github URL
 REPO=`git config remote.origin.url`
 SSH_REPO=${REPO/https:\/\/github.com\//git@github.com:}
@@ -35,8 +39,8 @@ git checkout $TARGET_BRANCH || git checkout --orphan $TARGET_BRANCH
 
 # remove old docs. mv new docs.
 rm -rf doc doc_cn
-mv ../doc_cn/html doc_cn
-mv ../doc/html doc
+mv ../doc/cn/html doc_cn
+mv ../doc/en/html doc
 
 # Check is there anything changed.
 set +e

paddle/utils/Stat.cpp

@@ -137,6 +137,9 @@ void StatSet::printSegTimerStatus() {
 
 void StatSet::printBarrierTimerStatus() {
   ReadLockGuard guard(lock_);
+  if (barrierStatSet_.empty()) {
+    return;
+  }
   // control barrierAbstact in runtime, so enable compliation
   LOG(INFO) << std::setiosflags(std::ios::left) << std::setfill(' ')
             << "======= BarrierStatSet status ======" << std::endl;

paddle/utils/Stat.h

@@ -258,28 +258,41 @@ inline StatSet& registerTimerArg2(uint64_t threshold = -1,
 // The default arguments are shown in the following line:
 // REGISTER_TIMER(statName, threshold = -1, statSet = globalStat)
 // TODO(yuyang18,wangyanfei01): if UNIQUE_NAME is needed
-#define REGISTER_TIMER(statName, ...)                                       \
-  static StatPtr __stat = registerTimerArg2(__VA_ARGS__).getStat(statName); \
-  TimerOnce __timerOnce(__stat.get(), "", registerTimerArg1(__VA_ARGS__));
+#define REGISTER_TIMER(statName, ...)                             \
+  static ::paddle::StatPtr __stat =                               \
+      ::paddle::registerTimerArg2(__VA_ARGS__).getStat(statName); \
+  ::paddle::TimerOnce __timerOnce(                                \
+      __stat.get(), "", ::paddle::registerTimerArg1(__VA_ARGS__));
 
 #define REGISTER_TIMER_SET(statName, start, ...)                            \
-  static StatPtr __stat = registerTimerArg2(__VA_ARGS__).getStat(statName); \
-  TimerOnce __timerOnce(                                                    \
-      __stat.get(), "", registerTimerArg1(__VA_ARGS__), false, start);
+  static ::paddle::StatPtr __stat =                                         \
+      ::paddle::registerTimerArg2(__VA_ARGS__).getStat(statName);           \
+  ::paddle::TimerOnce __timerOnce(__stat.get(),                             \
+                                  "",                                       \
+                                  ::paddle::registerTimerArg1(__VA_ARGS__), \
+                                  false,                                    \
+                                  start);
 
 // dynmaic timer, support to discriminate runtime entity, used in pserver
-#define REGISTER_TIMER_DYNAMIC(statName, ...)                        \
-  StatPtr __stat = registerTimerArg2(__VA_ARGS__).getStat(statName); \
-  TimerOnce __timerOnce(__stat.get(), "", registerTimerArg1(__VA_ARGS__));
-
-#define REGISTER_TIMER_DYNAMIC_SET(statName, start, ...)             \
-  StatPtr __stat = registerTimerArg2(__VA_ARGS__).getStat(statName); \
-  TimerOnce __timerOnce(                                             \
-      __stat.get(), "", registerTimerArg1(__VA_ARGS__), false, start);
-
-#define REGISTER_TIMER_INFO(statName, info)             \
-  static StatPtr __stat = globalStat.getStat(statName); \
-  TimerOnce __timerOnce(__stat.get(), info, 10 * 1000000LU /*threshold*/);
+#define REGISTER_TIMER_DYNAMIC(statName, ...)                     \
+  ::paddle::StatPtr __stat =                                      \
+      ::paddle::registerTimerArg2(__VA_ARGS__).getStat(statName); \
+  ::paddle::TimerOnce __timerOnce(                                \
+      __stat.get(), "", ::paddle::registerTimerArg1(__VA_ARGS__));
+
+#define REGISTER_TIMER_DYNAMIC_SET(statName, start, ...)                    \
+  ::paddle::StatPtr __stat =                                                \
+      ::paddle::registerTimerArg2(__VA_ARGS__).getStat(statName);           \
+  ::paddle::TimerOnce __timerOnce(__stat.get(),                             \
+                                  "",                                       \
+                                  ::paddle::registerTimerArg1(__VA_ARGS__), \
+                                  false,                                    \
+                                  start);
+
+#define REGISTER_TIMER_INFO(statName, info)                                 \
+  static ::paddle::StatPtr __stat = ::paddle::globalStat.getStat(statName); \
+  ::paddle::TimerOnce __timerOnce(                                          \
+      __stat.get(), info, 10 * 1000000LU /*threshold*/);
 
 #endif  // DISABLE_TIMER
 

proto/ModelConfig.proto

@@ -248,6 +248,13 @@ message ImageConfig {
   optional uint32 img_size_y = 9;
 }
 
+message PriorBoxConfig {
+  repeated uint32 min_size = 1;
+  repeated uint32 max_size = 2;
+  repeated float aspect_ratio = 3;
+  repeated float variance = 4;
+}
+
 message LayerInputConfig {
   required string input_layer_name = 1;
   optional string input_parameter_name = 2;
@@ -263,6 +270,7 @@ message LayerInputConfig {
   optional BilinearInterpConfig bilinear_interp_conf = 10;
   optional MaxOutConfig maxout_conf = 11;
   optional SppConfig spp_conf = 12;
+  optional PriorBoxConfig priorbox_conf = 13;
 }
 
 message LayerConfig {

python/paddle/trainer/PyDataProvider2.py

@@ -278,7 +278,7 @@ def provider(input_types=None,
                                 custom calculate one sample's batch_size.
 
                                 It is very danger to set it to false and use
-                                calc_batch_size together. Default is false.
+                                calc_batch_size together. Default is true.
     :type can_over_batch_size: bool
 
     :param calc_batch_size: a method to calculate each sample's batch size.

python/paddle/trainer/config_parser.py

@@ -498,9 +498,16 @@ class Input(Cfg):
             is_static=None,
             is_shared=None,
             update_hooks=None,
-            input_layer_argument=None, ):
+            input_layer_argument=None,
+            make_layer_name_in_submodel=True, ):
+        """
+        @param make_layer_name_in_submodel True by defalut, you might need to
+        set it carefully when adding Input in config_parser.py.
+        """
         self.add_keys(locals())
-        self.input_layer_name = MakeLayerNameInSubmodel(input_layer_name)
+        self.input_layer_name = MakeLayerNameInSubmodel(
+            input_layer_name
+        ) if make_layer_name_in_submodel else input_layer_name
 
 
 # Define a projection for iexed layer
@@ -1582,6 +1589,27 @@ class PrintLayer(LayerBase):
         super(PrintLayer, self).__init__(name, 'print', 0, inputs)
 
 
+@config_layer('priorbox')
+class PriorBoxLayer(LayerBase):
+    def __init__(self, name, inputs, size, min_size, max_size, aspect_ratio,
+                 variance):
+        super(PriorBoxLayer, self).__init__(name, 'priorbox', 0, inputs)
+        config_assert(len(inputs) == 2, 'PriorBoxLayer must have 2 inputs')
+        input_layer = self.get_input_layer(1)
+        config_assert(
+            input_layer.type == 'data',
+            'Expecting the second input layer of an priorbox layer to be '
+            'a data layer')
+        config_assert(input_layer.width > 0, 'The data layer must set width')
+        config_assert(input_layer.height > 0, 'The data layer must set height')
+        config_assert(len(variance) == 4, 'The variance must have 4 inputs')
+        self.config.inputs[0].priorbox_conf.min_size.extend(min_size)
+        self.config.inputs[0].priorbox_conf.max_size.extend(max_size)
+        self.config.inputs[0].priorbox_conf.aspect_ratio.extend(aspect_ratio)
+        self.config.inputs[0].priorbox_conf.variance.extend(variance)
+        self.config.size = size
+
+
 @config_layer('data')
 class DataLayer(LayerBase):
     def __init__(self, name, size, height=None, width=None, device=None):
@@ -1848,7 +1876,8 @@ class BatchNormLayer(LayerBase):
                     initial_std=0.0,
                     initial_mean=0.0,
                     is_static=True,
-                    is_shared=is_shared, ))
+                    is_shared=is_shared,
+                    make_layer_name_in_submodel=False, ))
 
         parallel_nn = bool(int(g_command_config_args.get("parallel_nn", 0)))
         cudnn_version = int(g_command_config_args.get("cudnn_version", 0))
@@ -1880,7 +1909,7 @@ class BatchNormLayer(LayerBase):
         # when either of it is non-zero. 
         if input_layer.width != 0 or input_layer.height != 0:
             self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
-                               image_conf.channels, True)
+                               image_conf.channels, False)
         else:
             self.set_layer_size(input_layer.size)
 

python/paddle/trainer_config_helpers/layers.py

@@ -106,6 +106,7 @@ __all__ = [
     'maxout_layer',
     'out_prod_layer',
     'print_layer',
+    'priorbox_layer',
     'spp_layer',
 ]
 
@@ -171,6 +172,7 @@ class LayerType(object):
     SPP_LAYER = "spp"
 
     PRINT_LAYER = "print"
+    PRIORBOX_LAYER = "priorbox"
 
     CTC_LAYER = "ctc"
     WARP_CTC_LAYER = "warp_ctc"
@@ -934,6 +936,52 @@ def print_layer(input, name=None):
     # this layer don't return anything, can not be input of other layer.
 
 
+@wrap_name_default("priorbox")
+def priorbox_layer(input,
+                   image,
+                   aspect_ratio,
+                   variance,
+                   min_size,
+                   max_size=[],
+                   name=None):
+    """
+    Compute the priorbox and set the variance. This layer is necessary for ssd.
+
+    :param name: The Layer Name.
+    :type name: basestring
+    :param input: The input layer.
+    :type input: LayerOutput
+    :param image: The network input image.
+    :type image: LayerOutput
+    :param aspect_ratio: The aspect ratio.
+    :type aspect_ratio: list
+    :param variance: The bounding box variance.
+    :type min_size: The min size of the priorbox width/height.
+    :param min_size: list
+    :type max_size: The max size of the priorbox width/height. Could be NULL.
+    :param max_size: list
+    :return: LayerOutput
+    """
+    # plus one for ratio 1.
+    num_filters = (len(aspect_ratio) * 2 + 1 + len(max_size)) * 4
+    size = (input.size / input.num_filters) * num_filters * 2
+    Layer(
+        name=name,
+        type=LayerType.PRIORBOX_LAYER,
+        inputs=[input.name, image.name],
+        size=size,
+        min_size=min_size,
+        max_size=max_size,
+        aspect_ratio=aspect_ratio,
+        variance=variance)
+    return LayerOutput(
+        name,
+        LayerType.PRIORBOX_LAYER,
+        parents=[input, image],
+        num_filters=num_filters,
+        size=size)
+
+
 @wrap_name_default("seq_pooling")
 @wrap_bias_attr_default(has_bias=False)
 @wrap_param_default(['pooling_type'], default_factory=lambda _: MaxPooling())