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提交 a357bd6f 编写于 作者: C chengduoZH
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Fix conflict and Add test_pooling3D_layer.protostr

上级 d747c5d5 5a7359c4
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Showing with 2772 addition and 265 deletion
cmake/external/mkldnn.cmake
浏览文件 @ a357bd6f
......@@ -51,7 +51,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "v0.9"
GIT_TAG "v0.10"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
......
cmake/external/mklml.cmake
浏览文件 @ a357bd6f
......@@ -28,7 +28,7 @@ INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.20170720")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.9/${MKLML_VER}.tgz")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.10/${MKLML_VER}.tgz")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
......@@ -54,7 +54,8 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${MKLML_SOURCE_DIR}
DOWNLOAD_DIR ${MKLML_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate -qO- ${MKLML_URL} | tar xz -C ${MKLML_DOWNLOAD_DIR}
DOWNLOAD_COMMAND wget --no-check-certificate ${MKLML_URL} -c -q -O ${MKLML_VER}.tgz
&& tar zxf ${MKLML_VER}.tgz
DOWNLOAD_NO_PROGRESS 1
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLML_INSTALL_ROOT}
......
doc/getstarted/build_and_install/index_cn.rst
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......@@ -6,14 +6,12 @@
安装流程
++++++++
PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜像，ubuntu的deb安装包等。我们推荐使用Docker镜像来部署环境，同时欢迎贡献更多的安装包。
PaddlePaddle提供Docker镜像来部署环境。
.. toctree::
:maxdepth: 1
docker_install_cn.rst
ubuntu_install_cn.rst
编译流程
......
doc/getstarted/build_and_install/index_en.rst
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......@@ -8,14 +8,13 @@ Install PaddlePaddle
:maxdepth: 1
docker_install_en.rst
ubuntu_install_en.rst
Build from Source
-----------------
.. warning::
Please use :code:`deb` package or :code:`docker` image to install paddle. The building guide is used for hacking or contributing PaddlePaddle source code.
Please use :code:`docker` image to install paddle. The building guide is used for hacking or contributing PaddlePaddle source code.
.. toctree::
:maxdepth: 1
......
doc/getstarted/build_and_install/ubuntu_install_cn.rst 已删除 100644 → 0
浏览文件 @ d747c5d5
Ubuntu部署PaddlePaddle
===================================
PaddlePaddle提供了ubuntu 14.04 deb安装包。
安装
------
安装包的下载地址是\: https://github.com/PaddlePaddle/Paddle/releases
它包含四个版本\:
* cpu版本: 支持主流x86处理器平台, 使用了avx指令集。
* cpu-noavx版本：支持主流x86处理器平台，没有使用avx指令集。
* gpu版本：支持主流x86处理器平台，支持nvidia cuda平台，使用了avx指令集。
* gpu-noavx版本：支持主流x86处理器平台，支持nvidia cuda平台，没有使用avx指令集。
下载完相关安装包后，执行:
.. code-block:: shell
sudo apt-get install gdebi
gdebi paddle-*-cpu.deb
或者:
.. code-block:: shell
dpkg -i paddle-*-cpu.deb
apt-get install -f
在 :code:`dpkg -i` 的时候如果报一些依赖未找到的错误是正常的，
在 :code:`apt-get install -f` 里会继续安装 PaddlePaddle。
安装完成后，可以使用命令 :code:`paddle version` 查看安装后的paddle 版本:
.. code-block:: shell
PaddlePaddle 0.8.0b1, compiled with
with_avx: ON
with_gpu: OFF
with_double: OFF
with_python: ON
with_rdma: OFF
with_timer: OFF
with_predict_sdk:
可能遇到的问题
--------------
libcudart.so/libcudnn.so找不到
++++++++++++++++++++++++++++++
安装完成后，运行 :code:`paddle train` 报错\:
.. code-block:: shell
0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH.
原因是未设置cuda运行时环境变量。 如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中，并设置：
.. code-block:: shell
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
doc/getstarted/build_and_install/ubuntu_install_en.rst 已删除 100644 → 0
浏览文件 @ d747c5d5
Debian Package installation guide
=================================
PaddlePaddle supports :code:`deb` pacakge. The installation of this :code:`deb` package is tested in ubuntu 14.04, but it should be support other debian based linux, too.
There are four versions of debian package, :code:`cpu`, :code:`gpu`, :code:`cpu-noavx`, :code:`gpu-noavx`. And :code:`noavx` version is used to support CPU which does not contain :code:`AVX` instructions. The download url of :code:`deb` package is \: https://github.com/baidu/Paddle/releases/
After downloading PaddlePaddle deb packages, you can use :code:`gdebi` install.
.. code-block:: bash
gdebi paddle-*.deb
If :code:`gdebi` is not installed, you can use :code:`sudo apt-get install gdebi` to install it.
Or you can use following commands to install PaddlePaddle.
.. code-block:: bash
dpkg -i paddle-*.deb
apt-get install -f
And if you use GPU version deb package, you need to install CUDA toolkit and cuDNN, and set related environment variables(such as LD_LIBRARY_PATH) first. It is normal when `dpkg -i` get errors. `apt-get install -f` will continue install paddle, and install dependences.
paddle/function/CMakeLists.txt
浏览文件 @ a357bd6f
......@@ -21,6 +21,8 @@ if(USE_NNPACK)
endif()
endif()
list(APPEND cpp_files neon/NeonDepthwiseConv.cpp)
add_library(paddle_function STATIC ${cpp_files} ${cu_objs})
add_dependencies(paddle_function ${external_project_dependencies})
add_dependencies(paddle_function paddle_proto)
......@@ -42,11 +44,11 @@ if(WITH_GPU)
add_simple_unittest(RowConvOpTest)
add_simple_unittest(BlockExpandOpTest)
add_simple_unittest(CropOpTest)
add_simple_unittest(DepthwiseConvOpTest)
endif()
add_simple_unittest(Im2ColTest)
add_simple_unittest(GemmConvOpTest)
add_simple_unittest(DepthwiseConvOpTest)
endif()
add_style_check_target(paddle_function ${h_files})
......
paddle/function/DepthwiseConvOpTest.cpp
浏览文件 @ a357bd6f
......@@ -34,4 +34,13 @@ TEST(DepthwiseConv, BackwardFilter) {
}
#endif
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
TEST(DepthwiseConv, Forward) {
DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
"GemmConv-CPU", "NeonDepthwiseConv-CPU", forward);
}
#endif
} // namespace paddle
paddle/function/Im2Col.h
浏览文件 @ a357bd6f
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "TensorShape.h"
#include "TensorType.h"
#include "neon/neon_util.h"
namespace paddle {
......@@ -93,4 +94,95 @@ public:
int paddingWidth);
};
template <class T>
struct Padding {
static void run(const T* src,
T* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
memcpy(dest, src, inputWidth * sizeof(T));
dest += inputWidth;
src += inputWidth;
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
}
}
};
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <>
struct Padding<float> {
static void run(const float* src,
float* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
int step = inputWidth >> 2;
int remain = inputWidth & 3;
for (int s = 0; s < step; s++) {
float32x4_t s0 = vld1q_f32(src);
vst1q_f32(dest, s0);
src += 4;
dest += 4;
}
for (int r = 0; r < remain; r++) {
*dest++ = *src++;
}
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
}
}
};
#endif
} // namespace paddle
paddle/function/neon/NeonDepthwiseConv.cpp 0 → 100644
浏览文件 @ a357bd6f
/* 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 "neon_util.h"
#include "paddle/function/ConvOp.h"
#include "paddle/function/Im2Col.h"
namespace paddle {
namespace neon {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <int filterSize, int stride>
struct DepthwiseConvKernel {};
inline float32_t conv3x3(float32x4_t r0,
float32x4_t r1,
float32x4_t r2,
float32x4_t k0,
float32x4_t k1,
float32x4_t k2) {
float32x4_t tmp;
tmp = vmulq_f32(r0, k0);
tmp = vmlaq_f32(tmp, r1, k1);
tmp = vmlaq_f32(tmp, r2, k2);
return vaddvq_f32(tmp);
}
inline float32_t conv4x4(float32x4_t r0,
float32x4_t r1,
float32x4_t r2,
float32x4_t r3,
float32x4_t k0,
float32x4_t k1,
float32x4_t k2,
float32x4_t k3) {
float32x4_t tmp;
tmp = vmulq_f32(r0, k0);
tmp = vmlaq_f32(tmp, r1, k1);
tmp = vmlaq_f32(tmp, r2, k2);
tmp = vmlaq_f32(tmp, r3, k3);
return vaddvq_f32(tmp);
}
/**
* Each step calculates four elements of the output.
* First step:
* R0[0, 1, 2, 3...] * K[0][0]
* R0[1, 2, 3, 4...] * K[0][1]
* R0[2, 3, 4, 5...] * K[0][2]
* R1[0, 1, 2, 3...] * K[1][0]
* R1[1, 2, 3, 4...] * K[1][1]
* R1[2, 3, 4, 5...] * K[1][2]
* R2[0, 1, 2, 3...] * K[2][0]
* R2[1, 2, 3, 4...] * K[2][1]
* + R2[2, 3, 4, 5...] * K[2][2]
* ------------------------------
* Output[0, 1, 2, 3]
*/
template <>
struct DepthwiseConvKernel<3, 1> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 9) {
// Load the filters
float32x4_t k[3];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 3);
k[2] = vld1q_f32(filterData + 6);
k[0] = vsetq_lane_f32(0.f, k[0], 3);
k[1] = vsetq_lane_f32(0.f, k[1], 3);
k[2] = vsetq_lane_f32(0.f, k[2], 3);
const float* r0 =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
const float* r1 = r0 + inputWidth;
const float* r2 = r0 + inputWidth * 2;
float32x4_t input[3][3];
for (int h = 0; h < outputHeight; h++) {
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t tmp;
input[0][0] = vld1q_f32(r0);
tmp = vld1q_f32(r0 + 4);
input[0][1] = vextq_f32(input[0][0], tmp, 1);
input[0][2] = vextq_f32(input[0][0], tmp, 2);
input[1][0] = vld1q_f32(r1);
tmp = vld1q_f32(r1 + 4);
input[1][1] = vextq_f32(input[1][0], tmp, 1);
input[1][2] = vextq_f32(input[1][0], tmp, 2);
input[2][0] = vld1q_f32(r2);
tmp = vld1q_f32(r2 + 4);
input[2][1] = vextq_f32(input[2][0], tmp, 1);
input[2][2] = vextq_f32(input[2][0], tmp, 2);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 4;
r1 += 4;
r2 += 4;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
*outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]);
r0++;
r1++;
r2++;
outputData++;
}
r0 += 2;
r1 += 2;
r2 += 2;
}
}
}
};
/**
* Each step calculates four elements of the output.
* First step:
* R0[0, 2, 4, 6...] * K[0][0]
* R0[1, 3, 5, 7...] * K[0][1]
* R0[2, 4, 6, 8...] * K[0][2]
* R1[0, 2, 4, 6...] * K[1][0]
* R1[1, 3, 5, 7...] * K[1][1]
* R1[2, 4, 6, 8...] * K[1][2]
* R2[0, 2, 4, 6...] * K[2][0]
* R2[1, 3, 5, 7...] * K[2][1]
* R2[2, 4, 6, 8...] * K[2][2]
* ------------------------------
* Output[0, 1, 2, 3]
*/
template <>
struct DepthwiseConvKernel<3, 2> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 9) {
// Load the filters
float32x4_t k[3];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 3);
k[2] = vld1q_f32(filterData + 6);
k[0] = vsetq_lane_f32(0.f, k[0], 3);
k[1] = vsetq_lane_f32(0.f, k[1], 3);
k[2] = vsetq_lane_f32(0.f, k[2], 3);
const float* start =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
float32x4_t input[3][3];
for (int h = 0; h < outputHeight; h++) {
const float* r0 = start + 2 * h * inputWidth;
const float* r1 = start + (2 * h + 1) * inputWidth;
const float* r2 = start + (2 * h + 2) * inputWidth;
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t data1;
float32x4x2_t data2;
data2 = vld2q_f32(r0);
input[0][0] = data2.val[0];
input[0][1] = data2.val[1];
data1 = vld1q_f32(r0 + 8);
input[0][2] = vextq_f32(data2.val[0], data1, 1);
data2 = vld2q_f32(r1);
input[1][0] = data2.val[0];
input[1][1] = data2.val[1];
data1 = vld1q_f32(r1 + 8);
input[1][2] = vextq_f32(data2.val[0], data1, 1);
data2 = vld2q_f32(r2);
input[2][0] = data2.val[0];
input[2][1] = data2.val[1];
data1 = vld1q_f32(r2 + 8);
input[2][2] = vextq_f32(data2.val[0], data1, 1);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 8;
r1 += 8;
r2 += 8;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
*outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]);
r0 += 2;
r1 += 2;
r2 += 2;
outputData++;
}
}
}
}
};
/**
* Each step calculates four elements of the output.
*/
template <>
struct DepthwiseConvKernel<4, 1> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 16) {
// Load the filters
float32x4_t k[4];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 4);
k[2] = vld1q_f32(filterData + 8);
k[3] = vld1q_f32(filterData + 12);
const float* r0 =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
const float* r1 = r0 + inputWidth;
const float* r2 = r0 + inputWidth * 2;
const float* r3 = r0 + inputWidth * 3;
float32x4_t input[4][4];
for (int h = 0; h < outputHeight; h++) {
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4_t tmp;
input[0][0] = vld1q_f32(r0);
tmp = vld1q_f32(r0 + 4);
input[0][1] = vextq_f32(input[0][0], tmp, 1);
input[0][2] = vextq_f32(input[0][0], tmp, 2);
input[0][3] = vextq_f32(input[0][0], tmp, 3);
input[1][0] = vld1q_f32(r1);
tmp = vld1q_f32(r1 + 4);
input[1][1] = vextq_f32(input[1][0], tmp, 1);
input[1][2] = vextq_f32(input[1][0], tmp, 2);
input[1][3] = vextq_f32(input[1][0], tmp, 3);
input[2][0] = vld1q_f32(r2);
tmp = vld1q_f32(r2 + 4);
input[2][1] = vextq_f32(input[2][0], tmp, 1);
input[2][2] = vextq_f32(input[2][0], tmp, 2);
input[2][3] = vextq_f32(input[2][0], tmp, 3);
input[3][0] = vld1q_f32(r3);
tmp = vld1q_f32(r3 + 4);
input[3][1] = vextq_f32(input[3][0], tmp, 1);
input[3][2] = vextq_f32(input[3][0], tmp, 2);
input[3][3] = vextq_f32(input[3][0], tmp, 3);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 4;
r1 += 4;
r2 += 4;
r3 += 4;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
float32x4_t i3 = vld1q_f32(r3);
*outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]);
r0++;
r1++;
r2++;
r3++;
outputData++;
}
r0 += 3;
r1 += 3;
r2 += 3;
r3 += 3;
}
}
}
};
/**
* Each step calculates four elements of the output.
*/
template <>
struct DepthwiseConvKernel<4, 2> {
static void run(const float* inputData,
const float* filterData,
int inputHeight,
int inputWidth,
int outputChannels,
int outputHeight,
int outputWidth,
int filterMultiplier,
float* outputData) {
const int steps = outputWidth >> 2;
const int remain = outputWidth & 3;
for (int c = 0; c < outputChannels; c++, filterData += 16) {
// Load the filters
float32x4_t k[4];
k[0] = vld1q_f32(filterData);
k[1] = vld1q_f32(filterData + 4);
k[2] = vld1q_f32(filterData + 8);
k[3] = vld1q_f32(filterData + 12);
const float* start =
inputData + (c / filterMultiplier) * (inputHeight * inputWidth);
float32x4_t input[4][4];
for (int h = 0; h < outputHeight; h++) {
const float* r0 = start + 2 * h * inputWidth;
const float* r1 = start + (2 * h + 1) * inputWidth;
const float* r2 = start + (2 * h + 2) * inputWidth;
const float* r3 = start + (2 * h + 3) * inputWidth;
for (int s = 0; s < steps; s++) {
// Load the inputs
float32x4x2_t data1;
float32x4x2_t data2;
data1 = vld2q_f32(r0);
data2 = vld2q_f32(r0 + 8);
input[0][0] = data1.val[0];
input[0][1] = data1.val[1];
input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r1);
data2 = vld2q_f32(r1 + 8);
input[1][0] = data1.val[0];
input[1][1] = data1.val[1];
input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r2);
data2 = vld2q_f32(r2 + 8);
input[2][0] = data1.val[0];
input[2][1] = data1.val[1];
input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1);
data1 = vld2q_f32(r3);
data2 = vld2q_f32(r3 + 8);
input[3][0] = data1.val[0];
input[3][1] = data1.val[1];
input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1);
input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1);
float32x4_t tmp1 = vdupq_n_f32(0.f);
float32x4_t tmp2 = vdupq_n_f32(0.f);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1);
tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2);
tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3);
tmp1 = vaddq_f32(tmp1, tmp2);
vst1q_f32(outputData, tmp1);
r0 += 8;
r1 += 8;
r2 += 8;
r3 += 8;
outputData += 4;
}
for (int r = 0; r < remain; r++) {
float32x4_t i0 = vld1q_f32(r0);
float32x4_t i1 = vld1q_f32(r1);
float32x4_t i2 = vld1q_f32(r2);
float32x4_t i3 = vld1q_f32(r3);
*outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]);
r0 += 2;
r1 += 2;
r2 += 2;
r3 += 2;
outputData++;
}
}
}
}
};
template <DeviceType Device>
class NeonDepthwiseConvFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
size_t batchSize = input[0];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
size_t filterWidth = getFilterWidth(filter);
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
size_t filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
// only support strideH() == strideW() and filterHeight == filterWidth.
CHECK_EQ(strideH(), strideW());
CHECK_EQ(filterHeight, filterWidth);
float* inputData = inputs[0].data<float>();
float* filterData = inputs[1].data<float>();
float* outputData = outputs[0].data<float>();
// padding the input
float* inputPadding = inputData;
if (paddingH() > 0 || paddingW() > 0) {
int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) *
(inputWidth + 2 * paddingW());
resizeBuffer<Device>(newSize);
inputPadding = reinterpret_cast<float*>(memory_->getBuf());
Padding<float>::run(inputData,
inputPadding,
batchSize * inputChannels,
inputHeight,
inputWidth,
paddingH(),
paddingW());
// height and width of padding data
inputHeight += 2 * paddingH();
inputWidth += 2 * paddingW();
}
std::function<void(
const float*, const float*, int, int, int, int, int, int, float*)>
DepthWiseConv;
if (filterWidth == 3 && strideW() == 1) {
DepthWiseConv = DepthwiseConvKernel<3, 1>::run;
} else if (filterWidth == 3 && strideW() == 2) {
DepthWiseConv = DepthwiseConvKernel<3, 2>::run;
} else if (filterWidth == 4 && strideW() == 1) {
DepthWiseConv = DepthwiseConvKernel<4, 1>::run;
} else if (filterWidth == 4 && strideW() == 2) {
DepthWiseConv = DepthwiseConvKernel<4, 2>::run;
} else {
LOG(FATAL) << "Not supported";
}
for (size_t i = 0; i < batchSize; i++) {
DepthWiseConv(inputPadding,
filterData,
inputHeight,
inputWidth,
outputChannels,
outputHeight,
outputWidth,
filterMultiplier,
outputData);
inputPadding += inputChannels * inputHeight * inputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
}
};
REGISTER_TYPED_FUNC(NeonDepthwiseConv, CPU, NeonDepthwiseConvFunction);
#endif
} // namespace neon
} // namespace paddle
paddle/function/neon/neon_util.h 0 → 100644
浏览文件 @ a357bd6f
/* 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
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
#include <arm_neon.h>
namespace paddle {
namespace neon {
inline float32x4_t vld1q_f32_aligned(const float* p) {
return vld1q_f32(
(const float*)__builtin_assume_aligned(p, sizeof(float32x4_t)));
}
#ifndef __aarch64__
inline float32_t vaddvq_f32(float32x4_t a) {
float32x2_t v = vadd_f32(vget_high_f32(a), vget_low_f32(a));
return vget_lane_f32(vpadd_f32(v, v), 0);
}
inline float32x4_t vmlaq_laneq_f32(float32x4_t a,
float32x4_t b,
float32x4_t v,
const int lane) {
return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane));
}
#endif
} // namespace neon
} // namespace paddle
#endif
paddle/gserver/layers/CostLayer.h
浏览文件 @ a357bd6f
......@@ -318,7 +318,9 @@ public:
void forwardImp(Matrix& output, Argument& label, Matrix& cost) override;
void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad) {}
void backwardImp(Matrix& outputValue,
Argument& label,
Matrix& outputGrad) override {}
};
/**
......
paddle/gserver/layers/CrossEntropyOverBeam.cpp 0 → 100644
浏览文件 @ a357bd6f
/* 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 "CrossEntropyOverBeam.h"
namespace paddle {
void CostForOneSequence::calValidExpandStep() {
validExpansionCount_ = 0;
goldAsExtraPath_ = true;
for (size_t i = 0; i < beams_->expansionCount; ++i) {
real gold = static_cast<real>(beams_->gold[i]);
if (i) {
real* start = beams_->candidateIds[i - 1]->getData();
goldRowIds_[i] = std::count_if(
start,
start + goldRowIds_[i - 1] * beamSize_ + goldColIds_[i - 1],
[](const real& val) { return val != -1.; });
} else {
goldRowIds_[i] = 0;
}
real* start =
beams_->candidateIds[i]->getData() + goldRowIds_[i] * beamSize_;
real* findEnd = std::find(start, start + beamSize_, gold);
validExpansionCount_++;
if (start + beamSize_ == findEnd) return;
goldColIds_[i] = findEnd - start;
}
if (goldColIds_[beams_->expansionCount - 1] != -1) goldAsExtraPath_ = false;
}
size_t CostForOneSequence::initLastExpansion() {
int beamId = validExpansionCount_ - 1;
const MatrixPtr candidates = beams_->candidateIds[beamId];
size_t height = candidates->getHeight();
/* initialization the last expansion. */
size_t pathCount = std::count_if(candidates->getData(),
candidates->getData() + height * beamSize_,
[](const real& val) { return val != -1; });
/*
* if the gold sequence falls off the beam during search, add the gold
* sequence as the last path into the all expanded candidates.
*/
if (goldAsExtraPath_) goldIdsInFinalExpansion_ = pathCount++;
pathRowIdsInEachBeam_.clear();
pathRowIdsInEachBeam_.resize(validExpansionCount_,
std::vector<int>(pathCount, 0));
parentIdsInBeam_.clear();
parentIdsInBeam_.resize(pathCount, 0);
if (goldAsExtraPath_) {
/* add gold sequence into the total expansion. */
pathRowIdsInEachBeam_[beamId].back() =
beams_->gold[beamId] +
getSeqStartPos(beamId, goldRowIds_[validExpansionCount_ - 1]);
parentIdsInBeam_.back() = goldRowIds_[validExpansionCount_ - 1];
} else {
size_t goldOffset = goldRowIds_[beamId] * beamSize_ + goldColIds_[beamId];
goldIdsInFinalExpansion_ =
std::count_if(candidates->getData(),
candidates->getData() + goldOffset,
[](const real& val) { return val != -1.; });
}
/*
* TODO(caoying): fix this, store the indices of selected candidate
* paths into Argument.ids
*/
real* ids = candidates->getData();
size_t curIdx = 0;
for (size_t i = 0; i < height; ++i) {
int basePos = getSeqStartPos(beamId, i);
for (size_t j = 0; j < beamSize_; ++j) {
int id = ids[i * beamSize_ + j];
if (id == -1) continue;
pathRowIdsInEachBeam_[beamId][curIdx] = id + basePos;
parentIdsInBeam_[curIdx++] = i;
}
}
return pathCount;
}
void CostForOneSequence::constructTotalExpansion() {
/*
* construct the entire expanded beam by begining with the last search
* in which gold falls off the beam.
*/
size_t totalPathCount = initLastExpansion();
for (int beamId = validExpansionCount_ - 2; beamId >= 0; --beamId) {
const MatrixPtr candidates = beams_->candidateIds[beamId];
real* ids = candidates->getData();
int lastParentIdInBeam = -1;
int basePos = -1;
for (size_t i = 0;
i < (goldAsExtraPath_ ? totalPathCount - 1 : totalPathCount);
++i) {
int id = ids[parentIdsInBeam_[i]];
int parentRowId = std::div(parentIdsInBeam_[i], beamSize_).quot;
if (parentIdsInBeam_[i] != lastParentIdInBeam)
basePos = getSeqStartPos(beamId, parentRowId);
pathRowIdsInEachBeam_[beamId][i] = id + basePos;
lastParentIdInBeam = parentIdsInBeam_[i];
parentIdsInBeam_[i] = parentRowId;
if (goldAsExtraPath_)
pathRowIdsInEachBeam_[beamId][totalPathCount - 1] =
beams_->gold[beamId] + getSeqStartPos(beamId, goldRowIds_[beamId]);
}
}
}
real CostForOneSequence::globallyNormalizedScore() {
expandedPathScores_.resize(validExpansionCount_);
Matrix::resizeOrCreate(
softmaxOut_, 1, pathRowIdsInEachBeam_[0].size(), false, false);
softmaxOut_->zeroMem();
MatrixPtr tmp = Matrix::create(
softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false);
for (size_t i = 0; i < validExpansionCount_; ++i) {
Matrix::resizeOrCreate(expandedPathScores_[i],
pathRowIdsInEachBeam_[i].size(),
1,
false,
false);
expandedPathScores_[i]->zeroMem();
IVectorPtr rowIds = IVector::create(pathRowIdsInEachBeam_[i].data(),
pathRowIdsInEachBeam_[i].size(),
false);
expandedPathScores_[i]->selectRows(*(beams_->scores[i]), *rowIds);
tmp->add(*expandedPathScores_[i]);
}
softmaxOut_->softmax(*softmaxOut_);
return -std::log(softmaxOut_->getData()[goldIdsInFinalExpansion_]);
}
real CostForOneSequence::forward() {
calValidExpandStep();
constructTotalExpansion();
return globallyNormalizedScore();
}
void CostForOneSequence::backward() {
/*
* when softmax layer is the output layer, and it is combined with
* cross-entropy as cost. The derivate with regard to softmax's input
* is simply:
*
* grad_i = softmax_out_i - target_i,
*
* and here hard label is used.
*/
softmaxOut_->getData()[goldIdsInFinalExpansion_] -= 1.;
MatrixPtr tmp = Matrix::create(
softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false);
for (size_t i = 0; i < validExpansionCount_; ++i) {
IVectorPtr rowIds = IVector::create(pathRowIdsInEachBeam_[i].data(),
pathRowIdsInEachBeam_[i].size(),
false);
/*
beams_->scoreGrad[i] has been intialized outside this class, this
class only keeps a pointer pointing to the original input gradients,
so here does not need to allocate or initalize the memory.
*/
tmp->addToRows(*beams_->scoreGrad[i], *rowIds);
}
}
REGISTER_LAYER(cross_entropy_over_beam, CrossEntropyOverBeam);
bool CrossEntropyOverBeam::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
CHECK_EQ(0U, inputLayers_.size() % 3) << "Error input number.";
beamExpanCount_ = inputLayers_.size() / 3;
candidateScores_.resize(beamExpanCount_);
candidateScoreGrad_.resize(beamExpanCount_);
candidateInBeam_.resize(beamExpanCount_);
goldSequence_.resize(beamExpanCount_);
gradToInputs_.resize(beamExpanCount_);
setNeedSequenceInfo(false);
return true;
}
void CrossEntropyOverBeam::checkInputs() {
batchSize_ = 0;
for (size_t i = 0; i < beamExpanCount_; ++i) {
const Argument& scores = getInput(i * 3);
const Argument& selCandidates = getInput(i * 3 + 1);
const Argument& goldSeq = getInput(i * 3 + 2);
if (i) {
CHECK(scores.hasSubseq()) << "input " << i << " "
<< inputLayers_[i * 3]->getName()
<< " should be a nested sequence";
CHECK_EQ(getInputValue(i * 3 + 1)->getWidth(), beamSize_);
CHECK_EQ(scores.getNumSequences(), batchSize_);
CHECK_EQ(scores.getNumSubSequences(), selCandidates.getBatchSize());
} else {
CHECK(scores.hasSeq()) << "input " << i << " "
<< inputLayers_[i]->getName()
<< " should be a sequence";
batchSize_ = scores.getNumSequences();
beamSize_ = getInputValue(i * 3 + 1)->getWidth();
CHECK_EQ(batchSize_, selCandidates.getBatchSize());
}
CHECK_EQ(1U, scores.value->getWidth());
CHECK_EQ(batchSize_, goldSeq.getBatchSize());
}
}
void CrossEntropyOverBeam::copyInputsToCpu() {
auto copyValue = [](const MatrixPtr& src, MatrixPtr& trg) {
if (dynamic_cast<GpuMatrix*>(src.get())) {
Matrix::resizeOrCreate(
trg, src->getHeight(), src->getWidth(), false, false);
trg->copyFrom(*src);
} else {
trg = std::move(src);
}
};
auto copyIds = [](const IVectorPtr& src, IVectorPtr& trg) {
if (dynamic_cast<GpuIVector*>(src.get())) {
IVector::resizeOrCreate(trg, src->getSize(), false);
trg->copyFrom(*src);
} else {
trg = std::move(src);
}
};
beamSplitPos_.clear();
beamSplitPos_.resize(batchSize_, std::vector<int>(beamExpanCount_, 0));
for (size_t i = 0; i < beamExpanCount_; ++i) {
copyValue(getInputValue(i * 3), candidateScores_[i]);
copyValue(getInputValue(i * 3 + 1), candidateInBeam_[i]);
copyIds(getInput(i * 3 + 2).ids, goldSequence_[i]);
if (i) {
ICpuGpuVectorPtr seqInfo = getInput(i * 3).sequenceStartPositions;
const int* seqStarts = seqInfo->getMutableData(false);
ICpuGpuVectorPtr subSeqInfo = getInput(i * 3).subSequenceStartPositions;
const int* subSeqStarts = subSeqInfo->getMutableData(false);
size_t seqId = 1;
for (size_t subSeqId = 0; subSeqId < subSeqInfo->getSize() - 1;
++subSeqId) {
CHECK_LT(seqId, seqInfo->getSize());
if (subSeqStarts[subSeqId] == seqStarts[seqId]) {
beamSplitPos_[seqId][i] = beamSplitPos_[seqId - 1][i];
seqId++;
}
beamSplitPos_[seqId - 1][i]++;
}
} else {
for (size_t j = 0; j < batchSize_; ++j) beamSplitPos_[j][i] = j + 1;
}
}
}
void CrossEntropyOverBeam::splitBatchBeams() {
beamCosts_.resize(batchSize_);
beamPerSeq_.resize(batchSize_, BeamExpansion(beamExpanCount_));
for (size_t i = 0; i < beamExpanCount_; ++i) {
int* seqStarts =
getInput(i * 3).sequenceStartPositions->getMutableData(false);
int* subSeqStarts = nullptr;
int maxLen = 0;
if (i) {
subSeqStarts =
getInput(i * 3).subSequenceStartPositions->getMutableData(false);
maxLen = getInput(i * 3).subSequenceStartPositions->getSize() - 1;
} else {
maxLen = getInput(i).sequenceStartPositions->getSize() - 1;
}
for (size_t j = 0; j < batchSize_; ++j) {
beamPerSeq_[j].scores[i] =
Matrix::create(candidateScores_[i]->getData() + seqStarts[j],
seqStarts[j + 1] - seqStarts[j],
1,
false,
false);
beamPerSeq_[j].scoreGrad[i] =
Matrix::create(candidateScoreGrad_[i]->getData() + seqStarts[j],
seqStarts[j + 1] - seqStarts[j],
1,
false,
false);
int offset = j ? beamSplitPos_[j - 1][i] : 0;
int height = beamSplitPos_[j][i] - (j ? beamSplitPos_[j - 1][i] : 0);
CHECK_GE(maxLen, offset + height);
beamPerSeq_[j].seqInfo[i] = IVector::create(
(i ? subSeqStarts : seqStarts) + offset, height + 1, false);
beamPerSeq_[j].candidateIds[i] =
Matrix::create(candidateInBeam_[i]->getData() + offset * beamSize_,
height,
beamSize_,
false,
false);
beamPerSeq_[j].gold[i] = goldSequence_[i]->getData()[j];
CHECK_LE(beamPerSeq_[j].gold[i], seqStarts[j + 1] - seqStarts[j]);
}
}
}
void CrossEntropyOverBeam::resizeOutput() {
Matrix::resizeOrCreate(output_.value, batchSize_, 1, false, false);
output_.value->zeroMem();
for (size_t i = 0; i < beamExpanCount_; ++i) {
MatrixPtr inGrad = getInputGrad(i * 3);
if (dynamic_cast<GpuMatrix*>(inGrad.get())) {
Matrix::resizeOrCreate(candidateScoreGrad_[i],
inGrad->getHeight(),
inGrad->getWidth(),
false,
false);
} else {
candidateScoreGrad_[i] = std::move(inGrad);
}
candidateScoreGrad_[i]->zeroMem();
}
}
void CrossEntropyOverBeam::copyGradToGpu(size_t copyCount) {
for (size_t i = 0; i < beamExpanCount_; ++i) {
if (dynamic_cast<GpuMatrix*>(getInputGrad(i * 3).get()))
getInputGrad(i * 3)->copyFrom(*candidateScoreGrad_[i]);
if (i == copyCount - 1) break;
}
}
void CrossEntropyOverBeam::forward(PassType passType) {
Layer::forward(passType);
checkInputs();
copyInputsToCpu();
resizeOutput();
splitBatchBeams();
MatrixPtr outputValue = getOutputValue();
for (size_t i = 0; i < batchSize_; ++i) {
beamCosts_[i].setData(
std::move(std::make_shared<BeamExpansion>(beamPerSeq_[i])), beamSize_);
outputValue->getData()[i] = beamCosts_[i].forward();
}
}
void CrossEntropyOverBeam::backward(const UpdateCallback& callback) {
for (size_t i = 0; i < batchSize_; ++i) {
beamCosts_[i].backward();
copyGradToGpu(beamCosts_[i].getValidExpansionCount());
}
}
} // namespace paddle
paddle/gserver/layers/CrossEntropyOverBeam.h 0 → 100644
浏览文件 @ a357bd6f
/* 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 "CrossEntropyOverBeam.h"
#include "Layer.h"
namespace paddle {
/* This struct stores the beams in all search steps for a single sequence. */
struct BeamExpansion {
std::vector<MatrixPtr> scores;
std::vector<IVectorPtr> seqInfo;
std::vector<MatrixPtr> candidateIds;
std::vector<int> gold;
std::vector<MatrixPtr> scoreGrad;
size_t expansionCount;
explicit BeamExpansion(int n) {
expansionCount = n;
scores.resize(expansionCount);
seqInfo.resize(expansionCount);
candidateIds.resize(expansionCount);
scoreGrad.resize(expansionCount);
gold.resize(expansionCount);
}
};
typedef std::shared_ptr<BeamExpansion> BeamExpansionPtr;
class CostForOneSequence {
public:
CostForOneSequence()
: beamSize_(0), validExpansionCount_(0), goldAsExtraPath_(false) {}
void setData(const BeamExpansionPtr bPtr, size_t beamSize) {
beams_ = bPtr;
beamSize_ = beamSize;
expandedPathScores_.clear();
expandedPathScores_.resize(beams_->expansionCount);
goldRowIds_.clear();
goldRowIds_.resize(beams_->expansionCount, 0);
goldColIds_.clear();
goldColIds_.resize(beams_->expansionCount, -1);
}
size_t getValidExpansionCount() { return validExpansionCount_; }
real forward();
void backward();
private:
void calValidExpandStep();
void constructTotalExpansion();
size_t initLastExpansion();
real globallyNormalizedScore();
int getSeqStartPos(size_t beamId, size_t rowId) {
CHECK_GT(beams_->seqInfo[beamId]->getSize() - 1, rowId);
int* starts = beams_->seqInfo[beamId]->getData();
return starts[rowId] - starts[0];
}
size_t beamSize_;
size_t validExpansionCount_;
bool goldAsExtraPath_;
std::vector<int> goldRowIds_;
std::vector<int> goldColIds_;
BeamExpansionPtr beams_;
std::vector<std::vector<int>> pathRowIdsInEachBeam_;
std::vector<int> parentIdsInBeam_;
size_t goldIdsInFinalExpansion_;
std::vector<MatrixPtr> expandedPathScores_;
MatrixPtr softmaxOut_;
};
class CrossEntropyOverBeam : public Layer {
public:
explicit CrossEntropyOverBeam(const LayerConfig& config) : Layer(config) {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
private:
void checkInputs();
void copyInputsToCpu();
void resizeOutput();
void copyGradToGpu(size_t copyCount);
void splitBatchBeams();
size_t beamExpanCount_;
size_t batchSize_;
size_t beamSize_;
/*
* the process of constructing beams is not friendly to GPU, currently, this
* layer only runs on CPU, if any of its inputs is on GPU memory, then copy
* it to CPU memory.
*/
std::vector<MatrixPtr> candidateScores_;
std::vector<MatrixPtr> candidateScoreGrad_;
std::vector<MatrixPtr> candidateInBeam_;
std::vector<MatrixPtr> gradToInputs_;
std::vector<IVectorPtr> goldSequence_;
std::vector<std::vector<int>> beamSplitPos_;
/*
* split entire bath of beams into beam per sequnence and store the result
* into this member.
*/
std::vector<BeamExpansion> beamPerSeq_;
/* beamCosts_ is used to propagate error in one sequence. */
std::vector<CostForOneSequence> beamCosts_;
};
} // namespace paddle
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @ a357bd6f
......@@ -29,6 +29,10 @@ namespace paddle {
REGISTER_LAYER(exconv, ExpandConvLayer);
REGISTER_LAYER(exconvt, ExpandConvLayer);
inline bool isDepthwiseConv(int channels, int groups) {
return channels == groups;
}
bool ExpandConvLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
/* Initialize the basic convolutional parent class */
......@@ -47,14 +51,27 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
if (useGpu_ && (size_t)groups_[i] == (size_t)channels_[i] && !isDeconv_) {
// Convolution Layer uses the GemmConv function by default.
convType = "GemmConv";
convGradInputType = "GemmConvGradInput";
convGradFilterType = "GemmConvGradFilter";
// If depth wise convolution and useGpu == true
if (useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) {
convType = "DepthwiseConv";
convGradInputType = "DepthwiseConvGradInput";
convGradFilterType = "DepthwiseConvGradFilter";
} else {
convType = "GemmConv";
convGradInputType = "GemmConvGradInput";
convGradFilterType = "GemmConvGradFilter";
}
// If depth wise convolution and useGpu == false and ARM-NEON
if (!useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
if ((filterSize_[i] == filterSizeY_[i]) &&
(filterSize_[i] == 3 || filterSize_[i] == 4) &&
(stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2)) {
convType = "NeonDepthwiseConv";
}
#endif
}
if (FLAGS_use_nnpack && !isDeconv_) {
......
paddle/gserver/layers/Layer.cpp
浏览文件 @ a357bd6f
......@@ -41,7 +41,7 @@ namespace paddle {
Layer::Layer(const LayerConfig& config, bool useGpu)
: config_(config),
useGpu_(useGpu),
deviceId_(-1),
deviceId_(CPU_DEVICE),
needSequenceInfo_(true) {}
bool Layer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) {
......
paddle/gserver/layers/Layer.h
浏览文件 @ a357bd6f
......@@ -59,7 +59,12 @@ protected:
LayerConfig config_;
/// whether to use GPU
bool useGpu_;
/// Device Id. CPU is -1, and GPU is 0, 1, 2 ...
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum PADDLE_DEVICE_ID {
MKLDNN_DEVICE = -2,
CPU_DEVICE = -1,
};
/// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ...
int deviceId_;
/// Input layers
std::vector<LayerPtr> inputLayers_;
......@@ -77,6 +82,7 @@ protected:
Argument output_;
/// Several outputs stored on different devices, used in 'parallel_nn' case,
/// and record them by deviceId_.
/// Also used in 'use_mkldnn' case.
std::vector<Argument> outputOtherDevice_;
/// If there are several outputs, map them by each name.
std::map<std::string, Argument*> outputMap_;
......@@ -172,6 +178,13 @@ protected:
return inputLayer.getOutput(deviceId_);
}
/**
* Get the argument of input layer with deviceId.
*/
const Argument& getInput(size_t inputIndex, int deviceId) const {
return inputLayers_[inputIndex]->getOutput(deviceId);
}
/**
* Get the forward-input value.
*/
......@@ -186,6 +199,13 @@ protected:
return inputLayer.getOutput(deviceId_).value;
}
/**
* Get the forward-input value with deviceId.
*/
const MatrixPtr& getInputValue(int inputIndex, int deviceId) {
return inputLayers_[inputIndex]->getOutput(deviceId).value;
}
/**
* Get the forward-input grad.
*/
......@@ -200,6 +220,13 @@ protected:
return inputLayer.getOutput(deviceId_).grad;
}
/**
* Get the forward-input grad.
*/
const MatrixPtr& getInputGrad(int inputIndex, int deviceId) {
return inputLayers_[inputIndex]->getOutput(deviceId).grad;
}
/**
* Get the forward-input label.
*/
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ a357bd6f
......@@ -61,43 +61,42 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
return;
}
// TODO(TJ): dst format should get from wgtVal_
int dstFmt = PARAM_FORMAT_MKLDNN_OI;
int srcFmt = weight_->getParameterPtr()->getHeaderFormat();
if (srcFmt == dstFmt) {
return;
}
// The weight_ is transposed from initial paddle weight
MatrixPtr paddleWgt = Matrix::create(
weight_->getW()->getData(), iLayerSize_, oc_, false, false);
// TODO(TJ): remove this print when do not need differ weights
std::ostringstream ostr;
paddleWgt->print(ostr);
VLOG(MKLDNN_ALL) << "Initial Weight from paddle: " << std::endl << ostr.str();
// The mkldnn weight is transposed from initial paddle matrix
MatrixPtr paddleWgtT;
paddleWgt->transpose(paddleWgtT, true);
weight_->getW()->copyFrom(*paddleWgtT);
weight_->getParameterPtr()->setHeaderFormat(dstFmt);
CHECK(wgtVal_) << "should have been initialized";
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
auto targetDim = wgtVal_->getDims();
auto srcFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
hasInitedWgt_ = true;
}
void MKLDNNFcLayer::convertWeightsToPaddle() {
MatrixPtr dnnWgt = weight_->getW();
MatrixPtr paddleWgt;
dnnWgt->transpose(paddleWgt, true);
// copy paddle weight and override on weight_
MatrixPtr dnnWgtT = Matrix::create(
dnnWgt->getData(), dnnWgt->getWidth(), dnnWgt->getHeight(), false, false);
dnnWgtT->copyFrom(*paddleWgt);
CHECK(wgtVal_) << "should have been initialized";
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
auto targetDim = wgtVal_->getDims();
auto dstFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo;
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
void MKLDNNFcLayer::convertOutputToOtherDevice() {
copyOutputInfoToOtherDevice();
// find other cpu device and reorder output to cpu device
int cnt = 0;
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
// fc cpu output value do not need convert
// just share point
outputOtherDevice_[i].value = output_.value;
++cnt;
}
}
if (cnt > 1) {
LOG(WARNING) << "should not have more than one CPU devie";
}
}
void MKLDNNFcLayer::reshape() {
const Argument& input = getInput(0);
const Argument& input = getInput(0, getPrev(0)->getDeviceId());
int batchSize = input.getBatchSize();
if (bs_ == batchSize) {
return;
......@@ -111,10 +110,6 @@ void MKLDNNFcLayer::reshape() {
if (iw_ == 0) {
iw_ = 1;
}
hasSpatial_ = true;
if (ih_ == 1 && iw_ == 1) {
hasSpatial_ = false;
}
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
ic_ = iLayerSize_ / (ih_ * iw_);
CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
......@@ -135,37 +130,53 @@ void MKLDNNFcLayer::reshape() {
void MKLDNNFcLayer::resetFwd() {
bool hasBias = biases_ && biases_->getW();
real* iData = getInputValue(0)->getData();
real* oData = getOutputValue()->getData();
real* wData = weight_->getW()->getData();
real* bData = hasBias ? biases_->getW()->getData() : NULL;
// TODO(TJ): below create should be covered in MkldnnMatrix
// create memory desc
memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
: createMD({bs_, ic_}, format::nc);
memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
: createMD({oc_, ic_}, format::oi);
memory::desc bMD = bData != NULL ? createMD({oc_}, format::x)
: createMD({}, format::format_undef);
memory::desc oMD = createMD({bs_, oc_}, format::nc);
// create memory primitive desc and memory self
inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
wgtVal_.reset(new memory(memory::primitive_desc(wMD, engine_), wData));
outVal_.reset(new memory(memory::primitive_desc(oMD, engine_), oData));
const MatrixPtr& wgt = weight_->getW();
const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr;
const MatrixPtr& out = output_.value;
if (inputIsOnlyMKLDNN()) {
const MatrixPtr& in = getInputValue(0);
inVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(in);
CHECK(inVal_) << "Input should be MKLDNNMatrix";
} else {
CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
const MatrixPtr& in = getInputValue(0, CPU_DEVICE);
inVal_ = MKLDNNMatrix::create(
in, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
}
inVal_->downSpatial();
wgtVal_ = MKLDNNMatrix::create(
wgt, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
wgtVal_->downSpatial();
biasVal_ =
hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr;
outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_);
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
if (!outputIsOnlyMKLDNN()) {
convertOutputToOtherDevice();
}
// create forward handle
prop_kind pk = prop_kind::forward;
fc_fwd::desc fwdDesc = bData != NULL ? fc_fwd::desc(pk, iMD, wMD, bMD, oMD)
: fc_fwd::desc(pk, iMD, wMD, oMD);
fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
biasVal_->getMemoryDesc(),
outVal_->getMemoryDesc())
: fc_fwd::desc(pk,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
outVal_->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
if (bData != NULL) {
biasVal_.reset(new memory(memory::primitive_desc(bMD, engine_), bData));
if (hasBias) {
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
} else {
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
}
printValueFormatFlow();
pipelineFwd_.clear();
pipelineFwd_.push_back(*fwd_);
}
......@@ -175,45 +186,46 @@ void MKLDNNFcLayer::resetBwd() {
return;
}
needResetBwd_ = false;
bool hasBias = biases_ && biases_->getWGrad();
real* iData = getInputValue(0)->getData();
real* iDiff = getInputGrad(0) != nullptr ? getInputGrad(0)->getData() : NULL;
real* oDiff = getOutputGrad()->getData();
real* wDiff = weight_->getWGrad()->getData();
real* bDiff = hasBias ? biases_->getWGrad()->getData() : NULL;
/// backward weight
// create memory desc for backward memory
memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
: createMD({bs_, ic_}, format::nc);
memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
: createMD({oc_, ic_}, format::oi);
memory::desc oMD = createMD({bs_, oc_}, format::nc);
memory::desc bMD = bDiff != NULL ? createMD({oc_}, format::x)
: createMD({}, format::format_undef);
if (inVal_) {
// update data
inVal_->set_data_handle(iData);
} else {
inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
}
// create memory primitive desc and memory self
wgtGrad_.reset(new memory(memory::primitive_desc(wMD, engine_), wDiff));
outGrad_.reset(new memory(memory::primitive_desc(oMD, engine_), oDiff));
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, iMD, wMD, oMD);
CHECK(inVal_) << "Should have input value";
const MatrixPtr& wgt = weight_->getWGrad();
const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr;
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
// for MKLDNN device:
// can not directly cast outputgrad to mkldnnmatrix,
// since each layer can not write the inputgrad to mkldnn inputgrad.
// So just create from matrix with outputvalue format.
// for CPU device:
// fc do not need to convert from cpu device since output is always nc format
// only need create from cpu device
const MatrixPtr& out = getOutput(device).grad;
outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc());
wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc());
biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc())
: nullptr;
// create memory primitive desc
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward,
inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
fc_bwdWgt::desc bwdWgtDesc = bDiff != NULL
? fc_bwdWgt::desc(iMD, wMD, bMD, oMD)
: fc_bwdWgt::desc(iMD, wMD, oMD);
fc_bwdWgt::desc bwdWgtDesc = hasBias
? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
biasGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
if (bDiff != NULL) {
biasGrad_.reset(new memory(memory::primitive_desc(bMD, engine_), bDiff));
if (hasBias) {
bwdWgt_.reset(
new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
} else {
......@@ -223,15 +235,26 @@ void MKLDNNFcLayer::resetBwd() {
pipelineBwd_.push_back(*bwdWgt_);
/// backward data
if (iDiff == NULL) {
device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
const MatrixPtr& in = getInputGrad(0, device);
if (in == nullptr) {
return;
}
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(iMD, wMD, oMD);
if (getInput(0, device).getAllCount() > 1) {
// TODO(TJ): use outputMaps_ ways when merge outgrad done
} else {
inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc());
}
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
inGrad_.reset(new memory(memory::primitive_desc(iMD, engine_), iDiff));
CHECK(wgtVal_) << "Should have weight memory";
bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
printGradFormatFlow();
pipelineBwd_.push_back(*bwdData_);
}
......@@ -241,11 +264,7 @@ void MKLDNNFcLayer::forward(PassType passType) {
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
// update input data
// since it might be changed if this is after data layer
real* iData = getInputValue(0)->getData();
inVal_->set_data_handle(iData);
syncInputValue();
// just submit forward pipeline
stream_->submit(pipelineFwd_);
......@@ -267,10 +286,7 @@ void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
resetBwd();
// update diff
real* oDiff = getOutputGrad()->getData();
outGrad_->set_data_handle(oDiff);
syncOutputGrad();
// just sumbmit backward pipeline
stream_->submit(pipelineBwd_);
}
......
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ a357bd6f
......@@ -32,16 +32,13 @@ protected:
// if has already init the weight
bool hasInitedWgt_;
// if input layer has image size info (ih>1 && iw>1)
bool hasSpatial_;
// fc weight and bias
std::unique_ptr<Weight> weight_;
std::unique_ptr<Weight> biases_;
public:
explicit MKLDNNFcLayer(const LayerConfig& config)
: MKLDNNLayer(config), hasInitedWgt_(false), hasSpatial_(true) {}
: MKLDNNLayer(config), hasInitedWgt_(false) {}
~MKLDNNFcLayer() {}
......@@ -75,6 +72,8 @@ protected:
* only would be called when needed
*/
void resetBwd();
void convertOutputToOtherDevice() override;
};
} // namespace paddle
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ a357bd6f
......@@ -18,9 +18,9 @@ limitations under the License. */
#include "Layer.h"
#include "MKLDNNBase.h"
#include "mkldnn.hpp"
#include "paddle/math/MKLDNNMatrix.h"
DECLARE_bool(use_mkldnn);
DECLARE_bool(use_mkldnn_wgt);
namespace paddle {
......@@ -52,15 +52,15 @@ protected:
std::vector<mkldnn::primitive> pipelineFwd_;
std::vector<mkldnn::primitive> pipelineBwd_;
// TODO(TJ): change below memory as MKLDNNMatrixPtr type
std::shared_ptr<mkldnn::memory> inVal_;
std::shared_ptr<mkldnn::memory> inGrad_;
std::shared_ptr<mkldnn::memory> outVal_;
std::shared_ptr<mkldnn::memory> outGrad_;
std::shared_ptr<mkldnn::memory> wgtVal_;
std::shared_ptr<mkldnn::memory> wgtGrad_;
std::shared_ptr<mkldnn::memory> biasVal_;
std::shared_ptr<mkldnn::memory> biasGrad_;
// MKLDNNMatrixPtr
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
MKLDNNMatrixPtr outVal_;
MKLDNNMatrixPtr outGrad_;
MKLDNNMatrixPtr wgtVal_;
MKLDNNMatrixPtr wgtGrad_;
MKLDNNMatrixPtr biasVal_;
MKLDNNMatrixPtr biasGrad_;
public:
explicit MKLDNNLayer(const LayerConfig& config)
......@@ -83,17 +83,21 @@ public:
virtual bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "and set use_mkldnn=True";
CHECK(!useGpu_) << "Do not support GPU yet";
// set device id before Layer::init
setDevice(MKLDNN_DEVICE);
// change param device to MKLDNN device
setParamsDevice(MKLDNN_DEVICE, parameterMap);
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
<< "and set use_mkldnn=True";
stream_.reset(new MKLDNNStream());
engine_ = CPUEngine::Instance().getEngine();
// TODO(TJ): deivecId
return true;
}
......@@ -109,6 +113,12 @@ public:
*/
virtual void convertWeightsToPaddle() {}
/**
* convert MKLDNN output to other device.
* only support CPU device yet
*/
virtual void convertOutputToOtherDevice() {}
/**
* print info about sizes
*/
......@@ -118,14 +128,124 @@ public:
<< ", oh: " << oh_ << ", ow: " << ow_;
}
// TODO(TJ): move to MkldnnMatrix
// create memory desc
inline mkldnn::memory::desc createMD(
mkldnn::memory::dims dims,
mkldnn::memory::format fmt,
mkldnn::memory::data_type type = mkldnn::memory::data_type::f32) {
// TODO(TJ): isFmtSuppoted(fmt)
return mkldnn::memory::desc(dims, type, fmt);
/**
* Print the mkldnn memory format flow of value
*/
virtual void printValueFormatFlow() {
if (inVal_ && outVal_) {
VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat()
<< " >>> " << outVal_->getFormat();
}
}
/**
* Print the mkldnn memory format flow of grad
*/
virtual void printGradFormatFlow() {
if (inGrad_ && outGrad_) {
VLOG(MKLDNN_FMTS) << "grad format flow --- " << inGrad_->getFormat()
<< " <<< " << outGrad_->getFormat();
}
}
protected:
/**
* copy image size and sequence info to other device
* @note: can not directly use Layer::copyOutputToOtherDevice since here only
* copy base info and do not copy data value
*/
void copyOutputInfoToOtherDevice() {
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].setFrameHeight(output_.getFrameHeight());
outputOtherDevice_[i].setFrameWidth(output_.getFrameWidth());
outputOtherDevice_[i].sequenceStartPositions =
output_.sequenceStartPositions;
outputOtherDevice_[i].subSequenceStartPositions =
output_.subSequenceStartPositions;
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
}
}
/**
* If input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
*/
bool inputIsOnlyMKLDNN(int index = 0) {
int prevDevice = getPrev(index)->getDeviceId();
if (prevDevice == MKLDNN_DEVICE) {
return true;
} else {
// do not support GPU yet
CHECK_EQ(prevDevice, CPU_DEVICE) << "Only support CPU yet";
return false;
}
}
/**
* If output only has MKLDNN device.
* Otherwise, other devices should only using CPU device.
*/
bool outputIsOnlyMKLDNN() {
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
CHECK_EQ(outputOtherDevice_[i].deviceId, CPU_DEVICE)
<< "Only support other device is CPU yet";
}
return outputOtherDevice_.size() == 0;
}
/**
* Sync input value data
*/
void syncInputValue() {
if (inputIsOnlyMKLDNN()) {
return;
}
real* iData = getInputValue(0, CPU_DEVICE)->getData();
// update input data
// since it might be changed if this is after data layer
inVal_->updateData(iData);
}
/**
* Sync output grad data
*/
void syncOutputGrad() {
if (outputIsOnlyMKLDNN()) {
return;
}
// update diff
real* oDiff = getOutput(CPU_DEVICE).grad->getData();
outGrad_->updateData(oDiff);
}
/**
* Set deviceId of this layer.
*/
void setDevice(int id) { deviceId_ = id; }
/**
* Set deviceId of the params used in this layer.
*/
void setParamsDevice(int id, const ParameterMap& parameterMap) {
for (auto& inputConfig : config_.inputs()) {
if (inputConfig.has_input_parameter_name()) {
ParameterPtr parameter;
std::string name = inputConfig.input_parameter_name();
CHECK(mapGet(name, parameterMap, &parameter))
<< "Cannot find input parameter " << name << " for layer "
<< getName();
parameter->setDevice(id);
}
}
if (config_.has_bias_parameter_name()) {
ParameterPtr parameter;
std::string name = config_.bias_parameter_name();
CHECK(mapGet(name, parameterMap, &parameter))
<< "Cannot find bias parameter " << name << " for layer "
<< getName();
parameter->setDevice(id);
}
}
};
......
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ a357bd6f
......@@ -34,6 +34,13 @@ add_unittest_without_exec(test_CRFLayerGrad
add_test(NAME test_CRFLayerGrad
COMMAND test_CRFLayerGrad)
################ test_CrossEntropyOverBeam ####################
add_unittest_without_exec(test_CrossEntropyOverBeam
test_CrossEntropyOverBeamGrad.cpp
LayerGradUtil.cpp)
add_test(NAME test_CrossEntropyOverBeam
COMMAND test_CrossEntropyOverBeam)
################ test_SeqSliceLayerGrad ####################
add_unittest_without_exec(test_SeqSliceLayerGrad
test_SeqSliceLayerGrad.cpp
......
paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp 0 → 100644
浏览文件 @ a357bd6f
/* Copyright (c) 2016 Baidu, Inc. 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 <random>
#include <sstream>
#include <gtest/gtest.h>
#include "ModelConfig.pb.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/trainer/Trainer.h"
#include "LayerGradUtil.h"
#include "paddle/testing/TestUtil.h"
using namespace paddle; // NOLINT
DECLARE_int32(gpu_id);
DECLARE_bool(thread_local_rand_use_global_seed);
const size_t MAX_SEQ_NUM = 23;
const size_t MAX_SEQ_LEN = 50;
const size_t MAX_BEAM_SIZE = 27;
const size_t SEED = (size_t)(time(NULL));
struct SingleBeamExpansion {
vector<int> seqStartPos;
vector<int> subSeqStartPos;
vector<real> candidateScores;
// TODO(caoying): store this into Argument.ids
vector<real> selectedIndices;
vector<int> groundTruth;
vector<size_t> inBeam;
vector<int> rowIdxInBeam;
vector<int> colIdxInBeam;
void resetGroundTruth(size_t n) {
groundTruth.clear();
groundTruth.resize(n, -1);
inBeam.clear();
inBeam.resize(n, 0);
rowIdxInBeam.clear();
rowIdxInBeam.resize(n, -1);
colIdxInBeam.clear();
colIdxInBeam.resize(n, -1);
}
};
inline float randFloat() {
return static_cast<float>(rand()) / static_cast<float>(RAND_MAX);
}
void genRand(real* numbers, size_t n) {
default_random_engine generator;
uniform_real_distribution<real> distribution(0.0, 1.0);
for (size_t i = 0; i < n; ++i) numbers[i] = distribution(generator);
}
vector<real> randSampling(real range, int n) {
CHECK_GE(range, n);
vector<real> num(range);
iota(begin(num), end(num), 0.);
if (range == n) return num;
random_shuffle(begin(num), end(num));
num.resize(n);
sort(begin(num), end(num));
return num;
}
void genCandidateScores(bool hasSubseq,
size_t beamSize,
SingleBeamExpansion& prevBeam,
SingleBeamExpansion& curBeam) {
vector<int>& seqStartPos = curBeam.seqStartPos;
seqStartPos.resize(1, 0);
vector<int>& subSeqStartPos = curBeam.subSeqStartPos;
subSeqStartPos.resize(1, 0);
srand(SEED);
if (prevBeam.selectedIndices.size()) {
if (prevBeam.subSeqStartPos.size() > 1) {
int seqIdx = 1;
// samples in previous beam are nested sequences.
for (size_t i = 1; i < prevBeam.subSeqStartPos.size(); ++i) {
for (size_t j = 0; j < beamSize; ++j) {
if (prevBeam.selectedIndices[(i - 1) * beamSize + j] == -1.) break;
subSeqStartPos.push_back(1 + (rand() % MAX_SEQ_LEN) +
subSeqStartPos.back());
}
if (prevBeam.seqStartPos[seqIdx] == prevBeam.subSeqStartPos[i]) {
seqStartPos.push_back(subSeqStartPos.back());
seqIdx++;
}
}
} else {
for (size_t i = 0; i <= prevBeam.selectedIndices.size(); ++i) {
if (i && i % beamSize == 0) {
seqStartPos.push_back(subSeqStartPos.back());
if (i == prevBeam.selectedIndices.size()) break;
}
if (prevBeam.selectedIndices[i] == -1.) continue;
subSeqStartPos.push_back(subSeqStartPos.back() +
(1 + (rand() % MAX_SEQ_LEN)));
}
}
} else {
// the first beam expansion
int seqNum = 1 + (rand() % MAX_SEQ_NUM);
for (int i = 0; i < seqNum; ++i) {
if (hasSubseq) {
for (size_t j = 0; j < 1 + (rand() % MAX_SEQ_NUM); ++j)
subSeqStartPos.push_back(subSeqStartPos.back() +
(1 + (rand() % MAX_SEQ_LEN)));
seqStartPos.push_back(subSeqStartPos.back());
} else {
seqStartPos.push_back(seqStartPos.back() +
(1 + (rand() % MAX_SEQ_LEN)));
}
}
}
size_t totalSeqNum = hasSubseq ? subSeqStartPos.back() : seqStartPos.back();
curBeam.candidateScores.resize(totalSeqNum, 0.);
genRand(curBeam.candidateScores.data(), totalSeqNum);
}
void genSelectedIndices(size_t beamSize,
vector<int>& seqStartPos,
vector<real>& selectedIndices) {
size_t selectedIdsCount = beamSize * (seqStartPos.size() - 1);
selectedIndices.resize(selectedIdsCount, -1.);
for (size_t i = 0; i < seqStartPos.size() - 1; ++i) {
int seqLen = seqStartPos[i + 1] - seqStartPos[i];
int n = min(seqLen, static_cast<int>(beamSize));
vector<real> ids = randSampling(seqLen, n);
memcpy(selectedIndices.data() + i * beamSize,
ids.data(),
sizeof(real) * ids.size());
}
}
void genGroundTruth(vector<SingleBeamExpansion>& beamExpansions,
size_t beamSize) {
SingleBeamExpansion& beam = beamExpansions[1];
size_t seqNum = beam.seqStartPos.size() - 1;
for (size_t i = 2; i < beamExpansions.size(); ++i)
CHECK_EQ(seqNum, beamExpansions[i].seqStartPos.size() - 1);
srand(SEED);
// initialize the first beam.
beam.resetGroundTruth(seqNum);
for (size_t i = 0; i < seqNum; ++i) {
if (randFloat() > 0.5) {
/*
* force the randomly generated label falls in the beam by chance 0.5.
* otherwise, when sequence length is relatively long and beam size is
* relatively small, the gold sequences falls off the beam at in the
* first search.
*/
real* begPos = beam.selectedIndices.data() + i * beamSize;
beam.colIdxInBeam[i] =
rand() % count_if(begPos, begPos + beamSize, [](const real& val) {
return val != -1.;
});
beam.groundTruth[i] =
beam.selectedIndices[i * beamSize + beam.colIdxInBeam[i]];
beam.inBeam[i] = 1;
} else {
int label = rand() % (beam.seqStartPos[i + 1] - beam.seqStartPos[i]);
beam.groundTruth[i] = label;
real* begPos = beam.selectedIndices.data() + i * beamSize;
real* endPos = begPos + beamSize;
real* lblPos = find(begPos, endPos, real(label));
if (lblPos != endPos) {
beam.inBeam[i] = 1;
beam.colIdxInBeam[i] = lblPos - begPos;
}
}
beam.rowIdxInBeam[i] = i;
}
// iterate over each beam expansions
for (size_t i = 2; i < beamExpansions.size(); ++i) {
SingleBeamExpansion& curBeam = beamExpansions[i];
SingleBeamExpansion& prevBeam = beamExpansions[i - 1];
curBeam.resetGroundTruth(seqNum);
// iterate over each sequence
for (size_t j = 0; j < seqNum; ++j) {
if (!prevBeam.inBeam[j]) continue;
// gold sequence falls in the beam in previous search.
real* begPos = prevBeam.selectedIndices.data();
int offset =
prevBeam.rowIdxInBeam[j] * beamSize + prevBeam.colIdxInBeam[j];
curBeam.rowIdxInBeam[j] = count_if(
begPos, begPos + offset, [](const real& val) { return val != -1.; });
if (randFloat() > 0.5) {
// force the randomly generated label falls in the beam by chance 0.5.
real* start =
curBeam.selectedIndices.data() + curBeam.rowIdxInBeam[j] * beamSize;
int n = rand() % count_if(start, start + beamSize, [](const real& val) {
return val != -1.;
});
curBeam.colIdxInBeam[j] = n;
curBeam.groundTruth[j] = *(start + n);
curBeam.inBeam[j] = 1;
} else {
CHECK_LE(curBeam.rowIdxInBeam[j] + 1,
curBeam.subSeqStartPos.size() - 1);
int start = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j]];
int end = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j] + 1];
CHECK_GT(size_t(end), size_t(start));
int label = rand() % (end - start);
curBeam.groundTruth[j] = label;
real* findBeg =
curBeam.selectedIndices.data() + curBeam.rowIdxInBeam[j] * beamSize;
real* lblPos =
find(findBeg, findBeg + beamSize, static_cast<real>(label));
if (lblPos != (findBeg + beamSize)) {
curBeam.inBeam[j] = 1;
curBeam.colIdxInBeam[j] = lblPos - findBeg;
}
}
}
}
}
void genOneBeam(size_t beamSize,
bool hasSubseq,
SingleBeamExpansion& prevBeam,
SingleBeamExpansion& curBeam) {
genCandidateScores(hasSubseq, beamSize, prevBeam, curBeam);
genSelectedIndices(beamSize,
hasSubseq ? curBeam.subSeqStartPos : curBeam.seqStartPos,
curBeam.selectedIndices);
}
void genRandomBeamExpansion(size_t expansionCount,
size_t beamSize,
vector<SingleBeamExpansion>& beamExpansions) {
beamExpansions.clear();
beamExpansions.resize(expansionCount + 1);
// beamExpansions[0] is reserved.
for (size_t i = 1; i <= expansionCount; ++i)
genOneBeam(beamSize, bool(i - 1), beamExpansions[i - 1], beamExpansions[i]);
genGroundTruth(beamExpansions, beamSize);
}
void testCrossEntropyOverBeam(bool useGpu,
size_t beamSize,
vector<SingleBeamExpansion>& beams) {
TestConfig config;
config.layerConfig.set_type("cross_entropy_over_beam");
size_t seqNum = 0;
for (size_t i = 1; i < beams.size(); ++i) {
const SingleBeamExpansion& beam = beams[i];
// create scores for all the candidates
MatrixPtr candidateScorePtr =
Matrix::create(beam.candidateScores.size(), 1, false, false);
candidateScorePtr->copyFrom(beam.candidateScores.data(),
beam.candidateScores.size());
ostringstream paramName;
paramName << "candidate_scores_" << i;
if (beam.subSeqStartPos.size() > 1) {
seqNum = beam.subSeqStartPos.size() - 1;
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
paramName.str(),
candidateScorePtr,
beam.seqStartPos,
beam.subSeqStartPos});
} else {
seqNum = beam.seqStartPos.size() - 1;
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
paramName.str(),
candidateScorePtr,
beam.seqStartPos});
}
config.layerConfig.add_inputs();
// create indices for the selected candidates
MatrixPtr selectedCandidates =
Matrix::create(seqNum, beamSize, false, false);
selectedCandidates->copyFrom(beam.selectedIndices.data(),
beam.selectedIndices.size());
paramName.clear();
paramName << "selected_candidates_" << i;
config.inputDefs.push_back(
{INPUT_SELF_DEFINE_DATA, paramName.str(), selectedCandidates});
config.layerConfig.add_inputs();
// create the ground truth
paramName.clear();
paramName << "label_" << i;
config.inputDefs.push_back(
{INPUT_SELF_DEFINE_DATA, paramName.str(), beam.groundTruth});
config.layerConfig.add_inputs();
}
testLayerGrad(
config, "cross_entropy_over_beam", seqNum, false, useGpu, false);
}
TEST(Layer, CrossEntropyOverBeam) {
LOG(INFO) << "SEED = " << SEED;
const size_t beamSize = 1 + rand() % MAX_BEAM_SIZE;
LOG(INFO) << "beamSize = " << beamSize;
// TODO(caoying): test with random beam expansions.
const size_t expansionCount = 3;
vector<SingleBeamExpansion> beams;
genRandomBeamExpansion(expansionCount, beamSize, beams);
for (bool useGpu : {false, true})
testCrossEntropyOverBeam(useGpu, beamSize, beams);
}
int main(int argc, char** argv) {
initMain(argc, argv);
hl_start();
hl_init(FLAGS_gpu_id);
FLAGS_thread_local_rand_use_global_seed = true;
srand(SEED);
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
paddle/math/Allocator.h
浏览文件 @ a357bd6f
......@@ -48,7 +48,13 @@ public:
*/
virtual void* alloc(size_t size) {
void* ptr;
#ifdef PADDLE_USE_MKLDNN
// refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
// memory alignment
CHECK_EQ(posix_memalign(&ptr, 4096ul, size), 0);
#else
CHECK_EQ(posix_memalign(&ptr, 32ul, size), 0);
#endif
CHECK(ptr) << "Fail to allocate CPU memory: size=" << size;
return ptr;
}
......
paddle/math/CMakeLists.txt
浏览文件 @ a357bd6f
......@@ -14,6 +14,17 @@
#
file(GLOB MATH_HEADERS . *.h)
file(GLOB MATH_SOURCES . *.cpp)
if(NOT WITH_MKLDNN)
set(DNN_HEADER "${CMAKE_CURRENT_SOURCE_DIR}/MKLDNNMatrix.h")
set(DNN_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/MKLDNNMatrix.cpp")
list(REMOVE_ITEM MATH_HEADERS "${DNN_HEADER}")
list(REMOVE_ITEM MATH_SOURCES "${DNN_SOURCE}")
message(STATUS "Skip compiling with MKLDNNMatrix")
else()
message(STATUS "Compile with MKLDNNMatrix")
endif()
set(MATH_SOURCES
"${PADDLE_SOURCE_DIR}/paddle/math/BaseMatrix.cu"
"${PADDLE_SOURCE_DIR}/paddle/math/TrainingAlgorithmOp.cu"
......
paddle/math/MKLDNNMatrix.cpp 0 → 100644
浏览文件 @ a357bd6f
/* Copyright (c) 2017 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 "MKLDNNMatrix.h"
using namespace mkldnn; // NOLINT
namespace paddle {
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) {
memory::desc md = pd.desc();
size_t ndims = md.data.ndims;
int* dims = md.data.dims;
CHECK(ndims > 0) << "Input dims should not be empty";
size_t cnts = 1;
for (size_t i = 0; i < ndims; ++i) {
cnts *= dims[i];
}
if (m == nullptr) {
size_t height = dims[0];
size_t width = cnts / dims[0];
m = Matrix::create(height, width, false, false);
}
CHECK(m) << " Matrix should not be empty";
CpuMatrixPtr cpuMatrix = std::dynamic_pointer_cast<CpuMatrix>(m);
CHECK(cpuMatrix) << "Only support create from CPU matrix yet";
CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match";
return std::make_shared<MKLDNNMatrix>(
m->getData(), m->getHeight(), m->getWidth(), pd);
}
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m,
memory::dims dims,
memory::format fmt,
engine& eg,
mkldnn::memory::data_type dtype) {
return create(m, memory::primitive_desc(memory::desc(dims, dtype, fmt), eg));
}
void MKLDNNMatrix::reorderDataFrom(const MKLDNNMatrixPtr& m,
memory::format srcFmt,
memory::dims targetDim) {
memory::format dstFmt = getFormat();
if (srcFmt == dstFmt) {
return;
}
CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal";
reorderOnce(getData(), m->getData(), srcFmt, dstFmt, targetDim);
}
void MKLDNNMatrix::reorderDataTo(const MKLDNNMatrixPtr& m,
memory::format dstFmt,
memory::dims targetDim) {
memory::format srcFmt = getFormat();
if (srcFmt == dstFmt) {
return;
}
CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal";
reorderOnce(getData(), m->getData(), srcFmt, dstFmt, targetDim);
}
void MKLDNNMatrix::reorderOnce(void* srcData,
void* dstData,
memory::format srcFmt,
memory::format dstFmt,
memory::dims dm) {
CHECK(srcData);
CHECK(dstData);
MatrixPtr tmpSrc;
if (dstData == srcData) {
// inplace data
size_t sz = 1;
for (size_t i = 0; i < dm.size(); ++i) {
sz *= dm[i];
}
tmpSrc = Matrix::create(sz, 1, false, false);
tmpSrc->copyFrom((real*)srcData, sz);
srcData = tmpSrc->getData();
}
auto dtype = this->getDtype();
auto srcMD = memory::desc(dm, dtype, srcFmt);
auto dstMD = memory::desc(dm, dtype, dstFmt);
auto eg = this->getEngine();
auto src = memory(memory::primitive_desc(srcMD, eg), srcData);
auto dst = memory(memory::primitive_desc(dstMD, eg), dstData);
auto r = reorder(src, dst);
stream(stream::kind::eager).submit({r}).wait();
}
void MKLDNNMatrix::downSpatial() {
int fmt = getFormat();
if (!(fmt == memory::format::nchw || fmt == memory::format::oihw)) {
// only support nchw and oihw yet, later can support more like nhwc, ihwo
return;
}
// TODO(TJ): change H(height) and W(width) if support nhwc or more
const int H = 2, W = 3;
memory::dims srcDims = getDims();
if (srcDims[H] != 1 || srcDims[W] != 1) {
// can not down spatial
return;
}
memory::dims dstDims = memory::dims{srcDims[0], srcDims[1]};
memory::format dstFmt;
switch (fmt) {
case memory::format::nchw:
dstFmt = memory::format::nc;
break;
case memory::format::oihw:
dstFmt = memory::format::oi;
break;
default:
LOG(FATAL) << "unsupported format";
}
memory::desc md = memory::desc(dstDims, getDtype(), dstFmt);
memory::primitive_desc pd = memory::primitive_desc(md, getEngine());
mkldnn_primitive_t result;
mkldnn::error::wrap_c_api(
mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr),
"could not create a memory primitive");
reset(result);
set_data_handle(getData());
}
} // namespace paddle
paddle/math/MKLDNNMatrix.h 0 → 100644
浏览文件 @ a357bd6f
/* Copyright (c) 2017 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 <vector>
#include "Matrix.h"
#include "mkldnn.hpp"
#include "paddle/parameter/Parameter.h"
namespace paddle {
class MKLDNNMatrix;
typedef std::shared_ptr<MKLDNNMatrix> MKLDNNMatrixPtr;
/**
* @brief MKLDNN Matrix.
*
*/
class MKLDNNMatrix : public CpuMatrix, public mkldnn::memory {
public:
MKLDNNMatrix(real* data,
size_t height,
size_t width,
mkldnn::memory::primitive_desc pd)
: CpuMatrix(data, height, width, false), mkldnn::memory(pd, data) {}
~MKLDNNMatrix() {}
/**
* Create MKLDNNMatrix from a MatrixPtr and memory primitive_desc
*/
static MKLDNNMatrixPtr create(MatrixPtr m, mkldnn::memory::primitive_desc pd);
/**
* Create MKLDNNMatrix from a MatrixPtr and memory details info
*/
static MKLDNNMatrixPtr create(
MatrixPtr m,
mkldnn::memory::dims dims,
mkldnn::memory::format fmt,
mkldnn::engine& eg,
mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32);
public:
/**
* Reorder this MKLDNNMatrix from other format.
* Support inplace reorder.
* @note: this function would only reorder the data layout.
* will NOT change this original dim or format info
*/
void reorderDataFrom(const MKLDNNMatrixPtr& m,
memory::format srcFmt,
memory::dims targetDim);
/**
* Reorder this MKLDNNMatrix to other format.
* Support inplace reorder.
* @note: this function would only reorder the data layout.
* will NOT change the dst dim or format info
*/
void reorderDataTo(const MKLDNNMatrixPtr& m,
memory::format dstFmt,
memory::dims targetDim);
/**
* Dimensionality reduction.
* Change format "nchw --> nc" or "oihw --> oi" if the h and w are both 1
*/
void downSpatial();
/**
* Update the memory data handle.
* Caution: This will not check the buffer size of the data,
* it should be coverd by user.
*/
void updateData(void* data) { set_data_handle(data); }
/**
* Get primitive descriptor.
*/
mkldnn::memory::primitive_desc getPrimitiveDesc() {
return this->get_primitive_desc();
}
/**
* Get memory descriptor.
*/
mkldnn::memory::desc getMemoryDesc() { return getPrimitiveDesc().desc(); }
/**
* Get dimensions.
*/
mkldnn::memory::dims getDims() {
mkldnn::memory::desc md = getMemoryDesc();
const int* src = md.data.dims;
int ndims = md.data.ndims;
mkldnn::memory::dims dst;
dst.resize(ndims);
for (int i = 0; i < ndims; ++i) {
dst[i] = src[i];
}
return dst;
}
/**
* Get format.
*/
mkldnn::memory::format getFormat() {
return (mkldnn::memory::format)(getMemoryDesc().data.format);
}
/**
* Get memory data type.
*/
mkldnn::memory::data_type getDtype() {
return (mkldnn::memory::data_type)(getMemoryDesc().data.data_type);
}
/**
* Get engine.
*/
mkldnn::engine getEngine() { return getPrimitiveDesc().get_engine(); }
protected:
/**
* Do reorder once.
* Can support inplace.
*/
void reorderOnce(void* srcData,
void* dstData,
memory::format srcFmt,
memory::format dstFmt,
memory::dims dm);
};
} // namespace paddle
paddle/parameter/Argument.cpp
浏览文件 @ a357bd6f
......@@ -679,6 +679,7 @@ void Argument::reorganizeSeqInfo(
const ICpuGpuVectorPtr subSeqStartPos,
std::vector<std::vector<int>>& reorganizedSeqInfo) {
CHECK(seqStartPos);
reorganizedSeqInfo.clear();
int seqNum = seqStartPos->getSize() - 1;
int* seqStarts = seqStartPos->getMutableData(false);
......
paddle/parameter/Parameter.h
浏览文件 @ a357bd6f
......@@ -281,7 +281,11 @@ public:
/**
* @brief Set the format in header.
*/
void setHeaderFormat(int32_t fmt) { headerFormat_ = fmt; }
void setHeaderFormat(int32_t fmt) {
CHECK(isHeaderFormatSupported(fmt)) << "Unsupported format version: "
<< fmt;
headerFormat_ = fmt;
}
/**
* @brief Parameter Update Hook.
......
paddle/pserver/LightNetwork.cpp
浏览文件 @ a357bd6f
......@@ -22,7 +22,6 @@ limitations under the License. */
#include <arpa/inet.h>
#include <net/if.h>
#include <net/if_arp.h>
#include <sys/ioctl.h>
#include <sstream>
......
python/paddle/trainer/config_parser.py
浏览文件 @ a357bd6f
......@@ -1688,6 +1688,21 @@ class MultiClassCrossEntropySelfNormCostLayer(LayerBase):
self.config.softmax_selfnorm_alpha = softmax_selfnorm_alpha
@config_layer('cross_entropy_over_beam')
class CrossEntropyOverBeamLayer(LayerBase):
def __init__(self, name, inputs, **xargs):
config_assert(len(inputs) % 3 == 0, "Error input number.")
super(CrossEntropyOverBeamLayer, self).__init__(
name, 'cross_entropy_over_beam', 0, inputs, **xargs)
input_num = len(inputs) / 3
for i in range(input_num):
input_layer = self.get_input_layer(i * 3)
config_assert(input_layer.size == 1, (
"Inputs for this layer are made up of "
"several triples, in which the first one is scores over "
"all candidate paths, whose size should be equal to 1."))
@config_layer('fc')
class FCLayer(LayerBase):
layer_type = 'fc'
......@@ -2386,6 +2401,7 @@ def define_cost(class_name, cost_type):
define_cost('MultiClassCrossEntropy', 'multi-class-cross-entropy')
define_cost('CrossEntropyOverBeamCostLayer', 'cross_entropy_over_beam')
define_cost('RankingCost', 'rank-cost')
define_cost('AucValidation', 'auc-validation')
define_cost('PnpairValidation', 'pnpair-validation')
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ a357bd6f
......@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import collections
import inspect
......@@ -106,6 +105,8 @@ __all__ = [
'nce_layer',
'cross_entropy_with_selfnorm',
'cross_entropy',
'BeamInput',
'cross_entropy_over_beam',
'multi_binary_label_cross_entropy',
'sum_cost',
'rank_cost',
......@@ -227,6 +228,7 @@ class LayerType(object):
HUBER_CLASSIFICATION = 'huber_classification'
CROSS_ENTROPY = 'multi-class-cross-entropy'
CROSS_ENTROPY_WITH_SELFNORM = 'multi_class_cross_entropy_with_selfnorm'
CROSS_ENTROPY_OVER_BEAM = 'cross_entropy_over_beam'
SOFT_BIN_CLASS_CROSS_ENTROPY = 'soft_binary_class_cross_entropy'
MULTI_BIN_LABEL_CROSS_ENTROPY = 'multi_binary_label_cross_entropy'
SUM_COST = 'sum_cost'
......@@ -4217,8 +4219,12 @@ def __cost_input__(input, label, weight=None):
"""
inputs and parents for cost layers.
"""
ipts = [Input(input.name), Input(label.name)]
parents = [input, label]
if isinstance(input, LayerOutput):
input = [input]
if isinstance(label, LayerOutput):
label = [label]
ipts = [Input(ipt.name) for ipt in (input + label)]
parents = [ipt for ipt in (input + label)]
if weight is not None:
assert weight.size == 1
ipts.append(Input(weight.name))
......@@ -5205,17 +5211,6 @@ def warp_ctc_layer(input,
building process, PaddlePaddle will clone the source codes, build and
install it to :code:`third_party/install/warpctc` directory.
To use warp_ctc layer, you need to specify the path of :code:`libwarpctc.so`,
using following methods:
1. Set it in :code:`paddle.init` (python api) or :code:`paddle_init` (c api),
such as :code:`paddle.init(use_gpu=True,
warpctc_dir=your_paddle_source_dir/third_party/install/warpctc/lib)`.
2. Set environment variable LD_LIBRARY_PATH on Linux or DYLD_LIBRARY_PATH
on Mac OS. For instance, :code:`export
LD_LIBRARY_PATH=your_paddle_source_dir/third_party/install/warpctc/lib:$LD_LIBRARY_PATH`.
More details of CTC can be found by referring to `Connectionist Temporal
Classification: Labelling Unsegmented Sequence Data with Recurrent
Neural Networks <http://machinelearning.wustl.edu/mlpapers/paper_files/
......@@ -5923,10 +5918,10 @@ def multi_binary_label_cross_entropy(input,
if input.activation is None or \
not isinstance(input.activation, SigmoidActivation):
logger.log(
logging.WARN,
"%s is not recommend for multi_binary_label_cross_entropy's activation, "
"maybe the sigmoid is better" % repr(input.activation))
logger.log(logging.WARN,
("%s is not a recommended activation for "
"multi_binary_label_cross_entropy, sigmoid is better") %
repr(input.activation))
Layer(
name=name,
......@@ -5941,6 +5936,113 @@ def multi_binary_label_cross_entropy(input,
size=1)
class BeamInput(object):
"""
Define the input for cross_entropy_over_beam layer.
A beam is made up of a triple: the first one is scores over all
candidates; the second one is indices of top k selected candidates; the
third one is the index of ground truth, which is also always called
gold.
"""
def __init__(self, candidate_scores, selected_candidates, gold):
assert isinstance(candidate_scores, LayerOutput)
self.candidate_scores = candidate_scores
assert candidate_scores.size == 1
assert isinstance(selected_candidates, LayerOutput)
self.selected_candidates = selected_candidates
assert isinstance(gold, LayerOutput)
self.gold = gold
@wrap_name_default()
@layer_support()
def cross_entropy_over_beam(input, name=None):
"""
This layer is used in learning to search models, which is to solve complex
joint prediction problems based on learning to search through a
problem-defined search space.
Specifically, the learning to search process for this layer begins with
searching a target sequence from a nested sequence. In the first search
step, top beam size sequences with highest scores, indices of these top k
sequences in the original nested sequence, and the ground truth (also
called gold) altogether (a triple) make up of the first beam.
Then, several special positions, for example, start and end positions
that define meaningful segments are searched. In these searches, top k
positions with highest scores are selected, and then sequence, starting
from the selected starts till ends of the sequences (or a fixed position)
are taken to search next.
We call the possible top k results returned in one search the beam. This
search process can be repeated for pre-defined turns and leads to several
beam expansions.
Finally, the layer cross_entropy_over_beam takes all the beam expansions
which contain several candidate targets found along the multi-step search.
cross_entropy_over_beam calculates cross entropy over the expanded beams
which all the candidates in the beam as the normalized factor.
Note that, if gold falls off the beam at search step t, then the cost is
calculated over the beam at step t.
This cost layer always works together with kmax_sequence_score_layer,
sub_nested_seq_layer, and sequence_slice_layer to trim the input to form a
sub-search space.
The example usage is:
.. code-block:: python
cost = cross_entropy_over_beam(input=[
BeamInput(
candidate_scores=beam1_candidates,
selected_candidates=beam1_topk,
gold=gold1),
BeamInput(
candidate_scores=beam2_candidates,
selected_candidates=beam2_topk,
gold=gold2),
])
:param input: input beams for this layer.
:type input: BeamInput
:param name: input beams for this layer.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, BeamInput):
input = [input]
else:
assert isinstance(input, list), (
'input for cross_entropy_over_beam shold be a python list '
'of BeamInput object.')
for ipt in input:
assert isinstance(ipt, BeamInput), (
'input for cross_entropy_over_beam '
'should be a BeamInput object.')
ipts = []
parents = []
for beam in input:
parents += [beam.candidate_scores, beam.selected_candidates, beam.gold]
ipts += [
beam.candidate_scores.name, beam.selected_candidates.name,
beam.gold.name
]
Layer(name=name, type=LayerType.CROSS_ENTROPY_OVER_BEAM, inputs=ipts)
return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1)
@wrap_name_default()
@layer_support()
def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
......
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @ a357bd6f
......@@ -9,6 +9,6 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer
test_seq_slice_layer test_pooling3D_layer)
test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer)
export whole_configs=(test_split_datasource)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr 0 → 100644
浏览文件 @ a357bd6f
type: "nn"
layers {
name: "sentence_states"
type: "data"
size: 32
active_type: ""
}
layers {
name: "sentence_scores"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__kmax_sequence_score_layer_0__"
type: "kmax_seq_score"
active_type: ""
inputs {
input_layer_name: "sentence_scores"
}
beam_size: 5
}
layers {
name: "__sub_nested_seq_layer_0__"
type: "sub_nested_seq"
size: 32
active_type: ""
inputs {
input_layer_name: "sentence_states"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_0__"
}
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 1
active_type: ""
inputs {
input_layer_name: "__sub_nested_seq_layer_0__"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_1__"
type: "kmax_seq_score"
active_type: ""
inputs {
input_layer_name: "sentence_scores"
}
beam_size: 5
}
layers {
name: "__seq_slice_layer_0__"
type: "seq_slice"
size: 32
active_type: ""
inputs {
input_layer_name: "__sub_nested_seq_layer_0__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_1__"
}
select_first: true
}
layers {
name: "__fc_layer_1__"
type: "fc"
size: 1
active_type: ""
inputs {
input_layer_name: "__seq_slice_layer_0__"
input_parameter_name: "___fc_layer_1__.w0"
}
bias_parameter_name: "___fc_layer_1__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_2__"
type: "kmax_seq_score"
active_type: ""
inputs {
input_layer_name: "__fc_layer_1__"
}
beam_size: 5
}
layers {
name: "sentences_ids"
type: "data"
size: 1
active_type: ""
}
layers {
name: "start_ids"
type: "data"
size: 1
active_type: ""
}
layers {
name: "end_ids"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__cross_entropy_over_beam_0__"
type: "cross_entropy_over_beam"
active_type: ""
inputs {
input_layer_name: "sentence_scores"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_0__"
}
inputs {
input_layer_name: "sentences_ids"
}
inputs {
input_layer_name: "__fc_layer_0__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_1__"
}
inputs {
input_layer_name: "start_ids"
}
inputs {
input_layer_name: "__fc_layer_1__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_2__"
}
inputs {
input_layer_name: "end_ids"
}
}
parameters {
name: "___fc_layer_0__.w0"
size: 32
initial_mean: 0.0
initial_std: 0.176776695297
dims: 32
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 1
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_1__.w0"
size: 32
initial_mean: 0.0
initial_std: 0.176776695297
dims: 32
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_1__.wbias"
size: 1
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "sentence_scores"
input_layer_names: "sentences_ids"
input_layer_names: "sentence_states"
input_layer_names: "start_ids"
input_layer_names: "end_ids"
output_layer_names: "__cross_entropy_over_beam_0__"
sub_models {
name: "root"
layer_names: "sentence_states"
layer_names: "sentence_scores"
layer_names: "__kmax_sequence_score_layer_0__"
layer_names: "__sub_nested_seq_layer_0__"
layer_names: "__fc_layer_0__"
layer_names: "__kmax_sequence_score_layer_1__"
layer_names: "__seq_slice_layer_0__"
layer_names: "__fc_layer_1__"
layer_names: "__kmax_sequence_score_layer_2__"
layer_names: "sentences_ids"
layer_names: "start_ids"
layer_names: "end_ids"
layer_names: "__cross_entropy_over_beam_0__"
input_layer_names: "sentence_scores"
input_layer_names: "sentences_ids"
input_layer_names: "sentence_states"
input_layer_names: "start_ids"
input_layer_names: "end_ids"
output_layer_names: "__cross_entropy_over_beam_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_pooling3D_layer.protostr 0 → 100644
浏览文件 @ a357bd6f
type: "nn"
layers {
name: "data_2d"
type: "data"
size: 6000
active_type: ""
height: 20
width: 10
}
layers {
name: "pool___2d"
type: "pool"
size: 840
active_type: ""
inputs {
input_layer_name: "data_2d"
pool_conf {
pool_type: "avg-projection"
channels: 30
size_x: 5
stride: 3
output_x: 4
img_size: 10
padding: 1
size_y: 5
stride_y: 3
output_y: 7
img_size_y: 20
padding_y: 1
}
}
height: 7
width: 4
}
layers {
name: "data_3d_1"
type: "data"
size: 60000
active_type: ""
height: 20
width: 10
depth: 10
}
layers {
name: "pool_3d_1"
type: "pool3d"
size: 3360
active_type: ""
inputs {
input_layer_name: "data_3d_1"
pool_conf {
pool_type: "avg-projection"
channels: 30
size_x: 5
stride: 3
output_x: 4
img_size: 10
padding: 1
size_y: 5
stride_y: 3
output_y: 7
img_size_y: 20
padding_y: 1
size_z: 5
stride_z: 3
output_z: 4
img_size_z: 10
padding_z: 1
}
}
height: 7
width: 4
depth: 4
}
layers {
name: "pool_3d_2"
type: "pool3d"
size: 3360
active_type: ""
inputs {
input_layer_name: "data_3d_1"
pool_conf {
pool_type: "max-projection"
channels: 30
size_x: 5
stride: 3
output_x: 4
img_size: 10
padding: 1
size_y: 5
stride_y: 3
output_y: 7
img_size_y: 20
padding_y: 1
size_z: 5
stride_z: 3
output_z: 4
img_size_z: 10
padding_z: 1
}
}
height: 7
width: 4
depth: 4
}
input_layer_names: "data_2d"
output_layer_names: "pool___2d"
output_layer_names: "pool_3d_1"
output_layer_names: "pool_3d_2"
sub_models {
name: "root"
layer_names: "data_2d"
layer_names: "pool___2d"
layer_names: "data_3d_1"
layer_names: "pool_3d_1"
layer_names: "pool_3d_2"
input_layer_names: "data_2d"
output_layer_names: "pool___2d"
output_layer_names: "pool_3d_1"
output_layer_names: "pool_3d_2"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py 0 → 100644
浏览文件 @ a357bd6f
#!/usr/bin/env python
#coding=utf-8
from paddle.trainer_config_helpers import *
beam_size = 5
# the first beam expansion.
sentence_states = data_layer(name="sentence_states", size=32)
sentence_scores = data_layer(name="sentence_scores", size=1)
topk_sentence_ids = kmax_sequence_score_layer(
input=sentence_scores, beam_size=beam_size)
# the second beam expansion.
topk_sen = sub_nested_seq_layer(
input=sentence_states, selected_indices=topk_sentence_ids)
start_pos_scores = fc_layer(input=topk_sen, size=1, act=LinearActivation())
topk_start_pos_ids = kmax_sequence_score_layer(
input=sentence_scores, beam_size=beam_size)
# the final beam expansion.
topk_start_spans = seq_slice_layer(
input=topk_sen, starts=topk_start_pos_ids, ends=None)
end_pos_scores = fc_layer(
input=topk_start_spans, size=1, act=LinearActivation())
topk_end_pos_ids = kmax_sequence_score_layer(
input=end_pos_scores, beam_size=beam_size)
# define the cost
sentence_idx = data_layer(name="sentences_ids", size=1)
start_idx = data_layer(name="start_ids", size=1)
end_idx = data_layer(name="end_ids", size=1)
cost = cross_entropy_over_beam(input=[
BeamInput(
candidate_scores=sentence_scores,
selected_candidates=topk_sentence_ids,
gold=sentence_idx), BeamInput(
candidate_scores=start_pos_scores,
selected_candidates=topk_start_pos_ids,
gold=start_idx), BeamInput(
candidate_scores=end_pos_scores,
selected_candidates=topk_end_pos_ids,
gold=end_idx)
])
outputs(cost)
python/paddle/v2/inference.py
浏览文件 @ a357bd6f
......@@ -70,7 +70,7 @@ class Inference(object):
item = [each_result[each_field] for each_field in field]
yield item
def infer(self, input, field='value', **kwargs):
def infer(self, input, field='value', flatten_result=True, **kwargs):
"""
Infer a data by model.
:param input: input data batch. Should be python iterable object.
......@@ -83,7 +83,10 @@ class Inference(object):
retv = [[] for i in xrange(len(result))]
for i, item in enumerate(result):
retv[i].append(item)
retv = [numpy.concatenate(out) for out in retv]
if flatten_result:
retv = [numpy.concatenate(out) for out in retv]
if len(retv) == 1:
return retv[0]
else:
......
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