diff --git a/doc/howto/dev/write_docs_cn.rst b/doc/howto/dev/write_docs_cn.rst
index 36e5d420c986fc8d88eefee4aa221dba0a0480f2..731a63f945c29ba78538b3d71289b234e569354d 100644
--- a/doc/howto/dev/write_docs_cn.rst
+++ b/doc/howto/dev/write_docs_cn.rst
@@ -5,15 +5,13 @@
PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。
-如何构建PaddlePaddle的文档
-==========================
+如何构建文档
+============
-PaddlePaddle的文档构建有直接构建和基于Docker构建两种方式，我们提供了一个构建脚本build_docs.sh来进行构建。
-PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使用基于Docker来构建PaddlePaddle的文档。
+PaddlePaddle的文档构建有两种方式。
-
-使用Docker构建PaddlePaddle的文档
---------------------------------
+使用Docker构建
+--------------
使用Docker构建PaddlePaddle的文档，需要在系统里先安装好Docker工具包。Docker安装请参考 `Docker的官网 `_ 。安装好Docker之后可以使用源码目录下的脚本构建文档，即
@@ -21,58 +19,46 @@ PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使
cd TO_YOUR_PADDLE_CLONE_PATH
cd paddle/scripts/tools/build_docs
- bash build_docs.sh with_docker
-
-编译完成后，会在当前目录生成两个子目录\:
-
-* doc 英文文档目录
-* doc_cn 中文文档目录
+ sh build_docs.sh
+编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。
打开浏览器访问对应目录下的index.html即可访问本地文档。
-
-
-直接构建PaddlePaddle的文档
---------------------------
-
-因为PaddlePaddle的v2 api文档生成过程依赖于py_paddle Python包，用户需要首先确认py_paddle包已经安装。
-
-.. code-block:: bash
-
- python -c "import py_paddle"
-
-如果提示错误，那么用户需要在本地编译安装PaddlePaddle，请参考 `源码编译文档 `_ 。
-注意，用户在首次编译安装PaddlePaddle时，请将WITH_DOC选项关闭。在编译安装正确之后，请再次确认py_paddle包已经安装，即可进行下一步操作。
+直接构建
+--------
如果提示正确，可以执行以下命令编译生成文档，即
.. code-block:: bash
cd TO_YOUR_PADDLE_CLONE_PATH
- cd paddle/scripts/tools/build_docs
- bash build_docs.sh local
-
-编译完成之后，会在当前目录生成两个子目录\:
-
-* doc 英文文档目录
-* doc_cn 中文文档目录
+ mkdir -p build
+ cd build
+ cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
+ make gen_proto_py
+ make paddle_docs paddle_docs_cn
+编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。
打开浏览器访问对应目录下的index.html即可访问本地文档。
-如何书写PaddlePaddle的文档
-==========================
+如何书写文档
+============
PaddlePaddle文档使用 `sphinx`_ 自动生成，用户可以参考sphinx教程进行书写。
-如何更新www.paddlepaddle.org文档
-================================
+如何更新文档主题
+================
+
+PaddlePaddle文档主题在 `TO_YOUR_PADDLE_CLONE_PATH/doc_theme` 文件夹下，包含所有和前端网页设计相关的文件。
-开发者给PaddlePaddle代码增加的注释以PR的形式提交到github中，提交方式可参见 `贡献文档 `_ 。
+如何更新doc.paddlepaddle.org
+============================
+
+更新的文档以PR的形式提交到github中，提交方式参见 `贡献文档 `_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的，用户可以分别查看最新的 `中文文档 `_ 和
`英文文档 `_ 。
-
.. _cmake: https://cmake.org/
.. _sphinx: http://www.sphinx-doc.org/en/1.4.8/
diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index c0838d9b759110fd706577386d2c81bda6876223..3371962c635c3731f00a6af2a6e287ece33397cd 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -9,6 +9,7 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
+nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
cc_test(variable_test SRCS variable_test.cc)
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 154068fef69bc96edbd85b731fe8091b3b1ff823..568f4e89819c8345d8908634f6fa56f09483a763 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -18,8 +18,10 @@
#ifndef PADDLE_ONLY_CPU
#include
#include
+#include
#endif
+#include
#include "paddle/framework/ddim.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/enforce.h"
@@ -32,7 +34,8 @@ template
using Vector = std::vector;
#else
template
-using Vector = thrust::host_vector;
+using Vector = thrust::host_vector<
+ T, thrust::system::cuda::experimental::pinned_allocator>;
#endif
using LoD = std::vector>;
diff --git a/paddle/framework/lod_tensor_test.cu b/paddle/framework/lod_tensor_test.cu
new file mode 100644
index 0000000000000000000000000000000000000000..1079a36a2e7b24f6f8a5bcbb296355567305a765
--- /dev/null
+++ b/paddle/framework/lod_tensor_test.cu
@@ -0,0 +1,52 @@
+/*
+ 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
+#include
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/platform/assert.h"
+
+#include
+
+__global__ void test(size_t* a, int size) {
+ for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size;
+ i += blockDim.x * gridDim.x) {
+ a[i] *= 2;
+ }
+}
+
+TEST(LoDTensor, LoDInGPU) {
+ paddle::framework::Tensor tensor;
+ paddle::framework::LoDTensor lod_tensor;
+ paddle::platform::GPUPlace place(0);
+
+ paddle::framework::LoD src_lod;
+ src_lod.push_back(std::vector{0, 2, 4, 6, 8, 10, 12, 14});
+
+ tensor.Resize({14, 16});
+ tensor.mutable_data(place);
+
+ lod_tensor.set_lod(src_lod);
+ lod_tensor.set_tensor(&tensor);
+ CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
+ CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
+
+ auto lod = lod_tensor.lod();
+
+ test<<<1, 8>>>(lod[0].data(), lod[0].size());
+ cudaDeviceSynchronize();
+
+ for (size_t i = 0; i < src_lod[0].size(); ++i) {
+ CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
+ }
+}
diff --git a/paddle/gserver/layers/Layer.h b/paddle/gserver/layers/Layer.h
index edef36194aabdb9c122ec3423deb036169a34d7c..4002a3d0747a86ab7b495ffe52247521831b71b8 100644
--- a/paddle/gserver/layers/Layer.h
+++ b/paddle/gserver/layers/Layer.h
@@ -49,6 +49,12 @@ struct LayerState {
};
typedef std::shared_ptr LayerStatePtr;
+/// Paddle device ID, MKLDNN is -2, CPU is -1
+enum PADDLE_DEVICE_ID {
+ MKLDNN_DEVICE = -2,
+ CPU_DEVICE = -1,
+};
+
/**
* @brief Base class for layer.
* Define necessary variables and functions for every layer.
@@ -59,11 +65,6 @@ protected:
LayerConfig config_;
/// whether to use GPU
bool useGpu_;
- /// 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
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp
index 53433cef35a377a73f87b041fdcfadd848dd2ec9..f70343251ad4fbb99f9614618f6d1bff1174f15e 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.cpp
+++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp
@@ -14,7 +14,6 @@ limitations under the License. */
#include "MKLDNNFcLayer.h"
#include "paddle/utils/Logging.h"
-#include "paddle/utils/Stat.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
@@ -40,6 +39,8 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap,
oc_ = getSize();
oh_ = 1;
ow_ = 1;
+ ih_ = 1;
+ iw_ = 1;
// input size can not change in FC
iLayerSize_ = inputLayers_[0]->getSize();
@@ -77,67 +78,53 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
-void MKLDNNFcLayer::reshape() {
- const Argument& input = getInput(0, getPrev(0)->getDeviceId());
- int batchSize = input.getBatchSize();
- if (bs_ == batchSize) {
- return;
- }
- bs_ = batchSize;
- ih_ = input.getFrameHeight();
- iw_ = input.getFrameWidth();
- if (ih_ == 0) {
- ih_ = 1;
- }
- if (iw_ == 0) {
- iw_ = 1;
- }
- CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
- ic_ = iLayerSize_ / (ih_ * iw_);
- CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
- CHECK_EQ(size_t(oc_), getSize());
- printSizeInfo();
+void MKLDNNFcLayer::reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+ reshapeInput(bs, ih, iw);
- // reset output
- output_.setFrameHeight(oh_);
- output_.setFrameWidth(ow_);
- resetOutput(bs_, oc_);
+ CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
+ ic = iLayerSize_ / (ih * iw);
+ CHECK_EQ(size_t(ic * ih * iw), iLayerSize_) << "not divisible";
+ CHECK_EQ(size_t(oc), getSize());
- // reset mkldnn forward
- resetFwd();
- needResetBwd_ = true;
+ reshapeOutput(oh, ow);
+ resizeOutput(bs, oc);
- convertWeightsFromPaddle();
+ printSizeInfo();
}
-void MKLDNNFcLayer::resetFwd() {
+void MKLDNNFcLayer::resetFwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) {
+ pipeline.clear();
bool hasBias = biases_ && biases_->getW();
- const MatrixPtr& wgt = weight_->getW();
- const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr;
- const MatrixPtr& out = output_.value;
+ const MatrixPtr& wgtVal = weight_->getW();
+ const MatrixPtr& biasVal = hasBias ? biases_->getW() : nullptr;
+ const MatrixPtr& outVal = output_.value;
if (inputIsOnlyMKLDNN()) {
- const MatrixPtr& in = getInputValue(0);
- inVal_ = std::dynamic_pointer_cast(in);
- CHECK(inVal_) << "Input should be MKLDNNMatrix";
+ const MatrixPtr& inVal = getInputValue(0);
+ in = std::dynamic_pointer_cast(inVal);
+ CHECK(in) << "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_);
+ const MatrixPtr& inVal = getInputValue(0, CPU_DEVICE);
+ in = MKLDNNMatrix::create(
+ inVal, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
+ }
+ in->downSpatial();
+ wgt = MKLDNNMatrix::create(
+ wgtVal, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
+ wgt->downSpatial();
+ bias = hasBias ? MKLDNNMatrix::create(biasVal, {oc_}, format::x, engine_)
+ : nullptr;
+ out = MKLDNNMatrix::create(outVal, {bs_, oc_}, format::nc, engine_);
// change original output value to mkldnn output value
- output_.value = std::dynamic_pointer_cast(outVal_);
+ output_.value = std::dynamic_pointer_cast(out);
if (!outputIsOnlyMKLDNN()) {
- copyOutputInfoToOtherDevice();
// fc cpu output value do not need create convert
// just share point
getOutput(CPU_DEVICE).value->setData(output_.value->getData());
@@ -146,27 +133,31 @@ void MKLDNNFcLayer::resetFwd() {
// create forward handle
prop_kind pk = prop_kind::forward;
fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk,
- inVal_->getMemoryDesc(),
- wgtVal_->getMemoryDesc(),
- biasVal_->getMemoryDesc(),
- outVal_->getMemoryDesc())
+ in->getMemoryDesc(),
+ wgt->getMemoryDesc(),
+ bias->getMemoryDesc(),
+ out->getMemoryDesc())
: fc_fwd::desc(pk,
- inVal_->getMemoryDesc(),
- wgtVal_->getMemoryDesc(),
- outVal_->getMemoryDesc());
+ in->getMemoryDesc(),
+ wgt->getMemoryDesc(),
+ out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
if (hasBias) {
- fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
+ fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *bias, *out));
} else {
- fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
+ fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *out));
}
printValueFormatFlow();
- pipelineFwd_.clear();
- pipelineFwd_.push_back(*fwd_);
+ pipeline.push_back(*fwd_);
}
-void MKLDNNFcLayer::resetBwd() {
+void MKLDNNFcLayer::resetBwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) {
+ pipeline.clear();
if (!needResetBwd_) {
return;
}
@@ -175,8 +166,8 @@ void MKLDNNFcLayer::resetBwd() {
/// backward weight
CHECK(inVal_) << "Should have input value";
- const MatrixPtr& wgt = weight_->getWGrad();
- const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr;
+ const MatrixPtr& wgtGrad = weight_->getWGrad();
+ const MatrixPtr& biasGrad = hasBias ? biases_->getWGrad() : nullptr;
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
@@ -187,107 +178,66 @@ void MKLDNNFcLayer::resetBwd() {
// 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;
+ const MatrixPtr& outGrad = getOutput(device).grad;
+ out = MKLDNNMatrix::create(outGrad, outVal_->getPrimitiveDesc());
+ wgt = MKLDNNMatrix::create(wgtGrad, wgtVal_->getPrimitiveDesc());
+ bias = hasBias ? MKLDNNMatrix::create(biasGrad, biasVal_->getPrimitiveDesc())
+ : nullptr;
// create memory primitive desc
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward,
inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc());
+ wgt->getMemoryDesc(),
+ out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
fc_bwdWgt::desc bwdWgtDesc = hasBias
? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- biasGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc())
+ wgt->getMemoryDesc(),
+ bias->getMemoryDesc(),
+ out->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc());
+ wgt->getMemoryDesc(),
+ out->getMemoryDesc());
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
if (hasBias) {
- bwdWgt_.reset(
- new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
+ bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt, *bias));
} else {
- bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_));
+ bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt));
}
- pipelineBwd_.clear();
- pipelineBwd_.push_back(*bwdWgt_);
+ pipeline.push_back(*bwdWgt_);
/// backward data
- const MatrixPtr& in = inputLayers_[0]->getOutput().grad;
- if (in == nullptr) {
+ const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
+ if (inGrad == nullptr) {
return;
}
if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) {
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
} else {
- inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc());
+ in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
}
- fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMemoryDesc(),
- wgtGrad_->getMemoryDesc(),
- outGrad_->getMemoryDesc());
+ fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(
+ inVal_->getMemoryDesc(), wgt->getMemoryDesc(), out->getMemoryDesc());
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
CHECK(wgtVal_) << "Should have weight memory";
- bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
+ bwdData_.reset(new fc_bwdData(bwdDataPD, *out, *wgtVal_, *in));
printGradFormatFlow();
- pipelineBwd_.push_back(*bwdData_);
+ pipeline.push_back(*bwdData_);
}
void MKLDNNFcLayer::updateInputData() {
- if (inputLayers_[0]->getType() != "data") {
- return;
- }
- real* iData = getInputValue(0, CPU_DEVICE)->getData();
- inVal_->setData(iData);
+ inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
-void MKLDNNFcLayer::forward(PassType passType) {
- Layer::forward(passType);
- reshape();
-
- {
- REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
- updateInputData();
-
- // just submit forward pipeline
- stream_->submit(pipelineFwd_);
- }
-
- /* activation */ {
- REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
- forwardActivation();
- }
-}
-
-void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
- /* Do derivation */ {
- REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
- backwardActivation();
- }
-
- {
- REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
- resetBwd();
-
- // just sumbmit backward pipeline
- stream_->submit(pipelineBwd_);
- }
-
- {
- REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
- weight_->getParameterPtr()->incUpdate(callback);
- if (biases_ && biases_->getWGrad()) {
- biases_->getParameterPtr()->incUpdate(callback);
- }
+void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
+ weight_->getParameterPtr()->incUpdate(callback);
+ if (biases_ && biases_->getWGrad()) {
+ biases_->getParameterPtr()->incUpdate(callback);
}
}
} // namespace paddle
diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h
index 4ad67a16e056a718c45a28babcf22a7cd571b15c..3119f863496df092da13c08bf733f13c42e53780 100644
--- a/paddle/gserver/layers/MKLDNNFcLayer.h
+++ b/paddle/gserver/layers/MKLDNNFcLayer.h
@@ -45,35 +45,28 @@ public:
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
- void convertWeightsFromPaddle() override;
-
- void convertWeightsToPaddle() override;
+ void reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
- void forward(PassType passType) override;
+ void resetFwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
- void backward(const UpdateCallback& callback) override;
+ void resetBwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
void updateInputData() override;
-protected:
- /**
- * reshape the input image sizes
- * and reset output buffer size
- * and reset mkldnn forward
- */
- void reshape();
-
- /**
- * reset the forward primitve and memory
- * only would be called when input size changes
- */
- void resetFwd();
-
- /**
- * reset the backward primitve and memory for mkldnn fc
- * only would be called when needed
- */
- void resetBwd();
+ void updateWeights(const UpdateCallback& callback) override;
+
+ void convertWeightsFromPaddle() override;
+
+ void convertWeightsToPaddle() override;
};
} // namespace paddle
diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h
index 543364edceff684bdcd002a8f4f10e7ce5e6953b..169679c8297542cac4a43f5a8e1af311ad9282df 100644
--- a/paddle/gserver/layers/MKLDNNLayer.h
+++ b/paddle/gserver/layers/MKLDNNLayer.h
@@ -19,6 +19,7 @@ limitations under the License. */
#include "MKLDNNBase.h"
#include "mkldnn.hpp"
#include "paddle/math/MKLDNNMatrix.h"
+#include "paddle/utils/Stat.h"
DECLARE_bool(use_mkldnn);
@@ -33,6 +34,8 @@ typedef std::shared_ptr MKLDNNLayerPtr;
*/
class MKLDNNLayer : public Layer {
protected:
+ // input value element count
+ size_t inputElemenCnt_;
// batch size
int bs_;
// input image channel, height and width
@@ -52,7 +55,7 @@ protected:
std::vector pipelineFwd_;
std::vector pipelineBwd_;
- // MKLDNNMatrixPtr
+ // MKLDNNMatrixPtr with internal format
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
MKLDNNMatrixPtr outVal_;
@@ -65,6 +68,7 @@ protected:
public:
explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config),
+ inputElemenCnt_(0),
bs_(0),
ic_(0),
ih_(0),
@@ -95,12 +99,104 @@ public:
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
+ checkCPUOutputsNumber();
stream_.reset(new MKLDNNStream());
engine_ = CPUEngine::Instance().getEngine();
return true;
}
+ void forward(PassType passType) override {
+ passType_ = passType;
+
+ {
+ REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
+ CHECK(!inputLayers_.empty());
+ copySeqInfoToOutputs();
+ size_t elemenCnt = inputLayers_[0]->getOutput().value->getElementCnt();
+ if (inputElemenCnt_ != elemenCnt) {
+ // reset when input total sizes changed, not only the batchsize
+ inputElemenCnt_ = elemenCnt;
+ reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
+ resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
+ convertWeightsFromPaddle();
+ needResetBwd_ = true;
+ }
+
+ if (inputLayers_[0]->getType() == "data") {
+ updateInputData();
+ }
+
+ stream_->submit(pipelineFwd_);
+ }
+
+ /* activation */ {
+ REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
+ forwardActivation();
+ }
+ }
+
+ void backward(const UpdateCallback& callback) override {
+ /* Do derivation */ {
+ REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
+ backwardActivation();
+ }
+
+ {
+ REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
+ if (needResetBwd_) {
+ resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
+ needResetBwd_ = false;
+ }
+
+ stream_->submit(pipelineBwd_);
+ }
+
+ {
+ REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
+ updateWeights(callback);
+ }
+ }
+
+ /**
+ * reshape the input image sizes
+ * and reset output image and buffer size
+ * output channel can not be changed
+ */
+ virtual void reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0;
+
+ /**
+ * reset the mkldnn forward primitve and memory
+ * only would be called when input size changes
+ */
+ virtual void resetFwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) = 0;
+
+ /**
+ * reset the mkldnn backward primitve and memory for mkldnn fc
+ * only would be called when needed
+ */
+ virtual void resetBwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) = 0;
+
+ /**
+ * Update input value data when input layer is "data" type.
+ * Since the input value data address might be changed.
+ */
+ virtual void updateInputData() {}
+
+ /**
+ * Update weights and biases if necessary.
+ */
+ virtual void updateWeights(const UpdateCallback& callback) {}
+
/**
* convert weight from paddle format to mkldnn format
* weight_ will be override
@@ -114,10 +210,38 @@ public:
virtual void convertWeightsToPaddle() {}
/**
- * Update input value data when input layer is "data" type.
- * Since the input value data address might be changed.
+ * add this interface as public for unit test
*/
- virtual void updateInputData() {}
+ void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
+
+protected:
+ /**
+ * reshape the input image sizes and input batchsize
+ */
+ virtual void reshapeInput(int& batchsize, int& height, int& width) {
+ const Argument& input = inputLayers_[0]->getOutput();
+ batchsize = input.getBatchSize();
+ int h = input.getFrameHeight();
+ int w = input.getFrameWidth();
+ if (h != 0) {
+ height = h;
+ }
+ if (w != 0) {
+ width = w;
+ }
+ }
+
+ /**
+ * reshape output image sizes
+ */
+ virtual void reshapeOutput(size_t height, size_t width) {
+ output_.setFrameHeight(height);
+ output_.setFrameWidth(width);
+ for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
+ outputOtherDevice_[i].setFrameHeight(height);
+ outputOtherDevice_[i].setFrameWidth(width);
+ }
+ }
/**
* print info about sizes
@@ -133,8 +257,8 @@ public:
*/
virtual void printValueFormatFlow() {
if (inVal_ && outVal_) {
- VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat()
- << " >>> " << outVal_->getFormat();
+ VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>> "
+ << outVal_->getFormat();
}
}
@@ -143,36 +267,12 @@ public:
*/
virtual void printGradFormatFlow() {
if (inGrad_ && outGrad_) {
- VLOG(MKLDNN_FMTS) << "grad format flow --- " << inGrad_->getFormat()
- << " <<< " << outGrad_->getFormat();
+ VLOG(MKLDNN_FMTS) << 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() {
- int cnt = 0;
- 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 (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
- ++cnt;
- }
- }
- if (cnt > 1) {
- LOG(WARNING) << "should not have more than one CPU devie";
- }
- }
-
/**
* If input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
@@ -205,6 +305,7 @@ protected:
*/
void setDevice(int id) { deviceId_ = id; }
+private:
/**
* Set deviceId of the params used in this layer.
*/
@@ -228,6 +329,42 @@ protected:
parameter->setDevice(id);
}
}
+
+ /**
+ * Check the cpu device number of outputOtherDevice_.
+ * should have only one at most.
+ */
+ void checkCPUOutputsNumber(int max = 1) {
+ int cnt = 0;
+ for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
+ if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
+ ++cnt;
+ }
+ }
+ CHECK_LE(cnt, max) << "too much CPU devies";
+ }
+
+ /**
+ * copy SeqInfo from input layer to this output and other output devices.
+ * @note: do not use getInput(0) since it used this deviceId_,
+ * use "inputLayers_[0]->getOutput()" instead.
+ */
+ void copySeqInfoToOutputs() {
+ if (inputLayers_.empty() || !needSequenceInfo_) {
+ return;
+ }
+ const Argument& input = inputLayers_[0]->getOutput();
+ output_.sequenceStartPositions = input.sequenceStartPositions;
+ output_.subSequenceStartPositions = input.subSequenceStartPositions;
+ output_.cpuSequenceDims = input.cpuSequenceDims;
+ for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
+ outputOtherDevice_[i].sequenceStartPositions =
+ output_.sequenceStartPositions;
+ outputOtherDevice_[i].subSequenceStartPositions =
+ output_.subSequenceStartPositions;
+ outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
+ }
+ }
};
} // namespace paddle
diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp
index de1635be2af37cd0ba49010199a417090865b0e4..2f48e5b2d3ffc9337ed1314f6db6549e56263fdd 100644
--- a/paddle/gserver/tests/MKLDNNTester.cpp
+++ b/paddle/gserver/tests/MKLDNNTester.cpp
@@ -63,8 +63,12 @@ void MKLDNNTester::reset(const TestConfig& dnn,
initTestLayer(
configs_[i], &(layerMaps_[i]), &(parameters_[i]), &(testLayers_[i]));
}
- dnnLayer_ = testLayers_[DNN];
refLayer_ = testLayers_[REF];
+ dnnLayer_ = std::dynamic_pointer_cast(testLayers_[DNN]);
+ CHECK(dnnLayer_);
+ // for comparison with Paddle reference results,
+ // need manually add cpu device output for test
+ dnnLayer_->addOutputArgument(CPU_DEVICE);
EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
@@ -109,20 +113,22 @@ void MKLDNNTester::randomBotDatas() {
void MKLDNNTester::randomTopDiffs() {
refLayer_->getOutputGrad()->randomizeUniform();
- dnnLayer_->getOutputGrad()->copyFrom(*(refLayer_->getOutputGrad()));
- VLOG(lvl_) << "Random dom Backward Input, TopDiff: ";
+ dnnLayer_->getOutput(CPU_DEVICE)
+ .grad->copyFrom(*(refLayer_->getOutputGrad()));
+ VLOG(lvl_) << "Random Backward Input, TopDiff: ";
printMatrix(refLayer_->getOutputGrad());
}
void MKLDNNTester::checkForward() {
- printTopDatas();
- double delta = compareMatrix(testLayers_[DNN]->getOutputValue(),
- testLayers_[REF]->getOutputValue());
VLOG(MKLDNN_ALL) << "Check Forward";
+ printTopDatas();
+ double delta = compareMatrix(dnnLayer_->getOutput(-1).value,
+ refLayer_->getOutputValue());
EXPECT_LE(fabs(delta), eps_);
}
void MKLDNNTester::checkBackwardData() {
+ VLOG(MKLDNN_ALL) << "Check Backward Data";
// TODO(TJ): uncomment me when batch norm ready
// const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
@@ -144,14 +150,12 @@ void MKLDNNTester::checkBackwardData() {
}
void MKLDNNTester::checkBackwardWgts() {
+ VLOG(MKLDNN_ALL) << "Check Backward Weight";
CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
vector dnnWgts; // used to temply save mkldnn weights
saveWgt(parameters_[DNN], dnnWgts);
- const MKLDNNLayerPtr dnnlayer =
- std::dynamic_pointer_cast(dnnLayer_);
- CHECK(dnnlayer);
- dnnlayer->convertWeightsToPaddle();
+ dnnLayer_->convertWeightsToPaddle();
for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
@@ -189,38 +193,38 @@ void MKLDNNTester::restoreWgt(const vector& from,
}
// clear parameters grad
-void MKLDNNTester::clearWgtDiffs() {
+void MKLDNNTester::clearWgtDiffs(size_t id) {
+ CHECK_LE(id, parameters_.size());
for (size_t n = 0; n < parameters_.size(); ++n) {
- for (size_t i = 0; i < parameters_[n].size(); ++i) {
- const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
- if (grad) {
- grad->zeroMem();
+ if (id == n || id == parameters_.size()) {
+ for (size_t i = 0; i < parameters_[n].size(); ++i) {
+ const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
+ if (grad) {
+ grad->zeroMem();
+ }
}
}
}
}
-void MKLDNNTester::clearBotDiffs() {
- // dnn and ref
+void MKLDNNTester::clearBotDiffs(size_t id) {
+ CHECK_LE(id, dataLayers_.size());
for (size_t n = 0; n < dataLayers_.size(); ++n) {
- // all inputs layers
- for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
- dataLayers_[n][i]->getOutputGrad()->zeroMem();
+ if (id == n || id == dataLayers_.size()) {
+ // clear inputs layers of this specific layer
+ for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+ dataLayers_[n][i]->getOutputGrad()->zeroMem();
+ }
}
}
}
-void MKLDNNTester::clearBotDiffs(int n) {
- CHECK_LT(n, NUM);
- // all inputs layers
- for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
- dataLayers_[n][i]->getOutputGrad()->zeroMem();
- }
-}
-
-void MKLDNNTester::clearTopDatas() {
+void MKLDNNTester::clearTopDatas(size_t id) {
+ CHECK_LE(id, testLayers_.size());
for (size_t i = 0; i < testLayers_.size(); ++i) {
- testLayers_[i]->getOutputValue()->zeroMem();
+ if (id == i || id == testLayers_.size()) {
+ testLayers_[i]->getOutputValue()->zeroMem();
+ }
}
}
@@ -300,16 +304,24 @@ void MKLDNNTester::runOnce() {
checkForward();
// test backward
+ // simple updater
+ UpdateCallback updateCallback = [](Parameter* para) {
+ auto& grad = para->getBuf(PARAMETER_GRADIENT);
+ auto& value = para->getBuf(PARAMETER_VALUE);
+ real lr = 1e-3;
+ value->add(*grad, lr);
+ };
randomTopDiffs();
- dnnLayer_->backward(nullptr);
- refLayer_->backward(nullptr);
+ dnnLayer_->backward(updateCallback);
+ refLayer_->backward(updateCallback);
checkBackwardData();
checkBackwardWgts();
// clear buffers
// ref code will addto the diff, dnn code will writeto it
- // and clearTopDatas() and clearWgtDiffs() should be coverd by test layers
+ // and clearTopDatas(REF) should be coverd by ref layers
clearBotDiffs(REF);
+ clearWgtDiffs(REF);
}
void MKLDNNTester::run(const TestConfig& dnn,
diff --git a/paddle/gserver/tests/MKLDNNTester.h b/paddle/gserver/tests/MKLDNNTester.h
index e55e4493ffdfe45b8cfdee423febd1878b8b3d8a..5ac885638cde7693a0c847733e7a6149c1b7e6c2 100644
--- a/paddle/gserver/tests/MKLDNNTester.h
+++ b/paddle/gserver/tests/MKLDNNTester.h
@@ -18,6 +18,7 @@ limitations under the License. */
#include
#include "LayerGradUtil.h"
#include "paddle/gserver/layers/MKLDNNBase.h"
+#include "paddle/gserver/layers/MKLDNNLayer.h"
namespace paddle {
@@ -40,7 +41,8 @@ protected:
vector layerMaps_;
vector> parameters_;
vector testLayers_;
- LayerPtr dnnLayer_, refLayer_;
+ LayerPtr refLayer_;
+ MKLDNNLayerPtr dnnLayer_;
/// run some iterations, all the result should pass
size_t iter_;
@@ -88,10 +90,10 @@ private:
void checkBackwardData();
void checkBackwardWgts();
- void clearWgtDiffs();
- void clearBotDiffs();
- void clearBotDiffs(int n); // clear specific layer
- void clearTopDatas();
+ // clear specific layer, clear all when id equals NUM
+ void clearWgtDiffs(size_t id = NUM);
+ void clearBotDiffs(size_t id = NUM);
+ void clearTopDatas(size_t id = NUM);
void printTopDatas();
void printMatrix(const MatrixPtr& m);
diff --git a/paddle/operators/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc
index 186a33edcec88bd5e51091a524a778eeb27ad526..4f380388b108dc173d847f027ba5c9db387a87f8 100644
--- a/paddle/operators/math/im2col_test.cc
+++ b/paddle/operators/math/im2col_test.cc
@@ -119,4 +119,4 @@ TEST(math, im2col) {
#ifndef PADDLE_ONLY_CPU
testIm2col();
#endif
-}
\ No newline at end of file
+}
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 227b75aff86089d0b21bdae7e6e402292bce67d9..3958b53c22c383e5e2298bfdc4e8490d4148118f 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -17,6 +17,7 @@ limitations under the License. */
#include
#include "paddle/framework/backward.h"
+#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
@@ -58,6 +59,8 @@ namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using LoD = framework::LoD;
static size_t UniqueIntegerGenerator() {
static std::atomic generator;
@@ -117,6 +120,60 @@ PYBIND11_PLUGIN(core) {
return self.data()[offset];
});
+ py::class_(m, "LoDTensor", R"DOC(LoD(Leval of Ddetails) Tensor.
+
+The tensor and LoD info should be created before creating the LoDTensor, then
+call the set_tensor and set_lod functions to set them.
+
+)DOC")
+ .def("__init__",
+ [](LoDTensor &instance,
+ const std::vector> &lod,
+ Tensor *t) {
+#ifdef PADDLE_ONLY_CPU
+ new (&instance) LoDTensor(lod, t);
+#else
+ paddle::framework::LoD new_lod;
+ new_lod.reserve(lod.size());
+ std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+ new (&instance) LoDTensor(new_lod, t);
+#endif
+ })
+ .def("set_tensor",
+ [](LoDTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
+ .def("set_lod",
+ [](LoDTensor &self, const std::vector> &lod) {
+#ifdef PADDLE_ONLY_CPU
+ self.set_lod(lod);
+#else
+ paddle::framework::LoD new_lod;
+ new_lod.reserve(lod.size());
+ std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+ self.set_lod(new_lod);
+#endif
+ })
+ .def("tensor",
+ [](LoDTensor &self) -> Tensor & { return self.tensor(); },
+ py::return_value_policy::reference)
+ .def("lod", [](LoDTensor &self) -> std::vector> {
+#ifdef PADDLE_ONLY_CPU
+ return self.lod();
+#else
+ auto lod = self.lod();
+ std::vector> new_lod;
+ new_lod.reserve(lod.size());
+ std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod),
+ [](paddle::framework::Vector item) ->
+ std::vector {
+ std::vector v;
+ v.reserve(item.size());
+ std::copy(item.begin(), item.end(), std::back_inserter(v));
+ return v;
+ });
+ return new_lod;
+#endif
+ });
+
py::class_(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
@@ -128,6 +185,11 @@ All parameter, weight, gradient are variables in Paddle.
.def("get_tensor",
[](Variable &self) -> Tensor * { return self.GetMutable(); },
py::return_value_policy::reference)
+ .def("get_lod_tensor",
+ [](Variable &self) -> LoDTensor * {
+ return self.GetMutable();
+ },
+ py::return_value_policy::reference)
.def("get_net",
[](Variable &self) -> operators::NetOp * {
return self.GetMutable();
diff --git a/python/paddle/v2/framework/tests/test_tensor.py b/python/paddle/v2/framework/tests/test_tensor.py
index 1af39818a305215b45219b8c5f0a10630fd64279..f26ed4964c521be1cd839b39d7244f96c653cb1a 100644
--- a/python/paddle/v2/framework/tests/test_tensor.py
+++ b/python/paddle/v2/framework/tests/test_tensor.py
@@ -3,7 +3,7 @@ import unittest
import numpy
-class TestScope(unittest.TestCase):
+class TestTensor(unittest.TestCase):
def test_int_tensor(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
@@ -20,8 +20,8 @@ class TestScope(unittest.TestCase):
tensor.set(tensor_array, place)
tensor_array_2 = numpy.array(tensor)
- self.assertEqual(1.0, tensor_array_2[3, 9])
- self.assertEqual(2.0, tensor_array_2[19, 11])
+ self.assertEqual(1, tensor_array_2[3, 9])
+ self.assertEqual(2, tensor_array_2[19, 11])
def test_float_tensor(self):
scope = core.Scope()
@@ -43,6 +43,84 @@ class TestScope(unittest.TestCase):
self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
+ def test_int_lod_tensor(self):
+ places = [core.CPUPlace(), core.GPUPlace(0)]
+ for place in places:
+ scope = core.Scope()
+ var = scope.new_var("test_tensor")
+ var_lod = scope.new_var("test_lod_tensor")
+
+ tensor = var.get_tensor()
+ lod_tensor = var_lod.get_lod_tensor()
+
+ tensor.set_dims([4, 4, 6])
+ tensor.alloc_int(place)
+ array = numpy.array(tensor)
+ array[0, 0, 0] = 3
+ array[3, 3, 5] = 10
+ tensor.set(array, place)
+
+ lod_tensor.set_tensor(tensor)
+ lod_tensor.set_lod([[0, 2, 4]])
+
+ lod_v = numpy.array(lod_tensor.tensor())
+ self.assertTrue(numpy.alltrue(array == lod_v))
+
+ lod = lod_tensor.lod()
+ self.assertEqual(0, lod[0][0])
+ self.assertEqual(2, lod[0][1])
+ self.assertEqual(4, lod[0][2])
+
+ def test_float_lod_tensor(self):
+ places = [core.CPUPlace(), core.GPUPlace(0)]
+ for place in places:
+ scope = core.Scope()
+ var = scope.new_var("test_tensor")
+ var_lod = scope.new_var("test_lod_tensor")
+
+ tensor = var.get_tensor()
+ lod_tensor = var_lod.get_lod_tensor()
+
+ tensor.set_dims([5, 2, 3, 4])
+ tensor.alloc_float(place)
+
+ tensor_array = numpy.array(tensor)
+ self.assertEqual((5, 2, 3, 4), tensor_array.shape)
+ tensor_array[0, 0, 0, 0] = 1.0
+ tensor_array[0, 0, 0, 1] = 2.0
+ tensor.set(tensor_array, place)
+
+ lod_tensor.set_tensor(tensor)
+
+ lod_v = numpy.array(lod_tensor.tensor())
+ self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
+ self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
+ self.assertEqual(len(lod_tensor.lod()), 0)
+
+ lod_py = [[0, 2, 5], [0, 2, 4, 5]]
+ lod_tensor.set_lod(lod_py)
+ lod = lod_tensor.lod()
+ self.assertListEqual(lod_py, lod)
+
+ def test_lod_tensor_init(self):
+ scope = core.Scope()
+ var = scope.new_var("test_tensor")
+ place = core.CPUPlace()
+ tensor = var.get_tensor()
+ tensor.set_dims([5, 2, 3, 4])
+ tensor.alloc_float(place)
+ tensor_array = numpy.array(tensor)
+ tensor_array[0, 0, 0, 0] = 1.0
+ tensor_array[0, 0, 0, 1] = 2.0
+ tensor.set(tensor_array, place)
+ lod_py = [[0, 2, 5], [0, 2, 4, 5]]
+
+ lod_tensor = core.LoDTensor(lod_py, tensor)
+ lod_v = numpy.array(lod_tensor.tensor())
+ self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
+ self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
+ self.assertListEqual(lod_py, lod_tensor.lod())
+
if __name__ == '__main__':
unittest.main()