diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md
new file mode 100644
index 0000000000000000000000000000000000000000..fa3c5d7990213cf2b0d236e66e592dd2699da876
--- /dev/null
+++ b/doc/design/ops/dist_train.md
@@ -0,0 +1,106 @@
+# Design Doc: Operation Graph Based Parameter Server
+
+## Abstract
+
+We propose an approach to implement the parameter server. In this
+approach, there is no fundamental difference between the trainer and
+the parameter server: they both run subgraphs, but subgraphs of
+different purposes.
+
+## Background
+
+The previous implementations of the parameter server does not run a
+subgraph. parameter initialization, optimizer computation, network
+communication and checkpointing are implemented twice on both the
+trainer and the parameter server.
+
+It would be great if we can write code once and use them on both the
+trainer and the parameter server: reduces code duplication and
+improves extensibility. Given that after the current refactor, we are
+representing everything as a computing graph on the
+trainer. Representing everything as a computing graph on the parameter
+server becomes a natural extension.
+
+## Design
+
+### Graph Converter
+
+The *graph converter* converts the user-defined operation (OP) graph
+into subgraphs to be scheduled on different nodes with the following
+steps:
+
+1. OP placement: the OPs will be placed on different nodes according
+ to heuristic that minimizes estimated total computation
+ time. Currently we will use a simple heuristic that puts parameter
+ varable on parameter server workers and everything else on trainer
+ workers.
+
+1. Add communication OPs to enable the communication between nodes.
+
+We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
+
+Below is an example of converting the user defined graph to the
+subgraphs for the trainer and the parameter server:
+
+
+
+After converting:
+
+
+
+1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
+1. Operators are added to the subgraphs.
+ - *Send* sends data to the connected *Recv* operator. The
+ scheduler on the receive node will only schedule *Recv* operator
+ to run when the *Send* operator has ran (the *Send* OP will mark
+ the *Recv* OP runnable automatically).
+ - *Enueue* enqueues the input variable, it can block until space
+ become available in the queue.
+ - *Dequeue* outputs configurable numbers of tensors from the
+ queue. It will block until the queue have the required number of
+ tensors.
+
+
+### Benefits
+
+- Model parallelism become easier to implement: it's an extension to
+ the trainer - parameter server approach. we already have the
+ communication OPs, but need to extend the graph converter's
+ placement functionality.
+
+- User-defined optimizer is easier to add - user can now express it as
+ a subgraph.
+
+- No more duplication logic inside the trainer and the parameter
+ server mentioned in the background section.
+
+### Challenges
+
+- It might be hard for the graph converter to cut a general graph
+ (without any hint for which subgraph is the optimizer). We may need
+ to label which subgraph inside the OP graph is the optimizer.
+
+- It's important to balance the parameter shards of on multiple
+ parameter server. If a single parameter is very big (some
+ word-embedding, fully connected, softmax layer), we need to
+ automatically partition the single parameter onto different
+ parameter servers when possible (only element-wise optimizer depends
+ on the parameter variable).
+
+### Discussion
+
+- In the "Aync SGD" figure, the "W" variable on the parameter server
+ could be read and wrote concurrently, what is our locking strategy?
+ E.g., each variable have a lock cpp method to be invoked by every
+ OP, or, have a lock OP.
+
+- Can the Enqueue OP be implemented under our current tensor design
+ (puts the input tensor into the queue tensor)?
+
+- *Dequeue* OP will have variable numbers of output (depends on the
+ `min_count` attribute), does our current design support it? (similar
+ question for the *Add* OP)
+
+
+### References:
+[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
diff --git a/doc/design/ops/src/dist-graph.graffle b/doc/design/ops/src/dist-graph.graffle
new file mode 100644
index 0000000000000000000000000000000000000000..941399c6ced8d5f65b6c595522b770c88259df4b
Binary files /dev/null and b/doc/design/ops/src/dist-graph.graffle differ
diff --git a/doc/design/ops/src/dist-graph.png b/doc/design/ops/src/dist-graph.png
new file mode 100644
index 0000000000000000000000000000000000000000..3546b09f1c2ee3e4f60f519d5e47f823f08051a7
Binary files /dev/null and b/doc/design/ops/src/dist-graph.png differ
diff --git a/doc/design/ops/src/local-graph.graffle b/doc/design/ops/src/local-graph.graffle
new file mode 100644
index 0000000000000000000000000000000000000000..19e509bd9af3c1e9a3f5e0f16ddd281457a339c5
Binary files /dev/null and b/doc/design/ops/src/local-graph.graffle differ
diff --git a/doc/design/ops/src/local-graph.png b/doc/design/ops/src/local-graph.png
new file mode 100644
index 0000000000000000000000000000000000000000..ada51200f793a9bb18911e7d63cfdb3244b967d7
Binary files /dev/null and b/doc/design/ops/src/local-graph.png differ
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/framework/operator.cc b/paddle/framework/operator.cc
index 790cfc4746b1d34da413fa3c29a266f962c6dde6..e1e122091f7759b1a68f1f982bc2a35e8241f9f0 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -123,6 +123,15 @@ OperatorBase::OperatorBase(const std::string& type,
CheckAllInputOutputSet();
}
+std::vector OperatorBase::InputVars() const {
+ std::vector ret_val;
+ for (auto& o : outputs_) {
+ ret_val.reserve(ret_val.size() + o.second.size());
+ ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
+ }
+ return ret_val;
+}
+
std::vector OperatorBase::OutputVars(bool has_intermediate) const {
std::vector ret_val;
if (has_intermediate) {
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index 9a98d4d3be0d1cb875d614b263f1e4365ede4113..4600b06009bcef7d0774d25b816aac4733f30795 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -94,11 +94,14 @@ class OperatorBase {
const VariableNameMap& Inputs() const { return inputs_; }
const VariableNameMap& Outputs() const { return outputs_; }
+
//! Get a input with argument's name described in `op_proto`
std::string Input(const std::string& name) const;
//! Get a input which has multiple variables.
const std::vector& Inputs(const std::string& name) const;
+ std::vector InputVars() const;
+
//! Get a output with argument's name described in `op_proto`
std::string Output(const std::string& name) const;
//! Get an output which has multiple variables.
@@ -311,9 +314,9 @@ class InferShapeContext {
}
template
- std::vector MultiOutput(const std::string& name) const {
+ std::vector MultiOutput(const std::string& name) const {
auto names = op_.Outputs(name);
- std::vector res;
+ std::vector res;
res.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h
index ce938b21437195fed8c1adad4329fd139f3f96ab..4b5a2ae523f2f7fde5445f0534cd99969ad9d59e 100644
--- a/paddle/framework/tensor.h
+++ b/paddle/framework/tensor.h
@@ -81,6 +81,9 @@ class Tensor {
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
+ /*! Return the numel of the memory block. */
+ inline int64_t numel() const;
+
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
@@ -162,6 +165,12 @@ class Tensor {
/*! points to dimensions of memory block. */
DDim dims_;
+ /**
+ * A cache of the number of elements in a tensor.
+ * Would be 0 for an uninitialized tensor.
+ */
+ int64_t numel_;
+
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h
index 637f04ae0037bd402d855b8bcde8087bfe8328d1..642b53efc7095d25712ca324638f5fe9b8316c0c 100644
--- a/paddle/framework/tensor_impl.h
+++ b/paddle/framework/tensor_impl.h
@@ -24,7 +24,7 @@ inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tenosr holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_GE(
- holder_->size(), product(dims_) * sizeof(T) + offset_,
+ holder_->size(), numel() * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
@@ -54,11 +54,11 @@ inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
template
inline T* Tensor::mutable_data(platform::Place place) {
static_assert(std::is_pod::value, "T must be POD");
- PADDLE_ENFORCE_GT(product(dims_), 0,
+ PADDLE_ENFORCE_GT(numel(), 0,
"Tensor's numel must be larger than zero to call "
"Tensor::mutable_data. Call Tensor::set_dim first.");
/* some versions of boost::variant don't have operator!= */
- int64_t size = product(dims_) * sizeof(T);
+ int64_t size = numel() * sizeof(T);
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
@@ -97,7 +97,7 @@ inline void Tensor::CopyFrom(const Tensor& src,
auto dst_ptr = static_cast(mutable_data(dst_place));
- auto size = product(src.dims_) * sizeof(T);
+ auto size = src.numel() * sizeof(T);
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
memory::Copy(boost::get(dst_place), dst_ptr,
@@ -131,7 +131,7 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
PADDLE_ENFORCE_LT(begin_idx, end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE_NE(dims_[0], 1, "Can not slice a tensor with dims_[0] = 1.");
- size_t base = product(dims_) / dims_[0];
+ size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
DDim dst_dims = dims_;
@@ -143,11 +143,14 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
inline Tensor& Tensor::Resize(const DDim& dims) {
dims_ = dims;
+ numel_ = product(dims_);
return *this;
}
inline const DDim& Tensor::dims() const { return dims_; }
+inline int64_t Tensor::numel() const { return numel_; }
+
template
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
Tensor res;
diff --git a/paddle/gserver/layers/BatchNormBaseLayer.cpp b/paddle/gserver/layers/BatchNormBaseLayer.cpp
index 1ceaaaa206ee3cbc5421238574c7f310011ccaa5..f7a80e23e1bd49549bec57b360587adc6b423794 100644
--- a/paddle/gserver/layers/BatchNormBaseLayer.cpp
+++ b/paddle/gserver/layers/BatchNormBaseLayer.cpp
@@ -62,14 +62,18 @@ void BatchNormBaseLayer::calFeatureMapSize() {
const ImageConfig& conf = config_.inputs(0).image_conf();
imageH_ = inputLayers_[0]->getOutput().getFrameHeight();
imageW_ = inputLayers_[0]->getOutput().getFrameWidth();
+ imageD_ = inputLayers_[0]->getOutput().getFrameDepth();
+
+ if (0 == imageD_) imageD_ = conf.img_size_z();
if (imageH_ == 0 && imageW_ == 0) {
imageH_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
imageW_ = conf.img_size();
} else {
getOutput().setFrameHeight(imageH_);
getOutput().setFrameWidth(imageW_);
+ getOutput().setFrameDepth(imageD_);
}
- imgPixels_ = imageH_ * imageW_;
+ imgPixels_ = imageH_ * imageW_ * imageD_;
}
} // namespace paddle
diff --git a/paddle/gserver/layers/BatchNormBaseLayer.h b/paddle/gserver/layers/BatchNormBaseLayer.h
index 230bafc31d96bbd49481a7ed135be6888688627e..e721d2d267a31cae46407673b8b1281e87055608 100644
--- a/paddle/gserver/layers/BatchNormBaseLayer.h
+++ b/paddle/gserver/layers/BatchNormBaseLayer.h
@@ -80,6 +80,7 @@ protected:
/// Height or width of input image feature.
/// Both of them are 1 if the input is fully-connected layer.
+ int imageD_;
int imageH_;
int imageW_;
/// Height * Width.
diff --git a/paddle/gserver/layers/CudnnBatchNormLayer.cpp b/paddle/gserver/layers/CudnnBatchNormLayer.cpp
index 44ba2c4b7d1562d2ce839b5f4b4de1af35e6925f..49a9540c0b6e36b59ed786287ff5c4569b69a6a5 100644
--- a/paddle/gserver/layers/CudnnBatchNormLayer.cpp
+++ b/paddle/gserver/layers/CudnnBatchNormLayer.cpp
@@ -37,7 +37,7 @@ bool CudnnBatchNormLayer::init(const LayerMap& layerMap,
}
void CudnnBatchNormLayer::reshape(int batchSize) {
- hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_, imageW_);
+ hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_ * imageD_, imageW_);
}
void CudnnBatchNormLayer::forward(PassType passType) {
@@ -104,7 +104,7 @@ void CudnnBatchNormLayer::forward(PassType passType) {
EPS,
batchSize,
channels_,
- imageH_,
+ imageH_ * imageD_,
imageW_);
}
}
diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp
index 1b59ed60c57fe3bbfa814befa8a63408a2621715..3eea638649e8ebfdd7efa18615977a9e1344c695 100644
--- a/paddle/gserver/layers/DeConv3DLayer.cpp
+++ b/paddle/gserver/layers/DeConv3DLayer.cpp
@@ -53,27 +53,27 @@ bool DeConv3DLayer::init(const LayerMap &layerMap,
size_t DeConv3DLayer::getSize() {
CHECK_NE(inputLayers_.size(), 0UL);
- outputH_.clear();
- outputW_.clear();
- outputD_.clear();
+ imgSizeW_.clear();
+ imgSizeH_.clear();
+ imgSizeD_.clear();
N_.clear();
NOut_.clear();
size_t layerSize = 0;
for (size_t i = 0; i < inputLayers_.size(); ++i) {
- outputW_.push_back(
- imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true));
- outputH_.push_back(imageSize(
- imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
- outputD_.push_back(imageSize(
- imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
- NOut_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
- N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
+ imgSizeW_.push_back(
+ imageSize(outputW_[i], filterSize_[i], padding_[i], stride_[i], true));
+ imgSizeH_.push_back(imageSize(
+ outputH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
+ imgSizeD_.push_back(imageSize(
+ outputD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
+ NOut_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
+ N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
layerSize += NOut_[i] * numFilters_;
}
- getOutput().setFrameHeight(outputH_[0]);
- getOutput().setFrameWidth(outputW_[0]);
- getOutput().setFrameDepth(outputD_[0]);
+ getOutput().setFrameHeight(imgSizeH_[0]);
+ getOutput().setFrameWidth(imgSizeW_[0]);
+ getOutput().setFrameDepth(imgSizeD_[0]);
return layerSize;
}
@@ -103,9 +103,9 @@ void DeConv3DLayer::forward(PassType passType) {
}
colBuf_->col2Vol(outMat->getData() + n * outMat->getStride(),
numFilters_,
- outputD_[i],
- outputH_[i],
- outputW_[i],
+ imgSizeD_[i],
+ imgSizeH_[i],
+ imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
@@ -144,9 +144,9 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) {
colBuf_->vol2Col(
getOutputGrad()->getData() + n * getOutputGrad()->getStride(),
numFilters_,
- outputD_[i],
- outputH_[i],
- outputW_[i],
+ imgSizeD_[i],
+ imgSizeH_[i],
+ imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
diff --git a/paddle/gserver/layers/DetectionOutputLayer.cpp b/paddle/gserver/layers/DetectionOutputLayer.cpp
index 8ab838e191314ab25469631626c0b0564d7fffda..0cf0a92bf4bd8f9b8eba2016b2377d9dfb18c70a 100644
--- a/paddle/gserver/layers/DetectionOutputLayer.cpp
+++ b/paddle/gserver/layers/DetectionOutputLayer.cpp
@@ -139,7 +139,13 @@ void DetectionOutputLayer::forward(PassType passType) {
allDecodedBBoxes,
&allIndices);
- resetOutput(numKept, 7);
+ if (numKept > 0) {
+ resetOutput(numKept, 7);
+ } else {
+ MatrixPtr outV = getOutputValue();
+ outV = NULL;
+ return;
+ }
MatrixPtr outV = getOutputValue();
getDetectionOutput(confBuffer_->getData(),
numKept,
diff --git a/paddle/gserver/layers/DetectionUtil.cpp b/paddle/gserver/layers/DetectionUtil.cpp
index 3e61adc66e60c54250e4f323452aa13045310879..d83674f45a70212a8adc94a31ff58eb0e01baa00 100644
--- a/paddle/gserver/layers/DetectionUtil.cpp
+++ b/paddle/gserver/layers/DetectionUtil.cpp
@@ -469,7 +469,7 @@ size_t getDetectionIndices(
const size_t numClasses,
const size_t backgroundId,
const size_t batchSize,
- const size_t confThreshold,
+ const real confThreshold,
const size_t nmsTopK,
const real nmsThreshold,
const size_t keepTopK,
diff --git a/paddle/gserver/layers/DetectionUtil.h b/paddle/gserver/layers/DetectionUtil.h
index fe4f9f075e4cf011c97f68f49598a828d62327b3..641ed873b4c8645b6455e5ef5e63593e3005b770 100644
--- a/paddle/gserver/layers/DetectionUtil.h
+++ b/paddle/gserver/layers/DetectionUtil.h
@@ -275,7 +275,7 @@ size_t getDetectionIndices(
const size_t numClasses,
const size_t backgroundId,
const size_t batchSize,
- const size_t confThreshold,
+ const real confThreshold,
const size_t nmsTopK,
const real nmsThreshold,
const size_t keepTopK,
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 8318c8c519a4cec1610eadd28320ee5ce0b4147d..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();
@@ -77,111 +78,86 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
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(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
+ reshapeInput(bs, ih, iw);
-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();
+ ic = iLayerSize_ / (ih * iw);
+ CHECK_EQ(size_t(ic * ih * iw), iLayerSize_) << "not divisible";
+ CHECK_EQ(size_t(oc), getSize());
- // reset output
- output_.setFrameHeight(oh_);
- output_.setFrameWidth(ow_);
- resetOutput(bs_, oc_);
+ reshapeOutput(oh, ow);
+ resizeOutput(bs, oc);
- // reset mkldnn forward
- resetFwd();
- needResetBwd_ = true;
-
- 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()) {
- convertOutputToOtherDevice();
+ // fc cpu output value do not need create convert
+ // just share point
+ getOutput(CPU_DEVICE).value->setData(output_.value->getData());
}
// 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;
}
@@ -190,8 +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;
@@ -202,101 +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
- device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
- const MatrixPtr& in = getInputGrad(0, device);
- if (in == nullptr) {
+ const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
+ if (inGrad == nullptr) {
return;
}
- if (getInput(0, device).getAllCount() > 1) {
- // TODO(TJ): use outputMaps_ ways when merge outgrad done
+ 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::forward(PassType passType) {
- Layer::forward(passType);
- reshape();
-
- {
- REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
- syncInputValue();
-
- // just submit forward pipeline
- stream_->submit(pipelineFwd_);
- }
-
- /* activation */ {
- REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
- forwardActivation();
- }
+void MKLDNNFcLayer::updateInputData() {
+ inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
-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();
-
- syncOutputGrad();
- // 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 e138a6faf181c412949218458e7ecf800a0d6a07..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 reshape(
+ int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
- void convertWeightsToPaddle() override;
+ void resetFwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
- void forward(PassType passType) override;
+ void resetBwd(std::vector& pipeline,
+ MKLDNNMatrixPtr& in,
+ MKLDNNMatrixPtr& wgt,
+ MKLDNNMatrixPtr& bias,
+ MKLDNNMatrixPtr& out) override;
- void backward(const UpdateCallback& callback) 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 convertOutputToOtherDevice() override;
+ 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 b983b833d510b823c5d4cff0b9390173e4cefc89..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() {}
/**
- * convert MKLDNN output to other device.
- * only support CPU device yet
+ * add this interface as public for unit test
+ */
+ void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
+
+protected:
+ /**
+ * reshape the input image sizes and input batchsize
*/
- virtual void convertOutputToOtherDevice() {}
+ 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();
}
}
@@ -143,29 +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() {
- 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.
@@ -193,37 +300,12 @@ protected:
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; }
+private:
/**
* Set deviceId of the params used in this layer.
*/
@@ -247,6 +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/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp
index 92cd61cdd515d5c693df086c9575a5f197c00cee..d7eee6eaf078dab8d48adc4c7ee758a433672ac6 100644
--- a/paddle/gserver/layers/SwitchOrderLayer.cpp
+++ b/paddle/gserver/layers/SwitchOrderLayer.cpp
@@ -24,10 +24,12 @@ bool SwitchOrderLayer::init(const LayerMap& layerMap,
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
auto& img_conf = config_.inputs(0).image_conf();
+ size_t inD = img_conf.img_size_z();
size_t inH =
img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size();
size_t inW = img_conf.img_size();
size_t inC = img_conf.channels();
+ inH = inH * inD;
inDims_ = TensorShape({0, inC, inH, inW});
outDims_ = TensorShape(4);
@@ -64,9 +66,10 @@ void SwitchOrderLayer::setInDims() {
MatrixPtr input = inputLayers_[0]->getOutputValue();
size_t batchSize = input->getHeight();
inDims_.setDim(0, batchSize);
-
+ int d = inputLayers_[0]->getOutput().getFrameDepth();
+ d = (d == 0 ? 1 : d);
int h = inputLayers_[0]->getOutput().getFrameHeight();
- if (h != 0) inDims_.setDim(2, h);
+ if (h != 0) inDims_.setDim(2, h * d);
int w = inputLayers_[0]->getOutput().getFrameWidth();
if (w != 0) inDims_.setDim(3, w);
int totalCount = input->getElementCnt();
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/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp
index d1f3bc241fa621cb0070125980996e8627e40fd6..090bde7b203652e3ffb1662b8f5b8937885d2608 100644
--- a/paddle/gserver/tests/test_LayerGrad.cpp
+++ b/paddle/gserver/tests/test_LayerGrad.cpp
@@ -1703,6 +1703,55 @@ TEST(Layer, BatchNormalizationLayer) {
#endif
}
+void testBatchNorm3DLayer(const string& type, bool trans, bool useGpu) {
+ TestConfig config;
+ const int CHANNELS = 10;
+ const int IMG_SIZE = 16;
+ const int IMG_SIZE_Y = 8;
+ const int IMG_SIZE_Z = 8;
+ size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y * IMG_SIZE_Z;
+ config.layerConfig.set_type(type);
+ config.layerConfig.set_size(size);
+ config.layerConfig.set_active_type("sigmoid");
+ config.biasSize = CHANNELS;
+ config.inputDefs.push_back({INPUT_DATA,
+ "layer_0",
+ /* dim= */ size,
+ /* paraSize= */ CHANNELS});
+
+ config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS});
+ config.inputDefs.back().isStatic = true;
+ config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS});
+ config.inputDefs.back().isStatic = true;
+
+ LayerInputConfig* input = config.layerConfig.add_inputs();
+ config.layerConfig.add_inputs();
+ config.layerConfig.add_inputs();
+
+ ImageConfig* img_conf = input->mutable_image_conf();
+ img_conf->set_channels(CHANNELS);
+ img_conf->set_img_size(IMG_SIZE);
+ img_conf->set_img_size_y(IMG_SIZE_Y);
+ img_conf->set_img_size_z(IMG_SIZE_Z);
+
+ testLayerGrad(config,
+ "batch_norm",
+ 64,
+ /* trans= */ trans,
+ useGpu,
+ /* useWeight */ true);
+}
+
+TEST(Layer, testBatchNorm3DLayer) {
+ testBatchNorm3DLayer("batch_norm", false, false);
+#ifndef PADDLE_ONLY_CPU
+ testBatchNorm3DLayer("batch_norm", false, true);
+ if (hl_get_cudnn_lib_version() >= int(4000)) {
+ testBatchNorm3DLayer("cudnn_batch_norm", false, true);
+ }
+#endif
+}
+
void testConvOperator(bool isDeconv) {
TestConfig config;
const int NUM_FILTERS = 16;
@@ -2253,26 +2302,27 @@ void test3DDeConvLayer(const string& type, bool trans, bool useGpu) {
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_stride_z(2);
- conv->set_img_size(IMAGE_SIZE);
- conv->set_img_size_y(IMAGE_SIZE_Y);
- conv->set_img_size_z(IMAGE_SIZE_Z);
- conv->set_output_x(imageSize(conv->img_size(),
+ conv->set_output_x(IMAGE_SIZE);
+ conv->set_output_y(IMAGE_SIZE_Y);
+ conv->set_output_z(IMAGE_SIZE_Z);
+
+ conv->set_img_size(imageSize(conv->output_x(),
conv->filter_size(),
conv->padding(),
conv->stride(),
true));
- conv->set_output_y(imageSize(conv->img_size_y(),
- conv->filter_size_y(),
- conv->padding_y(),
- conv->stride_y(),
- true));
- conv->set_output_z(imageSize(conv->img_size_z(),
- conv->filter_size_z(),
- conv->padding_z(),
- conv->stride_z(),
- true));
- config.layerConfig.set_size(conv->output_x() * conv->output_y() *
- conv->output_z() * NUM_FILTERS);
+ conv->set_img_size_y(imageSize(conv->output_y(),
+ conv->filter_size_y(),
+ conv->padding_y(),
+ conv->stride_y(),
+ true));
+ conv->set_img_size_z(imageSize(conv->output_z(),
+ conv->filter_size_z(),
+ conv->padding_z(),
+ conv->stride_z(),
+ true));
+ config.layerConfig.set_size(conv->img_size() * conv->img_size_y() *
+ conv->img_size_z() * NUM_FILTERS);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
config.inputDefs.push_back(
diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp
index 0a355e2644cce572ce90ecf5c9d2a5b7b395bc61..c4063e5069854242d9f93886b66580385557ca73 100644
--- a/paddle/math/MKLDNNMatrix.cpp
+++ b/paddle/math/MKLDNNMatrix.cpp
@@ -33,14 +33,12 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) {
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(m);
CHECK(cpuMatrix) << "Only support create from CPU matrix yet";
-
- CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match";
- return std::make_shared(
- m->getData(), m->getHeight(), m->getWidth(), pd);
+ CHECK_EQ(cpuMatrix->getElementCnt(), cnts) << "Count size does not match";
+ return std::make_shared(cpuMatrix, pd);
}
MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m,
@@ -138,7 +136,7 @@ void MKLDNNMatrix::downSpatial() {
mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr),
"could not create a memory primitive");
reset(result);
- set_data_handle(getData());
+ set_data_handle(data_);
}
} // namespace paddle
diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h
index e50f698b495713e6f15ab7a12a7ee7487662040f..eef3b429e6fa0087aeac3f5aed9dff983b06e826 100644
--- a/paddle/math/MKLDNNMatrix.h
+++ b/paddle/math/MKLDNNMatrix.h
@@ -30,11 +30,10 @@ typedef std::shared_ptr MKLDNNMatrixPtr;
*/
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(CpuMatrixPtr m, mkldnn::memory::primitive_desc pd)
+ : CpuMatrix(m->getData(), m->getHeight(), m->getWidth(), false),
+ mkldnn::memory(pd, m->getData()),
+ m_(m) {}
~MKLDNNMatrix() {}
@@ -81,11 +80,29 @@ public:
void downSpatial();
/**
- * Update the memory data handle.
+ * set 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); }
+ void setData(real* data) {
+ set_data_handle(data);
+ CpuMatrix::setData(data);
+ m_.reset();
+ }
+
+ /**
+ * override Matrix::getData
+ * check data before return
+ */
+ real* getData() override {
+ CHECK_EQ((void*)data_, get_data_handle());
+ return data_;
+ }
+
+ const real* getData() const override {
+ CHECK_EQ((void*)data_, get_data_handle());
+ return data_;
+ }
/**
* Get primitive descriptor.
@@ -143,6 +160,10 @@ protected:
memory::format srcFmt,
memory::format dstFmt,
memory::dims dm);
+
+private:
+ // save the CpuMatrixPtr in case the buffer released outside
+ CpuMatrixPtr m_;
};
} // namespace paddle
diff --git a/paddle/operators/concat_op.cc b/paddle/operators/concat_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..0ebefbab26ec8fdf316f852fbb7f6d9f3bbc48eb
--- /dev/null
+++ b/paddle/operators/concat_op.cc
@@ -0,0 +1,79 @@
+/* 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 "paddle/operators/concat_op.h"
+#include
+
+namespace paddle {
+namespace operators {
+using framework::Tensor;
+
+class ConcatOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto ins = ctx.MultiInput("X");
+ auto *out = ctx.Output("Out");
+ size_t axis = static_cast(ctx.Attr("axis"));
+ size_t n = ins.size();
+
+ PADDLE_ENFORCE_GT(n, 1, "Input tensors count should > 1.");
+
+ auto out_dims = ins[0]->dims();
+ size_t in_zero_dims_size = out_dims.size();
+ for (size_t i = 1; i < n; i++) {
+ for (size_t j = 0; j < in_zero_dims_size; j++) {
+ if (j == axis) {
+ out_dims[axis] += ins[i]->dims()[j];
+ continue;
+ }
+ PADDLE_ENFORCE_EQ(out_dims[j], ins[i]->dims()[j],
+ "Input tensors should have the same "
+ "elements except the specify axis.")
+ }
+ }
+ out->Resize(out_dims);
+ }
+};
+
+class ConcatOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ ConcatOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "the input tensors of concat operator.").AsDuplicable();
+ AddOutput("Out", "the output tensor of concat operator.");
+ AddComment(R"DOC(
+ Join the input tensors along with the axis.
+ Examples:
+ Input[0] = [[1,2],[3,4]]
+ Input[1] = [[5,6]]
+ axis = 0
+ Output = [[1,2],
+ [3,4],
+ [5,6]]
+ )DOC");
+ AddAttr("axis", "The axis which the inputs will be joined with.")
+ .SetDefault(0);
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(concat, ops::ConcatOp, ops::ConcatOpMaker)
+REGISTER_OP_CPU_KERNEL(concat,
+ ops::ConcatKernel)
diff --git a/paddle/operators/concat_op.cu b/paddle/operators/concat_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..38fee7473dbb2ba97fe95b6632db7a1749cf3bbe
--- /dev/null
+++ b/paddle/operators/concat_op.cu
@@ -0,0 +1,19 @@
+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/concat_op.h"
+
+namespace ops = paddle::operators;
+// TODO(Yancey1989) Add GPU kernel
diff --git a/paddle/operators/concat_op.h b/paddle/operators/concat_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..f977054fdf8aa0164db726b94a21c57f770dd674
--- /dev/null
+++ b/paddle/operators/concat_op.h
@@ -0,0 +1,64 @@
+/* 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
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template
+class ConcatKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const override {
+ auto ins = ctx.MultiInput("X");
+ auto* out = ctx.Output("Out");
+ int64_t axis = static_cast(ctx.Attr("axis"));
+ size_t n = ins.size();
+ size_t output_axis_dim = 0;
+ size_t before = 1, after = 1;
+ for (size_t i = 0; i < n; i++) {
+ output_axis_dim += ins[i]->dims()[axis];
+ }
+ auto& input_zero = ins[0];
+ for (int64_t i = 0; i < input_zero->dims().size(); i++) {
+ if (i == axis) {
+ continue;
+ }
+ if (i < axis) {
+ before *= input_zero->dims()[i];
+ } else {
+ after *= input_zero->dims()[i];
+ }
+ }
+ size_t output_offset = 0;
+ for (size_t i = 0; i < n; i++) {
+ auto& in = ins[i];
+ auto axis_dim = in->dims()[axis];
+ for (size_t j = 0; j < before; j++) {
+ size_t len = axis_dim * after * sizeof(T);
+ const T* src = in->data() + axis_dim * after * j;
+ T* out_data = out->mutable_data(platform::CPUPlace());
+ T* dest = out_data + output_offset + output_axis_dim * after * j;
+ memcpy(dest, src, len);
+ }
+ output_offset += axis_dim * after;
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index 9e2bcebe3b5432c157fac895a9bbab5164193dbb..0dc509952578497671a128374f77ce616a520909 100644
--- a/paddle/operators/cos_sim_op.h
+++ b/paddle/operators/cos_sim_op.h
@@ -42,7 +42,7 @@ class CosSimKernel : public framework::OpKernel {
output_y_norm->mutable_data(context.GetPlace());
auto dims = input_x->dims();
- int size = static_cast(framework::product(dims));
+ int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix::From(*input_x, new_dims);
auto y = EigenMatrix::From(*input_y, new_dims);
@@ -72,7 +72,7 @@ class CosSimGradKernel : public framework::OpKernel {
auto* input_grad_z = context.Input(framework::GradVarName("Out"));
auto dims = input_x->dims();
- int size = static_cast(framework::product(dims));
+ int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix::From(*input_x, new_dims);
auto y = EigenMatrix::From(*input_y, new_dims);
diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc
index 6574880c0eb6324b2dd175e39a364d2ef46e735e..3d76516405960c502a46997108049b2db5cab6bf 100644
--- a/paddle/operators/gaussian_random_op.cc
+++ b/paddle/operators/gaussian_random_op.cc
@@ -31,7 +31,7 @@ class CPUGaussianRandomKernel : public framework::OpKernel {
}
engine.seed(seed);
std::normal_distribution dist(mean, std);
- int64_t size = framework::product(tensor->dims());
+ int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu
index d9dbc1dcfe6a6676938d64be93c879ea69148018..2d63b3049988cfc3135a87a57dad56b970df3eab 100644
--- a/paddle/operators/gaussian_random_op.cu
+++ b/paddle/operators/gaussian_random_op.cu
@@ -50,8 +50,8 @@ class GPUGaussianRandomKernel : public framework::OpKernel {
T mean = static_cast(context.Attr("mean"));
T std = static_cast(context.Attr("std"));
thrust::counting_iterator index_sequence_begin(0);
- ssize_t N = framework::product(tensor->dims());
- thrust::transform(index_sequence_begin, index_sequence_begin + N,
+ int64_t size = tensor->numel();
+ thrust::transform(index_sequence_begin, index_sequence_begin + size,
thrust::device_ptr(data),
GaussianGenerator(mean, std, seed));
}
diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu
index 27eee3436af8107cef2aa3577ea238be49edf1af..708344046760691aa2da562eb1ee3d8b130c5f18 100644
--- a/paddle/operators/lookup_table_op.cu
+++ b/paddle/operators/lookup_table_op.cu
@@ -70,7 +70,7 @@ class LookupTableCUDAKernel : public framework::OpKernel {
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
- size_t K = product(ids_t->dims());
+ size_t K = ids_t->numel();
auto ids = ids_t->data();
auto table = table_t->data();
auto output = output_t->mutable_data(context.GetPlace());
@@ -91,7 +91,7 @@ class LookupTableGradCUDAKernel : public framework::OpKernel {
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
- int K = product(ids_t->dims());
+ int K = ids_t->numel();
const int32_t* ids = ids_t->data();
const T* d_output = d_output_t->data();
T* d_table = d_table_t->mutable_data(context.GetPlace());
diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h
index 877b36cef4ea9cdaaaf37c97d5e5bfce55b91436..a1298906dd4b4209644fe06584f70169519de01c 100644
--- a/paddle/operators/lookup_table_op.h
+++ b/paddle/operators/lookup_table_op.h
@@ -35,7 +35,7 @@ class LookupTableKernel : public framework::OpKernel {
auto ids = ids_t->data();
auto table = table_t->data();
auto output = output_t->mutable_data(context.GetPlace());
- for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
+ for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
@@ -61,7 +61,7 @@ class LookupTableGradKernel : public framework::OpKernel {
t.device(context.GetEigenDevice()) =
t.constant(static_cast(0));
- for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
+ for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
for (int j = 0; j < D; ++j) {
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/operators/mean_op.h b/paddle/operators/mean_op.h
index 9848af280b62729bef9243052ceae0b7d8f4c6f5..ce31e178d8e375dc59be80a6c05133201308da70 100644
--- a/paddle/operators/mean_op.h
+++ b/paddle/operators/mean_op.h
@@ -49,12 +49,11 @@ class MeanGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto OG = context.Input(framework::GradVarName("Out"));
- PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
- "Mean Gradient should be scalar");
+ PADDLE_ENFORCE(OG->numel() == 1, "Mean Gradient should be scalar");
auto IG = context.Output(framework::GradVarName("X"));
IG->mutable_data(context.GetPlace());
- T ig_size = (T)framework::product(IG->dims());
+ T ig_size = static_cast(IG->numel());
Eigen::DSizes bcast(ig_size);
EigenVector::Flatten(*IG).device(context.GetEigenDevice()) =
diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc
index 069fb5e1abc657aa02a50fde352ce88d078c36e1..a4876feb2edf77bd422fa2a7687b0fa7d55dae47 100644
--- a/paddle/operators/minus_op.cc
+++ b/paddle/operators/minus_op.cc
@@ -31,8 +31,7 @@ class MinusOp : public framework::OperatorWithKernel {
auto *right_tensor = ctx.Input("Y");
PADDLE_ENFORCE_EQ(
- framework::product(left_tensor->dims()),
- framework::product(right_tensor->dims()),
+ left_tensor->numel(), right_tensor->numel(),
"Minus operator must take two tensor with same num of elements");
ctx.Output("Out")->Resize(left_tensor->dims());
}
diff --git a/paddle/operators/name_convention.md b/paddle/operators/name_convention.md
new file mode 100644
index 0000000000000000000000000000000000000000..a090e0b5450509affdd739f63df618595f204f97
--- /dev/null
+++ b/paddle/operators/name_convention.md
@@ -0,0 +1,59 @@
+## Operator's Parameter Name Convention
+
+To make the operator document itself more clear, we recommend operator names obey the listing conventions.
+
+### OpProtoMaker names
+
+When defining an operator in Paddle, a corresponding [OpProtoMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L170) (TODO: OpProtoMaker Doc)need to be defined. All the Input/Output and Attributes will write into the [OpProto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L61) , and will be used in client language to create operator.
+
+- Input/Output.
+ - Input/Output names follow the **CamelCase**. e.g. `X`, `Y`, `Matrix`, `LastAxisInMatrix`. Input/Output much more like Variables, we prefer to meaningful English words.
+ - If an operator's Input/Output are tensors in math, not match to any meaningful words, input name should starts from `X`. e.g. `X`, `Y`, and output name should starts from `Out`. e.g. `Out`. This rule intends making operators which have few inputs/outputs unified.
+
+- Attribute.
+ - Attribute name follows the **camelCase**. e.g. `x`, `y`, `axis`, `rowwiseMatrix`. Also, attribute name prefers to meaningful English words.
+
+- Comments.
+ - Input/Output/Attr comment follow the format of **(type,default value) usage**, corresponding to which type it can be and how it will be used in the operator. e.g. Attribute in Accumulator`"gamma" `,`(float, default 1.0) Accumulation multiplier`.
+ - Operator comment format of` R"DOC(your comment here)DOC"`. You should explain the input/output of the operator first. If there is math calculation in this operator, you should write the equation in the comment. e.g. `Out = X + Y`.
+
+- Order.
+ - Follow the order of Input/Output, then Attribute, then Comments. See the example in best practice.
+
+### Best Practice
+
+Here we give some examples to show how these rules will be used.
+
+- The operator has one input, one output. e.g.`relu`, inputs: `X`, outputs: `Out`.
+
+- The operator has two input, one output. e.g. `rowwise_add`, inputs : `X`, `Y`, outputs : `Out`.
+
+- The operator contains attribute. e.g. `cosine`, inputs : `X`, `axis`, outputs : `Out`.
+
+ We give a full example of Accumulator Operator.
+
+```c++
+class AccumulateOpMaker : public framework::OpProtoAndCheckerMaker {
+public:
+ AccumulateOpMaker(framework::OpProto *proto,
+ framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor. If the output size is not the same as input size, the output tensor is first reshaped and initialized to zero, and only then, accumulation is done.");
+ AddOutput("Out", "(Tensor) Accumulated output tensor");
+ AddAttr("gamma", "(float, default 1.0) Accumulation multiplier");
+ AddComment(R"DOC(
+Accumulate operator accumulates the input tensor to the output tensor. If the
+output tensor already has the right size, we add to it; otherwise, we first
+initialize the output tensor to all zeros, and then do accumulation. Any
+further calls to the operator, given that no one else fiddles with the output
+in the interim, will do simple accumulations.
+Accumulation is done as shown:
+
+Out = 1*X + gamma*Out
+
+where X is the input tensor, Y is the output tensor and gamma is the multiplier
+argument.
+)DOC");
+ }
+};
+```
diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..b7061153d2bf13982f14f233e87a87daeeebf5fd
--- /dev/null
+++ b/paddle/operators/reshape_op.cc
@@ -0,0 +1,107 @@
+
+/* 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 "paddle/operators/reshape_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ReshapeOp : public framework::OperatorWithKernel {
+ public:
+ ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
+ const framework::VariableNameMap &outputs,
+ const framework::AttributeMap &attrs)
+ : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ // input check
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null");
+ auto shape = ctx.Attr>("shape");
+ PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
+ for (auto dim : shape) {
+ PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
+ }
+ // capacity check
+ int64_t capacity =
+ std::accumulate(shape.begin(), shape.end(), 1, std::multiplies());
+ auto *in = ctx.Input("X");
+ int64_t in_size = framework::product(in->dims());
+ PADDLE_ENFORCE_EQ(capacity, in_size,
+ "The size of Input(X) mismatches with Attr(shape).");
+ // resize output
+ std::vector shape_int64(shape.size(), 0);
+ std::transform(shape.begin(), shape.end(), shape_int64.begin(),
+ [](int a) { return static_cast(a); });
+ auto out_dims = framework::make_ddim(shape_int64);
+ ctx.Output("Out")->Resize(out_dims);
+ }
+};
+
+class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ ReshapeOpMaker(framework::OpProto *proto,
+ framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "The input tensor of reshape operator.");
+ AddOutput("Out", "The output tensor of reshape operator.");
+ AddAttr>("shape", "Target shape of reshape operator.");
+ AddComment(R"DOC(Reshape operator
+
+Reshape Input(X) into the shape specified by Attr(shape).
+
+An example:
+Given a 2-D tensor X with 2 rows and 2 columns
+
+ [[1, 2], [3, 4]]
+
+with target shape = [1, 4], the reshape operator will transform
+the tensor X into a 1-D tensor:
+
+ [1, 2, 3, 4]
+
+)DOC");
+ }
+};
+
+class ReshapeGradOp : public framework::OperatorWithKernel {
+ public:
+ ReshapeGradOp(const std::string &type,
+ const framework::VariableNameMap &inputs,
+ const framework::VariableNameMap &outputs,
+ const framework::AttributeMap &attrs)
+ : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+ "Input(Out@GRAD) shouldn't be null.");
+ auto dims = ctx.Input("X")->dims();
+ auto *d_in = ctx.Output(framework::GradVarName("X"));
+ d_in->Resize(dims);
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad,
+ ops::ReshapeGradOp);
+REGISTER_OP_CPU_KERNEL(reshape,
+ ops::ReshapeKernel);
+REGISTER_OP_CPU_KERNEL(
+ reshape_grad, ops::ReshapeGradKernel);
diff --git a/paddle/operators/reshape_op.cu b/paddle/operators/reshape_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..23dbe089d3b37aabedf9ef166f7bbfbf67da7e0a
--- /dev/null
+++ b/paddle/operators/reshape_op.cu
@@ -0,0 +1,22 @@
+/* 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 "paddle/operators/reshape_op.h"
+
+REGISTER_OP_GPU_KERNEL(
+ reshape,
+ paddle::operators::ReshapeKernel);
+REGISTER_OP_GPU_KERNEL(
+ reshape_grad,
+ paddle::operators::ReshapeGradKernel);
diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..873acf30782d390cdca5e7e864c76e1f743f9a7c
--- /dev/null
+++ b/paddle/operators/reshape_op.h
@@ -0,0 +1,55 @@
+/* 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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template
+class ReshapeKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const {
+ auto* out = ctx.Output("Out");
+ auto* in = ctx.Input("X");
+ out->mutable_data(ctx.GetPlace());
+
+ auto shape = ctx.Attr>("shape");
+ std::vector shape_int64(shape.size(), 0);
+ std::transform(shape.begin(), shape.end(), shape_int64.begin(),
+ [](int a) { return static_cast(a); });
+ auto out_dims = framework::make_ddim(shape_int64);
+ out->CopyFrom(*in, ctx.GetPlace());
+ out->Resize(out_dims);
+ }
+};
+
+template
+class ReshapeGradKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& ctx) const {
+ auto* d_out = ctx.Input(framework::GradVarName("Out"));
+ auto* d_x = ctx.Output(framework::GradVarName("X"));
+ d_x->mutable_data(ctx.GetPlace());
+
+ auto in_dims = d_x->dims();
+ d_x->CopyFrom(*d_out, ctx.GetPlace());
+ d_x->Resize(in_dims);
+ }
+};
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc
index dc30644a5e7e33d4289e48cac093aa5fde7e75e7..9f51d3efa8ecba894a1023b9de2df451ca85916c 100644
--- a/paddle/operators/squared_l2_distance_op.cc
+++ b/paddle/operators/squared_l2_distance_op.cc
@@ -41,8 +41,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
int rank = framework::arity(x_dims);
PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
- PADDLE_ENFORCE_EQ(framework::product(x_dims) / x_dims[0],
- framework::product(y_dims) / y_dims[0],
+ PADDLE_ENFORCE_EQ(x->numel() / x_dims[0], y->numel() / y_dims[0],
"Product of dimensions expcet the first dimension of "
"input and target must be equal.");
PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
@@ -50,8 +49,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
"or to 1.");
ctx.Output("sub_result")
- ->Resize({static_cast(x_dims[0]),
- static_cast(framework::product(x_dims) / x_dims[0])});
+ ->Resize({x_dims[0], x->numel() / x_dims[0]});
ctx.Output("Out")->Resize({x_dims[0], 1});
}
};
diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h
index ad3347a0b35f3385c5adbcd7ceaa94fe134105e3..097ac04fc09a10b3b624f491a847e281e41a802c 100644
--- a/paddle/operators/squared_l2_distance_op.h
+++ b/paddle/operators/squared_l2_distance_op.h
@@ -39,7 +39,7 @@ class SquaredL2DistanceKernel : public framework::OpKernel {
auto in0_dims = in0->dims();
auto in1_dims = in1->dims();
- int cols = framework::product(in0_dims) / in0_dims[0];
+ int cols = in0->numel() / in0_dims[0];
// reduce dimensions except the first
auto x =
EigenMatrix::From(*in0, framework::make_ddim({in0_dims[0], cols}));
@@ -82,7 +82,7 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel {
auto x_dims = x_g->dims();
auto y_dims = y_g->dims();
- int cols = framework::product(x_dims) / x_dims[0];
+ int cols = x_g->numel() / x_dims[0];
// calculate gradient
auto grad_mat = 2 *
(out_grad.broadcast(Eigen::array({{1, cols}}))) *
diff --git a/paddle/operators/sum_op.cc b/paddle/operators/sum_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..5805826ee8a555ca6dfc1ca81feaadffea9e1012
--- /dev/null
+++ b/paddle/operators/sum_op.cc
@@ -0,0 +1,73 @@
+/* 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 "paddle/operators/sum_op.h"
+#include
+
+namespace paddle {
+namespace operators {
+using framework::Tensor;
+
+class SumOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto ins = ctx.MultiInput("X");
+ auto *out = ctx.Output("Out");
+ int N = ins.size();
+
+ auto in_dim = ins[0]->dims();
+
+ PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
+ for (int i = 1; i < N; i++) {
+ auto dim = ins[i]->dims();
+ PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");
+ }
+ out->Resize(in_dim);
+ }
+};
+
+class SumOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "the input tensors of sum operator.").AsDuplicable();
+ AddOutput("Out", "the output tensor of sum operator.");
+ AddComment(R"DOC(
+ Sum the input tensors.
+ )DOC");
+ }
+};
+
+class SumGradOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ auto outputs = ctx.MultiOutput(framework::GradVarName("X"));
+ auto dims = ctx.Input(framework::GradVarName("Out"))->dims();
+ for (auto output : outputs) {
+ output->Resize(dims);
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp);
+REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel);
+REGISTER_OP_CPU_KERNEL(sum_grad,
+ ops::SumGradKernel);
diff --git a/paddle/operators/sum_op.cu b/paddle/operators/sum_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..a465cf3659ba7c51338abadfc62962fb6755a39d
--- /dev/null
+++ b/paddle/operators/sum_op.cu
@@ -0,0 +1,18 @@
+/* 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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/sum_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(sum, ops::SumKernel);
+REGISTER_OP_GPU_KERNEL(sum_grad,
+ ops::SumGradKernel);
diff --git a/paddle/operators/sum_op.h b/paddle/operators/sum_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..0b1e9ebaa38d455fb5e3ce8c1a39cbbcdad9a940
--- /dev/null
+++ b/paddle/operators/sum_op.h
@@ -0,0 +1,65 @@
+/* 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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template
+using EigenVector = framework::EigenVector;
+
+template
+class SumKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto ins = context.MultiInput("X");
+ auto* out = context.Output("Out");
+ out->mutable_data(context.GetPlace());
+
+ auto place = context.GetEigenDevice();
+ auto result = EigenVector::Flatten(*out);
+
+ int N = ins.size();
+ auto in = EigenVector::Flatten(*(ins[0]));
+ result.device(place) = in;
+ for (int i = 1; i < N; i++) {
+ auto in = EigenVector::Flatten(*(ins[i]));
+ result.device(place) = result + in;
+ }
+ }
+};
+
+template
+class SumGradKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto* input = context.Input(framework::GradVarName("Out"));
+ auto outs = context.MultiOutput(framework::GradVarName("X"));
+ for (auto out : outs) {
+ out->mutable_data(context.GetPlace());
+ }
+
+ auto place = context.GetEigenDevice();
+ auto in = EigenVector::Flatten(*input);
+ for (auto out : outs) {
+ auto result = EigenVector::Flatten(*out);
+ result.device(place) = in;
+ }
+ }
+};
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc
index f2aeef6c310df8535e67fa3906301a87f8ec4694..b8fbc9b52aecdb5c8d985b5de9bcd7cb85835b60 100644
--- a/paddle/operators/uniform_random_op.cc
+++ b/paddle/operators/uniform_random_op.cc
@@ -35,7 +35,7 @@ class CPUUniformRandomKernel : public framework::OpKernel {
std::uniform_real_distribution dist(
static_cast(context.Attr("min")),
static_cast(context.Attr("max")));
- int64_t size = framework::product(tensor->dims());
+ int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu
index c2c041b144b6ca1f019f972e1301b756ec1c9301..6614b53b3f990d10c82633f3c1f079acea0cd827 100644
--- a/paddle/operators/uniform_random_op.cu
+++ b/paddle/operators/uniform_random_op.cu
@@ -53,8 +53,8 @@ class GPUUniformRandomKernel : public framework::OpKernel {
T min = static_cast(context.Attr("min"));
T max = static_cast(context.Attr("max"));
thrust::counting_iterator index_sequence_begin(0);
- ssize_t N = framework::product(tensor->dims());
- thrust::transform(index_sequence_begin, index_sequence_begin + N,
+ int64_t size = tensor->numel();
+ thrust::transform(index_sequence_begin, index_sequence_begin + size,
thrust::device_ptr(data),
UniformGenerator(min, max, seed));
}
diff --git a/paddle/platform/enforce.h b/paddle/platform/enforce.h
index 81448897e95eb05f4ce7de8683d98e05bade77cb..64fcbd93b6c4d5d9b36f2636c3ef4f7327f08d25 100644
--- a/paddle/platform/enforce.h
+++ b/paddle/platform/enforce.h
@@ -25,10 +25,6 @@ limitations under the License. */
#include "paddle/string/printf.h"
#include "paddle/string/to_string.h"
-#ifdef __GNUC__
-#include // for __cxa_demangle
-#endif
-
#ifndef PADDLE_ONLY_CPU
#include "paddle/platform/dynload/cublas.h"
@@ -46,19 +42,6 @@ limitations under the License. */
namespace paddle {
namespace platform {
-namespace {
-#ifdef __GNUC__
-inline std::string demangle(std::string name) {
- int status = -4; // some arbitrary value to eliminate the compiler warning
- std::unique_ptr res{
- abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free};
- return (status == 0) ? res.get() : name;
-}
-#else
-inline std::string demangle(std::string name) { return name; }
-#endif
-}
-
struct EnforceNotMet : public std::exception {
std::exception_ptr exp_;
std::string err_str_;
@@ -79,7 +62,7 @@ struct EnforceNotMet : public std::exception {
Dl_info info;
for (int i = 0; i < size; ++i) {
if (dladdr(call_stack[i], &info)) {
- auto demangled = demangle(info.dli_sname);
+ auto demangled = info.dli_sname;
auto addr_offset = static_cast(call_stack[i]) -
static_cast(info.dli_saddr);
sout << string::Sprintf("%-3d %*0p %s + %zd\n", i,
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 53985933ed143c7faba6f7e2a704445697c1f58e..16a2368aae5fff7445654161db4fd6a97d5bebfc 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"
@@ -49,13 +50,18 @@ USE_OP(minus);
USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
+USE_CPU_ONLY_OP(concat);
USE_OP(top_k);
USE_OP(squared_l2_distance);
+USE_OP(sum);
+USE_OP(reshape);
namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using LoD = framework::LoD;
static size_t UniqueIntegerGenerator() {
static std::atomic generator;
@@ -115,6 +121,60 @@ 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.
@@ -126,6 +186,11 @@ 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();
@@ -216,7 +281,10 @@ All parameter, weight, gradient are variables in Paddle.
-> std::map> {
return op.Outputs();
})
+ .def("output_vars",
+ [](const OperatorBase &op) { return op.OutputVars(true); })
.def("inputs", [](const OperatorBase &op) { return op.Inputs(); })
+ .def("input_vars", [](const OperatorBase &op) { return op.InputVars(); })
.def("__str__", &OperatorBase::DebugString)
.def("no_intermediate_outputs",
[](const OperatorBase &op) { return op.OutputVars(false); })
diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index e57f793ac42b19037e9ca43a5e4a3ac5447dc34c..2ac455d771bf78377ce4ee7d921393d3b3958e3c 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -30,6 +30,8 @@ Configuring cmake in /paddle/build ...
-DCMAKE_BUILD_TYPE=Release
-DWITH_DOC=OFF
-DWITH_GPU=${WITH_GPU:-OFF}
+ -DWITH_MKLDNN=${WITH_MKLDNN:-ON}
+ -DWITH_MKLML=${WITH_MKLML:-ON}
-DWITH_AVX=${WITH_AVX:-OFF}
-DWITH_GOLANG=${WITH_GOLANG:-ON}
-DWITH_SWIG_PY=ON
@@ -50,6 +52,8 @@ cmake .. \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_DOC=OFF \
-DWITH_GPU=${WITH_GPU:-OFF} \
+ -DWITH_MKLDNN=${WITH_MKLDNN:-ON} \
+ -DWITH_MKLML=${WITH_MKLML:-ON} \
-DWITH_AVX=${WITH_AVX:-OFF} \
-DWITH_GOLANG=${WITH_GOLANG:-ON} \
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \
diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto
index 7d7fc23a4691646dfce4c162a445864c748501d9..ebf0911d6ea0b39d51447859ae2aef485b50b0e6 100644
--- a/proto/ModelConfig.proto
+++ b/proto/ModelConfig.proto
@@ -271,6 +271,7 @@ message ImageConfig {
// The size of input feature map.
required uint32 img_size = 8;
optional uint32 img_size_y = 9;
+ optional uint32 img_size_z = 10 [ default = 1 ];
}
message PriorBoxConfig {
@@ -519,6 +520,7 @@ message LayerConfig {
// for HuberRegressionLoss
optional double delta = 57 [ default = 1.0 ];
+ // for 3D data
optional uint64 depth = 58 [ default = 1 ];
// for switch order layer
diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 11dc84ae20679bb73735f9119739fca5ea7fa673..4f68a8953446ffa0510df65c5b214d09b913cff8 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -1332,6 +1332,12 @@ def parse_image(image, input_layer_name, image_conf):
get_img_size(input_layer_name, image_conf.channels)
+def parse_image3d(image, input_layer_name, image_conf):
+ image_conf.channels = image.channels
+ image_conf.img_size, image_conf.img_size_y, image_conf.img_size_z = \
+ get_img3d_size(input_layer_name, image_conf.channels)
+
+
def parse_norm(norm, input_layer_name, norm_conf):
norm_conf.norm_type = norm.norm_type
config_assert(
@@ -2028,6 +2034,7 @@ class ParameterReluLayer(LayerBase):
config_assert(input_layer.size % partial_sum == 0,
"a wrong setting for partial_sum")
self.set_layer_size(input_layer.size)
+ self.config.partial_sum = partial_sum
self.create_input_parameter(0, input_layer.size / partial_sum)
@@ -2365,9 +2372,11 @@ class BatchNormLayer(LayerBase):
name,
inputs,
bias=True,
+ img3D=False,
use_global_stats=True,
moving_average_fraction=0.9,
batch_norm_type=None,
+ mean_var_names=None,
**xargs):
if inputs is None:
inputs = []
@@ -2409,24 +2418,69 @@ class BatchNormLayer(LayerBase):
input_layer = self.get_input_layer(0)
image_conf = self.config.inputs[0].image_conf
- parse_image(self.inputs[0].image, input_layer.name, image_conf)
-
- # Only pass the width and height of input to batch_norm layer
- # when either of it is non-zero.
- if input_layer.width != 0 or input_layer.height != 0:
- self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
- image_conf.channels, False)
+ if img3D:
+ parse_image3d(self.inputs[0].image, input_layer.name, image_conf)
+ # Only pass the width and height of input to batch_norm layer
+ # when either of it is non-zero.
+ if input_layer.width != 0 or input_layer.height != 0:
+ self.set_cnn_layer(
+ input_layer_name=name,
+ depth=image_conf.img_size_z,
+ height=image_conf.img_size_y,
+ width=image_conf.img_size,
+ channels=image_conf.channels,
+ is_print=True)
+ else:
+ self.set_layer_size(input_layer.size)
else:
- self.set_layer_size(input_layer.size)
+ parse_image(self.inputs[0].image, input_layer.name, image_conf)
+ # Only pass the width and height of input to batch_norm layer
+ # when either of it is non-zero.
+ if input_layer.width != 0 or input_layer.height != 0:
+ self.set_cnn_layer(
+ input_layer_name=name,
+ height=image_conf.img_size_y,
+ width=image_conf.img_size,
+ channels=image_conf.channels,
+ is_print=True)
+ else:
+ self.set_layer_size(input_layer.size)
psize = self.calc_parameter_size(image_conf)
dims = [1, psize]
+ if mean_var_names is not None:
+ assert len(mean_var_names) == 2
+ self.inputs[1].parameter_name = mean_var_names[0]
+ self.inputs[2].parameter_name = mean_var_names[1]
+
self.create_input_parameter(0, psize)
self.create_input_parameter(1, psize, dims)
self.create_input_parameter(2, psize, dims)
self.create_bias_parameter(bias, psize)
+ def set_cnn_layer(self,
+ input_layer_name,
+ depth=None,
+ height=None,
+ width=None,
+ channels=None,
+ is_print=True):
+ depthIsNone = False
+ if depth is None:
+ depth = 1
+ depthIsNone = True
+ size = depth * height * width * channels
+ self.set_layer_size(size)
+ self.set_layer_height_width(height, width)
+ self.set_layer_depth(depth)
+ if is_print and depthIsNone:
+ print("output for %s: c = %d, h = %d, w = %d, size = %d" %
+ (input_layer_name, channels, height, width, size))
+ elif is_print:
+ print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" %
+ (input_layer_name, channels, depth, height, width, size))
+
def calc_parameter_size(self, image_conf):
return image_conf.channels
@@ -2688,9 +2742,20 @@ class AddToLayer(LayerBase):
super(AddToLayer, self).__init__(
name, 'addto', 0, inputs=inputs, **xargs)
config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer')
- for input_index in xrange(len(self.inputs)):
- input_layer = self.get_input_layer(input_index)
- self.set_layer_size(input_layer.size)
+
+ if len(self.inputs) > 1:
+ for input_index in xrange(len(self.inputs)):
+ assert self.get_input_layer(0).height == self.get_input_layer(
+ input_index).height
+ assert self.get_input_layer(0).width == self.get_input_layer(
+ input_index).width
+ assert self.get_input_layer(0).depth == self.get_input_layer(
+ input_index).depth
+
+ self.set_layer_size(self.get_input_layer(0).size)
+ self.set_layer_height_width(self.get_input_layer(0).height, \
+ self.get_input_layer(0).width)
+ self.set_layer_depth(self.get_input_layer(0).depth)
self.create_bias_parameter(bias, self.config.size)
@@ -3370,11 +3435,20 @@ class ConcatenateLayer(LayerBase):
name, 'concat', 0, inputs=inputs, **xargs)
size = 0
for input_index in xrange(len(self.inputs)):
+ assert self.get_input_layer(0).height == self.get_input_layer(
+ input_index).height
+ assert self.get_input_layer(0).width == self.get_input_layer(
+ input_index).width
+ assert self.get_input_layer(0).depth == self.get_input_layer(
+ input_index).depth
input_layer = self.get_input_layer(input_index)
input = self.inputs[input_index]
if self.config.size == 0:
size += input_layer.size
+ self.set_layer_height_width(self.get_input_layer(0).height, \
+ self.get_input_layer(0).width)
+ self.set_layer_depth(self.get_input_layer(0).depth)
self.set_layer_size(size)
@@ -3675,8 +3749,8 @@ class SwitchOrderLayer(LayerBase):
def __init__(self, name, inputs, reshape, **xargs):
super(SwitchOrderLayer, self).__init__(
name, 'switch_order', 0, inputs=inputs, **xargs)
- self.config.reshape_conf.heightAxis.extend(reshape['height'])
- self.config.reshape_conf.widthAxis.extend(reshape['width'])
+ self.config.reshape_conf.height_axis.extend(reshape['height'])
+ self.config.reshape_conf.width_axis.extend(reshape['width'])
# Deprecated, use a new layer specific class instead
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index cba45bd3afa178ab4dd3a50f0947b144e7466e53..4b1d80d3db924bfa2ad0e081f785d8f5dd719fce 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -354,6 +354,10 @@ class LayerOutput(object):
def height(self):
return cp.g_layer_map[self.full_name].height
+ @property
+ def depth(self):
+ return cp.g_layer_map[self.full_name].depth
+
def set_input(self, input):
"""
Set the input for a memory layer. Can only be used for memory layer
@@ -943,7 +947,7 @@ def data_layer(name, size, depth=None, height=None, width=None,
if height is not None and width is not None:
num_filters = size / (width * height * depth)
assert num_filters * width * height * depth == size, \
- "size=%s width=%s height=%s depth=%s" % (size, width, height, depth)
+ "size=%s width=%s height=%s depth=%s" % (size, width, height, depth)
return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters)
@@ -1219,7 +1223,8 @@ def detection_output_layer(input_loc,
name=None):
"""
Apply the NMS to the output of network and compute the predict bounding
- box location.
+ box location. The output of this layer could be None if there is no valid
+ bounding box.
:param name: The Layer Name.
:type name: basestring
@@ -2953,13 +2958,15 @@ def img_cmrnorm_layer(input,
def batch_norm_layer(input,
act=None,
name=None,
+ img3D=False,
num_channels=None,
bias_attr=None,
param_attr=None,
layer_attr=None,
batch_norm_type=None,
moving_average_fraction=0.9,
- use_global_stats=None):
+ use_global_stats=None,
+ mean_var_names=None):
"""
Batch Normalization Layer. The notation of this layer as follow.
@@ -3026,6 +3033,8 @@ def batch_norm_layer(input,
:math:`runningMean = newMean*(1-factor)
+ runningMean*factor`
:type moving_average_fraction: float.
+ :param mean_var_names: [mean name, variance name]
+ :type mean_var_names: string list
:return: LayerOutput object.
:rtype: LayerOutput
"""
@@ -3039,6 +3048,7 @@ def batch_norm_layer(input,
(batch_norm_type == "cudnn_batch_norm")
l = Layer(
name=name,
+ img3D=img3D,
inputs=Input(
input.name, image=Image(channels=num_channels), **param_attr.attr),
active_type=act.name,
@@ -3047,6 +3057,7 @@ def batch_norm_layer(input,
bias=ParamAttr.to_bias(bias_attr),
moving_average_fraction=moving_average_fraction,
use_global_stats=use_global_stats,
+ mean_var_names=mean_var_names,
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
@@ -6410,7 +6421,7 @@ def gated_unit_layer(input,
@wrap_name_default('switch_order')
def switch_order_layer(input,
name=None,
- reshape=None,
+ reshape_axis=None,
act=None,
layer_attr=None):
"""
@@ -6421,8 +6432,9 @@ def switch_order_layer(input,
The example usage is:
.. code-block:: python
+ reshape_axis = 3
+ switch = switch_order(input=layer, name='switch', reshape_axis=reshape_axis)
reshape = {'height':[ 0, 1, 2], 'width':[3]}
- switch = switch_order(input=layer, name='switch', reshape=reshape)
:param input: The input layer.
:type input: LayerOutput
@@ -6434,6 +6446,11 @@ def switch_order_layer(input,
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
+ assert reshape_axis != None and (reshape_axis > 0 and reshape_axis < 4)
+ height = [ele for ele in xrange(reshape_axis)]
+ width = [ele for ele in range(reshape_axis, 4)]
+ reshape = {'height': height, 'width': width}
+
l = Layer(
name=name,
inputs=input.name,
@@ -6444,6 +6461,7 @@ def switch_order_layer(input,
return LayerOutput(
name=name,
layer_type=LayerType.SWITCH_ORDER_LAYER,
+ activation=act,
parents=input,
size=l.config.size)
diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
index df872a90ff388f0d96cef44763dbd076bc768ab9..8a204a96f3ef57673cef65306d0bf8e8c3409751 100755
--- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
+++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh
@@ -10,6 +10,6 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer
-test_conv3d_layer test_deconv3d_layer)
+test_conv3d_layer test_deconv3d_layer test_BatchNorm3D)
export whole_configs=(test_split_datasource)
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr
index 1a577b8d9b1e1915236ba6afcfa97040d70c707a..5ddf6052df021b055390a42c25ce6c0d650e4aee 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr
@@ -62,6 +62,7 @@ layers {
moving_average_fraction: 0.9
height: 227
width: 227
+ depth: 1
}
layers {
name: "__crmnorm_0__"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
index 2818389b16cca75f5030b75fc4de8c89c06c5e02..c0252b945b4c7fd6b4dad8770e3e1dccb88df28a 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
@@ -62,6 +62,7 @@ layers {
moving_average_fraction: 0.9
height: 256
width: 256
+ depth: 1
}
layers {
name: "__crmnorm_0__"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr
new file mode 100644
index 0000000000000000000000000000000000000000..832ed24a31dd2bedba9a4fce77d7a088d1796fdb
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr
@@ -0,0 +1,92 @@
+type: "nn"
+layers {
+ name: "data3D"
+ type: "data"
+ size: 360
+ active_type: ""
+ height: 6
+ width: 20
+ depth: 3
+}
+layers {
+ name: "__batch_norm_0__"
+ type: "batch_norm"
+ size: 360
+ active_type: "relu"
+ inputs {
+ input_layer_name: "data3D"
+ input_parameter_name: "___batch_norm_0__.w0"
+ image_conf {
+ channels: 1
+ img_size: 20
+ img_size_y: 6
+ img_size_z: 3
+ }
+ }
+ inputs {
+ input_layer_name: "data3D"
+ input_parameter_name: "___batch_norm_0__.w1"
+ }
+ inputs {
+ input_layer_name: "data3D"
+ input_parameter_name: "___batch_norm_0__.w2"
+ }
+ bias_parameter_name: "___batch_norm_0__.wbias"
+ moving_average_fraction: 0.9
+ height: 6
+ width: 20
+ depth: 3
+}
+parameters {
+ name: "___batch_norm_0__.w0"
+ size: 1
+ initial_mean: 1.0
+ initial_std: 0.0
+ initial_strategy: 0
+ initial_smart: false
+}
+parameters {
+ name: "___batch_norm_0__.w1"
+ size: 1
+ initial_mean: 0.0
+ initial_std: 0.0
+ dims: 1
+ dims: 1
+ initial_strategy: 0
+ initial_smart: false
+ is_static: true
+ is_shared: true
+}
+parameters {
+ name: "___batch_norm_0__.w2"
+ size: 1
+ initial_mean: 0.0
+ initial_std: 0.0
+ dims: 1
+ dims: 1
+ initial_strategy: 0
+ initial_smart: false
+ is_static: true
+ is_shared: true
+}
+parameters {
+ name: "___batch_norm_0__.wbias"
+ size: 1
+ initial_mean: 0.0
+ initial_std: 0.0
+ dims: 1
+ dims: 1
+ initial_strategy: 0
+ initial_smart: false
+}
+input_layer_names: "data3D"
+output_layer_names: "__batch_norm_0__"
+sub_models {
+ name: "root"
+ layer_names: "data3D"
+ layer_names: "__batch_norm_0__"
+ input_layer_names: "data3D"
+ output_layer_names: "__batch_norm_0__"
+ is_recurrent_layer_group: false
+}
+
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr
index b110e91498ce7d112987714bd769868179141c54..8a1399efad0ff339e35f69400ac654a4787a6018 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr
@@ -74,6 +74,9 @@ layers {
inputs {
input_layer_name: "__bidirectional_gru_0___bw"
}
+ height: 0
+ width: 0
+ depth: 1
}
parameters {
name: "___bidirectional_gru_0___fw_transform.w0"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
index 64d227565f2b21ff43d4391c682ca90c0f47908e..94ad56cab063df9e6a11bb1c293727fb9dec810f 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
@@ -14,6 +14,29 @@ layers {
input_layer_name: "input"
input_parameter_name: "___prelu_layer_0__.w0"
}
+ partial_sum: 1
+}
+layers {
+ name: "__prelu_layer_1__"
+ type: "prelu"
+ size: 300
+ active_type: ""
+ inputs {
+ input_layer_name: "input"
+ input_parameter_name: "___prelu_layer_1__.w0"
+ }
+ partial_sum: 1
+}
+layers {
+ name: "__prelu_layer_2__"
+ type: "prelu"
+ size: 300
+ active_type: ""
+ inputs {
+ input_layer_name: "input"
+ input_parameter_name: "___prelu_layer_2__.w0"
+ }
+ partial_sum: 5
}
parameters {
name: "___prelu_layer_0__.w0"
@@ -23,14 +46,32 @@ parameters {
initial_strategy: 0
initial_smart: true
}
+parameters {
+ name: "___prelu_layer_1__.w0"
+ size: 300
+ initial_mean: 0.0
+ initial_std: 0.057735026919
+ initial_strategy: 0
+ initial_smart: true
+}
+parameters {
+ name: "___prelu_layer_2__.w0"
+ size: 60
+ initial_mean: 0.0
+ initial_std: 0.129099444874
+ initial_strategy: 0
+ initial_smart: true
+}
input_layer_names: "input"
-output_layer_names: "__prelu_layer_0__"
+output_layer_names: "__prelu_layer_2__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__prelu_layer_0__"
+ layer_names: "__prelu_layer_1__"
+ layer_names: "__prelu_layer_2__"
input_layer_names: "input"
- output_layer_names: "__prelu_layer_0__"
+ output_layer_names: "__prelu_layer_2__"
is_recurrent_layer_group: false
}
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr
index 8133aa9c8d3e7c6843d1b27b70e87d394a1e0e47..046037936a6d85f54095c65f206e468aa69065d7 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr
@@ -16,6 +16,9 @@ layers {
inputs {
input_layer_name: "data"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_1__"
@@ -28,6 +31,9 @@ layers {
inputs {
input_layer_name: "__addto_0__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_2__"
@@ -40,6 +46,9 @@ layers {
inputs {
input_layer_name: "__addto_1__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_3__"
@@ -52,6 +61,9 @@ layers {
inputs {
input_layer_name: "__addto_2__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_4__"
@@ -64,6 +76,9 @@ layers {
inputs {
input_layer_name: "__addto_3__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_5__"
@@ -76,6 +91,9 @@ layers {
inputs {
input_layer_name: "__addto_4__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_6__"
@@ -88,6 +106,9 @@ layers {
inputs {
input_layer_name: "__addto_5__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_7__"
@@ -100,6 +121,9 @@ layers {
inputs {
input_layer_name: "__addto_6__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_8__"
@@ -112,6 +136,9 @@ layers {
inputs {
input_layer_name: "__addto_7__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_9__"
@@ -124,6 +151,9 @@ layers {
inputs {
input_layer_name: "__addto_8__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_10__"
@@ -136,6 +166,9 @@ layers {
inputs {
input_layer_name: "__addto_9__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_11__"
@@ -148,6 +181,9 @@ layers {
inputs {
input_layer_name: "__addto_10__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_12__"
@@ -160,6 +196,9 @@ layers {
inputs {
input_layer_name: "__addto_11__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_13__"
@@ -172,6 +211,9 @@ layers {
inputs {
input_layer_name: "__addto_12__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_14__"
@@ -184,6 +226,9 @@ layers {
inputs {
input_layer_name: "__addto_13__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_15__"
@@ -196,6 +241,9 @@ layers {
inputs {
input_layer_name: "__addto_14__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_16__"
@@ -208,6 +256,9 @@ layers {
inputs {
input_layer_name: "__addto_15__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_17__"
@@ -220,6 +271,9 @@ layers {
inputs {
input_layer_name: "__addto_16__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_18__"
@@ -232,6 +286,9 @@ layers {
inputs {
input_layer_name: "__addto_17__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_19__"
@@ -244,6 +301,9 @@ layers {
inputs {
input_layer_name: "__addto_18__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_20__"
@@ -256,6 +316,9 @@ layers {
inputs {
input_layer_name: "__addto_19__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_21__"
@@ -268,6 +331,9 @@ layers {
inputs {
input_layer_name: "__addto_20__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_22__"
@@ -280,6 +346,9 @@ layers {
inputs {
input_layer_name: "__addto_21__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_23__"
@@ -292,6 +361,9 @@ layers {
inputs {
input_layer_name: "__addto_22__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_24__"
@@ -304,6 +376,9 @@ layers {
inputs {
input_layer_name: "__addto_23__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_25__"
@@ -316,6 +391,9 @@ layers {
inputs {
input_layer_name: "__addto_24__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_26__"
@@ -328,6 +406,9 @@ layers {
inputs {
input_layer_name: "__addto_25__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_27__"
@@ -340,6 +421,9 @@ layers {
inputs {
input_layer_name: "__addto_26__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_28__"
@@ -352,6 +436,9 @@ layers {
inputs {
input_layer_name: "__addto_27__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_29__"
@@ -364,6 +451,9 @@ layers {
inputs {
input_layer_name: "__addto_28__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_30__"
@@ -376,6 +466,9 @@ layers {
inputs {
input_layer_name: "__addto_29__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__addto_31__"
@@ -388,6 +481,9 @@ layers {
inputs {
input_layer_name: "__addto_30__"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__fc_layer_0__"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr
index d0ad388165007b8f96f059e5b003c52f756383e5..7a2f3eab38808a031c27cf7ab9d6273952e389eb 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr
@@ -22,6 +22,9 @@ layers {
inputs {
input_layer_name: "b"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__concat_0__"
@@ -34,6 +37,9 @@ layers {
inputs {
input_layer_name: "b"
}
+ height: 0
+ width: 0
+ depth: 1
}
layers {
name: "__concat_1__"
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
new file mode 100644
index 0000000000000000000000000000000000000000..a991b22252ba10eed895efd931108c2d8b0e52f1
--- /dev/null
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py
@@ -0,0 +1,11 @@
+from paddle.trainer_config_helpers import *
+
+settings(batch_size=1000, learning_rate=1e-4)
+
+#data = data_layer(name='data', size=180, width=30, height=6)
+#batchNorm = batch_norm_layer(data, num_channels=1)
+#outputs(batchNorm)
+
+data3D = data_layer(name='data3D', size=120 * 3, width=20, height=6, depth=3)
+batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True)
+outputs(batchNorm3D)
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
index 2e3057f323db22ffc3911cce30ec2e8bb95e3dbe..aae90fab32db78a70c2169ed8fafb930433f4136 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
@@ -2,5 +2,7 @@ from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300)
prelu = prelu_layer(input=data)
+prelu = prelu_layer(input=data, partial_sum=1)
+prelu = prelu_layer(input=data, partial_sum=5)
outputs(prelu)
diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py
index 7589cc9917f26375d595e200245d5ba099bc38d7..e66bf67d7949057486eb54c46f39128fad5dae55 100644
--- a/python/paddle/v2/event.py
+++ b/python/paddle/v2/event.py
@@ -53,10 +53,13 @@ class BeginPass(object):
class EndPass(WithMetric):
"""
Event On One Pass Training Complete.
+ To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')"
+ in your event_handler call back
"""
- def __init__(self, pass_id, evaluator):
+ def __init__(self, pass_id, evaluator, gm):
self.pass_id = pass_id
+ self.gm = gm
WithMetric.__init__(self, evaluator)
@@ -73,10 +76,13 @@ class BeginIteration(object):
class EndIteration(WithMetric):
"""
Event On One Batch Training Complete.
+ To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')"
+ in your event_handler call back
"""
- def __init__(self, pass_id, batch_id, cost, evaluator):
+ def __init__(self, pass_id, batch_id, cost, evaluator, gm):
self.pass_id = pass_id
self.batch_id = batch_id
self.cost = cost
+ self.gm = gm
WithMetric.__init__(self, evaluator)
diff --git a/python/paddle/v2/framework/op.py b/python/paddle/v2/framework/op.py
index c1585bcffcceb75292853018179066c9f614261e..9e665adad2d3ad91d183c6815fbd7135ac4e8965 100644
--- a/python/paddle/v2/framework/op.py
+++ b/python/paddle/v2/framework/op.py
@@ -43,7 +43,6 @@ class OpDescCreationMethod(object):
if len(args) != 0:
raise ValueError("Only keyword arguments are supported.")
op_desc = framework_pb2.OpDesc()
-
for input_parameter in self.__op_proto__.inputs:
input_arguments = kwargs.get(input_parameter.name, [])
if is_str(input_arguments):
@@ -142,8 +141,8 @@ def create_op_creation_method(op_proto):
return OpInfo(
method=__impl__,
name=op_proto.type,
- inputs=[var.name for var in op_proto.inputs],
- outputs=[var.name for var in op_proto.outputs],
+ inputs=[(var.name, var.duplicable) for var in op_proto.inputs],
+ outputs=[(var.name, var.duplicable) for var in op_proto.outputs],
attrs=[attr.name for attr in op_proto.attrs])
@@ -180,9 +179,15 @@ class OperatorFactory(object):
return self.op_methods.get(t)
def get_op_input_names(self, type):
+ return map(lambda x: x[0], self.get_op_info(type).inputs)
+
+ def get_op_inputs(self, type):
return self.get_op_info(type).inputs
def get_op_output_names(self, type):
+ return map(lambda x: x[0], self.get_op_info(type).outputs)
+
+ def get_op_outputs(self, type):
return self.get_op_info(type).outputs
def get_op_attr_names(self, type):
diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt
index ef910f939be0b9d3cb5e6d49e69a00daa191b1c6..6b22c0008210b492d00dee42e967ca14d0948b20 100644
--- a/python/paddle/v2/framework/tests/CMakeLists.txt
+++ b/python/paddle/v2/framework/tests/CMakeLists.txt
@@ -19,8 +19,6 @@ py_test(test_scatter_op SRCS test_scatter_op.py)
py_test(test_fill_zeros_like_op SRCS test_fill_zeros_like_op.py)
py_test(test_top_k_op SRCS test_top_k_op.py)
-py_test(gradient_checker SRCS gradient_checker.py)
-
py_test(test_rowwise_add_op SRCS test_rowwise_add_op.py)
py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
@@ -33,5 +31,8 @@ py_test(test_sgd_op SRCS test_sgd_op.py)
py_test(test_gradient_checker SRCS test_gradient_checker.py)
py_test(test_lookup_table SRCS test_lookup_table.py)
py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)
+py_test(test_sum_op SRCS test_sum_op.py)
py_test(mnist SRCS mnist.py)
+py_test(test_concat_op SRCS test_concat_op.py)
py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
+py_test(test_reshape_op SRCS test_reshape_op.py)
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
deleted file mode 100644
index fdb06b7988935ebbe53f72f4eba89d75ac2502d4..0000000000000000000000000000000000000000
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ /dev/null
@@ -1,311 +0,0 @@
-import unittest
-
-import numpy
-import itertools
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-
-__all__ = ['get_numeric_gradient']
-
-
-def create_op(op_type):
- # TODO need to set attrs
- kwargs = dict()
- for in_name in Operator.get_op_input_names(op_type):
- kwargs[in_name] = in_name
- for out_name in Operator.get_op_output_names(op_type):
- kwargs[out_name] = out_name
-
- return Operator(op_type, **kwargs)
-
-
-def grad_var_name(var_name):
- return var_name + "@GRAD"
-
-
-def empty_var_name():
- return "@EMPTY@"
-
-
-def get_numeric_gradient(op,
- input_values,
- output_name,
- input_to_check,
- delta=0.005,
- local_scope=None,
- in_place=False):
- """
- Get Numeric Gradient for an operator's input.
-
- :param op: C++ operator instance, could be an network
- :param input_values: The input variables. Should be an dictionary, key is
- variable name. Value is numpy array.
- :param output_name: The final output variable name.
- :param input_to_check: The input variable need to get gradient.
- :param delta: The perturbation value for numeric gradient method. The
- smaller delta is, the more accurate result will get. But if that delta is
- too small, it could occur numerical stability problem.
- :param local_scope: The local scope used for get_numeric_gradient.
- :return: The gradient array in numpy format.
- """
- if local_scope is None:
- local_scope = core.Scope()
-
- # Create all input variable in local_scope
- for var_name in input_values:
- var = local_scope.new_var(var_name)
- tensor = var.get_tensor()
- tensor.set_dims(input_values[var_name].shape)
- tensor.alloc_float(core.CPUPlace())
- tensor.set(input_values[var_name], core.CPUPlace())
-
- # Create all output variable in local_scope
- opts = op.outputs()
- for key in opts:
- for output in opts[key]:
- if local_scope.find_var(output) is None:
- local_scope.new_var(output).get_tensor()
- op.infer_shape(local_scope)
-
- # allocate output memory
- for key in opts:
- for output in opts[key]:
- local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace(
- ))
-
- cpu_ctx = core.DeviceContext.create(core.CPUPlace())
-
- def get_output():
- op.run(local_scope, cpu_ctx)
- return numpy.array(local_scope.find_var(output_name).get_tensor()).sum()
-
- def product(dim):
- return reduce(lambda a, b: a * b, dim, 1)
-
- def restore_inputs():
- for var_name in input_values:
- tensor_ = local_scope.find_var(var_name).get_tensor()
- tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace())
-
- # get the input tensor that we want to get it's numeric gradient.
- tensor_to_check = local_scope.find_var(input_to_check).get_tensor()
- tensor_size = product(tensor_to_check.get_dims())
- # prepare a numpy array to store the gradient.
- gradient_flat = numpy.zeros(shape=(tensor_size, ), dtype='float32')
-
- # we only compute gradient of one element each time.
- # we use a for loop to compute the gradient of every element.
- for i in xrange(tensor_size):
- if in_place:
- restore_inputs()
- # get one input element throw it's index i.
- origin = tensor_to_check.get_float_element(i)
-
- # add delta to it, run op and then get the sum of the result tensor.
- x_pos = origin + delta
- tensor_to_check.set_float_element(i, x_pos)
- y_pos = get_output()
-
- # plus delta to this element, run op and get the sum of the result tensor.
- if in_place:
- restore_inputs()
- x_neg = origin - delta
- tensor_to_check.set_float_element(i, x_neg)
- y_neg = get_output()
-
- # restore old value
- tensor_to_check.set_float_element(i, origin)
-
- # compute the gradient of this element and store it into a numpy array.
- gradient_flat[i] = (y_pos - y_neg) / delta / 2
-
- # reshape the gradient result to the shape of the source tensor.
- return gradient_flat.reshape(tensor_to_check.get_dims())
-
-
-class GradientChecker(unittest.TestCase):
- def __get_gradient(self, forward_op, backward_op, input_value, grad_names,
- place):
- """Get the input gradients after running forward and backward operators
- on the given places.
-
- :param forward_op: forward operator
- :type forward_op: Operator
- :param backward_op: backward operator
- :type backward_op: Operator
- :param input_value: input values.
- :type input_value: dict{string:numpy.array}
- :param grad_names: the names of returned input gradients.
- :type input_value: a list of string
- :param place: the device type.
- :type place: CPUPlace or GPUPlace
- :return: the input grdients of given grad_names.
- :rtype: a list of numpy.array
- """
- scope = core.Scope()
- ctx = core.DeviceContext.create(place)
-
- inputs = forward_op.inputs()
- in_names = [item for k in inputs for item in inputs[k]]
- outputs = forward_op.outputs()
- out_names = [item for k in outputs for item in outputs[k]]
-
- # create input var and set value
- for name, value in input_value.iteritems():
- if name not in in_names:
- raise ValueError(name + "does not exist in Op's inputs.")
- var = scope.new_var(name).get_tensor()
- var.set_dims(value.shape)
- var.set(value, place)
-
- # run forward op
- for out_name in out_names:
- scope.new_var(out_name)
- forward_op.infer_shape(scope)
- forward_op.run(scope, ctx)
-
- # set output var's shape
- # set output grad to ones
- for name in out_names:
- out_tensor = scope.find_var(name).get_tensor()
- grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
- grad_tensor.set_dims(out_tensor.shape())
- data = numpy.ones(out_tensor.shape(), dtype=numpy.float32)
- grad_tensor.set(data, place)
-
- # run backward op
- backward_outs = backward_op.outputs()
- backward_names = [
- item for key in backward_outs for item in backward_outs[key]
- ]
- for name in backward_names:
- scope.new_var(name)
-
- backward_op.infer_shape(scope)
- backward_op.run(scope, ctx)
-
- outs = [
- numpy.array(scope.find_var(name).get_tensor())
- for name in grad_names
- ]
- return outs
-
- def compare_grad(self, forward_op, input_value, no_grad_set=None):
- """ Compare the input gradients between CPU and GPU for the given forward
- operator.
-
- :param forward_op: forward operator
- :type forward_op: Operator
- :param input_value: input values.
- :type input_value: dict{string:numpy.array}
- :param no_grad_set: the set of variables names without gradients.
- :type no_grad_set: a set of string
- :raises: AssertionError, there is different gradient value.
- """
- if no_grad_set is None:
- no_grad_set = set()
- backward_op = core.Operator.backward(forward_op, no_grad_set)
- # return if not compile with GPU or not implementing GPU kernel
- if not (core.is_compile_gpu() and backward_op.support_gpu()):
- return
-
- outputs = backward_op.outputs()
- out_names = [item for k in outputs for item in outputs[k]]
- out_names = filter(lambda x: x != empty_var_name(), out_names)
- cpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
- out_names, core.CPUPlace())
- gpu_grads = self.__get_gradient(forward_op, backward_op, input_value,
- out_names, core.GPUPlace(0))
-
- for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads,
- out_names):
- self.assertTrue(
- numpy.allclose(
- c_grad, g_grad, atol=1e-4),
- "output name: " + name + " has diff")
-
- def __assert_is_close(self, numeric_grads, analytic_grads, names,
- max_relative_error, msg_prefix):
- """Use relative error for the comparison.
-
- :param numeric_grads: the numerical graidents.
- :type numeric_grads: a list of numpy.array
- :param analytic_grads: the analytical graidents.
- :type analytic_grads: a list of numpy.array
- :param name: the names of gradients, used to print for debug.
- :type names: a list of string
- :param msg_prefix: string info, used to print for debug.
- :type msf_prefix: string
- """
- for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
- abs_a = numpy.abs(a)
- # if abs_a is nearly zero, then use abs error for a, not relative
- # error.
- abs_a[abs_a < 1e-3] = 1
-
- diff_mat = numpy.abs(a - b) / abs_a
- max_diff = numpy.max(diff_mat)
-
- def err_msg():
- offset = numpy.argmax(diff_mat > max_relative_error)
- return "%s Variable %s max gradient diff %f over limit %f, the first " \
- "error element is %d" % (
- msg_prefix, name, max_diff, max_relative_error, offset)
-
- self.assertLessEqual(max_diff, max_relative_error, err_msg())
-
- def check_grad(self,
- forward_op,
- input_vars,
- inputs_to_check,
- output_name,
- no_grad_set=None,
- only_cpu=False,
- in_place=False,
- max_relative_error=0.005):
- """
- :param forward_op: used to create backward_op
- :param input_vars: numpy value of input variable. The following
- computation will use these variables.
- :param inputs_to_check: inputs var names that should check gradient.
- :param output_name: the output variable name of forward network.
- :param max_relative_error: The relative tolerance parameter.
- :param no_grad_set: used when create backward ops
- :param only_cpu: only compute and check gradient on cpu kernel.
- :return:
- """
- if no_grad_set is None:
- no_grad_set = set()
-
- no_tmp_out = forward_op.no_intermediate_outputs()
- if len(no_tmp_out) != 1:
- raise ValueError("non temp out_names should be 1")
-
- inputs = forward_op.inputs()
- in_names = [item for k in inputs for item in inputs[k]]
- for no_grad in no_grad_set:
- if no_grad not in in_names:
- raise ValueError("no_grad should be in in_names")
- if no_grad in inputs_to_check:
- raise ValueError("no_grad should not be in inputs_to_check")
-
- backward_op = core.Operator.backward(forward_op, no_grad_set)
-
- places = [core.CPUPlace()]
- if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu():
- places.append(core.GPUPlace(0))
-
- # get numerical gradients
- numeric_grads = [
- get_numeric_gradient(
- forward_op, input_vars, output_name, name, in_place=in_place)
- for name in inputs_to_check
- ]
-
- check_names = [grad_var_name(name) for name in inputs_to_check]
- for place in places:
- analytic_grads = self.__get_gradient(forward_op, backward_op,
- input_vars, check_names, place)
- self.__assert_is_close(numeric_grads, analytic_grads, check_names,
- max_relative_error,
- "Gradient Check On %s" % str(place))
diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fec4c9109bf247abb2068177583acb47a8ebd97
--- /dev/null
+++ b/python/paddle/v2/framework/tests/op_test.py
@@ -0,0 +1,281 @@
+import unittest
+import numpy as np
+import itertools
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+
+
+def grad_var_name(var_name):
+ return var_name + "@GRAD"
+
+
+def create_op(scope, op_type, inputs, outputs, attrs):
+ kwargs = dict()
+
+ for in_name, in_dup in Operator.get_op_inputs(op_type):
+ if in_name in inputs:
+ kwargs[in_name] = []
+ if in_dup:
+ sub_in = inputs[in_name]
+ for sub_in_name, _ in sub_in:
+ var = scope.new_var(sub_in_name)
+ kwargs[in_name].append(sub_in_name)
+ else:
+ var = scope.new_var(in_name)
+ kwargs[in_name].append(in_name)
+
+ for out_name, out_dup in Operator.get_op_outputs(op_type):
+ if out_name in outputs:
+ kwargs[out_name] = []
+ if out_dup:
+ sub_in = outputs[out_name]
+ for sub_in_name, _ in sub_in:
+ var = scope.new_var(sub_in_name)
+ kwargs[out_name].append(sub_in_name)
+ else:
+ var = scope.new_var(out_name)
+ kwargs[out_name].append(out_name)
+
+ for attr_name in Operator.get_op_attr_names(op_type):
+ if attr_name in attrs:
+ kwargs[attr_name] = attrs[attr_name]
+ return Operator(op_type, **kwargs)
+
+
+def set_input(scope, op, inputs, place):
+ for in_name, in_dup in Operator.get_op_inputs(op.type()):
+ if in_name in inputs:
+ if in_dup:
+ sub_in = inputs[in_name]
+ for sub_in_name, sub_in_array in sub_in:
+ var = scope.find_var(sub_in_name)
+ tensor = var.get_tensor()
+ tensor.set_dims(sub_in_array.shape)
+ tensor.set(sub_in_array, place)
+ else:
+ var = scope.find_var(in_name)
+ tensor = var.get_tensor()
+ arr = inputs[in_name]
+ tensor.set_dims(arr.shape)
+ tensor.set(arr, place)
+
+
+def set_output_grad(scope, op, outputs, place):
+ for out_name, out_dup in Operator.get_op_outputs(op.type()):
+ if out_name in outputs:
+ if out_dup:
+ sub_out = outputs[out_name]
+ for sub_out_name, _ in sub_out:
+ out_tensor = scope.find_var(sub_out_name).get_tensor()
+ grad_tensor = scope.new_var(grad_var_name(
+ sub_out_name)).get_tensor()
+ grad_tensor.set_dims(out_tensor.shape())
+ data = np.ones(out_tensor.shape(), dtype=np.float32)
+ grad_tensor.set(data, place)
+ else:
+ out_tensor = scope.find_var(out_name).get_tensor()
+ grad_tensor = scope.new_var(grad_var_name(out_name)).get_tensor(
+ )
+ grad_tensor.set_dims(out_tensor.shape())
+ data = np.ones(out_tensor.shape(), dtype=np.float32)
+ grad_tensor.set(data, place)
+
+
+def get_numeric_gradient(scope,
+ op,
+ inputs,
+ input_to_check,
+ output_name,
+ delta=0.005,
+ in_place=False):
+
+ set_input(scope, op, inputs, core.CPUPlace())
+ op.infer_shape(scope)
+
+ tensor_to_check = scope.find_var(input_to_check).get_tensor()
+
+ def product(dim):
+ return reduce(lambda a, b: a * b, dim, 1)
+
+ ctx = core.DeviceContext.create(core.CPUPlace())
+
+ def get_output():
+ op.run(scope, ctx)
+ return np.array(scope.find_var(output_name).get_tensor()).sum()
+
+ tensor_to_check = scope.find_var(input_to_check).get_tensor()
+ tensor_size = product(tensor_to_check.get_dims())
+ gradient_flat = np.zeros(shape=(tensor_size, ), dtype='float32')
+ # we only compute gradient of one element each time.
+ # we use a for loop to compute the gradient of every element.
+ for i in xrange(tensor_size):
+ if in_place:
+ set_input(scope, op, inputs, core.CPUPlace())
+
+ # get one input element throw it's index i.
+ origin = tensor_to_check.get_float_element(i)
+ # add delta to it, run op and then get the sum of the result tensor.
+ x_pos = origin + delta
+ tensor_to_check.set_float_element(i, x_pos)
+ y_pos = get_output()
+
+ if in_place:
+ set_input(scope, op, inputs, core.CPUPlace())
+
+ x_neg = origin - delta
+ tensor_to_check.set_float_element(i, x_neg)
+ y_neg = get_output()
+
+ tensor_to_check.set_float_element(i, origin)
+ gradient_flat[i] = (y_pos - y_neg) / delta / 2
+
+ return gradient_flat.reshape(tensor_to_check.get_dims())
+
+
+def get_backward_op(scope, op, no_grad_set):
+ backward_op = core.Operator.backward(op, no_grad_set)
+ for input in backward_op.input_vars():
+ var = scope.new_var(input)
+ var.get_tensor()
+ for output in backward_op.output_vars():
+ var = scope.new_var(output)
+ var.get_tensor()
+ return backward_op
+
+
+def get_gradient(scope, op, inputs, outputs, grad_name, place,
+ no_grad_set=None):
+ ctx = core.DeviceContext.create(place)
+
+ set_input(scope, op, inputs, place)
+
+ op.infer_shape(scope)
+ op.run(scope, ctx)
+
+ if no_grad_set is None:
+ no_grad_set = set()
+
+ backward_op = get_backward_op(scope, op, no_grad_set)
+ set_output_grad(scope, op, outputs, place)
+
+ backward_op.infer_shape(scope)
+ backward_op.run(scope, ctx)
+
+ out = np.array(scope.find_var(grad_name).get_tensor())
+ return out
+
+
+class OpTest(unittest.TestCase):
+ def check_output_with_place(self, place):
+ self.scope = core.Scope()
+ op_inputs = self.inputs if hasattr(self, "inputs") else dict()
+ op_attrs = self.attrs if hasattr(self, "attrs") else dict()
+ self.op = create_op(self.scope, self.op_type, op_inputs, self.outputs,
+ op_attrs)
+ if isinstance(place, core.GPUPlace) and not self.op.support_gpu():
+ return
+ set_input(self.scope, self.op, self.inputs, place)
+ self.op.infer_shape(self.scope)
+ ctx = core.DeviceContext.create(place)
+ self.op.run(self.scope, ctx)
+
+ for out_name, out_dup in Operator.get_op_outputs(self.op.type()):
+ if out_dup:
+ sub_out = self.outputs[out_name]
+ for sub_out_name in sub_out:
+ actual = np.array(
+ self.scope.find_var(sub_out_name).get_tensor())
+ expect = sub_out[sub_out_name]
+ self.assertTrue(
+ np.allclose(
+ actual, expect, atol=1e-05),
+ "output name: " + out_name + "has diff")
+ else:
+ actual = np.array(self.scope.find_var(out_name).get_tensor())
+ expect = self.outputs[out_name]
+ self.assertTrue(
+ np.allclose(
+ actual, expect, atol=1e-05),
+ "output name: " + out_name + "has diff")
+
+ def check_output(self):
+ places = [core.CPUPlace()]
+ if core.is_compile_gpu():
+ places.append(core.GPUPlace(0))
+ for place in places:
+ self.check_output_with_place(place)
+
+ def __assert_is_close(self, numeric_grads, analytic_grads, names,
+ max_relative_error, msg_prefix):
+
+ for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
+ abs_a = np.abs(a)
+ abs_a[abs_a < 1e-3] = 1
+
+ diff_mat = np.abs(a - b) / abs_a
+ max_diff = np.max(diff_mat)
+
+ def err_msg():
+ offset = np.argmax(diff_mat > max_relative_error)
+ return "%s Variable %s max gradient diff %f over limit %f, the first " \
+ "error element is %d" % (
+ msg_prefix, name, max_diff, max_relative_error, offset)
+
+ self.assertLessEqual(max_diff, max_relative_error, err_msg())
+
+ def check_grad(self,
+ inputs_to_check,
+ output_name,
+ no_grad_set=None,
+ in_place=False,
+ max_relative_error=0.005):
+ self.scope = core.Scope()
+ op_inputs = self.inputs if hasattr(self, "inputs") else dict()
+ op_attrs = self.attrs if hasattr(self, "attrs") else dict()
+ self.op = create_op(self.scope, self.op_type, op_inputs, self.outputs,
+ op_attrs)
+ if no_grad_set is None:
+ no_grad_set = set()
+
+ numeric_grads = [
+ get_numeric_gradient(
+ self.scope,
+ self.op,
+ self.inputs,
+ input_to_check,
+ output_name,
+ in_place=in_place) for input_to_check in inputs_to_check
+ ]
+ grad_names = [
+ grad_var_name(input_to_check) for input_to_check in inputs_to_check
+ ]
+
+ cpu_place = core.CPUPlace()
+ cpu_analytic_grads = [
+ get_gradient(self.scope, self.op, self.inputs, self.outputs,
+ grad_name, cpu_place, no_grad_set)
+ for grad_name in grad_names
+ ]
+
+ self.__assert_is_close(numeric_grads, cpu_analytic_grads, grad_names,
+ max_relative_error,
+ "Gradient Check On %s" % str(cpu_place))
+
+ if core.is_compile_gpu() and self.op.support_gpu():
+ gpu_place = core.GPUPlace(0)
+ gpu_analytic_grads = [
+ get_gradient(self.scope, self.op, self.inputs, self.outputs,
+ grad_name, gpu_place, no_grad_set)
+ for grad_name in grad_names
+ ]
+
+ self.__assert_is_close(numeric_grads, gpu_analytic_grads,
+ grad_names, max_relative_error,
+ "Gradient Check On %s" % str(gpu_place))
+
+ for c_grad, g_grad, name in itertools.izip(
+ cpu_analytic_grads, gpu_analytic_grads, grad_names):
+ self.assertTrue(
+ np.allclose(
+ c_grad, g_grad, atol=1e-4),
+ "output name: " + name + " has diff")
diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py
deleted file mode 100644
index 370f27eaf658dadbf7e82262c118140a10d15c41..0000000000000000000000000000000000000000
--- a/python/paddle/v2/framework/tests/op_test_util.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-
-
-class OpTestMeta(type):
- """
- Operator Test ClassMeta.
-
- It injects `test_all` method into user's OperatorTest class, to make Python
- unittest module run that method.
-
- The `test_all` read what value is stored in `self`. It use self's values to
- create and run a operator, and check whether that op is OK or not.
-
- See `test_add_two_op` for example usage.
- """
-
- def __new__(cls, name, bases, attrs):
- obj = super(OpTestMeta, cls).__new__(cls, name, bases, attrs)
-
- def test_all(self):
- scope = core.Scope()
- kwargs = dict()
- places = [core.CPUPlace()]
- if core.is_compile_gpu():
- places.append(core.GPUPlace(0))
-
- for place in places:
- for in_name in Operator.get_op_input_names(self.type):
- if hasattr(self, "inputs") and in_name in self.inputs:
- kwargs[in_name] = in_name
- var = scope.new_var(in_name).get_tensor()
- arr = self.inputs[in_name]
- var.set_dims(arr.shape)
- var.set(arr, place)
- else:
- kwargs[in_name] = "@EMPTY@"
-
- for out_name in Operator.get_op_output_names(self.type):
- if not hasattr(self, "outputs"):
- raise ValueError(
- "The test op must set self.outputs dict.")
- if out_name not in self.outputs:
- raise ValueError("The %s is not in self.outputs dict." %
- (out_name))
- kwargs[out_name] = out_name
- scope.new_var(out_name).get_tensor()
-
- for attr_name in Operator.get_op_attr_names(self.type):
- if hasattr(self, "attrs") and attr_name in self.attrs:
- kwargs[attr_name] = self.attrs[attr_name]
-
- op = Operator(self.type, **kwargs)
- if isinstance(place, core.GPUPlace) and not op.support_gpu():
- return
-
- op.infer_shape(scope)
-
- ctx = core.DeviceContext.create(place)
- op.run(scope, ctx)
-
- for out_name in Operator.get_op_output_names(self.type):
- actual = numpy.array(scope.find_var(out_name).get_tensor())
- expect = self.outputs[out_name]
- self.assertTrue(
- numpy.allclose(
- actual, expect, atol=1e-05),
- "output name: " + out_name + " has diff")
-
- obj.test_all = test_all
- return obj
diff --git a/python/paddle/v2/framework/tests/test_add_two_op.py b/python/paddle/v2/framework/tests/test_add_two_op.py
index a578e74eca9a3c4327a4881f853028e2347c98ad..3ca34d9b9fc2b7b54cc25ca0e0d1a08a71e37c52 100644
--- a/python/paddle/v2/framework/tests/test_add_two_op.py
+++ b/python/paddle/v2/framework/tests/test_add_two_op.py
@@ -1,23 +1,20 @@
import unittest
+import numpy as np
+from op_test import OpTest
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-
-from op_test_util import OpTestMeta
-
-
-class TestAddOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
+class TestAddOp(OpTest):
def setUp(self):
- self.type = "add"
+ self.op_type = "add"
self.inputs = {
- 'X': numpy.random.random((102, 105)).astype("float32"),
- 'Y': numpy.random.random((102, 105)).astype("float32")
+ 'X': np.random.random((102, 105)).astype("float32"),
+ 'Y': np.random.random((102, 105)).astype("float32")
}
self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
+ def test_check_output(self):
+ self.check_output()
+
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_concat_op.py b/python/paddle/v2/framework/tests/test_concat_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..656563f96e52df30951ec0ec7042ad9c530e90b2
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_concat_op.py
@@ -0,0 +1,22 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestConcatOp(OpTest):
+ def setUp(self):
+ self.op_type = "concat"
+ x0 = np.random.random((2, 3, 2, 5)).astype('float32')
+ x1 = np.random.random((2, 3, 3, 5)).astype('float32')
+ x2 = np.random.random((2, 3, 4, 5)).astype('float32')
+ axis = 2
+ self.inputs = {'X': [('x0', x0), ('x1', x1), ('x2', x2)]}
+ self.attrs = {'axis': axis}
+ self.outputs = {'Out': np.concatenate((x0, x1, x2), axis=axis)}
+
+ def test_check_output(self):
+ self.check_output()
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_cos_sim_op.py b/python/paddle/v2/framework/tests/test_cos_sim_op.py
index 32013a7999a4be42e5974b9ac751d5d911730994..797cbd8cc5cf7f73d58ca713d02667731d5c8a0e 100644
--- a/python/paddle/v2/framework/tests/test_cos_sim_op.py
+++ b/python/paddle/v2/framework/tests/test_cos_sim_op.py
@@ -1,17 +1,14 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
-class TestCosSimOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestCosSimOp(OpTest):
def setUp(self):
- self.type = "cos_sim"
+ self.op_type = "cos_sim"
self.inputs = {
- 'X': np.random.random((32, 64)).astype("float32"),
- 'Y': np.random.random((32, 64)).astype("float32")
+ 'X': np.random.random((10, 5)).astype("float32"),
+ 'Y': np.random.random((10, 5)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
@@ -23,38 +20,20 @@ class TestCosSimOp(unittest.TestCase):
'Out': np.expand_dims(expect_out, 1)
}
+ def test_check_output(self):
+ self.check_output()
-class TestCosSimGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("cos_sim")
- self.inputs = {
- 'X': np.random.random((10, 5)).astype("float32"),
- 'Y': np.random.random((10, 5)).astype("float32")
- }
-
- def test_cpu_gpu_compare(self):
- self.compare_grad(self.op, self.inputs)
-
- def test_normal(self):
- self.check_grad(
- self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.05)
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05)
- def test_ignore_x(self):
+ def test_check_grad_ingore_x(self):
self.check_grad(
- self.op,
- self.inputs, ["Y"],
- "Out",
- max_relative_error=0.05,
- no_grad_set={"X"})
+ ['Y'], 'Out', max_relative_error=0.05, no_grad_set=set('X'))
- def test_ignore_y(self):
+ def test_check_grad_ignore_y(self):
self.check_grad(
- self.op,
- self.inputs, ["X"],
- "Out",
- max_relative_error=0.05,
- no_grad_set={"Y"})
+ ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
index d4277f2a42ce2e66e37405ccd3b2ee444d403d1a..c2fc102a8b8de82da5c3fc5fee273790325908f8 100644
--- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py
+++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py
@@ -1,36 +1,27 @@
import unittest
import numpy
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
-class TestCrossEntropy(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestCrossEntropy(OpTest):
def setUp(self):
- self.type = "onehot_cross_entropy"
+ self.op_type = "onehot_cross_entropy"
batch_size = 30
class_num = 10
- X = numpy.random.random((batch_size, class_num)).astype("float32")
- label = 5 * numpy.ones(batch_size).astype("int32")
+ X = numpy.random.uniform(0.1, 1.0,
+ [batch_size, class_num]).astype("float32")
+ label = (class_num / 2) * numpy.ones(batch_size).astype("int32")
self.inputs = {'X': X, 'label': label}
Y = []
for i in range(0, batch_size):
Y.append(-numpy.log(X[i][label[i]]))
self.outputs = {'Y': numpy.array(Y).astype("float32")}
+ def test_check_output(self):
+ self.check_output()
-class CrossEntropyGradOpTest(GradientChecker):
def test_check_grad(self):
- op = create_op("onehot_cross_entropy")
- batch_size = 30
- class_num = 10
- inputs = {
- "X": numpy.random.uniform(
- 0.1, 1.0, [batch_size, class_num]).astype("float32"),
- "label": (class_num / 2) * numpy.ones(batch_size).astype("int32")
- }
- self.check_grad(op, inputs, set("X"), "Y")
+ self.check_grad(['X'], 'Y')
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py b/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
index e5c862605fb11a5ea1426cf8f9054589dc377ff1..2473daaba24438819f3f55ccc40fe1c64ee59960 100644
--- a/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
+++ b/python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
@@ -1,16 +1,17 @@
import unittest
-from op_test_util import OpTestMeta
-import numpy
+import numpy as np
+from op_test import OpTest
-class TestFillZerosLikeOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestFillZerosLikeOp(OpTest):
def setUp(self):
- self.type = "fill_zeros_like"
- self.inputs = {'Src': numpy.random.random((219, 232)).astype("float32")}
- self.outputs = {'Dst': numpy.zeros_like(self.inputs['Src'])}
+ self.op_type = "fill_zeros_like"
+ self.inputs = {'Src': np.random.random((219, 232)).astype("float32")}
+ self.outputs = {'Dst': np.zeros_like(self.inputs["Src"])}
+
+ def test_check_output(self):
+ self.check_output()
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_gather_op.py b/python/paddle/v2/framework/tests/test_gather_op.py
index e3de3fd0a1dddb3edb0de5987bd71d8a176d97ef..b0ab429ef1b53640dfb696f6ea2f7b745564b874 100644
--- a/python/paddle/v2/framework/tests/test_gather_op.py
+++ b/python/paddle/v2/framework/tests/test_gather_op.py
@@ -1,30 +1,20 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
+import numpy as np
+from op_test import OpTest
-class TestGatherOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestGatherOp(OpTest):
def setUp(self):
- self.type = "gather"
- xnp = numpy.random.random((10, 20)).astype("float32")
- self.inputs = {
- 'X': xnp,
- 'Index': numpy.array([1, 3, 5]).astype("int32")
- }
- self.outputs = {'Out': self.inputs['X'][self.inputs['Index']]}
+ self.op_type = "gather"
+ xnp = np.random.random((10, 20)).astype("float32")
+ self.inputs = {'X': xnp, 'Index': np.array([1, 3, 5]).astype("int32")}
+ self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
+ def test_check_output(self):
+ self.check_output()
-class TestGatherGradOp(GradientChecker):
- def test_gather_grad(self):
- op = create_op("gather")
- xnp = numpy.random.random((10, 20)).astype("float32")
- inputs = {'X': xnp, 'Index': numpy.array([1, 3, 5]).astype("int32")}
- self.check_grad(op, inputs, set("X"), "Out")
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Out')
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_gaussian_random_op.py b/python/paddle/v2/framework/tests/test_gaussian_random_op.py
index f95ed70b58d611b3233a21d3f2a34c864ae4d1b3..1f9e4db783c9907a22db72c8a6ff06c7ca0735da 100644
--- a/python/paddle/v2/framework/tests/test_gaussian_random_op.py
+++ b/python/paddle/v2/framework/tests/test_gaussian_random_op.py
@@ -14,11 +14,11 @@ class GaussianRandomTest(unittest.TestCase):
def gaussian_random_test(self, place):
scope = core.Scope()
- scope.new_var("Out").get_tensor()
+ scope.new_var('Out').get_tensor()
op = Operator(
"gaussian_random",
- Out="Out",
+ Out='Out',
dims=[1000, 784],
mean=.0,
std=1.,
@@ -27,10 +27,10 @@ class GaussianRandomTest(unittest.TestCase):
op.infer_shape(scope)
context = core.DeviceContext.create(place)
op.run(scope, context)
- tensor = numpy.array(scope.find_var("Out").get_tensor())
+ tensor = numpy.array(scope.find_var('Out').get_tensor())
self.assertAlmostEqual(numpy.mean(tensor), .0, delta=0.1)
self.assertAlmostEqual(numpy.std(tensor), 1., delta=0.1)
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py
index e8a7f848dffa0529c8cb0d6599286ce0e228d180..abeb01cb34158a43b5dcce5e39efc0e21e9fe638 100644
--- a/python/paddle/v2/framework/tests/test_gradient_checker.py
+++ b/python/paddle/v2/framework/tests/test_gradient_checker.py
@@ -1,42 +1,44 @@
import unittest
-import numpy
-from paddle.v2.framework.op import Operator
-from gradient_checker import GradientChecker
-from gradient_checker import get_numeric_gradient
+import numpy as np
+import paddle.v2.framework.core as core
+from op_test import get_numeric_gradient
+from op_test import create_op
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
- add_op = Operator("add", X="X", Y="Y", Out="Z")
- x = numpy.random.random((10, 1)).astype("float32")
- y = numpy.random.random((10, 1)).astype("float32")
-
- arr = get_numeric_gradient(add_op, {"X": x, "Y": y}, "Z", "X")
+ x = np.random.random((10, 1)).astype("float32")
+ y = np.random.random((10, 1)).astype("float32")
+ z = x + y
+ scope = core.Scope()
+ add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict())
+ arr = get_numeric_gradient(scope, add_op, {'X': x, 'Y': y}, 'X', 'Out')
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
def test_softmax_op(self):
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
- shiftx = x - numpy.max(x)
- exps = numpy.exp(shiftx)
- return exps / numpy.sum(exps)
+ shiftx = x - np.max(x)
+ exps = np.exp(shiftx)
+ return exps / np.sum(exps)
def label_softmax_grad(Y, dY):
dX = Y * 0.0
for i in range(Y.shape[0]):
- d = numpy.dot(Y[i, :], dY[i, :])
+ d = np.dot(Y[i, :], dY[i, :])
dX[i, :] = Y[i, :] * (dY[i, :] - d)
return dX
- softmax_op = Operator("softmax", X="X", Y="Y")
-
- X = numpy.random.random((2, 2)).astype("float32")
- Y = numpy.apply_along_axis(stable_softmax, 1, X)
- dY = numpy.ones(Y.shape)
+ X = np.random.random((2, 2)).astype("float32")
+ Y = np.apply_along_axis(stable_softmax, 1, X)
+ dY = np.ones(Y.shape)
dX = label_softmax_grad(Y, dY)
- arr = get_numeric_gradient(softmax_op, {"X": X}, "Y", "X")
- numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
+ scope = core.Scope()
+ softmax_op = create_op(scope, "softmax", {"X": X}, {"Y": Y}, dict())
+
+ arr = get_numeric_gradient(scope, softmax_op, {"X": X}, "X", "Y")
+ np.testing.assert_almost_equal(arr, dX, decimal=1e-2)
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py
index 19eb464baa555fb67a994f3cfb4d3ed628367c73..b259bb67e832adcb31b0ab4e992738be2b85f884 100644
--- a/python/paddle/v2/framework/tests/test_lookup_table.py
+++ b/python/paddle/v2/framework/tests/test_lookup_table.py
@@ -1,31 +1,22 @@
import unittest
import numpy as np
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
-class TestSigmoidOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestLookupTableOp(OpTest):
def setUp(self):
- self.type = 'lookup_table'
- table = np.random.random((17, 31)).astype('float32')
- ids = np.random.randint(0, 17, 4).astype('int32')
+ self.op_type = "lookup_table"
+ table = np.random.random((17, 31)).astype("float32")
+ ids = np.random.randint(0, 17, 4).astype("int32")
self.inputs = {'W': table, 'Ids': ids}
self.outputs = {'Out': table[ids]}
+ def test_check_output(self):
+ self.check_output()
-class TestSigmoidGradOp(GradientChecker):
- def test_grad(self):
- op = create_op('lookup_table')
- table = np.random.random((17, 31)).astype('float32')
- ids = np.random.randint(0, 17, 4).astype('int32')
- inputs = {'W': table, 'Ids': ids}
- # comapre gradients
- self.compare_grad(op, inputs, set(['Ids']))
- # check gradients
- self.check_grad(op, inputs, set('W'), 'Out')
+ def test_check_grad(self):
+ self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_mean_op.py b/python/paddle/v2/framework/tests/test_mean_op.py
index f32b3160d651a290823223c46c45bb3b6950a505..7823abd8f813aad6462c98a9ace9a13dc286a157 100644
--- a/python/paddle/v2/framework/tests/test_mean_op.py
+++ b/python/paddle/v2/framework/tests/test_mean_op.py
@@ -1,24 +1,20 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
import numpy as np
+from op_test import OpTest
-class TestMeanOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestMeanOp(OpTest):
def setUp(self):
- self.type = "mean"
- self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
- self.outputs = {'Out': np.mean(self.inputs['X'])}
+ self.op_type = "mean"
+ self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
+ self.outputs = {'Out': np.mean(self.inputs["X"])}
+ def test_check_output(self):
+ self.check_output()
-class MeanGradOpTest(GradientChecker):
- def test_normal(self):
- op = create_op("mean")
- inputs = {"X": np.random.random((10, 10)).astype("float32")}
- self.check_grad(op, inputs, set("X"), "Out")
+ def test_checkout_grad(self):
+ self.check_grad(['X'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_minus_op.py b/python/paddle/v2/framework/tests/test_minus_op.py
index 5abdd4a69bf3faa2f3341f338e195815389a7cef..dea797a1fea34265d0a32e097f413f421abf2521 100644
--- a/python/paddle/v2/framework/tests/test_minus_op.py
+++ b/python/paddle/v2/framework/tests/test_minus_op.py
@@ -1,30 +1,23 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
-class MinusOpTest(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class MinusOpTest(OpTest):
def setUp(self):
- self.type = "minus"
+ self.op_type = "minus"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((32, 84)).astype("float32")
}
self.outputs = {'Out': (self.inputs['X'] - self.inputs['Y'])}
+ def test_check_output(self):
+ self.check_output()
-class MinusGradTest(GradientChecker):
- def test_left(self):
- op = create_op("minus")
- inputs = {
- "X": np.random.random((10, 10)).astype("float32"),
- "Y": np.random.random((10, 10)).astype("float32")
- }
- self.check_grad(op, inputs, ["X", 'Y'], "Out")
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py
index 8c827e242e866b267e0fc4b73c31bafa0ccc7c48..b3d95a56b88e510734da54f36ff21ccd7e1baabb 100644
--- a/python/paddle/v2/framework/tests/test_mul_op.py
+++ b/python/paddle/v2/framework/tests/test_mul_op.py
@@ -1,27 +1,35 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
-from paddle.v2.framework.op import Operator
+from op_test import OpTest
-class TestMulOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestMulOp(OpTest):
def setUp(self):
- self.type = "mul"
+ self.op_type = "mul"
self.inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
-class TestMulOp2(unittest.TestCase):
- __metaclass__ = OpTestMeta
+ def test_check_grad_ingore_x(self):
+ self.check_grad(
+ ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
+ def test_check_grad_ingore_y(self):
+ self.check_grad(
+ ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
+
+
+class TestMulOp2(OpTest):
def setUp(self):
- self.type = "mul"
+ self.op_type = "mul"
self.inputs = {
'X': np.random.random((15, 4, 12, 10)).astype("float32"),
'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32")
@@ -32,72 +40,20 @@ class TestMulOp2(unittest.TestCase):
self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9))
}
+ def test_check_output(self):
+ self.check_output()
-class TestMulGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("mul")
- self.inputs = {
- 'X': np.random.random((32, 84)).astype("float32"),
- 'Y': np.random.random((84, 100)).astype("float32")
- }
-
- def test_cpu_gpu_compare(self):
- self.compare_grad(self.op, self.inputs)
-
- def test_normal(self):
- # mul op will enlarge the relative error
- self.check_grad(
- self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
-
- def test_ignore_x(self):
- self.check_grad(
- self.op,
- self.inputs, ["Y"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"X"})
-
- def test_ignore_y(self):
- self.check_grad(
- self.op,
- self.inputs, ["X"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"Y"})
-
-
-class TestMulGradTest2(GradientChecker):
- def setUp(self):
- self.op = Operator(
- "mul", X="X", Y="Y", Out="Out", x_num_col_dims=2, y_num_col_dims=2)
- self.inputs = {
- "X": np.random.random((15, 4, 12, 10)).astype("float32"),
- "Y": np.random.random((4, 30, 8, 2, 9)).astype("float32")
- }
-
- def test_cpu_gpu_compare(self):
- self.compare_grad(self.op, self.inputs)
-
- def test_normal(self):
- self.check_grad(
- self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
- def test_ignore_x(self):
+ def test_check_grad_ingore_x(self):
self.check_grad(
- self.op,
- self.inputs, ["Y"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"X"})
+ ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set('X'))
- def test_ignore_y(self):
+ def test_check_grad_ignore_y(self):
self.check_grad(
- self.op,
- self.inputs, ["X"],
- "Out",
- max_relative_error=0.5,
- no_grad_set={"Y"})
+ ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py
index e4b7cd480cb36249bb64ba3cab9a4b220d812346..50cfb855f2b01d8fd32342855d46716da7e07856 100644
--- a/python/paddle/v2/framework/tests/test_net.py
+++ b/python/paddle/v2/framework/tests/test_net.py
@@ -35,5 +35,5 @@ Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}
self.assertEqual(expected, "\n" + str(net))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_reshape_op.py b/python/paddle/v2/framework/tests/test_reshape_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..16bb6bb2af67f7d32a2fafc1cb37412084ec0829
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_reshape_op.py
@@ -0,0 +1,21 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestReshapeOp(OpTest):
+ def setUp(self):
+ self.op_type = "reshape"
+ self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
+ self.attrs = {'shape': [10 * 20]}
+ self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py
index 8378c1cd21c21fd31da9b82d2cdaaff332f291d7..336645bd993ff743cbe20bb5cae5cd278db57ce7 100644
--- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py
+++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py
@@ -1,68 +1,51 @@
import unittest
import numpy as np
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
-class TestRowwiseAddOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
- def setUp(self):
- self.type = "rowwise_add"
- self.inputs = {
- 'X': np.random.random((32, 84)).astype("float32"),
- 'b': np.random.random(84).astype("float32")
- }
- self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
-
-
-class TestRowwiseAddOp2(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestRowwiseAddOp(OpTest):
def setUp(self):
- self.type = "rowwise_add"
+ self.op_type = "rowwise_add"
self.inputs = {
- 'X': np.random.random((13, 6, 7, 8)).astype("float32"),
- 'b': np.random.random((7, 8)).astype("float32")
+ 'X': np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
+ 'b': np.random.uniform(0.1, 1, [10]).astype("float32")
}
self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
+ def test_check_output(self):
+ self.check_output()
-class TestRowwiseAddGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("rowwise_add")
- self.inputs = {
- "X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
- "b": np.random.uniform(0.1, 1, [10]).astype("float32")
- }
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'b'], 'Out')
- def test_normal(self):
- self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
+ def test_check_grad_ingore_b(self):
+ self.check_grad(['X'], 'Out', no_grad_set=set('b'))
- def test_ignore_b(self):
- self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
+ def test_check_grad_ingore_x(self):
+ self.check_grad(['b'], 'Out', no_grad_set=set('X'))
- def test_ignore_x(self):
- self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
-
-class TestRowwiseAddGradOp2(GradientChecker):
+class TestRowwiseAddOp2(OpTest):
def setUp(self):
- self.op = create_op("rowwise_add")
+ self.op_type = "rowwise_add"
self.inputs = {
- "X": np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"),
- "b": np.random.uniform(0.1, 1, [2, 5]).astype("float32")
+ 'X': np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"),
+ 'b': np.random.uniform(0.1, 1, [2, 5]).astype("float32")
}
+ self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
+
+ def test_check_output(self):
+ self.check_output()
- def test_normal(self):
- self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
+ def test_check_grad_normal(self):
+ self.check_grad(['X', 'b'], 'Out')
- def test_ignore_b(self):
- self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
+ def test_check_grad_ignore_b(self):
+ self.check_grad(['X'], 'Out', no_grad_set=set('b'))
- def test_ignore_x(self):
- self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
+ def test_check_grad_ignore_x(self):
+ self.check_grad(['b'], 'Out', no_grad_set=set('X'))
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_scale_and_identity_op.py b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py
index 69b301c376ee7a4ebb2e2dadc645c7d10f823a08..05d76d428299c8176d1a6adf6da15a203fa7502a 100644
--- a/python/paddle/v2/framework/tests/test_scale_and_identity_op.py
+++ b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py
@@ -1,43 +1,34 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
import numpy as np
-from paddle.v2.framework.op import Operator
+from op_test import OpTest
-class IdentityTest(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class IdentityTest(OpTest):
def setUp(self):
- self.type = "identity"
- self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+ self.op_type = "identity"
+ self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.outputs = {'Out': self.inputs['X']}
+ def test_check_output(self):
+ self.check_output()
-class IdentityGradOpTest(GradientChecker):
- def test_normal(self):
- op = create_op("identity")
- inputs = {"X": np.random.random((10, 10)).astype("float32")}
- self.check_grad(op, inputs, set("X"), "Out")
-
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Out')
-class ScaleTest(unittest.TestCase):
- __metaclass__ = OpTestMeta
+class ScaleTest(OpTest):
def setUp(self):
- self.type = "scale"
- self.inputs = {'X': np.random.random((32, 784)).astype("float32")}
+ self.op_type = "scale"
+ self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.attrs = {'scale': -2.3}
self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']}
+ def test_check_output(self):
+ self.check_output()
-class ScaleGradTest(GradientChecker):
- def test_normal(self):
- op = Operator("scale", X="X", Out="Out", scale=3.2)
- self.check_grad(op,
- {"X": np.random.random((10, 10)).astype("float32")},
- set("X"), "Out")
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py
index c1f9444889372104e39ded78fc7207a59b80a293..33c73c52631a09ea0fefdeb9467991ae9c04321c 100644
--- a/python/paddle/v2/framework/tests/test_scatter_op.py
+++ b/python/paddle/v2/framework/tests/test_scatter_op.py
@@ -1,37 +1,24 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
-import numpy
-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
+import numpy as np
+from op_test import OpTest
-class TestScatterOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestScatterOp(OpTest):
def setUp(self):
- self.type = "scatter"
- ref_np = numpy.ones((3, 3)).astype("float32")
- index_np = numpy.array([1, 2]).astype("int32")
- updates_np = numpy.random.random((2, 3)).astype("float32")
- output_np = numpy.copy(ref_np)
+ self.op_type = "scatter"
+ ref_np = np.ones((3, 3)).astype("float32")
+ index_np = np.array([1, 2]).astype("int32")
+ updates_np = np.random.random((2, 3)).astype("float32")
+ output_np = np.copy(ref_np)
output_np[index_np] += updates_np
self.inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np}
self.outputs = {'Out': output_np}
+ def test_check_output(self):
+ self.check_output()
-class TestScatterGradOp(GradientChecker):
- def test_scatter_grad(self):
- op = create_op("scatter")
- # test data setup
- ref_np = numpy.ones((3, 10)).astype("float32")
- index_np = numpy.array([1, 2]).astype("int32")
- updates_np = numpy.random.random((2, 10)).astype("float32")
- output_np = numpy.copy(ref_np)
- output_np[index_np] += updates_np
- inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np}
- self.check_grad(
- op, inputs, set(["Updates", "Ref"]), "Out", in_place=True)
+ def test_check_grad(self):
+ self.check_grad(['Updates', 'Ref'], 'Out', in_place=True)
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_sgd_op.py b/python/paddle/v2/framework/tests/test_sgd_op.py
index e5f9ef865e84f1a78e28884ad7e2e758f9ca8054..557cf15ace63e336462c7dcdbbc10f30aeedc6f4 100644
--- a/python/paddle/v2/framework/tests/test_sgd_op.py
+++ b/python/paddle/v2/framework/tests/test_sgd_op.py
@@ -1,21 +1,22 @@
import unittest
-import numpy
-from op_test_util import OpTestMeta
+import numpy as np
+from op_test import OpTest
-class TestSGD(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSGD(OpTest):
def setUp(self):
- self.type = "sgd"
- w = numpy.random.random((102, 105)).astype("float32")
- g = numpy.random.random((102, 105)).astype("float32")
+ self.op_type = "sgd"
+ w = np.random.random((102, 105)).astype("float32")
+ g = np.random.random((102, 105)).astype("float32")
lr = 0.1
self.inputs = {'param': w, 'grad': g}
self.attrs = {'learning_rate': lr}
self.outputs = {'param_out': w - lr * g}
+ def test_check_output(self):
+ self.check_output()
+
if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py
index 273c2e5ab1a84d12621fe9568c4cf22073b6aed4..2316e49eff7bb1cdb53acb3889a6ef05060b59f3 100644
--- a/python/paddle/v2/framework/tests/test_sigmoid_op.py
+++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py
@@ -1,27 +1,21 @@
import unittest
import numpy as np
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
+from op_test import OpTest
-class TestSigmoidOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSigmoid(OpTest):
def setUp(self):
- self.type = "sigmoid"
- self.inputs = {'X': np.random.random((15, 31)).astype("float32")}
+ self.op_type = "sigmoid"
+ self.inputs = {
+ 'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32")
+ }
self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
+ def test_check_output(self):
+ self.check_output()
-class TestSigmoidGradOp(GradientChecker):
- def test_grad(self):
- op = create_op("sigmoid")
- inputs = {"X": np.random.uniform(0.1, 1, [11, 17]).astype("float32")}
- # compare gpu and cpu results for backward op.
- # this test will be skiped if only compiling CPU version.
- self.compare_grad(op, inputs)
- # check gradients
- self.check_grad(op, inputs, set("X"), "Y", max_relative_error=0.007)
+ def test_check_grad(self):
+ self.check_grad(["X"], "Y", max_relative_error=0.007)
if __name__ == '__main__':
diff --git a/python/paddle/v2/framework/tests/test_softmax_op.py b/python/paddle/v2/framework/tests/test_softmax_op.py
index 0d590fa7065bdd2df0e3f2aea5464f0524d70670..1b948f252fa631e9886840b377de2996e110dc91 100644
--- a/python/paddle/v2/framework/tests/test_softmax_op.py
+++ b/python/paddle/v2/framework/tests/test_softmax_op.py
@@ -1,9 +1,6 @@
import unittest
-
import numpy as np
-
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
def stable_softmax(x):
@@ -13,26 +10,21 @@ def stable_softmax(x):
return exps / np.sum(exps)
-class TestSoftmaxOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSoftmaxOp(OpTest):
def setUp(self):
- self.type = "softmax"
- self.inputs = {"X": np.random.random((10, 10)).astype("float32")}
+ self.op_type = "softmax"
+ self.inputs = {
+ 'X': np.random.uniform(0.1, 1, [10, 10]).astype("float32")
+ }
self.outputs = {
- "Y": np.apply_along_axis(stable_softmax, 1, self.inputs["X"])
+ 'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
}
+ def test_check_output(self):
+ self.check_output()
-class TestSoftmaxGradOp(GradientChecker):
- def setUp(self):
- self.op = create_op("softmax")
- self.inputs = {
- "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32")
- }
-
- def test_softmax_grad(self):
- self.check_grad(self.op, self.inputs, ["X"], "Y")
+ def test_check_grad(self):
+ self.check_grad(['X'], 'Y')
if __name__ == "__main__":
diff --git a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
index 2bcdf37df434c9a089d75438d876114156261a5c..dc6ebf5d30369231b4918a168bbdf25c7096c808 100644
--- a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
+++ b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
@@ -1,17 +1,14 @@
import unittest
-from op_test_util import OpTestMeta
-from gradient_checker import GradientChecker, create_op
import numpy as np
+from op_test import OpTest
-class TestSquaredL2DistanceOp_f0(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestSquaredL2DistanceOp_f0(OpTest):
def setUp(self):
- self.type = 'squared_l2_distance'
+ self.op_type = "squared_l2_distance"
self.inputs = {
- 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'),
- 'Y': np.random.uniform(0.1, 1., (32, 64)).astype('float32')
+ 'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"),
+ 'Y': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32")
}
sub_res = self.inputs['X'] - self.inputs['Y']
output = sub_res * sub_res
@@ -20,15 +17,19 @@ class TestSquaredL2DistanceOp_f0(unittest.TestCase):
'Out': np.expand_dims(output.sum(1), 1)
}
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
-class TestSquaredL2DistanceOp_f1(unittest.TestCase):
- __metaclass__ = OpTestMeta
+class TestSquaredL2DistanceOp_f1(OpTest):
def setUp(self):
- self.type = 'squared_l2_distance'
+ self.op_type = "squared_l2_distance"
self.inputs = {
- 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'),
- 'Y': np.random.uniform(0.1, 1., (1, 64)).astype('float32')
+ 'X': np.random.uniform(0.1, 0.6, (2, 3)).astype("float32"),
+ 'Y': np.random.uniform(0.1, 0.6, (1, 3)).astype("float32")
}
sub_res = self.inputs['X'] - self.inputs['Y']
output = sub_res * sub_res
@@ -37,53 +38,34 @@ class TestSquaredL2DistanceOp_f1(unittest.TestCase):
'Out': np.expand_dims(output.sum(1), 1)
}
+ def test_check_output(self):
+ self.check_output()
-class TestSquaredL2DistanceOp_f2(unittest.TestCase):
- __metaclass__ = OpTestMeta
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
+
+class TestSquaredL2DistanceOp_f2(OpTest):
def setUp(self):
- self.type = 'squared_l2_distance'
+ self.op_type = "squared_l2_distance"
self.inputs = {
- 'X': np.random.uniform(0.1, 1., (32, 64, 128)).astype('float32'),
- 'Y': np.random.uniform(0.1, 1., (1, 64, 128)).astype('float32')
+ 'X': np.random.uniform(0.1, 0.6, (2, 3, 4)).astype("float32"),
+ 'Y': np.random.uniform(0.1, 0.6, (1, 3, 4)).astype("float32")
}
sub_res = self.inputs['X'] - self.inputs['Y']
- sub_res = sub_res.reshape((32, 64 * 128))
+ sub_res = sub_res.reshape((2, 3 * 4))
output = sub_res * sub_res
self.outputs = {
'sub_result': sub_res,
'Out': np.expand_dims(output.sum(1), 1)
}
+ def test_check_output(self):
+ self.check_output()
-class TestSquaredL2DistanceGradOp(GradientChecker):
- def test_squared_l2_distance_b0(self):
- op = create_op("squared_l2_distance")
- inputs = {
- 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'),
- 'Y': np.random.uniform(0.1, .6, (2, 3)).astype('float32')
- }
- self.compare_grad(op, inputs)
- self.check_grad(op, inputs, set(["X", "Y"]), "Out")
-
- def test_squared_l2_distance_b1(self):
- op = create_op("squared_l2_distance")
- inputs = {
- 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'),
- 'Y': np.random.uniform(0.1, .6, (1, 3)).astype('float32')
- }
- self.compare_grad(op, inputs)
- self.check_grad(op, inputs, set(["X", "Y"]), "Out")
-
- def test_squared_l2_distance_b2(self):
- op = create_op("squared_l2_distance")
- inputs = {
- 'X': np.random.uniform(0.1, .6, (2, 3, 4)).astype('float32'),
- 'Y': np.random.uniform(0.1, .6, (1, 3, 4)).astype('float32')
- }
- self.compare_grad(op, inputs)
- self.check_grad(op, inputs, set(["X", "Y"]), "Out")
+ def test_check_grad(self):
+ self.check_grad(['X', 'Y'], 'Out')
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_sum_op.py b/python/paddle/v2/framework/tests/test_sum_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..60254291e2ab9215e2bc37c12d5e2e1ca6d33d5d
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_sum_op.py
@@ -0,0 +1,24 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestSumOp(OpTest):
+ def setUp(self):
+ self.op_type = "sum"
+ x0 = np.random.random((3, 4)).astype('float32')
+ x1 = np.random.random((3, 4)).astype('float32')
+ x2 = np.random.random((3, 4)).astype('float32')
+ self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
+ y = x0 + x1 + x2
+ self.outputs = {'Out': y}
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad(self):
+ self.check_grad(['x0'], 'Out')
+
+
+if __name__ == "__main__":
+ unittest.main()
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()
diff --git a/python/paddle/v2/framework/tests/test_top_k_op.py b/python/paddle/v2/framework/tests/test_top_k_op.py
index e841d96d26bba13b2780c41ea7a209fd470cad3b..cab799256d791889c295aa7f9048080f5caaf2dc 100644
--- a/python/paddle/v2/framework/tests/test_top_k_op.py
+++ b/python/paddle/v2/framework/tests/test_top_k_op.py
@@ -1,14 +1,11 @@
import unittest
import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+from op_test import OpTest
-class TestTopkOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestTopkOp(OpTest):
def setUp(self):
- self.type = "top_k"
+ self.op_type = "top_k"
k = 1
input = np.random.random((32, 84)).astype("float32")
output = np.ndarray((32, k))
@@ -25,11 +22,9 @@ class TestTopkOp(unittest.TestCase):
self.outputs = {'Out': output, 'Indices': indices}
-class TestTopkOp3d(unittest.TestCase):
- __metaclass__ = OpTestMeta
-
+class TestTopkOp3d(OpTest):
def setUp(self):
- self.type = "top_k"
+ self.op_type = "top_k"
k = 1
input = np.random.random((32, 2, 84)).astype("float32")
input_flat_2d = input.reshape(64, 84)
@@ -48,5 +43,5 @@ class TestTopkOp3d(unittest.TestCase):
self.outputs = {'Out': output, 'Indices': indices}
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/framework/tests/test_uniform_random_op.py b/python/paddle/v2/framework/tests/test_uniform_random_op.py
index c3d2bb44da3977c0899b2609a8efe15b7e1789f2..76a5e36e56ab08230bdc2597d209fcf5d1d2acb0 100644
--- a/python/paddle/v2/framework/tests/test_uniform_random_op.py
+++ b/python/paddle/v2/framework/tests/test_uniform_random_op.py
@@ -14,11 +14,11 @@ class UniformRandomTest(unittest.TestCase):
def uniform_random_test(self, place):
scope = core.Scope()
- scope.new_var("X").get_tensor()
+ scope.new_var('X').get_tensor()
op = Operator(
"uniform_random",
- Out="X",
+ Out='X',
dims=[1000, 784],
min=-5.0,
max=10.0,
@@ -27,9 +27,9 @@ class UniformRandomTest(unittest.TestCase):
op.infer_shape(scope)
ctx = core.DeviceContext.create(place)
op.run(scope, ctx)
- tensor = numpy.array(scope.find_var("X").get_tensor())
+ tensor = numpy.array(scope.find_var('X').get_tensor())
self.assertAlmostEqual(tensor.mean(), 2.5, delta=0.1)
-if __name__ == '__main__':
+if __name__ == "__main__":
unittest.main()
diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py
index 8acea6155c588f2e8e5ad009cd8f0a0c09afb92b..e80456d9bbeb3c34ac9eab873a84dbf8f06e34df 100644
--- a/python/paddle/v2/inference.py
+++ b/python/paddle/v2/inference.py
@@ -2,6 +2,7 @@ import numpy
import collections
import topology
import minibatch
+import cPickle
__all__ = ['infer', 'Inference']
@@ -25,11 +26,23 @@ class Inference(object):
:type parameters: paddle.v2.parameters.Parameters
"""
- def __init__(self, output_layer, parameters):
+ def __init__(self, parameters, output_layer=None, fileobj=None):
import py_paddle.swig_paddle as api
- topo = topology.Topology(output_layer)
- gm = api.GradientMachine.createFromConfigProto(
- topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
+
+ if output_layer is not None:
+ topo = topology.Topology(output_layer)
+ gm = api.GradientMachine.createFromConfigProto(
+ topo.proto(), api.CREATE_MODE_TESTING, [api.PARAMETER_VALUE])
+ self.__data_types__ = topo.data_type()
+ elif fileobj is not None:
+ tmp = cPickle.load(fileobj)
+ gm = api.GradientMachine.createByConfigProtoStr(
+ tmp['protobin'], api.CREATE_MODE_TESTING,
+ [api.PARAMETER_VALUE])
+ self.__data_types__ = tmp['data_type']
+ else:
+ raise ValueError("Either output_layer or fileobj must be set")
+
for param in gm.getParameters():
val = param.getBuf(api.PARAMETER_VALUE)
name = param.getName()
@@ -43,7 +56,6 @@ class Inference(object):
# called here, but it's better to call this function in one place.
param.setValueUpdated()
self.__gradient_machine__ = gm
- self.__data_types__ = topo.data_type()
def iter_infer(self, input, feeding=None):
from data_feeder import DataFeeder
diff --git a/python/paddle/v2/topology.py b/python/paddle/v2/topology.py
index a20e878d0817d0a75e9c47a44f8765deca99225c..2db66be2505dde38a501edf45984e1f36beb351d 100644
--- a/python/paddle/v2/topology.py
+++ b/python/paddle/v2/topology.py
@@ -18,6 +18,7 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig
import paddle.trainer_config_helpers as conf_helps
import layer as v2_layer
import config_base
+import cPickle
__all__ = ['Topology']
@@ -100,6 +101,14 @@ class Topology(object):
return layer
return None
+ def serialize_for_inference(self, stream):
+ protobin = self.proto().SerializeToString()
+ data_type = self.data_type()
+ cPickle.dump({
+ 'protobin': protobin,
+ 'data_type': data_type
+ }, stream, cPickle.HIGHEST_PROTOCOL)
+
def __check_layer_type__(layer):
if not isinstance(layer, config_base.Layer):
diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py
index 0654a301049dcb347b79879076a869a0c14a07ae..ca95ef13bd440ac0ba3d46f6e4680d4d7aa94c42 100644
--- a/python/paddle/v2/trainer.py
+++ b/python/paddle/v2/trainer.py
@@ -174,13 +174,18 @@ class SGD(object):
pass_id=pass_id,
batch_id=batch_id,
cost=cost,
- evaluator=batch_evaluator))
+ evaluator=batch_evaluator,
+ gm=self.__gradient_machine__))
self.__parameter_updater__.finishBatch(cost)
batch_evaluator.finish()
self.__parameter_updater__.finishPass()
pass_evaluator.finish()
- event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
+ event_handler(
+ v2_event.EndPass(
+ pass_id,
+ evaluator=pass_evaluator,
+ gm=self.__gradient_machine__))
self.__gradient_machine__.finish()
def test(self, reader, feeding=None):