From ff3ec3c438e9c68b663fc42b4362a5b780e5f741 Mon Sep 17 00:00:00 2001
From: Zhuoyuan <chenzhuoyuan07@gmail.com>
Date: Mon, 28 Aug 2017 11:37:15 -0700
Subject: [PATCH 01/72] switch_op.h
---
paddle/operators/switch_op.h | 143 +++++++++++++++++++++++++++++++++++
1 file changed, 143 insertions(+)
create mode 100644 paddle/operators/switch_op.h
diff --git a/paddle/operators/switch_op.h b/paddle/operators/switch_op.h
new file mode 100644
index 0000000000..f72726bce1
--- /dev/null
+++ b/paddle/operators/switch_op.h
@@ -0,0 +1,143 @@
+/* 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 "glog/logging.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/operator.h"
+#include "paddle/framework/ddim.h"
+#include "paddle/operators/gather.h"
+
+namespace paddle {
+namespace operators {
+
+using namespace paddle::framework;
+
+template <typename Place, typename T>
+class CondOp final : public OperatorBase {
+public:
+ void Init() override;
+
+ /**
+ * InferShape must be called before Run.
+ */
+ virtual void InferShape(const std::shared_ptr<Scope>& scope) const override {
+ scope_t = scope.NewScope();
+ scope_f = scope.NewScope();
+ net_op_t->InferShape(scope_t);
+ net_op_f->InferShape(scope_f);
+ tensor_t = new Tensor();
+ tensor_f = new Tensor();
+ { // True branch
+ for (auto& input : net_op_t->Inputs()) {
+ auto var_name = input.second;
+ if (!scope_t.FindVar(var_name) {
+ scope_t.NewVar(var_name)->GetMutable<Tensor>();
+ }
+ }
+ }
+ { // False branch
+ for (auto& input : net_op_f->Inputs()) {
+ auto var_name = input.second;
+ if (!scope_f.FindVar(var_name) {
+ scope_f.NewVar(var_name)->GetMutable<Tensor>();
+ }
+ }
+ }
+ }
+
+ virtual void Run(const std::shared_ptr<Scope>& scope,
+ const platform::DeviceContext& dev_ctx) const override {
+ auto* cond = context.Input<Tensor>("Cond");
+ // Step 1: get the index
+ true_index.clear();
+ false_index.clear();
+ for(int i = 0; i < cond->dims()[0]; ++i) {
+ if (cond->data<bool>()[i])
+ true_index.push_back(i);
+ else:
+ false_index.push_back(i);
+ }
+ framework::DDim dim_ = paddle::framework::make_ddim({0});
+ dim_[0] = true_index.size();
+ tensor_t->Resize(dim_);
+ // set value
+ for (int i = 0; i < dim_[0]; ++i)
+ tensor_t->mutable_data<int>()[i] = true_index[i];
+ dim_[0] = false_index.size();
+ tensor_f->Resize(dim_);
+ // set value
+ for (int i = 0; i < dim_[0]; ++i)
+ tensor_f->mutable_data<int>()[i] = false_index[i];
+
+ // Step 2: collect data by calling gather
+ { // True branch
+ for (auto& input : net_op_t->Inputs()) {
+ auto var_name = input.second;
+ // find Tensor
+ Tensor* Tensor_parent = scope.FindVar(var_name)->GetMutable<Tensor>();
+ Tensor* Tensor_child = scope_t.FindVar(var_name)->GetMutable<Tensor>();
+ Gather<T>(dev_ctx.GetPlace(), tensor_parent, tensor_t, tensor_child);
+ }
+
+ }
+ }
+
+private:
+ Scope* scope_t;
+ Scope* scope_f;
+
+ // subnet_t
+ std::unique_ptr<framework::OperatorBase> net_op_t;
+ // NetOp* net_op_t;
+ // subnet_f
+ std::unique_ptr<framework::OperatorBase> net_op_f;
+ // NetOp* net_op_f;
+
+ // T_index
+ vector<int> true_index;
+ Tensor* tensor_t;
+ // F_index
+ vector<int> false_index;
+ Tensor* tensor_f;
+};
+
+class CondOpMaker : public OpProtoAndCheckerMaker {
+public:
+ IfElseOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("Cond", "The condition, which is a bool vector");
+ AddAttr<std::string>("subnet_t", "The subnet network to be called when Cond[i] == true");
+ AddAttr<std::string>("subnet_f", "The subnet network to be called when Cond[i] == false");
+ AddOutput("Out", "The output of if-else op");
+ AddComment(R"DOC(
+Sample dependent Cond Operator:
+The equation is: Out[i] = subnet_t[i], if Cond[i] == true
+Out[i] = subnet_t[i], if Cond[i] == false
+)DOC");
+ }
+};
+
+class CondGradientOp final : public OperatorBase {
+public:
+ void Init() override;
+
+ virtual void InferShape(const std::shared_ptr<Scope>& scope) const override;
+
+ virtual void Run(const std::shared_ptr<Scope>& scope,
+ const platform::DeviceContext& dev_ctx) const override;
+};
+
+} // namespace operators
+} // namespace paddle
From f075141b9144b6fdb91ccc2af9216601d70568c5 Mon Sep 17 00:00:00 2001
From: Zhuoyuan <chenzhuoyuan07@gmail.com>
Date: Mon, 28 Aug 2017 11:37:49 -0700
Subject: [PATCH 02/72] switch_op.cc
---
paddle/operators/switch_op.cc | 120 ++++++++++++++++++++++++++++++++++
1 file changed, 120 insertions(+)
create mode 100644 paddle/operators/switch_op.cc
diff --git a/paddle/operators/switch_op.cc b/paddle/operators/switch_op.cc
new file mode 100644
index 0000000000..09574a89a3
--- /dev/null
+++ b/paddle/operators/switch_op.cc
@@ -0,0 +1,120 @@
+#include "paddle/operators/switch_op.h"
+
+namespace paddle {
+namespace operators {
+
+// namespace if_else{
+
+
+void CondOp::Init() override {
+}
+
+void InferShape(const std::shared_ptr<Scope>& scope) const override {
+ subnet_t = GetAttr<std::string>("subnet_t");
+ subnet_f = GetAttr<std::string>("subnet_f");
+
+ // Create two Nets
+ // I use the same style as Recurrent_op, but does it create the net?
+ // can be called like
+ Variable* net_t = scope.FindVar(subnet_t);
+ Variable* net_f = scope.FindVar(subnet_f);
+
+ net_op_t = scope.FindVar(net_t)->GetMutable<NetOp>();
+ net_op_f = scope.FindVar(net_f)->GetMutable<NetOp>();
+
+ // Create two scopes
+ scope_t = scope.NewScope();
+ scope_f = scope.NewScope();
+
+ // check cond of size (batch_size), type bool
+ net_op_t->InferShape(scope_t);
+ net_op_f->InferShape(scope_f);
+
+ // check net_op_t and net_op_f of exactly same shape?
+}
+
+void IfElseOp::Run(const std::shared_ptr<Scope>& scope,
+ const platform::DeviceContext& dev_ctx) const {
+ /* step 1: create two subnets and scopes, supposed done in Infershape() */
+
+ /* step 2: get true and false index */
+ cond = Input(name.cond);
+ // get condition tensor
+ auto cond_tensor = scope.get<Tensor>(cond);
+ // tensor to cpu, whatever device it used to be in
+ cond_cpu.CopyFrom(cond_tensor, platform::CPUPlace());
+
+ size_t batch_size = cond_cpu.dims()[0];
+
+ // keep index of true and false to slice, clear them first before each batch
+ true_index.clear();
+ false_index.clear();
+
+ // get a DDim type variable dims, check dimension
+ auto dims = input0.dims();
+ for(int i=0; i<dims; i++) {
+ if (cond_cpu->data[i])
+ true_index.push_back(i);
+ else
+ false_index.push_back(i);
+ }
+
+ // turn true_index and false_index to tensors
+ Tensor* true_index_tensor = new Tensor(true_index);
+ Tensor* false_index_tensor = new Tensor(false_index);
+
+ /* Step 3: Gather */
+ { // True Scope
+ // Create new stuff
+ for (auto& input : net_op_t->inputs_) {
+ scope_t.NewVar(input);
+ if (input.type() != PARAMETER) { // gather and slice required
+ // Get Tensor and gather
+ Tensor* input_gather_ = scope_t.FindVar(input)->GetMutable<Tensor>();
+ Tensor* input_full_ = scope.FindVar(input)->GetMutable<Tensor>();
+ input_gather_ = Gather(input_full_, true_index_tensor);
+ }
+ }
+
+ for (auto& output : net_op->outputs_) {
+ scope_t.NewVar(output);
+ }
+
+ net_op_t.Run();
+ }
+
+ { // False Scope
+ // Create new stuff
+ for (auto& input : net_op_f->inputs_) {
+ scope_f.NewVar(input);
+ if (input.type() != PARAMETER) { // gather and slice required
+ // Get Tensor and gather
+ Tensor* input_gather_ = scope_f.FindVar(input)->GetMutable<Tensor>();
+ Tensor* input_full_ = scope.FindVar(input)->GetMutable<Tensor>();
+ input_gather_ = Gather(input_full_, false_index_tensor);
+ }
+ }
+
+ for (auto& output : net_op->outputs_) {
+ scope_t.NewVar(output);
+ }
+
+ net_op_f.Run();
+ }
+
+ /* Merge Output Together by scatter update */
+ for (auto& ouput : outputs_) {
+ Tensor* output_t = scope_t->FindVar(output)->GetMutable<Tensor>();
+ Tensor* output_f = scope_f->FindVar(output)->GetMutable<Tensor>();
+ Tensor* output_tensor = scope->FindVar(output)->GetMutable<Tensor>();
+ Scatter(output_t, output_tensor, true_index_tensor);
+ Scatter(output_f, output_tensor, false_index_tensor);
+ }
+}
+
+} // namespace operators
+} // namespace paddle
+
+REGISTER_OP(ifelse_op,
+ paddle::operators::IfElseOp,
+ paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
From 95b41bec3ea3ff6d95463b2564ae981b9d688fca Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Tue, 29 Aug 2017 11:44:29 -0700
Subject: [PATCH 03/72] Add LoD Tensor design doc
---
paddle/framework/lod_tensor.md | 122 +++++++++++++++++++++++++++++++++
1 file changed, 122 insertions(+)
create mode 100644 paddle/framework/lod_tensor.md
diff --git a/paddle/framework/lod_tensor.md b/paddle/framework/lod_tensor.md
new file mode 100644
index 0000000000..a9a1a113d1
--- /dev/null
+++ b/paddle/framework/lod_tensor.md
@@ -0,0 +1,122 @@
+# LoD (Level-of-Detail) Tensor
+
+PaddlePaddle's RNN doesn't require that all instances have the same length. To do so, we introduce an extension to Tensor, namely, LoD Tensor.
+
+## Challenge of Variable-length Inputs
+
+People usually represent a mini-batch by a Tensor. For example, a mini-batch of 32 images, each of size 32x32, is a 10x32x32 Tensor. So a transformation, T, of all images can be a matrix multiplication of the 32x32xO-dimensional tensor T and the 10x32x32 Tensor.
+
+Another example is that each mini-batch contains 32 sentences, where each word is a D-dimensional one-hot vector. If all sentences have the same length L, we can represent this mini-batch by a 32xLxD tensor. However, in most cases, sentences have variable lengths, and we will need an index data structure to record these variable lengths.
+
+## LoD as a Solution
+
+### Mini-Batch of variable-length sentenses
+
+Let's imagine a mini-batch of 3 variable lengths sentences, containing 3, 1, and 2 words respectively. We can represent it by a (3+1+2)xD tensor plus some index information:
+
+```
+ 3
+3 1 2
+||| | ||
+```
+
+Each `|` represents a D-dimensional word vectors. The number 3 on top indicate 3 sentences, and numbers 3, 1, and 2 on the second level represent the number of words in each sentence.
+
+### Mini-Batch of variable-length videos
+
+This approach generalizes to the case where elements are not words, but higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. If a mini-batch contains 3 videos of 3, 1, and 2 frames respectively. The underlying tensor is of size (3+1+2)x640x480. The index information illustrates as:
+
+```
+ 3
+3 1 2
+口口口 口 口口
+```
+
+where each `口` represents an image.
+
+### Mini-Batch of fixed-size images
+
+Let's get back to a typical example, image classification, where each mini-batch has M fixed-sized images. The LoD Tensor representation is
+
+```
+ M
+1 1 1 1 1
+口口口口 ... 口
+```
+
+The many 1's on the second level seem duplicated. For this particular case of 2 levels and the second level always have length 1, we can ignore the LoD index.
+
+### Design and summarization
+
+In summary, as long as that the essential elements (words or images) have the same size, we can represent mini-batches by a LoD Tensor:
+
+- The underlying tensor has size LxD1xD2x..., where D1xD2... is the size of the essential elements, and
+- the first dimension size L has an additon property -- a LoD index as a nested vector:
+
+ ```c++
+ typedef std::vector<std::vector> > LoD;
+ ```
+
+- The LoD index can is not necessary when there are only two levels and all elements of the second level have length 1.
+
+## Slicing of LoD Tensor
+
+Consider that we have a network with three levels of RNN: the top level one handles articles, the second level one handles sentences, and the basic level one handles words. This network requires that mini-batches represented by 4 level LoD Tensor, for example,
+
+```
+ 3
+3 1 2
+3 2 4 1 2 3
+||| || |||| | || |||
+```
+
+To allow each level of RNN to handle its input, we define **the slicing of a LoD Tensor is defined as getting the j-th sequence on level i, or the <i,j>-slice**
+
+For example, the <2,1>-slice of above slice is
+
+```
+2
+||
+```
+
+and the <1,2>-slice of above example is
+
+```
+2
+2 3
+|| |||
+```
+
+Let's go on slicing this slice. Its <1,1>-slice is
+
+```
+3
+|||
+```
+
+### The General Slicing Algorithm
+
+The algorithm, with over-simplified data structure, is defined as
+
+```c++
+typedef vector<vector<int> > LoD;
+
+struct LoDTensor {
+ LoD lod_;
+ float* tensor_;
+};
+
+LoDTensor Slice(const LoDTensor& lodt, int level, int sequence) {
+
+}
+```
+
+### Slicing the Top Level
+
+Please be aware that an RNN operator only slices the top level of a LoD Tensor to get the step inputs.
+
+```c++
+LoDTensor Slice(const LoDTensor& lodt, int sequence) {
+
+}
+```
From f6457e650192bfb1f5ac886585c17d2557f7f604 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Tue, 29 Aug 2017 11:44:41 -0700
Subject: [PATCH 04/72] Add LoD Tensor design doc
---
paddle/framework/lod_tensor.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/paddle/framework/lod_tensor.md b/paddle/framework/lod_tensor.md
index a9a1a113d1..8dfe3ee823 100644
--- a/paddle/framework/lod_tensor.md
+++ b/paddle/framework/lod_tensor.md
@@ -1,4 +1,4 @@
-# LoD (Level-of-Detail) Tensor
+# Design Doc: LoD (Level-of-Detail) Tensor
PaddlePaddle's RNN doesn't require that all instances have the same length. To do so, we introduce an extension to Tensor, namely, LoD Tensor.
From ca9be82f6c45691b6661a4be54a05a369f579295 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Wed, 30 Aug 2017 13:05:33 +0800
Subject: [PATCH 05/72] add how to use eigen cn doc
---
doc/howto/dev/new_op_cn.md | 7 +-
doc/howto/dev/use_eigen_cn.md | 140 ++++++++++++++++++++++++++++++++++
2 files changed, 146 insertions(+), 1 deletion(-)
create mode 100644 doc/howto/dev/use_eigen_cn.md
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index ebd2cf3ff0..55c99fa7af 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -169,6 +169,8 @@ class MulKernel : public framework::OpKernel {
`MulKernel`需要重写`Compute`接口，该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型，同样可获取到输入输出和属性参数，`Compute`函数里写具体实现时。
注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
+
+为了使得`OpKernel`的计算过程书写较为简单，CPU、GPU的代码可以复用，我们通常借助Eigen unsupported Tensor模块来实现。关于在paddle中如何使用Eigen库，请参考对应的使用[文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)
到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。
@@ -188,9 +190,12 @@ REGISTER_OP_CPU_KERNEL(mul_grad,
- `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
-在 `.cu`文件中注册GPU Kernel。
+在 `.cu`文件中注册GPU Kernel。请注意，如果GPU Kernel的实现是基于Eigen unsupported模块，那么在 `.cu`的最前面请加上宏定义 `#define EIGEN_USE_GPU`
```c++
+// if use Eigen unsupported module before include head files
+#define EIGEN_USE_GPU
+
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mul_grad,
diff --git a/doc/howto/dev/use_eigen_cn.md b/doc/howto/dev/use_eigen_cn.md
new file mode 100644
index 0000000000..d22ff4799c
--- /dev/null
+++ b/doc/howto/dev/use_eigen_cn.md
@@ -0,0 +1,140 @@
+## 在Paddle中如何使用Eigen
+
+神经网络本质上是一个计算图，计算需要的数据存放在`Tensor`中，而计算过程是由`Operartor`来描述的。在执行时，`Operator`调用对应`OpKernel`中的`Compute`接口，实现对`Tensor`的操作。
+
+
+### Eigen Tensor模块
+
+Eigen Tensor模块对element-wise计算提供了强大的支持，并且书写一份代码，可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块，因此可能测试不够完备，文档较少。
+
+关于Eigen Tensor模块的详细介绍请参考[文档](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md)
+
+
+### paddle::framework::Tensor
+
+Paddle Tensor定义在framework目录下，其主要接口如下：
+
+```
+class Tensor {
+ public:
+ /*! Return a pointer to mutable memory block. */
+ template <typename T>
+ inline T* data();
+
+ /**
+ * @brief Return a pointer to mutable memory block.
+ * @note If not exist, then allocation.
+ */
+ template <typename T>
+ inline T* mutable_data(platform::Place place);
+
+ /**
+ * @brief Return a pointer to mutable memory block.
+ *
+ * @param[in] dims The dimensions of the memory block.
+ * @param[in] place The place of the memory block.
+ *
+ * @note If not exist, then allocation.
+ */
+ template <typename T>
+ inline T* mutable_data(DDim dims, platform::Place place);
+
+ /*! Resize the dimensions of the memory block. */
+ inline Tensor& Resize(const DDim& dims);
+
+ /*! Return the dimensions of the memory block. */
+ inline const DDim& dims() const;
+
+ private:
+ /*! holds the memory block if allocated. */
+ std::shared_ptr<Placeholder> holder_;
+
+ /*! points to dimensions of memory block. */
+ DDim dim_;
+};
+```
+
+`Placeholder`的作用的延迟分配内存，即我们可以先定义一个Tensor，然后使用Resize接口设置Tensor的大小，最后再调用mutable_data接口分配实际的内存。
+
+```
+paddle::framework::Tensor t;
+paddle::platform::CPUPlace place;
+// set size first
+t.Resize({2, 3});
+// allocate memory on CPU later
+t.mutable_data(place);
+```
+
+下面以AddOp为例说明Tensor的使用过程：
+
+- InferShape
+
+在运行神经网络计算图时，我们先调用每个`Operator`的`InferShape`接口，根据输入Tensor的大小来设置输出Tensor的大小，`Resize`接口会被调用。
+
+```
+void InferShape(const framework::InferShapeContext &ctx) const override {
+ PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
+ ctx.Input<Tensor>("Y")->dims(),
+ "Two input of Add Op's dimension must be same.");
+ ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
+}
+```
+
+
+- Run
+
+`Operator`的`Run`接口最终会调用对应`OpKernel`的`Compute`接口，在这时真正的分配内存，`mutable_data`接口会被调用。
+
+```
+void Compute(const framework::ExecutionContext& context) const override {
+ auto* input0 = context.Input<Tensor>("X");
+ auto* input1 = context.Input<Tensor>("Y");
+ auto* output = context.Output<Tensor>("Out");
+
+ output->mutable_data<T>(context.GetPlace());
+
+ auto X = EigenVector<T>::Flatten(*input0);
+ auto Y = EigenVector<T>::Flatten(*input1);
+ auto Z = EigenVector<T>::Flatten(*output);
+
+ auto place = context.GetEigenDevice<Place>();
+
+ Z.device(place) = X + Y;
+}
+```
+
+
+### paddle::framework::Tensor到EigenTensor的转换
+
+如上一小节所示，在具体的计算中，我们需要先把输入Tensor和输出Tensor转换为Eigen支持的格式。我们在[eigen.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen.h)中提供了一些全局函数用来实现paddle::framework::Tensor到EigenTensor/EigenMatrix/EigenVector/EigenScalar的转换。
+
+以EigenTensor为例，做一个介绍
+
+```
+Tensor t;
+float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
+for (int i = 0; i < 1 * 2 * 3; i++) {
+ p[i] = static_cast<float>(i);
+}
+
+EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
+```
+
+From是EigenTensor模板struct提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。
+
+需要额外注意的是，EigenVector<T>::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor，在这里用EigenVector来表示；而EigenVector<T>::Flatten方法是把paddle中的一个Tensor进行reshape操作，压扁成为Eigen的一维Tensor，类型仍然为EigenVector。
+
+更多的转换方法请参考eigen_test.cc中的[单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen_test.cc)。
+
+
+
+### 实现计算
+
+当需要完成计算时，我们需要等式左边的EigenTensor调用device接口：
+
+```
+auto place = context.GetEigenDevice<Place>();
+Z.device(place) = X + Y;
+```
+
+由于Eigen Tensor模块的文档较少，我们可以参考TensorFlow的[kernels](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/kernels)模块下的相关`OpKernel`的计算代码。
From 1dfc5d87ff4b4f40272e387d598a1bec5477d127 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Wed, 30 Aug 2017 17:06:16 +0800
Subject: [PATCH 06/72] add more details
---
doc/howto/dev/use_eigen_cn.md | 9 +++++++--
1 file changed, 7 insertions(+), 2 deletions(-)
diff --git a/doc/howto/dev/use_eigen_cn.md b/doc/howto/dev/use_eigen_cn.md
index d22ff4799c..49a726959a 100644
--- a/doc/howto/dev/use_eigen_cn.md
+++ b/doc/howto/dev/use_eigen_cn.md
@@ -120,7 +120,7 @@ for (int i = 0; i < 1 * 2 * 3; i++) {
EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
```
-From是EigenTensor模板struct提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。
+From是EigenTensor模板提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。
需要额外注意的是，EigenVector<T>::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor，在这里用EigenVector来表示；而EigenVector<T>::Flatten方法是把paddle中的一个Tensor进行reshape操作，压扁成为Eigen的一维Tensor，类型仍然为EigenVector。
@@ -130,11 +130,16 @@ From是EigenTensor模板struct提供的一个接口，可以实现从paddle::fra
### 实现计算
-当需要完成计算时，我们需要等式左边的EigenTensor调用device接口：
+当需要完成计算时，我们需要等式左边的EigenTensor调用device接口。在这里需要注意的是，这里的EigenTensor之间的运算只是改变了原有Tensor中的数据，而不会改变原有Tensor的shape信息。
```
+auto X = EigenVector<T>::Flatten(*input0);
+auto Y = EigenVector<T>::Flatten(*input1);
+auto Z = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
Z.device(place) = X + Y;
```
+在这段代码中，input0/input1/output可以是任意维度的Tensor。我们调用了EigenVector的Flatten接口，把任意维度的Tensor转为了一维的EigenVector。而在计算结束之后，input0/input1/output的原有shape信息不变。如果想改变原有Tensor的shape信息，可以调用Resize接口进行改变。
+
由于Eigen Tensor模块的文档较少，我们可以参考TensorFlow的[kernels](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/kernels)模块下的相关`OpKernel`的计算代码。
From c20a01d67d928442a0b8fb187277906ddd54ea04 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Thu, 31 Aug 2017 11:26:59 +0800
Subject: [PATCH 07/72] Add cuDNN Wrapper.
---
paddle/platform/CMakeLists.txt | 1 +
paddle/platform/cudnn_helper.cc | 15 +++
paddle/platform/cudnn_helper.h | 215 ++++++++++++++++++++++++++++++++
paddle/platform/macros.h | 23 ++++
4 files changed, 254 insertions(+)
create mode 100644 paddle/platform/cudnn_helper.cc
create mode 100644 paddle/platform/cudnn_helper.h
create mode 100644 paddle/platform/macros.h
diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt
index 120eb1e4af..ef722e984c 100644
--- a/paddle/platform/CMakeLists.txt
+++ b/paddle/platform/CMakeLists.txt
@@ -22,3 +22,4 @@ ENDIF()
cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)
+nv_test(cudnn_helper SRCS cudnn_helper.cc)
diff --git a/paddle/platform/cudnn_helper.cc b/paddle/platform/cudnn_helper.cc
new file mode 100644
index 0000000000..764285e2ba
--- /dev/null
+++ b/paddle/platform/cudnn_helper.cc
@@ -0,0 +1,15 @@
+/* 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/platform/cudnn_helper.h"
diff --git a/paddle/platform/cudnn_helper.h b/paddle/platform/cudnn_helper.h
new file mode 100644
index 0000000000..91047236a2
--- /dev/null
+++ b/paddle/platform/cudnn_helper.h
@@ -0,0 +1,215 @@
+/* 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 <cudnn.h>
+#include "paddle/platform/dynload/cudnn.h"
+#include "paddle/platform/enforce.h"
+#include "paddle/platform/macros.h"
+
+namespace paddle {
+namespace platform {
+
+enum class DataLayout {
+ kNHWC,
+ kNCHW,
+ kNCHW_VECT_C,
+};
+
+enum class PoolingMode {
+ kMaximum,
+ kAverage,
+};
+
+template <typename T>
+class CudnnDataType;
+
+template <>
+class CudnnDataType<float> {
+ public:
+ static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
+ typedef const float ScalingParamType;
+ static ScalingParamType* kOne() {
+ static ScalingParamType v = 1.0;
+ return &v;
+ }
+ static const ScalingParamType* kZero() {
+ static ScalingParamType v = 0.0;
+ return &v;
+ }
+};
+
+template <>
+class CudnnDataType<double> {
+ public:
+ static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
+ typedef const double ScalingParamType;
+ static ScalingParamType* kOne() {
+ static ScalingParamType v = 1.0;
+ return &v;
+ }
+ static ScalingParamType* kZero() {
+ static ScalingParamType v = 0.0;
+ return &v;
+ }
+};
+
+inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) {
+ switch (order) {
+ case DataLayout::kNHWC:
+ return CUDNN_TENSOR_NHWC;
+ case DataLayout::kNCHW:
+ return CUDNN_TENSOR_NCHW;
+ default:
+ PADDLE_THROW("Unknown cudnn equivalent for order");
+ }
+ return CUDNN_TENSOR_NCHW;
+}
+
+class ScopedTensorDescriptor {
+ public:
+ ScopedTensorDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnCreateTensorDescriptor(&desc_));
+ }
+ ~ScopedTensorDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnDestroyTensorDescriptor(desc_));
+ }
+
+ inline cudnnTensorDescriptor_t descriptor(const cudnnTensorFormat_t format,
+ const cudnnDataType_t type,
+ const std::vector<int>& dims) {
+ // the format is not used now, but it maybe useful feature
+ std::vector<int> strides(dims.size());
+ strides[dims.size() - 1] = 1;
+ for (int i = dims.size() - 1; i >= 0; i++) {
+ strides[i] = dims[i + 1] * strides[i];
+ }
+ PADDLE_ENFORCE(cudnnSetTensorNdDescriptor(desc_, type, dims.size(),
+ dims.data(), strides.data()));
+ return desc_;
+ }
+
+ template <typename T>
+ inline cudnnTensorDescriptor_t descriptor(const DataLayout& order,
+ const std::vector<int>& dims) {
+ return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
+ dims);
+ }
+
+ private:
+ cudnnTensorDescriptor_t desc_;
+ DISABLE_COPY_AND_ASSIGN(ScopedTensorDescriptor);
+};
+
+class ScopedFilterDescriptor {
+ public:
+ ScopedFilterDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnCreateFilterDescriptor(&desc_));
+ }
+ ~ScopedFilterDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnDestroyFilterDescriptor(desc_));
+ }
+
+ inline cudnnFilterDescriptor_t descriptor(const cudnnTensorFormat_t format,
+ const cudnnDataType_t type,
+ const std::vector<int>& kernel) {
+ // filter layout: output input spatial_dim_y spatial_dim_x
+ PADDLE_ENFORCE(cudnnSetFilterNdDescriptor(desc_, type, format,
+ kernel.size(), kernel.data()));
+ return desc_;
+ }
+
+ template <typename T>
+ inline cudnnFilterDescriptor_t descriptor(const DataLayout& order,
+ const std::vector<int>& kernel) {
+ return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
+ kernel);
+ }
+
+ private:
+ cudnnFilterDescriptor_t desc_;
+ DISABLE_COPY_AND_ASSIGN(ScopedFilterDescriptor);
+};
+
+class ScopedConvolutionDescriptor {
+ public:
+ ScopedConvolutionDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnCreateConvolutionDescriptor(&desc_));
+ }
+ ~ScopedConvolutionDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnDestroyConvolutionDescriptor(desc_));
+ }
+
+ inline cudnnConvolutionDescriptor_t descriptor(
+ cudnnDataType_t type, const std::vector<int>& pads,
+ const std::vector<int>& strides, const std::vector<int>& dilations) {
+ PADDLE_ENFORCE_EQ(pads.size(), strides.size());
+ PADDLE_ENFORCE_EQ(pads.size(), dilations.size());
+ PADDLE_ENFORCE(cudnnSetConvolutionNdDescriptor(
+ desc_, pads.size(), pads.data(), strides.data(), dilations.data(),
+ CUDNN_CROSS_CORRELATION, type));
+ }
+
+ template <typename T>
+ inline cudnnConvolutionDescriptor_t descriptor(
+ const std::vector<int>& pads, const std::vector<int>& strides,
+ const std::vector<int>& dilations) {
+ return descriptor(CudnnDataType<T>::type, pads, strides, dilations);
+ }
+
+ private:
+ cudnnConvolutionDescriptor_t desc_;
+ DISABLE_COPY_AND_ASSIGN(ScopedConvolutionDescriptor);
+};
+
+class ScopedPoolingDescriptor {
+ public:
+ ScopedPoolingDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnCreatePoolingDescriptor(&desc_));
+ }
+ ~ScopedPoolingDescriptor() {
+ PADDLE_ENFORCE(dynload::cudnnDestroyPoolingDescriptor(desc_));
+ }
+
+ inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode,
+ cudnnDataType_t type,
+ const std::vector<int>& kernel,
+ const std::vector<int>& pads,
+ const std::vector<int>& strides) {
+ PADDLE_ENFORCE_EQ(kernel.size(), pads.size());
+ PADDLE_ENFORCE_EQ(kernel.size(), strides.size());
+ PADDLE_ENFORCE(cudnnSetPoolingNdDescriptor(
+ desc_, (mode == PoolingMode::kMaximum
+ ? CUDNN_POOLING_MAX
+ : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING),
+ CUDNN_PROPAGATE_NAN, // Always propagate nans.
+ kernel.size(), kernel.data(), pads.data(), strides.data()));
+ }
+
+ template <typename T>
+ inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode,
+ const std::vector<int>& kernel,
+ const std::vector<int>& pads,
+ const std::vector<int>& strides) {
+ return descriptor(mode, CudnnDataType<T>::type, kernel, pads, strides);
+ }
+
+ private:
+ cudnnPoolingDescriptor_t desc_;
+ DISABLE_COPY_AND_ASSIGN(ScopedPoolingDescriptor);
+};
+
+} // namespace platform
+} // namespace paddle
diff --git a/paddle/platform/macros.h b/paddle/platform/macros.h
new file mode 100644
index 0000000000..4a04a38c0c
--- /dev/null
+++ b/paddle/platform/macros.h
@@ -0,0 +1,23 @@
+/* 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
+
+// Disable the copy and assignment operator for a class.
+#ifndef DISABLE_COPY_AND_ASSIGN
+#define DISABLE_COPY_AND_ASSIGN(classname) \
+ private: \
+ classname(const classname&) = delete; \
+ classname& operator=(const classname&) = delete
+#endif
From f03811b06fc099486824cc8baaee3bacbc687523 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Thu, 31 Aug 2017 16:33:56 +0800
Subject: [PATCH 08/72] init refine LODTensor
---
paddle/framework/lod_tensor.cc | 41 +++++++----
paddle/framework/lod_tensor.h | 104 ++++++++--------------------
paddle/framework/lod_tensor_test.cc | 84 ++++++++++++----------
3 files changed, 102 insertions(+), 127 deletions(-)
diff --git a/paddle/framework/lod_tensor.cc b/paddle/framework/lod_tensor.cc
index 2b17890774..71eac4a10b 100644
--- a/paddle/framework/lod_tensor.cc
+++ b/paddle/framework/lod_tensor.cc
@@ -19,25 +19,24 @@
namespace paddle {
namespace framework {
-LODTensor::LOD LODTensor::LOD::SliceLevels(size_t level_begin,
- size_t level_end) const {
+LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end) {
LOD new_lod;
new_lod.reserve(level_end - level_begin);
for (size_t i = level_begin; i < level_end; i++) {
- new_lod.emplace_back(at(i));
+ new_lod.emplace_back(in.at(i));
}
return new_lod;
}
-LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin,
- size_t elem_end) const {
+LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin,
+ size_t elem_end) {
// slice the lod.
LOD new_lod;
- new_lod.reserve(size() - level);
- auto start = this->at(level)[elem_begin];
- auto end = this->at(level)[elem_end];
+ new_lod.reserve(in.size() - level);
+ auto start = in.at(level)[elem_begin];
+ auto end = in.at(level)[elem_end];
- for (auto it = this->begin() + level; it != this->end(); it++) {
+ for (auto it = in.begin() + level; it != in.end(); it++) {
auto it_begin = std::find(it->begin(), it->end(), start);
auto it_end = std::find(it_begin, it->end(), end);
PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
@@ -49,11 +48,11 @@ LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin,
[start](int v) { return v - start; });
PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LOD");
}
- PADDLE_ENFORCE_LE(new_lod.size(), this->size());
+ PADDLE_ENFORCE_LE(new_lod.size(), in.size());
return new_lod;
}
-bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) {
+bool operator==(const LOD& a, const LOD& b) {
if (a.size() != b.size()) {
return false;
}
@@ -70,9 +69,27 @@ bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) {
}
}
}
-
return true;
}
+void LODTensor::SliceLevels(size_t level_begin, size_t level_end) {
+ auto new_lod = framework::SliceLevels(lod_, level_begin, level_end);
+ lod_ = new_lod;
+}
+
+void LODTensor::SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) {
+ PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
+ NumLevels());
+ PADDLE_ENFORCE(elem_begin < NumElements(level),
+ "element begin [%d] out of range [%d]", elem_begin,
+ NumElements(level));
+ PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
+ "element end [%d] out of range [%d]", elem_end,
+ NumElements(level));
+
+ auto new_lod = framework::SliceInLevel(lod_, level, elem_begin, elem_end);
+ lod_ = new_lod;
+}
+
} // namespace framework
} // namespace paddle
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 9e27aec38d..7a9aebf50a 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -15,7 +15,7 @@
#pragma once
#include <memory>
-#if !defined(PADDLE_ONLY_CPU)
+#ifndef PADDLE_ONLY_CPU
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#endif
@@ -27,33 +27,31 @@
namespace paddle {
namespace framework {
+#ifdef PADDLE_ONLY_CPU
+template <typename T>
+using Vector = std::vector<T>;
+#else
+template <typename T>
+using Vector = thrust::host_vector<T>;
+#endif
+
+using LOD = std::vector<Vector<size_t>>;
+
+LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end);
+
+LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin,
+ size_t elem_end);
+
+bool operator==(const LOD& a, const LOD& b);
+
/*
* LODTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
-class LODTensor : public Tensor {
+struct LODTensor {
public:
-// Level save offsets of each unit.
-#ifdef PADDLE_ONLY_CPU
- template <typename T>
- using Vector = std::vector<T>;
-#else
- template <typename T>
- using Vector = thrust::host_vector<T>;
-#endif
- // LoD stores offsets of each level of units, the largest units level first,
- // then the smaller units level. Each Level stores the offsets of units in
- // Tesor.
- class LOD : public std::vector<Vector<size_t>> {
- public:
- LOD SliceLevels(size_t level_begin, size_t level_end) const;
- LOD SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) const;
- };
-
LODTensor() {}
- explicit LODTensor(const LOD &lod) : lod_(lod) {}
-
- virtual Tensor *Clone() const { return new LODTensor(lod_); }
+ LODTensor(const LOD& lod, Tensor* t) : lod_(lod), tensor_(t) {}
/*
* Get a element from LOD.
@@ -79,71 +77,23 @@ class LODTensor : public Tensor {
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
// the last offset is the end of last element
- return lod_[level].size() - 1;
+ return (lod_)[level].size() - 1;
}
/*
- * Slice of levels[level_begin:level_end], with tensor shared.
+ * Slice of levels[level_begin:level_end]
*/
- template <typename T>
- LODTensor SliceLevels(size_t level_begin, size_t level_end) const;
+ void SliceLevels(size_t level_begin, size_t level_end);
/*
- * Slice of elements of a level, [elem_begin: elem_end], with tensor shared.
+ * Slice of elements of a level, [elem_begin: elem_end]
* @note: low performance in slice lod_.
*/
- template <typename T>
- LODTensor SliceInLevel(size_t level, size_t elem_begin,
- size_t elem_end) const;
-
- /*
- * Copy other's lod_'s content, free to mutate.
- */
- void CopyLOD(const LODTensor &other) { lod_ = other.lod_; }
- /*
- * Determine whether LODTensor has a valid LOD info.
- */
- const LOD &lod() const { return lod_; }
- LOD *mutable_lod() { return &lod_; }
-
- virtual ~LODTensor() {}
+ void SliceInLevel(size_t level, size_t elem_begin, size_t elem_end);
- private:
+ public:
LOD lod_;
+ Tensor* tensor_; // not owned
};
-
-bool operator==(const LODTensor::LOD &a, const LODTensor::LOD &b);
-
-template <typename T>
-LODTensor LODTensor::SliceLevels(size_t level_begin, size_t level_end) const {
- auto new_lod = lod_.SliceLevels(level_begin, level_end);
- // slice levels just need to update LOD info, each level will contains the
- // whole tensor_, so no need to modify tensor_.
- LODTensor new_tensor(new_lod);
- new_tensor.ShareDataWith<T>(*this);
- return new_tensor;
-}
-
-template <typename T>
-LODTensor LODTensor::SliceInLevel(size_t level, size_t elem_begin,
- size_t elem_end) const {
- PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
- NumLevels());
- PADDLE_ENFORCE(elem_begin < NumElements(level),
- "element begin [%d] out of range [%d]", elem_begin,
- NumElements(level));
- PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
- "element end [%d] out of range [%d]", elem_end,
- NumElements(level));
-
- auto new_lod = lod_.SliceInLevel(level, elem_begin, elem_end);
-
- // slice elements just need to update LOD info, because offsets are not
- // changed, so the original tensor_ can be reused.
- LODTensor new_tensor(new_lod);
- new_tensor.ShareDataWith<T>(*this);
- return new_tensor;
-}
-
} // namespace framework
} // namespace paddle
diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc
index 2881136ced..b75d86b5b4 100644
--- a/paddle/framework/lod_tensor_test.cc
+++ b/paddle/framework/lod_tensor_test.cc
@@ -21,16 +21,34 @@
namespace paddle {
namespace framework {
+// TEST(LODTensor, test) {
+// LOD lod;
+// lod.push_back(std::vector<size_t>{0, 10, 20});
+// lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
+// lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
+
+// ASSERT_EQ(lod.size(), 3UL);
+
+// Tensor tensor;
+// tensor.Resize({20 /*batch size*/, 128 /*dim*/});
+// // malloc memory
+// platform::CPUPlace place;
+// tensor.mutable_data<float>(place);
+
+// LODTensor lod_tensor(lod, &tensor);
+
+// ASSERT_EQ(lod_tensor.NumLevels(), 3UL);
+// }
+
class LODTensorTester : public ::testing::Test {
public:
virtual void SetUp() override {
- lod_tensor.reset(new LODTensor);
// tensor's batch_size: 30
// 3 levels
// 0 10 20
// 0 5 10 15 20
// 0 2 5 7 10 12 15 20
- LODTensor::LOD lod;
+ LOD lod;
lod.push_back(std::vector<size_t>{0, 10, 20});
lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
@@ -41,75 +59,65 @@ class LODTensorTester : public ::testing::Test {
// malloc memory
tensor.mutable_data<float>(place);
- lod_tensor.reset(new LODTensor(lod));
- lod_tensor->Resize({20 /*batch size*/, 128 /*dim*/});
-
- lod_tensor->ShareDataWith<float>(tensor);
- // lod_tensor->ShareDataWith<Tensor>(tensor);
+ lod_tensor.lod_ = lod;
+ lod_tensor.tensor_ = &tensor;
}
protected:
- std::unique_ptr<LODTensor> lod_tensor;
platform::CPUPlace place;
Tensor tensor;
+ LODTensor lod_tensor;
};
-TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor->NumLevels(), 3UL); }
+TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor.NumLevels(), 3UL); }
TEST_F(LODTensorTester, NumElements) {
- ASSERT_EQ(lod_tensor->NumElements(0), 2UL);
- ASSERT_EQ(lod_tensor->NumElements(1), 4UL);
- ASSERT_EQ(lod_tensor->NumElements(2), 8UL);
+ ASSERT_EQ(lod_tensor.NumElements(0), 2UL);
+ ASSERT_EQ(lod_tensor.NumElements(1), 4UL);
+ ASSERT_EQ(lod_tensor.NumElements(2), 8UL);
}
TEST_F(LODTensorTester, SliceLevels) {
// slice 1 level
for (size_t level = 0; level < 3UL; ++level) {
- auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 1);
+ LODTensor new_lod_tensor = lod_tensor;
+ new_lod_tensor.SliceLevels(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
- ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
- // ASSERT_EQ(new_lod_tensor, *lod_tensor);
+ ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
+ ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
+ lod_tensor.tensor_->data<float>());
}
// slice 2 level
for (size_t level = 0; level < 2UL; ++level) {
- auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 2);
+ LODTensor new_lod_tensor = lod_tensor;
+ new_lod_tensor.SliceLevels(level, level + 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
- ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
- ASSERT_EQ(new_lod_tensor.NumElements(1),
- lod_tensor->NumElements(level + 1));
- ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
+ ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
+ ASSERT_EQ(new_lod_tensor.NumElements(1), lod_tensor.NumElements(level + 1));
+ ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
+ lod_tensor.tensor_->data<float>());
}
}
TEST_F(LODTensorTester, SliceInLevel) {
size_t level = 0;
- auto new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
+ LODTensor new_lod_tensor = lod_tensor;
+ new_lod_tensor.SliceInLevel(level, 0, 2);
EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL);
EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
- ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
+ ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
+ lod_tensor.tensor_->data<float>());
level = 1;
- new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
+ new_lod_tensor = lod_tensor;
+ new_lod_tensor.SliceInLevel(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
- ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
-}
-
-TEST_F(LODTensorTester, ShareLOD) {
- LODTensor new_lod_tensor;
- new_lod_tensor.CopyLOD(*lod_tensor);
- ASSERT_EQ(new_lod_tensor.lod(), lod_tensor->lod());
-}
-
-TEST_F(LODTensorTester, CopyLOD) {
- LODTensor new_lod_tensor;
- new_lod_tensor.CopyLOD(*lod_tensor);
- bool equals = std::equal(lod_tensor->lod().begin(), lod_tensor->lod().end(),
- new_lod_tensor.lod().begin());
- ASSERT_TRUE(equals);
+ ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
+ lod_tensor.tensor_->data<float>());
}
} // namespace framework
From 039784426641162686cfaae556a5f325dcf6fda4 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Thu, 31 Aug 2017 16:34:50 +0800
Subject: [PATCH 09/72] remove unused codes
---
paddle/framework/lod_tensor_test.cc | 19 -------------------
1 file changed, 19 deletions(-)
diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc
index b75d86b5b4..30c8925ad0 100644
--- a/paddle/framework/lod_tensor_test.cc
+++ b/paddle/framework/lod_tensor_test.cc
@@ -21,25 +21,6 @@
namespace paddle {
namespace framework {
-// TEST(LODTensor, test) {
-// LOD lod;
-// lod.push_back(std::vector<size_t>{0, 10, 20});
-// lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
-// lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
-
-// ASSERT_EQ(lod.size(), 3UL);
-
-// Tensor tensor;
-// tensor.Resize({20 /*batch size*/, 128 /*dim*/});
-// // malloc memory
-// platform::CPUPlace place;
-// tensor.mutable_data<float>(place);
-
-// LODTensor lod_tensor(lod, &tensor);
-
-// ASSERT_EQ(lod_tensor.NumLevels(), 3UL);
-// }
-
class LODTensorTester : public ::testing::Test {
public:
virtual void SetUp() override {
From 835572afe70e3c0a0f11ff2f40a53b899b7adda6 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Thu, 31 Aug 2017 16:51:02 +0800
Subject: [PATCH 10/72] make LODTensor class instead struct
---
paddle/framework/lod_tensor.h | 12 ++++++++++--
paddle/framework/lod_tensor_test.cc | 20 ++++++++++----------
2 files changed, 20 insertions(+), 12 deletions(-)
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 7a9aebf50a..9e6b6b4aca 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -48,11 +48,19 @@ bool operator==(const LOD& a, const LOD& b);
* LODTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
-struct LODTensor {
+class LODTensor {
public:
LODTensor() {}
LODTensor(const LOD& lod, Tensor* t) : lod_(lod), tensor_(t) {}
+ void set_lod(const LOD& lod) { lod_ = lod; }
+
+ void set_tensor(Tensor* tensor) { tensor_ = tensor; }
+
+ Tensor& tensor() { return *tensor_; }
+
+ LOD lod() { return lod_; }
+
/*
* Get a element from LOD.
*/
@@ -91,7 +99,7 @@ struct LODTensor {
*/
void SliceInLevel(size_t level, size_t elem_begin, size_t elem_end);
- public:
+ private:
LOD lod_;
Tensor* tensor_; // not owned
};
diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc
index 30c8925ad0..9a351605ed 100644
--- a/paddle/framework/lod_tensor_test.cc
+++ b/paddle/framework/lod_tensor_test.cc
@@ -40,8 +40,8 @@ class LODTensorTester : public ::testing::Test {
// malloc memory
tensor.mutable_data<float>(place);
- lod_tensor.lod_ = lod;
- lod_tensor.tensor_ = &tensor;
+ lod_tensor.set_lod(lod);
+ lod_tensor.set_tensor(&tensor);
}
protected:
@@ -65,8 +65,8 @@ TEST_F(LODTensorTester, SliceLevels) {
new_lod_tensor.SliceLevels(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
- ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
- lod_tensor.tensor_->data<float>());
+ ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+ lod_tensor.tensor().data<float>());
}
// slice 2 level
for (size_t level = 0; level < 2UL; ++level) {
@@ -75,8 +75,8 @@ TEST_F(LODTensorTester, SliceLevels) {
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
ASSERT_EQ(new_lod_tensor.NumElements(1), lod_tensor.NumElements(level + 1));
- ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
- lod_tensor.tensor_->data<float>());
+ ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+ lod_tensor.tensor().data<float>());
}
}
@@ -88,8 +88,8 @@ TEST_F(LODTensorTester, SliceInLevel) {
EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
- ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
- lod_tensor.tensor_->data<float>());
+ ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+ lod_tensor.tensor().data<float>());
level = 1;
new_lod_tensor = lod_tensor;
@@ -97,8 +97,8 @@ TEST_F(LODTensorTester, SliceInLevel) {
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
- ASSERT_EQ(new_lod_tensor.tensor_->data<float>(),
- lod_tensor.tensor_->data<float>());
+ ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
+ lod_tensor.tensor().data<float>());
}
} // namespace framework
From 207132226c39c864a0808428cb8593bafafc4407 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Thu, 31 Aug 2017 20:49:33 +0800
Subject: [PATCH 11/72] Add unit testing for cuDNN wrapper.
---
paddle/platform/CMakeLists.txt | 4 +-
paddle/platform/cudnn_helper.h | 41 +++++----
paddle/platform/cudnn_helper_test.cc | 121 +++++++++++++++++++++++++
paddle/platform/dynload/CMakeLists.txt | 2 +-
paddle/platform/dynload/cudnn.h | 8 ++
5 files changed, 157 insertions(+), 19 deletions(-)
create mode 100644 paddle/platform/cudnn_helper_test.cc
diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt
index ef722e984c..9a3ad8eb64 100644
--- a/paddle/platform/CMakeLists.txt
+++ b/paddle/platform/CMakeLists.txt
@@ -22,4 +22,6 @@ ENDIF()
cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)
-nv_test(cudnn_helper SRCS cudnn_helper.cc)
+
+nv_library(cudnn_helper SRCS cudnn_helper.cc DEPS dynload_cuda)
+nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
diff --git a/paddle/platform/cudnn_helper.h b/paddle/platform/cudnn_helper.h
index 91047236a2..6a43f49cfc 100644
--- a/paddle/platform/cudnn_helper.h
+++ b/paddle/platform/cudnn_helper.h
@@ -14,7 +14,9 @@ limitations under the License. */
#pragma once
+#ifndef PADDLE_ONLY_CPU
#include <cudnn.h>
+#include "glog/logging.h"
#include "paddle/platform/dynload/cudnn.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/macros.h"
@@ -93,11 +95,11 @@ class ScopedTensorDescriptor {
// the format is not used now, but it maybe useful feature
std::vector<int> strides(dims.size());
strides[dims.size() - 1] = 1;
- for (int i = dims.size() - 1; i >= 0; i++) {
- strides[i] = dims[i + 1] * strides[i];
+ for (int i = dims.size() - 2; i >= 0; i--) {
+ strides[i] = dims[i + 1] * strides[i + 1];
}
- PADDLE_ENFORCE(cudnnSetTensorNdDescriptor(desc_, type, dims.size(),
- dims.data(), strides.data()));
+ PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor(
+ desc_, type, dims.size(), dims.data(), strides.data()));
return desc_;
}
@@ -126,8 +128,8 @@ class ScopedFilterDescriptor {
const cudnnDataType_t type,
const std::vector<int>& kernel) {
// filter layout: output input spatial_dim_y spatial_dim_x
- PADDLE_ENFORCE(cudnnSetFilterNdDescriptor(desc_, type, format,
- kernel.size(), kernel.data()));
+ PADDLE_ENFORCE(dynload::cudnnSetFilterNdDescriptor(
+ desc_, type, format, kernel.size(), kernel.data()));
return desc_;
}
@@ -157,9 +159,21 @@ class ScopedConvolutionDescriptor {
const std::vector<int>& strides, const std::vector<int>& dilations) {
PADDLE_ENFORCE_EQ(pads.size(), strides.size());
PADDLE_ENFORCE_EQ(pads.size(), dilations.size());
- PADDLE_ENFORCE(cudnnSetConvolutionNdDescriptor(
+
+#if CUDNN_VERSION < 6000
+ // cudnn v5 does not support dilation conv, the argument is called upscale
+ // instead of dilations and it is must be one.
+ for (size_t i = 0; i < dilations.size(); ++i) {
+ PADDLE_ENFORCE_EQ(
+ dilations[i], 1,
+ "Dilations conv is not supported in this cuDNN version");
+ }
+#endif
+
+ PADDLE_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor(
desc_, pads.size(), pads.data(), strides.data(), dilations.data(),
CUDNN_CROSS_CORRELATION, type));
+ return desc_;
}
template <typename T>
@@ -184,26 +198,18 @@ class ScopedPoolingDescriptor {
}
inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode,
- cudnnDataType_t type,
const std::vector<int>& kernel,
const std::vector<int>& pads,
const std::vector<int>& strides) {
PADDLE_ENFORCE_EQ(kernel.size(), pads.size());
PADDLE_ENFORCE_EQ(kernel.size(), strides.size());
- PADDLE_ENFORCE(cudnnSetPoolingNdDescriptor(
+ PADDLE_ENFORCE(dynload::cudnnSetPoolingNdDescriptor(
desc_, (mode == PoolingMode::kMaximum
? CUDNN_POOLING_MAX
: CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING),
CUDNN_PROPAGATE_NAN, // Always propagate nans.
kernel.size(), kernel.data(), pads.data(), strides.data()));
- }
-
- template <typename T>
- inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode,
- const std::vector<int>& kernel,
- const std::vector<int>& pads,
- const std::vector<int>& strides) {
- return descriptor(mode, CudnnDataType<T>::type, kernel, pads, strides);
+ return desc_;
}
private:
@@ -213,3 +219,4 @@ class ScopedPoolingDescriptor {
} // namespace platform
} // namespace paddle
+#endif
diff --git a/paddle/platform/cudnn_helper_test.cc b/paddle/platform/cudnn_helper_test.cc
new file mode 100644
index 0000000000..729f2f8a10
--- /dev/null
+++ b/paddle/platform/cudnn_helper_test.cc
@@ -0,0 +1,121 @@
+/* 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/platform/cudnn_helper.h"
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+TEST(CudnnHelper, ScopedTensorDescriptor) {
+ using paddle::platform::ScopedTensorDescriptor;
+ using paddle::platform::DataLayout;
+
+ ScopedTensorDescriptor tensor_desc;
+ std::vector<int> shape = {2, 4, 6, 6};
+ auto desc = tensor_desc.descriptor<float>(DataLayout::kNCHW, shape);
+
+ cudnnDataType_t type;
+ int nd;
+ std::vector<int> dims(4);
+ std::vector<int> strides(4);
+ paddle::platform::dynload::cudnnGetTensorNdDescriptor(
+ desc, 4, &type, &nd, dims.data(), strides.data());
+
+ EXPECT_EQ(nd, 4);
+ for (size_t i = 0; i < dims.size(); ++i) {
+ EXPECT_EQ(dims[i], shape[i]);
+ }
+ EXPECT_EQ(strides[3], 1);
+ EXPECT_EQ(strides[2], 6);
+ EXPECT_EQ(strides[1], 36);
+ EXPECT_EQ(strides[0], 144);
+}
+
+TEST(CudnnHelper, ScopedFilterDescriptor) {
+ using paddle::platform::ScopedFilterDescriptor;
+ using paddle::platform::DataLayout;
+
+ ScopedFilterDescriptor filter_desc;
+ std::vector<int> shape = {2, 3, 3};
+ auto desc = filter_desc.descriptor<float>(DataLayout::kNCHW, shape);
+
+ cudnnDataType_t type;
+ int nd;
+ cudnnTensorFormat_t format;
+ std::vector<int> kernel(3);
+ paddle::platform::dynload::cudnnGetFilterNdDescriptor(desc, 3, &type, &format,
+ &nd, kernel.data());
+
+ EXPECT_EQ(GetCudnnTensorFormat(DataLayout::kNCHW), format);
+ EXPECT_EQ(nd, 3);
+ for (size_t i = 0; i < shape.size(); ++i) {
+ EXPECT_EQ(kernel[i], shape[i]);
+ }
+}
+
+TEST(CudnnHelper, ScopedConvolutionDescriptor) {
+ using paddle::platform::ScopedConvolutionDescriptor;
+
+ ScopedConvolutionDescriptor conv_desc;
+ std::vector<int> src_pads = {2, 2, 2};
+ std::vector<int> src_strides = {1, 1, 1};
+ std::vector<int> src_dilations = {1, 1, 1};
+ auto desc = conv_desc.descriptor<float>(src_pads, src_strides, src_dilations);
+
+ cudnnDataType_t type;
+ cudnnConvolutionMode_t mode;
+ int nd;
+ std::vector<int> pads(3);
+ std::vector<int> strides(3);
+ std::vector<int> dilations(3);
+ paddle::platform::dynload::cudnnGetConvolutionNdDescriptor(
+ desc, 3, &nd, pads.data(), strides.data(), dilations.data(), &mode,
+ &type);
+
+ EXPECT_EQ(nd, 3);
+ for (size_t i = 0; i < src_pads.size(); ++i) {
+ EXPECT_EQ(pads[i], src_pads[i]);
+ EXPECT_EQ(strides[i], src_strides[i]);
+ EXPECT_EQ(dilations[i], src_dilations[i]);
+ }
+ EXPECT_EQ(mode, CUDNN_CROSS_CORRELATION);
+}
+
+TEST(CudnnHelper, ScopedPoolingDescriptor) {
+ using paddle::platform::ScopedPoolingDescriptor;
+ using paddle::platform::PoolingMode;
+
+ ScopedPoolingDescriptor pool_desc;
+ std::vector<int> src_kernel = {2, 2, 5};
+ std::vector<int> src_pads = {1, 1, 2};
+ std::vector<int> src_strides = {2, 2, 3};
+ auto desc = pool_desc.descriptor(PoolingMode::kMaximum, src_kernel, src_pads,
+ src_strides);
+
+ cudnnPoolingMode_t mode;
+ cudnnNanPropagation_t nan_t = CUDNN_PROPAGATE_NAN;
+ int nd;
+ std::vector<int> kernel(3);
+ std::vector<int> pads(3);
+ std::vector<int> strides(3);
+ paddle::platform::dynload::cudnnGetPoolingNdDescriptor(
+ desc, 3, &mode, &nan_t, &nd, kernel.data(), pads.data(), strides.data());
+
+ EXPECT_EQ(nd, 3);
+ for (size_t i = 0; i < src_pads.size(); ++i) {
+ EXPECT_EQ(kernel[i], src_kernel[i]);
+ EXPECT_EQ(pads[i], src_pads[i]);
+ EXPECT_EQ(strides[i], src_strides[i]);
+ }
+ EXPECT_EQ(mode, CUDNN_POOLING_MAX);
+}
diff --git a/paddle/platform/dynload/CMakeLists.txt b/paddle/platform/dynload/CMakeLists.txt
index d205ead845..ceb66f84b6 100644
--- a/paddle/platform/dynload/CMakeLists.txt
+++ b/paddle/platform/dynload/CMakeLists.txt
@@ -1,2 +1,2 @@
cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags)
-nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc)
+nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc DEPS dynamic_loader)
diff --git a/paddle/platform/dynload/cudnn.h b/paddle/platform/dynload/cudnn.h
index ef0dd85b08..0120625b7c 100644
--- a/paddle/platform/dynload/cudnn.h
+++ b/paddle/platform/dynload/cudnn.h
@@ -62,19 +62,27 @@ extern void* cudnn_dso_handle;
#define CUDNN_DNN_ROUTINE_EACH(__macro) \
__macro(cudnnSetTensor4dDescriptor); \
__macro(cudnnSetTensor4dDescriptorEx); \
+ __macro(cudnnSetTensorNdDescriptor); \
+ __macro(cudnnGetTensorNdDescriptor); \
__macro(cudnnGetConvolutionNdForwardOutputDim); \
__macro(cudnnGetConvolutionForwardAlgorithm); \
__macro(cudnnCreateTensorDescriptor); \
__macro(cudnnDestroyTensorDescriptor); \
__macro(cudnnCreateFilterDescriptor); \
__macro(cudnnSetFilter4dDescriptor); \
+ __macro(cudnnSetFilterNdDescriptor); \
+ __macro(cudnnGetFilterNdDescriptor); \
__macro(cudnnSetPooling2dDescriptor); \
+ __macro(cudnnSetPoolingNdDescriptor); \
+ __macro(cudnnGetPoolingNdDescriptor); \
__macro(cudnnDestroyFilterDescriptor); \
__macro(cudnnCreateConvolutionDescriptor); \
__macro(cudnnCreatePoolingDescriptor); \
__macro(cudnnDestroyPoolingDescriptor); \
__macro(cudnnSetConvolution2dDescriptor); \
__macro(cudnnDestroyConvolutionDescriptor); \
+ __macro(cudnnSetConvolutionNdDescriptor); \
+ __macro(cudnnGetConvolutionNdDescriptor); \
__macro(cudnnCreate); \
__macro(cudnnDestroy); \
__macro(cudnnSetStream); \
From ed72af48cecb1462b14c2105e9451052fa583cde Mon Sep 17 00:00:00 2001
From: Xinghai Sun <sunxinghai1216@gmail.com>
Date: Fri, 1 Sep 2017 19:01:12 +0800
Subject: [PATCH 12/72] Add cos_sim op.
---
paddle/operators/CMakeLists.txt | 2 +-
paddle/operators/cos_sim_op.cc | 91 ++++++++++++++++++
paddle/operators/cos_sim_op.cu | 22 +++++
paddle/operators/cos_sim_op.h | 93 +++++++++++++++++++
paddle/pybind/pybind.cc | 1 +
.../paddle/v2/framework/tests/CMakeLists.txt | 1 +
.../v2/framework/tests/gradient_checker.py | 21 +++--
.../paddle/v2/framework/tests/op_test_util.py | 8 +-
.../v2/framework/tests/test_cos_sim_op.py | 40 ++++++++
9 files changed, 267 insertions(+), 12 deletions(-)
create mode 100644 paddle/operators/cos_sim_op.cc
create mode 100644 paddle/operators/cos_sim_op.cu
create mode 100644 paddle/operators/cos_sim_op.h
create mode 100644 python/paddle/v2/framework/tests/test_cos_sim_op.py
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index e5efcccb0e..25dbd236e6 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -56,7 +56,7 @@ list(REMOVE_ITEM GENERAL_OPS
op_library(net_op SRCS net_op.cc)
op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
-op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
+op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op)
op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
diff --git a/paddle/operators/cos_sim_op.cc b/paddle/operators/cos_sim_op.cc
new file mode 100644
index 0000000000..21a616522b
--- /dev/null
+++ b/paddle/operators/cos_sim_op.cc
@@ -0,0 +1,91 @@
+/* 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/cos_sim_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class CosSimOp : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null.");
+ PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
+ ctx.Input<Tensor>("Y")->dims(),
+ "Dimensions of Input(X) and Input(Y) must be the same.");
+
+ auto dims = ctx.Input<Tensor>("X")->dims();
+ ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
+ }
+};
+
+class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "The first input of cos_sim op.");
+ AddInput("Y", "The second input of cos_sim op.");
+ AddOutput("Out", "The output of cos_sim op.");
+ AddComment(R"DOC(
+Cosine Similarity Operator.
+
+The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y))
+)DOC");
+ }
+};
+
+class CosSimOpGrad : public framework::OperatorWithKernel {
+ public:
+ using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+ void InferShape(const framework::InferShapeContext &ctx) const override {
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+ "Input(Out@GRAD) should not be null.");
+
+ auto x_dims = ctx.Input<Tensor>("X")->dims();
+ auto y_dims = ctx.Input<Tensor>("Y")->dims();
+ auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+ PADDLE_ENFORCE_EQ(x_dims, y_dims,
+ "Dimensions of Input(X) and Input(Y) must be the same.");
+ PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
+ "1st dimension of Out@GRAD must equal to Input(X)");
+ PADDLE_ENFORCE_EQ(out_dims[1], 1,
+ "1st dimension of Out@GRAD must equal to Input(X)");
+
+ auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+ auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+ x_grad->Resize(x_dims);
+ y_grad->Resize(y_dims);
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(cos_sim, ops::CosSimOp, ops::CosSimOpMaker, cos_sim_grad,
+ ops::CosSimOpGrad);
+REGISTER_OP_CPU_KERNEL(cos_sim,
+ ops::CosSimKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+ cos_sim_grad, ops::CosSimGradKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/cos_sim_op.cu b/paddle/operators/cos_sim_op.cu
new file mode 100644
index 0000000000..0cb8fd26de
--- /dev/null
+++ b/paddle/operators/cos_sim_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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/cos_sim_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(cos_sim,
+ ops::CosSimKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+ cos_sim_grad, ops::CosSimGradKernel<paddle::platform::GPUPlace, float>);
diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
new file mode 100644
index 0000000000..5247087cc1
--- /dev/null
+++ b/paddle/operators/cos_sim_op.h
@@ -0,0 +1,93 @@
+/* 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 <typename T, int MajorType = Eigen::RowMajor,
+ typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class CosSimKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto* x = context.Input<Tensor>("X");
+ auto* y = context.Input<Tensor>("Y");
+ auto* z = context.Output<Tensor>("Out");
+
+ z->mutable_data<T>(context.GetPlace());
+
+ auto dims = x->dims();
+ int size = static_cast<int>(framework::product(dims));
+ auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
+ auto X = EigenMatrix<T>::From(*x, new_dims);
+ auto Y = EigenMatrix<T>::From(*y, new_dims);
+ auto Z = EigenMatrix<T>::From(*z, new_dims);
+
+ auto XY = (X * Y).sum(Eigen::array<int, 1>({1}));
+ auto XX = (X * X).sum(Eigen::array<int, 1>({1}));
+ auto YY = (Y * Y).sum(Eigen::array<int, 1>({1}));
+ auto place = context.GetEigenDevice<Place>();
+ Z.device(place) = XY / XX.sqrt() / YY.sqrt();
+ }
+};
+
+template <typename Place, typename T>
+class CosSimGradKernel : public framework::OpKernel {
+ public:
+ void Compute(const framework::ExecutionContext& context) const override {
+ auto* x = context.Input<Tensor>("X");
+ auto* y = context.Input<Tensor>("Y");
+ auto* z = context.Input<Tensor>("Out");
+ auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
+ auto* grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
+ auto* grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
+
+ grad_x->mutable_data<T>(context.GetPlace());
+ grad_y->mutable_data<T>(context.GetPlace());
+
+ auto dims = x->dims();
+ int size = static_cast<int>(framework::product(dims));
+ auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
+ auto X = EigenMatrix<T>::From(*x, new_dims);
+ auto Y = EigenMatrix<T>::From(*y, new_dims);
+ auto Z = EigenMatrix<T>::From(*z);
+ auto dX = EigenMatrix<T>::From(*grad_x, new_dims);
+ auto dY = EigenMatrix<T>::From(*grad_y, new_dims);
+ auto dZ = EigenMatrix<T>::From(*grad_z);
+
+ auto XX = (X * X).sum(Eigen::array<int, 1>({1}));
+ auto YY = (Y * Y).sum(Eigen::array<int, 1>({1}));
+ Eigen::DSizes<int, 2> bcast(1, dims[1]);
+ auto denominator_bcast = (XX.sqrt() * YY.sqrt()).broadcast(bcast);
+ auto Z_bcast = Z.broadcast(bcast);
+ auto dZ_bcast = dZ.broadcast(bcast);
+ auto place = context.GetEigenDevice<Place>();
+ dX.device(place) =
+ dZ_bcast * (Y / denominator_bcast - Z_bcast * X / XX.broadcast(bcast));
+ dY.device(place) =
+ dZ_bcast * (X / denominator_bcast - Z_bcast * Y / YY.broadcast(bcast));
+ // dX.device(place) = X;
+ // Y.device(place) = Y;
+ }
+};
+
+} // namespace operators
+} // namespace paddle
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 3bc150ccb7..a8ec35dfad 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -46,6 +46,7 @@ USE_OP(lookup_table);
USE_OP(scale);
USE_OP_ITSELF(identity);
USE_OP(minus);
+USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt
index 661ebd8964..e0f77d7973 100644
--- a/python/paddle/v2/framework/tests/CMakeLists.txt
+++ b/python/paddle/v2/framework/tests/CMakeLists.txt
@@ -4,6 +4,7 @@ py_test(test_scope SRCS test_scope.py)
py_test(test_tensor SRCS test_tensor.py)
py_test(test_mul_op SRCS test_mul_op.py)
+py_test(test_cos_sim_op SRCS test_cos_sim_op.py)
py_test(test_mean_op SRCS test_mean_op.py)
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
index 518f828bac..bf01ea4876 100644
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ b/python/paddle/v2/framework/tests/gradient_checker.py
@@ -36,13 +36,13 @@ def get_numeric_gradient(op,
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
+
+ :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 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
+ :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.
@@ -229,9 +229,9 @@ class GradientChecker(unittest.TestCase):
"""Use relative error for the comparison.
:param numeric_grads: the numerical graidents.
- :type numeric_grads: a list of numpy.array
+ :type numeric_grads: a list of numpy.array
:param analytic_grads: the analytical graidents.
- :type analytic_grads: a list of numpy.array
+ :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.
@@ -304,6 +304,13 @@ class GradientChecker(unittest.TestCase):
# get analytical gradients according to different device
analytic_grads = self.__get_gradient(forward_op, backward_op,
input_vars, check_names, place)
+ #print(numeric_grads[0], numeric_grads[0].shape)
+ print("dim0: ", numeric_grads[0], numeric_grads[0].shape)
+ print("dim0: ", analytic_grads[0], analytic_grads[0].shape)
+ print("---------------------")
+ print("dim1: ", numeric_grads[1], numeric_grads[1].shape)
+ print("dim1: ", analytic_grads[1], analytic_grads[1].shape)
+ assert False
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_util.py b/python/paddle/v2/framework/tests/op_test_util.py
index 3bc05a0fec..a4899355b5 100644
--- a/python/paddle/v2/framework/tests/op_test_util.py
+++ b/python/paddle/v2/framework/tests/op_test_util.py
@@ -6,13 +6,13 @@ 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
+
+ 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.
"""
diff --git a/python/paddle/v2/framework/tests/test_cos_sim_op.py b/python/paddle/v2/framework/tests/test_cos_sim_op.py
new file mode 100644
index 0000000000..f3b04d25f2
--- /dev/null
+++ b/python/paddle/v2/framework/tests/test_cos_sim_op.py
@@ -0,0 +1,40 @@
+import unittest
+import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
+
+
+class TestCosSimOp(unittest.TestCase):
+ __metaclass__ = OpTestMeta
+
+ def setUp(self):
+ self.type = "cos_sim"
+ self.inputs = {
+ 'X': np.random.random((32, 84)).astype("float32"),
+ 'Y': np.random.random((32, 84)).astype("float32")
+ }
+ expect = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
+ np.linalg.norm(self.inputs['X'], axis=1) / \
+ np.linalg.norm(self.inputs['Y'], axis=1)
+ expect = np.expand_dims(expect, 1)
+ self.outputs = {'Out': expect}
+
+
+class CosSimGradOpTest(GradientChecker):
+ def test_cos_sim(self):
+ op = create_op("cos_sim")
+ #inputs = {
+ #'X': np.random.random((2, 2)).astype("float32"),
+ #'Y': np.random.random((2, 2)).astype("float32")
+ #}
+ inputs = {
+ 'X': np.array([[0.9, 0.6], [1.9, 1.6]]).astype("float32"),
+ 'Y': np.array([[0.7, 0.8], [1.7, 1.8]]).astype("float32")
+ }
+ print(inputs)
+ self.check_grad(
+ op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
+
+
+if __name__ == '__main__':
+ unittest.main()
From 9d2909be2abcb9b8728f7c4de7437c07a1254b8b Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 1 Sep 2017 19:27:52 +0800
Subject: [PATCH 13/72] rename add_op to add_two_op
---
paddle/framework/CMakeLists.txt | 2 +-
paddle/operators/{add_op.cc => add_two_op.cc} | 2 +-
paddle/operators/{add_op.cu => add_two_op.cu} | 2 +-
paddle/operators/{add_op.h => add_two_op.h} | 0
4 files changed, 3 insertions(+), 3 deletions(-)
rename paddle/operators/{add_op.cc => add_two_op.cc} (98%)
rename paddle/operators/{add_op.cu => add_two_op.cu} (95%)
rename paddle/operators/{add_op.h => add_two_op.h} (100%)
diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index c0838d9b75..e138517b6b 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -25,7 +25,7 @@ cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
-cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
+cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_two_op)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
diff --git a/paddle/operators/add_op.cc b/paddle/operators/add_two_op.cc
similarity index 98%
rename from paddle/operators/add_op.cc
rename to paddle/operators/add_two_op.cc
index 8ab748ed71..bc99e306e0 100644
--- a/paddle/operators/add_op.cc
+++ b/paddle/operators/add_two_op.cc
@@ -12,7 +12,7 @@ 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/add_op.h"
+#include "paddle/operators/add_two_op.h"
namespace paddle {
namespace operators {
diff --git a/paddle/operators/add_op.cu b/paddle/operators/add_two_op.cu
similarity index 95%
rename from paddle/operators/add_op.cu
rename to paddle/operators/add_two_op.cu
index cec5f558cb..acc03b2c8b 100644
--- a/paddle/operators/add_op.cu
+++ b/paddle/operators/add_two_op.cu
@@ -14,7 +14,7 @@
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
-#include "paddle/operators/add_op.h"
+#include "paddle/operators/add_two_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(add_two,
diff --git a/paddle/operators/add_op.h b/paddle/operators/add_two_op.h
similarity index 100%
rename from paddle/operators/add_op.h
rename to paddle/operators/add_two_op.h
From ba1aa60700db8cf8abb4b7efbf6c67774f12c3c7 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Fri, 1 Sep 2017 21:48:38 +0800
Subject: [PATCH 14/72] fix the problem that long message will be truncated.
---
paddle/gserver/layers/PrintLayer.cpp | 11 ++++++++++-
1 file changed, 10 insertions(+), 1 deletion(-)
diff --git a/paddle/gserver/layers/PrintLayer.cpp b/paddle/gserver/layers/PrintLayer.cpp
index 0a1e17b9aa..e83ae34bbe 100644
--- a/paddle/gserver/layers/PrintLayer.cpp
+++ b/paddle/gserver/layers/PrintLayer.cpp
@@ -48,7 +48,16 @@ public:
<< inputLayers_.size() << ") at " << getName();
}
s << format.substr(pos);
- LOG(INFO) << s.str();
+
+ const std::string delimiter("\n");
+ std::string content = s.str();
+ std::string::size_type foundPos = 0;
+ std::string::size_type prevPos = 0;
+ while ((foundPos = content.find(delimiter, prevPos)) != std::string::npos) {
+ LOG(INFO) << content.substr(prevPos, foundPos - prevPos);
+ prevPos = foundPos + delimiter.size();
+ }
+ LOG(INFO) << content.substr(prevPos);
}
void backward(const UpdateCallback& callback) override {}
From 91215bcef907baffdf52bb7894a0a33d0253c16f Mon Sep 17 00:00:00 2001
From: Xinghai Sun <sunxinghai1216@gmail.com>
Date: Sat, 2 Sep 2017 17:25:30 +0800
Subject: [PATCH 15/72] Fix a bug causing wrong gradient results in cos_sim op.
---
paddle/operators/cos_sim_op.cc | 32 ++++++++++----
paddle/operators/cos_sim_op.h | 34 +++++++++------
.../v2/framework/tests/gradient_checker.py | 7 ----
.../v2/framework/tests/test_cos_sim_op.py | 42 ++++++++++++-------
4 files changed, 71 insertions(+), 44 deletions(-)
diff --git a/paddle/operators/cos_sim_op.cc b/paddle/operators/cos_sim_op.cc
index 21a616522b..3760d0b161 100644
--- a/paddle/operators/cos_sim_op.cc
+++ b/paddle/operators/cos_sim_op.cc
@@ -25,14 +25,16 @@ class CosSimOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
- PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null.");
- PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Dimensions of Input(X) and Input(Y) must be the same.");
auto dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
+ ctx.Output<Tensor>("XNorm")->Resize({dims[0], 1});
+ ctx.Output<Tensor>("YNorm")->Resize({dims[0], 1});
}
};
@@ -43,6 +45,9 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X", "The first input of cos_sim op.");
AddInput("Y", "The second input of cos_sim op.");
AddOutput("Out", "The output of cos_sim op.");
+ AddOutput("XNorm", "Row norm of the first input.").AsIntermediate();
+ AddOutput("YNorm", "Row norm of the second input.").AsIntermediate();
+
AddComment(R"DOC(
Cosine Similarity Operator.
@@ -57,20 +62,31 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
- PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null.");
- PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
+ "Input(XNorm) must not be null.");
+ PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
+ "Input(YNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
- "Input(Out@GRAD) should not be null.");
+ "Input(Out@GRAD) must not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
+ auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
+ auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
PADDLE_ENFORCE_EQ(x_dims, y_dims,
"Dimensions of Input(X) and Input(Y) must be the same.");
+ PADDLE_ENFORCE_EQ(xnorm_dims[0], x_dims[0],
+ "1st dimension of XNorm must equal that of Input(X).");
+ PADDLE_ENFORCE_EQ(xnorm_dims[1], 1, "2st dimension of XNorm must be one.");
+ PADDLE_ENFORCE_EQ(ynorm_dims[0], y_dims[0],
+ "1st dimension of YNorm must equal that of Input(Y).");
+ PADDLE_ENFORCE_EQ(ynorm_dims[1], 1, "2st dimension of YNorm must be one.");
PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
- "1st dimension of Out@GRAD must equal to Input(X)");
- PADDLE_ENFORCE_EQ(out_dims[1], 1,
- "1st dimension of Out@GRAD must equal to Input(X)");
+ "1st dimension of Out@GRAD must equal that of Input(X)");
+ PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index 5247087cc1..69d35d8bc2 100644
--- a/paddle/operators/cos_sim_op.h
+++ b/paddle/operators/cos_sim_op.h
@@ -31,21 +31,27 @@ class CosSimKernel : public framework::OpKernel {
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto* z = context.Output<Tensor>("Out");
+ auto* x_norm = context.Output<Tensor>("XNorm");
+ auto* y_norm = context.Output<Tensor>("YNorm");
z->mutable_data<T>(context.GetPlace());
+ x_norm->mutable_data<T>(context.GetPlace());
+ y_norm->mutable_data<T>(context.GetPlace());
auto dims = x->dims();
int size = static_cast<int>(framework::product(dims));
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto X = EigenMatrix<T>::From(*x, new_dims);
auto Y = EigenMatrix<T>::From(*y, new_dims);
- auto Z = EigenMatrix<T>::From(*z, new_dims);
+ auto Z = EigenMatrix<T>::From(*z);
+ auto XNorm = EigenMatrix<T>::From(*x_norm);
+ auto YNorm = EigenMatrix<T>::From(*y_norm);
- auto XY = (X * Y).sum(Eigen::array<int, 1>({1}));
- auto XX = (X * X).sum(Eigen::array<int, 1>({1}));
- auto YY = (Y * Y).sum(Eigen::array<int, 1>({1}));
auto place = context.GetEigenDevice<Place>();
- Z.device(place) = XY / XX.sqrt() / YY.sqrt();
+ auto XY = (X * Y).sum(Eigen::array<int, 1>({1}));
+ XNorm.device(place) = (X * X).sum(Eigen::array<int, 1>({1})).sqrt();
+ YNorm.device(place) = (Y * Y).sum(Eigen::array<int, 1>({1})).sqrt();
+ Z.device(place) = XY / XNorm / YNorm;
}
};
@@ -56,6 +62,8 @@ class CosSimGradKernel : public framework::OpKernel {
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto* z = context.Input<Tensor>("Out");
+ auto* x_norm = context.Input<Tensor>("XNorm");
+ auto* y_norm = context.Input<Tensor>("YNorm");
auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
auto* grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
@@ -69,23 +77,23 @@ class CosSimGradKernel : public framework::OpKernel {
auto X = EigenMatrix<T>::From(*x, new_dims);
auto Y = EigenMatrix<T>::From(*y, new_dims);
auto Z = EigenMatrix<T>::From(*z);
+ auto X_norm = EigenMatrix<T>::From(*x_norm);
+ auto Y_norm = EigenMatrix<T>::From(*y_norm);
auto dX = EigenMatrix<T>::From(*grad_x, new_dims);
auto dY = EigenMatrix<T>::From(*grad_y, new_dims);
auto dZ = EigenMatrix<T>::From(*grad_z);
- auto XX = (X * X).sum(Eigen::array<int, 1>({1}));
- auto YY = (Y * Y).sum(Eigen::array<int, 1>({1}));
- Eigen::DSizes<int, 2> bcast(1, dims[1]);
- auto denominator_bcast = (XX.sqrt() * YY.sqrt()).broadcast(bcast);
+ Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
auto Z_bcast = Z.broadcast(bcast);
auto dZ_bcast = dZ.broadcast(bcast);
auto place = context.GetEigenDevice<Place>();
+ auto X_snorm_bcast = X_norm.square().eval().broadcast(bcast);
+ auto Y_snorm_bcast = Y_norm.square().eval().broadcast(bcast);
+ auto norm_prod_bcast = (X_norm * Y_norm).eval().broadcast(bcast);
dX.device(place) =
- dZ_bcast * (Y / denominator_bcast - Z_bcast * X / XX.broadcast(bcast));
+ dZ_bcast * (Y / norm_prod_bcast - Z_bcast * X / X_snorm_bcast);
dY.device(place) =
- dZ_bcast * (X / denominator_bcast - Z_bcast * Y / YY.broadcast(bcast));
- // dX.device(place) = X;
- // Y.device(place) = Y;
+ dZ_bcast * (X / norm_prod_bcast - Z_bcast * Y / Y_snorm_bcast);
}
};
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
index bf01ea4876..409b3caf33 100644
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ b/python/paddle/v2/framework/tests/gradient_checker.py
@@ -304,13 +304,6 @@ class GradientChecker(unittest.TestCase):
# get analytical gradients according to different device
analytic_grads = self.__get_gradient(forward_op, backward_op,
input_vars, check_names, place)
- #print(numeric_grads[0], numeric_grads[0].shape)
- print("dim0: ", numeric_grads[0], numeric_grads[0].shape)
- print("dim0: ", analytic_grads[0], analytic_grads[0].shape)
- print("---------------------")
- print("dim1: ", numeric_grads[1], numeric_grads[1].shape)
- print("dim1: ", analytic_grads[1], analytic_grads[1].shape)
- assert False
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/test_cos_sim_op.py b/python/paddle/v2/framework/tests/test_cos_sim_op.py
index f3b04d25f2..a19be47f76 100644
--- a/python/paddle/v2/framework/tests/test_cos_sim_op.py
+++ b/python/paddle/v2/framework/tests/test_cos_sim_op.py
@@ -10,30 +10,40 @@ class TestCosSimOp(unittest.TestCase):
def setUp(self):
self.type = "cos_sim"
self.inputs = {
- 'X': np.random.random((32, 84)).astype("float32"),
- 'Y': np.random.random((32, 84)).astype("float32")
+ 'X': np.random.random((32, 64)).astype("float32"),
+ 'Y': np.random.random((32, 64)).astype("float32")
+ }
+ expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
+ expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
+ expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
+ expect_x_norm / expect_y_norm
+ self.outputs = {
+ 'XNorm': np.expand_dims(expect_x_norm, 1),
+ 'YNorm': np.expand_dims(expect_y_norm, 1),
+ 'Out': np.expand_dims(expect_out, 1)
}
- expect = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
- np.linalg.norm(self.inputs['X'], axis=1) / \
- np.linalg.norm(self.inputs['Y'], axis=1)
- expect = np.expand_dims(expect, 1)
- self.outputs = {'Out': expect}
class CosSimGradOpTest(GradientChecker):
- def test_cos_sim(self):
+ def test_cos_sim_2d(self):
+ op = create_op("cos_sim")
+ inputs = {
+ 'X': np.random.random((10, 5)).astype("float32"),
+ 'Y': np.random.random((10, 5)).astype("float32")
+ }
+ self.compare_grad(op, inputs)
+ self.check_grad(
+ op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.05)
+
+ def test_cos_sim_3d(self):
op = create_op("cos_sim")
- #inputs = {
- #'X': np.random.random((2, 2)).astype("float32"),
- #'Y': np.random.random((2, 2)).astype("float32")
- #}
inputs = {
- 'X': np.array([[0.9, 0.6], [1.9, 1.6]]).astype("float32"),
- 'Y': np.array([[0.7, 0.8], [1.7, 1.8]]).astype("float32")
+ 'X': np.random.random((10, 5, 2)).astype("float32"),
+ 'Y': np.random.random((10, 5, 2)).astype("float32")
}
- print(inputs)
+ self.compare_grad(op, inputs)
self.check_grad(
- op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
+ op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.05)
if __name__ == '__main__':
From 6fcdc91638c226209aae1244c9dcaf6882378dca Mon Sep 17 00:00:00 2001
From: qiaolongfei <qiaolongfei@baidu.com>
Date: Sat, 2 Sep 2017 13:30:58 -0700
Subject: [PATCH 16/72] add op() to InferShapeContext
---
paddle/framework/operator.h | 5 +++++
paddle/framework/operator_test.cc | 12 ++++++------
paddle/operators/gaussian_random_op.cc | 6 +++---
paddle/operators/mul_op.cc | 4 ++--
paddle/operators/scale_op.h | 2 +-
paddle/operators/sgd_op.h | 2 +-
paddle/operators/uniform_random_op.cc | 6 +++---
7 files changed, 21 insertions(+), 16 deletions(-)
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index 8397570d26..9874a7c295 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -229,6 +229,10 @@ class InferShapeContext {
InferShapeContext(const OperatorBase& op, const Scope& scope)
: op_(op), scope_(scope) {}
+ const OperatorBase& op() const { return op_; }
+
+ const Scope& scope() const { return scope_; }
+
size_t InputSize(const std::string& name) const {
return op_.Inputs(name).size();
}
@@ -312,6 +316,7 @@ class InferShapeContext {
return res;
}
+ private:
const OperatorBase& op_;
const Scope& scope_;
};
diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc
index 1d7efb7b94..f7c9e6b196 100644
--- a/paddle/framework/operator_test.cc
+++ b/paddle/framework/operator_test.cc
@@ -122,10 +122,10 @@ class CPUKernelTest : public OpKernel {
public:
void Compute(const ExecutionContext& ctx) const {
std::cout << "this is cpu kernel" << std::endl;
- std::cout << ctx.op_.DebugString() << std::endl;
+ std::cout << ctx.op().DebugString() << std::endl;
cpu_kernel_run_num++;
- ASSERT_EQ(ctx.op_.Input("x"), "IN1");
- ASSERT_EQ(ctx.op_.Output("y"), "OUT1");
+ ASSERT_EQ(ctx.op().Input("x"), "IN1");
+ ASSERT_EQ(ctx.op().Output("y"), "OUT1");
}
};
@@ -148,7 +148,7 @@ class OpKernelTestMultiInputsProtoAndCheckerMaker
class CPUKernalMultiInputsTest : public OpKernel {
public:
void Compute(const ExecutionContext& ctx) const {
- auto xs = ctx.op_.Inputs("xs");
+ auto xs = ctx.op().Inputs("xs");
ASSERT_EQ(xs.size(), 3UL);
ASSERT_EQ(xs[0], "x0");
ASSERT_EQ(xs[1], "x1");
@@ -172,10 +172,10 @@ class CPUKernalMultiInputsTest : public OpKernel {
auto outTensor0 = ctx.MultiOutput<Tensor>("ys");
ASSERT_EQ(outTensor0.size(), 2U);
- auto k = ctx.op_.Input("k");
+ auto k = ctx.op().Input("k");
ASSERT_EQ(k, "k0");
- auto ys = ctx.op_.Outputs("ys");
+ auto ys = ctx.op().Outputs("ys");
ASSERT_EQ(ys.size(), 2UL);
ASSERT_EQ(ys[0], "y0");
ASSERT_EQ(ys[1], "y1");
diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc
index a85363ad81..9eb976fed1 100644
--- a/paddle/operators/gaussian_random_op.cc
+++ b/paddle/operators/gaussian_random_op.cc
@@ -19,13 +19,13 @@ template <typename T>
class CPUGaussianRandomKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
- float mean = context.op_.GetAttr<float>("mean");
- float std = context.op_.GetAttr<float>("std");
+ float mean = context.op().GetAttr<float>("mean");
+ float std = context.op().GetAttr<float>("std");
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed =
- static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
+ static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc
index 173cc3850c..5b8b5f6c11 100644
--- a/paddle/operators/mul_op.cc
+++ b/paddle/operators/mul_op.cc
@@ -29,10 +29,10 @@ class MulOp : public framework::OperatorWithKernel {
auto dim1 = ctx.Input<Tensor>("Y")->dims();
PADDLE_ENFORCE_EQ(dim0.size(), 2,
"input X(%s) should be a tensor with 2 dims, a matrix",
- ctx.op_.Input("X"));
+ ctx.op().Input("X"));
PADDLE_ENFORCE_EQ(dim1.size(), 2,
"input Y(%s) should be a tensor with 2 dims, a matrix",
- ctx.op_.Input("Y"));
+ ctx.op().Input("Y"));
PADDLE_ENFORCE_EQ(
dim0[1], dim1[0],
"First matrix's width must be equal with second matrix's height.");
diff --git a/paddle/operators/scale_op.h b/paddle/operators/scale_op.h
index aea64f1b04..760b26f5e9 100644
--- a/paddle/operators/scale_op.h
+++ b/paddle/operators/scale_op.h
@@ -27,7 +27,7 @@ class ScaleKernel : public framework::OpKernel {
auto* in = context.Input<framework::Tensor>("X");
tensor->mutable_data<T>(in->place());
- auto scale = static_cast<T>(context.op_.GetAttr<AttrType>("scale"));
+ auto scale = static_cast<T>(context.op().GetAttr<AttrType>("scale"));
auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
auto eigen_in = framework::EigenVector<T>::Flatten(*in);
diff --git a/paddle/operators/sgd_op.h b/paddle/operators/sgd_op.h
index a0b5000ffb..b54a7864b0 100644
--- a/paddle/operators/sgd_op.h
+++ b/paddle/operators/sgd_op.h
@@ -31,7 +31,7 @@ class SGDOpKernel : public framework::OpKernel {
auto param = ctx.Input<Tensor>("param");
auto grad = ctx.Input<Tensor>("grad");
auto param_out = ctx.Output<Tensor>("param_out");
- float lr = ctx.op_.GetAttr<float>("learning_rate");
+ float lr = ctx.op().GetAttr<float>("learning_rate");
param_out->mutable_data<T>(ctx.GetPlace());
diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc
index 29491137e6..dbff2a1365 100644
--- a/paddle/operators/uniform_random_op.cc
+++ b/paddle/operators/uniform_random_op.cc
@@ -27,15 +27,15 @@ class CPUUniformRandomKernel : public framework::OpKernel {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed =
- static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
+ static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
}
engine.seed(seed);
std::uniform_real_distribution<T> dist(
- static_cast<T>(context.op_.GetAttr<float>("min")),
- static_cast<T>(context.op_.GetAttr<float>("max")));
+ static_cast<T>(context.op().GetAttr<float>("min")),
+ static_cast<T>(context.op().GetAttr<float>("max")));
ssize_t size = framework::product(tensor->dims());
for (ssize_t i = 0; i < size; ++i) {
data[i] = dist(engine);
From 848c317aa84aea797654a3f0ee7a2db0fb0e8d09 Mon Sep 17 00:00:00 2001
From: qiaolongfei <qiaolongfei@baidu.com>
Date: Sat, 2 Sep 2017 14:13:05 -0700
Subject: [PATCH 17/72] update gpu code
---
paddle/operators/gaussian_random_op.cu | 6 +++---
paddle/operators/uniform_random_op.cu | 6 +++---
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu
index 018a4bfcb2..9769fbcd2b 100644
--- a/paddle/operators/gaussian_random_op.cu
+++ b/paddle/operators/gaussian_random_op.cu
@@ -43,13 +43,13 @@ class GPUGaussianRandomKernel : public framework::OpKernel {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed =
- static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
+ static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
if (seed == 0) {
std::random_device rd;
seed = rd();
}
- T mean = static_cast<T>(context.op_.GetAttr<float>("mean"));
- T std = static_cast<T>(context.op_.GetAttr<float>("std"));
+ T mean = static_cast<T>(context.op().GetAttr<float>("mean"));
+ T std = static_cast<T>(context.op().GetAttr<float>("std"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
ssize_t N = framework::product(tensor->dims());
thrust::transform(index_sequence_begin, index_sequence_begin + N,
diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu
index 1d6709934c..fc8397f134 100644
--- a/paddle/operators/uniform_random_op.cu
+++ b/paddle/operators/uniform_random_op.cu
@@ -46,13 +46,13 @@ class GPUUniformRandomKernel : public framework::OpKernel {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed =
- static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
+ static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
if (seed == 0) {
std::random_device rd;
seed = rd();
}
- T min = static_cast<T>(context.op_.GetAttr<float>("min"));
- T max = static_cast<T>(context.op_.GetAttr<float>("max"));
+ T min = static_cast<T>(context.op().GetAttr<float>("min"));
+ T max = static_cast<T>(context.op().GetAttr<float>("max"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
ssize_t N = framework::product(tensor->dims());
thrust::transform(index_sequence_begin, index_sequence_begin + N,
From 79b1f33a021a3d943d55734bc5692a57ca346a79 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Sat, 2 Sep 2017 15:51:50 -0700
Subject: [PATCH 18/72] Remove `grad_op_type` in REGISTER_OP
---
doc/howto/dev/new_op_cn.md | 4 ++--
paddle/framework/backward.md | 2 +-
paddle/framework/backward_test.cc | 12 +++++-------
paddle/framework/grad_op_builder_test.cc | 4 ++--
paddle/framework/op_registry.h | 25 ++++++++++--------------
paddle/operators/add_op.cc | 2 +-
paddle/operators/cross_entropy_op.cc | 3 +--
paddle/operators/gather_op.cc | 3 +--
paddle/operators/lookup_table_op.cc | 2 +-
paddle/operators/mean_op.cc | 2 +-
paddle/operators/minus_op.cc | 3 +--
paddle/operators/mul_op.cc | 2 +-
paddle/operators/rowwise_add_op.cc | 2 +-
paddle/operators/scale_op.cc | 2 +-
paddle/operators/scatter_op.cc | 3 +--
paddle/operators/sigmoid_op.cc | 3 +--
paddle/operators/softmax_op.cc | 3 +--
17 files changed, 32 insertions(+), 45 deletions(-)
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index 7f8da2da5a..ec79b7f42b 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -178,13 +178,13 @@ class MulKernel : public framework::OpKernel {
```c++
namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
```
- - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`，
+ - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，并且注册`ops::MulOpGrad`为其反向Op。
- `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md
index 8aa6728a95..9500c92a26 100644
--- a/paddle/framework/backward.md
+++ b/paddle/framework/backward.md
@@ -18,7 +18,7 @@ A backward network is built up with several backward operators. Backward operato
For example, we have got a `mul_op`, and we can register it's information and corresponding backward operator by the following macro:
```cpp
-REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
+REGISTER_OP(mul, MulOp, MulOpMaker, MulOpGrad);
```
`mul` is the operator's type. `MulOp` and `MulOpMaker` are the operator class and the operator maker class respectively.
diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc
index f100c4d054..f4d99f55b5 100644
--- a/paddle/framework/backward_test.cc
+++ b/paddle/framework/backward_test.cc
@@ -148,16 +148,14 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
namespace f = paddle::framework;
namespace ops = paddle::operators;
using EnforceNotMet = paddle::platform::EnforceNotMet;
-REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
- f::NOP);
-REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
-REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
+REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, f::NOP);
+REGISTER_OP(mul, f::NOP, f::MulOpMaker, f::NOP);
+REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
-REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP);
+REGISTER_OP(add, f::NOP, f::AddOpMaker, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
-REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
- f::NOP);
+REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, f::NOP);
TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp(
diff --git a/paddle/framework/grad_op_builder_test.cc b/paddle/framework/grad_op_builder_test.cc
index 902c2655e9..8a817a3e13 100644
--- a/paddle/framework/grad_op_builder_test.cc
+++ b/paddle/framework/grad_op_builder_test.cc
@@ -54,8 +54,8 @@ TEST(GradOpBuilder, AddTwo) {
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
}
-REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
-REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
+REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, f::NOP);
+REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h
index 2d09cde41e..64c7f23ab6 100644
--- a/paddle/framework/op_registry.h
+++ b/paddle/framework/op_registry.h
@@ -33,8 +33,7 @@ namespace framework {
class OpRegistry {
public:
template <typename OpType, typename ProtoMakerType, typename GradOpType>
- static void RegisterOp(const std::string& op_type,
- const std::string& grad_op_type) {
+ static void RegisterOp(const std::string& op_type) {
PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
"'%s' is registered more than once.", op_type);
OpInfo op_info;
@@ -43,9 +42,9 @@ class OpRegistry {
const VariableNameMap& outputs, const AttributeMap& attrs) {
return new OpType(type, inputs, outputs, attrs);
};
- op_info.grad_op_type_ = grad_op_type;
if (std::type_index(typeid(ProtoMakerType)) !=
std::type_index(typeid(NOPMaker))) {
+ op_info.grad_op_type_ = op_type + "_grad";
op_info.proto_ = new OpProto;
op_info.checker_ = new OpAttrChecker;
auto maker = ProtoMakerType(op_info.proto_, op_info.checker_);
@@ -55,15 +54,14 @@ class OpRegistry {
op_info.proto_->IsInitialized(),
"Fail to initialize %s's OpProto, because %s is not initialized",
op_type, op_info.proto_->InitializationErrorString());
+ // register gradient op
+ RegisterOp<GradOpType, NOPMaker, NOP>(op_info.grad_op_type_);
} else {
+ op_info.grad_op_type_ = "";
op_info.proto_ = nullptr;
op_info.checker_ = nullptr;
}
OpInfoMap::Instance().Insert(op_type, op_info);
- // register gradient op
- if (!grad_op_type.empty()) {
- RegisterOp<GradOpType, NOPMaker, NOP>(grad_op_type, "");
- }
}
static std::unique_ptr<OperatorBase> CreateOp(const std::string& type,
@@ -92,10 +90,8 @@ class Registrar {
template <typename OpType, typename ProtoMakerType, typename GradOpType>
class OpRegistrar : public Registrar {
public:
- explicit OpRegistrar(const char* op_type) { OpRegistrar(op_type, ""); }
- OpRegistrar(const char* op_type, const char* grad_op_type) {
- OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type,
- grad_op_type);
+ explicit OpRegistrar(const char* op_type) {
+ OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type);
}
};
@@ -121,8 +117,7 @@ class OpKernelRegistrar : public Registrar {
/**
* Macro to register Operator.
*/
-#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \
- grad_op_class) \
+#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_class) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
class _OpClass_##op_type##_ : public op_class { \
@@ -137,14 +132,14 @@ class OpKernelRegistrar : public Registrar {
}; \
static ::paddle::framework::OpRegistrar< \
_OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_> \
- __op_registrar_##op_type##__(#op_type, #grad_op_type); \
+ __op_registrar_##op_type##__(#op_type); \
int TouchOpRegistrar_##op_type() { \
__op_registrar_##op_type##__.Touch(); \
return 0; \
}
#define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
- REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP)
+ REGISTER_OP(op_type, op_class, op_maker_class, ::paddle::framework::NOP)
/**
* Macro to register OperatorKernel.
diff --git a/paddle/operators/add_op.cc b/paddle/operators/add_op.cc
index 8ab748ed71..6384d8c8ce 100644
--- a/paddle/operators/add_op.cc
+++ b/paddle/operators/add_op.cc
@@ -57,7 +57,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
+REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, ops::AddOpGrad);
REGISTER_OP_CPU_KERNEL(add_two,
ops::AddKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/cross_entropy_op.cc b/paddle/operators/cross_entropy_op.cc
index ab1e1c101a..ac76326262 100644
--- a/paddle/operators/cross_entropy_op.cc
+++ b/paddle/operators/cross_entropy_op.cc
@@ -67,8 +67,7 @@ OnehotCrossEntropy Operator.
namespace ops = paddle::operators;
REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
- ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
- ops::OnehotCrossEntropyGradientOp);
+ ops::OnehotCrossEntropyOpMaker, ops::OnehotCrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<float>);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad,
diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc
index 123bed296c..07fa704824 100644
--- a/paddle/operators/gather_op.cc
+++ b/paddle/operators/gather_op.cc
@@ -63,8 +63,7 @@ Out = X[Index]
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad,
- ops::GatherGradOp);
+REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, ops::GatherGradOp);
REGISTER_OP_CPU_KERNEL(gather,
ops::GatherOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/lookup_table_op.cc b/paddle/operators/lookup_table_op.cc
index 94d40890a7..c3108ba8ec 100644
--- a/paddle/operators/lookup_table_op.cc
+++ b/paddle/operators/lookup_table_op.cc
@@ -66,7 +66,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
- lookup_table_grad, ops::LookupTableOpGrad);
+ ops::LookupTableOpGrad);
REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>);
REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>);
diff --git a/paddle/operators/mean_op.cc b/paddle/operators/mean_op.cc
index d3d0e55a67..e66e0abb25 100644
--- a/paddle/operators/mean_op.cc
+++ b/paddle/operators/mean_op.cc
@@ -54,7 +54,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp);
+REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradOp);
REGISTER_OP_CPU_KERNEL(mean,
ops::MeanKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mean_grad,
diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc
index 1eee9644ba..b4afebcd97 100644
--- a/paddle/operators/minus_op.cc
+++ b/paddle/operators/minus_op.cc
@@ -81,7 +81,6 @@ class MinusGradOp : public NetOp {
USE_OP(scale);
USE_OP_ITSELF(identity);
namespace ops = paddle::operators;
-REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
- ops::MinusGradOp<float>);
+REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradOp<float>);
REGISTER_OP_CPU_KERNEL(minus,
ops::MinusKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc
index 173cc3850c..559d19e6bd 100644
--- a/paddle/operators/mul_op.cc
+++ b/paddle/operators/mul_op.cc
@@ -84,7 +84,7 @@ class MulOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc
index 6825dce332..63de91254f 100644
--- a/paddle/operators/rowwise_add_op.cc
+++ b/paddle/operators/rowwise_add_op.cc
@@ -74,7 +74,7 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
- rowwise_add_grad, ops::RowwiseAddGradOp);
+ ops::RowwiseAddGradOp);
REGISTER_OP_CPU_KERNEL(
rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/scale_op.cc b/paddle/operators/scale_op.cc
index 8e96a74c94..4e039688d4 100644
--- a/paddle/operators/scale_op.cc
+++ b/paddle/operators/scale_op.cc
@@ -97,7 +97,7 @@ class IdentityOp : public NetOp {
namespace ops = paddle::operators;
-REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
+REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>,
ops::ScaleGradOp<float>);
REGISTER_OP_CPU_KERNEL(scale,
ops::ScaleKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc
index f901edefa2..35c185ad80 100644
--- a/paddle/operators/scatter_op.cc
+++ b/paddle/operators/scatter_op.cc
@@ -77,8 +77,7 @@ Out[Index] = Ref[Index] + Updates
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, scatter_grad,
- ops::ScatterGradOp);
+REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, ops::ScatterGradOp);
REGISTER_OP_CPU_KERNEL(scatter,
ops::ScatterOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/sigmoid_op.cc b/paddle/operators/sigmoid_op.cc
index 761c6de8d4..f35b702384 100644
--- a/paddle/operators/sigmoid_op.cc
+++ b/paddle/operators/sigmoid_op.cc
@@ -53,8 +53,7 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
- ops::SigmoidOpGrad);
+REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, ops::SigmoidOpGrad);
REGISTER_OP_CPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/softmax_op.cc b/paddle/operators/softmax_op.cc
index 40c51a64c4..471bb288fb 100644
--- a/paddle/operators/softmax_op.cc
+++ b/paddle/operators/softmax_op.cc
@@ -62,8 +62,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
-REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, softmax_grad,
- ops::SoftmaxOpGrad);
+REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, ops::SoftmaxOpGrad);
REGISTER_OP_CPU_KERNEL(softmax,
ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
From fff380b54e1d8da774e0c183ad206ffbac5f9b4b Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:03:45 -0700
Subject: [PATCH 19/72] Add layers and operators design doc
---
doc/design/layers_operators.md | 71 ++++++++++++++++++++++++++++++++++
1 file changed, 71 insertions(+)
create mode 100644 doc/design/layers_operators.md
diff --git a/doc/design/layers_operators.md b/doc/design/layers_operators.md
new file mode 100644
index 0000000000..4cb590c85f
--- /dev/null
+++ b/doc/design/layers_operators.md
@@ -0,0 +1,71 @@
+In a DL system, we can compose one or more fine grained operators into a coarse grained one. For example, the FC layer can be composed of a multiplication operator and an add operator.
+
+Historically, some fine grained operations are known as operators, and some coarse level ones are known as layers. But we need a well-defined separation.
+
+In general, operators are those very fine grained operations, e.g., mul and add. In the implementation, we can write them as C++ functions:
+
+```c++
+template <typename T> T add(T x, T y) { return x + y; }
+template <typename T> T mul(T x, T y) { return x * y; }
+```
+
+Then we can wrap them into operators which are C++ classes and can be created from Python bindings by name. A C macro can do this. For example, the following macro invocation
+
+```c++
+#define MAKE_FUNCTION_OPERATOR(mul);
+```
+
+generates
+
+```c++
+class mulOp : public OperatorBase {...};
+REGISTER_OP(mulOp, "mul");
+```
+
+so that in Python we can create operator mul by:
+
+```python
+X1 = Var()
+X2 = Var()
+Y = Var()
+paddle.cpp.create_operator("mul", input=[X1, X2], output=Y)
+```
+
+Also, at the same time, we can compose a coarse level C++ operator class by composing functions `mul` and `add`:
+
+```c++
+class FCOp : public OperatorBase {
+ public:
+ void Run(...) {
+ add(mul(Input("X"), Input("W")), Input("b");
+ }
+};
+REGISTER_OP(FCOp, "fc");
+```
+
+We need to support such composition in Python as well. To do so, we need a higher level Python wrapping of operator creation than `paddle.cpp.create_operator`. This higher level operator API should be compatible with the layer API.
+
+Let's explain using an example. Suppose that we are going to compose the FC using mul and add in Python, we'd like to have Python functions `mul` and `add` defined in module `operator`:
+
+```python
+def mul(X1, X2):
+ O = Var
+ paddle.cpp.create_operator("mul", input={X1, Y1], output=O)
+ return O
+
+def add(X1, X2):
+ O = Var
+ paddle.cpp.create_operator("add", input={X1, X2], output=O)
+ return O
+```
+
+so that we can define
+
+```python
+def layer.fc(X):
+ W = Var()
+ b = Var()
+ return operator.add(operator.mul(X, W), b)
+```
+
+We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. This is how we differentiate layer and operators in PaddlePaddle.
From 6aef11baac1ff09b0fc69dd801ba1fe508374c31 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:05:37 -0700
Subject: [PATCH 20/72] Add title
---
doc/design/layers_operators.md | 2 ++
1 file changed, 2 insertions(+)
diff --git a/doc/design/layers_operators.md b/doc/design/layers_operators.md
index 4cb590c85f..83169260c8 100644
--- a/doc/design/layers_operators.md
+++ b/doc/design/layers_operators.md
@@ -1,3 +1,5 @@
+# Design Doc: Layers and Operators
+
In a DL system, we can compose one or more fine grained operators into a coarse grained one. For example, the FC layer can be composed of a multiplication operator and an add operator.
Historically, some fine grained operations are known as operators, and some coarse level ones are known as layers. But we need a well-defined separation.
From 887275efad3e9e7184b17ee6a141196756d8fc07 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:12:24 -0700
Subject: [PATCH 21/72] Add a table
---
doc/design/layers_operators.md | 16 +++++++++++++---
1 file changed, 13 insertions(+), 3 deletions(-)
diff --git a/doc/design/layers_operators.md b/doc/design/layers_operators.md
index 83169260c8..ee9e890e5c 100644
--- a/doc/design/layers_operators.md
+++ b/doc/design/layers_operators.md
@@ -50,12 +50,12 @@ We need to support such composition in Python as well. To do so, we need a high
Let's explain using an example. Suppose that we are going to compose the FC using mul and add in Python, we'd like to have Python functions `mul` and `add` defined in module `operator`:
```python
-def mul(X1, X2):
+def operator.mul(X1, X2):
O = Var
paddle.cpp.create_operator("mul", input={X1, Y1], output=O)
return O
-def add(X1, X2):
+def operator.add(X1, X2):
O = Var
paddle.cpp.create_operator("add", input={X1, X2], output=O)
return O
@@ -70,4 +70,14 @@ def layer.fc(X):
return operator.add(operator.mul(X, W), b)
```
-We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. This is how we differentiate layer and operators in PaddlePaddle.
+We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. So we have the following concepts in above illustrative example:
+
+| C++ functions/functors | mul | add | | |
+| C++ operator class | mulOp | addOp | FCOp | |
+| Python binding | operator.mul | operator.add | operator.fc | |
+| Python function | | | | layer.fc |
+
+This is how we differentiate layer and operators in PaddlePaddle:
+
+- those defined in C++ and have a lightweighted Python wrapper in module `operators` are operators; whereas
+- those who don't have C++ implementations but a Python implementation that compose C++ operators are known as layers.
From 7159062873bc097e9a9951eb009e5f625b23b4a9 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:13:37 -0700
Subject: [PATCH 22/72] Add table header
---
doc/design/layers_operators.md | 2 ++
1 file changed, 2 insertions(+)
diff --git a/doc/design/layers_operators.md b/doc/design/layers_operators.md
index ee9e890e5c..cda99888be 100644
--- a/doc/design/layers_operators.md
+++ b/doc/design/layers_operators.md
@@ -72,6 +72,8 @@ def layer.fc(X):
We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. So we have the following concepts in above illustrative example:
+| implementation | mul | add | fc | fc |
+---------------------------------------------------------------------------------
| C++ functions/functors | mul | add | | |
| C++ operator class | mulOp | addOp | FCOp | |
| Python binding | operator.mul | operator.add | operator.fc | |
From 90a442825c42740173ecf5ee3e64c24756bcfd56 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:14:51 -0700
Subject: [PATCH 23/72] Change table to verbatim
---
doc/design/layers_operators.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/doc/design/layers_operators.md b/doc/design/layers_operators.md
index cda99888be..131effbccf 100644
--- a/doc/design/layers_operators.md
+++ b/doc/design/layers_operators.md
@@ -72,12 +72,12 @@ def layer.fc(X):
We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. So we have the following concepts in above illustrative example:
-| implementation | mul | add | fc | fc |
----------------------------------------------------------------------------------
+```
| C++ functions/functors | mul | add | | |
| C++ operator class | mulOp | addOp | FCOp | |
| Python binding | operator.mul | operator.add | operator.fc | |
| Python function | | | | layer.fc |
+```
This is how we differentiate layer and operators in PaddlePaddle:
From 1f713f6822b96ccaff5684e547dac32590047d85 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:25:05 -0700
Subject: [PATCH 24/72] in response to comments from Jia-Yi
---
doc/design/layers_operators.md | 11 ++++++-----
1 file changed, 6 insertions(+), 5 deletions(-)
diff --git a/doc/design/layers_operators.md b/doc/design/layers_operators.md
index 131effbccf..e4f8d303e8 100644
--- a/doc/design/layers_operators.md
+++ b/doc/design/layers_operators.md
@@ -1,4 +1,4 @@
-# Design Doc: Layers and Operators
+# Design Doc: Functions, Operators, and Layers
In a DL system, we can compose one or more fine grained operators into a coarse grained one. For example, the FC layer can be composed of a multiplication operator and an add operator.
@@ -20,8 +20,8 @@ Then we can wrap them into operators which are C++ classes and can be created fr
generates
```c++
-class mulOp : public OperatorBase {...};
-REGISTER_OP(mulOp, "mul");
+template <typename T> class mulOp : public OperatorBase {...};
+REGISTER_OP(mulOp<float32>, "mul");
```
so that in Python we can create operator mul by:
@@ -36,12 +36,13 @@ paddle.cpp.create_operator("mul", input=[X1, X2], output=Y)
Also, at the same time, we can compose a coarse level C++ operator class by composing functions `mul` and `add`:
```c++
+template <typename T>
class FCOp : public OperatorBase {
public:
void Run(...) {
- add(mul(Input("X"), Input("W")), Input("b");
+ add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b");
}
-};
+};d
REGISTER_OP(FCOp, "fc");
```
From 305aebc005c4083ee3733688fc1ed23b5e1a8232 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:26:44 -0700
Subject: [PATCH 25/72] Rename file according to title
---
doc/design/{layers_operators.md => functions_operators_layers.md} | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename doc/design/{layers_operators.md => functions_operators_layers.md} (100%)
diff --git a/doc/design/layers_operators.md b/doc/design/functions_operators_layers.md
similarity index 100%
rename from doc/design/layers_operators.md
rename to doc/design/functions_operators_layers.md
From 2b3904a6a8c568f8bf20155f8f87a33c9f4e67b9 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Sat, 2 Sep 2017 17:34:10 -0700
Subject: [PATCH 26/72] Explain auto code generation
---
doc/design/functions_operators_layers.md | 21 +++++++++++++++++----
1 file changed, 17 insertions(+), 4 deletions(-)
diff --git a/doc/design/functions_operators_layers.md b/doc/design/functions_operators_layers.md
index e4f8d303e8..7a2e8fd0ac 100644
--- a/doc/design/functions_operators_layers.md
+++ b/doc/design/functions_operators_layers.md
@@ -42,7 +42,7 @@ class FCOp : public OperatorBase {
void Run(...) {
add(mul(Input<T>("X"), Input<T>("W")), Input<T>("b");
}
-};d
+};
REGISTER_OP(FCOp, "fc");
```
@@ -52,17 +52,17 @@ Let's explain using an example. Suppose that we are going to compose the FC usi
```python
def operator.mul(X1, X2):
- O = Var
+ O = Var()
paddle.cpp.create_operator("mul", input={X1, Y1], output=O)
return O
def operator.add(X1, X2):
- O = Var
+ O = Var()
paddle.cpp.create_operator("add", input={X1, X2], output=O)
return O
```
-so that we can define
+Above code snippets are automatically generated. Given them, users can define
```python
def layer.fc(X):
@@ -71,6 +71,19 @@ def layer.fc(X):
return operator.add(operator.mul(X, W), b)
```
+If we don't have `operator.mul` and `operator.add`, the definiton of `layer.fc` would be complicated:
+
+```python
+def layer.fc(X):
+ W = Var()
+ b = Var()
+ O1 = Var()
+ paddle.cpp.create_operator("mul", input=[X, W], output=O1)
+ O2 = Var()
+ paddle.cpp.create_operator("add", input=[O1, b], output=O2)
+ return O2
+```
+
We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. So we have the following concepts in above illustrative example:
```
From d156dfa93bd39beeb3f79422ae60e3ebdc5ac1c8 Mon Sep 17 00:00:00 2001
From: zchen0211 <chenzhuoyuan07@gmail.com>
Date: Sun, 3 Sep 2017 00:42:10 +0000
Subject: [PATCH 27/72] if else design doc
---
doc/design/if_else_op.md | 54 +++++++++++++
paddle/operators/switch_op.cc | 120 ----------------------------
paddle/operators/switch_op.h | 143 ----------------------------------
3 files changed, 54 insertions(+), 263 deletions(-)
create mode 100644 doc/design/if_else_op.md
delete mode 100644 paddle/operators/switch_op.cc
delete mode 100644 paddle/operators/switch_op.h
diff --git a/doc/design/if_else_op.md b/doc/design/if_else_op.md
new file mode 100644
index 0000000000..79bb543de1
--- /dev/null
+++ b/doc/design/if_else_op.md
@@ -0,0 +1,54 @@
+In an if_op, only inputs with condition satisfied will be run. The op could have multiple inputs and multiple outputs.
+We should have the following design:
+
+```python
+# A 1-d bool vector
+cond = Var()
+# create an op
+if = pd.if_op()
+
+with if.true_block() as block:
+ x1 = if.input(x1)
+ x2 = if.input(x2)
+ y = pd.add(x1, x2)
+ y2 = pd.fc(x1) # contains (w,b)
+ if.output(y)
+ if.output(y2)
+
+o1, o2 = if(cond)
+```
+
+In an if_op, only inputs with condition satisfied will be run.
+We should have the following design:
+```python
+# A 1-d bool vector
+cond = Var()
+# create an op
+if = pd.if_op()
+
+with if.true_block() as block:
+ x1 = if.input(x1)
+ x2 = if.input(x2)
+ y = pd.add(x1, x2)
+ y2 = pd.fc(x1) # contains (w,b)
+ if.output(y, name="y")
+ if.output(y2, name="y2")
+
+with if.false_block() as block:
+ x1 = if.input(x1)
+ x2 = if.input(x2)
+ y = pd.fc(x2)
+ y2 = pd.softmax(x1)
+ if.output(y, name="y")
+ if.output(y2, name="y2")
+
+o1, o2 = if(cond)
+```
+
+Some questions:
+ 1. how to know which inputs will be selected by condition?
+ e.g. True_block():
+ y = pd.fc(x)
+ # we will have x, w, b all as inputs
+ # but only x will be selected by cond, how can the block know?
+
diff --git a/paddle/operators/switch_op.cc b/paddle/operators/switch_op.cc
deleted file mode 100644
index 09574a89a3..0000000000
--- a/paddle/operators/switch_op.cc
+++ /dev/null
@@ -1,120 +0,0 @@
-#include "paddle/operators/switch_op.h"
-
-namespace paddle {
-namespace operators {
-
-// namespace if_else{
-
-
-void CondOp::Init() override {
-}
-
-void InferShape(const std::shared_ptr<Scope>& scope) const override {
- subnet_t = GetAttr<std::string>("subnet_t");
- subnet_f = GetAttr<std::string>("subnet_f");
-
- // Create two Nets
- // I use the same style as Recurrent_op, but does it create the net?
- // can be called like
- Variable* net_t = scope.FindVar(subnet_t);
- Variable* net_f = scope.FindVar(subnet_f);
-
- net_op_t = scope.FindVar(net_t)->GetMutable<NetOp>();
- net_op_f = scope.FindVar(net_f)->GetMutable<NetOp>();
-
- // Create two scopes
- scope_t = scope.NewScope();
- scope_f = scope.NewScope();
-
- // check cond of size (batch_size), type bool
- net_op_t->InferShape(scope_t);
- net_op_f->InferShape(scope_f);
-
- // check net_op_t and net_op_f of exactly same shape?
-}
-
-void IfElseOp::Run(const std::shared_ptr<Scope>& scope,
- const platform::DeviceContext& dev_ctx) const {
- /* step 1: create two subnets and scopes, supposed done in Infershape() */
-
- /* step 2: get true and false index */
- cond = Input(name.cond);
- // get condition tensor
- auto cond_tensor = scope.get<Tensor>(cond);
- // tensor to cpu, whatever device it used to be in
- cond_cpu.CopyFrom(cond_tensor, platform::CPUPlace());
-
- size_t batch_size = cond_cpu.dims()[0];
-
- // keep index of true and false to slice, clear them first before each batch
- true_index.clear();
- false_index.clear();
-
- // get a DDim type variable dims, check dimension
- auto dims = input0.dims();
- for(int i=0; i<dims; i++) {
- if (cond_cpu->data[i])
- true_index.push_back(i);
- else
- false_index.push_back(i);
- }
-
- // turn true_index and false_index to tensors
- Tensor* true_index_tensor = new Tensor(true_index);
- Tensor* false_index_tensor = new Tensor(false_index);
-
- /* Step 3: Gather */
- { // True Scope
- // Create new stuff
- for (auto& input : net_op_t->inputs_) {
- scope_t.NewVar(input);
- if (input.type() != PARAMETER) { // gather and slice required
- // Get Tensor and gather
- Tensor* input_gather_ = scope_t.FindVar(input)->GetMutable<Tensor>();
- Tensor* input_full_ = scope.FindVar(input)->GetMutable<Tensor>();
- input_gather_ = Gather(input_full_, true_index_tensor);
- }
- }
-
- for (auto& output : net_op->outputs_) {
- scope_t.NewVar(output);
- }
-
- net_op_t.Run();
- }
-
- { // False Scope
- // Create new stuff
- for (auto& input : net_op_f->inputs_) {
- scope_f.NewVar(input);
- if (input.type() != PARAMETER) { // gather and slice required
- // Get Tensor and gather
- Tensor* input_gather_ = scope_f.FindVar(input)->GetMutable<Tensor>();
- Tensor* input_full_ = scope.FindVar(input)->GetMutable<Tensor>();
- input_gather_ = Gather(input_full_, false_index_tensor);
- }
- }
-
- for (auto& output : net_op->outputs_) {
- scope_t.NewVar(output);
- }
-
- net_op_f.Run();
- }
-
- /* Merge Output Together by scatter update */
- for (auto& ouput : outputs_) {
- Tensor* output_t = scope_t->FindVar(output)->GetMutable<Tensor>();
- Tensor* output_f = scope_f->FindVar(output)->GetMutable<Tensor>();
- Tensor* output_tensor = scope->FindVar(output)->GetMutable<Tensor>();
- Scatter(output_t, output_tensor, true_index_tensor);
- Scatter(output_f, output_tensor, false_index_tensor);
- }
-}
-
-} // namespace operators
-} // namespace paddle
-
-REGISTER_OP(ifelse_op,
- paddle::operators::IfElseOp,
- paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
diff --git a/paddle/operators/switch_op.h b/paddle/operators/switch_op.h
deleted file mode 100644
index f72726bce1..0000000000
--- a/paddle/operators/switch_op.h
+++ /dev/null
@@ -1,143 +0,0 @@
-/* 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 "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
-#include "paddle/framework/ddim.h"
-#include "paddle/operators/gather.h"
-
-namespace paddle {
-namespace operators {
-
-using namespace paddle::framework;
-
-template <typename Place, typename T>
-class CondOp final : public OperatorBase {
-public:
- void Init() override;
-
- /**
- * InferShape must be called before Run.
- */
- virtual void InferShape(const std::shared_ptr<Scope>& scope) const override {
- scope_t = scope.NewScope();
- scope_f = scope.NewScope();
- net_op_t->InferShape(scope_t);
- net_op_f->InferShape(scope_f);
- tensor_t = new Tensor();
- tensor_f = new Tensor();
- { // True branch
- for (auto& input : net_op_t->Inputs()) {
- auto var_name = input.second;
- if (!scope_t.FindVar(var_name) {
- scope_t.NewVar(var_name)->GetMutable<Tensor>();
- }
- }
- }
- { // False branch
- for (auto& input : net_op_f->Inputs()) {
- auto var_name = input.second;
- if (!scope_f.FindVar(var_name) {
- scope_f.NewVar(var_name)->GetMutable<Tensor>();
- }
- }
- }
- }
-
- virtual void Run(const std::shared_ptr<Scope>& scope,
- const platform::DeviceContext& dev_ctx) const override {
- auto* cond = context.Input<Tensor>("Cond");
- // Step 1: get the index
- true_index.clear();
- false_index.clear();
- for(int i = 0; i < cond->dims()[0]; ++i) {
- if (cond->data<bool>()[i])
- true_index.push_back(i);
- else:
- false_index.push_back(i);
- }
- framework::DDim dim_ = paddle::framework::make_ddim({0});
- dim_[0] = true_index.size();
- tensor_t->Resize(dim_);
- // set value
- for (int i = 0; i < dim_[0]; ++i)
- tensor_t->mutable_data<int>()[i] = true_index[i];
- dim_[0] = false_index.size();
- tensor_f->Resize(dim_);
- // set value
- for (int i = 0; i < dim_[0]; ++i)
- tensor_f->mutable_data<int>()[i] = false_index[i];
-
- // Step 2: collect data by calling gather
- { // True branch
- for (auto& input : net_op_t->Inputs()) {
- auto var_name = input.second;
- // find Tensor
- Tensor* Tensor_parent = scope.FindVar(var_name)->GetMutable<Tensor>();
- Tensor* Tensor_child = scope_t.FindVar(var_name)->GetMutable<Tensor>();
- Gather<T>(dev_ctx.GetPlace(), tensor_parent, tensor_t, tensor_child);
- }
-
- }
- }
-
-private:
- Scope* scope_t;
- Scope* scope_f;
-
- // subnet_t
- std::unique_ptr<framework::OperatorBase> net_op_t;
- // NetOp* net_op_t;
- // subnet_f
- std::unique_ptr<framework::OperatorBase> net_op_f;
- // NetOp* net_op_f;
-
- // T_index
- vector<int> true_index;
- Tensor* tensor_t;
- // F_index
- vector<int> false_index;
- Tensor* tensor_f;
-};
-
-class CondOpMaker : public OpProtoAndCheckerMaker {
-public:
- IfElseOpMaker(OpProto *proto, OpAttrChecker *op_checker)
- : OpProtoAndCheckerMaker(proto, op_checker) {
- AddInput("Cond", "The condition, which is a bool vector");
- AddAttr<std::string>("subnet_t", "The subnet network to be called when Cond[i] == true");
- AddAttr<std::string>("subnet_f", "The subnet network to be called when Cond[i] == false");
- AddOutput("Out", "The output of if-else op");
- AddComment(R"DOC(
-Sample dependent Cond Operator:
-The equation is: Out[i] = subnet_t[i], if Cond[i] == true
-Out[i] = subnet_t[i], if Cond[i] == false
-)DOC");
- }
-};
-
-class CondGradientOp final : public OperatorBase {
-public:
- void Init() override;
-
- virtual void InferShape(const std::shared_ptr<Scope>& scope) const override;
-
- virtual void Run(const std::shared_ptr<Scope>& scope,
- const platform::DeviceContext& dev_ctx) const override;
-};
-
-} // namespace operators
-} // namespace paddle
From e39a2c9f22610385b1ed680c6e015e10e9bef241 Mon Sep 17 00:00:00 2001
From: Zhuoyuan <chenzhuoyuan07@gmail.com>
Date: Sat, 2 Sep 2017 17:43:30 -0700
Subject: [PATCH 28/72] update if else
---
doc/design/if_else_op.md | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/doc/design/if_else_op.md b/doc/design/if_else_op.md
index 79bb543de1..cd66bfabf2 100644
--- a/doc/design/if_else_op.md
+++ b/doc/design/if_else_op.md
@@ -47,8 +47,9 @@ o1, o2 = if(cond)
Some questions:
1. how to know which inputs will be selected by condition?
- e.g. True_block():
+ ```python
+ True_block():
y = pd.fc(x)
# we will have x, w, b all as inputs
# but only x will be selected by cond, how can the block know?
-
+```
From fd8df0806dd07af4fed9ae40ffe5ec571639d2c4 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Sat, 2 Sep 2017 20:37:48 -0700
Subject: [PATCH 29/72] Make operator Input/Output can return nullptr
---
paddle/framework/operator.cc | 32 +++++++++++++++--------
paddle/framework/operator.h | 50 ++++++++++++++++++++----------------
2 files changed, 50 insertions(+), 32 deletions(-)
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 7abbde610f..4d32795884 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -33,12 +33,18 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
-const std::string& OperatorBase::Input(const std::string& name) const {
+std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
- PADDLE_ENFORCE_EQ(ins.size(), 1UL,
- "Op %s input %s should contain only one variable", type_,
- name);
- return ins[0];
+ switch (ins.size()) {
+ case 0:
+ return kEmptyVarName;
+ case 1:
+ return ins[0];
+ default:
+ PADDLE_THROW("Op %s input %s should contain only one variable", type_,
+ name);
+ return "";
+ }
}
const std::vector<std::string>& OperatorBase::Inputs(
@@ -49,12 +55,18 @@ const std::vector<std::string>& OperatorBase::Inputs(
return it->second;
}
-const std::string& OperatorBase::Output(const std::string& name) const {
+std::string OperatorBase::Output(const std::string& name) const {
auto& outs = Outputs(name);
- PADDLE_ENFORCE_EQ(outs.size(), 1UL,
- "Op %s output %s should contain only one variable", type_,
- name);
- return outs[0];
+ switch (outs.size()) {
+ case 0:
+ return kEmptyVarName;
+ case 1:
+ return outs[0];
+ default:
+ PADDLE_THROW("Op %s output %s should contain only one variable", type_,
+ name);
+ return "";
+ }
}
const std::vector<std::string>& OperatorBase::Outputs(
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index 8397570d26..7759473ef3 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -95,12 +95,12 @@ 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`
- const std::string& Input(const std::string& name) const;
+ std::string Input(const std::string& name) const;
//! Get a input which has multiple variables.
const std::vector<std::string>& Inputs(const std::string& name) const;
//! Get a output with argument's name described in `op_proto`
- const std::string& Output(const std::string& name) const;
+ std::string Output(const std::string& name) const;
//! Get an output which has multiple variables.
//! TODO add a vector_view to prevent memory copy.
const std::vector<std::string>& Outputs(const std::string& name) const;
@@ -238,11 +238,21 @@ class InferShapeContext {
}
const Variable* InputVar(const std::string& name) const {
- return scope_.FindVar(op_.Input(name));
+ auto ipt = op_.Input(name);
+ if (ipt == kEmptyVarName) {
+ return nullptr;
+ } else {
+ return scope_.FindVar(ipt);
+ }
}
Variable* OutputVar(const std::string& name) const {
- return scope_.FindVar(op_.Output(name));
+ auto opt = op_.Output(name);
+ if (opt == kEmptyVarName) {
+ return nullptr;
+ } else {
+ return scope_.FindVar(opt);
+ }
}
const std::vector<const Variable*> MultiInputVar(
@@ -250,9 +260,11 @@ class InferShapeContext {
auto names = op_.Inputs(name);
std::vector<const Variable*> res;
res.reserve(names.size());
- std::transform(
- names.begin(), names.end(), std::back_inserter(res),
- [this](const std::string& name) { return scope_.FindVar(name); });
+ std::transform(names.begin(), names.end(), std::back_inserter(res),
+ [this](const std::string& name) {
+ return name != kEmptyVarName ? scope_.FindVar(name)
+ : nullptr;
+ });
return res;
}
@@ -260,24 +272,24 @@ class InferShapeContext {
auto names = op_.Outputs(name);
std::vector<const Variable*> res;
res.reserve(names.size());
- std::transform(
- names.begin(), names.end(), std::back_inserter(res),
- [this](const std::string& name) { return scope_.FindVar(name); });
+ std::transform(names.begin(), names.end(), std::back_inserter(res),
+ [this](const std::string& name) {
+ return name != kEmptyVarName ? scope_.FindVar(name)
+ : nullptr;
+ });
return res;
}
template <typename T>
const T* Input(const std::string& name) const {
auto* var = InputVar(name);
- PADDLE_ENFORCE_NOT_NULL(var, "Input(%s) should not be nullptr", name);
- return &var->Get<T>();
+ return var == nullptr ? nullptr : &var->Get<T>();
}
template <typename T>
T* Output(const std::string& name) const {
auto var = OutputVar(name);
- PADDLE_ENFORCE_NOT_NULL(var, "Output(%s) should not be nullptr", name);
- return var->GetMutable<T>();
+ return var == nullptr ? nullptr : var->GetMutable<T>();
}
template <typename T>
@@ -288,10 +300,7 @@ class InferShapeContext {
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
- PADDLE_ENFORCE_NOT_NULL(
- var, "MultiInput(%s:%s) should not be nullptr", name,
- sub_name);
- return &var->Get<T>();
+ return var == nullptr ? nullptr : &var->Get<T>();
});
return res;
}
@@ -304,10 +313,7 @@ class InferShapeContext {
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
- PADDLE_ENFORCE_NOT_NULL(
- var, "MultiOutput(%s:%s) should not be nullptr.", name,
- sub_name);
- return var->GetMutable<T>();
+ return var == nullptr ? nullptr : var->GetMutable<T>();
});
return res;
}
From 879866cc565ed5e2fe76e92bcb2268c6916f7ffd Mon Sep 17 00:00:00 2001
From: Zhuo Zhang <lispc@qq.com>
Date: Sun, 3 Sep 2017 15:57:14 +0800
Subject: [PATCH 30/72] add PyDataProvider2.InputType pretty print function
---
python/paddle/trainer/PyDataProvider2.py | 35 ++++++++++++++++++++++++
1 file changed, 35 insertions(+)
diff --git a/python/paddle/trainer/PyDataProvider2.py b/python/paddle/trainer/PyDataProvider2.py
index 7e305e2cd9..033c71cf8f 100644
--- a/python/paddle/trainer/PyDataProvider2.py
+++ b/python/paddle/trainer/PyDataProvider2.py
@@ -27,6 +27,14 @@ class SequenceType(object):
SEQUENCE = 1
SUB_SEQUENCE = 2
+ @classmethod
+ def tostring(cls, value):
+ for k in cls.__dict__:
+ if not k.startswith('__'):
+ if getattr(cls, k) == value:
+ return cls.__name__ + '.' + k
+ return 'INVALID(' + str(value) + ')'
+
# TODO(yuyang18): Add string data type here.
class DataType(object):
@@ -35,6 +43,14 @@ class DataType(object):
SparseValue = 2
Index = 3
+ @classmethod
+ def tostring(cls, value):
+ for k in cls.__dict__:
+ if not k.startswith('__'):
+ if getattr(cls, k) == value:
+ return cls.__name__ + '.' + k
+ return 'INVALID(' + str(value) + ')'
+
class CacheType(object):
NO_CACHE = 0 # No cache at all
@@ -69,6 +85,25 @@ class InputType(object):
self.seq_type = seq_type
self.type = tp
+ def __repr__(self):
+ """
+ Return a human readable representation like 'InputType(dim=25921, seq_type=SequenceType.NO_SEQUENCE, type=DataType.Dense)'
+ """
+ repr_str = type(self).__name__
+ repr_str += '('
+ serialize_func_map = {
+ 'dim': repr,
+ 'seq_type': SequenceType.tostring,
+ 'type': DataType.tostring
+ }
+ for idx, k in enumerate(self.__slots__):
+ if idx != 0:
+ repr_str += ', '
+ repr_str += (
+ k + '=' + serialize_func_map.get(k, repr)(getattr(self, k)))
+ repr_str += ')'
+ return repr_str
+
def dense_slot(dim, seq_type=SequenceType.NO_SEQUENCE):
"""
From d7a1e40e10bbe9778916c61755ae04592ebb19b7 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Sun, 3 Sep 2017 11:02:47 -0700
Subject: [PATCH 31/72] Simple Implementation
---
paddle/framework/operator.cc | 28 ++++++++--------------------
paddle/framework/operator.h | 20 ++++++--------------
2 files changed, 14 insertions(+), 34 deletions(-)
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 4d32795884..68fb469b56 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -35,16 +35,10 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
- switch (ins.size()) {
- case 0:
- return kEmptyVarName;
- case 1:
- return ins[0];
- default:
- PADDLE_THROW("Op %s input %s should contain only one variable", type_,
- name);
- return "";
- }
+ PADDLE_ENFORCE_LE(ins.size(), 1UL,
+ "Op %s input %s should contain only one variable", type_,
+ name);
+ return ins.empty() ? kEmptyVarName : ins[0];
}
const std::vector<std::string>& OperatorBase::Inputs(
@@ -57,16 +51,10 @@ const std::vector<std::string>& OperatorBase::Inputs(
std::string OperatorBase::Output(const std::string& name) const {
auto& outs = Outputs(name);
- switch (outs.size()) {
- case 0:
- return kEmptyVarName;
- case 1:
- return outs[0];
- default:
- PADDLE_THROW("Op %s output %s should contain only one variable", type_,
- name);
- return "";
- }
+ PADDLE_ENFORCE_LE(outs.size(), 1UL,
+ "Op %s output %s should contain only one variable", type_,
+ name);
+ return outs.empty() ? kEmptyVarName : outs[0];
}
const std::vector<std::string>& OperatorBase::Outputs(
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index 7759473ef3..60ca8b279e 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -239,20 +239,12 @@ class InferShapeContext {
const Variable* InputVar(const std::string& name) const {
auto ipt = op_.Input(name);
- if (ipt == kEmptyVarName) {
- return nullptr;
- } else {
- return scope_.FindVar(ipt);
- }
+ return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
}
Variable* OutputVar(const std::string& name) const {
auto opt = op_.Output(name);
- if (opt == kEmptyVarName) {
- return nullptr;
- } else {
- return scope_.FindVar(opt);
- }
+ return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
}
const std::vector<const Variable*> MultiInputVar(
@@ -262,8 +254,8 @@ class InferShapeContext {
res.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(res),
[this](const std::string& name) {
- return name != kEmptyVarName ? scope_.FindVar(name)
- : nullptr;
+ return name == kEmptyVarName ? nullptr
+ : scope_.FindVar(name);
});
return res;
}
@@ -274,8 +266,8 @@ class InferShapeContext {
res.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(res),
[this](const std::string& name) {
- return name != kEmptyVarName ? scope_.FindVar(name)
- : nullptr;
+ return name == kEmptyVarName ? nullptr
+ : scope_.FindVar(name);
});
return res;
}
From 32244b528b75889d4bee4b5291c9d85f40978700 Mon Sep 17 00:00:00 2001
From: zchen0211 <chenzhuoyuan07@gmail.com>
Date: Sun, 3 Sep 2017 11:11:40 -0700
Subject: [PATCH 32/72] if else modified
---
doc/design/if_else_op.md | 98 +++++++++++++++++++++-------------------
1 file changed, 52 insertions(+), 46 deletions(-)
diff --git a/doc/design/if_else_op.md b/doc/design/if_else_op.md
index 79bb543de1..2e2fbe99ad 100644
--- a/doc/design/if_else_op.md
+++ b/doc/design/if_else_op.md
@@ -1,54 +1,60 @@
-In an if_op, only inputs with condition satisfied will be run. The op could have multiple inputs and multiple outputs.
-We should have the following design:
+IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has M (M<=N) instances, each corresponds to a true element in `cond`.
```python
-# A 1-d bool vector
-cond = Var()
-# create an op
-if = pd.if_op()
-
-with if.true_block() as block:
- x1 = if.input(x1)
- x2 = if.input(x2)
- y = pd.add(x1, x2)
- y2 = pd.fc(x1) # contains (w,b)
- if.output(y)
- if.output(y2)
-
-o1, o2 = if(cond)
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+
+b = pd.create_ifop_builder(inputs=[x], output_num=1)
+with b.true_block():
+ x = b.inputs(0)
+ z = operator.add(x, y)
+ b.set_output(0, operator.softmax(z))
+
+out = b(cond)
```
-In an if_op, only inputs with condition satisfied will be run.
-We should have the following design:
+If we want the output still has N instances, we can use IfElseOp with a default value, whose minibatch size must be N:
+
```python
-# A 1-d bool vector
-cond = Var()
-# create an op
-if = pd.if_op()
-
-with if.true_block() as block:
- x1 = if.input(x1)
- x2 = if.input(x2)
- y = pd.add(x1, x2)
- y2 = pd.fc(x1) # contains (w,b)
- if.output(y, name="y")
- if.output(y2, name="y2")
-
-with if.false_block() as block:
- x1 = if.input(x1)
- x2 = if.input(x2)
- y = pd.fc(x2)
- y2 = pd.softmax(x1)
- if.output(y, name="y")
- if.output(y2, name="y2")
-
-o1, o2 = if(cond)
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+default_value = var()
+b = pd.create_ifelseop_builder(inputs=[x], output_num=1)
+with b.true_block():
+ x = b.inputs(0)
+ z = operator.add(x, y)
+ b.set_output(0, operator.softmax(z))
+
+with b.false_block():
+ x = b.inputs(0)
+ z = layer.fc(x)
+ b.set_output(0, operator.softmax(z))
+
+out = b(cond)
```
-Some questions:
- 1. how to know which inputs will be selected by condition?
- e.g. True_block():
- y = pd.fc(x)
- # we will have x, w, b all as inputs
- # but only x will be selected by cond, how can the block know?
+If only true_block is set in an IfElseOp, we can have a default value for false as:
+```python
+import paddle as pd
+
+x = var()
+y = var()
+cond = var()
+default_value = var()
+b = pd.create_ifelseop_builder(inputs=[x], output_num=1, default_value)
+
+with b.true_block():
+ x = b.inputs(0)
+ z = operator.add(x, y)
+ b.set_output(0, operator.softmax(z))
+
+out = b(cond)
+```
+where default_value is a list of vars for `cond` == False.
From d323831a0bee005a5f58d13d2691eaa338cc6d38 Mon Sep 17 00:00:00 2001
From: qiaolongfei <qiaolongfei@baidu.com>
Date: Sun, 3 Sep 2017 12:13:22 -0700
Subject: [PATCH 33/72] add GetAttr to InferShapeContext
---
paddle/framework/operator.h | 5 +++++
paddle/operators/gaussian_random_op.cc | 7 +++----
paddle/operators/gaussian_random_op.cu | 7 +++----
paddle/operators/scale_op.h | 2 +-
paddle/operators/sgd_op.h | 2 +-
paddle/operators/uniform_random_op.cc | 7 +++----
paddle/operators/uniform_random_op.cu | 7 +++----
7 files changed, 19 insertions(+), 18 deletions(-)
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index 9874a7c295..29fa3ffd87 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -233,6 +233,11 @@ class InferShapeContext {
const Scope& scope() const { return scope_; }
+ template <typename T>
+ inline const T& GetAttr(const std::string& name) const {
+ return op_.GetAttr<T>(name);
+ }
+
size_t InputSize(const std::string& name) const {
return op_.Inputs(name).size();
}
diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc
index 9eb976fed1..056447901d 100644
--- a/paddle/operators/gaussian_random_op.cc
+++ b/paddle/operators/gaussian_random_op.cc
@@ -19,13 +19,12 @@ template <typename T>
class CPUGaussianRandomKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
- float mean = context.op().GetAttr<float>("mean");
- float std = context.op().GetAttr<float>("std");
+ float mean = context.GetAttr<float>("mean");
+ float std = context.GetAttr<float>("std");
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
- unsigned int seed =
- static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
+ unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu
index 9769fbcd2b..833a82bbf2 100644
--- a/paddle/operators/gaussian_random_op.cu
+++ b/paddle/operators/gaussian_random_op.cu
@@ -42,14 +42,13 @@ class GPUGaussianRandomKernel : public framework::OpKernel {
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
- unsigned int seed =
- static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
+ unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
if (seed == 0) {
std::random_device rd;
seed = rd();
}
- T mean = static_cast<T>(context.op().GetAttr<float>("mean"));
- T std = static_cast<T>(context.op().GetAttr<float>("std"));
+ T mean = static_cast<T>(context.GetAttr<float>("mean"));
+ T std = static_cast<T>(context.GetAttr<float>("std"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
ssize_t N = framework::product(tensor->dims());
thrust::transform(index_sequence_begin, index_sequence_begin + N,
diff --git a/paddle/operators/scale_op.h b/paddle/operators/scale_op.h
index 760b26f5e9..65fb77eefa 100644
--- a/paddle/operators/scale_op.h
+++ b/paddle/operators/scale_op.h
@@ -27,7 +27,7 @@ class ScaleKernel : public framework::OpKernel {
auto* in = context.Input<framework::Tensor>("X");
tensor->mutable_data<T>(in->place());
- auto scale = static_cast<T>(context.op().GetAttr<AttrType>("scale"));
+ auto scale = static_cast<T>(context.GetAttr<AttrType>("scale"));
auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
auto eigen_in = framework::EigenVector<T>::Flatten(*in);
diff --git a/paddle/operators/sgd_op.h b/paddle/operators/sgd_op.h
index b54a7864b0..8422b622ee 100644
--- a/paddle/operators/sgd_op.h
+++ b/paddle/operators/sgd_op.h
@@ -31,7 +31,7 @@ class SGDOpKernel : public framework::OpKernel {
auto param = ctx.Input<Tensor>("param");
auto grad = ctx.Input<Tensor>("grad");
auto param_out = ctx.Output<Tensor>("param_out");
- float lr = ctx.op().GetAttr<float>("learning_rate");
+ float lr = ctx.GetAttr<float>("learning_rate");
param_out->mutable_data<T>(ctx.GetPlace());
diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc
index dbff2a1365..2d943c4508 100644
--- a/paddle/operators/uniform_random_op.cc
+++ b/paddle/operators/uniform_random_op.cc
@@ -26,16 +26,15 @@ class CPUUniformRandomKernel : public framework::OpKernel {
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
- unsigned int seed =
- static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
+ unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
}
engine.seed(seed);
std::uniform_real_distribution<T> dist(
- static_cast<T>(context.op().GetAttr<float>("min")),
- static_cast<T>(context.op().GetAttr<float>("max")));
+ static_cast<T>(context.GetAttr<float>("min")),
+ static_cast<T>(context.GetAttr<float>("max")));
ssize_t size = framework::product(tensor->dims());
for (ssize_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 fc8397f134..df993c0779 100644
--- a/paddle/operators/uniform_random_op.cu
+++ b/paddle/operators/uniform_random_op.cu
@@ -45,14 +45,13 @@ class GPUUniformRandomKernel : public framework::OpKernel {
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
- unsigned int seed =
- static_cast<unsigned int>(context.op().GetAttr<int>("seed"));
+ unsigned int seed = static_cast<unsigned int>(context.GetAttr<int>("seed"));
if (seed == 0) {
std::random_device rd;
seed = rd();
}
- T min = static_cast<T>(context.op().GetAttr<float>("min"));
- T max = static_cast<T>(context.op().GetAttr<float>("max"));
+ T min = static_cast<T>(context.GetAttr<float>("min"));
+ T max = static_cast<T>(context.GetAttr<float>("max"));
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
ssize_t N = framework::product(tensor->dims());
thrust::transform(index_sequence_begin, index_sequence_begin + N,
From 7d5bdbbfee3e0c5a4dba3057c6dfb028dbf28ab8 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Sun, 3 Sep 2017 12:28:18 -0700
Subject: [PATCH 34/72] Add GenerateTemporaryNames/CheckAllInputOutputSet
---
paddle/framework/op_info.h | 14 ++++++++++--
paddle/framework/operator.cc | 41 +++++++++++++++++++++++++++---------
paddle/framework/operator.h | 4 ++++
3 files changed, 47 insertions(+), 12 deletions(-)
diff --git a/paddle/framework/op_info.h b/paddle/framework/op_info.h
index 94245c6c44..b98d8f23a1 100644
--- a/paddle/framework/op_info.h
+++ b/paddle/framework/op_info.h
@@ -80,9 +80,19 @@ class OpInfoMap {
}
const OpInfo& Get(const std::string& type) const {
+ auto op_info_ptr = GetNullable(type);
+ PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not been registered",
+ type);
+ return *op_info_ptr;
+ }
+
+ const OpInfo* GetNullable(const std::string& type) const {
auto it = map_.find(type);
- PADDLE_ENFORCE(it != map_.end(), "Operator %s are not found", type);
- return it->second;
+ if (it == map_.end()) {
+ return nullptr;
+ } else {
+ return &it->second;
+ }
}
template <typename Callback>
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 68fb469b56..88a872deaa 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -119,16 +119,8 @@ OperatorBase::OperatorBase(const std::string& type,
const VariableNameMap& outputs,
const AttributeMap& attrs)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs) {
- static std::atomic<size_t> gUniqId(0UL);
- for (auto& output : outputs_) {
- for (auto& output_name : output.second) {
- if (output_name == kTempVarName) {
- output_name += type_;
- output_name += "@";
- output_name += std::to_string(gUniqId.fetch_add(1));
- }
- }
- }
+ GenerateTemporaryNames();
+ CheckAllInputOutputSet();
}
std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
@@ -156,6 +148,35 @@ std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
return ret_val;
}
+void OperatorBase::CheckAllInputOutputSet() const {
+ auto& info_map = OpInfoMap::Instance();
+ auto* op_info = info_map.GetNullable(Type());
+ if (op_info == nullptr) return;
+
+ for (auto& in : op_info->Proto().inputs()) {
+ PADDLE_ENFORCE(inputs_.find(in.name()) != inputs_.end(),
+ "input %s is not set", in.name());
+ }
+
+ for (auto& out : op_info->Proto().outputs()) {
+ PADDLE_ENFORCE(outputs_.find(out.name()) != outputs_.end(),
+ "output %s is not set", out.name());
+ }
+}
+
+void OperatorBase::GenerateTemporaryNames() {
+ static std::atomic<size_t> gUniqId(0UL);
+ for (auto& output : outputs_) {
+ for (auto& output_name : output.second) {
+ if (output_name == kTempVarName) {
+ output_name += type_;
+ output_name += "@";
+ output_name += std::to_string(gUniqId.fetch_add(1));
+ }
+ }
+ }
+}
+
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h
index 60ca8b279e..590e335fdc 100644
--- a/paddle/framework/operator.h
+++ b/paddle/framework/operator.h
@@ -127,6 +127,10 @@ class OperatorBase {
// IG (Inputs Gradients)
VariableNameMap outputs_;
AttributeMap attrs_;
+
+ private:
+ void GenerateTemporaryNames();
+ void CheckAllInputOutputSet() const;
};
// Macro for define a clone method.
From 13b43279dc543a6fcbeca3899e8c3fbbbda227c1 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Sun, 3 Sep 2017 14:47:53 -0700
Subject: [PATCH 35/72] Fix CI Test
---
paddle/framework/backward_test.cc | 6 +++---
paddle/framework/operator.cc | 6 +++---
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc
index f100c4d054..ad8003420d 100644
--- a/paddle/framework/backward_test.cc
+++ b/paddle/framework/backward_test.cc
@@ -127,8 +127,8 @@ class FillZeroOpMaker : public OpProtoAndCheckerMaker {
public:
FillZeroOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
- AddInput("x", "x");
- AddOutput("out", "out");
+ AddInput("Src", "x");
+ AddOutput("Dst", "out");
AddComment("");
}
};
@@ -138,7 +138,7 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "x").AsDuplicable();
- AddOutput("Y", "y");
+ AddOutput("Out", "out");
AddComment("");
}
};
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 88a872deaa..790cfc4746 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -151,16 +151,16 @@ std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
void OperatorBase::CheckAllInputOutputSet() const {
auto& info_map = OpInfoMap::Instance();
auto* op_info = info_map.GetNullable(Type());
- if (op_info == nullptr) return;
+ if (op_info == nullptr || op_info->proto_ == nullptr) return;
for (auto& in : op_info->Proto().inputs()) {
PADDLE_ENFORCE(inputs_.find(in.name()) != inputs_.end(),
- "input %s is not set", in.name());
+ "Type %s's input %s is not set", Type(), in.name());
}
for (auto& out : op_info->Proto().outputs()) {
PADDLE_ENFORCE(outputs_.find(out.name()) != outputs_.end(),
- "output %s is not set", out.name());
+ "Type %s's output %s is not set", Type(), out.name());
}
}
From e0c27a96ebdc45c72f5c148e14b5acfd3c13bdf5 Mon Sep 17 00:00:00 2001
From: zchen0211 <chenzhuoyuan07@gmail.com>
Date: Sun, 3 Sep 2017 21:56:14 +0000
Subject: [PATCH 36/72] if else slight modify
---
doc/design/if_else_op.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/doc/design/if_else_op.md b/doc/design/if_else_op.md
index daba92248e..7370c2a24f 100644
--- a/doc/design/if_else_op.md
+++ b/doc/design/if_else_op.md
@@ -7,7 +7,7 @@ x = var()
y = var()
cond = var()
-b = pd.create_ifop_builder(inputs=[x], output_num=1)
+b = pd.create_ifop(inputs=[x], output_num=1)
with b.true_block():
x = b.inputs(0)
z = operator.add(x, y)
@@ -25,7 +25,7 @@ x = var()
y = var()
cond = var()
default_value = var()
-b = pd.create_ifelseop_builder(inputs=[x], output_num=1)
+b = pd.create_ifelseop(inputs=[x], output_num=1)
with b.true_block():
x = b.inputs(0)
z = operator.add(x, y)
@@ -47,7 +47,7 @@ x = var()
y = var()
cond = var()
default_value = var()
-b = pd.create_ifelseop_builder(inputs=[x], output_num=1, default_value)
+b = pd.create_ifelseop(inputs=[x], output_num=1, default_value)
with b.true_block():
x = b.inputs(0)
From 8c048aa0996f57c7a94d83c0bd043fa40fb3ab9f Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Mon, 4 Sep 2017 11:15:45 +0800
Subject: [PATCH 37/72] Remove cudnn_helper.cc
---
paddle/platform/CMakeLists.txt | 1 -
paddle/platform/cudnn_helper.cc | 15 ---------------
paddle/platform/cudnn_helper.h | 22 ----------------------
paddle/platform/cudnn_helper_test.cc | 3 +--
4 files changed, 1 insertion(+), 40 deletions(-)
delete mode 100644 paddle/platform/cudnn_helper.cc
diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt
index 9a3ad8eb64..17bdac8749 100644
--- a/paddle/platform/CMakeLists.txt
+++ b/paddle/platform/CMakeLists.txt
@@ -23,5 +23,4 @@ cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)
-nv_library(cudnn_helper SRCS cudnn_helper.cc DEPS dynload_cuda)
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
diff --git a/paddle/platform/cudnn_helper.cc b/paddle/platform/cudnn_helper.cc
deleted file mode 100644
index 764285e2ba..0000000000
--- a/paddle/platform/cudnn_helper.cc
+++ /dev/null
@@ -1,15 +0,0 @@
-/* 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/platform/cudnn_helper.h"
diff --git a/paddle/platform/cudnn_helper.h b/paddle/platform/cudnn_helper.h
index 6a43f49cfc..24ddf3441c 100644
--- a/paddle/platform/cudnn_helper.h
+++ b/paddle/platform/cudnn_helper.h
@@ -14,9 +14,6 @@ limitations under the License. */
#pragma once
-#ifndef PADDLE_ONLY_CPU
-#include <cudnn.h>
-#include "glog/logging.h"
#include "paddle/platform/dynload/cudnn.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/macros.h"
@@ -42,30 +39,12 @@ template <>
class CudnnDataType<float> {
public:
static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
- typedef const float ScalingParamType;
- static ScalingParamType* kOne() {
- static ScalingParamType v = 1.0;
- return &v;
- }
- static const ScalingParamType* kZero() {
- static ScalingParamType v = 0.0;
- return &v;
- }
};
template <>
class CudnnDataType<double> {
public:
static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
- typedef const double ScalingParamType;
- static ScalingParamType* kOne() {
- static ScalingParamType v = 1.0;
- return &v;
- }
- static ScalingParamType* kZero() {
- static ScalingParamType v = 0.0;
- return &v;
- }
};
inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) {
@@ -219,4 +198,3 @@ class ScopedPoolingDescriptor {
} // namespace platform
} // namespace paddle
-#endif
diff --git a/paddle/platform/cudnn_helper_test.cc b/paddle/platform/cudnn_helper_test.cc
index 729f2f8a10..6bd85ae1ca 100644
--- a/paddle/platform/cudnn_helper_test.cc
+++ b/paddle/platform/cudnn_helper_test.cc
@@ -13,8 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/platform/cudnn_helper.h"
-#include "glog/logging.h"
-#include "gtest/gtest.h"
+#include <gtest/gtest.h>
TEST(CudnnHelper, ScopedTensorDescriptor) {
using paddle::platform::ScopedTensorDescriptor;
From faa4da4835e0622387e2ab1f6481875c5243c5c9 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Mon, 4 Sep 2017 13:02:27 +0800
Subject: [PATCH 38/72] fix ctc edit distance in v2 API.
---
.../gserver/evaluators/CTCErrorEvaluator.cpp | 56 +++++++++++++++----
1 file changed, 46 insertions(+), 10 deletions(-)
diff --git a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
index 132119015f..8e2dc020cd 100644
--- a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
+++ b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
@@ -26,6 +26,7 @@ private:
int numTimes_, numClasses_, numSequences_, blank_;
real deletions_, insertions_, substitutions_;
int seqClassficationError_;
+ mutable std::unordered_map<std::string, real> evalResults_;
std::vector<int> path2String(const std::vector<int>& path) {
std::vector<int> str;
@@ -183,6 +184,18 @@ private:
return stringAlignment(gtStr, recogStr);
}
+ void storeLocalValues() const {
+ evalResults_["error"] = numSequences_ ? totalScore_ / numSequences_ : 0;
+ evalResults_["deletion_error"] =
+ numSequences_ ? deletions_ / numSequences_ : 0;
+ evalResults_["insertion_error"] =
+ numSequences_ ? insertions_ / numSequences_ : 0;
+ evalResults_["substitution_error"] =
+ numSequences_ ? substitutions_ / numSequences_ : 0;
+ evalResults_["sequence_error"] =
+ (real)seqClassficationError_ / numSequences_;
+ }
+
public:
CTCErrorEvaluator()
: numTimes_(0),
@@ -245,16 +258,12 @@ public:
}
virtual void printStats(std::ostream& os) const {
- os << config_.name() << "="
- << (numSequences_ ? totalScore_ / numSequences_ : 0);
- os << " deletions error"
- << "=" << (numSequences_ ? deletions_ / numSequences_ : 0);
- os << " insertions error"
- << "=" << (numSequences_ ? insertions_ / numSequences_ : 0);
- os << " substitutions error"
- << "=" << (numSequences_ ? substitutions_ / numSequences_ : 0);
- os << " sequences error"
- << "=" << (real)seqClassficationError_ / numSequences_;
+ storeLocalValues();
+ os << config_.name() << "=" << evalResults_["error"];
+ os << " deletions error = " << evalResults_["deletion_error"];
+ os << " insertions error = " << evalResults_["insertion_error"];
+ os << " substitution error = " << evalResults_["substitution_error"];
+ os << " sequence error = " << evalResults_["sequence_error"];
}
virtual void distributeEval(ParameterClient2* client) {
@@ -272,6 +281,33 @@ public:
seqClassficationError_ = (int)buf[4];
numSequences_ = (int)buf[5];
}
+
+ void getNames(std::vector<std::string>* names) {
+ storeLocalValues();
+ names->reserve(names->size() + evalResults_.size());
+ for (auto it = evalResults_.begin(); it != evalResults_.end(); ++it) {
+ names->push_back(config_.name() + "." + it->first);
+ }
+ }
+
+ real getValue(const std::string& name, Error* err) const {
+ storeLocalValues();
+
+ const std::string delimiter(".");
+ std::string::size_type foundPos = name.find(delimiter, 0);
+ CHECK(foundPos != std::string::npos);
+
+ auto it = evalResults_.find(
+ name.substr(foundPos + delimiter.size(), name.length()));
+ if (it == evalResults_.end()) {
+ *err = Error("Evaluator does not have the key %s", name.c_str());
+ return 0.0f;
+ }
+
+ return it->second;
+ }
+
+ std::string getTypeImpl() const { return "ctc_edit_distance"; }
};
REGISTER_EVALUATOR(ctc_edit_distance, CTCErrorEvaluator);
From a60128aeb235c3e53b6785537bf3f67502ca079d Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 4 Sep 2017 14:11:43 +0800
Subject: [PATCH 39/72] move identity_op from scale_op.cc to be a single file
---
paddle/operators/CMakeLists.txt | 2 ++
paddle/operators/identity_op.cc | 54 +++++++++++++++++++++++++++++++++
paddle/operators/scale_op.cc | 31 +------------------
3 files changed, 57 insertions(+), 30 deletions(-)
create mode 100644 paddle/operators/identity_op.cc
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index e5efcccb0e..d85c3d575a 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -48,12 +48,14 @@ add_subdirectory(math)
list(REMOVE_ITEM GENERAL_OPS
net_op
+ identity_op
minus_op
mul_op
recurrent_op
scale_op)
op_library(net_op SRCS net_op.cc)
+op_library(identity_op SRCS identity_op.cc DEPS scale_op)
op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
diff --git a/paddle/operators/identity_op.cc b/paddle/operators/identity_op.cc
new file mode 100644
index 0000000000..be956bf3b3
--- /dev/null
+++ b/paddle/operators/identity_op.cc
@@ -0,0 +1,54 @@
+/* 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/net_op.h"
+#include "paddle/operators/scale_op.h"
+
+namespace paddle {
+namespace operators {
+
+// identity is a alias of scale op. This is also a example for creating a alias
+// operator.
+template <typename AttrType>
+class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+ IdentityOpMaker(framework::OpProto *proto,
+ framework::OpAttrChecker *op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("X", "input tensor of identity op");
+ AddOutput("Out", "output tensor of identity op");
+ AddComment("identity operator. Just a alias of scale op which scale = 1.0");
+ }
+};
+
+template <typename AttrType>
+class IdentityOp : public NetOp {
+ public:
+ IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
+ const framework::VariableNameMap &outputs,
+ const framework::AttributeMap &attrs)
+ : NetOp(type, inputs, outputs, attrs) {
+ AppendOp(framework::OpRegistry::CreateOp(
+ "scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
+ {{"scale", static_cast<AttrType>(1)}}));
+ }
+};
+
+} // namespace operators
+} // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
+ ops::IdentityOpMaker<float>);
diff --git a/paddle/operators/scale_op.cc b/paddle/operators/scale_op.cc
index 4e039688d4..57696d2ee6 100644
--- a/paddle/operators/scale_op.cc
+++ b/paddle/operators/scale_op.cc
@@ -48,7 +48,7 @@ The equation is: Out = scale*X
}
};
-// Identity Op's gradient is identity op, too.
+// Scale Op's gradient is scale op, too.
// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
template <typename AttrType>
class ScaleGradOp : public NetOp {
@@ -65,33 +65,6 @@ class ScaleGradOp : public NetOp {
}
};
-// identity is a alias of scale op. This is also a example for creating a alias
-// operator.
-template <typename AttrType>
-class IdentityOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
- IdentityOpMaker(framework::OpProto *proto,
- framework::OpAttrChecker *op_checker)
- : OpProtoAndCheckerMaker(proto, op_checker) {
- AddInput("X", "input tensor of identity op");
- AddOutput("Out", "output tensor of identity op");
- AddComment("identity operator. Just a alias of scale op which scale = 1.0");
- }
-};
-
-template <typename AttrType>
-class IdentityOp : public NetOp {
- public:
- IdentityOp(const std::string &type, const framework::VariableNameMap &inputs,
- const framework::VariableNameMap &outputs,
- const framework::AttributeMap &attrs)
- : NetOp(type, inputs, outputs, attrs) {
- AppendOp(framework::OpRegistry::CreateOp(
- "scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
- {{"scale", static_cast<AttrType>(1)}}));
- }
-};
-
} // namespace operators
} // namespace paddle
@@ -101,5 +74,3 @@ REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>,
ops::ScaleGradOp<float>);
REGISTER_OP_CPU_KERNEL(scale,
ops::ScaleKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp<float>,
- ops::IdentityOpMaker<float>);
From fb49bc2c99c5deb0d7ebc9507410b8e60b0bb27c Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Mon, 4 Sep 2017 14:15:50 +0800
Subject: [PATCH 40/72] rename mse_cost into square_error_cost.
---
.../paddle/trainer_config_helpers/layers.py | 29 +++++++++++--------
.../test_cost_layers_with_weight.protostr | 8 ++---
.../configs/test_cost_layers_with_weight.py | 2 +-
3 files changed, 22 insertions(+), 17 deletions(-)
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 2bd274fad2..47ac601e67 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -53,7 +53,7 @@ __all__ = [
'cos_sim',
'hsigmoid',
'conv_projection',
- 'mse_cost',
+ 'square_error_cost',
'regression_cost',
'classification_cost',
'LayerOutput',
@@ -4238,13 +4238,18 @@ def __cost_input__(input, label, weight=None):
@wrap_name_default()
@layer_support()
-def mse_cost(input, label, weight=None, name=None, coeff=1.0, layer_attr=None):
+def square_error_cost(input,
+ label,
+ weight=None,
+ name=None,
+ coeff=1.0,
+ layer_attr=None):
"""
- mean squared error cost:
+ sum of square error cost:
.. math::
- \\frac{1}{N}\sum_{i=1}^N(t_i-y_i)^2
+ cost = \\sum_{i=1}^N(t_i-y_i)^2
:param name: layer name.
:type name: basestring
@@ -4273,7 +4278,7 @@ def mse_cost(input, label, weight=None, name=None, coeff=1.0, layer_attr=None):
return LayerOutput(name, LayerType.COST, parents=parents, size=1)
-regression_cost = mse_cost
+regression_cost = square_error_cost
@wrap_name_default("cost")
@@ -5798,9 +5803,9 @@ def huber_regression_cost(input,
coeff=1.0,
layer_attr=None):
"""
- In statistics, the Huber loss is a loss function used in robust regression,
- that is less sensitive to outliers in data than the squared error loss.
- Given a prediction f(x), a label y and :math:`\delta`, the loss function
+ In statistics, the Huber loss is a loss function used in robust regression,
+ that is less sensitive to outliers in data than the squared error loss.
+ Given a prediction f(x), a label y and :math:`\delta`, the loss function
is defined as:
.. math:
@@ -5848,13 +5853,13 @@ def huber_classification_cost(input,
coeff=1.0,
layer_attr=None):
"""
- For classification purposes, a variant of the Huber loss called modified Huber
- is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
- a true binary class label :math:`y\in \left \{-1, 1 \right \}`, the modified Huber
+ For classification purposes, a variant of the Huber loss called modified Huber
+ is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
+ a true binary class label :math:`y\in \left \{-1, 1 \right \}`, the modified Huber
loss is defined as:
.. math:
- loss = \max \left ( 0, 1-yf(x) \right )^2, yf(x)\geq 1
+ loss = \max \left ( 0, 1-yf(x) \right )^2, yf(x)\geq 1
loss = -4yf(x), \text{otherwise}
The example usage is:
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers_with_weight.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers_with_weight.protostr
index 96fb1d4ebd..cec8a73db6 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers_with_weight.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers_with_weight.protostr
@@ -45,7 +45,7 @@ layers {
coeff: 1.0
}
layers {
- name: "__mse_cost_0__"
+ name: "__square_error_cost_0__"
type: "square_error"
size: 1
active_type: ""
@@ -130,7 +130,7 @@ input_layer_names: "label"
input_layer_names: "weight"
input_layer_names: "multi_class_label"
output_layer_names: "__cost_0__"
-output_layer_names: "__mse_cost_0__"
+output_layer_names: "__square_error_cost_0__"
output_layer_names: "__nce_layer_0__"
evaluators {
name: "classification_error_evaluator"
@@ -146,7 +146,7 @@ sub_models {
layer_names: "weight"
layer_names: "__fc_layer_0__"
layer_names: "__cost_0__"
- layer_names: "__mse_cost_0__"
+ layer_names: "__square_error_cost_0__"
layer_names: "multi_class_label"
layer_names: "__nce_layer_0__"
input_layer_names: "input"
@@ -154,7 +154,7 @@ sub_models {
input_layer_names: "weight"
input_layer_names: "multi_class_label"
output_layer_names: "__cost_0__"
- output_layer_names: "__mse_cost_0__"
+ output_layer_names: "__square_error_cost_0__"
output_layer_names: "__nce_layer_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers_with_weight.py b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers_with_weight.py
index c369062930..caa6aaa943 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers_with_weight.py
+++ b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers_with_weight.py
@@ -10,7 +10,7 @@ fc = fc_layer(input=data, size=10, act=SoftmaxActivation())
outputs(
classification_cost(
input=fc, label=lbl, weight=wt),
- mse_cost(
+ square_error_cost(
input=fc, label=lbl, weight=wt),
nce_layer(
input=fc,
From c2c5df29110c69f3cf64e627c78ce7897e487025 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Mon, 4 Sep 2017 14:56:13 +0800
Subject: [PATCH 41/72] update doc.
---
doc/api/v2/config/layer.rst | 4 ++--
doc/getstarted/basic_usage/index_en.rst | 2 +-
doc/getstarted/concepts/src/train.py | 2 +-
doc/getstarted/concepts/use_concepts_cn.rst | 4 ++--
doc/howto/usage/k8s/k8s_distributed_cn.md | 2 +-
5 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst
index 1329b77bb4..c94627a728 100644
--- a/doc/api/v2/config/layer.rst
+++ b/doc/api/v2/config/layer.rst
@@ -434,9 +434,9 @@ lambda_cost
.. autoclass:: paddle.v2.layer.lambda_cost
:noindex:
-mse_cost
+square_error_cost
--------
-.. autoclass:: paddle.v2.layer.mse_cost
+.. autoclass:: paddle.v2.layer.square_error_cost
:noindex:
rank_cost
diff --git a/doc/getstarted/basic_usage/index_en.rst b/doc/getstarted/basic_usage/index_en.rst
index 6775da20c2..2cc438ebbe 100644
--- a/doc/getstarted/basic_usage/index_en.rst
+++ b/doc/getstarted/basic_usage/index_en.rst
@@ -49,7 +49,7 @@ To recover this relationship between ``X`` and ``Y``, we use a neural network wi
x = data_layer(name='x', size=1)
y = data_layer(name='y', size=1)
y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
- cost = mse_cost(input=y_predict, label=y)
+ cost = square_error_cost(input=y_predict, label=y)
outputs(cost)
Some of the most fundamental usages of PaddlePaddle are demonstrated:
diff --git a/doc/getstarted/concepts/src/train.py b/doc/getstarted/concepts/src/train.py
index 7e604f23de..8aceb23406 100644
--- a/doc/getstarted/concepts/src/train.py
+++ b/doc/getstarted/concepts/src/train.py
@@ -8,7 +8,7 @@ paddle.init(use_gpu=False)
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(2))
y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
-cost = paddle.layer.mse_cost(input=y_predict, label=y)
+cost = paddle.layer.square_error_cost(input=y_predict, label=y)
# create parameters
parameters = paddle.parameters.create(cost)
diff --git a/doc/getstarted/concepts/use_concepts_cn.rst b/doc/getstarted/concepts/use_concepts_cn.rst
index f15b11bd78..25bf7ff1d0 100644
--- a/doc/getstarted/concepts/use_concepts_cn.rst
+++ b/doc/getstarted/concepts/use_concepts_cn.rst
@@ -81,7 +81,7 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
.. code-block:: bash
y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
- cost = paddle.layer.mse_cost(input=y_predict, label=y)
+ cost = paddle.layer.square_error_cost(input=y_predict, label=y)
其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。
@@ -147,4 +147,4 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
.. literalinclude:: src/train.py
:linenos:
-有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。
\ No newline at end of file
+有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。
diff --git a/doc/howto/usage/k8s/k8s_distributed_cn.md b/doc/howto/usage/k8s/k8s_distributed_cn.md
index 3121b3f59d..a9bebf0955 100644
--- a/doc/howto/usage/k8s/k8s_distributed_cn.md
+++ b/doc/howto/usage/k8s/k8s_distributed_cn.md
@@ -213,7 +213,7 @@ I1116 09:10:17.123440 50 Util.cpp:130] Calling runInitFunctions
I1116 09:10:17.123764 50 Util.cpp:143] Call runInitFunctions done.
[WARNING 2016-11-16 09:10:17,227 default_decorators.py:40] please use keyword arguments in paddle config.
[INFO 2016-11-16 09:10:17,239 networks.py:1282] The input order is [movie_id, title, genres, user_id, gender, age, occupation, rating]
-[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__mse_cost_0__]
+[INFO 2016-11-16 09:10:17,239 networks.py:1289] The output order is [__square_error_cost_0__]
I1116 09:10:17.392917 50 Trainer.cpp:170] trainer mode: Normal
I1116 09:10:17.613910 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
I1116 09:10:17.680917 50 PyDataProvider2.cpp:257] loading dataprovider dataprovider::process
From 0b478e991c95f838da41bfcbf11f1e2b80ac17eb Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Mon, 4 Sep 2017 15:12:56 +0800
Subject: [PATCH 42/72] follow comments.
---
paddle/gserver/evaluators/CTCErrorEvaluator.cpp | 17 ++++++++++-------
1 file changed, 10 insertions(+), 7 deletions(-)
diff --git a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
index 8e2dc020cd..928c77a088 100644
--- a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
+++ b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
@@ -14,6 +14,7 @@ limitations under the License. */
#include "Evaluator.h"
#include "paddle/gserver/gradientmachines/NeuralNetwork.h"
+#include "paddle/utils/StringUtil.h"
namespace paddle {
@@ -259,7 +260,7 @@ public:
virtual void printStats(std::ostream& os) const {
storeLocalValues();
- os << config_.name() << "=" << evalResults_["error"];
+ os << config_.name() << " error = " << evalResults_["error"];
os << " deletions error = " << evalResults_["deletion_error"];
os << " insertions error = " << evalResults_["insertion_error"];
os << " substitution error = " << evalResults_["substitution_error"];
@@ -293,12 +294,10 @@ public:
real getValue(const std::string& name, Error* err) const {
storeLocalValues();
- const std::string delimiter(".");
- std::string::size_type foundPos = name.find(delimiter, 0);
- CHECK(foundPos != std::string::npos);
+ std::vector<std::string> buffers;
+ paddle::str::split(name, '.', &buffers);
+ auto it = evalResults_.find(buffers[buffers.size() - 1]);
- auto it = evalResults_.find(
- name.substr(foundPos + delimiter.size(), name.length()));
if (it == evalResults_.end()) {
*err = Error("Evaluator does not have the key %s", name.c_str());
return 0.0f;
@@ -307,7 +306,11 @@ public:
return it->second;
}
- std::string getTypeImpl() const { return "ctc_edit_distance"; }
+ std::string getType(const std::string& name, Error* err) const {
+ getValue(name, err);
+ if (!err->isOK()) return "";
+ return "ctc_edit_distance";
+ }
};
REGISTER_EVALUATOR(ctc_edit_distance, CTCErrorEvaluator);
From 5b93ac7778754943914b3f9511208e55a3526f64 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 4 Sep 2017 15:49:33 +0800
Subject: [PATCH 43/72] package a new USE_NO_KERNEL_OP for USE_OP_ITSELF
---
doc/howto/dev/new_op_cn.md | 6 ++++++
paddle/framework/backward.cc | 2 +-
paddle/framework/op_registry.h | 2 ++
paddle/operators/minus_op.cc | 2 +-
paddle/operators/recurrent_op.cc | 2 +-
paddle/pybind/pybind.cc | 4 ++--
python/paddle/v2/framework/op.py | 2 +-
7 files changed, 14 insertions(+), 6 deletions(-)
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index ec79b7f42b..dfcbce9037 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -227,6 +227,12 @@ make mul_op
USE_CPU_ONLY_OP(gather);
```
+ 如果OP不带Kernel，则使用`USE_NO_KENREL_OP`:
+
+ ```
+ USE_NO_KENREL_OP(recurrent);
+ ```
+
使用`USE_OP`告知编译器需要链接该Op的目标文件，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc
index 6b4c612cd8..c5d4662215 100644
--- a/paddle/framework/backward.cc
+++ b/paddle/framework/backward.cc
@@ -182,7 +182,7 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
});
// process recurrent gradient op as a special operator.
- if (forwardOp.Type() == "recurrent_op") {
+ if (forwardOp.Type() == "recurrent") {
// NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or
// this will result in infinite loop.
const auto& rnnop =
diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h
index 64c7f23ab6..27f1927aed 100644
--- a/paddle/framework/op_registry.h
+++ b/paddle/framework/op_registry.h
@@ -194,6 +194,8 @@ class OpKernelRegistrar : public Registrar {
USE_OP_DEVICE_KERNEL(op_type, GPU)
#endif
+#define USE_NO_KERNEL_OP(op_type) USE_OP_ITSELF(op_type);
+
#define USE_CPU_ONLY_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_DEVICE_KERNEL(op_type, CPU);
diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc
index b4afebcd97..138cdbd563 100644
--- a/paddle/operators/minus_op.cc
+++ b/paddle/operators/minus_op.cc
@@ -79,7 +79,7 @@ class MinusGradOp : public NetOp {
} // namespace paddle
USE_OP(scale);
-USE_OP_ITSELF(identity);
+USE_NO_KERNEL_OP(identity);
namespace ops = paddle::operators;
REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradOp<float>);
REGISTER_OP_CPU_KERNEL(minus,
diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc
index 16bd249cb3..e826703c60 100644
--- a/paddle/operators/recurrent_op.cc
+++ b/paddle/operators/recurrent_op.cc
@@ -235,5 +235,5 @@ RecurrentGradientOp::RecurrentGradientOp(
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(
- recurrent_op, paddle::operators::RecurrentOp,
+ recurrent, paddle::operators::RecurrentOp,
paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 3bc150ccb7..6896422617 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -39,12 +39,12 @@ USE_OP(sigmoid);
USE_OP(softmax);
USE_OP(rowwise_add);
USE_OP(fill_zeros_like);
-USE_OP_ITSELF(recurrent_op);
+USE_NO_KERNEL_OP(recurrent);
USE_OP(gaussian_random);
USE_OP(uniform_random);
USE_OP(lookup_table);
USE_OP(scale);
-USE_OP_ITSELF(identity);
+USE_NO_KERNEL_OP(identity);
USE_OP(minus);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
diff --git a/python/paddle/v2/framework/op.py b/python/paddle/v2/framework/op.py
index 6ac656321e..e7e932f6fe 100644
--- a/python/paddle/v2/framework/op.py
+++ b/python/paddle/v2/framework/op.py
@@ -179,7 +179,7 @@ class OperatorFactory(object):
class __RecurrentOp__(object):
__proto__ = None
- type = 'recurrent_op'
+ type = 'recurrent'
def __init__(self):
# cache recurrent_op's proto
From 10eacac9a700c02909f2c714280d66246db954b1 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Mon, 4 Sep 2017 16:01:40 +0800
Subject: [PATCH 44/72] follow comments and fix unittest.
---
doc/getstarted/basic_usage/index_cn.rst | 4 ++--
doc/getstarted/concepts/use_concepts_cn.rst | 2 +-
python/paddle/v2/tests/test_layer.py | 5 +++--
3 files changed, 6 insertions(+), 5 deletions(-)
diff --git a/doc/getstarted/basic_usage/index_cn.rst b/doc/getstarted/basic_usage/index_cn.rst
index 428f58830e..b473944fc7 100644
--- a/doc/getstarted/basic_usage/index_cn.rst
+++ b/doc/getstarted/basic_usage/index_cn.rst
@@ -55,7 +55,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
# 线性计算网络层: ȳ = wx + b
ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
# 计算误差函数，即 ȳ 和真实 y 之间的距离
- cost = mse_cost(input= ȳ, label=y)
+ cost = square_error_cost(input= ȳ, label=y)
outputs(cost)
@@ -69,7 +69,7 @@ PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍
- **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。
- **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。
- - **回归误差代价层**：回归误差代价层 `mse_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
+ - **回归误差代价层**：回归误差代价层 `square_error_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
定义了网络结构并保存为 `trainer_config.py` 之后，运行以下训练命令：
diff --git a/doc/getstarted/concepts/use_concepts_cn.rst b/doc/getstarted/concepts/use_concepts_cn.rst
index 25bf7ff1d0..c243083794 100644
--- a/doc/getstarted/concepts/use_concepts_cn.rst
+++ b/doc/getstarted/concepts/use_concepts_cn.rst
@@ -83,7 +83,7 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
cost = paddle.layer.square_error_cost(input=y_predict, label=y)
-其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上均方误差层。
+其中，x与y为之前描述的输入层；而y_predict是接收x作为输入，接上一个全连接层；cost接收y_predict与y作为输入，接上平方误差层。
最后一层cost中记录了神经网络的所有拓扑结构，通过组合不同的layer，我们即可完成神经网络的搭建。
diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py
index 783a0ca85d..de932ad715 100644
--- a/python/paddle/v2/tests/test_layer.py
+++ b/python/paddle/v2/tests/test_layer.py
@@ -134,8 +134,9 @@ class CostLayerTest(unittest.TestCase):
cost3 = layer.cross_entropy_cost(input=inference, label=label)
cost4 = layer.cross_entropy_with_selfnorm_cost(
input=inference, label=label)
- cost5 = layer.mse_cost(input=inference, label=label)
- cost6 = layer.mse_cost(input=inference, label=label, weight=weight)
+ cost5 = layer.square_error_cost(input=inference, label=label)
+ cost6 = layer.square_error_cost(
+ input=inference, label=label, weight=weight)
cost7 = layer.multi_binary_label_cross_entropy_cost(
input=inference, label=label)
cost8 = layer.rank_cost(left=score, right=score, label=score)
From 740c8ba12a29418eefeb22a843bebb1781f300fe Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 4 Sep 2017 16:40:23 +0800
Subject: [PATCH 45/72] remove scatter_op.cu/gather_op.cu as they support
only_cpu now
---
paddle/operators/CMakeLists.txt | 24 ++++++++++++++++--------
paddle/operators/gather_op.cu | 20 --------------------
paddle/operators/scatter_op.cu | 20 --------------------
3 files changed, 16 insertions(+), 48 deletions(-)
delete mode 100644 paddle/operators/gather_op.cu
delete mode 100644 paddle/operators/scatter_op.cu
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index d85c3d575a..90185101c4 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -46,15 +46,20 @@ endfunction()
add_subdirectory(math)
-list(REMOVE_ITEM GENERAL_OPS
- net_op
- identity_op
- minus_op
- mul_op
- recurrent_op
- scale_op)
+set(ONLYCPU_OPS
+ net_op
+ gather_op
+ scatter_op)
+foreach(src ${ONLYCPU_OPS})
+ op_library(${src} SRCS ${src}.cc)
+endforeach()
-op_library(net_op SRCS net_op.cc)
+set(DEPS_OPS
+ identity_op
+ minus_op
+ mul_op
+ recurrent_op
+ scale_op)
op_library(identity_op SRCS identity_op.cc DEPS scale_op)
op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
@@ -62,6 +67,9 @@ op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op)
op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
+list(REMOVE_ITEM GENERAL_OPS
+ ${ONLYCPU_OPS}
+ ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
op_library(${src} SRCS ${src}.cc ${src}.cu)
endforeach()
diff --git a/paddle/operators/gather_op.cu b/paddle/operators/gather_op.cu
deleted file mode 100644
index 3f04a7b3f8..0000000000
--- a/paddle/operators/gather_op.cu
+++ /dev/null
@@ -1,20 +0,0 @@
-/* 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/gather_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(gather,
- ops::GatherOpKernel<paddle::platform::GPUPlace, float>);
diff --git a/paddle/operators/scatter_op.cu b/paddle/operators/scatter_op.cu
deleted file mode 100644
index 6716b47883..0000000000
--- a/paddle/operators/scatter_op.cu
+++ /dev/null
@@ -1,20 +0,0 @@
-/* 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/scatter_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(scatter,
- ops::ScatterOpKernel<paddle::platform::GPUPlace, float>);
From a523bea8e585bd63f4167e012a05b03ad435b574 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Mon, 4 Sep 2017 20:40:33 +0800
Subject: [PATCH 46/72] fix getType.
---
paddle/gserver/evaluators/CTCErrorEvaluator.cpp | 8 +++++---
paddle/gserver/evaluators/ChunkEvaluator.cpp | 8 +++++++-
paddle/gserver/evaluators/Evaluator.h | 13 ++++++++-----
3 files changed, 20 insertions(+), 9 deletions(-)
diff --git a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
index 928c77a088..92087fa32b 100644
--- a/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
+++ b/paddle/gserver/evaluators/CTCErrorEvaluator.cpp
@@ -21,7 +21,7 @@ namespace paddle {
/**
* calculate sequence-to-sequence edit distance
*/
-class CTCErrorEvaluator : public NotGetableEvaluator {
+class CTCErrorEvaluator : public Evaluator {
private:
MatrixPtr outActivations_;
int numTimes_, numClasses_, numSequences_, blank_;
@@ -307,8 +307,10 @@ public:
}
std::string getType(const std::string& name, Error* err) const {
- getValue(name, err);
- if (!err->isOK()) return "";
+ this->getValue(name, err);
+ if (!err->isOK()) {
+ return "";
+ }
return "ctc_edit_distance";
}
};
diff --git a/paddle/gserver/evaluators/ChunkEvaluator.cpp b/paddle/gserver/evaluators/ChunkEvaluator.cpp
index 1658282f3a..a2ab15eede 100644
--- a/paddle/gserver/evaluators/ChunkEvaluator.cpp
+++ b/paddle/gserver/evaluators/ChunkEvaluator.cpp
@@ -268,7 +268,13 @@ public:
}
// get type of evaluator
- std::string getTypeImpl() const { return "chunk"; }
+ std::string getType(const std::string& name, Error* err) const {
+ this->getValue(name, err);
+ if (!err->isOK()) {
+ return "";
+ }
+ return "chunk";
+ }
private:
void storeLocalValues() const {
diff --git a/paddle/gserver/evaluators/Evaluator.h b/paddle/gserver/evaluators/Evaluator.h
index b114500e2b..90203553e0 100644
--- a/paddle/gserver/evaluators/Evaluator.h
+++ b/paddle/gserver/evaluators/Evaluator.h
@@ -211,6 +211,7 @@ public:
*err = Error("Not implemented");
return .0f;
}
+
std::string getType(const std::string& name, Error* err) const {
*err = Error("Not implemented");
return "";
@@ -331,6 +332,7 @@ private:
protected:
std::string getTypeImpl() const;
};
+
/**
* @brief precision, recall and f1 score Evaluator
* \f[
@@ -358,6 +360,12 @@ public:
virtual void distributeEval(ParameterClient2* client);
+ void getNames(std::vector<std::string>* names);
+
+ real getValue(const std::string& name, Error* err) const;
+
+ std::string getType(const std::string& name, Error* err) const;
+
struct StatsInfo {
/// numbers of true positives
double TP;
@@ -428,11 +436,6 @@ private:
mutable std::unordered_map<std::string, real> values_;
void storeLocalValues() const;
- // Evaluator interface
-public:
- void getNames(std::vector<std::string>* names);
- real getValue(const std::string& name, Error* err) const;
- std::string getType(const std::string& name, Error* err) const;
};
/*
From 7c785426e4f15c7ef3a6659e1d32385ed5e48273 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Mon, 4 Sep 2017 22:04:08 +0800
Subject: [PATCH 47/72] add missing protostr for sub_nested_seq_layer.
---
..._layers.protostr => test_sub_nested_seq_select_layer.protostr} | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename python/paddle/trainer_config_helpers/tests/configs/protostr/{test_seq_select_layers.protostr => test_sub_nested_seq_select_layer.protostr} (100%)
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_select_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_sub_nested_seq_select_layer.protostr
similarity index 100%
rename from python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_select_layers.protostr
rename to python/paddle/trainer_config_helpers/tests/configs/protostr/test_sub_nested_seq_select_layer.protostr
From 447033296d927dd0b0c1240e2ecccaa667eb0fe8 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Mon, 4 Sep 2017 22:29:34 +0800
Subject: [PATCH 48/72] Make some operator correctly handle gradients for multi
inputs.
---
paddle/operators/mul_op.cc | 4 +--
paddle/operators/mul_op.h | 36 ++++++++++---------
paddle/operators/rowwise_add_op.cc | 6 ++--
paddle/operators/rowwise_add_op.h | 24 +++++++------
paddle/operators/scatter_op.cc | 4 +--
paddle/operators/scatter_op.h | 10 +++---
.../v2/framework/tests/gradient_checker.py | 25 +++++++++++--
.../paddle/v2/framework/tests/test_mul_op.py | 3 +-
8 files changed, 72 insertions(+), 40 deletions(-)
diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc
index 8d0f59745f..603dc7f4bd 100644
--- a/paddle/operators/mul_op.cc
+++ b/paddle/operators/mul_op.cc
@@ -75,8 +75,8 @@ class MulOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE(y_dims[1] == out_dims[1],
"Out@GRAD M X N must equal to Y dims 1, N ");
- x_grad->Resize(x_dims);
- y_grad->Resize(y_dims);
+ if (x_grad) x_grad->Resize(x_dims);
+ if (y_grad) y_grad->Resize(y_dims);
}
};
diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h
index 8facc02814..66ed2f81c7 100644
--- a/paddle/operators/mul_op.h
+++ b/paddle/operators/mul_op.h
@@ -31,13 +31,13 @@ template <typename Place, typename T>
class MulKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
- auto* X = context.Input<Tensor>("X");
- auto* Y = context.Input<Tensor>("Y");
- auto* Z = context.Output<Tensor>("Out");
- Z->mutable_data<T>(context.GetPlace());
+ auto* x = context.Input<Tensor>("X");
+ auto* y = context.Input<Tensor>("Y");
+ auto* z = context.Output<Tensor>("Out");
+ z->mutable_data<T>(context.GetPlace());
auto* device_context =
const_cast<platform::DeviceContext*>(context.device_context_);
- math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
+ math::matmul<Place, T>(*x, false, *y, false, 1, z, 0, device_context);
}
};
@@ -45,20 +45,24 @@ template <typename Place, typename T>
class MulGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
- auto* X = ctx.Input<Tensor>("X");
- auto* Y = ctx.Input<Tensor>("Y");
- auto* dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
+ auto* x = ctx.Input<Tensor>("X");
+ auto* y = ctx.Input<Tensor>("Y");
+ auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
- auto* dX = ctx.Output<Tensor>(framework::GradVarName("X"));
- auto* dY = ctx.Output<Tensor>(framework::GradVarName("Y"));
- dX->mutable_data<T>(ctx.GetPlace());
- dY->mutable_data<T>(ctx.GetPlace());
+ auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+ auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto* device_context =
const_cast<platform::DeviceContext*>(ctx.device_context_);
- // dX = dOut * Y'. dX: M x K, dOut : M x N, Y : K x N
- math::matmul<Place, T>(*dOut, false, *Y, true, 1, dX, 0, device_context);
- // dY = X' * dOut. dY: K x N, dOut : M x N, X : M x K
- math::matmul<Place, T>(*X, true, *dOut, false, 1, dY, 0, device_context);
+ if (dx) {
+ // dx = dout * y'. dx: M x K, dout : M x N, y : K x N
+ dx->mutable_data<T>(ctx.GetPlace());
+ math::matmul<Place, T>(*dout, false, *y, true, 1, dx, 0, device_context);
+ }
+ if (dy) {
+ dy->mutable_data<T>(ctx.GetPlace());
+ // dy = x' * dout. dy K x N, dout : M x N, x : M x K
+ math::matmul<Place, T>(*x, true, *dout, false, 1, dy, 0, device_context);
+ }
}
};
diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc
index 63de91254f..a9dfba3e95 100644
--- a/paddle/operators/rowwise_add_op.cc
+++ b/paddle/operators/rowwise_add_op.cc
@@ -64,8 +64,10 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
auto dims0 = ctx.Input<Tensor>("X")->dims();
auto dims1 = ctx.Input<Tensor>("b")->dims();
PADDLE_ENFORCE_EQ(1, dims1.size(), "b dims should be 1")
- ctx.Output<Tensor>(framework::GradVarName("X"))->Resize(dims0);
- ctx.Output<Tensor>(framework::GradVarName("b"))->Resize(dims1);
+ auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+ auto *db = ctx.Output<Tensor>(framework::GradVarName("b"));
+ if (dx) dx->Resize(dims0);
+ if (db) db->Resize(dims1);
}
};
diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h
index 1cbd8bb31a..4e926d9f29 100644
--- a/paddle/operators/rowwise_add_op.h
+++ b/paddle/operators/rowwise_add_op.h
@@ -51,20 +51,24 @@ template <typename Place, typename T>
class RowwiseAddGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
- auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
- auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
+ auto* dout = context.Input<Tensor>(framework::GradVarName("Out"));
+ auto* dx = context.Output<Tensor>(framework::GradVarName("X"));
auto* db = context.Output<Tensor>(framework::GradVarName("b"));
- dX->mutable_data<T>(context.GetPlace());
- db->mutable_data<T>(context.GetPlace());
- auto OutGrad = EigenMatrix<T>::From(*dOut);
+ auto out_grad = EigenMatrix<T>::From(*dout);
auto place = context.GetEigenDevice<Place>();
- EigenMatrix<T>::From(*dX).device(place) = OutGrad;
+ if (dx) {
+ dx->mutable_data<T>(context.GetPlace());
+ EigenMatrix<T>::From(*dx).device(place) = out_grad;
+ }
- // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
- // colwise add
- Eigen::array<int, 1> dims{{0}}; /* dimension to reduce */
- EigenVector<T>::Flatten(*db).device(place) = OutGrad.sum(dims);
+ if (db) {
+ db->mutable_data<T>(context.GetPlace());
+ // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
+ // colwise add
+ Eigen::array<int, 1> dims{{0}}; /* dimension to reduce */
+ EigenVector<T>::Flatten(*db).device(place) = out_grad.sum(dims);
+ }
}
};
} // namespace operators
diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc
index 35c185ad80..9b5068f07c 100644
--- a/paddle/operators/scatter_op.cc
+++ b/paddle/operators/scatter_op.cc
@@ -50,8 +50,8 @@ class ScatterGradOp : public framework::OperatorWithKernel {
auto *dRef = ctx.Output<Tensor>(framework::GradVarName("Ref"));
auto *Ref = ctx.Input<Tensor>("Ref");
- dRef->Resize(Ref->dims());
- dUpdates->Resize(Updates->dims());
+ if (dRef) dRef->Resize(Ref->dims());
+ if (dUpdates) dUpdates->Resize(Updates->dims());
}
};
diff --git a/paddle/operators/scatter_op.h b/paddle/operators/scatter_op.h
index e9595638a8..7551480211 100644
--- a/paddle/operators/scatter_op.h
+++ b/paddle/operators/scatter_op.h
@@ -49,10 +49,12 @@ class ScatterGradientOpKernel : public framework::OpKernel {
auto *dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
// In place gradient: dRef = dO
- dRef->ShareDataWith<T>(*dOut);
- dUpdates->mutable_data<T>(ctx.GetPlace());
- // Gradient by Gather: dUpdates += dO[Index]
- Gather<T>(ctx.GetPlace(), dOut, Index, dUpdates);
+ if (dRef) dRef->ShareDataWith<T>(*dOut);
+ if (dUpdates) {
+ dUpdates->mutable_data<T>(ctx.GetPlace());
+ // Gradient by Gather: dUpdates += dO[Index]
+ Gather<T>(ctx.GetPlace(), dOut, Index, dUpdates);
+ }
}
};
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
index 518f828bac..82ab7ad39b 100644
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ b/python/paddle/v2/framework/tests/gradient_checker.py
@@ -286,6 +286,9 @@ class GradientChecker(unittest.TestCase):
for no_grad in no_grad_set:
if no_grad not in in_names:
raise ValueError("no_grad should be in in_names")
+ if name 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()]
@@ -301,9 +304,25 @@ class GradientChecker(unittest.TestCase):
check_names = [grad_var_name(name) for name in inputs_to_check]
for place in places:
- # get analytical gradients according to different device
- analytic_grads = self.__get_gradient(forward_op, backward_op,
- input_vars, check_names, place)
+ # analytic_grads = self.__get_gradient(forward_op, backward_op,
+ # input_vars, check_names, place)
+ # In fact, the above two lines can be used to replace following
+ # codes. But most of the gradient operators need to handle the case
+ # where one of more of the gradient of the input is not needed.
+ # We change the unit test framework to explicitly test whether
+ # the operator correctly handles this through follow codes.
+ # In addtion, if all the inputs have no gradients, the NOP operator
+ # will be returned by core.Operator.backward(). The following codes
+ # do not test this case.
+ analytic_grads = []
+ for name in inputs_to_check:
+ no_grads = [name for name in no_grad_set]
+ no_grads.extend(filter(lambda x: x != name, inputs_to_check))
+ backward_op = core.Operator.backward(forward_op, set(no_grads))
+ # get analytical gradients according to different device
+ analytic_grads.extend(
+ self.__get_gradient(forward_op, backward_op, input_vars,
+ [grad_var_name(name)], 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/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py
index ee0d81a64e..81371b1d11 100644
--- a/python/paddle/v2/framework/tests/test_mul_op.py
+++ b/python/paddle/v2/framework/tests/test_mul_op.py
@@ -16,13 +16,14 @@ class TestMulOp(unittest.TestCase):
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
-class MulGradOpTest(GradientChecker):
+class TestMulGradOp(GradientChecker):
def test_mul(self):
op = create_op("mul")
inputs = {
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
+ self.compare_grad(op, inputs)
# mul op will enlarge the relative error
self.check_grad(
op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
From 9a3c69c268d65c340ed5d0ae9aee1a90ca50ee84 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 4 Sep 2017 10:45:11 -0700
Subject: [PATCH 49/72] Revert "Remove `grad_op_type` in `REGISTER_OP`"
---
doc/howto/dev/new_op_cn.md | 4 ++--
paddle/framework/backward.md | 2 +-
paddle/framework/backward_test.cc | 12 +++++++-----
paddle/framework/grad_op_builder_test.cc | 4 ++--
paddle/framework/op_registry.h | 25 ++++++++++++++----------
paddle/operators/add_op.cc | 2 +-
paddle/operators/cross_entropy_op.cc | 3 ++-
paddle/operators/gather_op.cc | 3 ++-
paddle/operators/lookup_table_op.cc | 2 +-
paddle/operators/mean_op.cc | 2 +-
paddle/operators/minus_op.cc | 3 ++-
paddle/operators/mul_op.cc | 2 +-
paddle/operators/rowwise_add_op.cc | 2 +-
paddle/operators/scale_op.cc | 2 +-
paddle/operators/scatter_op.cc | 3 ++-
paddle/operators/sigmoid_op.cc | 3 ++-
paddle/operators/softmax_op.cc | 3 ++-
17 files changed, 45 insertions(+), 32 deletions(-)
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index ec79b7f42b..7f8da2da5a 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -178,13 +178,13 @@ class MulKernel : public framework::OpKernel {
```c++
namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
```
- - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，并且注册`ops::MulOpGrad`为其反向Op。
+ - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`，
- `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md
index 9500c92a26..8aa6728a95 100644
--- a/paddle/framework/backward.md
+++ b/paddle/framework/backward.md
@@ -18,7 +18,7 @@ A backward network is built up with several backward operators. Backward operato
For example, we have got a `mul_op`, and we can register it's information and corresponding backward operator by the following macro:
```cpp
-REGISTER_OP(mul, MulOp, MulOpMaker, MulOpGrad);
+REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
```
`mul` is the operator's type. `MulOp` and `MulOpMaker` are the operator class and the operator maker class respectively.
diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc
index bf8b11e5f5..ad8003420d 100644
--- a/paddle/framework/backward_test.cc
+++ b/paddle/framework/backward_test.cc
@@ -148,14 +148,16 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
namespace f = paddle::framework;
namespace ops = paddle::operators;
using EnforceNotMet = paddle::platform::EnforceNotMet;
-REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, f::NOP);
-REGISTER_OP(mul, f::NOP, f::MulOpMaker, f::NOP);
-REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, f::NOP);
+REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad,
+ f::NOP);
+REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP);
+REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker);
REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker);
-REGISTER_OP(add, f::NOP, f::AddOpMaker, f::NOP);
+REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP);
REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker);
-REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, f::NOP);
+REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad,
+ f::NOP);
TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp(
diff --git a/paddle/framework/grad_op_builder_test.cc b/paddle/framework/grad_op_builder_test.cc
index 8a817a3e13..902c2655e9 100644
--- a/paddle/framework/grad_op_builder_test.cc
+++ b/paddle/framework/grad_op_builder_test.cc
@@ -54,8 +54,8 @@ TEST(GradOpBuilder, AddTwo) {
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
}
-REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, f::NOP);
-REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, f::NOP);
+REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP);
+REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h
index 64c7f23ab6..2d09cde41e 100644
--- a/paddle/framework/op_registry.h
+++ b/paddle/framework/op_registry.h
@@ -33,7 +33,8 @@ namespace framework {
class OpRegistry {
public:
template <typename OpType, typename ProtoMakerType, typename GradOpType>
- static void RegisterOp(const std::string& op_type) {
+ static void RegisterOp(const std::string& op_type,
+ const std::string& grad_op_type) {
PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type),
"'%s' is registered more than once.", op_type);
OpInfo op_info;
@@ -42,9 +43,9 @@ class OpRegistry {
const VariableNameMap& outputs, const AttributeMap& attrs) {
return new OpType(type, inputs, outputs, attrs);
};
+ op_info.grad_op_type_ = grad_op_type;
if (std::type_index(typeid(ProtoMakerType)) !=
std::type_index(typeid(NOPMaker))) {
- op_info.grad_op_type_ = op_type + "_grad";
op_info.proto_ = new OpProto;
op_info.checker_ = new OpAttrChecker;
auto maker = ProtoMakerType(op_info.proto_, op_info.checker_);
@@ -54,14 +55,15 @@ class OpRegistry {
op_info.proto_->IsInitialized(),
"Fail to initialize %s's OpProto, because %s is not initialized",
op_type, op_info.proto_->InitializationErrorString());
- // register gradient op
- RegisterOp<GradOpType, NOPMaker, NOP>(op_info.grad_op_type_);
} else {
- op_info.grad_op_type_ = "";
op_info.proto_ = nullptr;
op_info.checker_ = nullptr;
}
OpInfoMap::Instance().Insert(op_type, op_info);
+ // register gradient op
+ if (!grad_op_type.empty()) {
+ RegisterOp<GradOpType, NOPMaker, NOP>(grad_op_type, "");
+ }
}
static std::unique_ptr<OperatorBase> CreateOp(const std::string& type,
@@ -90,8 +92,10 @@ class Registrar {
template <typename OpType, typename ProtoMakerType, typename GradOpType>
class OpRegistrar : public Registrar {
public:
- explicit OpRegistrar(const char* op_type) {
- OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type);
+ explicit OpRegistrar(const char* op_type) { OpRegistrar(op_type, ""); }
+ OpRegistrar(const char* op_type, const char* grad_op_type) {
+ OpRegistry::RegisterOp<OpType, ProtoMakerType, GradOpType>(op_type,
+ grad_op_type);
}
};
@@ -117,7 +121,8 @@ class OpKernelRegistrar : public Registrar {
/**
* Macro to register Operator.
*/
-#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_class) \
+#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \
+ grad_op_class) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \
class _OpClass_##op_type##_ : public op_class { \
@@ -132,14 +137,14 @@ class OpKernelRegistrar : public Registrar {
}; \
static ::paddle::framework::OpRegistrar< \
_OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_> \
- __op_registrar_##op_type##__(#op_type); \
+ __op_registrar_##op_type##__(#op_type, #grad_op_type); \
int TouchOpRegistrar_##op_type() { \
__op_registrar_##op_type##__.Touch(); \
return 0; \
}
#define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \
- REGISTER_OP(op_type, op_class, op_maker_class, ::paddle::framework::NOP)
+ REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP)
/**
* Macro to register OperatorKernel.
diff --git a/paddle/operators/add_op.cc b/paddle/operators/add_op.cc
index 6384d8c8ce..8ab748ed71 100644
--- a/paddle/operators/add_op.cc
+++ b/paddle/operators/add_op.cc
@@ -57,7 +57,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, ops::AddOpGrad);
+REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
REGISTER_OP_CPU_KERNEL(add_two,
ops::AddKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/cross_entropy_op.cc b/paddle/operators/cross_entropy_op.cc
index ac76326262..ab1e1c101a 100644
--- a/paddle/operators/cross_entropy_op.cc
+++ b/paddle/operators/cross_entropy_op.cc
@@ -67,7 +67,8 @@ OnehotCrossEntropy Operator.
namespace ops = paddle::operators;
REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
- ops::OnehotCrossEntropyOpMaker, ops::OnehotCrossEntropyGradientOp);
+ ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad,
+ ops::OnehotCrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<float>);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad,
diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc
index 07fa704824..123bed296c 100644
--- a/paddle/operators/gather_op.cc
+++ b/paddle/operators/gather_op.cc
@@ -63,7 +63,8 @@ Out = X[Index]
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, ops::GatherGradOp);
+REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad,
+ ops::GatherGradOp);
REGISTER_OP_CPU_KERNEL(gather,
ops::GatherOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/lookup_table_op.cc b/paddle/operators/lookup_table_op.cc
index c3108ba8ec..94d40890a7 100644
--- a/paddle/operators/lookup_table_op.cc
+++ b/paddle/operators/lookup_table_op.cc
@@ -66,7 +66,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker,
- ops::LookupTableOpGrad);
+ lookup_table_grad, ops::LookupTableOpGrad);
REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel<float>);
REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel<float>);
diff --git a/paddle/operators/mean_op.cc b/paddle/operators/mean_op.cc
index e66e0abb25..d3d0e55a67 100644
--- a/paddle/operators/mean_op.cc
+++ b/paddle/operators/mean_op.cc
@@ -54,7 +54,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradOp);
+REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp);
REGISTER_OP_CPU_KERNEL(mean,
ops::MeanKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mean_grad,
diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc
index b4afebcd97..1eee9644ba 100644
--- a/paddle/operators/minus_op.cc
+++ b/paddle/operators/minus_op.cc
@@ -81,6 +81,7 @@ class MinusGradOp : public NetOp {
USE_OP(scale);
USE_OP_ITSELF(identity);
namespace ops = paddle::operators;
-REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradOp<float>);
+REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
+ ops::MinusGradOp<float>);
REGISTER_OP_CPU_KERNEL(minus,
ops::MinusKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc
index 8d0f59745f..5b8b5f6c11 100644
--- a/paddle/operators/mul_op.cc
+++ b/paddle/operators/mul_op.cc
@@ -84,7 +84,7 @@ class MulOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, ops::MulOpGrad);
+REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc
index 63de91254f..6825dce332 100644
--- a/paddle/operators/rowwise_add_op.cc
+++ b/paddle/operators/rowwise_add_op.cc
@@ -74,7 +74,7 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
- ops::RowwiseAddGradOp);
+ rowwise_add_grad, ops::RowwiseAddGradOp);
REGISTER_OP_CPU_KERNEL(
rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/scale_op.cc b/paddle/operators/scale_op.cc
index 4e039688d4..8e96a74c94 100644
--- a/paddle/operators/scale_op.cc
+++ b/paddle/operators/scale_op.cc
@@ -97,7 +97,7 @@ class IdentityOp : public NetOp {
namespace ops = paddle::operators;
-REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>,
+REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker<float>, scale_grad,
ops::ScaleGradOp<float>);
REGISTER_OP_CPU_KERNEL(scale,
ops::ScaleKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc
index 35c185ad80..f901edefa2 100644
--- a/paddle/operators/scatter_op.cc
+++ b/paddle/operators/scatter_op.cc
@@ -77,7 +77,8 @@ Out[Index] = Ref[Index] + Updates
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, ops::ScatterGradOp);
+REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, scatter_grad,
+ ops::ScatterGradOp);
REGISTER_OP_CPU_KERNEL(scatter,
ops::ScatterOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/sigmoid_op.cc b/paddle/operators/sigmoid_op.cc
index f35b702384..761c6de8d4 100644
--- a/paddle/operators/sigmoid_op.cc
+++ b/paddle/operators/sigmoid_op.cc
@@ -53,7 +53,8 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, ops::SigmoidOpGrad);
+REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker, sigmoid_grad,
+ ops::SigmoidOpGrad);
REGISTER_OP_CPU_KERNEL(sigmoid,
ops::SigmoidKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
diff --git a/paddle/operators/softmax_op.cc b/paddle/operators/softmax_op.cc
index 471bb288fb..40c51a64c4 100644
--- a/paddle/operators/softmax_op.cc
+++ b/paddle/operators/softmax_op.cc
@@ -62,7 +62,8 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
-REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, ops::SoftmaxOpGrad);
+REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker, softmax_grad,
+ ops::SoftmaxOpGrad);
REGISTER_OP_CPU_KERNEL(softmax,
ops::SoftmaxKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
From bb0a11cd14902971902987e2ed6f7107ae758bef Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Mon, 4 Sep 2017 13:01:18 -0700
Subject: [PATCH 50/72] Add a graph building example
---
doc/design/graph.md | 51 ++++++++++++++
.../images/graph_construction_example.bash | 11 +++
.../images/graph_construction_example.dot | 63 ++++++++++++++++++
.../images/graph_construction_example_all.png | Bin 0 -> 60087 bytes
..._construction_example_forward_backward.png | Bin 0 -> 55715 bytes
...raph_construction_example_forward_only.png | Bin 0 -> 32082 bytes
6 files changed, 125 insertions(+)
create mode 100644 doc/design/graph.md
create mode 100755 doc/design/images/graph_construction_example.bash
create mode 100644 doc/design/images/graph_construction_example.dot
create mode 100644 doc/design/images/graph_construction_example_all.png
create mode 100644 doc/design/images/graph_construction_example_forward_backward.png
create mode 100644 doc/design/images/graph_construction_example_forward_only.png
diff --git a/doc/design/graph.md b/doc/design/graph.md
new file mode 100644
index 0000000000..87f696f90f
--- /dev/null
+++ b/doc/design/graph.md
@@ -0,0 +1,51 @@
+# Design Doc: Computations as Graphs
+
+A primary goal of the refactorization of PaddlePaddle is a more flexible representation of deep learning computation, in particular, a graph of operators and variables, instead of sequences of layers as before.
+
+This document explains that the construction of a graph as three steps:
+
+- construct the forward part
+- construct the backward part
+- construct the optimization part
+
+Let us take the problem of image classification as a simple example. The application program that trains the model looks like:
+
+```python
+x = layer.data("images")
+l = layer.data("label")
+y = layer.fc(x)
+cost = layer.mse(y, l)
+optimize(cost)
+train(cost, reader=mnist.train())
+```
+
+### Forward Part
+
+The first four lines of above program build the forward part of the graph.
+
+
+
+In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x. `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b.
+
+In this example, all operators are created as `OpDesc` protobuf messages, and all variables are `VarDesc`. These protobuf messages are saved in a `BlockDesc` protobuf message.
+
+### Backward Part
+
+The fifth line `optimize(cost)` calls two functions, `ConstructBackwardGraph` and `ConstructOptimizationGraph`.
+
+`ConstructBackwardGraph` traverses the forward graph in the `BlockDesc` protobuf message and builds the backward part.
+
+
+
+According to the chain rule of gradient computation, `ConstructBackwardGraph` would
+
+1. create a gradient operator G for each operator F,
+1. make all inputs, outputs, and outputs' gradient of F as inputs of G,
+1. create gradients for all inputs of F, except for those who don't have gradients, like x and l, and
+1. make all these gradients as outputs of G.
+
+### Optimization Part
+
+For each parameter, like W and b created by `layer.fc`, marked as double circles in above graphs, `ConstructOptimizationGraph` creates an optimization operator to apply its gradient. Here results in the complete graph:
+
+
diff --git a/doc/design/images/graph_construction_example.bash b/doc/design/images/graph_construction_example.bash
new file mode 100755
index 0000000000..35e6997abd
--- /dev/null
+++ b/doc/design/images/graph_construction_example.bash
@@ -0,0 +1,11 @@
+cat ./graph_construction_example.dot | \
+ sed 's/color=red/color=red, style=invis/g' | \
+ sed 's/color=green/color=green, style=invis/g' | \
+ dot -Tpng > graph_construction_example_forward_only.png
+
+cat ./graph_construction_example.dot | \
+ sed 's/color=green/color=green, style=invis/g' | \
+ dot -Tpng > graph_construction_example_forward_backward.png
+
+cat ./graph_construction_example.dot | \
+ dot -Tpng > graph_construction_example_all.png
diff --git a/doc/design/images/graph_construction_example.dot b/doc/design/images/graph_construction_example.dot
new file mode 100644
index 0000000000..b77ddd448e
--- /dev/null
+++ b/doc/design/images/graph_construction_example.dot
@@ -0,0 +1,63 @@
+digraph ImageClassificationGraph {
+ ///////// The forward part /////////
+ FeedX [label="Feed", color=blue, shape=box];
+ FeedY [label="Feed", color=blue, shape=box];
+ FC [label="FC", color=blue, shape=box];
+ MSE [label="MSE", color=blue, shape=box];
+
+ x [label="x", color=blue, shape=oval];
+ l [label="l", color=blue, shape=oval];
+ y [label="y", color=blue, shape=oval];
+ W [label="W", color=blue, shape=doublecircle];
+ b [label="b", color=blue, shape=doublecircle];
+ cost [label="cost", color=blue, shape=oval];
+
+ FeedX -> x -> FC -> y -> MSE -> cost [color=blue];
+ FeedY -> l -> FC [color=blue];
+ W -> FC [color=blue];
+ b -> FC [color=blue];
+ l -> MSE [color=blue];
+
+ ////////// The backward part /////////
+ MSE_Grad [label="MSE_grad", color=red, shape=box];
+ FC_Grad [label="FC_grad", color=red, shape=box];
+
+ d_cost [label="d cost", color=red, shape=oval];
+ d_y [label="d y", color=red, shape=oval];
+ d_b [label="d b", color=red, shape=oval];
+ d_W [label="d W", color=red, shape=oval];
+
+ cost -> MSE_Grad [color=red];
+ d_cost -> MSE_Grad [color=red];
+ x -> MSE_Grad [color=red];
+ l -> MSE_Grad [color=red];
+ y -> MSE_Grad -> d_y [color=red];
+
+ x -> FC_Grad [color=red];
+ y -> FC_Grad [color=red];
+ d_y -> FC_Grad [color=red];
+ W -> FC_Grad -> d_W [color=red];
+ b -> FC_Grad -> d_b [color=red];
+
+ ////////// The optimizaiton part //////////
+
+ OPT_W [label="SGD", color=green, shape=box];
+ OPT_b [label="SGD", color=green, shape=box];
+
+ d_W -> OPT_W -> W [color=green];
+ d_b -> OPT_b -> b [color=green];
+
+ ////////// Groupings //////////
+
+ subgraph clusterMSE {
+ style=invis;
+ MSE;
+ MSE_Grad;
+ }
+
+ subgraph clusterFC {
+ style=invis;
+ FC;
+ FC_Grad;
+ }
+}
diff --git a/doc/design/images/graph_construction_example_all.png b/doc/design/images/graph_construction_example_all.png
new file mode 100644
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literal 0
HcmV?d00001
From 9f3bcd0e4608119cbafce9db7191a74bc8a8bfeb Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Mon, 4 Sep 2017 13:10:05 -0700
Subject: [PATCH 51/72] Update image
---
.../images/graph_construction_example.dot | 2 +-
.../images/graph_construction_example_all.png | Bin 60087 -> 58932 bytes
..._construction_example_forward_backward.png | Bin 55715 -> 54429 bytes
...raph_construction_example_forward_only.png | Bin 32082 -> 29878 bytes
4 files changed, 1 insertion(+), 1 deletion(-)
diff --git a/doc/design/images/graph_construction_example.dot b/doc/design/images/graph_construction_example.dot
index b77ddd448e..86f82fe05a 100644
--- a/doc/design/images/graph_construction_example.dot
+++ b/doc/design/images/graph_construction_example.dot
@@ -13,7 +13,7 @@ digraph ImageClassificationGraph {
cost [label="cost", color=blue, shape=oval];
FeedX -> x -> FC -> y -> MSE -> cost [color=blue];
- FeedY -> l -> FC [color=blue];
+ FeedY -> l [color=blue];
W -> FC [color=blue];
b -> FC [color=blue];
l -> MSE [color=blue];
diff --git a/doc/design/images/graph_construction_example_all.png b/doc/design/images/graph_construction_example_all.png
index 3ee61f9ef4544da786480f188403742d48a1d9e3..d769d70af759873802144f0dd0ddad21976a22eb 100644
GIT binary patch
literal 58932
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diff --git a/doc/design/images/graph_construction_example_forward_backward.png b/doc/design/images/graph_construction_example_forward_backward.png
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From e4494bb4417561ae59e26aec176e766214aa61f7 Mon Sep 17 00:00:00 2001
From: Yi Wang <yiwang01@baidu.com>
Date: Mon, 4 Sep 2017 13:12:48 -0700
Subject: [PATCH 52/72] Correct optimziation part
---
.../images/graph_construction_example.dot | 2 ++
.../images/graph_construction_example_all.png | Bin 58932 -> 55421 bytes
..._construction_example_forward_backward.png | Bin 54429 -> 47228 bytes
...raph_construction_example_forward_only.png | Bin 29878 -> 29192 bytes
4 files changed, 2 insertions(+)
diff --git a/doc/design/images/graph_construction_example.dot b/doc/design/images/graph_construction_example.dot
index 86f82fe05a..bedb6de011 100644
--- a/doc/design/images/graph_construction_example.dot
+++ b/doc/design/images/graph_construction_example.dot
@@ -44,6 +44,8 @@ digraph ImageClassificationGraph {
OPT_W [label="SGD", color=green, shape=box];
OPT_b [label="SGD", color=green, shape=box];
+ W -> OPT_W [color=green];
+ b -> OPT_b [color=green];
d_W -> OPT_W -> W [color=green];
d_b -> OPT_b -> b [color=green];
diff --git a/doc/design/images/graph_construction_example_all.png b/doc/design/images/graph_construction_example_all.png
index d769d70af759873802144f0dd0ddad21976a22eb..18d8330b60e12720bb993c8cf588d64ff8db1ea9 100644
GIT binary patch
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diff --git a/doc/design/images/graph_construction_example_forward_backward.png b/doc/design/images/graph_construction_example_forward_backward.png
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From 3d9d32a1c1462780ea1a5682a27ce7da090a4b74 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Mon, 4 Sep 2017 16:20:27 -0700
Subject: [PATCH 53/72] Invoke check_grad many times for no_grad_set
---
.../v2/framework/tests/gradient_checker.py | 23 +++-------------
.../paddle/v2/framework/tests/test_mul_op.py | 27 +++++++++++++++----
.../v2/framework/tests/test_rowwise_add_op.py | 16 ++++++++---
3 files changed, 37 insertions(+), 29 deletions(-)
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
index 82ab7ad39b..b8d7e4ea43 100644
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ b/python/paddle/v2/framework/tests/gradient_checker.py
@@ -286,7 +286,7 @@ class GradientChecker(unittest.TestCase):
for no_grad in no_grad_set:
if no_grad not in in_names:
raise ValueError("no_grad should be in in_names")
- if name in inputs_to_check:
+ 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)
@@ -304,25 +304,8 @@ class GradientChecker(unittest.TestCase):
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)
- # In fact, the above two lines can be used to replace following
- # codes. But most of the gradient operators need to handle the case
- # where one of more of the gradient of the input is not needed.
- # We change the unit test framework to explicitly test whether
- # the operator correctly handles this through follow codes.
- # In addtion, if all the inputs have no gradients, the NOP operator
- # will be returned by core.Operator.backward(). The following codes
- # do not test this case.
- analytic_grads = []
- for name in inputs_to_check:
- no_grads = [name for name in no_grad_set]
- no_grads.extend(filter(lambda x: x != name, inputs_to_check))
- backward_op = core.Operator.backward(forward_op, set(no_grads))
- # get analytical gradients according to different device
- analytic_grads.extend(
- self.__get_gradient(forward_op, backward_op, input_vars,
- [grad_var_name(name)], place))
+ 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/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py
index 81371b1d11..92d2b80e87 100644
--- a/python/paddle/v2/framework/tests/test_mul_op.py
+++ b/python/paddle/v2/framework/tests/test_mul_op.py
@@ -17,16 +17,33 @@ class TestMulOp(unittest.TestCase):
class TestMulGradOp(GradientChecker):
- def test_mul(self):
- op = create_op("mul")
- inputs = {
+ 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")
}
- self.compare_grad(op, inputs)
+
+ def test_normal(self):
# mul op will enlarge the relative error
self.check_grad(
- op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
+ 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"})
# TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library
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 45d569da29..403734e71a 100644
--- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py
+++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py
@@ -17,13 +17,21 @@ class TestRowwiseAddOp(unittest.TestCase):
class RowwiseAddGradOpTest(GradientChecker):
- def test_rowwise_add(self):
- op = create_op("rowwise_add")
- inputs = {
+ 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")
}
- self.check_grad(op, inputs, set(["X", "b"]), "Out")
+
+ def test_normal(self):
+ self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
+
+ def test_ignore_b(self):
+ self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
+
+ def test_ignore_x(self):
+ self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
if __name__ == '__main__':
From beafabc73e929e3790ba93687917a002ae0f3da0 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Tue, 5 Sep 2017 10:20:43 +0800
Subject: [PATCH 54/72] follow comments
---
doc/howto/dev/use_eigen_cn.md | 35 ++++++++++++++++++-----------------
1 file changed, 18 insertions(+), 17 deletions(-)
diff --git a/doc/howto/dev/use_eigen_cn.md b/doc/howto/dev/use_eigen_cn.md
index 49a726959a..1367323b71 100644
--- a/doc/howto/dev/use_eigen_cn.md
+++ b/doc/howto/dev/use_eigen_cn.md
@@ -7,14 +7,14 @@
Eigen Tensor模块对element-wise计算提供了强大的支持，并且书写一份代码，可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块，因此可能测试不够完备，文档较少。
-关于Eigen Tensor模块的详细介绍请参考[文档](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md)
+关于Eigen Tensor模块的详细介绍请参考[文档1](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md) 和[文档2](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md)
### paddle::framework::Tensor
Paddle Tensor定义在framework目录下，其主要接口如下：
-```
+```cpp
class Tensor {
public:
/*! Return a pointer to mutable memory block. */
@@ -54,9 +54,9 @@ class Tensor {
};
```
-`Placeholder`的作用的延迟分配内存，即我们可以先定义一个Tensor，然后使用Resize接口设置Tensor的大小，最后再调用mutable_data接口分配实际的内存。
+`Placeholder`的作用是延迟分配内存，即我们可以先定义一个Tensor，然后使用Resize接口设置Tensor的大小，最后再调用mutable_data接口分配实际的内存。
-```
+```cpp
paddle::framework::Tensor t;
paddle::platform::CPUPlace place;
// set size first
@@ -65,13 +65,14 @@ t.Resize({2, 3});
t.mutable_data(place);
```
+### paddle::framework::Tensor使用样例
下面以AddOp为例说明Tensor的使用过程：
- InferShape
在运行神经网络计算图时，我们先调用每个`Operator`的`InferShape`接口，根据输入Tensor的大小来设置输出Tensor的大小，`Resize`接口会被调用。
-```
+```cpp
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
@@ -85,7 +86,7 @@ void InferShape(const framework::InferShapeContext &ctx) const override {
`Operator`的`Run`接口最终会调用对应`OpKernel`的`Compute`接口，在这时真正的分配内存，`mutable_data`接口会被调用。
-```
+```cpp
void Compute(const framework::ExecutionContext& context) const override {
auto* input0 = context.Input<Tensor>("X");
auto* input1 = context.Input<Tensor>("Y");
@@ -93,13 +94,13 @@ void Compute(const framework::ExecutionContext& context) const override {
output->mutable_data<T>(context.GetPlace());
- auto X = EigenVector<T>::Flatten(*input0);
- auto Y = EigenVector<T>::Flatten(*input1);
- auto Z = EigenVector<T>::Flatten(*output);
+ auto x = EigenVector<T>::Flatten(*input0);
+ auto y = EigenVector<T>::Flatten(*input1);
+ auto z = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
- Z.device(place) = X + Y;
+ z.device(place) = x + y;
}
```
@@ -110,7 +111,7 @@ void Compute(const framework::ExecutionContext& context) const override {
以EigenTensor为例，做一个介绍
-```
+```cpp
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
@@ -122,7 +123,7 @@ EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
From是EigenTensor模板提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。
-需要额外注意的是，EigenVector<T>::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor，在这里用EigenVector来表示；而EigenVector<T>::Flatten方法是把paddle中的一个Tensor进行reshape操作，压扁成为Eigen的一维Tensor，类型仍然为EigenVector。
+在Eigen中，不同rank的Tensor是不同类型，Vector是rank为1的Tensor。需要额外注意的是，EigenVector<T>::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor，在这里用EigenVector来表示；而EigenVector<T>::Flatten方法是把paddle中的一个Tensor进行reshape操作，压扁成为Eigen的一维Tensor，类型仍然为EigenVector。
更多的转换方法请参考eigen_test.cc中的[单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen_test.cc)。
@@ -132,12 +133,12 @@ From是EigenTensor模板提供的一个接口，可以实现从paddle::framework
当需要完成计算时，我们需要等式左边的EigenTensor调用device接口。在这里需要注意的是，这里的EigenTensor之间的运算只是改变了原有Tensor中的数据，而不会改变原有Tensor的shape信息。
-```
-auto X = EigenVector<T>::Flatten(*input0);
-auto Y = EigenVector<T>::Flatten(*input1);
-auto Z = EigenVector<T>::Flatten(*output);
+```cpp
+auto x = EigenVector<T>::Flatten(*input0);
+auto y = EigenVector<T>::Flatten(*input1);
+auto z = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
-Z.device(place) = X + Y;
+z.device(place) = x + y;
```
在这段代码中，input0/input1/output可以是任意维度的Tensor。我们调用了EigenVector的Flatten接口，把任意维度的Tensor转为了一维的EigenVector。而在计算结束之后，input0/input1/output的原有shape信息不变。如果想改变原有Tensor的shape信息，可以调用Resize接口进行改变。
From ab55d7933bd7efbdddebbcee237323505d80244a Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Tue, 5 Sep 2017 10:36:46 +0800
Subject: [PATCH 55/72] revert scatter_op and other mirror changes.
---
doc/howto/dev/new_op_cn.md | 48 ++++++++++++++-----
paddle/operators/mul_op.h | 2 +-
paddle/operators/scatter_op.cc | 4 +-
paddle/operators/scatter_op.h | 10 ++--
.../paddle/v2/framework/tests/test_mul_op.py | 3 ++
.../v2/framework/tests/test_rowwise_add_op.py | 2 +-
6 files changed, 46 insertions(+), 23 deletions(-)
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index ec79b7f42b..5c523bf046 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -280,28 +280,50 @@ class TestMulOp(unittest.TestCase):
反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。
- ```
- class MulGradOpTest(GradientChecker):
- def test_mul(self):
- op = create_op("mul")
- inputs = {
+```
+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")
}
- self.compare_grad(op, inputs)
+
+ 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(
- op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
- ```
+ 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"})
+```
+
+下面解释一些关键的地方:
- 调用`create_op("mul")`创建反向Op对应的前向Op。
- - 定义输入`inputs`。
- 调用`compare_grad`函数对比CPU、GPU计算结果。
- - 调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。
- - 第一个参数`op` : 前向op。
- - 第二个参数`inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
- - 第三个参数`set(["X", "Y"])` : 指定对输入变量`X`、`Y`做梯度检测。
+ - `test_normal`中调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。
+ - 第一个参数`self.op` : 前向Op。
+ - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
+ - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
+ - `test_ignore_x`和`test_ignore_y`分支测试只需要计算一个输入梯度的情况。
### 编译和执行
diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h
index 66ed2f81c7..05a79e13b3 100644
--- a/paddle/operators/mul_op.h
+++ b/paddle/operators/mul_op.h
@@ -54,8 +54,8 @@ class MulGradKernel : public framework::OpKernel {
auto* device_context =
const_cast<platform::DeviceContext*>(ctx.device_context_);
if (dx) {
- // dx = dout * y'. dx: M x K, dout : M x N, y : K x N
dx->mutable_data<T>(ctx.GetPlace());
+ // dx = dout * y'. dx: M x K, dout : M x N, y : K x N
math::matmul<Place, T>(*dout, false, *y, true, 1, dx, 0, device_context);
}
if (dy) {
diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc
index 9b5068f07c..35c185ad80 100644
--- a/paddle/operators/scatter_op.cc
+++ b/paddle/operators/scatter_op.cc
@@ -50,8 +50,8 @@ class ScatterGradOp : public framework::OperatorWithKernel {
auto *dRef = ctx.Output<Tensor>(framework::GradVarName("Ref"));
auto *Ref = ctx.Input<Tensor>("Ref");
- if (dRef) dRef->Resize(Ref->dims());
- if (dUpdates) dUpdates->Resize(Updates->dims());
+ dRef->Resize(Ref->dims());
+ dUpdates->Resize(Updates->dims());
}
};
diff --git a/paddle/operators/scatter_op.h b/paddle/operators/scatter_op.h
index 7551480211..e9595638a8 100644
--- a/paddle/operators/scatter_op.h
+++ b/paddle/operators/scatter_op.h
@@ -49,12 +49,10 @@ class ScatterGradientOpKernel : public framework::OpKernel {
auto *dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
// In place gradient: dRef = dO
- if (dRef) dRef->ShareDataWith<T>(*dOut);
- if (dUpdates) {
- dUpdates->mutable_data<T>(ctx.GetPlace());
- // Gradient by Gather: dUpdates += dO[Index]
- Gather<T>(ctx.GetPlace(), dOut, Index, dUpdates);
- }
+ dRef->ShareDataWith<T>(*dOut);
+ dUpdates->mutable_data<T>(ctx.GetPlace());
+ // Gradient by Gather: dUpdates += dO[Index]
+ Gather<T>(ctx.GetPlace(), dOut, Index, dUpdates);
}
};
diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py
index 92d2b80e87..b58e4266d1 100644
--- a/python/paddle/v2/framework/tests/test_mul_op.py
+++ b/python/paddle/v2/framework/tests/test_mul_op.py
@@ -24,6 +24,9 @@ class TestMulGradOp(GradientChecker):
'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(
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 403734e71a..2ddb85e2e7 100644
--- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py
+++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py
@@ -16,7 +16,7 @@ class TestRowwiseAddOp(unittest.TestCase):
self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
-class RowwiseAddGradOpTest(GradientChecker):
+class TestRowwiseAddGradOp(GradientChecker):
def setUp(self):
self.op = create_op("rowwise_add")
self.inputs = {
From efd40b66cc4bb898fe152337aeda9f65ced33767 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Tue, 5 Sep 2017 14:17:13 +0800
Subject: [PATCH 56/72] update the doc for how to write the operators.
---
doc/howto/dev/new_op_cn.md | 141 +++++++++++++++++--------------------
1 file changed, 64 insertions(+), 77 deletions(-)
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index 3e71a0a592..6bd54137ba 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -23,19 +23,20 @@
- `framework::OperatorWithKernel`：继承自OperatorBase，Op有计算函数，称作有Kernel。
- `class OpProtoAndCheckerMaker`：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成
-依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下：
-
-
- 内容 | 定义位置
--------------- | :----------------------
-OpProtoMake定义 | `.cc`文件，Backward Op不需要定义OpProtoMake
-Op定义 | `.cc`文件
-Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`.cc`文件，GPU可在`.cu`文件。
-注册Op | Op注册在`.cc`文件；Kernel注册CPU在`.cc`文件，GPU在`.cu`文件
-
-
-下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。
+依据是否包含kernel，可以将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下：
+
+
+ 内容 | 定义位置
+-------------- | :----------------------
+OpProtoMake定义 | `*_op.cc`文件，Backward Op不需要定义OpProtoMake
+Op定义 | `*_op.cc`文件
+Kernel实现 | CPU、GPU共享Kernel在`*_op.h`文件，否则，CPU可以在`*_op.cc`文件，GPU可在`*_op.cu`文件。
+注册Op | Op注册在`*_op.cc`文件；Kernel注册CPU在`*_op.cc`文件，GPU在`*_op.cu`文件
+实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。
+
+
+下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。
## 实现C++类
@@ -43,8 +44,8 @@ Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`
### 1. 定义ProtoMaker类
矩阵乘的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释：
-
-```
+
+```cpp
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
@@ -59,20 +60,20 @@ The equation is: Out = X * Y
}
};
```
-
-[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数包括2个：
+
+[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数包括2个参数：
- `framework::OpProto` ： 前者存储Op的输入输出和参数属性，将用于Python API接口的生成。
- `framework::OpAttrChecker` ：后者用于检查参数属性的合法性。
-
+
构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加该Op的注释，这些函数会将对应内容添加到`OpProto`中。
-在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，该命名尽可能的规范。
+在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，命名请遵守命名规范。
+
-
再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子：
-
-```
+
+```cpp
template <typename AttrType>
class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
@@ -87,17 +88,17 @@ The equation is: Out = scale*X
}
};
```
-
- 在这个例子里，两处不同：
-
+
+ 这个例子有两处不同：
+
- `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中。
- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
-
+
### 2. 定义Operator类
-```c++
+```cpp
class MulOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
@@ -121,20 +122,20 @@ class MulOp : public framework::OperatorWithKernel {
```
[`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22)继承自`OperatorWithKernel`。`public`成员：
-
-```c++
+
+```cpp
using framework::OperatorWithKernel::OperatorWithKernel;
```
这句表示使用基类`OperatorWithKernel`的构造函数，也可写成：
-
-```c++
+
+```cpp
MulOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
-```
-
+```
+
还需要重写`InferShape`接口。`InferShape`为const函数，不能修改Op的成员变量，参数为`const framework::InferShapeContext &ctx`，通过该参数可获取到输入输出以及属性。它的功能是：
- 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法。
@@ -144,7 +145,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
### 3. 定义OpKernel类
-```C++
+```cpp
template <typename Place, typename T>
class MulKernel : public framework::OpKernel {
public:
@@ -163,34 +164,34 @@ class MulKernel : public framework::OpKernel {
`MulKernel`继承自`framework::OpKernel`，带有模板参数:
- `typename Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
-
+
- `typename T` : 表示数据类型，如`float`, `double`等。
-
+
`MulKernel`需要重写`Compute`接口，该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型，同样可获取到输入输出和属性参数，`Compute`函数里写具体实现时。
-
-注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
-
+
+注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
+
到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。
-
+
### 4. 注册Operator
在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
-```c++
+```cpp
namespace ops = paddle::operators;
REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(mul_grad,
ops::MulGradKernel<paddle::platform::CPUPlace, float>);
```
-
+
- `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`，
- `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
- `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
在 `.cu`文件中注册GPU Kernel。
-
-```c++
+
+```cpp
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mul_grad,
@@ -199,56 +200,42 @@ REGISTER_OP_GPU_KERNEL(mul_grad,
### 5. 编译
-在[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)文件中添加编译。
-
-```
-op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
-```
-
-下面命令可以编译：
-
+无需修改[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动加入编译。
+
+直接执行下面命令可进行编译：
+
```
make mul_op
```
## 绑定Python
-- 绑定Python
-
- 在 [`paddle/pybind/pybind.cc
+- 绑定Python
+
+ 在 [`paddle/pybind/pybind.cc
`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc)文件中添加该类：
```
USE_OP(mul);
```
如果只实现了CPU版本，则使用`USE_CPU_ONLY_OP`:
-
+
```
USE_CPU_ONLY_OP(gather);
```
-
+
如果OP不带Kernel，则使用`USE_NO_KENREL_OP`:
-
+
```
USE_NO_KENREL_OP(recurrent);
```
-
+
使用`USE_OP`告知编译器需要链接该Op的目标文件，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
-
-
+
+
- 生成库
- 在 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件添加类到`DEPS`中，使得该Op可以链接到生成的lib库中。
-
- ```
- if(WITH_PYTHON)
- cc_library(paddle_pybind SHARED
- SRCS pybind.cc
- DEPS pybind python backward
- mul_op
- minus_op)
- endif(WITH_PYTHON)
- ```
+ 无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动被添加链接至生成的lib库中。
## 实现单元测试
@@ -258,7 +245,7 @@ make mul_op
前向Op单测继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`，具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数，以及Python对比的输出值。
-```
+```python
import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
@@ -276,17 +263,17 @@ class TestMulOp(unittest.TestCase):
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
```
首先需要`import`必要的包,下面详细解释其他值：
-
+
- `self.type = "mul" ` : 定义类型，和注册的类型一致。
- `self.inputs` : 定义输入，类型为Numpy.array，并初始化。
- `self.outputs` : 定义输出，并得到Python结算结果。
-
+
### 反向Operator单测
反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。
- ```
+ ```python
class MulGradOpTest(GradientChecker):
def test_mul(self):
op = create_op("mul")
@@ -294,7 +281,7 @@ class TestMulOp(unittest.TestCase):
'X': np.random.random((32, 84)).astype("float32"),
'Y': np.random.random((84, 100)).astype("float32")
}
- self.compare_grad(op, inputs)
+ self.compare_grad(op, inputs)
# mul op will enlarge the relative error
self.check_grad(
op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
@@ -310,7 +297,7 @@ class TestMulOp(unittest.TestCase):
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
-### 编译和执行
+### 编译和执行
单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加编译：
From cdae0c754ec2f218ad06589fe669ebb00fb52e07 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 5 Sep 2017 15:51:47 +0800
Subject: [PATCH 57/72] fix Conv3d, DeConv3d (bias shape)
---
paddle/gserver/layers/Conv3DLayer.cpp | 23 +++++++++++++++++------
paddle/gserver/layers/DeConv3DLayer.cpp | 22 ++++++++++++++++------
2 files changed, 33 insertions(+), 12 deletions(-)
diff --git a/paddle/gserver/layers/Conv3DLayer.cpp b/paddle/gserver/layers/Conv3DLayer.cpp
index 7cc9937cce..3887aa58b2 100644
--- a/paddle/gserver/layers/Conv3DLayer.cpp
+++ b/paddle/gserver/layers/Conv3DLayer.cpp
@@ -42,10 +42,10 @@ bool Conv3DLayer::init(const LayerMap &layerMap,
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
- std::unique_ptr<Weight>(new Weight(1, numFilters_, biasParameter_));
+ std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
} else {
biases_ =
- std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
+ std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
}
}
return true;
@@ -224,20 +224,31 @@ void Conv3DLayer::bpropData(int i) {
}
void Conv3DLayer::bpropBiases() {
+ MatrixPtr biases = Matrix::create(biases_->getWGrad()->getData(),
+ 1,
+ biases_->getWGrad()->getElementCnt(),
+ false,
+ useGpu_);
MatrixPtr outGradMat = getOutputGrad();
+
if (this->sharedBiases_) {
- biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
+ biases->collectSharedBias(*outGradMat, 1.0f);
} else {
- biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
+ biases->collectBias(*outGradMat, 1.0f);
}
}
void Conv3DLayer::addBias() {
MatrixPtr outMat = getOutputValue();
+ MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
+ 1,
+ biases_->getW()->getElementCnt(),
+ false,
+ useGpu_);
if (this->sharedBiases_) {
- outMat->addSharedBias(*(biases_->getW()), 1.0f);
+ outMat->addSharedBias(*(bias), 1.0f);
} else {
- outMat->addBias(*(biases_->getW()), 1.0f);
+ outMat->addBias(*(bias), 1.0f);
}
}
diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp
index 7d5c772c89..2838980a97 100644
--- a/paddle/gserver/layers/DeConv3DLayer.cpp
+++ b/paddle/gserver/layers/DeConv3DLayer.cpp
@@ -42,10 +42,10 @@ bool DeConv3DLayer::init(const LayerMap &layerMap,
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
- std::unique_ptr<Weight>(new Weight(1, numFilters_, biasParameter_));
+ std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
} else {
biases_ =
- std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_));
+ std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
}
}
return true;
@@ -191,21 +191,31 @@ void DeConv3DLayer::bpropWeights(int i) {}
void DeConv3DLayer::bpropData(int i) {}
void DeConv3DLayer::bpropBiases() {
+ MatrixPtr biases = Matrix::create(biases_->getWGrad()->getData(),
+ 1,
+ biases_->getWGrad()->getElementCnt(),
+ false,
+ useGpu_);
const MatrixPtr &outGradMat = getOutputGrad();
if (this->sharedBiases_) {
- biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f);
+ biases->collectSharedBias(*outGradMat, 1.0f);
} else {
- biases_->getWGrad()->collectBias(*outGradMat, 1.0f);
+ biases->collectBias(*outGradMat, 1.0f);
}
}
void DeConv3DLayer::addBias() {
MatrixPtr outMat = getOutputValue();
+ MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
+ 1,
+ biases_->getW()->getElementCnt(),
+ false,
+ useGpu_);
if (this->sharedBiases_) {
- outMat->addSharedBias(*(biases_->getW()), 1.0f);
+ outMat->addSharedBias(*(bias), 1.0f);
} else {
- outMat->addBias(*(biases_->getW()), 1.0f);
+ outMat->addBias(*(bias), 1.0f);
}
}
From b3ff125d5598da6e362f360220eef816cf0d7f42 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Tue, 5 Sep 2017 16:29:57 +0800
Subject: [PATCH 58/72] follow comments.
---
doc/howto/dev/new_op_cn.md | 42 +++++++++++++++++++++++---------------
1 file changed, 25 insertions(+), 17 deletions(-)
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index 6bd54137ba..85c222163c 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -28,16 +28,18 @@
内容 | 定义位置
-------------- | :----------------------
-OpProtoMake定义 | `*_op.cc`文件，Backward Op不需要定义OpProtoMake
-Op定义 | `*_op.cc`文件
-Kernel实现 | CPU、GPU共享Kernel在`*_op.h`文件，否则，CPU可以在`*_op.cc`文件，GPU可在`*_op.cu`文件。
-注册Op | Op注册在`*_op.cc`文件；Kernel注册CPU在`*_op.cc`文件，GPU在`*_op.cu`文件
+OpProtoMake定义 | `.cc`文件，Backward Op不需要定义OpProtoMake
+Op定义 | `.cc`文件
+Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`.cc`文件，GPU可在`.cu`文件。
+注册Op | Op注册在`.cc`文件；Kernel注册CPU在`.cc`文件，GPU在`.cu`文件
+
实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。
下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。
+
## 实现C++类
@@ -200,13 +202,18 @@ REGISTER_OP_GPU_KERNEL(mul_grad,
### 5. 编译
-无需修改[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动加入编译。
+- 简单**无特殊依赖**的OP无需修改CMakeList.txt文件。[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt) 会自动将 `paddle/operators` 目录下新增的 `*_op.cc` 文件加入编译。
+- 较为复杂、**有额外依赖** 的operator仍需要修改[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)。如，`mul_op` 依赖 `math_function`，需要在`CMakeLists.txt`中添加如下内容：
-直接执行下面命令可进行编译：
+ ```
+ op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) +
+ ```
-```
-make mul_op
-```
+- 运行下面命令可以进行编译：
+
+ ```
+ make mul_op
+ ```
## 绑定Python
@@ -235,13 +242,13 @@ make mul_op
- 生成库
- 无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动被添加链接至生成的lib库中。
+ 无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会自动被添加链接到生成的lib库中。
## 实现单元测试
单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单测](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
-### 前向Operator单测
+### 前向Operator单元测试
前向Op单测继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`，具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数，以及Python对比的输出值。
@@ -269,7 +276,7 @@ class TestMulOp(unittest.TestCase):
- `self.outputs` : 定义输出，并得到Python结算结果。
-### 反向Operator单测
+### 反向Operator单元测试
反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。
@@ -297,21 +304,22 @@ class TestMulOp(unittest.TestCase):
- 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
-### 编译和执行
+### 编译和执行单元测试
-单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加编译：
+单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加以下内容将单测加入工程中：
```
py_test(test_mul_op SRCS test_mul_op.py)
```
-编译时需要打开`WITH_TESTING`, 即 `cmake paddle_dir -DWITH_TESTING=ON`，编译成功之后执行单测命令为：
+请注意，**不同于Op的编译测试，运行单元测试测时需要编译整个工程**，并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后，执行下面的命令来运行单测：
-```
+```bash
make test ARGS="-R test_mul_op -V"
```
+
或者:
-```
+```bash
ctest -R test_mul_op
```
From 020e45f715b1a1cea5dcbee10dacb055b3889523 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 5 Sep 2017 16:54:12 +0800
Subject: [PATCH 59/72] follow comments to revert add_two_op to add_op
---
paddle/framework/CMakeLists.txt | 2 +-
paddle/framework/grad_op_builder_test.cc | 4 ++--
paddle/operators/{add_two_op.cc => add_op.cc} | 7 +++----
paddle/operators/{add_two_op.cu => add_op.cu} | 5 ++---
paddle/operators/{add_two_op.h => add_op.h} | 0
paddle/pybind/pybind.cc | 2 +-
python/paddle/v2/framework/tests/test_add_two_op.py | 2 +-
python/paddle/v2/framework/tests/test_gradient_checker.py | 2 +-
python/paddle/v2/framework/tests/test_net.py | 4 ++--
python/paddle/v2/framework/tests/test_operator.py | 4 ++--
python/paddle/v2/framework/tests/test_recurrent_op.py | 2 +-
11 files changed, 16 insertions(+), 18 deletions(-)
rename paddle/operators/{add_two_op.cc => add_op.cc} (88%)
rename paddle/operators/{add_two_op.cu => add_op.cu} (82%)
rename paddle/operators/{add_two_op.h => add_op.h} (100%)
diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index e138517b6b..c0838d9b75 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -25,7 +25,7 @@ cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
-cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_two_op)
+cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
diff --git a/paddle/framework/grad_op_builder_test.cc b/paddle/framework/grad_op_builder_test.cc
index 902c2655e9..9e3ca563c6 100644
--- a/paddle/framework/grad_op_builder_test.cc
+++ b/paddle/framework/grad_op_builder_test.cc
@@ -3,7 +3,7 @@
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
-USE_OP(add_two);
+USE_OP(add);
namespace paddle {
namespace framework {
@@ -41,7 +41,7 @@ namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
- "add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
+ "add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(grad_add_op->Inputs().size(), 4UL);
diff --git a/paddle/operators/add_two_op.cc b/paddle/operators/add_op.cc
similarity index 88%
rename from paddle/operators/add_two_op.cc
rename to paddle/operators/add_op.cc
index bc99e306e0..8dbd47cf0d 100644
--- a/paddle/operators/add_two_op.cc
+++ b/paddle/operators/add_op.cc
@@ -12,7 +12,7 @@ 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/add_two_op.h"
+#include "paddle/operators/add_op.h"
namespace paddle {
namespace operators {
@@ -57,7 +57,6 @@ class AddOpGrad : public framework::OperatorWithKernel {
} // namespace paddle
namespace ops = paddle::operators;
-REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad);
+REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad);
-REGISTER_OP_CPU_KERNEL(add_two,
- ops::AddKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(add, ops::AddKernel<paddle::platform::CPUPlace, float>);
diff --git a/paddle/operators/add_two_op.cu b/paddle/operators/add_op.cu
similarity index 82%
rename from paddle/operators/add_two_op.cu
rename to paddle/operators/add_op.cu
index acc03b2c8b..fdf2c66c7c 100644
--- a/paddle/operators/add_two_op.cu
+++ b/paddle/operators/add_op.cu
@@ -14,8 +14,7 @@
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
-#include "paddle/operators/add_two_op.h"
+#include "paddle/operators/add_op.h"
namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(add_two,
- ops::AddKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(add, ops::AddKernel<paddle::platform::GPUPlace, float>);
diff --git a/paddle/operators/add_two_op.h b/paddle/operators/add_op.h
similarity index 100%
rename from paddle/operators/add_two_op.h
rename to paddle/operators/add_op.h
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 6896422617..dba3d8f285 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -30,7 +30,7 @@ limitations under the License. */
namespace py = pybind11;
-USE_OP(add_two);
+USE_OP(add);
USE_OP(onehot_cross_entropy);
USE_OP(sgd);
USE_OP(mul);
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 0def484edd..a578e74eca 100644
--- a/python/paddle/v2/framework/tests/test_add_two_op.py
+++ b/python/paddle/v2/framework/tests/test_add_two_op.py
@@ -11,7 +11,7 @@ class TestAddOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
- self.type = "add_two"
+ self.type = "add"
self.inputs = {
'X': numpy.random.random((102, 105)).astype("float32"),
'Y': numpy.random.random((102, 105)).astype("float32")
diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py
index e0b3151208..857427cdfb 100644
--- a/python/paddle/v2/framework/tests/test_gradient_checker.py
+++ b/python/paddle/v2/framework/tests/test_gradient_checker.py
@@ -7,7 +7,7 @@ from gradient_checker import get_numeric_gradient
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
- add_op = Operator('add_two', X="X", Y="Y", Out="Z")
+ 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")
diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py
index 9339cf28da..e4b7cd480c 100644
--- a/python/paddle/v2/framework/tests/test_net.py
+++ b/python/paddle/v2/framework/tests/test_net.py
@@ -15,7 +15,7 @@ def fc(X, W, Y):
class TestNet(unittest.TestCase):
def test_net_all(self):
net = core.Net.create()
- op1 = Operator("add_two", X="X", Y="Y", Out="Out")
+ op1 = Operator("add", X="X", Y="Y", Out="Out")
net.append_op(op1)
net2 = core.Net.create()
@@ -26,7 +26,7 @@ class TestNet(unittest.TestCase):
expected = '''
Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
- Op(add_two), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
+ Op(add), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
diff --git a/python/paddle/v2/framework/tests/test_operator.py b/python/paddle/v2/framework/tests/test_operator.py
index 1abc4eeb57..040556322d 100644
--- a/python/paddle/v2/framework/tests/test_operator.py
+++ b/python/paddle/v2/framework/tests/test_operator.py
@@ -193,10 +193,10 @@ class TestOpDescCreationMethod(unittest.TestCase):
class TestOpCreations(unittest.TestCase):
def test_all(self):
- add_op = op.Operator("add_two", X="a", Y="b", Out="z")
+ add_op = op.Operator("add", X="a", Y="b", Out="z")
self.assertIsNotNone(add_op)
# Invoke C++ DebugString()
- self.assertEqual('Op(add_two), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
+ self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
str(add_op))
diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py
index d6000ab9f9..22e680fd78 100644
--- a/python/paddle/v2/framework/tests/test_recurrent_op.py
+++ b/python/paddle/v2/framework/tests/test_recurrent_op.py
@@ -146,7 +146,7 @@ class TestRecurrentOp(unittest.TestCase):
stepnet = core.Net.create()
x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
- sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum")
+ sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
sig_op = Operator("sigmoid", X="sum", Y="h@alias")
for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
From e687f3f540d3a403ab376f6c533362a6e6c577ff Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Tue, 5 Sep 2017 17:37:12 +0800
Subject: [PATCH 60/72] Make attribute support for std::vector<std::pair<int,
int>>
---
paddle/framework/attribute.cc | 12 ++++++++++++
paddle/framework/attribute.h | 3 ++-
paddle/framework/framework.proto | 7 +++++++
python/paddle/v2/framework/op.py | 7 ++++++-
4 files changed, 27 insertions(+), 2 deletions(-)
diff --git a/paddle/framework/attribute.cc b/paddle/framework/attribute.cc
index 9eb07acdff..27132eaa0b 100644
--- a/paddle/framework/attribute.cc
+++ b/paddle/framework/attribute.cc
@@ -43,6 +43,10 @@ template <>
AttrType AttrTypeID<std::vector<std::string>>() {
return STRINGS;
}
+template <>
+AttrType AttrTypeID<std::vector<std::pair<int, int>>>() {
+ return INT_PAIRS;
+}
Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
switch (attr_desc.type()) {
@@ -76,6 +80,14 @@ Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
}
return val;
}
+ case paddle::framework::AttrType::INT_PAIRS: {
+ std::vector<std::pair<int, int>> val(attr_desc.int_pairs_size());
+ for (int i = 0; i < attr_desc.int_pairs_size(); ++i) {
+ val[i].first = attr_desc.int_pairs(i).first();
+ val[i].second = attr_desc.int_pairs(i).second();
+ }
+ return val;
+ }
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
diff --git a/paddle/framework/attribute.h b/paddle/framework/attribute.h
index 08b47cabd4..071879a9d4 100644
--- a/paddle/framework/attribute.h
+++ b/paddle/framework/attribute.h
@@ -28,7 +28,8 @@ namespace paddle {
namespace framework {
typedef boost::variant<boost::blank, int, float, std::string, std::vector<int>,
- std::vector<float>, std::vector<std::string>>
+ std::vector<float>, std::vector<std::string>,
+ std::vector<std::pair<int, int>>>
Attribute;
typedef std::unordered_map<std::string, Attribute> AttributeMap;
diff --git a/paddle/framework/framework.proto b/paddle/framework/framework.proto
index ae44a1ffd4..368136a972 100644
--- a/paddle/framework/framework.proto
+++ b/paddle/framework/framework.proto
@@ -22,8 +22,14 @@ enum AttrType {
INTS = 3;
FLOATS = 4;
STRINGS = 5;
+ INT_PAIRS = 6;
}
+message IntPair {
+ required int32 first = 1;
+ required int32 second = 2;
+};
+
// OpDesc describes an instance of a C++ framework::OperatorBase
// derived class type.
message OpDesc {
@@ -37,6 +43,7 @@ message OpDesc {
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
+ repeated IntPair int_pairs = 9;
};
message Var {
diff --git a/python/paddle/v2/framework/op.py b/python/paddle/v2/framework/op.py
index e7e932f6fe..0349407a85 100644
--- a/python/paddle/v2/framework/op.py
+++ b/python/paddle/v2/framework/op.py
@@ -94,9 +94,14 @@ class OpDescCreationMethod(object):
new_attr.floats.extend(user_defined_attr)
elif attr.type == framework_pb2.STRINGS:
new_attr.strings.extend(user_defined_attr)
+ elif attr.type == framework_pb2.INT_PAIRS:
+ for p in user_defined_attr:
+ pair = new_attr.pairs.add()
+ pair.first = p[0]
+ pair.second = p[1]
else:
raise NotImplementedError("Not support attribute type " +
- attr.type)
+ str(attr.type))
return op_desc
From 2beaf3aa2a966980177ea0a0d309fd7a7a594a1c Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 5 Sep 2017 18:12:42 +0800
Subject: [PATCH 61/72] auto find .cc or .cu in operator/CMakeLists.txt
---
paddle/operators/CMakeLists.txt | 54 +++++++++++++++------------------
1 file changed, 24 insertions(+), 30 deletions(-)
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 90185101c4..99b8e1967f 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -14,27 +14,31 @@ function(op_library TARGET)
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}"
"${multiValueArgs}" ${ARGN})
- foreach(src ${op_library_SRCS})
- if (${src} MATCHES ".*\\.cu$")
- list(APPEND cu_srcs ${src})
- elseif(${src} MATCHES ".*\\.cc$")
- list(APPEND cc_srcs ${src})
- else()
- message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu")
+ list(LENGTH op_library_SRCS op_library_SRCS_len)
+ if (${op_library_SRCS_len} EQUAL 0)
+ if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cc)
+ list(APPEND cc_srcs ${TARGET}.cc)
endif()
- endforeach()
+ if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
+ list(APPEND cu_srcs ${TARGET}.cc)
+ endif()
+ else()
+ foreach(src ${op_library_SRCS})
+ if (${src} MATCHES ".*\\.cu$")
+ list(APPEND cu_srcs ${src})
+ elseif(${src} MATCHES ".*\\.cc$")
+ list(APPEND cc_srcs ${src})
+ else()
+ message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu")
+ endif()
+ endforeach()
+ endif()
list(LENGTH cc_srcs cc_srcs_len)
if (${cc_srcs_len} EQUAL 0)
message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file")
endif()
- list(LENGTH cu_srcs cu_srcs_len)
- list(LENGTH op_library_DEPS dep_len)
- if (${cu_srcs_len} EQUAL 0 AND ${dep_len} EQUAL 0)
- message(WARNING "The op library ${TARGET} not support GPU!")
- endif()
-
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
@@ -46,32 +50,22 @@ endfunction()
add_subdirectory(math)
-set(ONLYCPU_OPS
- net_op
- gather_op
- scatter_op)
-foreach(src ${ONLYCPU_OPS})
- op_library(${src} SRCS ${src}.cc)
-endforeach()
-
set(DEPS_OPS
identity_op
minus_op
mul_op
recurrent_op
scale_op)
-op_library(identity_op SRCS identity_op.cc DEPS scale_op)
-op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op)
-op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
+op_library(identity_op DEPS scale_op)
+op_library(minus_op DEPS scale_op)
+op_library(mul_op DEPS math_function)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op)
-op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op)
+op_library(scale_op DEPS net_op)
-list(REMOVE_ITEM GENERAL_OPS
- ${ONLYCPU_OPS}
- ${DEPS_OPS})
+list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
- op_library(${src} SRCS ${src}.cc ${src}.cu)
+ op_library(${src})
endforeach()
set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
From 42513496f30c137e42b54fab6ce275845349bccf Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 5 Sep 2017 22:17:24 +0800
Subject: [PATCH 62/72] fix a type error in operator/CMakeLists.txt
---
paddle/operators/CMakeLists.txt | 2 +-
paddle/operators/add_op.cu | 2 --
2 files changed, 1 insertion(+), 3 deletions(-)
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 99b8e1967f..8a0ff1eb53 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -20,7 +20,7 @@ function(op_library TARGET)
list(APPEND cc_srcs ${TARGET}.cc)
endif()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
- list(APPEND cu_srcs ${TARGET}.cc)
+ list(APPEND cu_srcs ${TARGET}.cu)
endif()
else()
foreach(src ${op_library_SRCS})
diff --git a/paddle/operators/add_op.cu b/paddle/operators/add_op.cu
index fdf2c66c7c..d9c6d20a6c 100644
--- a/paddle/operators/add_op.cu
+++ b/paddle/operators/add_op.cu
@@ -12,8 +12,6 @@
See the License for the specific language governing permissions and
limitations under the License. */
-#define EIGEN_USE_GPU
-#include "paddle/framework/op_registry.h"
#include "paddle/operators/add_op.h"
namespace ops = paddle::operators;
From dc520da763f258e6ca1c79f79bb82bc55ec4cf14 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Tue, 5 Sep 2017 23:08:21 +0800
Subject: [PATCH 63/72] update doc of softmax_op.
---
paddle/operators/softmax_op.cc | 26 ++++++++++++++++++++++----
1 file changed, 22 insertions(+), 4 deletions(-)
diff --git a/paddle/operators/softmax_op.cc b/paddle/operators/softmax_op.cc
index 40c51a64c4..7d062ad67c 100644
--- a/paddle/operators/softmax_op.cc
+++ b/paddle/operators/softmax_op.cc
@@ -24,7 +24,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
- "The input of softmax op must be matrix");
+ "The input of softmax op must be a matrix.");
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
@@ -34,9 +34,27 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
SoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
- AddInput("X", "input of softmax");
- AddOutput("Y", "output of softmax");
- AddComment("Softmax Op");
+ AddInput("X",
+ "The input tensor of softmax. "
+ "2-D with shape [batch_size, input_feature_dimensions].");
+ AddOutput("Y", "The normalized values with the same shape as X.");
+ AddComment(R"DOC(
+The input of softmax operator is a 2-D tensor with shape N x K (N is the
+batch_size, K is the dimension of input feature). The output tensor has the
+same shape as the input tensor.
+
+For each row of the input tensor, the softmax operator squashes the
+K-dimensional vector of arbitrary real values to a K-dimensional vector of real
+values in the range [0, 1] that add up to 1. Specifically, it computes the
+exponential of the given dimension and the sum of exponential values of all
+the other dimensions in the K-dimensional vector input. Then the ratio of the
+exponential of the given dimension and the sum of exponential values of all
+the other dimensions is the output of the softmax operator.
+
+For each row `i` and each column `j` in X, we have:
+ Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j]))
+
+)DOC");
}
};
From bc0f9495f019d9f72bf9d6a82f97673c7dce0063 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 5 Sep 2017 13:43:51 -0700
Subject: [PATCH 64/72] Move two tests form `op_registry_test` to
`operator_test`
1. TEST(ProtoMaker, DuplicatedAttr)
2. TEST(ProtoMaker, DuplicatedInOut)
---
paddle/framework/op_registry_test.cc | 34 +---------------------------
paddle/framework/operator_test.cc | 34 ++++++++++++++++++++++++++++
2 files changed, 35 insertions(+), 33 deletions(-)
diff --git a/paddle/framework/op_registry_test.cc b/paddle/framework/op_registry_test.cc
index 50c45919c5..b43f6a8cc5 100644
--- a/paddle/framework/op_registry_test.cc
+++ b/paddle/framework/op_registry_test.cc
@@ -174,36 +174,4 @@ TEST(OpRegistry, CustomChecker) {
op->Run(scope, dev_ctx);
int test_attr = op->GetAttr<int>("test_attr");
ASSERT_EQ(test_attr, 4);
-}
-
-class TestAttrProtoMaker : public pd::OpProtoAndCheckerMaker {
- public:
- TestAttrProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
- : OpProtoAndCheckerMaker(proto, op_checker) {
- AddAttr<float>("scale", "scale of test op");
- AddAttr<float>("scale", "scale of test op");
- }
-};
-
-TEST(ProtoMaker, DuplicatedAttr) {
- pd::OpProto op_proto;
- pd::OpAttrChecker op_checker;
- auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
- ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
-}
-
-class TestInOutProtoMaker : public pd::OpProtoAndCheckerMaker {
- public:
- TestInOutProtoMaker(pd::OpProto* proto, pd::OpAttrChecker* op_checker)
- : OpProtoAndCheckerMaker(proto, op_checker) {
- AddInput("input", "input of test op");
- AddInput("input", "input of test op");
- }
-};
-
-TEST(ProtoMaker, DuplicatedInOut) {
- pd::OpProto op_proto;
- pd::OpAttrChecker op_checker;
- auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
- ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
-}
+}
\ No newline at end of file
diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc
index f7c9e6b196..8a1970c7a8 100644
--- a/paddle/framework/operator_test.cc
+++ b/paddle/framework/operator_test.cc
@@ -263,4 +263,38 @@ TEST(Operator, Clone) {
OperatorClone a("ABC", {}, {}, {});
auto b = a.Clone();
ASSERT_EQ(a.Type(), b->Type());
+}
+
+class TestAttrProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
+ public:
+ TestAttrProtoMaker(paddle::framework::OpProto* proto,
+ paddle::framework::OpAttrChecker* op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddAttr<float>("scale", "scale of test op");
+ AddAttr<float>("scale", "scale of test op");
+ }
+};
+
+TEST(ProtoMaker, DuplicatedAttr) {
+ paddle::framework::OpProto op_proto;
+ paddle::framework::OpAttrChecker op_checker;
+ auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
+ ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
+}
+
+class TestInOutProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
+ public:
+ TestInOutProtoMaker(paddle::framework::OpProto* proto,
+ paddle::framework::OpAttrChecker* op_checker)
+ : OpProtoAndCheckerMaker(proto, op_checker) {
+ AddInput("input", "input of test op");
+ AddInput("input", "input of test op");
+ }
+};
+
+TEST(ProtoMaker, DuplicatedInOut) {
+ paddle::framework::OpProto op_proto;
+ paddle::framework::OpAttrChecker op_checker;
+ auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
+ ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
}
\ No newline at end of file
From fd0e1e893f22e1ef27fb9f1e6d12c590d2fcdeea Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 5 Sep 2017 17:42:14 -0700
Subject: [PATCH 65/72] Fix warnings in lookup_op
---
paddle/operators/lookup_table_op.h | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h
index 4da8079b91..877b36cef4 100644
--- a/paddle/operators/lookup_table_op.h
+++ b/paddle/operators/lookup_table_op.h
@@ -30,12 +30,12 @@ class LookupTableKernel : public framework::OpKernel {
auto ids_t = context.Input<Tensor>("Ids"); // int tensor
auto output_t = context.Output<Tensor>("Out"); // float tensor
- size_t N = table_t->dims()[0];
- size_t D = table_t->dims()[1];
+ int N = table_t->dims()[0];
+ int D = table_t->dims()[1];
auto ids = ids_t->data<int32_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
- for (size_t i = 0; i < product(ids_t->dims()); ++i) {
+ for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
@@ -51,8 +51,8 @@ class LookupTableGradKernel : public framework::OpKernel {
auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
- size_t N = d_table_t->dims()[0];
- size_t D = d_table_t->dims()[1];
+ int N = d_table_t->dims()[0];
+ int D = d_table_t->dims()[1];
auto ids = ids_t->data<int32_t>();
const T* d_output = d_output_t->data<T>();
T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
@@ -61,10 +61,10 @@ class LookupTableGradKernel : public framework::OpKernel {
t.device(context.GetEigenDevice<platform::CPUPlace>()) =
t.constant(static_cast<T>(0));
- for (size_t i = 0; i < product(ids_t->dims()); ++i) {
+ for (ssize_t i = 0; i < product(ids_t->dims()); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
- for (size_t j = 0; j < D; ++j) {
+ for (int j = 0; j < D; ++j) {
d_table[ids[i] * D + j] += d_output[i * D + j];
}
}
From 5d9478094d8721f312d9fec323920e608ed23e66 Mon Sep 17 00:00:00 2001
From: lispc <lispc@qq.com>
Date: Wed, 6 Sep 2017 11:33:16 +0800
Subject: [PATCH 66/72] PyDataProvider2.InputType repr refine style
---
python/paddle/trainer/PyDataProvider2.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/python/paddle/trainer/PyDataProvider2.py b/python/paddle/trainer/PyDataProvider2.py
index 033c71cf8f..248da4ae8d 100644
--- a/python/paddle/trainer/PyDataProvider2.py
+++ b/python/paddle/trainer/PyDataProvider2.py
@@ -87,7 +87,8 @@ class InputType(object):
def __repr__(self):
"""
- Return a human readable representation like 'InputType(dim=25921, seq_type=SequenceType.NO_SEQUENCE, type=DataType.Dense)'
+ Return a human readable representation like 'InputType(dim=25921,
+ seq_type=SequenceType.NO_SEQUENCE, type=DataType.Dense)'
"""
repr_str = type(self).__name__
repr_str += '('
From 11163dfc7a6c1504c42acf77bd48d677d7167941 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Wed, 6 Sep 2017 11:54:33 +0800
Subject: [PATCH 67/72] make dim int to int64_t
---
paddle/framework/ddim.cc | 64 ++++++++++-----------
paddle/framework/ddim.h | 20 +++----
paddle/framework/ddim_test.cc | 4 +-
paddle/framework/dim.h | 67 +++++++++++-----------
paddle/framework/dim_test.cu | 4 +-
paddle/framework/eigen.h | 2 +-
paddle/framework/tensor_impl.h | 4 +-
paddle/operators/gaussian_random_op.cc | 10 +++-
paddle/operators/rnn/recurrent_op_utils.cc | 2 +-
paddle/operators/uniform_random_op.cc | 10 +++-
paddle/pybind/pybind.cc | 2 +-
paddle/pybind/tensor_py.h | 4 +-
12 files changed, 102 insertions(+), 91 deletions(-)
diff --git a/paddle/framework/ddim.cc b/paddle/framework/ddim.cc
index cfd3e8dfde..85b7de7974 100644
--- a/paddle/framework/ddim.cc
+++ b/paddle/framework/ddim.cc
@@ -21,16 +21,16 @@ namespace framework {
/// @cond HIDDEN
template <int i>
-Dim<i> make_dim(const int* d) {
+Dim<i> make_dim(const int64_t* d) {
return Dim<i>(*d, make_dim<i - 1>(d + 1));
}
template <>
-Dim<1> make_dim<1>(const int* d) {
+Dim<1> make_dim<1>(const int64_t* d) {
return Dim<1>(*d);
}
-void make_ddim(DDim& ddim, const int* dims, int n) {
+void make_ddim(DDim& ddim, const int64_t* dims, int n) {
switch (n) {
case 1:
ddim = make_dim<1>(dims);
@@ -67,13 +67,13 @@ void make_ddim(DDim& ddim, const int* dims, int n) {
/// @endcond
-DDim make_ddim(std::initializer_list<int> dims) {
+DDim make_ddim(std::initializer_list<int64_t> dims) {
DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size());
return result;
}
-DDim make_ddim(const std::vector<int>& dims) {
+DDim make_ddim(const std::vector<int64_t>& dims) {
DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size());
return result;
@@ -81,12 +81,12 @@ DDim make_ddim(const std::vector<int>& dims) {
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes errors
-class DynamicMutableIndexer : public boost::static_visitor<int&> {
+class DynamicMutableIndexer : public boost::static_visitor<int64_t&> {
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D>
- int& operator()(Dim<D>& dim) const {
+ int64_t& operator()(Dim<D>& dim) const {
return dim[idx_];
}
@@ -94,12 +94,12 @@ class DynamicMutableIndexer : public boost::static_visitor<int&> {
int idx_;
};
-class DynamicConstIndexer : public boost::static_visitor<int> {
+class DynamicConstIndexer : public boost::static_visitor<int64_t> {
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D>
- int operator()(const Dim<D>& dim) const {
+ int64_t operator()(const Dim<D>& dim) const {
return dim[idx_];
}
@@ -109,22 +109,22 @@ class DynamicConstIndexer : public boost::static_visitor<int> {
/// @endcond
-int& DDim::operator[](int idx) {
+int64_t& DDim::operator[](int idx) {
return boost::apply_visitor(DynamicMutableIndexer(idx), var);
}
-int DDim::operator[](int idx) const {
+int64_t DDim::operator[](int idx) const {
return boost::apply_visitor(DynamicConstIndexer(idx), var);
}
-ssize_t DDim::size() const { return arity(*this); }
+int64_t DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const {
if (var.which() != d.getVar().which()) {
return false;
} else {
- std::vector<int> v1 = vectorize(*this);
- std::vector<int> v2 = vectorize(d);
+ std::vector<int64_t> v1 = vectorize(*this);
+ std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) {
@@ -139,10 +139,10 @@ bool DDim::operator==(DDim d) const {
bool DDim::operator!=(DDim d) const { return !(*this == d); }
DDim DDim::operator+(DDim d) const {
- std::vector<int> v1 = vectorize(*this);
- std::vector<int> v2 = vectorize(d);
+ std::vector<int64_t> v1 = vectorize(*this);
+ std::vector<int64_t> v2 = vectorize(d);
- std::vector<int> v3;
+ std::vector<int64_t> v3;
assert(v1.size() == v2.size());
@@ -154,10 +154,10 @@ DDim DDim::operator+(DDim d) const {
}
DDim DDim::operator*(DDim d) const {
- std::vector<int> v1 = vectorize(*this);
- std::vector<int> v2 = vectorize(d);
+ std::vector<int64_t> v1 = vectorize(*this);
+ std::vector<int64_t> v2 = vectorize(d);
- std::vector<int> v3;
+ std::vector<int64_t> v3;
assert(v1.size() == v2.size());
@@ -168,15 +168,15 @@ DDim DDim::operator*(DDim d) const {
return make_ddim(v3);
}
-int get(const DDim& ddim, int idx) { return ddim[idx]; }
+int64_t get(const DDim& ddim, int idx) { return ddim[idx]; }
void set(DDim& ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : public boost::static_visitor<> {
- std::vector<int>& vector;
+ std::vector<int64_t>& vector;
- explicit VectorizeVisitor(std::vector<int>& v) : vector(v) {}
+ explicit VectorizeVisitor(std::vector<int64_t>& v) : vector(v) {}
template <typename T>
void operator()(const T& t) {
@@ -188,31 +188,31 @@ struct VectorizeVisitor : public boost::static_visitor<> {
};
/// @endcond
-std::vector<int> vectorize(const DDim& ddim) {
- std::vector<int> result;
+std::vector<int64_t> vectorize(const DDim& ddim) {
+ std::vector<int64_t> result;
VectorizeVisitor visitor(result);
boost::apply_visitor(visitor, ddim);
return result;
}
-struct ProductVisitor : public boost::static_visitor<ssize_t> {
+struct ProductVisitor : public boost::static_visitor<int64_t> {
template <int D>
- ssize_t operator()(const Dim<D>& dim) {
+ int64_t operator()(const Dim<D>& dim) {
return product(dim);
}
};
-ssize_t product(const DDim& ddim) {
+int64_t product(const DDim& ddim) {
ProductVisitor visitor;
return boost::apply_visitor(visitor, ddim);
}
struct SliceVectorizeVisitor : public boost::static_visitor<> {
- std::vector<int>& vector;
+ std::vector<int64_t>& vector;
int begin;
int end;
- SliceVectorizeVisitor(std::vector<int>& v, int b, int e)
+ SliceVectorizeVisitor(std::vector<int64_t>& v, int b, int e)
: vector(v), begin(b), end(e) {
PADDLE_ENFORCE(begin < end,
"Begin index must be less than end index in ddim slice.");
@@ -240,7 +240,7 @@ struct SliceVectorizeVisitor : public boost::static_visitor<> {
};
DDim slice_ddim(const DDim& dim, int begin, int end) {
- std::vector<int> vec;
+ std::vector<int64_t> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
boost::apply_visitor(visitor, dim);
@@ -280,7 +280,7 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
return os;
}
-DDim::DDim(std::initializer_list<int> init_list) {
+DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
}
} // namespace framework
diff --git a/paddle/framework/ddim.h b/paddle/framework/ddim.h
index 95f294b627..db30c52394 100644
--- a/paddle/framework/ddim.h
+++ b/paddle/framework/ddim.h
@@ -40,7 +40,7 @@ struct DDim {
template <int D>
explicit DDim(const Dim<D>& in) : var(in) {}
- /*implicit*/ DDim(std::initializer_list<int> init_list);
+ /*implicit*/ DDim(std::initializer_list<int64_t> init_list);
template <int D>
DDim& operator=(const Dim<D>& in) {
@@ -48,8 +48,8 @@ struct DDim {
return *this;
}
- int& operator[](int idx);
- int operator[](int idx) const;
+ int64_t& operator[](int idx);
+ int64_t operator[](int idx) const;
template <typename Visitor>
typename Visitor::result_type apply_visitor(Visitor& visitor) {
@@ -71,15 +71,15 @@ struct DDim {
DDim operator*(DDim d) const;
- ssize_t size() const;
+ int64_t size() const;
};
/**
- * \brief Make a DDim from std::vector<int>
+ * \brief Make a DDim from std::vector<int64_t>
*
* \param dims An vector of ints. Must be sized between [1, 9]
*/
-DDim make_ddim(const std::vector<int>& dims);
+DDim make_ddim(const std::vector<int64_t>& dims);
/**
* \brief Make a DDim from an initializer list
@@ -87,14 +87,14 @@ DDim make_ddim(const std::vector<int>& dims);
* \param dims An initializer list of ints. Must be sized between [1, 9]
*
*/
-DDim make_ddim(std::initializer_list<int> dims);
+DDim make_ddim(std::initializer_list<int64_t> dims);
-int get(const DDim& dim, int idx);
+int64_t get(const DDim& dim, int idx);
void set(DDim& dim, int idx, int val);
-std::vector<int> vectorize(const DDim& ddim);
+std::vector<int64_t> vectorize(const DDim& ddim);
-ssize_t product(const DDim& ddim);
+int64_t product(const DDim& ddim);
/**
* \brief Slice a ddim
diff --git a/paddle/framework/ddim_test.cc b/paddle/framework/ddim_test.cc
index 9d18a2972c..756232b1b5 100644
--- a/paddle/framework/ddim_test.cc
+++ b/paddle/framework/ddim_test.cc
@@ -12,7 +12,7 @@ TEST(DDim, Equality) {
EXPECT_EQ(ddim[2], 5);
// construct a DDim from a vector
- std::vector<int> vec({9, 1, 5});
+ std::vector<int64_t> vec({9, 1, 5});
paddle::framework::DDim vddim = paddle::framework::make_ddim(vec);
EXPECT_EQ(ddim[0], 9);
EXPECT_EQ(ddim[1], 1);
@@ -25,7 +25,7 @@ TEST(DDim, Equality) {
EXPECT_EQ(paddle::framework::get(ddim, 0), 6);
// vectorize a DDim
- std::vector<int> res_vec = paddle::framework::vectorize(vddim);
+ std::vector<int64_t> res_vec = paddle::framework::vectorize(vddim);
EXPECT_EQ(res_vec[0], 9);
EXPECT_EQ(res_vec[1], 1);
EXPECT_EQ(res_vec[2], 5);
diff --git a/paddle/framework/dim.h b/paddle/framework/dim.h
index 883fdc55eb..04d4b0e604 100644
--- a/paddle/framework/dim.h
+++ b/paddle/framework/dim.h
@@ -17,13 +17,13 @@ struct Dim {
static constexpr int dimensions = i;
template <typename... Args>
- HOSTDEVICE Dim(int _head, Args... _tail) : head(_head), tail(_tail...) {
+ HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
static_assert(sizeof...(_tail) == i - 1,
"Dim initialized with the wrong number of parameters");
}
HOSTDEVICE
- Dim(int _head, const Dim<i - 1>& _tail) : head(_head), tail(_tail) {}
+ Dim(int64_t _head, const Dim<i - 1>& _tail) : head(_head), tail(_tail) {}
HOSTDEVICE
Dim() : head(0), tail() {}
@@ -31,12 +31,12 @@ struct Dim {
/** Construct a Dim from a linear index and size. Uses Fortran order
* indexing. */
HOSTDEVICE
- Dim(int idx, const Dim<i>& size)
+ Dim(int64_t idx, const Dim<i>& size)
: head(idx % size.head), tail(idx / size.head, size.tail) {}
/** Construct a Dim with each dimension set to the given index */
HOSTDEVICE
- Dim(int idx) : head(idx), tail(idx) {}
+ Dim(int64_t idx) : head(idx), tail(idx) {}
HOSTDEVICE
bool operator==(const Dim<i>& o) const {
@@ -47,13 +47,13 @@ struct Dim {
bool operator!=(const Dim<i>& o) const { return !(*this == o); }
HOSTDEVICE
- int& operator[](int idx);
+ int64_t& operator[](int idx);
HOSTDEVICE
- int operator[](int idx) const;
+ int64_t operator[](int idx) const;
HOST std::string to_string() const;
- int head;
+ int64_t head;
Dim<i - 1> tail;
};
@@ -63,7 +63,7 @@ struct Dim<1> {
static constexpr int dimensions = 1;
HOSTDEVICE
- Dim(int _head) : head(_head) {}
+ Dim(int64_t _head) : head(_head) {}
HOSTDEVICE
Dim() : head(0) {}
@@ -86,11 +86,11 @@ struct Dim<1> {
bool operator!=(const Dim<1>& o) const { return !(*this == o); }
HOSTDEVICE
- int& operator[](int idx);
+ int64_t& operator[](int idx);
HOSTDEVICE
- int operator[](int idx) const;
+ int64_t operator[](int idx) const;
- int head;
+ int64_t head;
};
namespace {
@@ -100,12 +100,12 @@ template <int i>
struct DimGetter {
// Return a copy if Dim is const
template <typename D>
- HOSTDEVICE static int impl(const D& d) {
+ HOSTDEVICE static int64_t impl(const D& d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a reference if Dim is mutable
template <typename D>
- HOSTDEVICE static int& impl(D& d) {
+ HOSTDEVICE static int64_t& impl(D& d) {
return DimGetter<i - 1>::impl(d.tail);
}
};
@@ -115,18 +115,18 @@ template <>
struct DimGetter<0> {
// Return a copy if Dim is const
template <typename D>
- HOSTDEVICE static int impl(const D& d) {
+ HOSTDEVICE static int64_t impl(const D& d) {
return d.head;
}
// Return a reference if Dim is mutable
template <typename D>
- HOSTDEVICE static int& impl(D& d) {
+ HOSTDEVICE static int64_t& impl(D& d) {
return d.head;
}
};
template <int D>
-HOSTDEVICE int& indexer(Dim<D>& dim, int idx) {
+HOSTDEVICE int64_t& indexer(Dim<D>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
@@ -141,7 +141,7 @@ HOSTDEVICE int& indexer(Dim<D>& dim, int idx) {
}
template <>
-HOSTDEVICE int& indexer<1>(Dim<1>& dim, int idx) {
+HOSTDEVICE int64_t& indexer<1>(Dim<1>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx != 0) {
throw std::invalid_argument("Invalid index");
@@ -153,7 +153,7 @@ HOSTDEVICE int& indexer<1>(Dim<1>& dim, int idx) {
}
template <int D>
-HOSTDEVICE int indexer(const Dim<D>& dim, int idx) {
+HOSTDEVICE int64_t indexer(const Dim<D>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
@@ -168,7 +168,7 @@ HOSTDEVICE int indexer(const Dim<D>& dim, int idx) {
}
template <>
-HOSTDEVICE int indexer<1>(const Dim<1>& dim, int idx) {
+HOSTDEVICE int64_t indexer<1>(const Dim<1>& dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx != 0) {
throw std::invalid_argument("Invalid index");
@@ -182,73 +182,76 @@ HOSTDEVICE int indexer<1>(const Dim<1>& dim, int idx) {
} // namespace
// Static access to constant Dim
template <int i, int l>
-HOSTDEVICE int get(const Dim<l>& d) {
+HOSTDEVICE int64_t get(const Dim<l>& d) {
return DimGetter<i>::impl(d);
}
// Static access to mutable Dim
template <int i, int l>
-HOSTDEVICE int& get(Dim<l>& d) {
+HOSTDEVICE int64_t& get(Dim<l>& d) {
return DimGetter<i>::impl(d);
}
// Dynamic access to constant Dim
template <int l>
-HOSTDEVICE int Dim<l>::operator[](int i) const {
+HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
return indexer(*this, i);
}
// Dynamic access to mutable Dim
template <int l>
-HOSTDEVICE int& Dim<l>::operator[](int i) {
+HOSTDEVICE int64_t& Dim<l>::operator[](int i) {
return indexer(*this, i);
}
// Dynamic access to constant Dim
-inline HOSTDEVICE int Dim<1>::operator[](int i) const {
+inline HOSTDEVICE int64_t Dim<1>::operator[](int i) const {
return indexer(*this, i);
}
// Dynamic access to mutable Dim
-inline HOSTDEVICE int& Dim<1>::operator[](int i) { return indexer(*this, i); }
+inline HOSTDEVICE int64_t& Dim<1>::operator[](int i) {
+ return indexer(*this, i);
+}
// Dynamic access to constant Dim
// without std::enable_if will try to instantiate this on get<0>(d)
template <int l>
-HOSTDEVICE typename std::enable_if<(l > 0), int>::type get(const Dim<l>& d,
- int i) {
+HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l>& d,
+ int i) {
return d[i];
}
// Dynamic access to mutable Dim
template <int l>
-HOSTDEVICE typename std::enable_if<(l > 0), int&>::type get(Dim<l>& d, int i) {
+HOSTDEVICE typename std::enable_if<(l > 0), int64_t&>::type get(Dim<l>& d,
+ int i) {
return d[i];
}
// Dot product of two dims
template <int i>
-HOSTDEVICE int linearize(const Dim<i>& a, const Dim<i>& b) {
+HOSTDEVICE int64_t linearize(const Dim<i>& a, const Dim<i>& b) {
return a.head * b.head + linearize(a.tail, b.tail);
}
// Base case dot product of two Dims
// Notice it is inline because it is no longer a template
template <>
-HOSTDEVICE inline int linearize(const Dim<1>& a, const Dim<1>& b) {
+HOSTDEVICE inline int64_t linearize(const Dim<1>& a, const Dim<1>& b) {
return a.head * b.head;
}
// Product of a Dim
template <int i>
-HOSTDEVICE int product(const Dim<i>& a, int prod = 1) {
+HOSTDEVICE int64_t product(const Dim<i>& a, int prod = 1) {
return prod * a.head * product(a.tail);
}
// Base case product of a Dim
// Notice it is inline because it is no longer a template
template <>
-HOSTDEVICE inline int product(const Dim<1>& a, int prod) {
+HOSTDEVICE inline int64_t product(const Dim<1>& a, int prod) {
return prod * a.head;
}
diff --git a/paddle/framework/dim_test.cu b/paddle/framework/dim_test.cu
index 3898d0a447..f42b1eeadf 100644
--- a/paddle/framework/dim_test.cu
+++ b/paddle/framework/dim_test.cu
@@ -47,9 +47,9 @@ TEST(Dim, Equality) {
EXPECT_EQ(b[1], 11);
// dynamic access on GPU
- thrust::device_vector<int> r(1);
+ thrust::device_vector<int64_t> r(1);
dyn_idx_gpu<<<1, 1>>>(thrust::raw_pointer_cast(r.data()));
- int res = r[0];
+ int64_t res = r[0];
EXPECT_EQ(res, 6);
// ex_prefix_mul
diff --git a/paddle/framework/eigen.h b/paddle/framework/eigen.h
index a4667cc51f..2d8d9ae10c 100644
--- a/paddle/framework/eigen.h
+++ b/paddle/framework/eigen.h
@@ -28,7 +28,7 @@ struct EigenDim {
static Type From(const DDim& dims) {
PADDLE_ENFORCE(arity(dims) == D, "D must match arity(DDim)");
Type ret;
- for (int d = 0; d < arity(dims); d++) {
+ for (int64_t d = 0; d < arity(dims); d++) {
ret[d] = dims[d];
}
return ret;
diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h
index 7893e233b7..94f436294f 100644
--- a/paddle/framework/tensor_impl.h
+++ b/paddle/framework/tensor_impl.h
@@ -58,7 +58,7 @@ inline T* Tensor::mutable_data(platform::Place place) {
"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!= */
- size_t size = product(dims_) * sizeof(T);
+ int64_t size = product(dims_) * sizeof(T);
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
@@ -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.");
- int base = product(dims_) / dims_[0];
+ size_t base = product(dims_) / dims_[0];
Tensor dst;
dst.holder_ = holder_;
DDim dst_dims = dims_;
diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc
index 056447901d..583c9e5774 100644
--- a/paddle/operators/gaussian_random_op.cc
+++ b/paddle/operators/gaussian_random_op.cc
@@ -31,8 +31,8 @@ class CPUGaussianRandomKernel : public framework::OpKernel {
}
engine.seed(seed);
std::normal_distribution<T> dist(mean, std);
- ssize_t size = framework::product(tensor->dims());
- for (ssize_t i = 0; i < size; ++i) {
+ int64_t size = framework::product(tensor->dims());
+ for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
}
@@ -46,9 +46,13 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
auto dims = GetAttr<std::vector<int>>("dims");
+ std::vector<int64_t> temp(dims.size());
+ for (auto dim : dims) {
+ temp.push_back(static_cast<int64_t>(dim));
+ }
PADDLE_ENFORCE(dims.size() > 0UL,
"dims can be one int or array. dims must be set.");
- tensor->Resize(framework::make_ddim(dims));
+ tensor->Resize(framework::make_ddim(temp));
}
};
diff --git a/paddle/operators/rnn/recurrent_op_utils.cc b/paddle/operators/rnn/recurrent_op_utils.cc
index a9b65c30f2..69e723b401 100644
--- a/paddle/operators/rnn/recurrent_op_utils.cc
+++ b/paddle/operators/rnn/recurrent_op_utils.cc
@@ -61,7 +61,7 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
outlinks[i].internal);
f::DDim step_dims = step_scope_var->template GetMutable<Tensor>()->dims();
- std::vector<int> dims_vec = vectorize(step_dims);
+ std::vector<int64_t> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(f::make_ddim(dims_vec));
} else {
diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc
index 2d943c4508..3241d4289a 100644
--- a/paddle/operators/uniform_random_op.cc
+++ b/paddle/operators/uniform_random_op.cc
@@ -35,8 +35,8 @@ class CPUUniformRandomKernel : public framework::OpKernel {
std::uniform_real_distribution<T> dist(
static_cast<T>(context.GetAttr<float>("min")),
static_cast<T>(context.GetAttr<float>("max")));
- ssize_t size = framework::product(tensor->dims());
- for (ssize_t i = 0; i < size; ++i) {
+ int64_t size = framework::product(tensor->dims());
+ for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
}
}
@@ -52,7 +52,11 @@ class UniformRandomOp : public framework::OperatorWithKernel {
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto dims = GetAttr<std::vector<int>>("dims");
- tensor->Resize(framework::make_ddim(dims));
+ std::vector<int64_t> temp(dims.size());
+ for (auto dim : dims) {
+ temp.push_back(static_cast<int64_t>(dim));
+ }
+ tensor->Resize(framework::make_ddim(temp));
}
};
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 6896422617..7db629a68f 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -76,7 +76,7 @@ PYBIND11_PLUGIN(core) {
.def("get_dims",
[](const Tensor &self) { return vectorize(self.dims()); })
.def("set_dims",
- [](Tensor &self, const std::vector<int> &dim) {
+ [](Tensor &self, const std::vector<int64_t> &dim) {
self.Resize(make_ddim(dim));
})
.def("alloc_float",
diff --git a/paddle/pybind/tensor_py.h b/paddle/pybind/tensor_py.h
index 39ba60b4dc..95171acf72 100644
--- a/paddle/pybind/tensor_py.h
+++ b/paddle/pybind/tensor_py.h
@@ -85,7 +85,7 @@ void PyCPUTensorSetFromArray(
framework::Tensor &self,
py::array_t<T, py::array::c_style | py::array::forcecast> array,
paddle::platform::CPUPlace &place) {
- std::vector<int> dims;
+ std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
@@ -102,7 +102,7 @@ void PyCUDATensorSetFromArray(
framework::Tensor &self,
py::array_t<T, py::array::c_style | py::array::forcecast> array,
paddle::platform::GPUPlace &place) {
- std::vector<int> dims;
+ std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
From 857cd24a35bef9c182292ca709fe1dd9d0e52e19 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Wed, 6 Sep 2017 12:18:23 +0800
Subject: [PATCH 68/72] fix dim_test.cu bug
---
paddle/framework/dim_test.cu | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/paddle/framework/dim_test.cu b/paddle/framework/dim_test.cu
index f42b1eeadf..0a6a87669c 100644
--- a/paddle/framework/dim_test.cu
+++ b/paddle/framework/dim_test.cu
@@ -8,7 +8,7 @@ __global__ void test(paddle::framework::Dim<2>* o) {
o[0] = paddle::framework::make_dim(5, 6);
}
-__global__ void dyn_idx_gpu(int* o) {
+__global__ void dyn_idx_gpu(int64_t* o) {
auto d = paddle::framework::make_dim(5, 6);
o[0] = d[1];
}
From a5f1e6d6bdd094253cd6f47bc7bbefb1fbc3b1e4 Mon Sep 17 00:00:00 2001
From: Xinghai Sun <sunxinghai1216@gmail.com>
Date: Wed, 6 Sep 2017 12:58:00 +0800
Subject: [PATCH 69/72] Update cos_sim operator by following reviewer's
comments.
---
paddle/operators/cos_sim_op.cc | 4 +-
paddle/operators/cos_sim_op.h | 97 ++++++++++---------
.../v2/framework/tests/test_cos_sim_op.py | 38 +++++---
3 files changed, 76 insertions(+), 63 deletions(-)
diff --git a/paddle/operators/cos_sim_op.cc b/paddle/operators/cos_sim_op.cc
index 3760d0b161..c033af3b74 100644
--- a/paddle/operators/cos_sim_op.cc
+++ b/paddle/operators/cos_sim_op.cc
@@ -90,8 +90,8 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
- x_grad->Resize(x_dims);
- y_grad->Resize(y_dims);
+ if (x_grad) x_grad->Resize(x_dims);
+ if (y_grad) y_grad->Resize(y_dims);
}
};
diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index 69d35d8bc2..9e3ff26815 100644
--- a/paddle/operators/cos_sim_op.h
+++ b/paddle/operators/cos_sim_op.h
@@ -28,30 +28,30 @@ template <typename Place, typename T>
class CosSimKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
- auto* x = context.Input<Tensor>("X");
- auto* y = context.Input<Tensor>("Y");
- auto* z = context.Output<Tensor>("Out");
- auto* x_norm = context.Output<Tensor>("XNorm");
- auto* y_norm = context.Output<Tensor>("YNorm");
+ auto* input_x = context.Input<Tensor>("X");
+ auto* input_y = context.Input<Tensor>("Y");
+ auto* output_z = context.Output<Tensor>("Out");
+ auto* output_x_norm = context.Output<Tensor>("XNorm");
+ auto* output_y_norm = context.Output<Tensor>("YNorm");
- z->mutable_data<T>(context.GetPlace());
- x_norm->mutable_data<T>(context.GetPlace());
- y_norm->mutable_data<T>(context.GetPlace());
+ output_z->mutable_data<T>(context.GetPlace());
+ output_x_norm->mutable_data<T>(context.GetPlace());
+ output_y_norm->mutable_data<T>(context.GetPlace());
- auto dims = x->dims();
+ auto dims = input_x->dims();
int size = static_cast<int>(framework::product(dims));
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
- auto X = EigenMatrix<T>::From(*x, new_dims);
- auto Y = EigenMatrix<T>::From(*y, new_dims);
- auto Z = EigenMatrix<T>::From(*z);
- auto XNorm = EigenMatrix<T>::From(*x_norm);
- auto YNorm = EigenMatrix<T>::From(*y_norm);
+ auto x = EigenMatrix<T>::From(*input_x, new_dims);
+ auto y = EigenMatrix<T>::From(*input_y, new_dims);
+ auto z = EigenMatrix<T>::From(*output_z);
+ auto x_norm = EigenMatrix<T>::From(*output_x_norm);
+ auto y_norm = EigenMatrix<T>::From(*output_y_norm);
auto place = context.GetEigenDevice<Place>();
- auto XY = (X * Y).sum(Eigen::array<int, 1>({1}));
- XNorm.device(place) = (X * X).sum(Eigen::array<int, 1>({1})).sqrt();
- YNorm.device(place) = (Y * Y).sum(Eigen::array<int, 1>({1})).sqrt();
- Z.device(place) = XY / XNorm / YNorm;
+ auto xy = (x * y).sum(Eigen::array<int, 1>({1}));
+ x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({1})).sqrt();
+ y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({1})).sqrt();
+ z.device(place) = xy / x_norm / y_norm;
}
};
@@ -59,41 +59,44 @@ template <typename Place, typename T>
class CosSimGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
- auto* x = context.Input<Tensor>("X");
- auto* y = context.Input<Tensor>("Y");
- auto* z = context.Input<Tensor>("Out");
- auto* x_norm = context.Input<Tensor>("XNorm");
- auto* y_norm = context.Input<Tensor>("YNorm");
- auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
- auto* grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
- auto* grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
+ auto* input_x = context.Input<Tensor>("X");
+ auto* input_y = context.Input<Tensor>("Y");
+ auto* input_z = context.Input<Tensor>("Out");
+ auto* input_x_norm = context.Input<Tensor>("XNorm");
+ auto* input_y_norm = context.Input<Tensor>("YNorm");
+ auto* output_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
+ auto* output_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
+ auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
- grad_x->mutable_data<T>(context.GetPlace());
- grad_y->mutable_data<T>(context.GetPlace());
-
- auto dims = x->dims();
+ auto dims = input_x->dims();
int size = static_cast<int>(framework::product(dims));
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
- auto X = EigenMatrix<T>::From(*x, new_dims);
- auto Y = EigenMatrix<T>::From(*y, new_dims);
- auto Z = EigenMatrix<T>::From(*z);
- auto X_norm = EigenMatrix<T>::From(*x_norm);
- auto Y_norm = EigenMatrix<T>::From(*y_norm);
- auto dX = EigenMatrix<T>::From(*grad_x, new_dims);
- auto dY = EigenMatrix<T>::From(*grad_y, new_dims);
- auto dZ = EigenMatrix<T>::From(*grad_z);
+ auto x = EigenMatrix<T>::From(*input_x, new_dims);
+ auto y = EigenMatrix<T>::From(*input_y, new_dims);
+ auto z = EigenMatrix<T>::From(*input_z);
+ auto x_norm = EigenMatrix<T>::From(*input_x_norm);
+ auto y_norm = EigenMatrix<T>::From(*input_y_norm);
+ auto dz = EigenMatrix<T>::From(*input_grad_z);
Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
- auto Z_bcast = Z.broadcast(bcast);
- auto dZ_bcast = dZ.broadcast(bcast);
+ auto z_bcast = z.broadcast(bcast);
+ auto dz_bcast = dz.broadcast(bcast);
auto place = context.GetEigenDevice<Place>();
- auto X_snorm_bcast = X_norm.square().eval().broadcast(bcast);
- auto Y_snorm_bcast = Y_norm.square().eval().broadcast(bcast);
- auto norm_prod_bcast = (X_norm * Y_norm).eval().broadcast(bcast);
- dX.device(place) =
- dZ_bcast * (Y / norm_prod_bcast - Z_bcast * X / X_snorm_bcast);
- dY.device(place) =
- dZ_bcast * (X / norm_prod_bcast - Z_bcast * Y / Y_snorm_bcast);
+ auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast);
+ auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast);
+ auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast);
+ if (output_grad_x) {
+ output_grad_x->mutable_data<T>(context.GetPlace());
+ auto dx = EigenMatrix<T>::From(*output_grad_x, new_dims);
+ dx.device(place) =
+ dz_bcast * (y / norm_prod_bcast - z_bcast * x / x_snorm_bcast);
+ }
+ if (output_grad_y) {
+ output_grad_y->mutable_data<T>(context.GetPlace());
+ auto dy = EigenMatrix<T>::From(*output_grad_y, new_dims);
+ dy.device(place) =
+ dz_bcast * (x / norm_prod_bcast - z_bcast * y / y_snorm_bcast);
+ }
}
};
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 a19be47f76..32013a7999 100644
--- a/python/paddle/v2/framework/tests/test_cos_sim_op.py
+++ b/python/paddle/v2/framework/tests/test_cos_sim_op.py
@@ -24,26 +24,36 @@ class TestCosSimOp(unittest.TestCase):
}
-class CosSimGradOpTest(GradientChecker):
- def test_cos_sim_2d(self):
- op = create_op("cos_sim")
- inputs = {
+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")
}
- self.compare_grad(op, inputs)
+
+ def test_cpu_gpu_compare(self):
+ self.compare_grad(self.op, self.inputs)
+
+ def test_normal(self):
self.check_grad(
- op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.05)
+ self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.05)
- def test_cos_sim_3d(self):
- op = create_op("cos_sim")
- inputs = {
- 'X': np.random.random((10, 5, 2)).astype("float32"),
- 'Y': np.random.random((10, 5, 2)).astype("float32")
- }
- self.compare_grad(op, inputs)
+ def test_ignore_x(self):
+ self.check_grad(
+ self.op,
+ self.inputs, ["Y"],
+ "Out",
+ max_relative_error=0.05,
+ no_grad_set={"X"})
+
+ def test_ignore_y(self):
self.check_grad(
- op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.05)
+ self.op,
+ self.inputs, ["X"],
+ "Out",
+ max_relative_error=0.05,
+ no_grad_set={"Y"})
if __name__ == '__main__':
From 52f2bc1f9d84a05f1c73428124a1235fb141f193 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Wed, 6 Sep 2017 13:34:01 +0800
Subject: [PATCH 70/72] fix vector size error
---
paddle/operators/gaussian_random_op.cc | 3 ++-
paddle/operators/uniform_random_op.cc | 3 ++-
2 files changed, 4 insertions(+), 2 deletions(-)
diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc
index 583c9e5774..8bb61275ba 100644
--- a/paddle/operators/gaussian_random_op.cc
+++ b/paddle/operators/gaussian_random_op.cc
@@ -46,7 +46,8 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
auto dims = GetAttr<std::vector<int>>("dims");
- std::vector<int64_t> temp(dims.size());
+ std::vector<int64_t> temp;
+ temp.reserve(dims.size());
for (auto dim : dims) {
temp.push_back(static_cast<int64_t>(dim));
}
diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc
index 3241d4289a..40cef8942a 100644
--- a/paddle/operators/uniform_random_op.cc
+++ b/paddle/operators/uniform_random_op.cc
@@ -52,7 +52,8 @@ class UniformRandomOp : public framework::OperatorWithKernel {
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto dims = GetAttr<std::vector<int>>("dims");
- std::vector<int64_t> temp(dims.size());
+ std::vector<int64_t> temp;
+ temp.reserve(dims.size());
for (auto dim : dims) {
temp.push_back(static_cast<int64_t>(dim));
}
From 1e419d9ed3610c95b518982a026666ea3f699f27 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Wed, 6 Sep 2017 17:24:47 +0800
Subject: [PATCH 71/72] fix clang build and run error
---
paddle/operators/cos_sim_op.h | 15 +++++++++------
1 file changed, 9 insertions(+), 6 deletions(-)
diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h
index 9e3ff26815..9e2bcebe3b 100644
--- a/paddle/operators/cos_sim_op.h
+++ b/paddle/operators/cos_sim_op.h
@@ -23,6 +23,9 @@ using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+template <typename T, int MajorType = Eigen::RowMajor,
+ typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename Place, typename T>
class CosSimKernel : public framework::OpKernel {
@@ -43,14 +46,14 @@ class CosSimKernel : public framework::OpKernel {
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix<T>::From(*input_x, new_dims);
auto y = EigenMatrix<T>::From(*input_y, new_dims);
- auto z = EigenMatrix<T>::From(*output_z);
- auto x_norm = EigenMatrix<T>::From(*output_x_norm);
- auto y_norm = EigenMatrix<T>::From(*output_y_norm);
+ auto z = EigenVector<T>::Flatten(*output_z);
+ auto x_norm = EigenVector<T>::Flatten(*output_x_norm);
+ auto y_norm = EigenVector<T>::Flatten(*output_y_norm);
auto place = context.GetEigenDevice<Place>();
- auto xy = (x * y).sum(Eigen::array<int, 1>({1}));
- x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({1})).sqrt();
- y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({1})).sqrt();
+ auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
+ x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
+ y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
z.device(place) = xy / x_norm / y_norm;
}
};
From db5e726d160789f05fb8323c6915991e5a276339 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Wed, 6 Sep 2017 18:14:27 +0800
Subject: [PATCH 72/72] update the how to write op doc.
---
doc/howto/dev/new_op_cn.md | 183 +++++++++++++++++++++----------------
1 file changed, 102 insertions(+), 81 deletions(-)
diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md
index 0d29865447..58665e9f2b 100644
--- a/doc/howto/dev/new_op_cn.md
+++ b/doc/howto/dev/new_op_cn.md
@@ -45,7 +45,9 @@ Kernel实现 | CPU、GPU共享Kernel实现在`.h`文件中，否则，CPU
### 1. 定义ProtoMaker类
-矩阵乘的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释：
+矩阵乘法的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。
+
+首先定义`ProtoMaker`来描述该Op的输入、输出，并添加注释：
```cpp
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -63,17 +65,17 @@ The equation is: Out = X * Y
};
```
-[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数包括2个参数：
+[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数含有2个参数：
- `framework::OpProto` ： 前者存储Op的输入输出和参数属性，将用于Python API接口的生成。
- `framework::OpAttrChecker` ：后者用于检查参数属性的合法性。
-构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加该Op的注释，这些函数会将对应内容添加到`OpProto`中。
+构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加Op的注释。这些函数会将对应内容添加到`OpProto`中。
-在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，命名请遵守命名规范。
+上面的代码在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，命名请遵守命名规范。
-再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子：
+再以[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)为例：
```cpp
template <typename AttrType>
@@ -91,14 +93,16 @@ The equation is: Out = scale*X
};
```
- 这个例子有两处不同：
+这个例子有两处不同：
+
+- `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中，如果Op的某个输入不参与反向梯度的计算，请显示地调用`.NotInGradient()`进行设置。
- - `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中。
- - `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
+- `AddAttr<AttrType>("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。
### 2. 定义Operator类
+下面的点实现了MulOp的定义：
```cpp
class MulOp : public framework::OperatorWithKernel {
@@ -143,14 +147,27 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
- 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法。
- 2). 设置输出Tensor的形状。
-通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和要讲到的注册函数一起放在`.cc`中
+通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和下面将要介绍的注册函数一起放在`.cc`中
### 3. 定义OpKernel类
-```cpp
-template <typename Place, typename T>
-class MulKernel : public framework::OpKernel {
- public:
+`MulKernel`继承自`framework::OpKernel`，带有下面两个模板参数:
+
+- `typename Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
+
+- `typename T` : 表示数据类型，如`float`, `double`等。
+
+需要为`MulKernel`类重写`Compute`接口。
+- `Compute`接受一个输入参数：`const framework::ExecutionContext& context`。
+- 与`InferShapeContext`相比，`ExecutionContext`增加了设备类型，同样可获取到输入输出和属性参数。
+- `Compute`函数里实现`OpKernel`的具体计算逻辑。
+
+下面是 `MulKernel` `Compute`的实现：
+
+ ```cpp
+ template <typename Place, typename T>
+ class MulKernel : public framework::OpKernel {
+ public:
void Compute(const framework::ExecutionContext& context) const override {
auto* X = context.Input<Tensor>("X");
auto* Y = context.Input<Tensor>("Y");
@@ -160,50 +177,50 @@ class MulKernel : public framework::OpKernel {
const_cast<platform::DeviceContext*>(context.device_context_);
math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
}
-};
-```
+ };
+ ```
-`MulKernel`继承自`framework::OpKernel`，带有模板参数:
+需要注意：**不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。**
- - `typename Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
+`MulOp`的CPU、GPU实现共享同一个`Kernel`。`OpKernel`不共享的例子可以参考：[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
- - `typename T` : 表示数据类型，如`float`, `double`等。
+为了使`OpKernel`的计算过程书写更加简单，并且CPU、GPU的代码可以复用，我们通常借助 Eigen unsupported Tensor模块来实现`Compute`接口。关于在PaddlePaddle中如何使用Eigen库，请参考[使用文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)。
-`MulKernel`需要重写`Compute`接口，该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型，同样可获取到输入输出和属性参数，`Compute`函数里写具体实现时。
-注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。
+到此，前向Op实现完成。接下来，需要在`.cc`文件中注册该op和kernel。
+反向Op类的定义，反向OpKernel的定义与前向Op类似，这里不再赘述。**但需注意反向Op没有`ProtoMaker`**。
-为了使得`OpKernel`的计算过程书写较为简单，CPU、GPU的代码可以复用，我们通常借助Eigen unsupported Tensor模块来实现。关于在paddle中如何使用Eigen库，请参考对应的使用[文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)
+### 4. 注册Operator
-到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。
+- 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
-### 4. 注册Operator
+ ```cpp
+ namespace ops = paddle::operators;
+ REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
+ REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
+ REGISTER_OP_CPU_KERNEL(mul_grad,
+ ops::MulGradKernel<paddle::platform::CPUPlace, float>);
+ ```
-在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。
+ 在上面的代码中：
-```cpp
-namespace ops = paddle::operators;
-REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
-REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
-REGISTER_OP_CPU_KERNEL(mul_grad,
- ops::MulGradKernel<paddle::platform::CPUPlace, float>);
-```
+ - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`。
+ - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
+ - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
- - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`，
- - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。
- - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。
-在 `.cu`文件中注册GPU Kernel。请注意，如果GPU Kernel的实现是基于Eigen unsupported模块，那么在 `.cu`的最前面请加上宏定义 `#define EIGEN_USE_GPU`
+- 在 `.cu`文件中注册GPU Kernel。
+ - 请注意，如果GPU Kernel的实现基于Eigen unsupported模块，那么在 `.cu`的开始请加上宏定义 `#define EIGEN_USE_GPU`，代码示例如下：
-```cpp
-// if use Eigen unsupported module before include head files
-#define EIGEN_USE_GPU
+ ```cpp
+ // if use Eigen unsupported module before include head files
+ #define EIGEN_USE_GPU
-namespace ops = paddle::operators;
-REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
-REGISTER_OP_GPU_KERNEL(mul_grad,
- ops::MulGradKernel<paddle::platform::GPUPlace, float>);
-```
+ namespace ops = paddle::operators;
+ REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
+ REGISTER_OP_GPU_KERNEL(mul_grad,
+ ops::MulGradKernel<paddle::platform::GPUPlace, float>);
+ ```
### 5. 编译
@@ -225,7 +242,7 @@ REGISTER_OP_GPU_KERNEL(mul_grad,
- 绑定Python
在 [`paddle/pybind/pybind.cc
-`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc)文件中添加该类：
+`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) 使用`USE_OP`告知编译器需要链接的Op，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
```
USE_OP(mul);
@@ -242,50 +259,54 @@ REGISTER_OP_GPU_KERNEL(mul_grad,
USE_NO_KENREL_OP(recurrent);
```
- 使用`USE_OP`告知编译器需要链接该Op的目标文件，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
-
- 生成库
- 无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会自动被添加链接到生成的lib库中。
+ 无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动添加链接到生成的lib库中。
## 实现单元测试
-单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单测](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
+单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。
### 前向Operator单元测试
-前向Op单测继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`，具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数，以及Python对比的输出值。
+前向Op单元测试继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`。各项更加具体的单元测试在`OpTestMeta`里完成。测试前向Operator，需要：
-```python
-import unittest
-import numpy as np
-from gradient_checker import GradientChecker, create_op
-from op_test_util import OpTestMeta
+1. 在`setUp`函数定义输入、输出，以及相关的属性参数。
+2. 生成随机的输入数据。
+3. 在Python脚本中实现与前向operator相同的计算逻辑，得到输出值，与operator前向计算的输出进行对比。
-class TestMulOp(unittest.TestCase):
- __metaclass__ = OpTestMeta
- def setUp(self):
- self.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'])}
-```
- 首先需要`import`必要的包,下面详细解释其他值：
+ ```python
+ import unittest
+ import numpy as np
+ from gradient_checker import GradientChecker, create_op
+ from op_test_util import OpTestMeta
+
+ class TestMulOp(unittest.TestCase):
+ __metaclass__ = OpTestMeta
- - `self.type = "mul" ` : 定义类型，和注册的类型一致。
- - `self.inputs` : 定义输入，类型为Numpy.array，并初始化。
- - `self.outputs` : 定义输出，并得到Python结算结果。
+ def setUp(self):
+ self.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'])}
+ ```
+
+上面的代码首先导入依赖的包，下面是对`setUp`函数中操作的重要变量的详细解释：
+
+- `self.type = "mul" ` : 定义类型，与operator注册时注册的类型一致。
+- `self.inputs` : 定义输入，类型为`numpy.array`，并初始化。
+- `self.outputs` : 定义输出，并在Python脚本中完成与operator同样的计算逻辑，返回Python端的计算结果。
### 反向Operator单元测试
-反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。
+反向Op单元测试继承自`GradientChecker`，而`GradientChecker`继承自`unittest.TestCase`，因此，**反向单元测试函数需要以`test_`开头**。
-```cpp
+```python
class TestMulGradOp(GradientChecker):
def setUp(self):
self.op = create_op("mul")
@@ -319,27 +340,27 @@ class TestMulGradOp(GradientChecker):
no_grad_set={"Y"})
```
-下面解释一些关键的地方:
+下面解释代码中一些关键的地方:
- - 调用`create_op("mul")`创建反向Op对应的前向Op。
- - 调用`compare_grad`函数对比CPU、GPU计算结果。
- - `test_normal`中调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。
- - 第一个参数`self.op` : 前向Op。
- - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
- - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
- - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
- - `test_ignore_x`和`test_ignore_y`分支测试只需要计算一个输入梯度的情况。
+- 调用`create_op("mul")`创建反向Op对应的前向Op。
+- 调用`compare_grad`函数对比CPU、GPU计算结果。
+- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。
+ - 第一个参数`self.op` : 前向Op。
+ - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。
+ - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。
+ - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`
+- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。
### 编译和执行单元测试
-单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加以下内容将单测加入工程中：
+单元测试编写完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)中添加以下内容，将单元测试加入工程：
```
py_test(test_mul_op SRCS test_mul_op.py)
```
-请注意，**不同于Op的编译测试，运行单元测试测时需要编译整个工程**，并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后，执行下面的命令来运行单测：
+请注意，**不同于Op的编译测试，运行单元测试测时需要编译整个工程**，并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后，执行下面的命令来运行单元测试：
```bash
make test ARGS="-R test_mul_op -V"