From 68fb4e336e150205e8b226f0b680382d56cf8fb9 Mon Sep 17 00:00:00 2001 From: weixing02 Date: Mon, 28 May 2018 10:35:54 +0800 Subject: [PATCH] add fluid_guide --- .../Developer's_Guide_to_Paddle_Fluid.md | 1652 +++++++++++++++++ doc/fluid/images/1.png | Bin 0 -> 150990 bytes doc/fluid/images/2.png | Bin 0 -> 430358 bytes doc/fluid/images/3.png | Bin 0 -> 426753 bytes doc/fluid/images/4.png | Bin 0 -> 368509 bytes doc/fluid/images/LoDTensor.png | Bin 0 -> 111690 bytes doc/fluid/images/compile_run_time.png | Bin 0 -> 124693 bytes doc/fluid/images/executor.png | Bin 0 -> 193180 bytes doc/fluid/images/fluid_examples.png | Bin 0 -> 192866 bytes doc/fluid/images/fluid_module_1.png | Bin 0 -> 125435 bytes doc/fluid/images/fluid_module_2.png | Bin 0 -> 28896 bytes doc/fluid/images/layer.png | Bin 0 -> 125194 bytes doc/fluid/images/operator1.png | Bin 0 -> 111512 bytes doc/fluid/images/operator2.png | Bin 0 -> 141047 bytes doc/fluid/images/place.png | Bin 0 -> 19218 bytes doc/fluid/images/print_fluid_program.png | Bin 0 -> 93504 bytes doc/fluid/images/program_desc1.png | Bin 0 -> 44762 bytes doc/fluid/images/program_desc2.png | Bin 0 -> 42559 bytes doc/fluid/images/raw_input.png | Bin 0 -> 349671 bytes doc/fluid/images/scope_variable_tensor.png | Bin 0 -> 123940 bytes doc/fluid/images/sorted_input.png | Bin 0 -> 275024 bytes doc/fluid/images/transpiler.png | Bin 0 -> 54699 bytes doc/fluid/images/user_interface.png | Bin 0 -> 98027 bytes 23 files changed, 1652 insertions(+) create mode 100644 doc/fluid/getstarted/Developer's_Guide_to_Paddle_Fluid.md create mode 100644 doc/fluid/images/1.png create mode 100644 doc/fluid/images/2.png create mode 100644 doc/fluid/images/3.png create mode 100644 doc/fluid/images/4.png create mode 100644 doc/fluid/images/LoDTensor.png create mode 100644 doc/fluid/images/compile_run_time.png create mode 100644 doc/fluid/images/executor.png create mode 100644 doc/fluid/images/fluid_examples.png create mode 100644 doc/fluid/images/fluid_module_1.png create mode 100644 doc/fluid/images/fluid_module_2.png create mode 100644 doc/fluid/images/layer.png create mode 100644 doc/fluid/images/operator1.png create mode 100644 doc/fluid/images/operator2.png create mode 100644 doc/fluid/images/place.png create mode 100644 doc/fluid/images/print_fluid_program.png create mode 100644 doc/fluid/images/program_desc1.png create mode 100644 doc/fluid/images/program_desc2.png create mode 100644 doc/fluid/images/raw_input.png create mode 100644 doc/fluid/images/scope_variable_tensor.png create mode 100644 doc/fluid/images/sorted_input.png create mode 100644 doc/fluid/images/transpiler.png create mode 100644 doc/fluid/images/user_interface.png diff --git a/doc/fluid/getstarted/Developer's_Guide_to_Paddle_Fluid.md b/doc/fluid/getstarted/Developer's_Guide_to_Paddle_Fluid.md new file mode 100644 index 00000000000..33b67b01edc --- /dev/null +++ b/doc/fluid/getstarted/Developer's_Guide_to_Paddle_Fluid.md @@ -0,0 +1,1652 @@ + + + +# Paddle Fluid 开发者指南 + +--- + + +# 请大家加入Hi群:1600022 + +--- + +### ==1==. 为什么需要 PaddlePaddle Fluid? + +--- + +### 两个基础问题 + + + +1. 如何描述机器学习模型和优化过程? + - 完备自洽,表达能力足以支持潜在出现的各种计算需求 +1. 如何充分利用资源高效计算? + - 支持异步设备、多卡、分布式计算 + - 降低计算/计算优化的开发成本 + - …… + + + +--- + +### 如何描述模型和优化过程? + + + +||一组连续执行的layers|variable和operator构成的计算图|不再有模型的概念| +|:--|:--|:--|:--| +|2013|Caffe,Theano, Torch, PaddlePaddle|| +|2015||TensorFlow, MxNet, Caffe2, ONNX, n-graph| +|2016|||PyTorch, TensorFlow Eager Execution, **==PaddlePaddle Fluid==**| + + + +--- + + +###

目标 :smile:

+ + + +- 提高对各类机器学习任务的描述能力:能够描述潜在出现的任意机器学习模型。 +- 代码结构逻辑清晰,各模块充分解耦:内外部贡献者能够专注于自己所需的功能模块,基于框架进行再次开发。 +- 从设计上,留下技术优化的空间和潜力。 +- 代码解耦后降低多设备支持、计算优化等的开发成本。 +- 在统一的设计理念下,实现自动可伸缩,自动容错的分布式计算。 + + + +--- + +## ==2.== Design Overview + +--- + +# Fluid: 系统形态 + +- [编译器式的执行流程,区分编译时和运行时](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/fluid_compiler.md) +
+ +![100%](images/fluid-compiler.png) + +--- + + +#### 让我们在Fluid程序实例中,区分编译时和运行时 + +--- +### Fluid 编译时 + + + +- ==**定义前向计算**== + + ```python + x = fluid.layers.data(name='x',shape=[13], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + avg_cost = fluid.layers.mean(x=cost) + ``` + +- ==**添加反向、正则、优化**== + ```python + learning_rate = 0.01 + sgd_optimizer = fluid.optimizer.SGD(learning_rate) + sgd_optimizer.minimize(avg_cost) + ``` + + +--- + +### `Program` vs. 计算图 + + + +- 在科学计算领域,计算图是一种描述计算的经典方式。下图展示了从前向计算图(蓝色)开始,通过添加反向(红色)和优化算法相关(绿色)操作,构建出整个计算图的过程: + + ![60% center](images/graph_construction_example_all.png) + +- Fluid ==使用`Program`而不是计算图==来描述模型和优化过程。`Program`由`Block`、`Operator`和`Variable`构成,相关概念会在后文详细展开。 +- 编译时 Fluid 接受前向计算(这里可以先简单的理解为是一段有序的计算流)`Program`,为这段前向计算按照:前向 :arrow_right: 反向 :arrow_right: 梯度 clip :arrow_right: 正则 :arrow_right: 优化 的顺序,添加相关 `Operator`和`Variable`到`Program`到完整的计算。 + + + +--- + +### Fluid 运行时 + + + +- ==**读入数据**== + + ```python + train_reader = paddle.batch( + paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500), + batch_size=20) + feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) + ``` +- ==**定义执行程序的设备**== + ```python + place = fluid.CPUPlace() + feeder = fluid.DataFeeder(place=place,feed_list=[x, y]) + ``` + +- ==创建执行器(Executor),执行初始化 `Program`和训练`Program`== + + ```python + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + PASS_NUM = 100 + for pass_id in range(PASS_NUM): + for data in train_reader(): + avg_loss_value, = exe.run(fluid.default_main_program(), + feed=feeder.feed(data), + fetch_list=[avg_cost]) + print(avg_loss_value) + ``` + + +--- + +### 总结:框架做什么?用户做什么? +
+ + + +|构建训练|执行训练| +|---|---| +|用户:描述前向运算
框架:添加反向运算
框架:添加优化运算
框架:添加内存优化
框架:添加并行/多设备/分布式相关的计算单元|框架:创建Operator(计算)+ Variable(数据)
框架:创建`Block`
框架:内存管理/设备管理
框架:执行计算| + +
+ +--- + +###

总结:编译时

+ + +**用户编写一段Python程序,描述模型的前向计算** +1. 创建变量描述 `VarDesc` +1. 创建operators的描述 `OpDesc` +1. 创建operators的属性 +1. 推断变量的类型和形状,进行静态检查:`inferShape` +1. 规划变量的内存复用 +1. 创建反向计算 +1. 添加优化相关的Operators +1. (可选)添加多卡/多机相关的Operator,生成在多卡/多机上运行的程序 + + + +--- + +###

总结:运行时

+ + +**执行规划好的计算** +1. 创建`Executor` +1. 为将要执行的一段计算,在层级式的`Scope`空间中创建`Scope` +1. 创建`Block`,依次执行`Block` + +

+
+ Figure. 编译时运行时概览 +

+ +
+ +--- + +## ==3==. 用户如何描述计算? +--- + +### Fluid:==像写程序一样==定义计算 + + +- 顺序执行 + ```python + x = fluid.layers.data(name='x',shape=[13], dtype='float32') + y_predict = fluid.layers.fc(input=x, size=1, act=None) + y = fluid.layers.data(name='y', shape=[1], dtype='float32') + cost = fluid.layers.square_error_cost(input=y_predict, label=y) + ``` + +- 条件分支: [swith](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch.md)、[ifelse](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/if_else_op.md) + + ```python + a = fluid.Var(10) + b = fluid.Var(0) + + switch = fluid.switch() + with switch.block(): + with switch.case(fluid.less_equal(a, 10)): + fluid.print("Case 1") + with switch.case(fluid.larger(a, 0)): + fluid.print("Case 2") + with switch.default(): + fluid.print("Case 3") + ``` + +>[A Lisp cond form may be compared to a continued if-then-else as found in many algebraic programming languages](https://www.cs.cmu.edu/Groups/AI/html/cltl/clm/node84.html). + + + +--- + +### Fluid: ==像写程序一样==定义计算 + + + +- 循环:[while](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book/test_machine_translation.py#L105) + + ```python + d0 = layers.data("d0", shape=[10], dtype='float32') + data_array = layers.array_write(x=d0, i=i) + array_len = layers.fill_constant(shape=[1],dtype='int64', value=3) + + cond = layers.less_than(x=i, y=array_len) + while_op = layers.While(cond=cond) + with while_op.block(): + d = layers.array_read(array=data_array, i=i) + i = layers.increment(x=i, in_place=True) + layers.array_write(result, i=i, array=d) + layers.less_than(x=i, y=array_len, cond=cond) + ``` + +- 完整实例请点查看 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44) +- beam search [:arrow_right:]( https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book/test_machine_translation.py#L105) + + + +--- + +####

总结

+ + + +1. 用户层提供的描述语法具有完备性、自洽性,有能力支持对复杂计算过程描述 +1. 使用方式和核心概念可以类比编程语言,认知能够直接迁移 +1. 能够支持:定义问题,逐步求解 + + + +--- + + +## ==3.== 核心概念 + +--- +### 编译时概念 :==变量和计算的描述== + + + +- `VarDesc` + `TensorDesc` + `OpDesc` :arrow_right: `BlockDesc` :arrow_right: `ProgramDesc` + - https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto + +- 什么是 Fluid Program + + - 在Fluid中,一个神经网络任务(训练/预测)被描述为一段`Program` + - `Program`包含对`Variable`(数据)和 `Operator`(对数据的操作)的描述 + - `Variable` 和 `Operator` 被组织为多个可以嵌套的`Block`,构成一段完整的`Fluid Program` + + +>编译阶段最终,经过 Transpiler 的执行规划,变换处理,生成使用`protobuf`序列化后的`ProgramDesc`。可以发送给多卡或者网络中的其它计算节点执行 + + + +--- + +### 编译时概念 :==**[Transpiler](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/fluid_compiler.md)**== + + +1. 接受一段`ProgramDesc`作为输入,生成一段新的`ProgramDesc` + + - *Memory optimization transpiler*:向原始`ProgramDesc` 中插入 `FreeMemoryOps`,在一次迭代优化结束前提前释放内存,使得能够维持较小的 memory footprint + + - *Distributed training transpiler*:将原始的`ProgramDesc`中转化为对应的分布式版本,生成两段新的`ProgramDesc`: + 1. trainer进程执行的`ProgramDesc` + 1. parameter server执行的`ProgramDesc` + +1. ==**WIP**==: 接受一段`ProgramDesc`,生成可直接被`gcc`, `nvcc`, `icc`等编译的代码,编译后得到可执行文件 + + + +--- +### Transplier + +![70% center](images/transpiler.png) + +--- + +### 打印 `ProgramDesc` + +![85% center](images/print_fluid_program.png) + + + +- `default_startup_program`:创建可学习参数,对参数进行初始化 +- `default_main_program`:由用户定义的模型,包括了前向、反向、优化及所有必要的计算 + +- 打印可读的 `Program` + ```python + from paddle.v2.fluid import debuger + print debuger.pprint_program_codes(framework.default_main_program().desc) + ``` + + +--- +### 输出效果 + + + +|variable in block 0|variable in block 0| +|:--:|:--:| +|![73%](images/program_desc1.png)|![75%](images/program_desc2.png)| + + + +--- + +### 运行时概念 + + + +- 数据相关 + - `Tensor` / `LoDTensor` / `Variable` + - `Scope` + +- 计算相关 + - `Block` + - `Kernel`、`OpWithKernel`、`OpWithoutKernel` + + | | protobuf messages|C++ class objects| + |---|---|---| + |Data|[VarDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L107)|[Variable](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h#L24)| + |Operation|[OpDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L35)|[Operator](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L64)| + |Block|BlockDesc|Block| + +- 执行相关 :`Executor` + + + +--- +#### Tensor 和 LoD(Level-of-Detail) Tensor + + +- Tensor 是$n$-dimensional arry的推广,LoDTensor是在Tensor基础上附加了序列信息 +- Fluid中输入、输出,网络中的可学习参数全部统一使用LoDTensor(n-dimension array)表示 +- 一个mini-batch输入数据是一个LoDTensor + - 在Fluid中,RNN 处理变长序列无需padding,得益于 `LoDTensor`表示 + - 可以简单将 LoD 理解为:`std::vector>` + - 对非序列数据,LoD 信息为空 + + | | TensorFlow | PaddlePaddle | + |-----------------------|------------|--------------| + | RNN | Support | Support | + | recursive RNN | Support | Support | + | padding zeros | Must | No need | + | blob data type | Tensor | LoDTensor | + + + +--- +#### LoD 信息实例 + + + +![43% center](images/LoDTensor.png) + +- 图(a)的LoD 信息 + ```cpp + [0, 5, 8, 10, 14] + ``` +- 图(b)的 LoD 信息 + ```cpp + [[0, 5, 8, 10, 14] /*level=1*/, [0, 2, 3, 5, 7, 8, 10, 13, 14] /*level=2*/] + ``` + + +--- +#### Tensor, Variable, Scope 之间的关系 + +![40% center](images/scope_variable_tensor.png) + + + +1. `Block` 是一个实现层的概念,不在应用层暴露给用户。目前用户无法自行创建并利用`Block`,用户能够感知的只有`Program`这个概念。 +1. 逻辑上,可以将 `Block` 类比为编程语言中的大括号:定义了一段作用域,其中运行一段代码 +1. `Executor`会为每一个`Block`创建一个`Scope`,`Block`是可嵌套的,因此`Scope`也是可嵌套的 + + + +--- +### Executor + + + +|接口|说明| +|--|--| +![66% center](images/executor.png)|输入
1. `ProgramDesc`
2. `Scope`
3.`block_id`

解释执行步骤
1. 创建所有 Variables
2. 逐一创建 Operator 并运行| +
+ +--- +### Operator/OpWithKernel/Kernel + + +![50% center](images/operator1.png) + +- operator 无状态,Operator的核心是==Run==方法 +- 一个operator可以注册多个kernel +- operator 可以无 kernel:while_op 、ifelse op + + + +--- +#### Fluid Operator vs. PaddlePaddle layers + + +|Layer|Operator| +|--|--| +|![70% center](images/layer.png)|![73% center](images/operator2.png)| +|1. 内部维护状态
2. 包含forward和backward方法|1. 内部无状态
2. 只有Run方法| + +
+ +--- + + +### ==4.== 内存管理 + +--- +### 目标 + +- 为异构设备提供统一的内存分配、回收接口 +- 最小化管理内存所需的时间,最小化管理开销 +- 减少内存碎片 +- 将内存管理与计算(Operators/Kernels)完全剥离 +- 统一内存管理是内存优化的基础 + +--- + + + +### Memory 接口 + +- 内存管理模块向上层应用逻辑提供三个基础接口: + ```cpp + template + void* Alloc(Place place, size_t size); + + template + void Free(Place place, void* ptr); + + template + size_t Used(Place place); + + struct Usage : public boost::static_visitor { + size_t operator()(const platform::CPUPlace& cpu) const; + size_t operator()(const platform::CUDAPlace& gpu) const; + }; + ``` +- 模板参数 `Place` 指示内存分配发生的设备 +- 实现时,需特化支持的 `Place`, 提供以上三个接口的实现 + + + +--- +### 代码结构 + + + +内存管理模块可以理解为由以下两部分构成: + +1. SystemAllocator:实际从物理设备上分配、释放的内存的接口 +1. BuddyAllocator:内存管理算法 + + + +--- +### System Allocator + + + +- SystemAllocator 是实现物理内存分配、回收的基类 + - 不同设备上的内存分配和回收终将转化为标准接口调用 + - 为不同设备实现MemoryAllocator,继承自SystemAllocator + + ```cpp + class SystemAllocator { + public: + virtual ~SystemAllocator() {} + virtual void* Alloc(size_t& index, size_t size) = 0; + virtual void Free(void* p, size_t size, size_t index) = 0; + virtual bool UseGpu() const = 0; + }; + ``` + + +--- + +### CPU/GPU Allocator + + + +```cpp +class CPUAllocator : public SystemAllocator { + public: + virtual void* Alloc(size_t& index, size_t size); + virtual void Free(void* p, size_t size, size_t index); + virtual bool UseGpu() const; +}; + +#ifdef PADDLE_WITH_CUDA +class GPUAllocator : public SystemAllocator { + public: + virtual void* Alloc(size_t& index, size_t size); + virtual void Free(void* p, size_t size, size_t index); + virtual bool UseGpu() const; + private: + size_t gpu_alloc_size_ = 0; + size_t fallback_alloc_size_ = 0; +}; +#endif +``` +- CPUAllocator和GPUAllocator分别继承自SystemAllocator,分别调用相应的标准库函数实现物理内存的分配和释放。 +- 一旦大块、连续的物理内存分配之后,将通过内存管理算法实现内存的按块分配、回收、重用等。 + + + +--- +### CPU Allocator + + + +- CPU 内存的分配提供两种选项: + 1. non-pinned memory:可分页内存 + 2. pinned memory:页锁定内存 + - 分配过大的页锁定内存有可能因为系统可使用的分页内存减少,影响系统性能,默认CPU下分配的是可分页内存 + +- 通过gflags进行设置一次性分配内存的大小以及是否使用页锁定内存。 + + ```cpp + DEFINE_bool(use_pinned_memory, true, "If set, allocate cpu pinned memory."); + DEFINE_double(fraction_of_cpu_memory_to_use, 1, + "Default use 100% of CPU memory for PaddlePaddle," + "reserve the rest for page tables, etc"); + ``` + + + +--- +### GPU Allocator + + + +- 通过 cudaMalloc 分配GPU显存 +- GPUAllocator::Alloc 首先会计算指定GPU device上的可用显存 + - 如果可用显存小于请求分配大小,调用cudaMalloc进行分配 + - 如果可用显存不足,目前会报错退出。 +- 通过gflags控制GPU下一次性分配显存的大小: + + ```cpp + DEFINE_double(fraction_of_gpu_memory_to_use, 0.92, + "Default use 92% of GPU memory for PaddlePaddle," + "reserve the rest for page tables, etc"); + ``` + + + +--- +#### 内存管理算法: [Buddy Memory Allocation](https://en.wikipedia.org/wiki/Buddy_memory_allocation) + + + +- Memory Arena:一次性分配大块连续内存,之后会基于这块内存进行内存管理:动态分配、释放、重用内存块。 +- 伙伴内存分配: + - 将内存划分为 2 的幂次方个分区,使用 best-fit 方法来分配内存请求。 + - 当释放内存时,检查 buddy 块,查看相邻的内存块是否也已被释放。如果是,将内存块合并,以最小化内存碎片。 + - 分配的内存在物理内存的自然边界对齐,提高内存访问效率。 + - 算法的时间效率高,单使用 best-fit 方法的缘故,会产生一定的内存浪费 + + + +--- + +### Buddy Allocator + + + +- BuddyAllocator 是一个单例,每个设备(如: GPU/CPU(0)/GPU(1)) 拥有一个BuddyAllocator +- BuddyAllocator 内部拥有一个私有成员变量 SystemAllocator +- 当请求的内存超过BuddyAllocator管理的空余内存时,将会调用SystemAllocator去指定的设备上分配物理内存 + + + +--- +### 实例:CPU 下内存管理接口的实现 + + + +- 对上层应用,统一通过BuddyAllocator来实现内存的分配、释放以及用量查询 + ```cpp + template <> + void* Alloc(platform::CPUPlace place, size_t size) { + VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place); + void* p = GetCPUBuddyAllocator()->Alloc(size); + VLOG(10) << " pointer=" << p; + return p; + } + + template <> + void Free(platform::CPUPlace place, void* p) { + VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place); + GetCPUBuddyAllocator()->Free(p); + } + + template <> + size_t Used(platform::CPUPlace place) { + return GetCPUBuddyAllocator()->Used(); + } + ``` + + +--- + +### ==5.== 多设备支持 + +--- +### 多设备支持(一) + + + +- step 1:添加Place类型,由用户实现添加到框架 + - 可以将Place类型理解为一个整数加上一个枚举型,包括:设备号 + 设备类型 + + ![40% center](images/place.png) + +- DeviceContext + - 不同的Place会对应一个相应的DeviceContext,用于组织管理与设备相关的信息 + - 例如,GpuDeviceContext中会管理Cuda stream + - 目前实现中一些特殊的库也会对应有自己的DeviceContext:例如: + ```cpp + class MKLDNNDeviceContext : public CPUDeviceContext {……} + ``` + - 每种设备对应的DeviceContext需要管理的内容不尽相同,视具体需求来实现 + + + +--- + +### 多设备支持(二) + + + +- step 2: 增加KernelType,为相应的KernelType注册Kernel对象,由用户实现注册给框架 可以按照: + 1. Place 执行设备 + 1. DataType 执行数据类型 FP32/FP64/INT32/INT64 + 1. Memory layout: 运行时 Tensor 在内存中的排布格式 NCHW、 NHWC + 1. 使用的库 + + 来区分Kernel,为同一个operator注册多个 Kernel。 + + ```cpp + struct OpKernelType { + proto::DataType data_type_; + DataLayout data_layout_; + platform::Place place_; + LibraryType library_type_; + } + ``` + + + +--- + +### 多设备支持(三) + + + +step 3: 运行时的 KernelType 推断和Kernel切换,按需要修改Kernel推断和Kernel切换规则 +- Expected Kernel:期待调用的Kernel:由(1)`Place`和计算精度决定;或(2)用户在配置中显示指定使用的计算库,如`cudnn`、`mkldnn`等。 +- Actual Kernel:运行时从`Operator`的输入(`Variable`)可以推断出实际需要的`KernelType` +- 当Expected Kernel和Actual Kernel不一致的时候,框架会插入`data_transformer`或者`data_layerout_transform`等,保证Expected Kernel可以执行,包括: + - CPUPlace :arrow_right: GPUPlace :跨设备内存复制 + - NCHW :arrow_right: nChw8c :Layout转换 + - FP32 :arrow_right: FP16 :精度转换 _**尚未支持**_ + - …… +- 以上过程实现在OperatorWithKernel类的Run方法中 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.cc#L497) + + + +--- + +## ==6.== while_op + +--- +### while_op + + + +- 循环执行一段`Program`,直到条件operator判断循环条件不满足时终止循环 +- while_op 的特殊之处: + 1. while_op 没有 kernel + 1. while_op 拥有自己的`Block`,会形成一段嵌套的`Block` + 1. ==while_op 内部创建了一个 Executor,来循环执行`Block`== + +- while_op 输入输出 : LoDTensorArray + ```cpp + namespace paddle { + namespace framework { + using LoDTensorArray = std::vector; + } + } + ``` + - 每一次循环,从原始输入中“切出”一个片段 + - LoDTensorArray 在Python端暴露,是Fluid支持的基础数据结构之一,用户可以直接创建并使用 + + + +--- +### while_op [Run](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/while_op.cc#L42) 方法概览 + + + +```cpp + +void Run(const framework::Scope &scope, + const platform::Place &dev_place) const override { + PADDLE_ENFORCE_NOT_NULL(scope.FindVar(Input(kCondition))); + auto &cond = scope.FindVar(Input(kCondition))->Get(); + PADDLE_ENFORCE_EQ(cond.dims(), paddle::framework::make_ddim({1})); + + framework::Executor executor(dev_place); + auto *block = Attr(kStepBlock); + + auto *program = block->Program(); + auto step_scopes = + scope.FindVar(Output(kStepScopes))->GetMutable(); + + while (cond.data()[0]) { + auto ¤t_scope = scope.NewScope(); + step_scopes->push_back(¤t_scope); + executor.Run(*program, ¤t_scope, block->ID(), + false /*create_local_scope*/); + } +} + +``` + + + +--- + +### while_op 的重要应用:Dynamic RNN + +--- + +### 什么是 `dynamicRNN` ? + + +
+ +1. 用户可以自定义在一个时间步之内的计算, 框架接受序列输入数据,在其上循环调用用户定义的单步计算 +1. 可学习参数在多个时间步之间共享 +1. `dynamicRNN` 由 `while_op` 实现 +1. 如果`dynamicRNN`中定义了`memory`,将会构成一个循环神经网络,否则其行为就等于在输入序列上循环调用预定义的单步计算 + +
+ +--- + +#### `dynamic RNN` 用户接口 + + +

+ +

+ +- `dynamicRNN` 中的重要元素 + 1. **step input**: `dynamicRNN` 每个时间步的输入 + 1. **step function**: 用户定义的单步计算 + 1. **memory**: 用于形成循环连接 + 1. **external/static memory**:单步计算的每一步都可以全部读取到的外部输入 + +
+ +--- + +#### dynamicRNN 中的 Memory + + + +`dynamicRNN`中`memory`的行为非常类似于 C++ 中的引用变量 + - `memory` “指向” 一个operator的输出变量,记作: A + - `memory` 可以被 LoDTensor 初始化(当LoD信息为空时,为非序列,否则为序列),默认`memory`被初始化为零 + - `memory` 在 operator A 前向计算之后,进行前向计算 + - 当 `memory` 的前向计算会 "指向" A 的输出 LoDTensor + - `memory` 的输出可以是另一个 operator 的输入,于是形成了“循环”连接 + + + +--- + +### DynamicRNN 实现细节 + + + +- `while_op` 无法独立构成dynamicRNN,必须和一组相关的 operator 及数据结构配合 + - 依赖的 operators (这里仅列出最重要的,并非全部): + - `lod_rank_table` operator + - `lod_tensor_to_array` operator + - `array_to_lod_tensor` operator + - `shrink_memory` operator + - 依赖的数据结构 + - `TensorArray` + - `LoDRankTable` + +- 在Fluid中,RNN接受变长序列输入,无需填充,以上数据结构和相关的operator配合工作,实现了对变长输入以batch计算 + + + +--- + +### `dynamicRNN` 如何实现 batch 计算 ? + + + +- 问题: + - RNN 可以看作是一个展开的前向网络,前向网络的深度是最长序列的长度 + - 如果不对变长序列进行填充,将它们填充到一样长度,每个mini-batch输入将会不等长,每个样本展开长度不一致,导致前向和反向计算实现困难 + + + +---- +##### 实例 :RNN encoder-decoder with attention + + + +- 以机器翻译的RNN encoder-decoder 模型(涉及了`dynamicRNN`的所有设计要素)为例,下图是 RNN encoder-decoder 的原始输入: +

+
Figure. RNN encoder-decoder 原始batch 输入数据 +

+ +- source word sequences 是encoder RNN的输出,是一个LoDTensor +- target word sequences 是look_uptable的输入,是一个LoDTensor +- 上图中一个矩形方块是CPU/GPU内存中一片连续的内存空间,表示一个dense vector + +
+ +--- + +### `dynamicRNN` 如何实现 batch 计算 ? + + + +1. 对一个mini batch中不等长样本进行排序,最长样本变成batch中的第一个,最短样本是batch中最后一个 + - `LoDTensor` :arrow_right: `LoDRankTable` :heavy_plus_sign: `lod_rank_table operaator` + - 可以将`LoDRankTable`理解为对LoDTensor中的多个序列按照长度排序LoDRankTable 存储了排序之后的index + +2. 构建每个时间步的batch输入:随着时间步增加,每个时间步的batch输入可能会逐渐缩小 + - `TensorArray` :heavy_plus_sign: `lod_tensor_to_array` :arrow_right: `LoDTensor` (without LoD) +3. 每个时间步输出写入一个输出 `LoDTensorArray` +3. `dynamicRNN`循环结束后, 按照`LoDRankTable`中记录的信息对输出`LoDTensorArray`重排序,还原会原始输入顺序 + - `TensorArray` :heavy_plus_sign: `array_to_lod_tensor` :arrow_right: `LoDTensor` + + + +--- + +### 运行实例 + +

+ +

+ +--- +### 运行实例 + +

+ +

+ + + +- 执行到第5~7个batch时,batch size将会缩小 + + + +--- +### 运行实例 + +

+ +

+ + + +- 第5 ~ 7个batch时RNN的`memory`会发生什么? + - `memory` 指向某个operator的输出Tensor,在该operator前向计算之后,“取回”其计算结果 + - 5 ~ 7时,遇到了序列的结束,==下一个时间步计算不再需要在已经结束的序列上展开== + - 在`dynamicRNN`中`shrink_memory` operator 用来缩小`memory`的batch输入 + + + +--- +### 运行实例:batch 1 ~ 2 + +

+
Figure. 第1、2个batch输入dynamicRNN的batch输入 +

+ +--- +### 运行实例:batch 3 ~ 4 + +

+
Figure. 第3、4个batch输入dynamicRNN的batch输入 +

+ +--- + +### 运行实例:batch 5 ~ 7 + +

+
Figure. 第5、6、7个batch输入dynamicRNN的batch输入 +

+ +--- + +### ==7.== Fluid 代码结构 + +--- +### Fluid 代码结构 + + + + + + + + + + + + + + + +
代码结构模块结构
+

+ +

+
+

+ +

+
+ +--- + + +### ==8.== 文档总结 + +--- + + +- 设计概览 + - 重构概览 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/refactorization.md) + - fluid [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/fluid.md) + - fluid_compiler [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/fluid_compiler.md) +- 核心概念 + - variable 描述 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/var_desc.md) + - Tensor [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/tensor.md) + - LoDTensor [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) + - TensorArray [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/tensor_array.md) + - Program [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md) + - Block [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md) + - Scope [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) + +--- + +- 重要功能模块 + - backward [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/backward.md) + - 内存优化 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/memory_optimization.md) + - evaluator [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/executor.md) + - python API [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md) + - regularization [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/regularization.md) + +- 开发指南 + - 支持新设硬件设备库 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/support_new_device.md) + - 添加新的Operator [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_cn.md) + - 添加新的Kernel [:arrow_right:]( +https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_kernel_en.md) + + + +--- + +### ==9.== 开发指南 + +--- + +#### 建议开发环境:使用 Docker 编译和测试 + + + +Docker编译PaddlePaddle源码: [:arrow_right:](http://www.paddlepaddle.org/docs/develop/documentation/zh/howto/dev/build_cn.html) + +PaddlePaddle 在 Dockerhub 地址:[:arrow_right:]( + https://hub.docker.com/r/paddlepaddle/paddle/tags/) + +1. 获取PaddlePaddle的Docker镜像 + ```bash + docker pull paddlepaddle/paddle:latest-dev + ``` + +1. 启动 docker container + + ```bash + docker run -it -v $PWD/Paddle:/paddle paddlepaddle/paddle:latest-dev /bin/bash + ``` + +1. 进入docker container后,从源码编译,请参考文档 [:arrow_right:]( http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/build_from_source_cn.html) + + + +--- + +### 一些说明 + + + +1. PaddlePaddle的Docker镜像为了减小体积,默认没有安装vim,可以在容器中执行`apt-get install -y vim`来安装vim。 +1. 开发推荐使用tag为`latest-dev`的镜像,其中打包了所有编译依赖。`latest`及`lastest-gpu`是production镜像,主要用于运行PaddlePaddle程序。 +1. 在Docker中运行GPU程序,推荐使用nvidia-docker,[否则需要将CUDA库和设备挂载到Docker容器内](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/docker_install_cn.html#dockergpu)。 + + + ```bash + nvidia-docker run -it -v $PWD/Paddle:/paddle paddlepaddle/paddle:latest-dev /bin/bash + ``` + + + + + +--- + +### [如何贡献](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/contribute_to_paddle_cn.md) + + + +- ==提交PullRequest前请务必阅读==: [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/contribute_to_paddle_cn.md) +- 代码要求 + 1. 代码注释遵守 Doxygen 的样式 + 1. 确保编译器选项 WITH_STYLE_CHECK 已打开,并且编译能通过代码样式检查 + 1. 所有代码必须具有单元测试,且能够通过所有单元测试 +- 使用 `pre-commit` 钩子提交Pull Request + 1. 帮助格式化源代码(C++,Python) + 1. 在提交前自动检查一些基本事宜:如每个文件只有一个 EOL,Git 中不要添加大文件等 + 1. 安装pre-commit,并在PaddlePaddle根目录运行: + ```bash + ➜ pip install pre-commit + ➜ pre-commit install + ``` + + +--- + +### 如何贡献 + + + +1. 开始开发之前请先建立issue。 + - 让其它同学知道某项工作已经有人在进行,以避免多人开发同一功能的情况。 +1. 提交PR必须关联相关的issue。做法请参考:[:arrow_right:](https://help.github.com/articles/closing-issues-using-keywords/) + - 目的:为了在提交的版本中留有记录描述这个PR是为了开发什么样的功能,为了解决什么样的问题。 + - 当PR被merge后,关联的issue会被自动关闭。 +1. PR review 中,reviewer的每条comment都必须回复。 + - 如修改完可直接回复:Done。 + - 目的:review comment 中可能会有(1)询问类型的问题;(2)可以在下一个PR修改的问题;(3)comment意见不合理等。需要明确回复,以便reviewer和其他人有历史可查,便于区分是否已经进行修改,或者准备下一个PR修改,或者意见不合理可以不用进行修改。 + + + +--- + + +### ==10.== 添加新的 Operator + +--- + +### 概念简介 + + + +添加一个新的operator,会涉及实现以下C++类的派生类: + +1. `framework::OperatorBase`: Operator(简写,Op)基类。 +1. `framework::OpKernel`: Op计算函数的基类,称作Kernel。 +1. `framework::OperatorWithKernel`:继承自OperatorBase,Op有计算函数,称作有Kernel。 +1. `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,可以将Op分为两种: +1. 包含Kernel的Op:继承自OperatorWithKernel,==绝大多数operator都属于这一类== +1. 不包含kernel的Op,继承自OperatorBase,只有少量Op属于这一类,例如while_op,ifelse_op + +这里主要介绍带Kernel的Op如何编写。 + + + +--- + +#### 添加新的Operator需要修改/添加哪些文件? + + + + 内容 | 定义位置 +-------------- | :---------------------- +OpProtoMake定义 | `.cc`文件,Backward Op不需要OpProtoMaker +Op定义 | `.cc`文件 +Kernel实现 | CPU、CUDA共享Kernel实现在`.h`文件中,否则,CPU 实现在`.cc`文件中,CUDA 实现在`.cu`文件中。 +注册Op | Op注册实现在`.cc`文件;Kernel注册CPU实现在`.cc`文件中,CUDA实现在`.cu`文件中 + +- 添加 Operator 之前请阅读:[Operator 命名规范](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/op_documentation/name_convention.md)及[Operator Markdown注释规范](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/op_documentation/op_markdown_format.md)。 +- 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。 +- 根据文件名自动构建op和Python端绑定,请务必遵守以上命名,否则需要进一步修改PyBind相关文件及CMakeLists.txt。 + + +--- + +###### 实现带Kernel的Operator step1: 定义ProtoMaker类 + + + +下面均以[clip_op](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/clip_op.h)为例进行介绍 + +- clip_op计算公式:$Out = \min(\max(X, min), max)$ +- 首先定义`ProtoMaker`来描述该Op的输入、输出,并添加注释(*下面代码段的中注释进行了简化,实现时需按照规范添加注释*): + + ```cpp + template + class ClipOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ClipOpMaker(OpProto* proto, OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X","(Tensor)The input of clip op."); + AddOutput("Out", "(Tensor),The output of clip op."); + AddAttr( + "min", "(float),Minimum value."); + AddAttr( + "max", "(float),Maximum value."); + AddComment(R"DOC( + …… + )DOC"); + } + }; + ``` + + + +--- + +###### 实现带Kernel的Operator step2: 定义Operator类 + + + +下面的代码段实现了`clip_op`的定义: + +```cpp +class ClipOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of ClipOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of ClipOp should not be null."); + auto x_dims = ctx->GetInputDim("X"); + auto max = ctx->Attrs().Get("max"); + auto min = ctx->Attrs().Get("min"); + PADDLE_ENFORCE_LT(min, max, "max should be greater than min."); + ctx->SetOutputDim("Out", x_dims); + ctx->ShareLoD("X", /*->*/ "Out"); + } +}; +``` + + +--- + +### Operator 类中需要完成的工作 + + + +1. clip_op 继承自`OperatorWithKernel`, + + ```cpp + using framework::OperatorWithKernel::OperatorWithKernel; + ``` + 表示使用基类`OperatorWithKernel`的构造函数。 + +1. 重写`InferShape`接口。 + - `InferShape` 为const函数,不能修改Op的成员变 + - `InferShape` 的参数为 `const framework::InferShapeContext &ctx`,从中可获取到输入输出以及属性 + - `InferShape` 会被调用两次,一次是编译时(创建op),一次是运行时(调用op的`Run`方法时),需要完成以下功能: + 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。 + + + +--- + +### 补充说明 + + + +1. `InferShape`目前支持两种实现方式,二者最后都会生成一个functor注册给OpInfo结构体。 + 1. 继承framework::InferShapeBase,实现为一个functor(参考 [mul_op](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22)) + 1. override InferShape函数(参考 [clip_op](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/clip_op.cc#L24)) + +1. 什么是`functor` ? + + - 类或结构体仅重载了`()`,一般是可被多个kernel复用的计算函数。 + + + + ```cpp + template + class CrossEntropyFunctor { + public: + void operator()(const platform::CPUDeviceContext& ctx, + framework::Tensor* out, + const framework::Tensor* prob, + const framework::Tensor* labels, const bool softLabel) { + …… + } + }; + ``` + + + - 在 clip_op 内也会看到将一段计算函数抽象为functor的使用法: [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/clip_op.h#L27)。 + + + +--- + +###### 实现带Kernel的Operator step3: 定义OpKernel类 + + + +- `ClipKernel`继承自`framework::OpKernel`,带有下面两个模板参数: + 1. `typename DeviceContext`: 表示设备类型,不同设备共享同一个Kernel时,需添加该模板参数。不共享时,需要提供针对不同设备的特化实现。 + 1. `typename T` : 表示支持的数据类型,如`float`, `double`等 + +- 在`ClipKernel`类中重写`Compute`方法 + 1. `Compute`接受输入参数:`const framework::ExecutionContext& context` + - `ExecutionContext` 是从 `Scope`中将运行时Op的输入、输出`Variable`组织在一起,使得Op在调用`Compute`方法时,能够简单地通过名字拿到需要的输入输出`Variable` + - 与`InferShapeContext`相比,`ExecutionContext` 中增加了设备类型 + 1. 在`Compute`函数里实现`OpKernel`的具体计算逻辑 + + + +--- +#### ClipKernel 代码概览 + + + +```cpp +template +class ClipKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto max = context.Attr("max"); + auto min = context.Attr("min"); + auto* x = context.Input("X"); + auto* out = context.Output("Out"); + T* out_data = out->mutable_data(context.GetPlace()); + const T* x_data = x->data(); + int64_t numel = x->numel(); + Transform trans; + trans(context.template device_context(), x_data, + x_data + numel, out_data, ClipFunctor(min, max)); + } +}; +``` + +- 为了使`OpKernel`的计算过程书写更加简单,并且CPU、CUDA的代码可以复用, Fluid 使用 Eigen 作为基础的矩阵运算库 +- Fluid对Eigen unsupported Tensor提供了一些基本的封装,可以在`Compute`接口中直接调用 + - 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)。 + + + +--- +###### 实现带Kernel的Operator step4: 实现反向Op + + + +- ==**反向Op没有`ProtoMaker`**==,除此之外定义与实现方式前向Op完全一致,不再赘述 +- 这里仅对反向Op的输入输出进行说明: + 1. 反向Op的输入 + - 前向Op的输出 + - 反向传播过程中传递给当前Op的梯度 + - 需要注意,Fluid中,不区分Cost Op和中间层Op,所有Op都必须正确处理接收到的梯度 + 2. 反向Op的输出 + - 对可学习参数的求导结果 + - 对所有输入的求导结果 + + + + +--- + +###### 实现带Kernel的Operator step5: 注册Op及Kernel + + + +至此Op和Op kernel都已经实现完毕,接下来,需要在`.cc`和`cu`文件中注册op和kernel + +1. 在`.cc`文件中注册前向、反向Op类,注册CPU Kernel。 + + + + ```cpp + namespace ops = paddle::operators; + REGISTER_OP(clip, ops::ClipOp, ops::ClipOpMaker, clip_grad, + ops::ClipOpGrad); + REGISTER_OP_CPU_KERNEL( + clip, ops::ClipKernel); + REGISTER_OP_CPU_KERNEL( + clip_grad, ops::ClipGradKernel); + ``` + + - 在上面的代码片段中: + + 1. `REGISTER_OP` : 注册`ops::ClipOp`类,类型名为`clip`,该类的`ProtoMaker`为`ops::ClipOpMaker`,注册`ops::ClipOpGrad`,类型名为`clip_grad` + 1. `REGISTER_OP_WITHOUT_GRADIENT` : 用于注册没有反向的Op,例如:优化算法相关的Op + 1. `REGISTER_OP_CPU_KERNEL` :注册`ops::ClipKernel`类,并特化模板参数为`paddle::platform::CPUPlace`和`float`类型,同理,注册`ops::ClipGradKernel`类 + + +1. 按照同样方法,在`.cu`文件中注册GPU Kernel + - 如果CUDA Kernel的实现基于Eigen,需在 `.cu`的开始加上宏定义 `#define EIGEN_USE_GPU` + + + +--- + +##### 编译和Python端绑定 + + + +- 运行下面命令可以仅编译新添加的Op: + + ``` + make mul_op + ``` + - 需注意,运行单元测试需要编译整个工程 + +- 如果遵循前文的文件命名规则,构建过程中,会自动为新增的op添加Python端绑定,并链接到生成的lib库中 + + + +--- + +###### 实现带Kernel的Operator step6: 添加前向单测及梯度检测 + + + +- 新增Op的单元测试统一添加至:[python/paddle/v2/fluid/tests](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/v2/fluid/tests)目录 +- 前向Operator单测 + + 1. Op单元测试继承自`OpTest`,各项具体的单元测试在`TestClipOp`里完成,所有单测case都以`TestXX`命名 + 1. 单元测试Operator,需要: + 1. 在`setUp`函数定义输入、输出,以及相关的属性参数 + 1. 生成随机的输入数据 + 1. 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 + 1. 反向梯度检测流程测试框架已经实现,直接调用相应接口`check_grad`即可 + +- `clip_op` 单测代码请参考 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/test_clip_op.py),这里不再展开 + + + +--- +#### 编译执行单测 + + + +- `python/paddle/v2/framework/tests` 目录下新增的 `test_*.py` 单元测试会被自动加入工程进行编译 + + - 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" + ``` + + 或者: + + ``` + ctest -R test_mul_op + ``` + + +--- + +### 添加Op的一些注意事项 + + + +- 为每个Op创建单独的`*_op.h`(如有)、`*_op.cc`和`*_op.cu`(如有)。不允许一个文件中包含多个Op,将会导致编译出错。 +- 注册Op时的类型名,需要和该Op的名字一样。不允许在`A_op.cc`里面,注册`REGISTER_OP(B, ...)`,会导致单元测试出错。 +- 如果Op没有实现CUDA Kernel,不要创建空的`*_op.cu`,会导致单元测试出错。 +- 如果多个Op依赖一些共用的函数,可以创建非`*_op.*`格式的文件来存放,如`gather.h`文件。 + + + +--- + + +### ==10.== 使用相关问题 + +--- + +### 定义前向计算 + + + +- 当在python端执行时: + ```python + import paddle.v2.fluid as fluid + ``` + [`framework.py`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/framework.py#L1040)定义了两个全局`Program`: + ```python + # program is a global instance. + _main_program_ = Program() + _startup_program_ = Program() + ``` + +- 前向定义的过程就是不断往`mian_program`中添加Op和Variable +- 如果需要执行一个新的`mian_program`时,可以调用调用: + ```python + def switch_main_program(program): + """ + Switch the main program to a new program. + This funtion returns the previous main program. + """ + …… + ``` + + +--- + +### 自定义参数的初始化 + + + +- 调用`fluid.ParamAttr(……)`接口,自定义参数的初始化 + + ```python + w_param_attrs = ParamAttr(name=None, + initializer=UniformInitializer(low=-1.0, high=1.0, seed=0), + learning_rate=1.0, + regularizer=L1Decay(1.0), + trainable=True, + clip=GradientClipByValue(-1.0, 1.0), + ) + y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs) + ``` + +- 补充问题:如何创建 `Variable` + ```python + cur_program = Program() + cur_block = cur_program.current_block() + new_var = cur_block.create_var(name="X", shape=[-1, 16, 16], dtype="float32") + ``` + + + +--- + +### 添加反向Op + + + +- 调用`fluid.backward.append_backward(X)`(`X`是一个Variable),来为一段前向`ProgramDesc`添加反Op + + ```python + data = fluid.layers.data(name="data", shape=(2,3,4)) + out = fluid.layers.fc(input=data,size=128,act=None) + loss = fluid.layers.reduce_sum(out) + fluid.backward.append_backward(loss=loss) + ``` + +- 添加优化相关的Op + ```python + sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) + sgd_optimizer.minimize(loss) + ``` + +- 可以随时调用`print(fluid.default_main_program())`来输出当前的`main_program` + +- 当构建完成整个`Program`后,调用下面的接口执行内存优化: + ```python + fluid.memory_optimize(fluid.default_main_program()) + ``` + - _注:内存优化目前仍在持续开发中,有可能不够稳定。_ + + + +--- + +### 总结:编译时执行流程 + + + +- 用户定义前向计算 +- 添加反向Op到`default_main_program` +- 添加 gradient clipping Op 到 +- 添加 regularization Op 到`default_main_program` +- 为指定的优化算法,添加相关的状态 variable of optimizer 到`default_startup_program` + - 状态相关 variable是指如学习率, 历史 momentum, 二阶momentum等 +- 添加初始化 variable 的Op 到 `default_startup_program` +- 为整个网络最后一个op,添加设置其接受到的梯度的Op到`default_main_program` +- 进行内存优化规划 + + + +--- + +### Feed 数据 (一):通过 feed 字典 + + + +- 执行executor的run方法时,指定feed字典,feed op 会将指定的数据放到`x`和`y`两个Variable中 + ```python + y_data = np.random.randint(0, 8, [1]).astype("int32") + y_tensor = core.Tensor() + y_tensor.set(y_data, place) + + x_data = np.random.uniform(0.1, 1, [11, 8]).astype("float32") + x_tensor = core.Tensor() + x_tensor.set(x_data, place) + …… + cost = exe.run( + fluid.default_main_program(), + feed={'x': x_tensor, + 'y': y_tensor}, + fetchlist=[avg_cost]) + ``` + +- 这种方法较为底层,一般用于单测中 + + + +--- + +### Feed 数据 (二):使用 DataFeeder接口 + + + +- 编写一个data_reader函数,data_reader是一个Python generator + + ```python + def demo_reader(): + def random_generator(): + yield np.random.uniform(0.1, 1, [4]), np.random.randint(0, 1, [1]) + return random_generator + ``` +- 在训练任务中使用 DataFeeder 接口 + ```python + cost = exe.run( + fluid.default_main_program(), + feed={'x': x_tensor, + 'y': y_tensor}, + fetchlist=[avg_cost]) + + train_reader = paddle.batch( + paddle.reader.shuffle(demo_reader(), buf_size=500), batch_size=4) + feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) + for data in train_reader(): + cost = exe.run( + fluid.default_main_program(), + feed=feeder.feed(data), + fetch_list=[cost]) + ``` + + + +--- + +### 常见问题 + + + +- 如何使用 evaluator ? [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py#L168) + + ```python + accuracy = fluid.evaluator.Accuracy(input=predict, label=label) + for pass_id in range(PASS_NUM): + accuracy.reset() + for data in train_reader(): + loss, acc = exe.run(fluid.default_main_program(), + feed=feeder.feed(data), + fetch_list=[avg_cost] + accuracy.metrics) + pass_acc = accuracy.eval(exe) + # acc 当前一个batch 的 accuracy + # pass_acc 当前batch 的 accuracy + pass_total_acc = accuracy.eval(exe) # 整个pass的accuracy + ``` + +- 如何在训练中测试?[:arrow_right:](https://github.com/dzhwinter/benchmark/blob/master/fluid/vgg16.py#L144) +- 如何保存训练好的模型?[:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book/test_recognize_digits.py#L143) +- 如何加载训练好的模型进行预测?[:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book/test_recognize_digits.py#L154) +- 如何在同一个训练任务中定义多个Program,并交替运行? [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/demo/fc_gan.py) +- 如何profile?Fluid 实现了profile 工具,可以直接调用。请参考示例 [:arrow_right:](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/test_profiler.py) + + + + +--- + + + +# 谢谢 :heart: diff --git a/doc/fluid/images/1.png b/doc/fluid/images/1.png new file mode 100644 index 0000000000000000000000000000000000000000..67daf566f91aab570e60971c4ea8e2be876e214d GIT binary patch literal 150990 zcmeFZWmsFw7BC#7rL+awQd|qg3lxW-h2mP=LyI+7aCcfLUfkU^#obDQqQMOkRL1qoKNQ=cq}F@ML^fau_&|h4@xTOvRx$Qs z2j7V236kPl@>gne>MM+|g9(^sTz)&t@Y|s$xu>6h=b);1Eqd zTn^I8l81IDIc!@Q9R~n_XZ(*ZnxX010{{U15m;T@QCm>~XbiDtd;fVKg)S_;!>E2>aSKvd!CM9v;R`Earl$1 zI|kYRXkq7Id(HlzvG2MH{Q(84nmRzNoc@O8Qk z@_TPp-q6WCsY+COZC{Wc;@-`b=HhLR9P$k0N*Bl}EcDH0^nW0u6wTEnKlDui;y06&NUXixs1FTdCHz5pX)7dCy2e?mdS1aRE{XCA_^ zsR6{@X^*D<5e0xI7WR*WKY3t`{s0hYxAC3lAEt=*2t6*%mm-|K=a)?Tq>DkMRmU-h+PMUaBO4rPO0H 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