From 9065f11c7a1fe36cf49ec2efa02521723ca97b0d Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 19 Sep 2017 04:13:28 +0000 Subject: [PATCH] Deploy to GitHub Pages: 8141875cf608dfe066aebf0991565bfb5434e7ee --- .../_sources/design/refactorization.md.txt | 253 ++++++++ develop/doc/design/refactorization.html | 571 +++++++++++++++++ develop/doc/objects.inv | Bin 2584 -> 2608 bytes develop/doc/searchindex.js | 2 +- .../_sources/design/refactorization.md.txt | 253 ++++++++ develop/doc_cn/design/refactorization.html | 579 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 3119 -> 3142 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 1658 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/refactorization.md.txt create mode 100644 develop/doc/design/refactorization.html create mode 100644 develop/doc_cn/_sources/design/refactorization.md.txt create mode 100644 develop/doc_cn/design/refactorization.html diff --git a/develop/doc/_sources/design/refactorization.md.txt b/develop/doc/_sources/design/refactorization.md.txt new file mode 100644 index 00000000000..e105861e926 --- /dev/null +++ b/develop/doc/_sources/design/refactorization.md.txt @@ -0,0 +1,253 @@ +# Design Doc: Refactorization Overview + +The goal of refactorizaiton include: + +1. Make it easy for external contributors to write new elementory computaiton operations. +1. Make the codebase clean and readable. +1. Introduce a new design of computation representation -- a computation graph of operators and variables. +1. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing. + +## Computation Graphs + +1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs. + + 1. Please dig into [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a solid example. + +1. Users write Python programs to describe the graphs and run it (locally or remotely). + +1. A graph is composed of *variabels* and *operators*. + +1. The description of graphs must be able to be serialized/deserialized, so it + + 1. could to be sent to the cloud for distributed execution, and + 1. be sent to clients for mobile or enterprise deployment. + +1. The Python program do + + 1. *compilation*: runs a Python program to generate a protobuf message representation of the graph and send it to + 1. the C++ library `libpaddle.so` for local execution, + 1. the master process of a distributed training job for training, or + 1. the server process of a Kubernetes serving job for distributed serving. + 1. *execution*: according to the protobuf message, constructs instances of class `Variable` and `OperatorBase`, and run them. + +## Description and Realization + +At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph. + +At runtime, the C++ program realizes the graph and run it. + +| | Representation (protobuf messages) | Realization (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| + +The word *graph* is exchangable with *block* in this document. A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }. + +## Compilation and Execution + +1. Run an applicaton Python program to describe the graph. In particular, + + 1. create VarDesc to represent local/intermediate variables, + 1. create operators and set attributes, + 1. validate attribute values, + 1. inference the type and the shape of variables, + 1. plan for memory-reuse for variables, + 1. generate backward and optimization part of the Graph. + 1. possiblly split the graph for distributed training. + +1. The invocation of `train` or `infer` in the application Python program: + + 1. create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block, + 1. realize local variables defined in the BlockDesc message in the new scope, + 1. a scope is similar to the stack frame in programming languages, + + 1. create an instance of class `Block`, in which, + 1. realize operators in the BlockDesc message, + + 1. run the Block by calling + 1. `Block::Eval(vector* targets)` for forward and backward computations, or + 1. `Block::Eval(vector* targets)` for optimization. + + +## Intermediate Representation (IR) + +```text +Compile Time -> IR -> Runtime +``` + +### Benefit + +- Optimization + ```text + Compile Time -> IR -> Optimized IR -> Runtime + ``` +- Send automatically partitioned IR to different nodes. + - Automatic data parallel + ```text + Compile Time + |-> Single GPU IR + |-> [trainer-IR-0, trainer-IR-1, pserver-IR] + |-> Node-0 (runs trainer-IR-0) + |-> Node-1 (runs trainer-IR-1) + |-> Node-2 (runs pserver-IR) + ``` + - Automatic model parallel (planned for future) + +--- + +# Operator/OpWithKernel/OpKernel + +![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/49caf1fb70820fb4a6c217634317c9306f361f36/op_op_with_kern_class_diagram.dot) + +--- + +# Operator +![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot) + +* `Operator` is the fundamental building block as the user interface. + * Operator stores input/output variable name, and attributes. + * The `InferShape` interface is used to infer output variable shapes by its input shapes. + * Use `Run` to compute `input variables` to `output variables`. + +--- + +# OpWithKernel/Kernel + +![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/9d7f4eba185cf41c8e2fbfb40ae21890dbddcd39/op_with_kernel.dot) + +* `OpWithKernel` inherits `Operator`. +* `OpWithKernel` contains a Kernel map. + * `OpWithKernel::Run` get device's kernel, and invoke `OpKernel::Compute`. + * `OpKernelKey` is the map key. Only device place now, but may be data type later. + +--- + +# Why separate Kernel and Operator + +* Separate GPU and CPU code. + * Make Paddle can run without GPU. +* Make one operator (which is user interface) can contain many implementations. + * Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel. +--- + +# Libraries for Kernel development + +* `Eigen::Tensor` contains basic math and element-wise functions. + * Note that `Eigen::Tensor` has broadcast implementation. + * Limit number of `tensor.device(dev) = ` in your code. +* `thrust::tranform` and `std::transform`. + * `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel. + * `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`. +* Hand-writing `GPUKernel` and `CPU` code + * Do not write `.h`. CPU Kernel should be in `.cc`. CPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.) +--- +# Operator Register + +## Why register is necessary? +We need a method to build mappings between Op type names and Op classes. + +## How to do the register? + +Maintain a map, whose key is the type name and value is corresponding Op constructor. + +--- +# The Registry Map + +### `OpInfoMap` + +`op_type(string)` -> `OpInfo` + +`OpInfo`: + +- **`creator`**: The Op constructor. +- **`grad_op_type`**: The type of the gradient Op. +- **`proto`**: The Op's Protobuf, including inputs, outputs and required attributes. +- **`checker`**: Used to check attributes. + +--- +# Related Concepts + +### Op_Maker +It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)) + +### Register Macros +```cpp +REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class) +REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) +``` + +### `USE` Macros +make sure the registration process is executed and linked. + +--- +# Register Process +1. Write Op class, as well as its gradient Op class if there is. +2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes. +3. Invoke macro `REGISTER_OP`. The macro will + 1. call maker class to complete `proto` and `checker` + 2. with the completed `proto` and `checker`, build a new key-value pair in the `OpInfoMap` + +4. Invoke `USE` macro in where the Op is used to make sure it is linked. + +--- +# Backward Module (1/2) +### Create Backward Operator +- Mapping from forwarding Op to backward Op +![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png) + +--- +# Backward Module (2/2) +### Build Backward Network +- **Input** graph of forwarding operators +- **Output** graph of backward operators +- **corner case in construction** + - shared variable => insert `Add` operator + - no gradient => insert `fill_zero_grad` operator + - recursive netOp => call `Backward` recursively + - RNN Op => recursively call `Backward` on stepnet + + +--- +# Scope, Variable, Tensor + +* `Tensor` is an n-dimension array with type. + * Only dims and data pointers are stored in `Tensor`. + * All operators on `Tensor` is written in `Operator` or global functions. + * variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) +* `Variable` is the inputs and outputs of an operator. Not just `Tensor`. + * step_scopes in RNN is a variable and not a tensor. +* `Scope` is where variables store at. + * map + * `Scope` has a hierarchical structure. The local scope can get variable from its parent scope. + +--- +# Block (in design) +## the difference with original RNNOp +- as an operator is more intuitive than `RNNOp`, +- offers new interface `Eval(targets)` to deduce the minimal block to `Run`, +- fits the compile-time/ runtime separation design. + - during the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc` + - when graph executes, a Block with `BlockDesc` passed in creates `Op` and `Var` then `Run` + +--- +# Milestone +- take Paddle/books as the main line, the requirement of the models motivates framework refactoring, +- model migration + - framework development gives **priority support** to model migration, for example, + - the MNIST demo needs a Python interface, + - the RNN models require the framework to support `LoDTensor`. + - determine some timelines, + - heavily-relied Ops need to be migrated first, + - different models can be migrated parallelly. +- improve the framework at the same time +- accept imperfection, concentrated on solving the specific problem at the right price. + +--- +# Control the migration quality +- compare the performance of migrated models with old ones. +- follow google C style +- build the automatic workflow of generating Python/C++ documentations + - the documentation of layers and ops should be written inside the code + - take the documentation quality into account when doing PR + - preview the documentations, read and improve them from users' perspective diff --git a/develop/doc/design/refactorization.html b/develop/doc/design/refactorization.html new file mode 100644 index 00000000000..e6fb97c2509 --- /dev/null +++ b/develop/doc/design/refactorization.html @@ -0,0 +1,571 @@ + + + + + + + + + + + Design Doc: Refactorization Overview — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
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+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Design Doc: Refactorization Overview
    • +
+
+ +
+
+
+
+ +
+

Design Doc: Refactorization Overview

+

The goal of refactorizaiton include:

+
    +
  1. Make it easy for external contributors to write new elementory computaiton operations.
    2. +
  2. Make the codebase clean and readable.
    3. +
  3. Introduce a new design of computation representation – a computation graph of operators and variables.
    4. +
  4. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing.
    5. +
+
+

Computation Graphs

+
    +
  1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs.
    2. +
  2. Please dig into computation graphs for a solid example.
    3. +
  3. Users write Python programs to describe the graphs and run it (locally or remotely).
    4. +
  4. A graph is composed of variabels and operators.
    5. +
  5. The description of graphs must be able to be serialized/deserialized, so it
      +
    1. could to be sent to the cloud for distributed execution, and
      2. +
    2. be sent to clients for mobile or enterprise deployment.
      3. +
    +
    6. +
  6. The Python program do
      +
    1. compilation: runs a Python program to generate a protobuf message representation of the graph and send it to
        +
      1. the C++ library libpaddle.so for local execution,
        2. +
      2. the master process of a distributed training job for training, or
        3. +
      3. the server process of a Kubernetes serving job for distributed serving.
        4. +
      +
      2. +
    2. execution: according to the protobuf message, constructs instances of class Variable and OperatorBase, and run them.
      3. +
    +
    7. +
+
+
+

Description and Realization

+

At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph.

+

At runtime, the C++ program realizes the graph and run it.

+

| | Representation (protobuf messages) | Realization (C++ class objects) | +|—|—|—| +|Data|VarDesc|Variable| +|Operation|OpDesc|Operator| +|Block|BlockDesc|Block|

+

The word graph is exchangable with block in this document. A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }.

+
+
+

Compilation and Execution

+
    +
  1. Run an applicaton Python program to describe the graph. In particular,
      +
    1. create VarDesc to represent local/intermediate variables,
      2. +
    2. create operators and set attributes,
      3. +
    3. validate attribute values,
      4. +
    4. inference the type and the shape of variables,
      5. +
    5. plan for memory-reuse for variables,
      6. +
    6. generate backward and optimization part of the Graph.
      7. +
    7. possiblly split the graph for distributed training.
      8. +
    +
    2. +
  2. The invocation of train or infer in the application Python program:
      +
    1. create a new Scope instance in the scope hierarchy for each run of a block,
        +
      1. realize local variables defined in the BlockDesc message in the new scope,
        2. +
      2. a scope is similar to the stack frame in programming languages,
        3. +
      +
      2. +
    2. create an instance of class Block, in which,
        +
      1. realize operators in the BlockDesc message,
        2. +
      +
      3. +
    3. run the Block by calling
        +
      1. Block::Eval(vector<Variable>* targets) for forward and backward computations, or
        2. +
      2. Block::Eval(vector<Operator>* targets) for optimization.
        3. +
      +
      4. +
    +
    3. +
+
+
+

Intermediate Representation (IR)

+
Compile Time -> IR -> Runtime
+
+
+
+

Benefit

+
    +

  • Optimization

    +
    Compile Time -> IR -> Optimized IR -> Runtime
+
    +
    +
    • +

  • Send automatically partitioned IR to different nodes.

    +
      +

    • Automatic data parallel

      +
      Compile Time
+|-> Single GPU IR
+    |-> [trainer-IR-0, trainer-IR-1, pserver-IR]
+        |-> Node-0 (runs trainer-IR-0)
+        |-> Node-1 (runs trainer-IR-1)
+        |-> Node-2 (runs pserver-IR)
+
      +
      +
      • +

    • Automatic model parallel (planned for future)

      +
      • +
    +
    • +
+
+
+
+
+
+

Operator/OpWithKernel/OpKernel

+

class_diagram

+
+
+
+

Operator

+

class_diagram

+
    +
  • Operator is the fundamental building block as the user interface.
      +
    • Operator stores input/output variable name, and attributes.
      • +
    • The InferShape interface is used to infer output variable shapes by its input shapes.
      • +
    • Use Run to compute input variables to output variables.
      • +
    +
    • +
+
+
+
+

OpWithKernel/Kernel

+

class_diagram

+
    +
  • OpWithKernel inherits Operator.
    • +
  • OpWithKernel contains a Kernel map.
      +
    • OpWithKernel::Run get device’s kernel, and invoke OpKernel::Compute.
      • +
    • OpKernelKey is the map key. Only device place now, but may be data type later.
      • +
    +
    • +
+
+
+
+

Why separate Kernel and Operator

+
    +
  • Separate GPU and CPU code.
      +
    • Make Paddle can run without GPU.
      • +
    +
    • +
  • Make one operator (which is user interface) can contain many implementations.
      +
    • Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel.
      • +
    +
    • +
+
+
+
+

Libraries for Kernel development

+
    +
  • Eigen::Tensor contains basic math and element-wise functions.
      +
    • Note that Eigen::Tensor has broadcast implementation.
      • +
    • Limit number of tensor.device(dev) = in your code.
      • +
    +
    • +
  • thrust::tranform and std::transform.
      +
    • thrust has the same API as C++ standard library. Using transform can quickly implement a customized elementwise kernel.
      • +
    • thrust has more complex API, like scan, reduce, reduce_by_key.
      • +
    +
    • +
  • Hand-writing GPUKernel and CPU code
      +
    • Do not write .h. CPU Kernel should be in .cc. CPU kernel should be in .cu. (GCC cannot compile GPU code.)
      • +
    +
    • +
+
+
+
+

Operator Register

+
+

Why register is necessary?

+

We need a method to build mappings between Op type names and Op classes.

+
+
+

How to do the register?

+

Maintain a map, whose key is the type name and value is corresponding Op constructor.

+
+
+
+
+

The Registry Map

+
+

OpInfoMap

+

op_type(string) -> OpInfo

+

OpInfo:

+
    +
  • creator: The Op constructor.
    • +
  • grad_op_type: The type of the gradient Op.
    • +
  • proto: The Op’s Protobuf, including inputs, outputs and required attributes.
    • +
  • checker: Used to check attributes.
    • +
+
+
+
+ +
+
+

Register Process

+
    +
  1. Write Op class, as well as its gradient Op class if there is.
    2. +
  2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes.
    3. +
  3. Invoke macro REGISTER_OP. The macro will
      +
    1. call maker class to complete proto and checker
      2. +
    2. with the completed proto and checker, build a new key-value pair in the OpInfoMap
      3. +
    +
    4. +
  4. Invoke USE macro in where the Op is used to make sure it is linked.
    5. +
+
+
+
+

Backward Module (1/2)

+
+

Create Backward Operator

+
    +
  • Mapping from forwarding Op to backward Op +backward
    • +
+
+
+
+
+

Backward Module (2/2)

+
+

Build Backward Network

+
    +
  • Input graph of forwarding operators
    • +
  • Output graph of backward operators
    • +
  • corner case in construction
      +
    • shared variable => insert Add operator
      • +
    • no gradient => insert fill_zero_grad operator
      • +
    • recursive netOp => call Backward recursively
      • +
    • RNN Op => recursively call Backward on stepnet
      • +
    +
    • +
+
+
+
+
+

Scope, Variable, Tensor

+
    +
  • Tensor is an n-dimension array with type.
      +
    • Only dims and data pointers are stored in Tensor.
      • +
    • All operators on Tensor is written in Operator or global functions.
      • +
    • variable length Tensor design LoDTensor
      • +
    +
    • +
  • Variable is the inputs and outputs of an operator. Not just Tensor.
      +
    • step_scopes in RNN is a variable and not a tensor.
      • +
    +
    • +
  • Scope is where variables store at.
      +
    • map<string/*var name */, Variable>
      • +
    • Scope has a hierarchical structure. The local scope can get variable from its parent scope.
      • +
    +
    • +
+
+
+
+

Block (in design)

+
+

the difference with original RNNOp

+
    +
  • as an operator is more intuitive than RNNOp,
    • +
  • offers new interface Eval(targets) to deduce the minimal block to Run,
    • +
  • fits the compile-time/ runtime separation design.
      +
    • during the compilation, SymbolTable stores VarDescs and OpDescs and serialize to a BlockDesc
      • +
    • when graph executes, a Block with BlockDesc passed in creates Op and Var then Run
      • +
    +
    • +
+
+
+
+
+

Milestone

+
    +
  • take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
    • +
  • model migration
      +
    • framework development gives priority support to model migration, for example,
        +
      • the MNIST demo needs a Python interface,
        • +
      • the RNN models require the framework to support LoDTensor.
        • +
      +
      • +
    • determine some timelines,
      • +
    • heavily-relied Ops need to be migrated first,
      • +
    • different models can be migrated parallelly.
      • +
    +
    • +
  • improve the framework at the same time
    • +
  • accept imperfection, concentrated on solving the specific problem at the right price.
    • +
+
+
+
+

Control the migration quality

+
    +
  • compare the performance of migrated models with old ones.
    • +
  • follow google C style
    • +
  • build the automatic workflow of generating Python/C++ documentations
      +
    • the documentation of layers and ops should be written inside the code
      • +
    • take the documentation quality into account when doing PR
      • +
    • preview the documentations, read and improve them from users’ perspective
      • +
    +
    • +
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/refactorization.md.txt b/develop/doc_cn/_sources/design/refactorization.md.txt new file mode 100644 index 00000000000..e105861e926 --- /dev/null +++ b/develop/doc_cn/_sources/design/refactorization.md.txt @@ -0,0 +1,253 @@ +# Design Doc: Refactorization Overview + +The goal of refactorizaiton include: + +1. Make it easy for external contributors to write new elementory computaiton operations. +1. Make the codebase clean and readable. +1. Introduce a new design of computation representation -- a computation graph of operators and variables. +1. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing. + +## Computation Graphs + +1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs. + + 1. Please dig into [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a solid example. + +1. Users write Python programs to describe the graphs and run it (locally or remotely). + +1. A graph is composed of *variabels* and *operators*. + +1. The description of graphs must be able to be serialized/deserialized, so it + + 1. could to be sent to the cloud for distributed execution, and + 1. be sent to clients for mobile or enterprise deployment. + +1. The Python program do + + 1. *compilation*: runs a Python program to generate a protobuf message representation of the graph and send it to + 1. the C++ library `libpaddle.so` for local execution, + 1. the master process of a distributed training job for training, or + 1. the server process of a Kubernetes serving job for distributed serving. + 1. *execution*: according to the protobuf message, constructs instances of class `Variable` and `OperatorBase`, and run them. + +## Description and Realization + +At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph. + +At runtime, the C++ program realizes the graph and run it. + +| | Representation (protobuf messages) | Realization (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| + +The word *graph* is exchangable with *block* in this document. A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }. + +## Compilation and Execution + +1. Run an applicaton Python program to describe the graph. In particular, + + 1. create VarDesc to represent local/intermediate variables, + 1. create operators and set attributes, + 1. validate attribute values, + 1. inference the type and the shape of variables, + 1. plan for memory-reuse for variables, + 1. generate backward and optimization part of the Graph. + 1. possiblly split the graph for distributed training. + +1. The invocation of `train` or `infer` in the application Python program: + + 1. create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block, + 1. realize local variables defined in the BlockDesc message in the new scope, + 1. a scope is similar to the stack frame in programming languages, + + 1. create an instance of class `Block`, in which, + 1. realize operators in the BlockDesc message, + + 1. run the Block by calling + 1. `Block::Eval(vector* targets)` for forward and backward computations, or + 1. `Block::Eval(vector* targets)` for optimization. + + +## Intermediate Representation (IR) + +```text +Compile Time -> IR -> Runtime +``` + +### Benefit + +- Optimization + ```text + Compile Time -> IR -> Optimized IR -> Runtime + ``` +- Send automatically partitioned IR to different nodes. + - Automatic data parallel + ```text + Compile Time + |-> Single GPU IR + |-> [trainer-IR-0, trainer-IR-1, pserver-IR] + |-> Node-0 (runs trainer-IR-0) + |-> Node-1 (runs trainer-IR-1) + |-> Node-2 (runs pserver-IR) + ``` + - Automatic model parallel (planned for future) + +--- + +# Operator/OpWithKernel/OpKernel + +![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/49caf1fb70820fb4a6c217634317c9306f361f36/op_op_with_kern_class_diagram.dot) + +--- + +# Operator +![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot) + +* `Operator` is the fundamental building block as the user interface. + * Operator stores input/output variable name, and attributes. + * The `InferShape` interface is used to infer output variable shapes by its input shapes. + * Use `Run` to compute `input variables` to `output variables`. + +--- + +# OpWithKernel/Kernel + +![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/9d7f4eba185cf41c8e2fbfb40ae21890dbddcd39/op_with_kernel.dot) + +* `OpWithKernel` inherits `Operator`. +* `OpWithKernel` contains a Kernel map. + * `OpWithKernel::Run` get device's kernel, and invoke `OpKernel::Compute`. + * `OpKernelKey` is the map key. Only device place now, but may be data type later. + +--- + +# Why separate Kernel and Operator + +* Separate GPU and CPU code. + * Make Paddle can run without GPU. +* Make one operator (which is user interface) can contain many implementations. + * Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel. +--- + +# Libraries for Kernel development + +* `Eigen::Tensor` contains basic math and element-wise functions. + * Note that `Eigen::Tensor` has broadcast implementation. + * Limit number of `tensor.device(dev) = ` in your code. +* `thrust::tranform` and `std::transform`. + * `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel. + * `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`. +* Hand-writing `GPUKernel` and `CPU` code + * Do not write `.h`. CPU Kernel should be in `.cc`. CPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.) +--- +# Operator Register + +## Why register is necessary? +We need a method to build mappings between Op type names and Op classes. + +## How to do the register? + +Maintain a map, whose key is the type name and value is corresponding Op constructor. + +--- +# The Registry Map + +### `OpInfoMap` + +`op_type(string)` -> `OpInfo` + +`OpInfo`: + +- **`creator`**: The Op constructor. +- **`grad_op_type`**: The type of the gradient Op. +- **`proto`**: The Op's Protobuf, including inputs, outputs and required attributes. +- **`checker`**: Used to check attributes. + +--- +# Related Concepts + +### Op_Maker +It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)) + +### Register Macros +```cpp +REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class) +REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) +``` + +### `USE` Macros +make sure the registration process is executed and linked. + +--- +# Register Process +1. Write Op class, as well as its gradient Op class if there is. +2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes. +3. Invoke macro `REGISTER_OP`. The macro will + 1. call maker class to complete `proto` and `checker` + 2. with the completed `proto` and `checker`, build a new key-value pair in the `OpInfoMap` + +4. Invoke `USE` macro in where the Op is used to make sure it is linked. + +--- +# Backward Module (1/2) +### Create Backward Operator +- Mapping from forwarding Op to backward Op +![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png) + +--- +# Backward Module (2/2) +### Build Backward Network +- **Input** graph of forwarding operators +- **Output** graph of backward operators +- **corner case in construction** + - shared variable => insert `Add` operator + - no gradient => insert `fill_zero_grad` operator + - recursive netOp => call `Backward` recursively + - RNN Op => recursively call `Backward` on stepnet + + +--- +# Scope, Variable, Tensor + +* `Tensor` is an n-dimension array with type. + * Only dims and data pointers are stored in `Tensor`. + * All operators on `Tensor` is written in `Operator` or global functions. + * variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) +* `Variable` is the inputs and outputs of an operator. Not just `Tensor`. + * step_scopes in RNN is a variable and not a tensor. +* `Scope` is where variables store at. + * map + * `Scope` has a hierarchical structure. The local scope can get variable from its parent scope. + +--- +# Block (in design) +## the difference with original RNNOp +- as an operator is more intuitive than `RNNOp`, +- offers new interface `Eval(targets)` to deduce the minimal block to `Run`, +- fits the compile-time/ runtime separation design. + - during the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc` + - when graph executes, a Block with `BlockDesc` passed in creates `Op` and `Var` then `Run` + +--- +# Milestone +- take Paddle/books as the main line, the requirement of the models motivates framework refactoring, +- model migration + - framework development gives **priority support** to model migration, for example, + - the MNIST demo needs a Python interface, + - the RNN models require the framework to support `LoDTensor`. + - determine some timelines, + - heavily-relied Ops need to be migrated first, + - different models can be migrated parallelly. +- improve the framework at the same time +- accept imperfection, concentrated on solving the specific problem at the right price. + +--- +# Control the migration quality +- compare the performance of migrated models with old ones. +- follow google C style +- build the automatic workflow of generating Python/C++ documentations + - the documentation of layers and ops should be written inside the code + - take the documentation quality into account when doing PR + - preview the documentations, read and improve them from users' perspective diff --git a/develop/doc_cn/design/refactorization.html b/develop/doc_cn/design/refactorization.html new file mode 100644 index 00000000000..7e5b9b358a2 --- /dev/null +++ b/develop/doc_cn/design/refactorization.html @@ -0,0 +1,579 @@ + + + + + + + + + + + Design Doc: Refactorization Overview — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
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+
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+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Design Doc: Refactorization Overview
    • +
+
+ +
+
+
+
+ +
+

Design Doc: Refactorization Overview

+

The goal of refactorizaiton include:

+
    +
  1. Make it easy for external contributors to write new elementory computaiton operations.
    2. +
  2. Make the codebase clean and readable.
    3. +
  3. Introduce a new design of computation representation – a computation graph of operators and variables.
    4. +
  4. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing.
    5. +
+
+

Computation Graphs

+
    +
  1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs.
    2. +
  2. Please dig into computation graphs for a solid example.
    3. +
  3. Users write Python programs to describe the graphs and run it (locally or remotely).
    4. +
  4. A graph is composed of variabels and operators.
    5. +
  5. The description of graphs must be able to be serialized/deserialized, so it
      +
    1. could to be sent to the cloud for distributed execution, and
      2. +
    2. be sent to clients for mobile or enterprise deployment.
      3. +
    +
    6. +
  6. The Python program do
      +
    1. compilation: runs a Python program to generate a protobuf message representation of the graph and send it to
        +
      1. the C++ library libpaddle.so for local execution,
        2. +
      2. the master process of a distributed training job for training, or
        3. +
      3. the server process of a Kubernetes serving job for distributed serving.
        4. +
      +
      2. +
    2. execution: according to the protobuf message, constructs instances of class Variable and OperatorBase, and run them.
      3. +
    +
    7. +
+
+
+

Description and Realization

+

At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph.

+

At runtime, the C++ program realizes the graph and run it.

+

| | Representation (protobuf messages) | Realization (C++ class objects) | +|—|—|—| +|Data|VarDesc|Variable| +|Operation|OpDesc|Operator| +|Block|BlockDesc|Block|

+

The word graph is exchangable with block in this document. A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }.

+
+
+

Compilation and Execution

+
    +
  1. Run an applicaton Python program to describe the graph. In particular,
      +
    1. create VarDesc to represent local/intermediate variables,
      2. +
    2. create operators and set attributes,
      3. +
    3. validate attribute values,
      4. +
    4. inference the type and the shape of variables,
      5. +
    5. plan for memory-reuse for variables,
      6. +
    6. generate backward and optimization part of the Graph.
      7. +
    7. possiblly split the graph for distributed training.
      8. +
    +
    2. +
  2. The invocation of train or infer in the application Python program:
      +
    1. create a new Scope instance in the scope hierarchy for each run of a block,
        +
      1. realize local variables defined in the BlockDesc message in the new scope,
        2. +
      2. a scope is similar to the stack frame in programming languages,
        3. +
      +
      2. +
    2. create an instance of class Block, in which,
        +
      1. realize operators in the BlockDesc message,
        2. +
      +
      3. +
    3. run the Block by calling
        +
      1. Block::Eval(vector<Variable>* targets) for forward and backward computations, or
        2. +
      2. Block::Eval(vector<Operator>* targets) for optimization.
        3. +
      +
      4. +
    +
    3. +
+
+
+

Intermediate Representation (IR)

+
Compile Time -> IR -> Runtime
+
+
+
+

Benefit

+
    +

  • Optimization

    +
    Compile Time -> IR -> Optimized IR -> Runtime
+
    +
    +
    • +

  • Send automatically partitioned IR to different nodes.

    +
      +

    • Automatic data parallel

      +
      Compile Time
+|-> Single GPU IR
+    |-> [trainer-IR-0, trainer-IR-1, pserver-IR]
+        |-> Node-0 (runs trainer-IR-0)
+        |-> Node-1 (runs trainer-IR-1)
+        |-> Node-2 (runs pserver-IR)
+
      +
      +
      • +

    • Automatic model parallel (planned for future)

      +
      • +
    +
    • +
+
+
+
+
+
+

Operator/OpWithKernel/OpKernel

+

class_diagram

+
+
+
+

Operator

+

class_diagram

+
    +
  • Operator is the fundamental building block as the user interface.
      +
    • Operator stores input/output variable name, and attributes.
      • +
    • The InferShape interface is used to infer output variable shapes by its input shapes.
      • +
    • Use Run to compute input variables to output variables.
      • +
    +
    • +
+
+
+
+

OpWithKernel/Kernel

+

class_diagram

+
    +
  • OpWithKernel inherits Operator.
    • +
  • OpWithKernel contains a Kernel map.
      +
    • OpWithKernel::Run get device’s kernel, and invoke OpKernel::Compute.
      • +
    • OpKernelKey is the map key. Only device place now, but may be data type later.
      • +
    +
    • +
+
+
+
+

Why separate Kernel and Operator

+
    +
  • Separate GPU and CPU code.
      +
    • Make Paddle can run without GPU.
      • +
    +
    • +
  • Make one operator (which is user interface) can contain many implementations.
      +
    • Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel.
      • +
    +
    • +
+
+
+
+

Libraries for Kernel development

+
    +
  • Eigen::Tensor contains basic math and element-wise functions.
      +
    • Note that Eigen::Tensor has broadcast implementation.
      • +
    • Limit number of tensor.device(dev) = in your code.
      • +
    +
    • +
  • thrust::tranform and std::transform.
      +
    • thrust has the same API as C++ standard library. Using transform can quickly implement a customized elementwise kernel.
      • +
    • thrust has more complex API, like scan, reduce, reduce_by_key.
      • +
    +
    • +
  • Hand-writing GPUKernel and CPU code
      +
    • Do not write .h. CPU Kernel should be in .cc. CPU kernel should be in .cu. (GCC cannot compile GPU code.)
      • +
    +
    • +
+
+
+
+

Operator Register

+
+

Why register is necessary?

+

We need a method to build mappings between Op type names and Op classes.

+
+
+

How to do the register?

+

Maintain a map, whose key is the type name and value is corresponding Op constructor.

+
+
+
+
+

The Registry Map

+
+

OpInfoMap

+

op_type(string) -> OpInfo

+

OpInfo:

+
    +
  • creator: The Op constructor.
    • +
  • grad_op_type: The type of the gradient Op.
    • +
  • proto: The Op’s Protobuf, including inputs, outputs and required attributes.
    • +
  • checker: Used to check attributes.
    • +
+
+
+
+ +
+
+

Register Process

+
    +
  1. Write Op class, as well as its gradient Op class if there is.
    2. +
  2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes.
    3. +
  3. Invoke macro REGISTER_OP. The macro will
      +
    1. call maker class to complete proto and checker
      2. +
    2. with the completed proto and checker, build a new key-value pair in the OpInfoMap
      3. +
    +
    4. +
  4. Invoke USE macro in where the Op is used to make sure it is linked.
    5. +
+
+
+
+

Backward Module (1/2)

+
+

Create Backward Operator

+
    +
  • Mapping from forwarding Op to backward Op +backward
    • +
+
+
+
+
+

Backward Module (2/2)

+
+

Build Backward Network

+
    +
  • Input graph of forwarding operators
    • +
  • Output graph of backward operators
    • +
  • corner case in construction
      +
    • shared variable => insert Add operator
      • +
    • no gradient => insert fill_zero_grad operator
      • +
    • recursive netOp => call Backward recursively
      • +
    • RNN Op => recursively call Backward on stepnet
      • +
    +
    • +
+
+
+
+
+

Scope, Variable, Tensor

+
    +
  • Tensor is an n-dimension array with type.
      +
    • Only dims and data pointers are stored in Tensor.
      • +
    • All operators on Tensor is written in Operator or global functions.
      • +
    • variable length Tensor design LoDTensor
      • +
    +
    • +
  • Variable is the inputs and outputs of an operator. Not just Tensor.
      +
    • step_scopes in RNN is a variable and not a tensor.
      • +
    +
    • +
  • Scope is where variables store at.
      +
    • map<string/*var name */, Variable>
      • +
    • Scope has a hierarchical structure. The local scope can get variable from its parent scope.
      • +
    +
    • +
+
+
+
+

Block (in design)

+
+

the difference with original RNNOp

+
    +
  • as an operator is more intuitive than RNNOp,
    • +
  • offers new interface Eval(targets) to deduce the minimal block to Run,
    • +
  • fits the compile-time/ runtime separation design.
      +
    • during the compilation, SymbolTable stores VarDescs and OpDescs and serialize to a BlockDesc
      • +
    • when graph executes, a Block with BlockDesc passed in creates Op and Var then Run
      • +
    +
    • +
+
+
+
+
+

Milestone

+
    +
  • take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
    • +
  • model migration
      +
    • framework development gives priority support to model migration, for example,
        +
      • the MNIST demo needs a Python interface,
        • +
      • the RNN models require the framework to support LoDTensor.
        • +
      +
      • +
    • determine some timelines,
      • +
    • heavily-relied Ops need to be migrated first,
      • +
    • different models can be migrated parallelly.
      • +
    +
    • +
  • improve the framework at the same time
    • +
  • accept imperfection, concentrated on solving the specific problem at the right price.
    • +
+
+
+
+

Control the migration quality

+
    +
  • compare the performance of migrated models with old ones.
    • +
  • follow google C style
    • +
  • build the automatic workflow of generating Python/C++ documentations
      +
    • the documentation of layers and ops should be written inside the code
      • +
    • take the documentation quality into account when doing PR
      • +
    • preview the documentations, read and improve them from users’ perspective
      • +
    +
    • +
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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