Add MindIR Tutorial.

c1292f4d · gongchen · gong chen · b0e06c84 · c1292f4d · c1292f4d
10 changed file
--- a/docs/source_zh_cn/design.rst
+++ b/docs/source_zh_cn/design.rst
-设计文档
-===========
-
-.. toctree::
-  :maxdepth: 1
-
-  architecture
-  technical_white_paper
-  design/mindspore/distributed_training_design
-  design/mindinsight/profiler_design
-  design/mindinsight/training_visual_design
-  design/mindinsight/graph_visual_design
-  design/mindinsight/tensor_visual_design
-  design/mindarmour/differential_privacy_design
+设计文档
+===========
+
+.. toctree::
+  :maxdepth: 1
+
+  architecture
+  technical_white_paper
+  design/mindspore/ir
+  design/mindspore/distributed_training_design
+  design/mindinsight/profiler_design
+  design/mindinsight/training_visual_design
+  design/mindinsight/graph_visual_design
+  design/mindinsight/tensor_visual_design
+  design/mindarmour/differential_privacy_design
--- a/docs/source_zh_cn/design/mindspore/images/ir/cf.dot
+++ b/docs/source_zh_cn/design/mindspore/images/ir/cf.dot
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+}
--- a/docs/source_zh_cn/design/mindspore/images/ir/closure.png
+++ b/docs/source_zh_cn/design/mindspore/images/ir/closure.png
--- a/docs/source_zh_cn/design/mindspore/images/ir/hof.dot
+++ b/docs/source_zh_cn/design/mindspore/images/ir/hof.dot
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+}
--- a/docs/source_zh_cn/design/mindspore/images/ir/hof.png
+++ b/docs/source_zh_cn/design/mindspore/images/ir/hof.png
--- a/docs/source_zh_cn/design/mindspore/images/ir/ir.dot
+++ b/docs/source_zh_cn/design/mindspore/images/ir/ir.dot
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--- a/docs/source_zh_cn/design/mindspore/images/ir/ir.png
+++ b/docs/source_zh_cn/design/mindspore/images/ir/ir.png
--- a/docs/source_zh_cn/design/mindspore/ir.md
+++ b/docs/source_zh_cn/design/mindspore/ir.md
+# MindSpore IR（MindIR）
+
+`框架开发` `中级` `高级` `贡献者`
+
+<!-- TOC -->
+
+- [MindSpore IR（MindIR）](#mindspore-irmindir)
+    - [简介](#简介)
+    - [文法定义](#文法定义)
+    - [示例](#示例)
+    - [如何保存IR](#如何保存ir)
+    - [函数式语义](#函数式语义)
+        - [高阶函数](#高阶函数)
+        - [控制流](#控制流)
+        - [自由变量和闭包](#自由变量和闭包)
+    - [参考文献](#参考文献)
+
+<!-- /TOC -->
+
+<a href="https://gitee.com/mindspore/docs/blob/master/docs/source_zh_cn/design/mindspore/ir.md" target="_blank"><img src="../../_static/logo_source.png"></a>
+
+## 简介
+中间表示（IR）是程序编译过程中介于源语言和目标语言之间的程序表示，以方便编译器进行程序分析和优化，因此IR的设计需要考虑从源语言到目标语言的转换难度，同时考虑程序分析和优化的易用性和性能。
+
+MindIR是一种基于图表示的函数式IR，其最核心的目的是服务于自动微分变换。自动微分采用的是基于函数式编程框架的变换方法，因此IR采用了接近于ANF函数式的语义。此外，借鉴Sea of Nodes[1]和Thorin[2]的优秀设计，采用了一种基于显性依赖图的表示方式。
+
+## 文法定义
+ANF是函数式编程中常用且简洁的中间表示，其文法定义如下所示：
+```
+<aexp> ::= NUMBER | STRING | VAR | BOOLEAN | PRIMOP
+          |  (lambda (VAR …) <exp>)
+<cexp> ::= (<aexp> <aexp> …)
+          |  (if <aexp> <exp> <exp>)
+<exp> ::= (let ([VAR <cexp>]) <exp>) | <cexp> | <aexp>
+
+```
+ANF中表达式分为原子表达式（aexp）和复合表达式（cexp），原子表达式表示一个常数值或一个变量或一个匿名函数；复合表达式由多个原子表达式复合组成，表示一个匿名函数或原语函数调用，组合的第一个输入是调用的函数，其余输入是调用的参数。
+
+MindIR文法继承于ANF，其定义如下所示：
+```
+<ANode> ::= <ValueNode> | <ParameterNode>
+<ParameterNode> ::= Parameter
+<ValueNode> ::= Scalar | Named | Tensor | Type | Shape
+               | Primitive | MetaFuncGraph | FuncGraph 
+<CNode> ::= (<AnfNode> …)
+<AnfNode> ::= <CNode> | <ANode>
+```
+MindIR中的ANode对应于ANF的原子表达式，ANode有两个子类分别为ValueNode和ParameterNode。ValueNode表示常数节点，可承载一个常数值（标量、符号、张量、类型、维度等），也可以是一个原语函数（Primitive）或一个元函数（MetaFuncGraph）或一个普通函数（FuncGraph），因为在函数式编程中函数定义本身也是一个值。ParameterNode是参数节点，表示函数的形参。
+
+MindIR中CNode对应于ANF的复合表达式，表示一次函数调用。
+
+在MindSpore自动微分时，会计算ParameterNode和CNode的梯度贡献，并返回最终ParameterNode的梯度，而不计算ValueNode的梯度。
+
+## 示例
+下面以一段程序作为示例，对比理解MindIR。
+```python
+def func(x, y):
+    return x / y
+
+@ms_function
+def test_f(x, y):
+    a = x - 1
+    b = a + y
+    c = b * func(a, b)
+    return c
+```
+这段Python代码对应的ANF表达为：
+```
+lambda (x, y)
+    let a = x - 1 in
+    let b = a + y in
+    let func = lambda (x, y)
+        let ret = x / y in
+        ret end in
+    let %1 = func(a, b) in
+    let c = b * %1 in
+    c end
+```
+对应的MindIR为[ir.dot](./images/ir/ir.dot)：
+
+![](./images/ir/ir.png)
+
+在MindIR中，一个函数图（FuncGraph）表示一个普通函数的定义，函数图一般由ParameterNode、ValueNode和CNode组成有向无环图，可以清晰地表达出从参数到返回值的计算过程。在上图中可以看出，python代码中两个函数`test_f`和`func`转换成了两个函数图，其参数`x`和`y`转换为函数图的ParameterNode，每一个表达式转换为一个CNode。CNode的第一个输入链接着调用的函数，例如图中的`add`、`func`、`return`。值得注意的是这些节点均是`ValueNode`，因为它们被理解为常数函数值。CNode的其他输入链接这调用的参数，参数值可以来自于ParameterNode、ValueNode和其他CNode。
+
+在ANF中每个表达式都用let表达式绑定为一个变量，通过对变量的引用来表示对表达式输出的依赖，而在MindIR中每个表达式都绑定为一个节点，通过节点与节点之间的有向边表示依赖关系。
+
+## 如何保存IR
+通过`context.set_context(save_graphs=True)`来保存各个编译阶段的中间代码。被保存的中间代码有两种格式，一个是后缀名为`.ir`的文本格式，一个是后缀名为`.dot`的图形化格式。当网络规模不大时，建议使用更直观的图形化格式来查看，当网络规模较大时建议使用更高效的文本格式来查看。
+
+DOT文件可以通过graphviz转换为图片格式来查看，例如将dot转换为png的命令是`dot -Tpng *.dot -o *.png`。
+
+## 函数式语义
+MindIR较传统计算图的一个重要特性是不仅可以表达算子之间的数据依赖，还可以表达丰富的函数式语义。
+### 高阶函数
+在MindIR中，函数的定义是由一个子图来定义，但其本身可以是一个被传递的值，作为其他高阶函数的输入或输出。
+例如下面一个简单的示例中，函数`f`作为参数传入了函数`g`，因此函数`g`是一个接收函数输入的高阶函数，函数`f`真正的调用点是在函数`g`内部。
+
+```
+@ms_function
+def hof(x):
+    def f(x):
+        return x + 3
+    def g(function, x):
+        return function(x) * function(x)
+    res = g(f, x)
+    return res
+```
+
+对应的MindIR为[hof.dot](./images/ir/hof.dot)：
+![](./images/ir/hof.png)
+
+在实际网络训练脚本中，自动求导泛函`GradOperation`和优化器中常用到的`Partial`和`HyperMap`都是典型的高阶函数。高阶语义极大地提升了MindSpore表达的灵活性和简洁性。
+
+### 控制流
+控制流在MindIR中是以高阶函数选择调用的形式表达。这样的形式把控制流转换为高阶函数的数据流，从而使得自动微分算法更加强大。不仅可以支持数据流的自动微分，还可以支持条件跳转、循环和递归等控制流的自动微分。
+
+下面以一个简单的斐波那契用例来演示说明。
+```python
+@ms_function
+def fibonacci(n):
+    if(n < 1):
+        return 0
+    elif(n == 1):
+        return 1
+    else:
+        return fibonacci(n-1) + fibonacci(n-2)
+```
+
+对应的MindIR为[cf.dot](./images/ir/cf.dot)：
+![](./images/ir/cf.png)
+
+其中`fibonacci`是顶层函数图，在顶层中有两个函数图被`switch`选择调用。`✓fibonacci`是第一个`if`的True分支，`✗fibonacci`是第一个`if`的False分支。在`✗fibonacci`中被调用的`✓✗fibonacci`是`elif`的True分支，`✗✗fibonacci`是`elif`的False分支。这里需要理解的关键是在MindIR中，条件跳转和递归是以高阶控制流的形式表达的。例如，`✓fibonacci`和`✗fibonacci`是作为`switch`算子的参数传入，`switch`根据条件参数选择哪一个函数作为返回值。因此，`switch`是把输入的函数当成普通的值做了一个二元选择操作，并没有调用，而真正的函数调用是在紧随`switch`后的CNode上完成。
+
+
+### 自由变量和闭包
+闭包（closure）是一种编程语言特性，它指的是代码块和作用域环境的结合。自由变量(free variable)是指在代码块中引用作用域环境中的变量而非局部变量。在MindIR中，代码块是以函数图呈现的，而作用域环境可以理解为该函数被调用时的上下文环境，自由变量的捕获方式是值拷贝而非引用。
+
+一个典型的闭包用例如下：
+```python
+@ms_function
+def func_outer(a, b):
+    def func_inner(c):
+        return a + b + c
+    return func_inner
+
+@ms_function
+def ms_closure():
+    closure = func_outer(1, 2)
+    out1 = closure(1)
+    out2 = closure(2)
+    return out1, out2
+```
+
+对应的MindIR为[closure.dot](./images/ir/closure.dot)：
+![](./images/ir/closure.png)
+
+在例子中，`a`和`b`是自由变量，因为`func_inner`中变量`a`和`b`是引用的其父图`func_outer`中定义的参数。变量`closure`是一个闭包，它是函数`func_inner`与其上下文`func_outer(1, 2)`的结合。因此，`out1`的结果是4，因为其等价于`1+2+1`，`out2`的结果是5，因为其等价于`1+2+2`。
+
+## 参考文献
+[1] C. Click and M. Paleczny. A simple graph-based intermediate representation.
+SIGPLAN Not., 30:35–49, March 1995.
+
+[2] Roland Leißa, Marcel Köster, and Sebastian Hack. A graph-based higher-order intermediate
+representation. In Proceedings of the 13th Annual IEEE/ACM International Symposium on
+Code Generation and Optimization, pages 202–212. IEEE Computer Society, 2015.
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