Mathematica:使用 Simply 进行常见的子表达式消除和强度降低

发布于 2024-10-02 21:12:41 字数 3149 浏览 8 评论 0原文

因此,最近我一直在研究如何在编译器优化中充分利用 Mathematica 的模式匹配和术语重写……尝试高度优化作为循环内部部分的短代码块。减少计算表达式所需工作量的两种常见方法是识别多次出现的子表达式并存储结果,然后在后续点使用存储的结果来节省工作。另一种方法是尽可能使用更便宜的操作。例如,我的理解是,求平方根比加法和乘法需要更多的时钟周期。需要明确的是,我感兴趣的是评估表达式所需的浮点运算的成本,而不是 Mathematica 评估它需要多长时间。

我的第一个想法是,我将使用 Mathematica 的 simplify 函数来解决开发问题。可以指定一个复杂性函数来比较两个表达式的相对简单性。我打算使用相关算术运算的权重创建一个,并将表达式的 LeafCount 添加到其中,以考虑所需的赋值操作。这解决了强度方面的降低问题,但正是消除了常见的子表达式让我陷入了困境。

我正在考虑将公共子表达式消除添加到可能的转换函数中以简化使用。但对于大型表达式,可能有许多可能的子表达式可以被替换,并且在看到表达式之前不可能知道它们是什么。我编写了一个提供可能替换的函数,但您指定的转换函数似乎只需要返回一个可能的转换,至少从文档中的示例来看是这样。关于如何绕过这一限制有什么想法吗?有谁更好地了解简化如何使用可能暗示前进方向的转换函数?

我想象 Simplify 在幕后正在进行一些动态编程,尝试对表达式的不同部分进行不同的简化,并返回复杂度分数最低的简化。我尝试使用常见的代数简化(例如因子和集合)自己进行动态规划会更好吗?

编辑:我添加了生成可能要删除的子表达式的代码。

(*traverses entire expression tree storing each node*)
AllSubExpressions[x_, accum_] := Module[{result, i, len},
  len = Length[x];
  result = Append[accum, x];
  If[LeafCount[x] > 1,
   For[i = 1, i <= len, i++,
     result = ToSubExpressions2[x[[i]], result];
     ];
   ];
  Return[Sort[result, LeafCount[#1] > LeafCount[#2] &]]
  ]

CommonSubExpressions[statements_] := Module[{common, subexpressions},
subexpressions = AllSubExpressions[statements, {}];
  (*get the unique set of sub expressions*)
  common = DeleteDuplicates[subexpressions];
  (*remove constants from the list*)
  common = Select[common, LeafCount[#] > 1 &];
  (*only keep subexpressions that occur more than once*)
  common = Select[common, Count[subexpressions, #] > 1 &];
  (*output the list of possible subexpressions to replace with the \
number of occurrences*)
  Return[common];
  ]

从 CommonSubExpressions 返回的列表中选择公共子表达式后,执行替换的函数如下。

eliminateCSE[statements_, expr_] := Module[{temp},
temp = Unique["r"];
Prepend[ReplaceAll[statements, expr -> temp], temp[expr]]
]

冒着这个问题变得很长的风险,我将放一些示例代码。我认为尝试优化的一个不错的表达式是经典的 Runge-Kutta 求解微分方程的方法。

Input:
nextY=statements[y + 1/6 h (f[t, n] + 2 f[0.5 h + t, y + 0.5 h f[t, n]] + 
    2 f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]] + 
    f[h + t, 
     y + h f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]]])];
possibleTransformations=CommonSubExpressions[nextY]
transformed=eliminateCSE[nextY, First[possibleTransformations]]

Output:
{f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]], 
y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]], 
0.5 h f[0.5 h + t, y + 0.5 h f[t, n]], 
f[0.5 h + t, y + 0.5 h f[t, n]], y + 0.5 h f[t, n], 0.5 h f[t, n], 
0.5 h + t, f[t, n], 0.5 h}

statements[r1[f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]]], 
y + 1/6 h (2 r1 + f[t, n] + 2 f[0.5 h + t, y + 0.5 h f[t, n]] + 
 f[h + t, h r1 + y])]

最后,判断不同表达式的相对成本的代码如下。目前权重只是概念性的,因为这仍然是我正在研究的领域。

Input:
cost[e_] := 
 Total[MapThread[
   Count[e, #1, Infinity, Heads -> True]*#2 &, {{Plus, Times, Sqrt, 
     f}, {1, 2, 5, 10}}]]
cost[transformed]

Output:
100

So lately I have been toying around with how Mathematica's pattern matching and term rewriting might be put to good use in compiler optimizations...trying to highly optimize short blocks of code that are the inner parts of loops. Two common ways to reduce the amount of work it takes to evaluate an expression is to identify sub-expressions that occur more than once and store the result and then use the stored result at subsequent points to save work. Another approach is to use cheaper operations where possible. For instance, my understanding is that taking square roots take more clock cycles than additions and multiplications. To be clear, I am interested in the cost in terms of floating point operations that evaluating the expression would take, not how long it takes Mathematica to evaluate it.

My first thought was that I would tackle the problem developing using Mathematica's simplify function. It is possible to specify a complexity function that compares the relative simplicity of two expressions. I was going to create one using weights for the relevant arithmetic operations and add to this the LeafCount for the expression to account for the assignment operations that are required. That addresses the reduction in strength side, but it is the elimination of common subexpressions that has me tripped up.

I was thinking of adding common subexpression elimination to the possible transformation functions that simplify uses. But for a large expression there could be many possible subexpressions that could be replaced and it won't be possible to know what they are till you see the expression. I have written a function that gives the possible substitutions, but it seems like the transformation function you specify needs to just return a single possible transformation, at least from the examples in the documentation. Any thoughts on how one might get around this limitation? Does anyone have a better idea of how simplify uses transformation functions that might hint at a direction forward?

I imagine that behind the scenes that Simplify is doing some dynamic programming trying different simplifications on different parts of the expressions and returning the one with the lowest complexity score. Would I be better off trying to do this dynamic programming on my own using common algebraic simplifications such as factor and collect?

EDIT: I added the code that generates possible sub-expressions to remove

(*traverses entire expression tree storing each node*)
AllSubExpressions[x_, accum_] := Module[{result, i, len},
  len = Length[x];
  result = Append[accum, x];
  If[LeafCount[x] > 1,
   For[i = 1, i <= len, i++,
     result = ToSubExpressions2[x[[i]], result];
     ];
   ];
  Return[Sort[result, LeafCount[#1] > LeafCount[#2] &]]
  ]

CommonSubExpressions[statements_] := Module[{common, subexpressions},
subexpressions = AllSubExpressions[statements, {}];
  (*get the unique set of sub expressions*)
  common = DeleteDuplicates[subexpressions];
  (*remove constants from the list*)
  common = Select[common, LeafCount[#] > 1 &];
  (*only keep subexpressions that occur more than once*)
  common = Select[common, Count[subexpressions, #] > 1 &];
  (*output the list of possible subexpressions to replace with the \
number of occurrences*)
  Return[common];
  ]

Once a common sub-expression is chosen from the list returned by CommonSubExpressions the function that does the replacement is below.

eliminateCSE[statements_, expr_] := Module[{temp},
temp = Unique["r"];
Prepend[ReplaceAll[statements, expr -> temp], temp[expr]]
]

At the risk of this question getting long, I will put a little example code up. I thought a decent expression to try to optimize would be the classical Runge-Kutta method for solving differential equations.

Input:
nextY=statements[y + 1/6 h (f[t, n] + 2 f[0.5 h + t, y + 0.5 h f[t, n]] + 
    2 f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]] + 
    f[h + t, 
     y + h f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]]])];
possibleTransformations=CommonSubExpressions[nextY]
transformed=eliminateCSE[nextY, First[possibleTransformations]]

Output:
{f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]], 
y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]], 
0.5 h f[0.5 h + t, y + 0.5 h f[t, n]], 
f[0.5 h + t, y + 0.5 h f[t, n]], y + 0.5 h f[t, n], 0.5 h f[t, n], 
0.5 h + t, f[t, n], 0.5 h}

statements[r1[f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]]], 
y + 1/6 h (2 r1 + f[t, n] + 2 f[0.5 h + t, y + 0.5 h f[t, n]] + 
 f[h + t, h r1 + y])]

Finally, the code to judge the relative cost of different expressions is below. The weights are conceptual at this point as that is still an area I am researching.

Input:
cost[e_] := 
 Total[MapThread[
   Count[e, #1, Infinity, Heads -> True]*#2 &, {{Plus, Times, Sqrt, 
     f}, {1, 2, 5, 10}}]]
cost[transformed]

Output:
100

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与君绝 2024-10-09 21:12:54

通过添加选项来过滤掉列表中作为其他元素的子表达式的元素,对 @Yaroslav 的答案进行了轻微的补充:

subexpressionQ[a_, b_] := MemberQ[Cases[a, _, All], b]

filterOutSubexpressions[x_] := 
 Select[x, 
  s |-> Not@
    AnyTrue[x[[All, 1]] // 
      DeleteCases[s[[1]]], subexpressionQ[#, s[[1]]] &]]

Yaroslav 封装到函数中的代码:

subExpressionCount[s_] :=
 Module[{index,value,indices,values,items,indexQ,counts},
  index[downvalue_, dict_] := (downvalue[[1]] /. HoldPattern[dict[x_]] -> x) // ReleaseHold;
  value[downvalue_] := downvalue[[-1]];
  indices[dict_] := Map[#[[1]] /. {HoldPattern[dict[x_]] -> x} &, DownValues[dict]] // ReleaseHold;
  values[dict_] := Map[#[[-1]] &, DownValues[dict]];
  items[dict_] := Map[{index[#, dict], value[#]} &, DownValues[dict]];
  indexQ[dict_, index_] := If[MatchQ[dict[index], HoldPattern[dict[index]]], False, True];
  Map[(counts[#]=If[indexQ[counts,#],counts[#]+1,1];#)&,s,Infinity];
  items[counts]
  ]

注意: 我不清楚排序后 subExpressionCount 的输出是否可以与 Tally@Cases[#, _, All] & 不同。

示例:

subExpressionCount[
    x /. Solve[a*x^3 + b*x^2 + c*x + d == 0, x][[1]]] // 
   Select[#[[2]] > 1 && 
      LeafCount[#] > 10 &] // filterOutSubexpressions // 
 ReverseSortBy[Last]

{{-2 b^3 + 9 abc - 27 a^2 d + Sqrt[ 4 (-b^2 + 3 ac)^3 + (-2 b^3 + 9 abc - 27 a^ 2 d)^2], 2}}

A slight addition to @Yaroslav 's answer by adding the option to filter out elements in the list that are subexpressions of other elements:

subexpressionQ[a_, b_] := MemberQ[Cases[a, _, All], b]

filterOutSubexpressions[x_] := 
 Select[x, 
  s |-> Not@
    AnyTrue[x[[All, 1]] // 
      DeleteCases[s[[1]]], subexpressionQ[#, s[[1]]] &]]

The code by Yaroslav wrapped into a function :

subExpressionCount[s_] :=
 Module[{index,value,indices,values,items,indexQ,counts},
  index[downvalue_, dict_] := (downvalue[[1]] /. HoldPattern[dict[x_]] -> x) // ReleaseHold;
  value[downvalue_] := downvalue[[-1]];
  indices[dict_] := Map[#[[1]] /. {HoldPattern[dict[x_]] -> x} &, DownValues[dict]] // ReleaseHold;
  values[dict_] := Map[#[[-1]] &, DownValues[dict]];
  items[dict_] := Map[{index[#, dict], value[#]} &, DownValues[dict]];
  indexQ[dict_, index_] := If[MatchQ[dict[index], HoldPattern[dict[index]]], False, True];
  Map[(counts[#]=If[indexQ[counts,#],counts[#]+1,1];#)&,s,Infinity];
  items[counts]
  ]

Note: It is unclear to me whether the output of subExpressionCount can be different to Tally@Cases[#, _, All] & after ordering.

example :

subExpressionCount[
    x /. Solve[a*x^3 + b*x^2 + c*x + d == 0, x][[1]]] // 
   Select[#[[2]] > 1 && 
      LeafCount[#] > 10 &] // filterOutSubexpressions // 
 ReverseSortBy[Last]

{{-2 b^3 + 9 a b c - 27 a^2 d + Sqrt[ 4 (-b^2 + 3 a c)^3 + (-2 b^3 + 9 a b c - 27 a^2 d)^2], 2}}

春风十里 2024-10-09 21:12:53

我试图模仿此博客上出现的字典压缩功能: https://writings.stephenwolfram.com/2018/11/logic-explainability-and-the-future-of-understanding/

这是我所做的:

DictionaryCompress[expr_, count_, size_, func_] := Module[
  {t, s, rule, rule1, rule2},
  t = Tally@Level[expr, Depth[expr]];
  s = Sort[
    Select[{First@#, Last@#, Depth[First@#]} & /@ 
      t, (#[[2]] > count && #[[3]] > size) &], #1[[2]]*#1[[3]] < #2[[
        2]]*#2[[2]] &];
  rule = MapIndexed[First[#1] -> func @@ #2 &, s];
  rule = (# //. Cases[rule, Except[#]]) & /@ rule;
  rule1 = Select[rule, ! FreeQ[#, Plus] &];
  rule2 = Complement[rule, rule1];
  rule = rule1 //. (Reverse /@ rule2);
  rule = rule /. MapIndexed[ Last[#1] -> func @@ #2 &, rule];
  {
   expr //. rule,
   Reverse /@ rule
   }
  ];

poly = Sum[Subscript[c, k] x^k, {k, 0, 4}];
sol = Solve[poly == 0, x];
expr = x /. sol;
Column[{Column[
     MapIndexed[
      Style[TraditionalForm[Subscript[x, First[#2]] == #], 20] &, #[[
       1]]], Spacings -> 1],
    Column[Style[#, 20] & /@ #[[2]], Spacings -> 1, Frame -> All]
    }] &@DictionaryCompress[expr, 1, 1, 
  Framed[#, Background -> LightYellow] &]

在此处输入图像描述

I tried to mimic the dictionary compression function appears on this blog: https://writings.stephenwolfram.com/2018/11/logic-explainability-and-the-future-of-understanding/

Here is what I made:

DictionaryCompress[expr_, count_, size_, func_] := Module[
  {t, s, rule, rule1, rule2},
  t = Tally@Level[expr, Depth[expr]];
  s = Sort[
    Select[{First@#, Last@#, Depth[First@#]} & /@ 
      t, (#[[2]] > count && #[[3]] > size) &], #1[[2]]*#1[[3]] < #2[[
        2]]*#2[[2]] &];
  rule = MapIndexed[First[#1] -> func @@ #2 &, s];
  rule = (# //. Cases[rule, Except[#]]) & /@ rule;
  rule1 = Select[rule, ! FreeQ[#, Plus] &];
  rule2 = Complement[rule, rule1];
  rule = rule1 //. (Reverse /@ rule2);
  rule = rule /. MapIndexed[ Last[#1] -> func @@ #2 &, rule];
  {
   expr //. rule,
   Reverse /@ rule
   }
  ];

poly = Sum[Subscript[c, k] x^k, {k, 0, 4}];
sol = Solve[poly == 0, x];
expr = x /. sol;
Column[{Column[
     MapIndexed[
      Style[TraditionalForm[Subscript[x, First[#2]] == #], 20] &, #[[
       1]]], Spacings -> 1],
    Column[Style[#, 20] & /@ #[[2]], Spacings -> 1, Frame -> All]
    }] &@DictionaryCompress[expr, 1, 1, 
  Framed[#, Background -> LightYellow] &]

enter image description here

匿名。 2024-10-09 21:12:51

要识别重复的子表达式,您可以使用类似的东西

(*helper functions to add Dictionary-like functionality*)

index[downvalue_, 
   dict_] := (downvalue[[1]] /. HoldPattern[dict[x_]] -> x) // 
   ReleaseHold;
value[downvalue_] := downvalue[[-1]];
indices[dict_] := 
  Map[#[[1]] /. {HoldPattern[dict[x_]] -> x} &, DownValues[dict]] // 
   ReleaseHold;
values[dict_] := Map[#[[-1]] &, DownValues[dict]];
items[dict_] := Map[{index[#, dict], value[#]} &, DownValues[dict]];
indexQ[dict_, index_] := 
  If[MatchQ[dict[index], HoldPattern[dict[index]]], False, True];


(*count number of times each sub-expressions occurs *)
expr = Cos[x + Cos[Cos[x] + Sin[x]]] + Cos[Cos[x] + Sin[x]];
Map[(counts[#] = If[indexQ[counts, #], counts[#] + 1, 1]; #) &, expr, 
  Infinity];
items[counts] // Column

To identify repeating subexpressions, you could use something like this

(*helper functions to add Dictionary-like functionality*)

index[downvalue_, 
   dict_] := (downvalue[[1]] /. HoldPattern[dict[x_]] -> x) // 
   ReleaseHold;
value[downvalue_] := downvalue[[-1]];
indices[dict_] := 
  Map[#[[1]] /. {HoldPattern[dict[x_]] -> x} &, DownValues[dict]] // 
   ReleaseHold;
values[dict_] := Map[#[[-1]] &, DownValues[dict]];
items[dict_] := Map[{index[#, dict], value[#]} &, DownValues[dict]];
indexQ[dict_, index_] := 
  If[MatchQ[dict[index], HoldPattern[dict[index]]], False, True];


(*count number of times each sub-expressions occurs *)
expr = Cos[x + Cos[Cos[x] + Sin[x]]] + Cos[Cos[x] + Sin[x]];
Map[(counts[#] = If[indexQ[counts, #], counts[#] + 1, 1]; #) &, expr, 
  Infinity];
items[counts] // Column
亚希 2024-10-09 21:12:48

这里还有一些由作者实现的例程: http://stoney.sb.org/wordpress/2009/06/converting-symbolic-mathematica-expressions-to-c-code/

我将其打包成*.M文件并已修复一个错误(如果表达式没有重复的子表达式,它就会消失),我试图找到作者的联系信息,看看我是否可以将他修改后的代码上传到pastebin或其他地方。

编辑:我已获得作者的上传许可并将其粘贴到此处: http://pastebin.com/fjYiR0B3

There are also some routines here implemented here by this author: http://stoney.sb.org/wordpress/2009/06/converting-symbolic-mathematica-expressions-to-c-code/

I packaged it into a *.M file and have fixed a bug (if the expression has no repeated subexpressions the it dies), and I am trying to find the author's contact info to see if I can upload his modified code to pastebin or wherever.

EDIT: I have received permission from the author to upload it and have pasted it here: http://pastebin.com/fjYiR0B3

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