Code Monkey home page Code Monkey logo

creduce-windows's Introduction

This is intended as a guide to get CReduce up and running on Windows.

Table of Contents

Introduction
A Word on PATH and Shells
Preliminaries
Get a Perl Distribution
Build Clang Build unifdef.exe
Get CReduce
Build CReduce

Using CReduce

A Non-Toy Example

We assume that you know what CReduce is and what it does. More information about CReduce can be found at the project's home page.

CReduce is quite difficult to get working on Windows. The process is not well documented and there are many gotchas along the way. And if you stray from the path, things will go wrong. This guide is intended to be a complete verifiably reproducible sequence of steps that will allow you to get CReduce working on your Windows machine.

We assume that all commands (unless explicitly stated otherwise) are run from a cmd shell. NOT a git bash shell or any other kind of shell. Please do not use git bash unless the instructions explicitly call for it.

For each tool that we build or all, instructions will be given about whether it needs to be in your PATH. If the instructions indicate that it should be in PATH and you do not put it in PATH, you will be on your own to adjust the instructions as necessary.

Before starting, you should have the following software installed:

Flex is a fast lexer generator used by CReduce. Ninja is a build tool used (among other things) to build CMake-configured projects. CMake and Git are, well... CMake and Git. Download them all from the links above.

All of these packages provide binary distributions. You do not need to build anything from source. In all cases it should be safe to download whatever the latest version is.

Important: All of these tools should be in your PATH environment variable.

For the remainder of the document, we will assume that your source tree is rooted at a folder named src. All shell commands with no explicit instructions assume that are you are in src.

Download any Perl distribution you feel comfortable with. ActiveState and Strawberry Perl are popular choices.

Important: perl should be in your PATH environment variable.

Install the following Perl modules:

  • Exporter::Lite
  • File::Which
  • Getopt::Tabular
  • Regexp::Common
  • Term::ReadKey
$ cpan -i "Exporter::Lite"
// Note: The previous command will probably inform you that you need to download
// MinGW and dmake.  Accept and let it proceed.  You will only be prompted for this
// on the first package you install.
$ cpan -i "File::Which"
$ cpan -i "Getopt::Tabular"
$ cpan -i "Regexp::Common"
$ cpan -i "Term::ReadKey"

CReduce claims to be able to work with a version of clang/LLVM installed from a binary distribution package. However, i was not able to make this work due to the fact that the binary distribution does not ship with the .cmake configuration files, and CReduce seems to require them. So we will be building from source.

Important: Make sure you are in a MSVC C++ Developer Command Prompt for these steps. You can open one by searching for "x86 Native Tools Command Prompt for VS 2017" or similar. Note that when you try to run cmake, it might find gcc on your path due to an earlier step, and then try to build clang using gcc instead of MSVC. You don't want this. If this happens, delete gcc.exe and g++.exe from C:\Perl64\site\bin. If you ever try to install another package that needs them, it will always just re-download them same as it did in the earlier step.

Important: As of this writing, LLVM miscompiles with VS 2019. Please make sure you are using 2015 or 2017.

  1. Clone LLVM: (src) $ git clone https://github.com/llvm/llvm-project.git.
  2. Make a build directory: (src) $ mkdir llvm-build && cd llvm-build
  3. Configure the build.
  (src/llvm-build) $ 
    cmake -G Ninja
      -DCMAKE_BUILD_TYPE=Release
      -DLLVM_TARGETS_TO_BUILD=X86
      -DLLVM_ENABLE_PROJECTS=clang;lld
      ..\llvm-project\llvm
  1. Run the build and wait: (src/llvm-build) $ ninja

Note that, contrary to other steps, clang does not need to be in your PATH.

unifdef is an optional utility that can be used by CReduce to eliminate blocks of preprocessor logic. If you have it, CReduce can use it. If not, it will work anyway (but probably be slowe and the reduction might not be as good). Note that if you only ever reduce pre-processed source (e.g. cl.exe /EP foo.cpp) then you don't need this. Nevertheless, we don't want to stray from the path, because that's where things start going wrong.

  1. Clone the repo: (src) $ git clone git://dotat.at/unifdef.git
  2. Open a git bash shell (Git Bash is a tool that ships with Git for Windows) and cd to the directory where you cloned unifdef in step 1.
  3. Run this command: (src/unifdef) $ sh scripts/reversion.sh. You should see some output like this:
   version
     -> unifdef-2.11.25.65842ab.XX 2019-04-19 12:27:33 -0700
  1. Open src/unifdef/win32/unifdef.sln in a recent version of Visual Studio. I tested 2015 but 2017 should work equally well. Build the Release configuration.

Note: unifdef does not need to be in PATH. Later we will copy it into CReduces build tree which will allow creduce to find it.

  1. Clone creduce from its github repo.

(src) $ git clone https://github.com/csmith-project/creduce.git

  1. Make sure you are set up to track the llvm-svn-compatible branch or else you may fail building CReduce using a newer version of Clang.
(src) $ cd creduce
(src/creduce) $ git checkout -b creduce origin/llvm-svn-compatible
Switched to a new branch 'creduce'
Branch 'creduce' set up to track remote branch 'llvm-svn-compatible' from 'origin'.
  1. Make a build directory (we'll use this later):
(src/creduce) $ cd ..
(src) $ mkdir creduce-build

Finally! We're ready to actually build CReduce.

  1. Switch to your CReduce build directory created earlier and run CMake to configure it.
  (src/creduce-build) $ 
    cmake -G Ninja 
      -DCMAKE_BUILD_TYPE=Release 
      -DLLVM_DIR=src/llvm-build
      -DCMAKE_C_COMPILER=src/llvm-build/bin/clang-cl.exe
      -DCMAKE_CXX_COMPILER=src/llvm-build/bin/clang-cl.exe
      -DCMAKE_PREFIX_PATH=src/llvm-build
      ..\creduce

Important: CMake requires absolute paths. Replace src with the absolute path of the directory. Also, CMake requires forward slashes. Do not use backslashes anywhere.

  1. Build creduce. (src/creduce-build) $ ninja

  2. Install creduce (Note: You must be in an administrator command prompt): (src/creduce-build) $ ninja install

Note: You will get thousands of warnings, but you should not get any errors. Ignore the warnings.

  1. unifdef needs to be manually copied into the place where creduce expects it to be.
(src/creduce-build) $ mkdir unifdef && cd unifdef
(src/creduce-build/unifdef) $ copy src/unifdef/win32/Release/unifdef.exe

It's finally time to use CReduce! Let's look at how to actually use it.

Normally, creduce expects you to write your own interestingness test. This is a self-contained script which has all information about how to invoke the compiler as well as what makes the result interesting directly into the script. Then creduce will invoke your script once for each source file it wants to check the interestingness of. There are two big reasons this can often be a bit of a burden.

The first reason (which is not Windows specific) is that in a large majority of cases, an interestingness test boils down to "run the compiler with these flags, and the invocation was interesting if X was in the output, otherwise it's not interesting". It's painstaking to have to write a new script every single time that copies a lot of the boilerplate, hardcodes paths, etc. It would be nice if we could automate this.

This is compounded by the second reason (which is Windows specific). The "script" that creduce expects is something that can be run the same way an executable can be run. On Unixy systems this is fine, because you can write a shell script. On Windows though this means you need to use a batch file, and I can assure you that nobody wants to do anything non-trivial in a batch file. It's nice to be able to write our scripts in something like Python. So on Windows, we additionally need a "wrapper" batch file that just calls a Python script, but then this script has to be hardcoded as well, so now we need 2 scripts filled with boilerplate every time we want a new interestingness test.

First we'll look at how to write an interestingness test the "standard" way, which will motivate the next topic, which provides an easy way to write interestingness tests for the most common use cases.

You invoke creduce with $ perl path/to/creduce test.bat foo.cpp.

During the reduction process, creduce will make many temporary directories, copy the current version of the source into it, and run your script against it. For this reason, it's important that foo.cpp be relative and assumed to be located in the current directory. This way, whenever creduce runs your interestingness test, it will pick up the version of the source file in the current temporary directory.

Let's make this concrete by looking at an example of a toy program with something we want to reduce, and the scripts and creduce invocation needed to make this happen.

C:\warning\foo.cpp
int main(int argc, char **argv) {
  return 1.0;  // This should warn about double to int conversion.
}

First, we write test.py which will invoke the compiler, check for this warning, and return 0 if the warning is present, and 1 if the warning is not present.

# C:\reduce\test.py
import os
import subprocess
import sys

# IMPORTANT: Path to source file is in current working directory
args = ['cl.exe', '/W3', '/WX', 'foo.cpp']

obj = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)
(stdout, stderr) = obj.communicate()

if "warning treated as error" in stdout:
    sys.exit(0)

sys.exit(1)

Then we need to write our batch file wrapper which calls python with this script:

REM C:\reduce\test.bat
python C:\reduce\test.py

And finally, we can invoke creduce:

$ perl C:\src\creduce-build\creduce\creduce test.bat foo.cpp

If we run this, we should see creduce printing a bunch of output like this:

===< pass_comments :: 0 >===
===< pass_includes :: 0 >===
===< pass_line_markers :: 0 >===
===< pass_blank :: 0 >===
(2.6 %, 373 bytes)
===< pass_clang_binsrch :: replace-function-def-with-decl >===
===< pass_clang_binsrch :: remove-unused-function >===
===< pass_lines :: 0 >===
(5.2 %, 363 bytes)
===< pass_lines :: 1 >===

and if we wait long enough, it will return. So what did it do? Let's look at the original file now.

$ cat foo.cpp
int main(int argc, char **argv) ;

And we can see that our program is smaller.

That was a lot of work though. We had to write 2 files, hardcode a bunch of paths, and mess around with python subprocess module which you probably have to check the documentation for every time you use it. Let's see how we can make this easier.

Let's try this again. First put the original (pre-reduced file) back where it was.

$ copy foo.cpp.orig foo.cpp

Then download reduce.py and this time run reduce.py instead of running creduce directly.

$ python reduce.py --source=foo.cpp --creduce=C:\src\creduce-build\creduce\creduce --stdout="warning treated as error" --cflags="/c /O2 /W3 /WX"

And we get the exact same result! We didn't have to create any batch files, or python files, and we didn't have to worry about relative / absolute paths.

Note that BYO interestingness tests are obviously more powerful. They allow you to compile multiple files, have interesting tests based on the generated code or object file, link stuff together, and pretty much whatever you want. But for the majority of cases, the simple interestingness test should suffice and greatly simplify things.

Here we give an example of how CReduce can be used to find real problems by showing a non-trivial program that illustrates a compiler bug, and then using creduce to figure out how to make that compiler bug smaller.

To start with, I needed to find an ICE. I searched the Microsoft Developer Community website and decided to use this one which has something do with a std::vector. The repro is here:

// ice.cpp
#include <vector>
#include <cstdint>

int what();

enum class bingus_t : uint8_t {
  bungus,
  bingus,
  lumpus
};

int what() {

  struct a_struct_t {
	  bool is_small {true};
	  uint32_t delta_time {0};
	  bingus_t type {bingus_t::bungus};
	  uint32_t size {0};
	  uint32_t data_size {0};
  };
  struct another_struct_t {
	  std::vector<unsigned char> bytes {};
	  a_struct_t ans {};
  };

  std::vector<another_struct_t> tests {
	  {{0x00,0xFF,0x58,0x04,0x04,0x02,0x18,0x08},
      {true,0x00,bingus_t::lumpus,8,7}},
  };

  return 0;
}

But it's not immediately obvious what the problem is. Let's see how creduce can help.

First, preprocess the file so that creduce has a single self-contained source file to work with:

$ cl /c /P /EP /Fiice.pp.cpp ice.cpp

Next, let's make sure the ICE actually happens on the pre-processed file.

$ cl /c ice.pp.cpp
C:\creduction>cl /Z7 /c ice.pp.cpp
Microsoft (R) C/C++ Optimizing Compiler Version 19.20.27508.1 for x64
Copyright (C) Microsoft Corporation.  All rights reserved.

ice.pp.cpp
C:\creduction\ice.pp.cpp(26) : fatal error C1001: An internal error has occurred in the compiler.
(compiler file 'd:\agent\_work\1\s\src\vctools\Compiler\Utc\src\p2\main.c', line 160)
 To work around this problem, try simplifying or changing the program near the locations listed above.
Please choose the Technical Support command on the Visual C++
 Help menu, or open the Technical Support help file for more information
  cl!InvokeCompilerPassW()+0x84fd5

C:\creduction\ice.pp.cpp(26) : fatal error C1001: An internal error has occurred in the compiler.
(compiler file 'd:\agent\_work\1\s\src\vctools\Compiler\Utc\src\Common\error.c', line 835)
 To work around this problem, try simplifying or changing the program near the locations listed above.
Please choose the Technical Support command on the Visual C++
 Help menu, or open the Technical Support help file for more information

Now, let's try to reduce this to the smallest possible ICE.

$  python reduce.py --source=ice.pp.cpp --cores=12 --creduce=C:\src\creduce-build\creduce\creduce --stdout="compiler file 'd:\agent\_work\1\s\src\vctools\Compiler\Utc\src\p2\main.c', line 160" --stderr="compiler file 'd:\agent\_work\1\s\src\vctools\Compiler\Utc\src\p2\main.c', line 160" --cflags="/c"

Note that the text we're checking for includes the exact faulting line in the compiler, to make sure that the source file is only interesting if it produces the same ICE.

After all is said and done, we get this:

namespace std {
template <class a> class initializer_list {
public:
  initializer_list();
  initializer_list(a *, a *);
};
class b {
  int c;

public:
  template <class j> b(j);
};
template <class d> class e {
public:
  e(initializer_list<d>) : f(int()) {}
  ~e();
  b f;
};
struct g {};
struct h {
  e<char> bytes;
  g ans{};
};
e<h> i{{}};
} // namespace std

While creduce is pretty good, if you play around with this by hand, you can get it a little smaller:

namespace std {
template <class a> struct initializer_list {
  initializer_list();
  initializer_list(a *, a *);
};
}

template <class d> struct e {
  e(std::initializer_list<d>);
  ~e();
  int f;
};
struct g {};
struct h {
  e<char> bytes;
  g ans{};
};
e<h> i{{}};

But at least now it's more apparent what the bug is, and also makes for a better repro case.

creduce-windows's People

Contributors

zjturner avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.