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Some pointers on where to start, how to obtain the Clang's AST, tutorials or anything in this regard will be of great help!!! I have been trying to find some and I got this link which was created 2 years back. But for some reason, it is not working for me. The sample code, in the tutorial, gives me too many errors.

So I am not sure, if I build the code properly or some thing is wrong with the tutorial. But I would be happy to start from some other page as well. Start with the tutorial linked by sharth. Then go through Clang's Doxygen.

Start with SemaConsumer. Read a lot of source code. Clang is a moving target. If you are writing tools based on clang, then you need to recognize that clang is adding and fixing features daily, so you should be prepared to read a lot of code!

However, do note that the stability of the API comes at a cost of comprehensiveness. You won't be able to do everything with this API, but it is much easier to use. Basically it has such a flow in the case of llvm The last function handles the whole translation unit top level decls are already handled at this pointand calls EmitBackendOutput to do backend stuff.

So this function is a good spot where you can do something with the complete AST and before emitting backend output. Learn more. How to make use of Clang's AST?

Ask Question. Asked 9 years, 1 month ago. Active 2 years, 3 months ago.That post was and still is quite popular, but also severely out of date; it's time to fix that. Clang is moving forward very fast. Today, to develop tools similar to the one presented in that post one should use the "tooling" library libTooling. I hinted at its existence even in that post, but it was still at its infancy then. Now libTooling is a fairly mature layer through which it's possible to interact with Clang.

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So I rewrote the tool presented in the previous post using libTooling. The tool is very basic, but it demonstrates a lot of the machinery.

If you've ever tried to build an out-of-tree tool using LLVM or Clang as a library, you will not underestimate the utility of having this makefile available and continuously updated. The low-level details of the sample haven't changed much. Finally, a Rewriter is used to manage the textual changes to the source code.

This is how Clang-based source to source transformations work - analysis of the AST lets us find out where to apply changes, and a Rewriter is used to actually apply those changes [2]. The link between the two is the very accurate source location information managed by the AST for each node. What's really new about this sample is how it turns this low-level transformation logic into an actual tool.

With libTooling, we no longer need to laboriously set up an instance of the compiler front-end manually. The ClangTool class does it all for us, while CommonOptionsParser simplifies the command-line interface of the tool. All we need is to implement a FrontendAction - a very central abstraction within Clang for entities that produce stuff from the parsed AST [3].

MyFrontendAction is very simple.

Getting Started: Building and Running Clang

Note that ClangTool will create a new MyFrontendAction instance per file, so it's correct to have a new Rewriter in each instance. This is enabled by adding another layer of abstraction - the FrontendActionFactory. The factory pattern here is used to decouple the process of creating objects implementing FrontendAction from the concrete FrontendAction subclasses used by our tool. It also lets us customize the creation process to a greater extent - for example passing additional information into each MyFrontendAction object.

In our sample, advanced features aren't really needed so we use the convenience newFrontendActionFactory template which creates a basic factory behind the scenes. It's important to spend a few minutes to discuss how to invoke the tool.What does that mean, and why should you care? A front-end is a program that takes a piece of code and converts it from a flat string to a structured, tree-formed representation of the same program — an AST.

For example, renaming a variable without an AST is hard: You cannot simply search for the old name and replace it with a new name, as this will change too much for example, variables that have the old name but are in a different namespace, and so on. Front-ends have existed for decades. Most programs in this tutorial are well below 50 lines. This tutorial tells you how you do this.

怎样操作clang的ast, 比如添加、修改、删除节点,把一个AST插入到另一个AST,AST复制等?

So, do you have a large C code-base and want to create automated non-trivial refactorings? Then clang is for you. As example, we will write a program that collects a list of all globals defined and used in a program. For these input filesthis output will be generated.

The final program is close to production quality: It is for example able to process the source code of vim — the resulting html file is 2. Furthermore, the program can be used with any make-based program out of the box — no change to Makefiles or input file lists required.

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A short word of warning: clang is still work in prograss. The last time I checked that all programs work was Sep 28, Clang works on all platforms. In this tutorial I assume that you have some Unix-based platform, but in principle everything should work on Windows, too. There is no official release of clang yet, you have to get it from SVN and build it for yourself. Luckily, this is easy:. If you call. Note that this does a debug build of llvm and clang.

A front-end consists of multiple parts. First is usually a lexer, which converts the input from a stream of characters to a stream of tokens. For performance reasons, clang does not have a separate preprocessor program, but does preprocessing while lexing. Our first program will not do anything useful, it only constructs a Preprocessor and exits again. First is Diagnostic.Also, the Clang mailing list is very active and helpful if you ever find yourself stuck on something.

Kernel code especially drivers can be very complex and difficult to analyze, but Clang allows you to handle it with ease. The preprocessed AST is a lot easier to work with than the source-level C code, but you can always reference the original code with ease.

In fact, every data structure in Clang used to represent the code AST, CFG, etc can always relate back to the original source, which is quite useful for many analysis purposes refactoring, etc. Almost every compiler and static analysis tool uses an AST to represent the source code.

There are many subclasses of each; here are a few examples:. Clang can be used as a drop-in replacement for gcc and it offers some cool built-in static analysis tools.

As a programmer not just a normal user! Your code is the plugin itself and is run as a completely new instance for each source file, meaning you cannot keep any global information or other contextual information across different source files but you can still run it on multiple files sequentially.

A plugin is run by passing some options to your build system Clang, Make, etc via command-line arguments. LibTooling is usually for running analysis on some source code multiple files, if you want separately from your normal build process. A new instance of your analysis code and a new AST will be created for each new source file much like a Clang Pluginbut you are able to maintain contextual information across each source file because data items like global variables will persist.

LibClang is great when you need a stable API. It offers all the flexibility and full AST access of a Plugin without losing global context across source files. For this entire tutorial series, I use Clang 3.

Any other information you can provide? Your website can go viral, you need some initial traffic only.

Generic DAG Rewriter Infrastructure

How to do that??? Thanks for the tutorial. Could you produce a similar tutorial for doing this in Windows? Please help me how to proceed further.

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How to parse C programs with clang: A tutorial in 9 parts

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clang rewriter tutorial

How does Clang work?Clang is everywhere; It is the core of my favorite Vim plugin YouCompleteMeit recently got supported by Microsoft Visual Studioit is mentioned in numerous episodes of CppCast and it powers the excellent clang formatter. Naturally, I wanted to get a better understanding of how the clang front end works under the hood. In compiler design, a front end takes care of the analysis part, which means breaking up the source code into pieces according to a grammatical structure.

The result is an intermediate representation which is transformed in a target program by the back end, called synthesis. Optionally, there is an intermediate step between the frond- and back end for optimization. In concrete terms, the front end is responsible for parsing the source code, checking it for errors and turning the input code into an Abstract Syntax Tree AST. The latter is a structured representation, which can be used for different purposes such as creating a symbol table, performing type checking and finally generating code.

The AST is the part I'm mainly interested in, as it is clang's core, where all the interesting stuff happens. From the clang documentationI found the video below. Manuel Klimek gives an easy to follow explanation of the AST as well as its applications.

I highly recommend watching this video! Examples are the identifier table and the source manager to name a few. The AST is built using three groups of core classes: declarationsstatements and types.

If you follow the links to the doxygen, you'll see that these three classes form the base of a range of specializations. However, it is important to note that they do not inherit from a common base class. As a result, there is no common interface for visiting all the nodes in the tree.

Each node has dedicated traversal methods that allow you to navigate the tree. Further on I'll show how we can use visitors to do so efficiently, without needing a common API. Consider the if-statement in the code example below, which is represented by an IfStmt in the AST printed underneath.

It consists of a conditional Expr BinaryOperator and two Stmt s, one for the then-case and one for the else-case respectively. Now how do we traverse this sub-tree? Easy, by calling one of IfStmt 's dedicated methods. Each node in the tree is a specific class with dedicated methods. Dumping the AST, as illustrated above, is the easiest way to find the appropriate class representation.

Finding the right methods is just a matter of looking at the class reference. The location of a token is represented by the SourceLocation class. Because this object is embedded in many of the AST nodes, it is required to be very small. This is achieved by working together with the SourceManager to encode the actual location in the source file. In order to manipulate the AST, we need to be able to traverse the syntax tree.

Let's consider 3 available approaches to a rudimentary use case where we want to find all calls to doSomething in a given source file test. The first two use LibToolinga library that enables the creation of a stand-alone tool. However, when a high-level and stable interface is important, Clang's C interface LibClangis the preferred choice. In the third example we explore AST traversal with cursorswhich represent locations within the Abstract Syntax Tree.

VisitCallExpr for the example below.The goal of a compiler IR is to represent code - at various levels of abstraction which pose different sets of tradeoffs in terms of representational capabilities and ease of transformation.

However, the ability to represent code is not itself very useful - you also need to be able to implement those transformations. There are many different sorts of compiler transformations, but this document focuses on a particularly important class of transformation that comes up repeatedly at scale, and is important for the immediate goals of MLIR: that of pattern matching on a set of operations and replacing with another set.

Because MLIR spans such a wide range of different problems, a single infrastructure for performing graph-to-graph rewrites can help solve many diverse domain challenges, including TensorFlow graph level down to the machine code level.

As such, a natural abstraction for these graph-to-graph rewrites is that of DAG pattern matching: clients define DAG tile patterns, and each pattern includes a result DAG to produce and the cost of the result or, inversely, the benefit of doing the replacement. A common infrastructure efficiently finds and perform the rewrites.

While this concept is simple, the details are more nuanced. This proposal defines and explores a set of abstractions that we feel can solve a wide range of different problems, and can be applied to many different sorts of problems that MLIR is - and is expected to - face over time. There is a huge amount of related work to consider, given that pretty much every compiler in existence has to solve this problem many times over. Here are a few graph rewrite systems we have used, along with the pros and cons of this related work.

One unifying problem with all of these is that these systems are only trying to solve one particular and usually narrow problem: our proposal would like to solve many of these problems with a single infrastructure.

A degenerate but pervasive case of DAG-to-DAG pattern matching is constant folding: given an operation whose operands contain constants can often be folded to a result constant value. MLIR already has constant folding routines which provide a simpler API than a general DAG-to-DAG pattern matcher, and we expect it to remain because the simpler contract makes it applicable in some cases that a generic matcher would not.

For example, a DAG-rewrite can remove arbitrary nodes in the current function, which could invalidate iterators. Constant folding as an API does not remove any nodes, it just provides a list of constant values and allows the clients to update their data structures as necessary. The literature is full of source-to-source translators which transform identities in order to improve performance e.

It is well known in literature that DAG pattern matching is more powerful than tree pattern matching, but OTOH, DAG pattern matching can lead to duplication of computation which needs to be checked for. Compilers end up with a lot of peephole optimizers for various things, e. These generally match one or more operations and produce zero or more operations as a result. These passes have a lot of diversity, but also have a unifying structure: they mostly have a worklist outer loop which visits operations.

These systems are simple to set up, and pattern matching templates have some advantages they are extensible for new sorts of sub-patterns, look compact at point of use. OTOH, they have lots of well known problems, for example:. The instruction selection subsystem in LLVM is the result of many years worth of iteration and discovery, driven by the need for LLVM to support code generation for lots of targets, the complexity of code generators for modern instruction sets e.

X86and the fanatical pursuit of reusing code across targets. Eli wrote a nice short overview of how this works, and the LLVM documentation describes it in more depth including its advantages and limitations. It allows writing patterns like this.

clang rewriter tutorial

MLIR will face a wide range of pattern matching and graph rewrite problems, and one of the major advantages of having a common representation for code at multiple levels that it allows us to invest in - and highly leverage - a single infra for doing this sort of work.

This proposal includes support for defining pattern matching and rewrite algorithms on MLIR. Patterns should have a cost associated with them, and the common infrastructure should be responsible for sorting out the lowest cost match for a given application.

We separate the task of picking a particular locally optimal pattern from a given root node, the algorithm used to rewrite an entire graph given a particular set of goals, and the definition of the patterns themselves.This page gives you the shortest path to checking out Clang and demos a few options.

This should get you up and running with the minimum of muss and fuss. If you like what you see, please consider getting involved with the Clang community. Clang is released as part of regular LLVM releases. From Xcode 4. Note : Some Clang tests are sensitive to the line endings.

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If you're using git on Windows, make sure your core. We recommend that developers who want the fastest incremental builds use the Ninja build system. You can use the generated Visual Studio project files to edit Clang source code and generate a second build directory next to it for running the tests with these steps:.

The clang tool is the compiler driver and front-end, which is designed to be a drop-in replacement for the gcc command. Here are some examples of how to use the high-level driver:. The 'clang' driver is designed to work as closely to GCC as possible to maximize portability. The only major difference between the two is that Clang defaults to gnu99 mode while GCC defaults to gnu89 mode.

Note, the -cc1 argument indicates the compiler front-end, and not the driver, should be run. The compiler front-end has several additional Clang specific features which are not exposed through the GCC compatible driver interface. Quick Links Testing Coverage.

clang rewriter tutorial

Getting Started: Building and Running Clang This page gives you the shortest path to checking out Clang and demos a few options. Building Clang and Working with the Code On Unix-like Systems If you would like to check out and build Clang, the current procedure is as follows: Get the required tools.

Note also that Python is needed for running the test suite.

clang rewriter tutorial

Note: For subsequent Clang development, you can just run make clang. Source code control program. This is used for generating Visual Studio solution and project files.

It is used to run the clang test suite. The gnuwin32 packages are the oldest and most well-tested way to get these tools. However, the MSys utilities provided by git for Windows have been known to work. Cygwin has worked in the past, but is not well tested. Run CMake to generate the Visual Studio solution and project files: cd. To generate x86 binaries instead of x64, pass -A Win The above, if successful, will have created an LLVM. See the running examples from above. Using Ninja alongside Visual Studio We recommend that developers who want the fastest incremental builds use the Ninja build system.

You can use the generated Visual Studio project files to edit Clang source code and generate a second build directory next to it for running the tests with these steps: Check out clang and LLVM as described above Open a developer command prompt with the appropriate environment.