[cfe-dev] [RFC] Moving (parts of) the Cling REPL in Clang

Fri Jul 10 12:09:27 PDT 2020

On 7/10/20 8:09 PM, David Rector wrote:
> I do not know enough about cling, but I like what you describe very much, am particularly intrigued about how your approach could also be appropriated to do ahead-of-time constexpr metaprogramming as well, which also involves incrementally adding declarations to the translation unit.

   Wow, I do not think we have thought of something like that. Cling 
keeps a single clang compiler instance in memory and each new input just 
"adds" to it -- nothing fancy on the frontend. The more interesting part 
happens in CodeGen where we produce multiple llvm::Modules. Maybe some 
parts from that could be reused to pursue the direction you are 
intrigued about.

>
> Dave
>
>> On Jul 9, 2020, at 11:43 PM, JF Bastien via cfe-dev <cfe-dev at lists.llvm.org> wrote:
>>
>> I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.
>>
>>
>>> On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <cfe-dev at lists.llvm.org> wrote:
>>>
>>> I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.
>>>
>>> -Hal
>>>
>>> On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
>>>> Motivation
>>>> ===
>>>>
>>>> Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.
>>>>
>>>> Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.
>>>>
>>>> Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.
>>>>
>>>>
>>>> Background
>>>> ===
>>>>
>>>> Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.
>>>>
>>>>
>>>> Plans
>>>> ===
>>>>
>>>> The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.
>>>>
>>>>
>>>> Moving Parts of Cling Upstream
>>>> ---
>>>>
>>>> Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.
>>>>
>>>> A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
>>>> There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.
>>>>
>>>> Extend and Generalize the Language Interoperability Layer Around Cling
>>>> ---
>>>>
>>>> HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.
>>>>
>>>>
>>>> Extend and Generalize the OpenCL/CUDA Support in Cling
>>>> ---
>>>>
>>>> Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.
>>>>
>>>>
>>>>
>>>> The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.
>>>>
>>>>
>>>> Collaboration
>>>> ===
>>>>
>>>> Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).
>>>>
>>>>
>>>>
>>>> Many thanks!
>>>>
>>>>
>>>> David & Vassil
>>>>
>>>> References
>>>> ===
>>>> [1] ROOT GitHub https://github.com/root-project/root
>>>> [2] ROOT https://root.cern
>>>> [3] Cling https://github.com/root-project/cling
>>>> [4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
>>>> [5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
>>>> [6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
>>>> [7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
>>>> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
>>>> [9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>>>>
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>>> -- 
>>> Hal Finkel
>>> Lead, Compiler Technology and Programming Languages
>>> Leadership Computing Facility
>>> Argonne National Laboratory
>>>
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