[llvm-dev] About OpenMP dialect in MLIR (original) (raw)

Johannes Doerfert via llvm-dev llvm-dev at lists.llvm.org
Thu Feb 13 10🔞20 PST 2020

Hi Vinay,

Thanks for taking an interest and the detailed discussion.

To start by picking a few paragraph from your email to clarify a couple of things that lead to the current design or that might otherwise need clarification. We can talk about other points later as well.

[ Site notes: 1) I'm not an MLIR person. 2) It seems unfortnuate that we do not have a mlir-dev list. ]

1. With the current design, the number of transformations / optimizations that one can write on OpenMP constructs would become limited as there can be any custom loop structure with custom operations / types inside it.

OpenMP, as an input language, does not make many assumptions about the code inside of constructs*. So, inside a parallel can be almost anything the base language has to offer, both lexically and dynamically. Assuming otherwise is not going to work. Analyzing a "generic" OpenMP representation in order to determine if can be represented as a more restricted "op" seems at least plausible. You will run into various issue, some mentioned explicitly below. For starters, you still have to generate proper OpenMP runtime calls, e.g., from your GPU dialect, even if it is "just" to make sure the OMPD/OMPT interfaces expose useful information.

2. It would also be easier to transform the Loop nests containing OpenMP constructs if the body of the OpenMP operations is well defined (i.e., does not accept arbitrary loop structures). Having nested redundant "parallel" , "target" and "do" regions seems unnecessary.

As mentioned above, you cannot start with the assumption OpenMP input is structured this this way. You have to analyze it first. This is the same reason we cannot simply transform C/C++ for loops into affine.for without proper analysis of the loop body.

Now, more concrete. Nested parallel and target regions are not necessarily redundant, nor can/should we require the user not to have them. Nested parallelism can easily make sense, depending on the problem decomposition. Nested target will make a lot of sense with reverse offload, which is already in the standard, and it also should be allowed for the sake of a modular (user) code base.

3. There would also be new sets of loop structures in new dialects when C/C++ is compiled to MLIR. It would complicate the number of possible combinations inside the OpenMP region.

Is anyone working on this? If so, what is the timeline? I personally was not expecting Clang to switch over to MLIR any time soon but I am happy if someone wants to correct me on this. I mention this only because it interacts with the arguments I will make below.

E. Lowering of target constructs mentioned in ( 2(d) ) specifies direct lowering to LLVM IR ignoring all the advantages that MLIR provides. Being able to compile the code for heterogeneous hardware is one of the biggest advantages that MLIR brings to the table. That is being completely missed here. This also requires solving the problem of handling target information in MLIR. But that is a problem which needs to be solved anyway. Using GPU dialect also gives us an opportunity to represent offloading semantics in MLIR.

I'm unsure what the problem with "handling target information in MLIR" is but whatever design we end up with, we need to know about the target (triple) in all stages of the pipeline, even if it is just to pass it down.

Given the ability to represent multiple ModuleOps and the existence of GPU dialect, couldn't higher level optimizations on offloaded code be done at MLIR level?. The proposed design would lead us to the same problems that we are currently facing in LLVM IR.

Also, OpenMP codegen will automatically benefit from the GPU dialect based optimizations. For example, it would be way easier to hoist a memory reference out of GPU kernel in MLIR than in LLVM IR.

While I agree with the premise that you can potentially reuse MLIR transformations, it might not be as simple in practice.

As mentioned above, you cannot assume much about OpenMP codes, almost nothing for a lot of application codes I have seen. Some examples:

If you have a function call, or any synchronization event for that matter, located between two otherwise adjacent target regions (see below), you cannot assume the two target regions will be offloaded to the same device.

  #omp target
  {}
  foo();
  #omp target
  {}

Similarly, you cannot assume a omp parallel is allowed to be executed with more than a single thread, or that a omp [parallel] for does not have loop carried data-dependences, ... Data-sharing attributes are also something that has to be treated carefully:

x = 5;
#omp task
  x = 3;
print(x);

Should print 5, not 3.

I hope I convinced you that OpenMP is not trivially mappable to existing dialects without proper analysis. If not, please let me know why you expect it to be.

Now when it comes to code analyses, LLVM-IR offers a variety of interesting features, ranging from a mature set of passes to the cross-language LTO capabilities. We are working on the missing parts, e.g., heterogeneous llvm::Modules as we speak. Simple OpenMP optimizations are already present in LLVM and interesting ones are prototyped for a while now (let me know if you want to see more not-yet merged patches/optimizations). I also have papers, results, and talks that might be interesting here. Let me know if you need pointers to them.

Cheers, Johannes

On 02/13, Vinay Madhusudan via llvm-dev wrote:

Hi,

I have few questions / concerns regarding the design of OpenMP dialect in MLIR that is currently being implemented, mainly for the f18 compiler. Below, I summarize the current state of various efforts in clang / f18 / MLIR / LLVM regarding this. Feel free to add to the list in case I have missed something. 1. [May 2019] An OpenMPIRBuilder in LLVM was proposed for flang and clang frontends. Note that this proposal was before considering MLIR for FIR. a. llvm-dev proposal : http://lists.flang-compiler.org/pipermail/flang-devlists.flang-compiler.org/2019-May/000197.html b. Patches in review: https://reviews.llvm.org/D70290. This also includes the clang codegen changes. 2. [July - September 2019] OpenMP dialect for MLIR was discussed / proposed with respect to the f18 compilation stack (keeping FIR in mind). a. flang-dev discussion link: https://lists.llvm.org/pipermail/flang-dev/2019-September/000020.html b. Design decisions captured in PPT: https://drive.google.com/file/d/1vU6LsblsUYGA35B3y9PmBvtKOTXj1Fu/view c. MLIR google groups discussion: https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/4AjeawdHiw d. Target constructs design: http://lists.flang-compiler.org/pipermail/flang-devlists.flang-compiler.org/2019-September/000285.html e. SIMD constructs design: http://lists.flang-compiler.org/pipermail/flang-devlists.flang-compiler.org/2019-September/000278.html 3. [Jan 2020] OpenMP dialect RFC in llvm discourse : https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/397 4. [Jan- Feb 2020] Implementation of OpenMP dialect in MLIR: a. The first patch which introduces the OpenMP dialect was pushed. b. Review of barrier construct is in progress: https://reviews.llvm.org/D72962 I have tried to list below different topics of interest (to different people) around this work. Most of these are in the design phase (or very new) and multiple parties are interested with different sets of goals in mind. I. Flang frontend and its integration II. Fortran representation in MLIR / FIR development III. OpenMP development for flang, OpenMP builder in LLVM. IV. Loop Transformations in MLIR / LLVM with respect to OpenMP. It looks like the design has evolved over time and there is no one place which contains the latest design decisions that fits all the different pieces of the puzzle. I will try to deduce it from the above mentioned references. Please correct me If I am referring to anything which has changed. A. For most OpenMP design discussions, FIR examples are used (as seen in (2) and (3)). The MLIR examples mentioned in the design only talks about FIR dialect and LLVM dialect. This completely ignores the likes of standard, affine (where most loop transformations are supposed to happen) and loop dialects. I think it is critical to decouple the OpenMP dialect development in MLIR from the current flang / FIR effort. It would be useful if someone can mention these examples using existing dialects in MLIR and also how the different transformations / lowerings are planned. B. In latest RFC(3), it is mentioned that the initial OpenMP dialect version will be as follows, omp.parallel { omp.do { fir.do %i = 0 to %ub3 : !fir.integer { ... } } } and then after the "LLVM conversion" it is converted as follows: omp.parallel { %ub3 = omp.do %i = 0 to %ub3 : !llvm.integer { ... } }

a. Is it the same omp.do operation which now contains the bounds and induction variables of the loop after the LLVM conversion? If so, will the same operation have two different semantics during a single compilation? b. Will there be different lowerings for various loop operations from different dialects? loop.for and affine.for under omp operations would need different OpenMP / LLVM lowerings. Currently, both of them are lowered to the CFG based loops during the LLVM dialect conversion (which is much before the proposed OpenMP dialect lowering). There would be no standard way to represent OpenMP operations (especially the ones which involve loops) in MLIR. This would drastically complicate lowering. C. It is also not mentioned how clauses like firstprivate, shared, private, reduce, map, etc are lowered to OpenMP dialect. The example in the RFC contains FIR and LLVM types and nothing about std dialect types. Consider the below example: #pragma omp parallel for reduction(+:x) _for (int i = 0; i < N; ++i)_ _x += a[i];_ _How would the above be represented in OpenMP dialect? and What type would_ _"x" be in MLIR? It is not mentioned in the design as to how the various_ _SSA values for various OpenMP clauses are passed around in OpenMP_ _operations._ _D. Because of (A), (B) and (C), it would be beneficial to have an omp._ _paralleldo operation which has semantics similar to other loop structures_ _(may not be LoopLikeInterface) in MLIR. To me, it looks like having OpenMP_ _operations based on standard MLIR types and operations (scalars and memrefs_ _mainly) is the right way to go._ _Why not have omp.paralleldo operation with AffineMap based bounds, so as_ _to decouple it from Value/Type similar to affine.for?_ _1. With the current design, the number of transformations / optimizations_ _that one can write on OpenMP constructs would become limited as there can_ _be any custom loop structure with custom operations / types inside it._ _2. It would also be easier to transform the Loop nests containing OpenMP_ _constructs if the body of the OpenMP operations is well defined (i.e., does_ _not accept arbitrary loop structures). Having nested redundant "parallel" ,_ _"target" and "do" regions seems unnecessary._ _3. There would also be new sets of loop structures in new dialects when_ _C/C++ is compiled to MLIR. It would complicate the number of possible_ _combinations inside the OpenMP region._ _E. Lowering of target constructs mentioned in ( 2(d) ) specifies direct_ _lowering to LLVM IR ignoring all the advantages that MLIR provides. Being_ _able to compile the code for heterogeneous hardware is one of the biggest_ _advantages that MLIR brings to the table. That is being completely missed_ _here. This also requires solving the problem of handling target information_ _in MLIR. But that is a problem which needs to be solved anyway. Using GPU_ _dialect also gives us an opportunity to represent offloading semantics in_ _MLIR._ _Given the ability to represent multiple ModuleOps and the existence of GPU_ _dialect, couldn't higher level optimizations on offloaded code be done at_ _MLIR level?. The proposed design would lead us to the same problems that we_ _are currently facing in LLVM IR._ _Also, OpenMP codegen will automatically benefit from the GPU dialect based_ _optimizations. For example, it would be way easier to hoist a memory_ _reference out of GPU kernel in MLIR than in LLVM IR._ -------------- next part -------------- A non-text attachment was scrubbed... Name: signature.asc Type: application/pgp-signature Size: 228 bytes Desc: not available URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20200213/c0a311e4/attachment.sig>

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