Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
The ‘shard’ dialect defines a set of attributes, operations, and interfaces for working with tensor sharding and device communication. It’s inspired by [GSPMD](General and Scalable Parallelization for ML Computation Graphs). Originally, the dialect was called mesh, but it was renamed to better reflect what it actually does. Collective Communication Operations Device Groups In-group Devices Purity and Execution Model Operations shard.all_gather (shard::AllGatherOp) shard.all_reduce (shar...| MLIR
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Issues with MLIR can be reported through GitHub. Report the issue for the llvm-project repository at https://github.com/llvm/llvm-project/issues/new. If possible, attach the “mlir” label (label management may be limited to accounts that have a contribution history). Several other labels prefixed with “mlir:” are available if the issue can be classified further, for example, “mlir:core” can be used for issues with MLIR core libraries (mlir/lib/IR, mlir/lib/Interfaces, etc.) and “...| MLIR
Inspecting compilation There’s no silver bullet for debugging the compilation process. Standard debugging techniques (printf debugging, gdb/lldb, IDE graphical debuggers, etc.) are of course applicable, but below are MLIR-specific facilities that are quite useful before diving into a generic debug flow. These facilities assume that you have reduced your problem to a form that can be reproduced with mlir-opt or another program that hooks into MLIR’s option parsing, if this is not the case,...| MLIR
How to refer to MLIR in publications? Is there an accompanying paper? MLIR has been presented in the 2021 IEEE/ACM International Symposium on Code Generation and Optimization, the full text of the paper is available from IEEE. A pre-publication draft is available on arXiv but may be missing improvements and corrections. Please also note that MLIR keeps evolving and IR snippets presented in the paper may no longer use modern syntax, refer to the MLIR documentation for the new syntax.| MLIR
Everyone is welcome to contribute to MLIR. There are several ways of getting involved and contributing including reporting bugs, improving documentation and tutorials. Community Guidelines Please be mindful of the LLVM Code of Conduct, which pledges to foster an open and welcoming environment. Contributing code Please send pull-request on GitHub. If you don’t have write access to the repo, just leave a comment asking the reviewer to hit the merge button it for you.| MLIR
This document attempts to describe a few developer policies used in MLIR (such as coding standards used) as well as development approach (such as, testing methods). Style guide MLIR follows the LLVM style guide. We also adhere to the following (which deviate from or are not specified in the LLVM style guide): Adopts camelBack; Uses Doxygen-style (///) comments for top-level and class member definitions, regardless of them being visible as public APIs.| MLIR
Below is a list of projects that can be suitable for Google Summer of Code (GSOC) or just for someone to get started with contributing to MLIR. See also the “beginner” issues on the bugtracker. If you’re interested in one of these projects, feel free to discuss it on the MLIR section of the LLVM forums or on the MLIR channel of the LLVM discord server. The mentors are indicative and suggestion of first point of contact for starting on these projects.| MLIR
This glossary contains definitions of MLIR-specific terminology. It is intended to be a quick reference document. For terms which are well-documented elsewhere, definitions are kept brief and the header links to the more in-depth documentation. Block A sequential list of operations without control flow. Also called a basic block. Conversion The transformation of code represented in one dialect into a semantically equivalent representation in another dialect (i.e. inter-dialect conversion) or ...| MLIR
Quickstart commands Run all MLIR tests: Run integration tests (requires -DMLIR_INCLUDE_INTEGRATION_TESTS=ON): Run C++ unit tests: Run lit tests in a specific directory Run a specific lit test file Test categories lit and FileCheck tests Diagnostic tests Integration tests C++ Unit tests Contributor guidelines FileCheck best practices Test Formatting Best Practices Test Documentation Best Practices Quickstart commands These commands are explained below in more detail. All commands are run from ...| MLIR
The acc dialect is an MLIR dialect for representing the OpenACC programming model. OpenACC is a standardized directive-based model which is used with C, C++, and Fortran to enable programmers to expose parallelism in their code. The descriptive approach used by OpenACC allows targeting of parallel multicore and accelerator targets like GPUs by giving the compiler the freedom of how to parallelize for specific architectures. OpenACC also provides the ability to optimize the parallelism through...| MLIR
The AMDGPU dialect provides wrappers around AMD-specific functionality and LLVM intrinsics. These wrappers should be used in conjunction with more generic dialects, such as gpu and vector, when generating LLVM IR that will eventually be executed on AMD hardware. Operations amdgpu.dpp (amdgpu::DPPOp) amdgpu.ext_packed_fp8 (amdgpu::ExtPackedFp8Op) amdgpu.fat_raw_buffer_cast (amdgpu::FatRawBufferCastOp) amdgpu.gather_to_lds (amdgpu::GatherToLDSOp) amdgpu.lds_barrier (amdgpu::LDSBarrierOp) amdgpu...| MLIR
The Intel Advanced Matrix Extensions (AMX) provide a tile matrix multiply unit (TMUL), a tile control register (TILECFG), and eight tile registers TMM0 through TMM7 (TILEDATA). This AMX dialect provides a bridge between MLIR concepts such as vectors and memrefs and the lower level LLVM IR support of AMX. Note that since configuration changes (implicit at dialect level) are costly, it is highly recommended to use the AMX dialect on same-shaped vectors, at least within a single method.| MLIR
Operations arm_neon.2d.sdot (arm_neon::Sdot2dOp) arm_neon.intr.sdot (arm_neon::SdotOp) arm_neon.intr.smmla (arm_neon::SmmlaOp) arm_neon.intr.smull (arm_neon::SMullOp) arm_neon.intr.ummla (arm_neon::UmmlaOp) arm_neon.intr.usmmla (arm_neon::UsmmlaOp) Operations source arm_neon.2d.sdot (arm_neon::Sdot2dOp) Sdot op Syntax: operation ::= `arm_neon.2d.sdot` $a `,` $b `,` $c attr-dict `:` type($b) `,` type($c) `to` type($res) The two input vectors b and c have a 2D shape, consisting of either 2 or 4...| MLIR
Basic dialect to target Arm SVE architectures This dialect contains the definitions necessary to target specific Arm SVE scalable vector operations. Operations arm_sve.convert_from_svbool (arm_sve::ConvertFromSvboolOp) arm_sve.convert_to_svbool (arm_sve::ConvertToSvboolOp) arm_sve.dupq_lane (arm_sve::DupQLaneOp) arm_sve.intr.add (arm_sve::ScalableMaskedAddIIntrOp) arm_sve.intr.convert.from.svbool (arm_sve::ConvertFromSvboolIntrOp) arm_sve.intr.convert.to.svbool (arm_sve::ConvertToSvboolIntrOp...| MLIR
Basic dialect to target Arm SME. This dialect defines custom and LLVM IR intrinsic operations that are used to target Arm Scalable Matrix Extension. Through the available conversion and ArmSME passes you can, for example, lower a linalg.matmul operation to Arm SME FMOPA (floating-point outer product) operations. See one of the in-tree end-to-end integration tests for reference: Linalg/CPU/ArmSME/matmul.mlir Vector/CPU/ArmSME/outerproduct-f64.mlir In order to run ArmSME integration tests, incl...| MLIR
Types and operations for async dialect This dialect contains operations for modeling asynchronous execution. Operations async.add_to_group (async::AddToGroupOp) async.await (async::AwaitOp) async.await_all (async::AwaitAllOp) async.call (async::CallOp) async.coro.begin (async::CoroBeginOp) async.coro.end (async::CoroEndOp) async.coro.free (async::CoroFreeOp) async.coro.id (async::CoroIdOp) async.coro.save (async::CoroSaveOp) async.coro.suspend (async::CoroSuspendOp) async.create_group (async:...| MLIR
Bufferization in MLIR is the process of converting the tensor type to the memref type. Simply put, bufferization is the process of converting computations on the mathematical tensor construct to computations on physical memory buffers. The bufferization dialect contains operations/interfaces specific to the bufferization passes. An overview of the bufferization infrastructure and important conceptual details related to using the MLIR dialect conversion infrastructure can be found in bufferiza...| MLIR
This dialect contains low-level, i.e. non-region based, control flow constructs. These constructs generally represent control flow directly on SSA blocks of a control flow graph. Operations cf.assert (cf::AssertOp) cf.br (cf::BranchOp) cf.cond_br (cf::CondBranchOp) cf.switch (cf::SwitchOp) Operations source cf.assert (cf::AssertOp) Assert operation with message attribute Syntax: operation ::= `cf.assert` $arg `,` $msg attr-dict Assert operation at runtime with single boolean operand and an er...| MLIR
The complex dialect is intended to hold complex numbers creation and arithmetic ops. Operations complex.abs (complex::AbsOp) complex.add (complex::AddOp) complex.angle (complex::AngleOp) complex.atan2 (complex::Atan2Op) complex.bitcast (complex::BitcastOp) complex.conj (complex::ConjOp) complex.constant (complex::ConstantOp) complex.cos (complex::CosOp) complex.create (complex::CreateOp) complex.div (complex::DivOp) complex.eq (complex::EqualOp) complex.exp (complex::ExpOp) complex.expm1 (com...| MLIR
The Data Layout and Target Information (DLTI) dialect is intended to hold attributes and other components pertaining to descriptions of in-memory data layout and compilation targets. Attributes DataLayoutEntryAttr DataLayoutSpecAttr FunctionPointerAlignmentAttr MapAttr TargetDeviceSpecAttr TargetSystemSpecAttr Attributes DataLayoutEntryAttr An attribute to represent an entry of a data layout specification. A data layout entry attribute is a key-value pair where the key is a type or an identif...| MLIR
Dialect to generate C/C++ from MLIR. The EmitC dialect allows to convert operations from other MLIR dialects to EmitC ops. Those can be translated to C/C++ via the Cpp emitter. The following convention is followed: If template arguments are passed to an emitc.call_opaque operation, C++ is generated. If tensors are used, C++ is generated. If multiple return values are used within in a functions or an emitc.call_opaque operation, C++11 is required.| MLIR
Note: this dialect is more likely to change than others in the near future; use with caution. This dialect provides middle-level abstractions for launching GPU kernels following a programming model similar to that of CUDA or OpenCL. It provides abstractions for kernel invocations (and may eventually provide those for device management) that are not present at the lower level (e.g., as LLVM IR intrinsics for GPUs). Its goal is to abstract away device- and driver-specific manipulations to launc...| MLIR
The Index dialect The Index dialect contains operations for manipulating values of the builtin index type. The index type models target-specific values of pointer width, like intptr_t. Index values are typically used as loop bounds, array subscripts, tensor dimensions, etc. The operations in this dialect operate exclusively on scalar index types. The dialect and its operations treat the index type as signless and contains signed and unsigned versions of certain operations where the distinctio...| MLIR
IR Definition Language Dialect IRDL is an SSA-based declarative representation of dynamic dialects. It allows the definition of dialects, operations, attributes, and types, with a declarative description of their verifiers. IRDL code is meant to be generated and not written by hand. As such, the design focuses on ease of generation/analysis instead of ease of writing/reading. Users can define a new dialect with irdl.dialect, operations with irdl.operation, types with irdl.| MLIR
The math dialect is intended to hold mathematical operations on integer and floating types beyond simple arithmetics. Each operation works on scalar, vector or tensor type. On vector and tensor type operations apply elementwise unless explicitly specified otherwise. As an example, the floating point absolute value can be expressed as: // Scalar absolute value. %a = math.absf %b : f64 // Vector elementwise absolute value. %f = math.absf %g : vector<4xf32> // Tensor elementwise absolute value.| MLIR
The MLProgram dialect contains structural operations and types for defining a compiled Machine-Learning program, as created from common ML frameworks, such as TensorFlow, PyTorch, JAX, etc. It does not itself define computation ops common to such frameworks but establishes a common programming model for establishing modules, functions, globals and memory model components appropriate for such an abstract level of detail. This dialect is under active development, and while stability is an event...| MLIR
This dialect models the Message Passing Interface (MPI), version 4.0. It is meant to serve as an interfacing dialect that is targeted by higher-level dialects. The MPI dialect itself can be lowered to multiple MPI implementations and hide differences in ABI. The dialect models the functions of the MPI specification as close to 1:1 as possible while preserving SSA value semantics where it makes sense, and uses memref types instead of bare pointers.| MLIR
The NVGPU dialect provides a bridge between higher-level target-agnostic dialects (GPU and Vector) and the lower-level target-specific dialect (LLVM IR based NVVM dialect) for NVIDIA GPUs. This allow representing PTX specific operations while using MLIR high level dialects such as Memref and Vector for memory and target-specific register operands, respectively. Operations nvgpu.device_async_copy (nvgpu::DeviceAsyncCopyOp) nvgpu.device_async_create_group (nvgpu::DeviceAsyncCreateGroupOp) nvgpu...| MLIR
Operations nvvm.bar.warp.sync (NVVM::SyncWarpOp) nvvm.barrier (NVVM::BarrierOp) nvvm.barrier.arrive (NVVM::BarrierArriveOp) nvvm.barrier0 (NVVM::Barrier0Op) nvvm.breakpoint (NVVM::Breakpoint) nvvm.cluster.arrive (NVVM::ClusterArriveOp) nvvm.cluster.arrive.relaxed (NVVM::ClusterArriveRelaxedOp) nvvm.cluster.wait (NVVM::ClusterWaitOp) nvvm.convert.bf16x2.to.f8x2 (NVVM::ConvertBF16x2ToF8x2Op) nvvm.convert.f16x2.to.f8x2 (NVVM::ConvertF16x2ToF8x2Op) nvvm.convert.f32x2.to.f6x2 (NVVM::ConvertF32x2To...| MLIR
Interpreted pattern execution dialect The PDL Interpreter dialect provides a lower level abstraction compared to the PDL dialect, and is targeted towards low level optimization and interpreter code generation. The dialect operations encapsulates low-level pattern match and rewrite “primitives”, such as navigating the IR (Operation::getOperand), creating new operations (OpBuilder::create), etc. Many of the operations within this dialect also fuse branching control flow with some form of a ...| MLIR
High level pattern definition dialect PDL presents a high level abstraction for the rewrite pattern infrastructure available in MLIR. This abstraction allows for representing patterns transforming MLIR, as MLIR. This allows for applying all of the benefits that the general MLIR infrastructure provides, to the infrastructure itself. This means that pattern matching can be more easily verified for correctness, targeted by frontends, and optimized. PDL abstracts over various different aspects of...| MLIR
Pointer dialect Operations ptr.ptr_add (ptr::PtrAddOp) ptr.type_offset (ptr::TypeOffsetOp) Attributes GenericSpaceAttr SpecAttr Types PtrType Enums AtomicBinOp AtomicOrdering PtrAddFlags Operations source ptr.ptr_add (ptr::PtrAddOp) Pointer add operation Syntax: operation ::= `ptr.ptr_add` ($flags^)? $base `,` $offset attr-dict `:` type($base) `,` type($offset) The ptr_add operation adds an integer offset to a pointer to produce a new pointer. The input and output pointer types are always the...| MLIR
The quant dialect offers a framework for defining and manipulating quantized values. Central to this framework is the !quant.uniform data type, used to represent quantized values. This dialect also provides a suite of operations to handle and convert quantized values between their original floating-point representations and the optimized, lower bit-width integer representations. The quant dialect is instrumented with transformation passes to lower these operations into other core MLIR dialect...| MLIR
Operations rocdl.ballot (ROCDL::BallotOp) rocdl.barrier (ROCDL::BarrierOp) rocdl.cvt.f32.bf8 (ROCDL::CvtF32Bf8Op) rocdl.cvt.f32.fp8 (ROCDL::CvtF32Fp8Op) rocdl.cvt.pk.bf8.f32 (ROCDL::CvtPkBf8F32Op) rocdl.cvt.pk.f32.bf8 (ROCDL::CvtPkF32Bf8Op) rocdl.cvt.pk.f32.fp8 (ROCDL::CvtPkF32Fp8Op) rocdl.cvt.pk.fp8.f32 (ROCDL::CvtPkFp8F32Op) rocdl.cvt.pkrtz (ROCDL::CvtPkRtz) rocdl.cvt.scalef32.2xpk16.bf6.f32 (ROCDL::CvtScaleF322xPk16Bf6F32Op) rocdl.cvt.scalef32.2xpk16.fp6.f32 (ROCDL::CvtScaleF322xPk16Fp6F32...| MLIR
The scf (structured control flow) dialect contains operations that represent control flow constructs such as if and for. Being structured means that the control flow has a structure unlike, for example, gotos or asserts. Unstructured control flow operations are located in the cf (control flow) dialect. Originally, this dialect was developed as a common lowering stage for the affine and linalg dialects. Both convert to SCF loops instead of targeting branch-based CFGs directly.| MLIR
Description of operations & types within the Shape dialect as well as their usage. Types and operations for shape dialect This dialect contains operations for shape inference. Note: Unless explicitly stated, all functions that return a shape and take shapes as input, return the invalid shape if one of its operands is an invalid shape. This avoids flagging multiple errors for one verification failure. The dialect itself does not specify how errors should be combined (there are multiple differe...| MLIR
A dialect that models satisfiability modulo theories Operations smt.and (mlir::smt::AndOp) smt.apply_func (mlir::smt::ApplyFuncOp) smt.array.broadcast (mlir::smt::ArrayBroadcastOp) smt.array.select (mlir::smt::ArraySelectOp) smt.array.store (mlir::smt::ArrayStoreOp) smt.assert (mlir::smt::AssertOp) smt.bv.add (mlir::smt::BVAddOp) smt.bv.and (mlir::smt::BVAndOp) smt.bv.ashr (mlir::smt::BVAShrOp) smt.bv.cmp (mlir::smt::BVCmpOp) smt.bv.concat (mlir::smt::ConcatOp) smt.bv.constant (mlir::smt::BVC...| MLIR
The SparseTensor dialect supports all the attributes, types, operations, and passes that are required to make sparse tensor types first class citizens within the MLIR compiler infrastructure. The dialect forms a bridge between high-level operations on sparse tensors types and lower-level operations on the actual sparse storage schemes consisting of positions, coordinates, and values. Lower-level support may consist of fully generated code or may be provided by means of a small sparse runtime ...| MLIR
The tensor dialect is intended to hold core tensor creation and manipulation ops, which are not strongly associated with any particular other dialect or domain abstraction. The aim for ops in this dialect is that they make sense for any tensor element type. When this is not the case, the op is left to live in other dialects. Examples of element types that could be supported by the tensor dialect include:| MLIR
Operations ub.poison (ub::PoisonOp) Attributes PoisonAttr Operations source ub.poison (ub::PoisonOp) Poisoned constant operation. Syntax: operation ::= `ub.poison` attr-dict (`<` $value^ `>`)? `:` type($result) The poison operation materializes a compile-time poisoned constant value to indicate deferred undefined behavior. value attribute is needed to indicate an optional additional poison semantics (e.g. partially poisoned vectors), default value indicates results is fully poisoned. Examples...| MLIR
The SiFive Vector Coprocessor Interface (VCIX) provides a flexible mechanism to extend application processors with custom coprocessors and variable-latency arithmetic units. The interface offers throughput comparable to that of standard RISC-V vector instructions. To accelerate performance, system designers may use VCIX as a low-latency, high-throughput interface to a coprocessor https://www.sifive.com/document-file/sifive-vector-coprocessor-interface-vcix-software Operations vcix.v.iv (vcix:...| MLIR
Operations x86vector.avx.bcst_to_f32.packed (x86vector::BcstToPackedF32Op) x86vector.avx.cvt.packed.even.indexed_to_f32 (x86vector::CvtPackedEvenIndexedToF32Op) x86vector.avx.cvt.packed.odd.indexed_to_f32 (x86vector::CvtPackedOddIndexedToF32Op) x86vector.avx.intr.dot (x86vector::DotOp) x86vector.avx.rsqrt (x86vector::RsqrtOp) x86vector.avx512.cvt.packed.f32_to_bf16 (x86vector::CvtPackedF32ToBF16Op) x86vector.avx512.dot (x86vector::DotBF16Op) x86vector.avx512.mask.compress (x86vector::MaskComp...| MLIR
The XeGPU dialect that models Intel GPU’s ISA The XeGPU dialect closely models a subset of the Xe GPU’s ISA, providing an abstraction to support high-performance GEMM code generation. It serves as a bridge dialect in the MLIR gradual lowering process, working with MLIR memref and vector types, and complements the Arith, Math, Vector, and Memref dialects. XeGPU operations are introduced for special Xe instructions not modeled by the LLVM/SPIR-V dialect, such as DPAS and 2D block load and s...| MLIR
Overview Wrapping a Transformation in an Action Intercepting Actions MLIR-provided Handlers Debug Counters ExecutionContext See also the slides and the recording from the MLIR Open Meeting where this feature was demoed. Overview Action are means to encapsulate any transformation of any granularity in a way that can be intercepted by the framework for debugging or tracing purposes, including skipping a transformation programmatically (think about “compiler fuel” or “debug counters” in ...| MLIR
Overview Deprecated Passes What is One-Shot Bufferize? Goals of Bufferization Destination-Passing Style Tensor / Buffer Boundary Using One-Shot Bufferize Memory Layouts Extending One-Shot Bufferize Debugging Buffer Copies Overview Bufferization in MLIR is the process of converting ops with tensor semantics to ops with memref semantics. There are multiple MLIR passes that are related to bufferization. These passes typically run as one of the last steps in a pass pipeline, right before lowering...| MLIR
Before starting the tutorial on the Transform dialect, let us take a brief look at the concept of Structured operations and its implementation in the Linalg dialect. Note that the Transform dialect does not require Structured operations and vice versa. The two co-evolved at the beginning of the Transform dialect, which makes the subset of transformations for Structured operations the most mature and most suitable for the tutorial. If you are already familiar with this concept, skip to Chapter 1.| MLIR
Introduction The Transform dialect allows one to precisely target transformations at specific operations in the IR and to chain them, that is to apply a transformation to operations produced by the previous transformation. To achieve this, transformations are expressed as other operations in the IR. We call these the IR containing these operations transform IR. And we call the IR that is being transformed payload IR. Transform IR operations operate on values that may be associated with payloa...| MLIR
The Language The AST The Language This tutorial will be illustrated with a toy language that we’ll call “Toy” (naming is hard…). Toy is a tensor-based language that allows you to define functions, perform some math computation, and print results. Given that we want to keep things simple, the codegen will be limited to tensors of rank <= 2, and the only datatype in Toy is a 64-bit floating point type (aka ‘double’ in C parlance).| MLIR
Setting Up to Add New Transformations Before defining a new transform operation, we need to choose where its implementation should be located. While MLIR encourages upstream contributions, it is not always possible or even desirable to modify the main Transform dialect, for example, if the transformation is specific to some out-of-tree dialect that is not itself available upstream. The Transform dialect uses the dialect extension mechanism to allow additional operations to be injected without...| MLIR
Introduction: Multi-Level Intermediate Representation Interfacing with MLIR Opaque API Defining a Toy Dialect Defining Toy Operations Op vs Operation: Using MLIR Operations Using the Operation Definition Specification (ODS) Framework Complete Toy Example Now that we’re familiar with our language and the AST, let’s see how MLIR can help to compile Toy. Introduction: Multi-Level Intermediate Representation Other compilers, like LLVM (see the Kaleidoscope tutorial), offer a fixed set of pred...| MLIR
Optimize Transpose using C++ style pattern-match and rewrite Optimize Reshapes using DRR Creating a dialect that closely represents the semantics of an input language enables analyses, transformations and optimizations in MLIR that require high-level language information and are generally performed on the language AST. For example, clang has a fairly heavy mechanism for performing template instantiation in C++. We divide compiler transformations into two categories: local and global. In this ...| MLIR
Type Constraints and ApplyEach Trait A transform operation that applies to each payload operation individually and requires it to be of a specific kind is a repeated pattern. One can use Transform dialect types to specify the preconditions of the type. Specifically, we can change the expected operand type from the wide TransformHandleTypeInterface to the more narrow Transform_ConcreteOp<"func.call">. Furthermore, we use the TransformEachOpTrait trait to provide the skeleton implementation of ...| MLIR
Background: Grappling with an Extensible IR Shape Inference: Preparing for Code Generation Inlining Intraprocedural Shape Inference Background: Grappling with an Extensible IR Through dialects, MLIR allows for the representation of many different levels of abstraction; the Toy dialect that we have previously defined is one such example. Though these different dialects may represent different abstractions, there is often a set of common transformations and analyses that we would like to perform.| MLIR
Check the continuously-tested version of MLIR files under mlir/test/Examples/transform/Ch4. Up until now, we were applying transform dialect scripts under the assumption that specific payload operations are identified by the caller when the transform dialect interpreter is invoked. This may be seen as contrary to the idea of driving transformations from a dialect since the transformation targets must be identified through mechanisms external to the transform dialect interpreter, for example, ...| MLIR
Conversion Target Conversion Patterns Partial Lowering Design Considerations With Partial Lowering Complete Toy Example Taking Advantage of Affine Optimization At this point, we are eager to generate actual code and see our Toy language take life. We will use LLVM to generate code, but just showing the LLVM builder interface here wouldn’t be very exciting. Instead, we will show how to perform progressive lowering through a mix of dialects coexisting in the same function.| MLIR
Lowering to LLVM Conversion Target Type Converter Conversion Patterns Full Lowering CodeGen: Getting Out of MLIR Emitting LLVM IR Setting up a JIT In the previous chapter, we introduced the dialect conversion framework and partially lowered many of the Toy operations to affine loop nests for optimization. In this chapter, we will finally lower to LLVM for code generation. Lowering to LLVM For this lowering, we will again use the dialect conversion framework to perform the heavy lifting.| MLIR
Defining a struct in Toy Defining a struct in MLIR Defining the Type Class Exposing to ODS Parsing and Printing Operating on StructType In the previous chapter, we demonstrated an end-to-end compilation flow from our Toy front-end to LLVM IR. In this chapter, we will extend the Toy language to support a new composite struct type. Defining a struct in Toy The first thing we need to define is the interface of this type in our toy source language.| MLIR
This chapter demonstrates how a schedule from the Halide DSL can be implemented using Transform dialect for structured ops. Note that the IR below is pseudo-code with types removed for brevity. It may also get out of sync with the current syntax. Always refer to the source code in mlir/examples/transform/ChH as the source of truth. Channeled Convolution The Transform dialect provides a substrate for implementing “transformation directive” domain-specific languages (DSLs) in MLIR.| MLIR
Attribute / Type Constraints Attribute / Type Constraints When defining the arguments of an operation in TableGen, users can specify either plain attributes/types or use attribute/type constraints to levy additional requirements on the attribute value or operand type. def My_Type1 : MyDialect_Type<"Type1", "type1"> { ... } def My_Type2 : MyDialect_Type<"Type2", "type2"> { ... } // Plain type let arguments = (ins MyType1:$val); // Type constraint let arguments = (ins AnyTypeOf<[MyType1, MyType...| MLIR
CMake best practices TableGen Targets Library Targets CMake best practices Public dialects are typically separated into at least 3 directories: mlir/include/mlir/Dialect/Foo (for public include files) mlir/lib/Dialect/Foo (for sources) mlir/lib/Dialect/Foo/IR (for operations) mlir/lib/Dialect/Foo/Transforms (for transforms) mlir/test/Dialect/Foo (for tests) Along with other public headers, the ‘include’ directory contains a TableGen file in the ODS format, describing the operations in the...| MLIR
Generating Aliases OpAsmDialectInterface OpAsmAttrInterface and OpAsmTypeInterface Suggesting SSA/Block Names Defining Default Dialect Generating Aliases AsmPrinter can generate aliases for frequently used types and attributes when not printing them in generic form. For example, !my_dialect.type<a=3,b=4,c=5,d=tuple,e=another_type> and #my_dialect.attr<a=3> can be aliased to !my_dialect_type and #my_dialect_attr. There are mainly two ways to hook into the AsmPrinter. One is the attribute/type ...| MLIR
Data layout information allows the compiler to answer questions related to how a value of a particular type is stored in memory. For example, the size of a value or its address alignment requirements. It enables, among others, the generation of various linear memory addressing schemes for containers of abstract types and deeper reasoning about vectors. The data layout subsystem is designed to scale to MLIR’s open type and operation system.| MLIR
This document describes how to define dialect attributes and types. LangRef Refresher Attributes Types Attributes and Types Adding a new Attribute or Type definition Class Name CMake Targets Documentation Mnemonic Parameters Traits Interfaces Builders Parsing and Printing Verification Storage Classes Mutable attributes and types Extra declarations Mnemonic Alias in Assembly Registering with the Dialect LangRef Refresher Before diving into how to define these constructs, below is a quick refre...| MLIR
Source Locations Diagnostic Engine Constructing a Diagnostic Diagnostic Appending arguments Attaching notes Managing Metadata InFlight Diagnostic Diagnostic Configuration Options Print Operation On Diagnostic Print StackTrace On Diagnostic Common Diagnostic Handlers Scoped Diagnostic Handler SourceMgr Diagnostic Handler SourceMgr Diagnostic Verifier Handler Parallel Diagnostic Handler This document presents an introduction to using and interfacing with MLIR’s diagnostics infrastructure. See...| MLIR
This document describes a framework in MLIR in which to perform operation conversions between, and within dialects. This framework allows for transforming illegal operations to those supported by a provided conversion target, via a set of pattern-based operation rewriting patterns. The dialect conversion framework consists of the following components: A Conversion Target A set of Rewrite Patterns A Type Converter (Optional) Modes of Conversion Conversion Target Recursive Legality Rewrite Patt...| MLIR
Introduction Positioning Inception Evolution Prior Art Lessons from ONNX Lessons from LIFT Lessons from XLA Lessons from Halide and TVM Lessons from Tensor Comprehensions Lessons from Polyhedral compilers Lessons from the Affine dialect Core Guiding Principles Transformations and Simplicity First Preservation of Information Composable and Declarative Transformations Suitability for Search and Machine Learning Extensibility and Future-Proofness Key Observations Algorithms + Data Structures = P...| MLIR
Warning: Linalg’s OpDSL is currently being deprecated, with its operations slowly being moved into TableGen’s ODS format. Please refer to the MLIR Restructuring discussion for more in-depth information. Python based DSL for authoring Linalg op definitions and generating linalg.generic IR based on them for samples. The Linalg OpDSL is a high level DSL for constructing structured op definitions in a way that can be exported to built-in, named structured ops via YAML-based definitions or use...| MLIR
This document describes the mechanisms of producing LLVM IR from MLIR. The overall flow is two-stage: conversion of the IR to a set of dialects translatable to LLVM IR, for example LLVM Dialect or one of the hardware-specific dialects derived from LLVM IR intrinsics such as AMX, X86Vector or ArmNeon; translation of MLIR dialects to LLVM IR. This flow allows the non-trivial transformation to be performed within MLIR using MLIR APIs and makes the translation between MLIR and LLVM IR simple and ...| MLIR
MLIR LSP Language Server : mlir-lsp-server Supporting custom dialects Features PDLL LSP Language Server : mlir-pdll-lsp-server Compilation Database Features TableGen LSP Language Server : tblgen-lsp-server Compilation Database Features Language Server Design Communication and Transport Language Server Protocol Language-Specific Server Editor Plugins Visual Studio Code This document describes the tools and utilities related to supporting LSP IDE language extensions for various MLIR-related lan...| MLIR
This document describes the MLIR bytecode format and its encoding. This format is versioned and stable: we don’t plan to ever break compatibility, that is a dialect should be able to deserialize any older bytecode. Similarly, we support back-deployment so that an older version of the format can be targetted. That said, it is important to realize that the promises of the bytecode format are made assuming immutable dialects: the format allows backward and forward compatibility, but only when ...| MLIR
Current status: Under development, API unstable, built by default. Design Scope Object Model Naming Convention and Ownership Model Nullity Type Hierarchies Auxiliary Types Printing Common Patterns Indexed Components Iterable Components Extending the API Extensions for Dialect Attributes and Types Extensions for Interfaces Design Many languages can interoperate with C but have a harder time with C++ due to name mangling and memory model differences. Although the C API for MLIR can be used dire...| MLIR
Current status: Under development and not enabled by default Building Pre-requisites CMake variables Recommended development practices Design Use cases Composable modules Submodules Loader Use the C-API Ownership in the Core IR Optionality and argument ordering in the Core IR User-level API Context Management Inspecting IR Objects Creating IR Objects Style Properties vs get*() methods repr methods CamelCase vs snake_case Prefer pseudo-containers Provide one stop helpers for common things Test...| MLIR
How to Use it Write the script for testing interestingness Available reduction strategies Operation elimination Rewrite patterns into simpler forms Reduce with built-in optimization passes Build a custom mlir-reduce Future works An MLIR input may trigger bugs after series of transformations. To root cause the problem or help verification after fixes, developers want to be able to reduce the size of a reproducer for a bug. This document describes mlir-reduce, which is similar to bugpoint, a to...| MLIR
This document tries to provide some context about MLIR important changes in the context of LLVM releases. It is updated on a best effort basis. At the moment the MLIR community does not qualify the LLVM release branch specifically, it is a snapshot of the MLIR development at the time of the release. LLVM 20 LLVM 18 Properties: beyond attributes LLVM 17 Bytecode Properties: beyond attributes Action: Tracing and Debugging MLIR-based Compilers Transform Dialect Others LLVM 20 All the MLIR runner...| MLIR
Tool to simplify rewriting .mlir files. There are a couple of build in rewrites discussed below along with usage. Note: This is still in very early stage. Its so early its less a tool than a growing collection of useful functions: to use its best to do what’s needed on a brance by just hacking it (dialects registered, rewrites etc) to say help ease a rename, upstream useful utility functions, point to ease others migrating, and then bin eventually.| MLIR
The existing documentation about MLIR focuses on long term vision, how its pieces fit together, and the benefits of modular and composable infrastructure in the vast and distant future. While this viewpoint appeals to some, it causes concern for others who are more concerned about the “here and now” - why does it make sense to make a “revolutionary” change when any individual problem can be fixed in place? This document explains that adoption of MLIR to solve graph based problems isn...| MLIR
MLIR embraces polyhedral compiler techniques for their many advantages representing and transforming dense numerical kernels, but it uses a form that differs significantly from other polyhedral frameworks. Disclaimer / Warning This document is a very early design proposal (which has since been accepted) that explored the tradeoffs of using this simplified form vs the traditional polyhedral schedule list form. At some point, this document could be dusted off and written as a proper academic pa...| MLIR
Operations omp.atomic.capture (omp::AtomicCaptureOp) omp.atomic.read (omp::AtomicReadOp) omp.atomic.update (omp::AtomicUpdateOp) omp.atomic.write (omp::AtomicWriteOp) omp.barrier (omp::BarrierOp) omp.cancel (omp::CancelOp) omp.cancellation_point (omp::CancellationPointOp) omp.critical (omp::CriticalOp) omp.critical.declare (omp::CriticalDeclareOp) omp.declare_mapper (omp::DeclareMapperOp) omp.declare_mapper.info (omp::DeclareMapperInfoOp) omp.declare_reduction (omp::DeclareReductionOp) omp.di...| MLIR
Canonicalization is an important part of compiler IR design: it makes it easier to implement reliable compiler transformations and to reason about what is better or worse in the code, and it forces interesting discussions about the goals of a particular level of IR. Dan Gohman wrote an article exploring these issues; it is worth reading if you’re not familiar with these concepts. Most compilers have canonicalization passes, and sometimes they have many different ones (e.| MLIR
MatchOpInterface (MatchOpInterface) Methods:| MLIR
Function boundary ABI Inserting bufferization.dealloc operations Supported interfaces Limitations Example Buffer Deallocation Simplification Pass Lower Deallocations Pass Generic Lowering Specialized Lowerings One-Shot Bufferize does not deallocate any buffers that it allocates. After running One-Shot Bufferize, the resulting IR may have a number of memref.alloc ops, but no memref.dealloc ops. Buffer dellocation is delegated to the -ownership-based-buffer-deallocation pass. On a high level, b...| MLIR
Introduction Defining Patterns Benefit Root Operation Name (Optional) matchAndRewrite implementation Application Recursion Debug Names and Labels Initialization Construction Pattern Rewriter Pattern Application Common Pattern Drivers Dialect Conversion Driver Walk Pattern Rewrite Driver Greedy Pattern Rewrite Driver Debugging Pattern Filtering Common Pass Utilities This document details the design and API of the pattern rewriting infrastructure present in MLIR, a general DAG-to-DAG transforma...| MLIR
This document details the PDL Language (PDLL), a custom frontend language for writing pattern rewrites targeting MLIR. Note: This document assumes a familiarity with MLIR concepts; more specifically the concepts detailed within the MLIR Pattern Rewriting and Operation Definition Specification (ODS) documentation. Introduction Rationale Why build a new language instead of improving TableGen DRR? Why not build a DSL in “X”? Language Specification Includes Patterns Variables Operation Expres...| MLIR
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org
Multi-Level IR Compiler Framework| mlir.llvm.org