mlir.llvm.orgMLIR - Multi-Level IR Compiler

Multi-Level IR Compiler Framework Community Forums Chat Debugging Tips FAQ Source Doxygen GitHub Bugs Logo Assets Youtube Channel Multi-Level Intermediate Representation Overview The project is a novel approach to building reusable and extensible compiler infrastructure. aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together. Weekly Public Meeting We host a weekly public meeting about and the ecosystem. To be notified of the next meeting, please subscribe to the Announcements category on Discourse. You can register to this public calendar to keep up-to-date with the schedule. If you’d like to discuss a particular topic or have questions, please add it to the agenda doc . The meetings are recorded and published in the talks section. More resources For more information on , please see: The section of the LLVM forums for any questions. Real-time discussion on the channel of the LLVM discord server. Previous talks . What is for? is intended to be a hybrid IR which can support multiple different requirements in a unified infrastructure. For example, this includes: The ability to represent dataflow graphs (such as in TensorFlow), including dynamic shapes, the user-extensible op ecosystem, TensorFlow variables, etc. Optimizations and transformations typically done on such graphs (e.g. in Grappler). Ability to host high-performance-computing-style loop optimizations across kernels (fusion, loop interchange, tiling, etc.), and to transform memory layouts of data. Code generation lowering” transformations such as DMA insertion, explicit cache management, memory tiling, and vectorization for 1D and 2D register architectures. Ability to represent target-specific operations, e.g. accelerator-specific high-level operations. Quantization and other graph transformations done on a Deep-Learning graph. Polyhedral primitives . Hardware Synthesis Tools / HLS . is a common IR that also supports hardware specific operations. Thus, any investment into the infrastructure surrounding (e.g. the compiler passes that work on it) should yield good returns; many targets can use that infrastructure and will benefit from it. is a powerful representation, but it also has non-goals. We do not try to support low level machine code generation algorithms (like register allocation and instruction scheduling). They are a better fit for lower level optimizers (such as LLVM). Also, we do not intend to be a source language that end-users would themselves write kernels in (analogous to CUDA C++). On the other hand, provides the backbone for representing any such DSL and integrating it in the ecosystem. Compiler infrastructure We benefited from experience gained from building other IRs (LLVM IR, XLA HLO, and Swift SIL) when building . The framework encourages existing best practices, e.g. writing and maintaining an IR spec, building an IR verifier, providing the ability to dump and parse files to text, writing extensive unit tests with the FileCheck tool, and building the infrastructure as a set of modular libraries that can be combined in new ways. Other lessons have been incorporated and integrated into the design in subtle ways. For example, LLVM has non-obvious design mistakes that prevent a multithreaded compiler from working on multiple functions in an LLVM module at the same time. solves these problems by having limited SSA scope to reduce the use-def chains and by replacing cross-function references with explicit symbol reference . Citing Please see the FAQ entry on how to cite in publications. Next - Users of Edit on GitHub Powered by Hugo . Theme by TechDoc . Designed by Thingsym . Home Users of Related Publications Talks Deprecations & Current Refactoring Getting Started + Reporting Issues Debugging Tips FAQ How to Contribute Developer Guide Open Projects Glossary Testing Guide Code Documentation + Bindings + Python Bindings Tools + : Language Server Protocol Reduce Action: Tracing and Debugging -based Compilers Buffer Deallocation - Internals Bufferization Data Layout Modeling Defining Dialects + Defining Dialect Attributes and Types Operation Definition Specification (ODS) Diagnostic Infrastructure Dialect Conversion Dialects + ’acc’ Dialect ’affine’ Dialect ’amdgpu’ Dialect ’amx’ Dialect ’arith’ Dialect ’arm_neon’ Dialect ’arm_sve’ Dialect ’ArmSME’ Dialect ’async’ Dialect ’bufferization’ Dialect ’cf’ Dialect ’complex’ Dialect ’dlti’ Dialect ’emitc’ Dialect ’func’ Dialect ’gpu’ Dialect ’index’ Dialect ’irdl’ Dialect ’linalg’ Dialect + Linalg OpDSL ’llvm’ Dialect ’math’ Dialect ’memref’ Dialect ’mesh’ Dialect ’ml_program’ Dialect ’mpi’ Dialect ’nvgpu’ Dialect ’nvvm’ Dialect ’omp’ Dialect ’pdl_interp’ Dialect ’pdl’ Dialect ’polynomial’ Dialect ’quant’ Dialect ’rocdl’ Dialect ’scf’ Dialect ’shape’ Dialect ’sparse_tensor’ Dialect ’tensor’ Dialect ’ub’ Dialect ’vcix’ Dialect ’vector’ Dialect ’x86vector’ Dialect ’xegpu’ Dialect Builtin Dialect OpInterface definitions SPIR-V Dialect Tensor Operator Set Architecture (TOSA) Dialect Transform Dialect Interfaces LLVM IR Target Bytecode Format C API Language Reference Release Notes Operation Canonicalization Ownership-based Buffer Deallocation Pass Infrastructure Passes Pattern Rewriting : Generic DAG-to-DAG Rewriting PDLL - PDL Language Quantization Rationale + Generic DAG Rewriter Infrastructure Rationale Linalg Dialect Rationale: The Case For Compiler-Friendly Custom Operations Rationale : Incremental Application to Graph Algorithms in ML Frameworks : The case for a simplified polyhedral form Side Effects & Speculation Usage of ’const’ in , for core IR types Shape Inference SPIR-V Dialect to LLVM Dialect conversion manual Symbols and Symbol Tables Table-driven Declarative Rewrite Rule (DRR) Traits + The `Broadcastable` Trait Tutorials + Creating a Dialect Quickstart tutorial to adding graph rewrite Toy Tutorial + Chapter 1: Toy Language and AST Chapter 2: Emitting Basic Chapter 3: High-level Language-Specific Analysis and Transformation Chapter 4: Enabling Generic Transformation with Interfaces Chapter 5: Partial Lowering to Lower-Level Dialects for Optimization Chapter 6: Lowering to LLVM and CodeGeneration Chapter 7: Adding a Composite Type to Toy Transform Dialect Tutorial + Chapter 0: A Primer on Structured” Linalg Operations Chapter 1: Combining Existing Transformations Chapter 2: Adding a Simple New Transformation Operation Chapter 3: More than Simple Transform Operations Chapter 4: Matching Payload with Transform Operations Chapter H: Reproducing Halide Schedule Understanding the IR Structure Writing DataFlow Analyses...