forked from OSchip/llvm-project
141 lines
6.8 KiB
Markdown
141 lines
6.8 KiB
Markdown
# Multi-Level Intermediate Representation Overview
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The MLIR project aims to define a common intermediate representation (IR) that
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will unify the infrastructure required to execute high performance machine
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learning models in TensorFlow and similar ML frameworks. This project will
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include the application of HPC techniques, along with integration of search
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algorithms like reinforcement learning. This project aims to reduce the cost to
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bring up new hardware, and improve usability for existing TensorFlow users.
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Note that this repository contains the core of the MLIR framework. The
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TensorFlow compilers we are building on top of MLIR will be part of the
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main TensorFlow repository soon.
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# How to Contribute
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Thank you for your interest in contributing to MLIR! If you want to contribute
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to MLIR, be sure to review the [contribution guidelines](CONTRIBUTING.md).
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## More resources
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For more information on MLIR, please see:
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* [The MLIR draft specification](g3doc/LangRef.md), which describes the IR
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itself.
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* [The MLIR rationale document](g3doc/Rationale.md), covering motivation
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behind some decisions.
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* Previous external [talks](#mlir-talks).
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Join the [MLIR mailing list](https://groups.google.com/a/tensorflow.org/forum/#!forum/mlir)
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to hear about announcements and discussions.
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We also have an [MLIR SIG](https://github.com/tensorflow/community/blob/master/sigs/mlir/CHARTER.md)
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which was created to enable collaboration and form a strong
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engineering-driven open community. We have weekly 'Open Design Meetings'. If you’d like
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to discuss a particular topic or have questions, please add it to the [agenda doc](https://docs.google.com/document/d/1y_9f1AbfgcoVdJh4_aM6-BaSHvrHl8zuA5G4jv_94K8/edit#).
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Details on how to join the meeting are in the agenda doc. You
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should also get an invite when you join the mailing list.
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Please be mindful of the [TensorFlow Code of Conduct](https://github.com/tensorflow/tensorflow/blob/master/CODE_OF_CONDUCT.md),
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which pledges to foster an open and welcoming environment.
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## What is MLIR for?
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MLIR is intended to be a hybrid IR which can support multiple different
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requirements in a unified infrastructure. For example, this includes:
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* The ability to represent all TensorFlow graphs, including dynamic shapes,
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the user-extensible op ecosystem, TensorFlow variables, etc.
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* Optimizations and transformations typically done on a TensorFlow graph, e.g.
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in Grappler.
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* Quantization and other graph transformations done on a TensorFlow graph or
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the TF Lite representation.
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* Representation of kernels for ML operations in a form suitable for
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optimization.
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* Ability to host high-performance-computing-style loop optimizations across
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kernels (fusion, loop interchange, tiling, etc) and to transform memory
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layouts of data.
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* Code generation "lowering" transformations such as DMA insertion, explicit
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cache management, memory tiling, and vectorization for 1D and 2D register
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architectures.
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* Ability to represent target-specific operations, e.g. the MXU on TPUs.
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MLIR is a common IR that also supports hardware specific operations. Thus,
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any investment into the infrastructure surrounding MLIR (e.g. the compiler
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passes that work on it) should yield good returns; many targets can use that
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infrastructure and will benefit from it.
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MLIR is a powerful representation, but it also has non-goals. We do not try to
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support low level machine code generation algorithms (like register allocation
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and instruction scheduling). They are a better fit for lower level optimizers
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(such as LLVM). Also, we do not intend MLIR to be a source language that
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end-users would themselves write kernels in (analogous to CUDA C++). While we
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would love to see a kernel language happen someday, that will be an independent
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project that compiles down to MLIR.
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## Compiler infrastructure
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We benefited from experience gained from building other IRs (HLO, LLVM and SIL)
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when building MLIR. We will directly adopt existing best practices, e.g. writing
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and maintaining an IR spec, building an IR verifier, providing the ability to
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dump and parse MLIR files to text, writing extensive unit tests with the
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[FileCheck](https://llvm.org/docs/CommandGuide/FileCheck.html) tool, and
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building the infrastructure as a set of modular libraries that can be combined
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in new ways. We plan to use the infrastructure developed by the XLA team for
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performance analysis and benchmarking.
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Other lessons have been incorporated and integrated into the design in subtle
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ways. For example, LLVM has non-obvious design mistakes that prevent a
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multithreaded compiler from working on multiple functions in an LLVM module at
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the same time. MLIR solves these problems by having per-function constant pools
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and by making references explicit with `function_ref`.
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# Getting started with MLIR
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The following instructions for compiling and testing MLIR assume that you have
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`git`, [`ninja`](https://ninja-build.org/), and a working C++ toolchain. In the
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future, we aim to align on the same level of platform support as
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[LLVM](https://llvm.org/docs/GettingStarted.html#requirements). For now, MLIR
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has been tested on Linux and macOS, with recent versions of clang and with
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gcc 7.
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```sh
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git clone https://github.com/llvm/llvm-project.git
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git clone https://github.com/tensorflow/mlir llvm-project/llvm/projects/mlir
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mkdir llvm-project/build
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cd llvm-project/build
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cmake -G Ninja ../llvm -DLLVM_BUILD_EXAMPLES=ON -DLLVM_TARGETS_TO_BUILD="host"
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cmake --build . --target check-mlir
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```
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To compile and test on Windows using Visual Studio 2017:
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```bat
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REM In shell with Visual Studio environment set up, e.g., with command such as
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REM $visual-studio-install\Auxiliary\Build\vcvarsall.bat" x64
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REM invoked.
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git clone https://github.com/llvm/llvm-project.git
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git clone https://github.com/tensorflow/mlir llvm-project\llvm\projects\mlir
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mkdir llvm-project\build
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cd llvm-project\build
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cmake ..\llvm -G "Visual Studio 15 2017 Win64" -DLLVM_BUILD_EXAMPLES=ON -DLLVM_TARGETS_TO_BUILD="host" -DCMAKE_BUILD_TYPE=Release -Thost=x64
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cmake --build . --target check-mlir
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```
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As a starter, you may try [the tutorial](g3doc/Tutorials/Toy/Ch-1.md) on
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building a compiler for a Toy language.
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# MLIR talks
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* "[MLIR Primer: A Compiler Infrastructure for the End of Moore’s Law](https://ai.google/research/pubs/pub48035.pdf)"
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* Chris Lattner & Jacques Pienaar, Google at
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[Compilers for Machine Learning](https://www.c4ml.org/) workshop at
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[CGO 2019](http://cgo.org/cgo2019/)
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* "[MLIR: Multi-Level Intermediate Representation for Compiler
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Infrastructure](https://llvm.org/devmtg/2019-04/talks.html#Keynote_1)"
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* Tatiana Shpeisman & Chris Lattner, Google at
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[EuroLLVM 2019](https://llvm.org/devmtg/2019-04)
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* "[Tutorial: Building a Compiler with MLIR](https://llvm.org/devmtg/2019-04/talks.html#Tutorial_1)"
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* Mehdi Amini, Jacques Pienaar, Nicolas Vasilache, Google at
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[EuroLLVM 2019](https://llvm.org/devmtg/2019-04)
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