Static cast size_t -> int64_t instead of vice versa for equals comparisons
These were just introduced by a previous CL moving MemRef getRank to return int64_t. size_t could be smaller than 64 bits and in equals comparisons, signed vs unsigned doesn't matter. In these cases, we know right now that the particular int64_t is not larger than max size_t (because it currently comes directly from a size() call), the alternative cast plus equals comparison is always safe, so we might as well do it that way and no longer require reasoning deeper into the callstack.
We are already assuming that size() calls fit into int64_t in a number of other cases like the aforementioned getRank() (since exabytes of RAM are rare). If we want to avoid this assumption we will have to come up with a principled way to do it throughout.
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PiperOrigin-RevId: 250980297
The MLIR project aims to define a common intermediate representation (IR) that will unify the infrastructure required to execute high performance machine learning models in TensorFlow and similar ML frameworks. This project will include the application of HPC techniques, along with integration of search algorithms like reinforcement learning. This project aims to reduce the cost to bring up new hardware, and improve usability for existing TensorFlow users.
Note that this repository contains the core of the MLIR framework. The TensorFlow compilers we are building on top of MLIR will be part of the main TensorFlow repository soon.
We'd love to accept your patches and contributions to this project soon. But we are not yet ready to accept community contributions at this time.
For more information on MLIR, please see:
Join the MLIR mailing list to hear about announcements and discussions. Please be mindful of the TensorFlow Code of Conduct, which pledges to foster an open and welcoming environment.
MLIR is intended to be a hybrid IR which can support multiple different requirements in a unified infrastructure. For example, this includes:
MLIR is a common IR that also supports hardware specific operations. Thus, any investment into the infrastructure surrounding MLIR (e.g. the compiler passes that work on it) should yield good returns; many targets can use that infrastructure and will benefit from it.
MLIR 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 MLIR to be a source language that end-users would themselves write kernels in (analogous to CUDA C++). While we would love to see a kernel language happen someday, that will be an independent project that compiles down to MLIR.
We benefitted from experience gained from building other IRs (HLO, LLVM and SIL) when building MLIR. We will directly adopt existing best practices, e.g. writing and maintaining an IR spec, building an IR verifier, providing the ability to dump and parse MLIR 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. We plan to use the infrastructure developed by the XLA team for performance analysis and benchmarking.
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. MLIR solves these problems by having per-function constant pools and by making references explicit with function_ref.
The following instructions assume that you have git, ninja, and a working C++ toolchain. In the future, we aim to align on the same level of platform support as LLVM. For now, MLIR has been tested on Linux and macOS, with recent versions of clang and with gcc 7.
git clone https://github.com/llvm/llvm-project.git git clone https://github.com/tensorflow/mlir llvm-project/llvm/projects/mlir mkdir llvm-project/build cd llvm-project/build cmake -G Ninja ../llvm -DLLVM_BUILD_EXAMPLES=ON -DLLVM_ENABLE_CXX1Y=Y -DLLVM_TARGETS_TO_BUILD="host" cmake --build . --target check-mlir
As a starter, you may try the tutorial on building a compiler for a Toy language.