DISCLAIMER: This is not an officially-supported Google project. It is a sandbox for quick iteration and experimentation on projects related to the IREE project, MLIR, and LLVM.
This repository contains experimental work by the IREE team closely related to LLVM and MLIR, usually with the aim of upstreaming in some form. The main project is at https://github.com/google/iree.
As an experimental project, build greenness, documentation, and polish are likely to be minimal, as it instead prioritizes easy experimentation.
Licensed under the Apache license with LLVM Exceptions. See LICENSE for more information.
This project builds as part of the LLVM External Projects facility (see
documentation for the LLVM_EXTERNAL_PROJECTS
config setting). There are many
ways to set this up. We recommend using our configure.py
script below.
It is left to the reader to adapt paths if deviating. We assume below that
projects are checked out to $HOME/src
.
TODO: Simplify instructions.
In your $HOME/src
directory, check out each project:
Required:
git clone https://github.com/google/iree-llvm-sandbox
We use the following environment variables in these instructions:
LLVM_SOURCE_DIR
: $HOME/src/llvm-projectIREE_LLVM_SANDBOX_SOURCE_DIR
: $HOME/src/iree-llvm-sandboxIREE_LLVM_SANDBOX_BUILD_DIR
: $HOME/src/iree-llvm-sandbox/build
Follow the instructions for MLIR Python Bindings:
which python
python -m venv ~/.venv/mlirdev
source ~/.venv/mlirdev/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
The sandbox can be optionally built with or without IREE integration (for accessing IREE specific IR and evaluating on IREE compatible targets):
Checkout the IREE GitHub repo next to this directory and initialize submodules:
(cd .. && git clone https://github.com/google/iree --recurse-submodules=third_party/llvm-project)
And configure/build the project:
python configure.py --iree-path=../iree
Note that the third_party/llvm-project
bundled with IREE will be used.
You must checkout llvm-project at a compatible commit next to this directory.
(cd .. && git clone https://github.com/llvm/llvm-project.git)
And configure/build the project:
python configure.py
source .env && export PYTHONPATH
# Sanity check (should not error).
python -c "import mlir.iree_sandbox"
# Run a matmul.
cd ${IREE_LLVM_SANDBOX_SOURCE_DIR}; \
python -m python.matmul.bench
cd "${IREE_LLVM_SANDBOX_BUILD_DIR}"
./bin/mlir-proto-opt \
../iree-llvm-sandbox/test/test_constant.mlir \
-linalg-comprehensive-module-bufferize
TODOs:
- hook up a lit test target.
- re-add npcomp instructions once it is upgraded to use the same build setup.
Python tests come with a tool to perform as simple randomized search. The search is going to randomly instantiate a given op to some cocnrete dimensions and type variables and try to compile it using mlir.
The results are persisted in the output/
folder by default in a structure that
includes a name of the expert compiler, the name of the op and the
success/failure/timeout status code. The results contain the full program output
(including potential compilation errors) and an accompanying .sh
file that can
be used to re-run the same configuration again.
To run the search with default settings:
export PATH="${IREE_LLVM_SANDBOX_BUILD_DIR}/bin:$PATH"
cd ${IREE_LLVM_SANDBOX_SOURCE_DIR}
alias search_cli="python -m python.local_search.search_cli"
search_cli
To run with a different linalg op, use --op
flag:
search_cli --op matvec
To specify the name of the expert compilers, use --expert
(see experts.py
for all available expert definitions):
search_cli --experts ExpertCompiler1
To specify the possible types, use --types
flag:
search_cli --types f32,f64
Alternatively, one can also force some variables to concrete values, while
others will ramain random using --assign
:
search_cli --assign M=16 N=32 K=64
To specify range of possible values for dimensions, use --range
flag (where
numbers correspond to arguments of the corresponding range
function in
Python):
search_cli --range 128,256,8
The search can be run using multiple processes at once, via --par
flag:
search_cli --par 72
Each process collects the fixed number of random samples, customized via
--samples
flag:
search_cli --samples 100
One can see a ranked list, based on llvm-mca performance estimates:
alias rank_cli="python -m python.local_search.rank_cli"
rank_cli
You can customize the --op
, the number of the output results (--limit
) and
the metric used for ranking (--by
) through additional command-line flags.
The metrics are coming from either runtime
or mca
input files that can be
specified using --input
flag. By default results are ranked by the measured
runtime.