llvm-capstone/mlir/utils/mbr

MBR - MLIR Benchmark Runner

MBR is a tool to run benchmarks. It measures compilation and running times of benchmark programs. It uses MLIR's python bindings for MLIR benchmarks.

Installation

To build and enable MLIR benchmarks, pass -DMLIR_ENABLE_PYTHON_BENCHMARKS=ON while building MLIR. If you make some changes to the mbr files itself, build again with -DMLIR_ENABLE_PYTHON_BENCHMARKS=ON.

Writing benchmarks

As mentioned in the intro, this tool measures compilation and running times. An MBR benchmark is a python function that returns two callables, a compiler and a runner. Here's an outline of a benchmark; we explain its working after the example code.

def benchmark_something():
    # Preliminary setup
    def compiler():
        # Compiles a program and creates an "executable object" that can be
        # called to invoke the compiled program.
        ...

    def runner(executable_object):
        # Sets up arguments for executable_object and calls it. The
        # executable_object is returned by the compiler.
        # Returns an integer representing running time in nanoseconds.
        ...

    return compiler, runner

The benchmark function's name must be prefixed by "benchmark_" and benchmarks must be in the python files prefixed by "benchmark_ for them to be discoverable. The file and function prefixes are configurable using the configuration file mbr/config.ini relative to this README's directory.

A benchmark returns two functions, a compiler and a runner. The compiler returns a callable which is accepted as an argument by the runner function. So the two functions work like this

1. compiler: configures and returns a callable.
2. runner: takes that callable in as input, sets up its arguments, and calls it. Returns an int representing running time in nanoseconds.

The compiler callable is optional if there is no compilation step, for example, for benchmarks involving numpy. In that case, the benchmarks look like this.

def benchmark_something():
    # Preliminary setup
    def runner():
        # Run the program and return the running time in nanoseconds.
        ...

    return None, runner

In this case, the runner does not take any input as there is no compiled object to invoke.

Running benchmarks

MLIR benchmarks can be run like this

PYTHONPATH=<path_to_python_mlir_core> <other_env_vars> python <llvm-build-path>/bin/mlir-mbr --machine <machine_identifier> --revision <revision_string> --result-stdout <path_to_start_search_for_benchmarks>

For a description of command line arguments, run

python mlir/utils/mbr/mbr/main.py -h

And to learn more about the other arguments, check out the LNT's documentation page here.

If you want to run only specific benchmarks, you can use the positional argument top_level_path appropriately.

1. If you want to run benchmarks in a specific directory or a file, set top_level_path to that.
2. If you want to run a specific benchmark function, set the top_level_path to the file containing that benchmark function, followed by a ::, and then the benchmark function name. For example, mlir/benchmark/python/benchmark_sparse.py::benchmark_sparse_mlir_multiplication.

Configuration

Various aspects about the framework can be configured using the configuration file in the mbr/config.ini relative to the directory of this README.