README.md

Install Exo

Using Exo

If you're just using Exo, install it using pip:

$ pip install exo-lang

Developing Exo

If you plan to work on the compiler directly, clone this repository and run the following commands:

$ git submodule update --init --recursive
$ python3.9 -m venv ~/.venv/exo
$ source ~/.venv/exo/bin/activate

This will checkout all the required submodules and enable the Exo virtual environment. Next, install the compiler:

$ python -m pip install -U pip setuptools wheel
$ python -m pip install -r requirements.txt
$ pre-commit install

This will make sure you have the submodules checked out and that the pre-commit scripts (that run an autoformatter, maybe other tools in the future) run.

If you're feeling ambitious, you can also install Exo from source.

Examples

Take a look at exo/examples for scheduling examples.

Exo's scheduling API

Top-level Python function decorator

1. @proc - decorates a Python function which is parsed and compiled as Exo. Replaces the function with a Procedure object.
2. @instr - same as @proc, but accepts a hardware instruction as a format string.
3. @config - decorates a Python class which is parsed and compiled as an Exo configuration object

Procedure object methods

Introspection operations

• .name() returns the procedure name.
• .check_effects() forces Exo to run effect checking on the procedure.
• .show_effects() prints the effects of the procedure.
• .show_effect(stmt) prints the effect of the stmt in the procedure.
• .is_instr() returns true if the procedure has a hardware instruction string.
• .get_instr() returns the hardware instruction string.
• .get_ast() returns a QAST, which is an AST representation suitable for introspection.

Execution / interpretation operations

• .compile_c(directory, filename) compiles the procedure into C and stores in filename in the directory.
• .interpret(**args) runs Exo interpreter on the procedure.

Scheduling operations on Procedure objects

Buffer related operations

Operation	Description
.data_reuse(buf1, buf2)	Reuses a buffer buf1 in the use site of buf2 and removes the allocation of buf2.
.inline_window(win_stmt)	Removes the window statement win_stmt, which is an alias to the window, and inlines the windowing in its use site.
.expand_dim(stmt, alloc_dim, indexing)	Expands the dimension of the allocation statement stmt with dimension alloc_dim of indexing indexing.
.bind_expr(new_name, expr)	Binds the right hand side expression expr to a newly allocated buffer named new_name
.stage_mem(win_expr, new_name, stmt_start, stmt_end=None)	Stages the buffer win_expr to the new window expression new_name in statement block (stmt_start to stmt_end), and adds an initialization loop and a write-back loop.
.rearrange_dim(alloc, dimensions)	Takes an allocation statement and a list of integers to map the dimension. It rearranges the dimensions of alloc in dimension order. E.g., if alloc were foo[N,M,K] and the dimension were [2,0,1], it would become foo[K,N,M] after this operation.
.lift_alloc(alloc, n_lifts=1, keep_dims=False)	Lifts the allocation statement alloc out of n_lifts number of scopes. If and For statements are the only statements in Exo which introduce a scope. When lifting the allocation out of a for loop, it will expand its dimension to the loop bound if keep_dims is True.

Loop related operations

Operation	Description
.split(loop, split_const, iter_vars, tail='guard', perfect=False)	Splits loop into an outer and an inner loop. The inner loop bound is split_const and the outer and inner loop names are specified by a list of strings iter_vars. If perfect is True, it will not introduce a tail case. tail specifies the tail strategies, where the options are guard, cut, and cut_and_guard.
.fuse_loop(loop1, loop2)	Fuses two adjacent loops with a common iteration variable.
.partition_loop(loop, num)	Partitions loop into two loops, the first running between 0 and num and the second between num+1 and loop's original bound.
.reorder(loop1, loop2)	Reorders two nested loops. loop2 should be nested directly inside loop1. loop1 will be nested inside loop2 after this operation.
.unroll(loop)	Unrolls the loop. The loop needs to have a constant bound.
.fission_after(stmt, n_lifts=1)	Fissions the n_lifts number of loops around the stmt. The fissioned loops around the stmt need to be directly nested with each other and the statements before and after the stmt should not have any allocation dependencies.
.remove_loop(loop)	Replaces the loop with its body if the body is idempotent. The system must be able to prove that the loop runs at least once.

Config related operations

Operation	Description
.bind_config(expr, config, field)	Binds the right hand side expr to config.field. It will replace the use site of expr with config.field and introduces a config statement of config.field = expr.
.configwrite_root(config, field, expr)	Inserts the config statement config.field = expr in the beginning of the procedure.
.configwrite_after(stmt, config, field, expr)	Inserts the config statement config.field = expr after stmt.
.delete_config(stmt)	Deletes the configuration statement.

Other scheduling operations

Operation	Description
.add_assertion(assertion)	Asserts the truth of the expression assertion at the beginning of the procedure.
.lift_if(if, n_lifts=1)	Lifts the if statement if out of n_lifts number of scopes. This is similar to reorder(), but for if statements.
.assert_if(if, bool)	Unsafely asserts that the if condition is always True or False. This can be used to remove branches.
.delete_pass()	Deletes a Pass statement in the procedure.
.reorder_stmts(stmt1, stmt2)	Reorder two adjacent statements stmt1 and stmt2. After this operation, the order will be stmt2 stmt1.
.reorder_before(stmt)	Move the statement stmt before the previous statement. This is a shorthand for reorder_stmts().
.replace(subproc, stmt)	Replace the statement with a call to subproc. This operation is one of our contributions and is explained in detail in the paper.
.replace_all(subproc)	Eagerly replace every matching statement with a call to subproc.
.inline(call_site)	Inline the function call.
.is_eq(another_proc)	Returns True if another_proc is equivalent to the procedure.
.call_eqv(eqv_proc, call_site)	Replace the function call statement of call_site with a call to an equivalent procedure eqv_proc.
.repeat(directive, *args)	Continue to run the directive until it fails. The directive and its arguments are given separately, e.g. proc.repeat(Procedure.inline, "proc_to_inline(_)")
.simplify()	Simplify the code in the procedure body. Tries to reduce expressions to constants and eliminate dead branches and loops. Uses branch conditions to simplify expressions inside the branches.
.rename(new_name)	Rename this procedure to new_name.
.make_instr(instr_string)	Converts this procedure to an instruction procedure with instruction instr_string.
.partial_eval(*args, **kwargs)	Specializes this procedure to the given argument values.
.set_precision(name, type)	Sets the precision type of name to type.
.set_window(name, is_window)	If is_window is True, it sets the buffer name to window type, instead of a tensor type.
.set_memory(name, mem_type)	Sets a buffer name's memory type to mem_type.

Exo's repository structure

In this repository, folders are structured as follows:

1. src/exo is where the core Exo implementation resides.
   • API.py defines the stable API. Documentation for this API can be found in the section below.
   • libs/ contains some common memory definitions (memories.py) and custom malloc implementations. These could be user-defined, but we provide them for convenience.
   • platforms/ contains instruction definitions that are part of the release. These could be user-defined, but we provide them for convenience.
   • Other files are implementation details of Exo (e.g., typecheck.py implements typecheck), but we will not dwell on these as they are not exposed to users.
2. apps/ contains some sample applications written in Exo.
3. dependencies/ contains submodules that Exo's apps and testing depends on.
4. examples/ contains a Python notebook that we used for live demos. This should be ignored.
5. tests/ contains the Exo test suite.

Build Exo from source

Self-contained install with Python

If you don't want to use your system version of python (e.g. if it's too old), you can install Exo and a compatible version of Python with Nix.

First, install Nix (if you don't have it) using either the systemwide installer or the portable install (no root required for portable):

$ wget https://github.com/DavHau/nix-portable/releases/download/v009/nix-portable
$ chmod +x nix-portable

Then launch a shell which includes Exo and a compatible version of Python:

$ git clone [email protected]:exo-lang/exo.git
$ cd exo/

# with a systemwide nix installation
$ nix --experimental-features 'nix-command flakes' develop

# or with a portable nix installation
$ PATH_TO_NIX_PORTABLE/nix-portable nix develop

This is a virtualenv-like environment that you will need to enter each time you wish to use Exo.

Manual install

We make active use of newer Python 3.x features, so please use the same version of Python as our CI if you're getting errors about unsupported features.

Setting up Exo for development is like any other Python project. We strongly recommend you use a virtual environment.

$ git clone [email protected]:exo-lang/exo.git
$ cd exo/
$ git submodule update --init --recursive
$ python -m venv ~/.venv/exo
$ source ~/.venv/exo/bin/activate
(exo) $ python -m pip install -U pip setuptools wheel
(exo) $ python -m pip install -r requirements.txt
(exo) $ python -m build
(exo) $ pip install dist/*.whl

PySMT

Depending on your setup, getting PySMT to work correctly may be difficult. You need to independently install a solver such as Z3 or CVC4, and even then getting the PySMT library to correctly locate that solver may be difficult. We have included the z3-solver package as a requirement, which will hopefully avoid this issue, but you can also install z3 (or your choice of solver) independently.

Notes for Testing

Dependencies

Build system (required)

The Exo test harness generates C code and as such needs to compile and link using an unknown (i.e. system) compiler. To do this, it generates CMake build files and invokes CMake behind the scenes.

Therefore, you must have CMake 3.21 or newer installed.

By default, CMake will use Ninja as its backend, but this may be overridden by setting the environment variable CMAKE_GENERATOR to Unix Makefiles, in case you do not wish to install Ninja.

SDE (optional)

For testing x86 features on processors which don't support them (e.g., AVX-512 or AMX), we rely on the Intel Software Development Emulator as an optional dependency. Tests which rely on this (namely for AMX) look for sde64 either in the path defined by the SDE_PATH environment variable or in the system PATH, and are skipped if it is not available.

Running tests

To run the tests, simply type

pytest

in the root of the project.

Running Coverage Testing

To run pytest with coverage tests, execute

pytest --cov=./ --cov-report=html

Then, if you want to see annotated source files, open ./htmlcov/index.html.

Publication

The first paper on Exo was published at PLDI '22. You can download the paper from ACM Digital Library. If you use Exo, please cite both the compiler and the paper!

@inproceedings{pldi22:exo,
  title        = {Exocompilation for Productive Programming of Hardware Accelerators},
  author       = {
    Ikarashi, Yuka and Bernstein, Gilbert Louis and Reinking, Alex and Genc,
    Hasan and Ragan-Kelley, Jonathan
  },
  year         = 2022,
  booktitle    = {
    Proceedings of the 43rd ACM SIGPLAN International Conference on Programming
    Language Design and Implementation
  },
  location     = {San Diego, CA, USA},
  publisher    = {Association for Computing Machinery},
  address      = {New York, NY, USA},
  series       = {PLDI 2022},
  pages        = {703–718},
  doi          = {10.1145/3519939.3523446},
  isbn         = 9781450392655,
  url          = {https://doi.org/10.1145/3519939.3523446},
  abstract     = {
    High-performance kernel libraries are critical to exploiting accelerators
    and specialized instructions in many applications. Because compilers are
    difficult to extend to support diverse and rapidly-evolving hardware
    targets, and automatic optimization is often insufficient to guarantee
    state-of-the-art performance, these libraries are commonly still coded and
    optimized by hand, at great expense, in low-level C and assembly. To better
    support development of high-performance libraries for specialized hardware,
    we propose a new programming language, Exo, based on the principle of
    exocompilation: externalizing target-specific code generation support and
    optimization policies to user-level code. Exo allows custom hardware
    instructions, specialized memories, and accelerator configuration state to
    be defined in user libraries. It builds on the idea of user scheduling to
    externalize hardware mapping and optimization decisions. Schedules are
    defined as composable rewrites within the language, and we develop a set of
    effect analyses which guarantee program equivalence and memory safety
    through these transformations. We show that Exo enables rapid development
    of state-of-the-art matrix-matrix multiply and convolutional neural network
    kernels, for both an embedded neural accelerator and x86 with AVX-512
    extensions, in a few dozen lines of code each.
  },
  numpages     = 16,
  keywords     = {
    program optimization, user-schedulable languages, user-extensible backend
    & scheduling, instruction abstraction, scheduling, hardware
    accelerators
  }
}