Accera

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Open source cross-platform compiler for compute-intensive loops used in AI algorithms, from Microsoft Research

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![Accera logo](https://microsoft.github.io/Accera/assets/logos/Accera_darktext.png)
<div style="margin-bottom:30px"></div>

<a href="https://pypi.org/project/accera/"><img src="https://badge.fury.io/py/accera.svg" alt="PyPI package version"/></a> <a href="https://pypi.org/project/accera/"><img src="https://img.shields.io/pypi/pyversions/accera" alt="Python versions"/></a> ![MIT License](https://img.shields.io/pypi/l/accera)

# Problem at Hand
Writing highly optimized compute-intensive code in a traditional programming language is strenuous and time-consuming. Not only does it require advanced engineering skills such as fluency in Assembly language, but a deep understanding of computer architecture is also indispensable. Manual optimization of even the simplest numerical algorithms demands a significant engineering effort. Needless to say, a highly optimized numerical code is often prone to bugs, lacks readability, and offers little to no usability. Code maintenance becomes a nightmare resulting in the reimplementation of the same logic every time an architecture level change is introduced.

# Accera: An Optimized Solution

Accera is a compiler that enables you to experiment with loop optimizations without hand-writing Assembly code. With Accera, these problems and impediments can be addressed in an optimized way. It is available as a Python library and supports cross-compiling to a wide range of [processor targets](https://github.com/microsoft/Accera/blob/main/accera/python/accera/Targets.py).

Accera has THREE primary goals:

* Performance: To guarantee the fastest implementation for any compute-intensive algorithm.
* Readability: To ensure effective implementation of algorithms without sacrificing the readability of code.
* Writability: To provide a user-friendly programming model, designed for agility and maintainability.

## Install

To install for Linux, macOS, or Windows (requires Python 3.7-3.10):

```shell
pip install accera
```

See the [Install Instructions](https://microsoft.github.io/Accera/Install/) for more details on installing pre-built Python 3 packages and how to build Accera from the source.

### Quickstart

In this example, we will:

* Implement matrix multiplication with a ReLU activation (matmul + ReLU), commonly used in machine learning algorithms.
* Generate two implementations: a naive algorithm and loop-based transformations.
* Compare the execution time of both implementations.

#### Run in your browser

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/Accera/demos?labpath=docs%2Fdemos%2Fquickstart.ipynb)

No installation is required. This will launch a Jupyter notebook with the quickstart example running in the cloud.

#### Run on your machine

1. Create a Python 3 script called `quickstart.py`:

```python
    import accera as acc

# define placeholder inputs/output
    A = acc.Array(role=acc.Role.INPUT, shape=(512, 512))
    B = acc.Array(role=acc.Role.INPUT, shape=(512, 512))
    C = acc.Array(role=acc.Role.INPUT_OUTPUT, shape=(512, 512))

# implement the logic for matmul and relu
    matmul = acc.Nest(shape=(512, 512, 512))
    i1, j1, k1 = matmul.get_indices()
    @matmul.iteration_logic
    def _():
        C[i1, j1] += A[i1, k1] * B[k1, j1]

relu = acc.Nest(shape=(512, 512))
    i2, j2 = relu.get_indices()
    @relu.iteration_logic
    def _():
        C[i2, j2] = acc.max(C[i2, j2], 0.0)

package = acc.Package()

# fuse the i and j indices of matmul and relu, add to the package
    schedule = acc.fuse(matmul.create_schedule(), relu.create_schedule(), partial=2)
    package.add(schedule, args=(A, B, C), base_name="matmul_relu_fusion_naive")

# transform the schedule, add to the package
    i, j, f, k = schedule.get_indices()
    ii, jj = schedule.tile({
        i: 16,
        j: 16
    }) # loop tiling
    schedule.reorder(j, i, f, k, jj, ii) # loop reordering
    plan = schedule.create_plan()
    plan.unroll(ii) # loop unrolling
    package.add(plan, args=(A, B, C), base_name="matmul_relu_fusion_transformed")

# build a dynamically-linked package (a .dll or .so) that exports both functions
    print(package.build(name="hello_accera", format=acc.Package.Format.HAT_DYNAMIC))
    ```

2. Ensure that you have a compiler in your PATH:

* Windows: Install Microsoft Visual Studio and run `vcvars64.bat` to setup the command prompt.
    * Linux/macOS: Install gcc

Don't have a compiler handy? We recommend trying Accera in your browser instead [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/Accera/demos?labpath=docs%2Fdemos%2Fquickstart.ipynb)

3. Install Accera:

```shell
    pip install accera
    ```

4. Generate the library that implements two versions of matmul + ReLU:

```shell
    python quickstart.py
    ```

5. To consume and compare the library functions, create a file called `benchmark.py` in the same location:

```python
    import hatlib as hat
    import numpy as np

# load the package
    _, functions = hat.load("hello_accera.hat")

# call one of the functions with test inputs
    A_test = np.random.rand(512, 512).astype(np.float32)
    B_test = np.random.rand(512, 512).astype(np.float32)
    C_test = np.zeros((512, 512)).astype(np.float32)
    C_numpy = np.maximum(C_test + A_test @ B_test, 0.0)

matmul_relu = functions["matmul_relu_fusion_transformed"]
    matmul_relu(A_test, B_test, C_test)

# check correctness
    np.testing.assert_allclose(C_test, C_numpy, atol=1e-3)

# benchmark all functions
    hat.run_benchmark("hello_accera.hat", batch_size=5, min_time_in_sec=5)
    ```

6. Run the benchmark to get the execution time results:

```shell
    python benchmark.py
    ```

#### Next Steps

The [Manual](https://microsoft.github.io/Accera/Manual/00%20Introduction/) is the best introductory resource for the Accera Python programming model.

In particular, the [schedule transformations](https://microsoft.github.io/Accera/Manual/03%20Schedules/#schedule-transformations) describe how you can experiment with different loop transformations with just a few lines of Python code.

Finally, the `.hat` format is just a C header file containing the metadata. Learn more about the [HAT format](https://github.com/microsoft/hat) and [benchmarking](https://github.com/microsoft/hat/tree/main/tools).

## How it works

In a nutshell, Accera takes the Python code that defines the loop schedule and algorithm while converting it into [MLIR](https://mlir.llvm.org/) intermediate representation (IR). Accera's compiler then takes this IR through a series of MLIR pipelines to perform transformations. The result is a binary library with a C header file. The library implements the algorithms that are defined in Python and it is compatible with the target.

To peek into the stages of IR transformation that Accera does, try replacing `format=acc.Package.Format.HAT_DYNAMIC` with `format=acc.Package.Format.MLIR_DYNAMIC` in `quickstart.py`, re-run the script, and search the `_tmp` subfolder for the intermediate `*.mlir` files. We plan to document these IR constructs in the future.

## Documentation

Get familiar with Accera's concepts and Python constructs in the [Documentation](https://microsoft.github.io/Accera/) page.

## Tutorials

Step-by-step examples are available on the [Tutorials](https://microsoft.github.io/Accera/Tutorials) page. We're working on adding more complementary examples and tutorials.

## Contributions

Accera is a research platform-in-progress that can certainly benefit from your contributions. We would love your feedback, recommendations, and feature requests. Not to mention that we are excited to answer your questions. Let’s collaborate! Please file a  [Github issue](https://github.com/microsoft/Accera/issues/new) or send us a pull request. Please review the [Microsoft Code of Conduct](https://opensource.microsoft.com/codeofconduct/) to learn more.

## Credits

Accera is built using several open source libraries, including: [LLVM](https://llvm.org/), [pybind11](https://pybind11.readthedocs.io/en/stable/), [toml++](https://marzer.github.io/tomlplusplus/), [tomlkit](https://github.com/sdispater/tomlkit), [vcpkg](https://vcpkg.io/en/index.html), [pyyaml](https://pyyaml.org/), and [HAT](https://github.com/microsoft/hat). For testing, we used [numpy](https://github.com/numpy/numpy) and [catch2](https://github.com/catchorg/Catch2).

## License

This project is released under the [MIT License](https://github.com/microsoft/Accera/blob/main/LICENSE).

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