cornell-zhang / allo

Allo: A Programming Model for Composable Accelerator Design

Accelerator Design Language

Documentation

Allo is an Accelerator Design Language (ADL) and compiler that facilitates the construction of large-scale, high-performance hardware accelerators in a modular and composable manner. Allo has several key features:

• Progressive hardware customizations: Allo decouples hardware customizations from algorithm specifications and treats each hardware customization as a primitive that performs a rewrite on the program. Allo not only decouples the loop-based transformations, but also extends the decoupling to memory, communication, and data types.
• Reusable parameterized kernel templates: Allo supports declaring type variables during kernel creation and instantiating the kernel when building the hardware executable, which is an important feature for building reusable hardware kernel libraries. Allo introduces a concise grammar for creating kernel templates, eliminating the need for users to possess complicated metaprogramming expertise.
• Composable schedules: Allo empowers users to construct kernels incrementally from the bottom up, adding customizations one at a time while validating the correctness of each submodule. Ultimately, multiple schedules are progressively integrated into a complete design using the .compose() primitive. This approach, unachievable by prior top-down methods, significantly enhances productivity and debuggability.

Installation

Please clone the Allo repository to your local machine.

git clone https://github.com/cornell-zhang/allo.git
cd allo

We recommend creating a new conda environment for Allo. Since we are using the latest Python features, the minimum Python version is 3.12.

conda create -n allo python=3.12
conda activate allo

Prerequisites

We need to first install the LLVM project and the hcl-mlir dialect. Users can choose to use our provided docker or build from source.

Docker

To simplify the installation process, we provide a docker image that has already installed the LLVM-18.x project. Please pull the image from Docker Hub, patch LLVM, and install the hcl dialect as described above.

# * The LLVM is installed in /root/llvm-project in the docker image, which has already been patched
# * A prebuilt hcl-dialect is installed in /root/hcl-dialect, but please note that it is not up-to-date
#   You can pull the latest hcl-dialect using `git pull` and rebuild it if needed
docker pull chhzh123/hcl-dialect:llvm-18.x-py3.12
docker run --rm -it chhzh123/hcl-dialect:llvm-18.x-py3.12

Build from source

Users can also choose to build LLVM and the hcl dialect from source. Please follow the instructions below.

# Make sure you are under the correct Python environment
bash build.sh

Install Allo

After installing LLVM and the hcl dialect, we can directly pip install Allo:

# Under the root directory of Allo
python3 -m pip install -e .

Getting Started

Below is a minimal example of leveraging Allo to customize a GEMM kernel:

import allo
from allo.ir.types import int32

# Allo kernel definition
def gemm(A: int32[32, 32], B: int32[32, 32]) -> int32[32, 32]:
    C: int32[32, 32] = 0
    for i, j, k in allo.grid(32, 32, 32):
        C[i, j] += A[i, k] * B[k, j]
    return C

# Schedule construction
s = allo.customize(gemm)

# Real-time transformation
s.split("i", factor=8)
print(s.module)

# Compilation
mod = s.build(target="llvm")

# Execution
import numpy as np
np_A = np.random.randint(0, 100, (32, 32)).astype(np.int32)
np_B = np.random.randint(0, 100, (32, 32)).astype(np.int32)
np_C = mod(np_A, np_B)

# Testing
golden_C = np.matmul(np_A, np_B)
np.testing.assert_allclose(np_C, golden_C, rtol=1e-5, atol=1e-5)

Publications

Please refer to our PLDI'24 paper for more details. If you use Allo in your research, please use the following bibtex entry to cite us:

@article{chen2024allo,
    author = {Hongzheng Chen and Niansong Zhang and Shaojie Xiang and Zhichen Zeng and Mengjia Dai and Zhiru Zhang},
    title = {Allo: A Programming Model for Composable Accelerator Design},
    journal = {Proc. ACM Program. Lang.},
    year = {2024},
    month = {jun},
    url = {https://doi.org/10.1145/3656401},
    doi = {10.1145/3656401},
    articleno = {171},
    volume = {8},
    number = {PLDI},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    issue_date = {June 2024},
}

Related Projects

• Accelerator Programming Languages: Exo, Halide, TVM
• Accelerator Design Languages: Dahlia, HeteroCL, PyLog, ScaleHLS, Spatial
• Compiler Frameworks: MLIR