The HPVM Compiler Infrastructure

This repository contains the source code and documentation for the HPVM Compiler Infrastructure.

HPVM is a compiler for heterogeneous parallel system. For more about what HPVM is, see our website and publications: PPoPP'18 paper, OOPSLA'19 paper, PPoPP'21 paper.

Read our online documentation for how to build, install, and use HPVM.

HPVM is currently at version 1.0.

Getting Started

Dependencies

The following components are mandatory for building HPVM:

• GCC (>=5.1)
• CMake (>=3.17)
• GNU Make (>=3.79) or Ninja (>=1.10)

Building HPVM

1. Install Anaconda, a Python package manager. This provides the command conda.

2. Clone this branch (hpvm-release-epochs0) of this repository:

   git clone -b hpvm-release-epochs0 --single-branch https://gitlab.engr.illinois.edu/llvm/hpvm.git

3. Find env.yaml in the repo's root, and use conda to create a virtual environment with Python 3.7 and all required packages installed:

   cd hpvm/
   conda env create -n hpvm -f ./env.yaml
   conda activate hpvm

4. Change directory to ./hpvm in the repo and run the installer with recommended options:

   cd hpvm/; ./install.sh -t "X86"

   This will download additional dependencies, configure HPVM and build HPVM.

   • If you have ninja installed (in the place of make), you may use ./install.sh --ninja -t "X86" to build HPVM with ninja.

Generating NVDLA Buffer from PyTorch DNN

Run test/epoch_dnn/main.py to generate the PyTorch DNN defined at test/epoch_dnn/torch_dnn/miniera.py into NVDLA, and evaluate it on real hardware:

cd test/epoch_dnn
# rm -r /tmp/miniera  # Do this if you have run main.py before
python main.py

This script will run through the whole code generation (putting everything under /tmp/miniera) and evaluation, including

1. Generate quantization scale calib.txt for INT8 quantization;
2. Export DNN to NVDLA buffer hpvm-mod.nvdla;
3. Select a sample of images from the dataset and put them as JPG files (images/$idx_$label.jpg); for example, the 0th image which has label 1 will be images/0000_1.jpg;
4. Copy NVDLA model and images to an NVDLA device via SCP;
5. Run the NVDLA model on every image we sampled;
6. Display the accuracy of this model.

HPVM Scheduler Backend

We provide the HPVM Backend pass DFG2LLVM_EPOCHS which converts those Dataflow Graph (DFG) nodes which represent specific tasks from the scheduler's task library (e.g FFT, Viterbi). To specify that a particular node corresponds to a scheduler task, users must specify the target hint to node be EPOCHS_TARGET.

__hpvm__hint(EPOCHS_TARGET)

If a particular node in the DFG is marked as above, the DFG2LLVM_EPOCHS backend pass would then identify which task from the scheduler's task library that node corresponds by using the user specified call to __hpvm__task. For example, to specify that a node represents a Vitterbi Task in the accelerator, the following call will be used:

__hpvm__task(VIT_TASK)

Using this information, alongside a task library configuration file (which describes the specific scheduler api to set-up and execute each task), the DFG2LLVM_EPOCHS pass generates the appropriate task specific api calls while respecting the task inter-dependency constraints (as denoted by the structure of the HPVM Dataflow Graph).

To add a new task type to this pass, users must simply add a new task type entry to __hpvm__task and create the entry for the name of the specific scheduler api calls to the task library configuration file.

Support

All questions can be directed to hpvm-dev@lists.cs.illinois.edu.