README.md

Keras Frontend

Install Keras Frontend after moving to directory /hpvm/hpvm/projects/keras

Requirements

• python == 3.6.x
• pip >= 18

If your system uses a different Python version, we recommend using the conda environment keras_python36.yml. Install this using:

conda env create -f keras_python36.yml --name keras_python36

Activate the conda environment before installing the pip package (below) using:

conda activate keras_python36

NOTE: This step must be performed each time (for each shell process) the frontend is to be used.

Installing the Keras Frontend Package

At the root of this project (/projects/keras/) install the Keras frontend pip package as:

pip3 install -e ./

NOTE: If you are using the conda environment, activate it prior to this step.

Suppported Operations

List of supported operations and limitations detailed in https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/docs/Support.md

Keras Benchmarks

Run the Keras benchmarks under hpvm/hpvm/test/dnn_benchmarks/keras

Download CNN Model Files

Prior to running the benchmarks, ensure you download the CNN model data (inputs and weights) if not done in automatic build script.

wget https://databank.illinois.edu/datafiles/o3izd/download -O model_params.tar.gz
tar -xf  model_params.tar.gz

Move extracted model_params directory to /test/dnn_benchmarks/model_params (Benchmarks expect data at this location)

Running Benchmaks

List of benchmarks and the expected accuracies:

Benchmark	Accuracy
alexnet.py	79.28
alexnet2.py	84.98
alexnet_imagenet.py	56.30
lenet.py	98.70
mobilenet_cifar10.py	84.42
resnet18_cifar10.py	89.56
resnet50_imagenet.py	75.10
vgg16_cifar10.py	89.96
vgg16_cifar100.py	66.50
vgg16_imagenet.py	69.46

Synopsis

python3 ${BENCH_NAME}.py  [hpvm_reload|keras_reload]  [frontend] [compile]

Command-line Parameters

hpvm_reload : Reloads HPVM weights ('.bin' binary format used in model_params found here: https://gitlab.engr.illinois.edu/llvm/hpvm/-/tree/approx_hpvm_reorg_keras/hpvm/test/dnn_benchmarks/model_params) from directory path specified in the reload_dir parameter set in code - this is described in "Parameters to Change in Code" (below).

keras_reload: Alternatively, reload weights in Keras .h5 file format with path to file specified in keras_model_file described in "Parameters to Change in Code" (below).

frontend: Invokes the HPVM frontend and dumps weights (in HPVM .bin format) in the output directory specified. The parameters that control where data and source files are dumped are specified by parameters data_dir and src_dir, respectively. These are described below.

compile: Optional Parameter. When specified, it compiles the HPVM-C code generated by the frontend into an HPVM binary under the directory specified by src_dir (described below). If src_dir path exists, a unique directory (which appends a unique ID) is created. The binary is built with the name HPVM_binary.

NOTE: Before running HPVM_binary necessary to set CUDA and CUDNN paths with:

source ${PATH_TO_YOUR_HPVM_ROOT}/hpvm/set_paths.sh

Parameters to Change in Code

The AlexNet source is commented with explanations on how to use the Keras frontend interface. AlexNet source is here.

• NAME: Benchmark Name - Can be set to any desired value

• reload_dir: Path to directory from where to reload weights in HPVM format. This directory is used to reload weights if hpvm_reload command-line option is used.

• keras_model_file: Path to Keras .h5 model file to reload weigths from. Either of reload_dir or keras_model_file can be used. keras_model_file is used when keras_reload commad-line parameter is used with the Benchmark script.

• data_dir: Directory to dump weights specified specified in constructor

• src_dir: Directory to dump ApproxHPVM sources in HPVM-C (C with HPVM compiler intrinsics) specified in constructor

• num_classes: number of output classes - dependent on the dataset used. For CIFAR10, num_classes is 10, CIFAR100 has 100 classes, for ImageNet, number of classes is 1000.

• batch_size: This parameter controls the size of each batch that is processed in HPVM. The batch size should be kept as large as the GPU memory can support. This parameter should be adapted according to the memory size of the deployed device.

Using the Frontend with Custom (New) Benchmarks

Any new benchmarks must inherit from the commom parent Benchmark class and override the virtual functions for building the model, training, and data preprocessing. These methods are described below:

def buildModel(self): Constructs and returns a keras model

def data_preprocess(self): returns X_train, y_train, X_test, y_test, X_tuner, and y_tuner data (in that order): These are described here:

• X_train: Training data (fp32) in NCHW format

• y_train: Training labels (int32)

• X_test: Testing/Evaluation data in NCHW format

• y_test: Testing/Evaluation labels

• X_tuner: Data to be used for autotuning

• y_tuner: Labels corresponding to tuning data

def trainModel(self, model, X_train, y_train, X_test, y_test): Trains the Keras model constructed in buildModel and is expected to return the trained keras model - training parameters should be tuned here.

Directly using Keras Frontend API

Alternate to extending the Benchmark class, users may directly invoke the Keras Frontend API. This can be done as:

from keras_frontend.approxhpvm_translator import translate_to_approxhpvm

# Construct and train your Keras Model (or load pre-trained weights)

translate_to_approxhpvm(model, data_dir, src_dir, test_data, test_labels, tune_data, tune_labels, batch_size, num_classes)

Automated Tests

scripts/test_benchmarks.py is an automated test script that evaluates the accuracy of each Benchmark in Keras and HPVM (after comilation using HPVM Compiler) and compares the accuracy of each binary to the known correct accuracy. Run from root of /test/dnn_benchmarks/keras:

python test_benchmarks.py