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Hashim Sharif authored
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HPVM Tensor Runtime

Dependencies

  • CUDA >= 9.1

  • cuDNN >= 7

Building Tensor Runtime

Tensor Runtime and the DNN sources using the Tensor runtime are built with the unified HPVM build system. These can also be separately built. HPVM Tensor Runtime can be built under the build directory as:

make -j ${NUM_THREADS} tensor_runtime

The tensor runtime is built as a static library under build/lib/liibtensor_runtime.a

TensorRT DNN Benchmarks

To assist development of tensor-based programs using only the tensor runtime, we include sources under dnn_sources that directly invoke the HPVM Tensor Runtime API calls for tensor operations, e.g., convolution, matrix multiplication, add, relu, among others.

Each benchmark can be build under your build directory as:

make -j ${NUM_THREADS} ${BENCHMARK}

Currently, 17 Benchmarks included:

lenet_mnist_fp32 lenet_mnist_fp16
alexnet_cifar10_fp32 alexnet_cifar10_fp16
alexnet2_cifar10_fp32 alexnet2_cifar10_fp16
vgg16_cifar10_fp32 vgg16_cifar10_fp16
vgg16_cifar100_fp32 vgg16_cifar100_fp16
mobilenet_cifar10_fp32 mobilenet_cifar10_fp16
resnet18_cifar10_fp32 resnet18_cifar10_fp16
alexnet_imagenet_fp32
vgg16_imagenet_fp32
resnet50_imagenet_fp32

_fp32 suffix denotes fp32 binaries - these use the FP32 API calls

_fp_16 suffix denotes fp16 binaries - these use FP16 (half precision) calls.