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.