This is an example of a CUDA extension for PyTorch which uses CuPy to compute the Hadamard product of two tensors.
For a more advanced PyTorch extension that uses CuPy as well, please see: https://github.com/szagoruyko/pyinn
Make sure to install CuPy, which can be done using
pip install cupy or alternatively using one of the provided binary packages as outlined in the CuPy repository.
There is no separate build process necessary, simply run
python run.py to test it. A minimal example of how the sample extension can be used is also shown below.
import torch import hadamard class Network(torch.nn.Module): def __init__(self): super(Network, self).__init__() # end def forward(self, input1, input2): return hadamard.Hadamard.apply(input1, input2) # end # end net = Network().cuda() input1 = torch.rand(64, 3, 128, 128).cuda() input2 = torch.rand(64, 3, 128, 128).cuda() input1 = input1.requires_grad_() input2 = input2.requires_grad_() output = net(input1, input2) expected = torch.mul(input1, input2) print(torch.sum(output.data - expected.data), '<-- should be 0.0')
Please refer to the appropriate file within this repository.