https://github.com/sniklaus/pytorch-extension

an example of a CUDA extension for PyTorch using CuPy which computes the Hadamard product of two tensors
https://github.com/sniklaus/pytorch-extension

cuda cupy deep-learning python pytorch

Last synced: 3 months ago
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an example of a CUDA extension for PyTorch using CuPy which computes the Hadamard product of two tensors

• Host: GitHub
• URL: https://github.com/sniklaus/pytorch-extension
• Owner: sniklaus
• License: gpl-3.0
• Created: 2017-07-01T04:49:16.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-03-01T17:01:28.000Z (11 months ago)
• Last Synced: 2024-08-04T00:12:49.915Z (6 months ago)
• Topics: cuda, cupy, deep-learning, python, pytorch
• Language: Python
• Homepage:
• Size: 41 KB
• Stars: 118
• Watchers: 6
• Forks: 14
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pytorch-extension

This is an example of a CUDA extension/function/layer for PyTorch which uses CuPy to compute the Hadamard product of two tensors.

For a more advanced extension that uses CuPy as well, please see: https://github.com/sniklaus/softmax-splatting




And yet another extension that uses CuPy can be found here: https://github.com/sniklaus/revisiting-sepconv

## setup

Make sure to install CuPy, which can be done using `pip install cupy` or alternatively using one of the provided [binary packages](https://docs.cupy.dev/en/stable/install.html#installing-cupy) as outlined in the CuPy repository.

## usage

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.

```python

import torch

import hadamard # the custom layer

class Network(torch.nn.Module):

	def __init__(self):

		super().__init__()

	# end

	def forward(self, tenOne, tenTwo):

		return hadamard.hadamard_func.apply(tenOne, tenTwo)

	# end

# end

netNetwork = Network().cuda()

tenOne = torch.rand(size=[64, 3, 128, 128], dtype=torch.float32, device=torch.device('cuda')).requires_grad_()

tenTwo = torch.rand(size=[64, 3, 128, 128], dtype=torch.float32, device=torch.device('cuda')).requires_grad_()

tenOut = netNetwork(tenOne, tenTwo)

tenExpected = torch.mul(tenOne, tenTwo)

print(torch.sum(tenOut.data - tenExpected.data), '<-- should be 0.0')

```

## license

Please refer to the appropriate file within this repository.