tensor-bridge 0.2.0

Transfer tensors between PyTorch, Jax and more

tensor-bridge

tensor-bridge is a light-weight library that achieves inter-library tensor transfer by native cudaMemcpy call with minimal overheads.

import torch
import jax
from tensor_bridge import copy_tensor


# PyTorch tensor
torch_data = torch.rand(2, 3, 4, device="cuda:0")

# Jax tensor
jax_data = jax.random.uniform(jax.random.key(123), shape=(2, 3, 4))

# Copy Jax tensor to PyTorch tensor
copy_tensor(torch_data, jax_data)

# And, other way around
copy_tensor(jax_data, torch_data)

:warning: Currently, this repository is under active development. Especially, transfer between different layout of tensors is not implemented yet. I recommend to try copy_tensor_with_assertion before starting experiments. copy_tensor_with_assertion will raise an error if copy doesn't work. If copy_tensor_with_assertion raises an error, you need to force the tensor to be contiguous:

# PyTorch example

# different layout raises an error
a = torch.rand(2, 3, device="cuda:0")
b = torch.rand(3, 2, device="cuda:0").transpose(0, 1)
copy_tensor_with_assertion(a, b)  # AssertionError !!

# make both tensors contiguous layout
b = b.contiguous()
copy_tensor_with_assertion(a, b)

Since copy_tensor_with_assertion does additional GPU-CPU transfer internally, make sure that you switch to copy_tensor in your experiments. Otherwise your training loop will be significantly slower.

Features

• Fast inter-library tensor copy.
• Inter-GPU copy (I believe this is supported with the current implementation. But, not tested yet.)

Supported deep learning libraries

• PyTorch
• Jax
• nnabla

Installation

PyPI

If pip installation doesn't work, please try installation from source code.

Python 3.10.x

You can install a pre-built package.

pip install tensor-bridge

Other Python version

Your macine needs to install nvcc to compile a native code and Cython to compile .pyx files.

pip install Cython==0.29.36
pip install tensor-bridge

Pre-built packages for other Python versions are in progress.

From souce code

Your macine needs to install nvcc to compile a native code and Cython to compile .pyx files.

git clone [e-mail]:takuseno/tensor-bridge
cd tensor-bridge
pip install Cython==0.29.36
pip install -e .

Unit test

Your machine needs to install NVIDIA's GPU and nvidia-driver to execute tests.

./bin/build-docker
./bin/test

Benchmark

To benchmark round trip copies between Jax and PyTorch:

./bin/build-docker
./bin/benchmark

This is result with my local desktop with RTX4070.

Benchmarking copy_tensor...
Average compute time: 1.3043880462646485e-05 sec
Benchmarking copy via CPU...
Average compute time: 0.0016725873947143555 sec
Benchmarking dlpack...
Average compute time: 7.467031478881836e-05 sec

copy_tensor is surprisingly faster than DLPack. Looking at PyTorch's implementation, it seems that PyTorch does additional CUDA stream synchronization, which adds additional compute time.