Fusing Taichi into PyTorch
PRs are always welcomed, please see TODOs and issues.
In differentiable rendering including neural rendering, rendering algorithms are transferred to the field of computer vision, but some rendering operations (e.g., ray tracing and direct volume rendering) are not easy to be expressed in tensor operations but in kernels. Differentiable kernels of Taichi enables fast, efficient and differentiable implementation of rendering algorithms while tensor operators provides math expressiveness.
Stannum bridges Taichi and PyTorch to have advantage of both kernel-based and operator-based parallelism.
Please see documentation.
Code sample of Tube:
from stannum import Tube
import taichi as ti
import torch
@ti.kernel
def mul(arr: ti.template(), out: ti.template()):
for i in arr:
out[i] = arr[i] * 2.0
if __name__ == "__main__":
ti.init(ti.cpu)
a = torch.ones(10, requires_grad=True)
tube = Tube() \
.register_input_tensor((10,), torch.float32, "arr") \
.register_output_tensor((10,), torch.float32, "out", True) \
.register_kernel(mul, ["arr", "out"]) \
.finish()
out = tube(a)
loss = out.sum()
loss.backward()
assert torch.allclose(out, torch.ones_like(out) * 2)
assert torch.allclose(a.grad, torch.ones_like(a) * 2)Install stannum with pip by
python -m pip install stannum
Make sure you have the following installed:
- PyTorch
- latest Taichi
- For performance concerns, we strongly recommend to use Taichi >= 1.1.3 (see Issue #9 for more information)
Please feel free to file issues. If a runtime error occurs from the dependencies of stannum, you may also want to check the upstream breaking change tracker.