Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Different tensor computation result between using CPU and GPU devices #255

Open
xiangfuli opened this issue Jul 5, 2023 · 1 comment
Open

Different tensor computation result between using CPU and GPU devices #255

xiangfuli opened this issue Jul 5, 2023 · 1 comment

Comments

@xiangfuli
Copy link

🐛 Describe the bug

During the development of the skew2vec function (trying to transform the skew-symmetric matrix to the 3D vector), I found when using this formula err_pose = (torch.mm(desired_pose.T, Rwb) - torch.mm(Rwb.T, desired_pose))(All tensor is 3x3 matrix) which is similar with torch.mm(A, B.T) - torch.mm(B, A.T), the result will not match the skew-symmetric characteristic and the difference is pretty small.

The implementation of skew2vec function is:

def skew2vec(input:torch.Tensor) -> torch.Tensor:
    v = input.tensor() if hasattr(input, 'ltype') else input
    assert v.shape[-2:] == (3, 3), "Last 2 dim should be (3, 3)"
    assert torch.equal(v.permute(0, 2, 1), -v), "Each matrix must be a skew matrix"

    return torch.stack([torch.stack([-v[..., 1, 2]], dim=-1),
                            torch.stack([ v[..., 0, 2]], dim=-1),
                            torch.stack([-v[..., 0, 1]], dim=-1)], dim=-1)

The following is the tensor calculation result when I use cuda type tensor.

tensor([[[ 0.00000000000000000000, -0.00632718438454868588, -0.18133159863325493122],
[ 0.00632718438454868675,  0.00000000000000000000, -0.18298103584576658198],
[ 0.18133159863325493122,  0.18298103584576663749, 0.00000000000000000000]]], device='cuda:0', dtype=torch.float64)

which will cause the second assertion in the skew2vec function being failed. The PyTorch official is aware of this NUMERICAL ACCURACY problem.

I also found some functions have added the rtol and atol parameters in the method, e.g. this mat2SO3 function

pypose/pypose/lietensor/convert.py

Line 258 in 3b31492

def mat2Sim3(mat, check=True, rtol=1e-5, atol=1e-5):
.

Maybe the best way to also add the rtol and atol in the skew2vec parameters and use torch.allclose to check the equality?

Versions

Collecting environment information...
PyTorch version: 2.0.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.26.3
Libc version: glibc-2.31

Python version: 3.10.9 (main, Mar  1 2023, 18:23:06) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1036-gcp-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 450.236.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.5
[pip3] numpydoc==1.5.0
[pip3] torch==2.0.1
[conda] blas                      1.0                         mkl
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-service               2.4.0           py310h7f8727e_0
[conda] mkl_fft                   1.3.1           py310hd6ae3a3_0
[conda] mkl_random                1.2.2           py310h00e6091_0
[conda] numpy                     1.23.5          py310hd5efca6_0
[conda] numpy-base                1.23.5          py310h8e6c178_0
[conda] numpydoc                  1.5.0           py310h06a4308_0
[conda] torch                     2.0.1                    pypi_0    pypi
@wang-chen
Copy link
Member

I agree with this change, @xiangfuli you may feel free to change it and create a PR. Thank you so much!

@xiangfuli xiangfuli mentioned this issue Jul 8, 2023
Open
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
Towards Next Major Release
Status: No status
Milestone
No milestone
Development

No branches or pull requests
2 participants
@wang-chen @xiangfuli