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module: deprecationmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulmodule: nnRelated to torch.nnRelated to torch.nntriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module
Description
🐛 Bug
Hi, it looks like torch::nn::init::orthogonal_ calls a deprecated function internally, which prints the following warning:
[W BatchLinearAlgebra.cpp:1940] Warning: torch.qr is deprecated in favor of torch.linalg.qr and will be removed in a future PyTorch release.
The boolean parameter 'some' has been replaced with a string parameter 'mode'.
Q, R = torch.qr(A, some)
should be replaced with
Q, R = torch.linalg.qr(A, 'reduced' if some else 'complete') (function operator())To Reproduce
Compile and run the following script:
#include <torch/torch.h>
int main() {
auto x = torch::empty({3, 5});
torch::nn::init::orthogonal_(x);
}Expected behavior
No warning
Environment
- PyTorch Version (e.g., 1.0): LibTorch 1.9.0
- OS (e.g., Linux): macOS 11.4
- How you installed PyTorch (
conda,pip, source): Downloaded from pytorch.org - Build command you used (if compiling from source): n/a
- Python version: n/a
- CUDA/cuDNN version: None
- GPU models and configuration: n/a
cc @jianyuh @nikitaved @pearu @mruberry @heitorschueroff @walterddr @IvanYashchuk @xwang233 @lezcano @albanD @jbschlosser
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module: deprecationmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulmodule: nnRelated to torch.nnRelated to torch.nntriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module