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Mean_cuda not implemented for complex types #46982
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function requestA request for a new function or the addition of new arguments/modes to an existing function.A request for a new function or the addition of new arguments/modes to an existing function.good first issuemodule: complexRelated to complex number support in PyTorchRelated to complex number support in PyTorchtriagedThis 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
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function requestA request for a new function or the addition of new arguments/modes to an existing function.A request for a new function or the addition of new arguments/modes to an existing function.good first issuemodule: complexRelated to complex number support in PyTorchRelated to complex number support in PyTorchtriagedThis 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
🐛 Bug
Tensor averaging not implemented for complex types
To Reproduce
Same for float,
torch.complex64,torch.complex32.Expected behaviour
Calculates mean across tensor, e.g.
Environment
PyTorch version: 1.8.0.dev20201027
Is debug build: True
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Manjaro Linux (x86_64)
GCC version: (GCC) 10.2.0
Clang version: 10.0.1
CMake version: version 3.18.3
Python version: 3.8 (64-bit runtime)
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: GeForce GTX 970
Nvidia driver version: 450.80.02
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.8.0.2
/usr/lib/libcudnn_adv_infer.so.8.0.2
/usr/lib/libcudnn_adv_train.so.8.0.2
/usr/lib/libcudnn_cnn_infer.so.8.0.2
/usr/lib/libcudnn_cnn_train.so.8.0.2
/usr/lib/libcudnn_ops_infer.so.8.0.2
/usr/lib/libcudnn_ops_train.so.8.0.2
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] numpy==1.19.2
[pip3] torch==1.8.0.dev20201027
[pip3] torchvision==0.9.0.dev20201027
[pip3] torchviz==0.0.1
[conda] blas 1.0 mkl
[conda] cudatoolkit 10.2.89 hfd86e86_1
[conda] mkl 2020.2 256
[conda] mkl-service 2.3.0 py38he904b0f_0
[conda] mkl_fft 1.2.0 py38h23d657b_0
[conda] mkl_random 1.1.1 py38h0573a6f_0
[conda] numpy 1.19.2 py38h54aff64_0
[conda] numpy-base 1.19.2 py38hfa32c7d_0
[conda] torch 1.8.0.dev20201026 pypi_0 pypi
[conda] torchvision 0.9.0.dev20201027 pypi_0 pypi
[conda] torchviz 0.0.1 pypi_0 pypi
cc @ezyang @anjali411 @dylanbespalko @mruberry