Repository: pytorch
Issue: addcdiv_ (in and out of place) not implemented for torch.float16 and cpu
Labels: low priority,triaged,enhancement,module: half

### 🐛 Describe the bug

Running addcdiv_ or addcdiv with torch.float16 and on cpu = 
RuntimeError: "addcdiv_cpu_out" not implemented for 'Half'

Probably not a pressing use case, but if people are still using fp16 it would prove an issue:
code to repro:
~~~
use_type = torch.float16

alpha = .80
tsize = 2000000
data_tensor = torch.randn(tsize,dtype=use_type)

atensor = torch.randn(tsize, dtype=use_type)
btensor = torch.randn(tsize, dtype=use_type)

#clones
data_clone = data_tensor.clone()
aclone = atensor.clone()
bclone = btensor.clone()

# inplace
data_tensor.addcdiv_(atensor, btensor,value=-alpha)
# out of place
data_clone = torch.addcdiv(data_clone, aclone, bclone, value=-alpha)

# verify
torch.equal(data_tensor, data_clone)
~~~

Results:
<img width="797" alt="addcdiv_half" src="https://user-images.githubusercontent.com/46302957/184212804-40a1b788-748e-45fa-87e3-8ad1570f574f.png">

You'll get the same if you comment the first addcdiv out and move to the out of place.



### Versions


Collecting environment information...
PyTorch version: 1.13.0.dev20220810+cu113
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.22.3
Libc version: glibc-2.27

Python version: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:56:21) [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-1081-aws-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: 11.6.124
GPU models and configuration:
GPU 0: NVIDIA A10G
GPU 1: NVIDIA A10G
GPU 2: NVIDIA A10G
GPU 3: NVIDIA A10G

Nvidia driver version: 510.47.03
cuDNN version: Probably one of the following:
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.1.1
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.1.1
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==1.22.4
[pip3] numpydoc==1.4.0
[pip3] torch==1.13.0.dev20220810+cu113
[pip3] torch-model-archiver==0.6.0b20220513
[pip3] torch-workflow-archiver==0.2.4b20220513
[pip3] torchserve==0.6.0b20220513
[pip3] torchtext==0.13.0
[pip3] torchvision==0.14.0.dev20220810+cu113
[conda] blas 2.115 mkl conda-forge
[conda] blas-devel 3.9.0 15_linux64_mkl conda-forge
[conda] captum 0.5.0 0 pytorch
[conda] cudatoolkit 11.6.0 hecad31d_10 conda-forge
[conda] libblas 3.9.0 15_linux64_mkl conda-forge
[conda] libcblas 3.9.0 15_linux64_mkl conda-forge
[conda] liblapack 3.9.0 15_linux64_mkl conda-forge
[conda] liblapacke 3.9.0 15_linux64_mkl conda-forge
[conda] magma-cuda116 2.6.1 0 pytorch
[conda] mkl 2022.1.0 h84fe81f_915 conda-forge
[conda] mkl-devel 2022.1.0 ha770c72_916 conda-forge
[conda] mkl-include 2022.1.0 h84fe81f_915 conda-forge
[conda] mkl-service 2.4.0 py39hb699420_0 conda-forge
[conda] mkl_fft 1.3.1 py39h1fd5c3a_3 conda-forge
[conda] mkl_random 1.2.2 py39h8b66066_1 conda-forge
[conda] mypy_extensions 0.4.3 py39hf3d152e_5 conda-forge
[conda] numpy 1.22.4 py39hc58783e_0 conda-forge
[conda] numpydoc 1.4.0 pyhd8ed1ab_1 conda-forge
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch 1.13.0.dev20220810+cu113 pypi_0 pypi
[conda] torch-model-archiver 0.6.0 py39_0 pytorch
[conda] torch-workflow-archiver 0.2.4 py39_0 pytorch
[conda] torchserve 0.6.0 py39_0 pytorch
[conda] torchtext 0.13.0 py39 pytorch
[conda] torchvision 0.14.0.dev20220810+cu113 pypi_0 pypi