Thanks for the code snippet.
I cannot reproduce the issue using the 1.6.0 conda binaries:
Collecting environment information...
PyTorch version: 1.6.0
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.3 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.10.2
Python version: 3.7 (64-bit runtime)
Is CUDA available: True
CUDA runtime version: 10.2.86
GPU models and configuration:
GPU 0: GeForce RTX 2080 Ti
GPU 1: TITAN V

Nvidia driver version: 450.51.06
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] numpy==1.19.1
[pip3] numpydoc==1.1.0
[pip3] torch==1.6.0
[pip3] torchvision==0.7.0
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               10.2.89              hfd86e86_1
[conda] mkl                       2020.2                      256
[conda] mkl-service               2.3.0            py37he904b0f_0
[conda] mkl_fft                   1.1.0            py37h23d657b_0
[conda] mkl_random                1.1.1            py37h0573a6f_0
[conda] numpy                     1.19.1           py37hbc911f0_0
[conda] numpy-base                1.19.1           py37hfa32c7d_0
[conda] numpydoc                  1.1.0                      py_0
[conda] pytorch                   1.6.0           py3.7_cuda10.2.89_cudnn7.6.5_0    pytorch
[conda] torchvision               0.7.0                py37_cu102    pytorch

and using an  Intel(R) Core(TM) i7-5820K CPU @ 3.30GHz.
However, based on the env outputs, it seems I’m using a newer mkl than you are.
Could you try to install the conda binaries (I’m unsure if the pip wheels ship with an older mkl version) and rerun the code?
If you are still facing this error, could you please create an issue on GitHub so that we could track and fix it?