Repository: pytorch
Issue: ## 🐛 Bug: QNNPACK tests failing on master on Nexus 6
Labels: oncall: mobile

## 🐛 Bug: QNNPACK tests failing on master on Nexus 6

QNNPACK tests not passing on Android armv7 on clean master (Nexus 6)

## To Reproduce

Steps to reproduce the behavior:

1. check out ebf743a63a  nshulga  (master*, HEAD) Fix bazel-test linking issue (#36157)

2. build the tests and push to device:
cd aten/src/ATen/native/quantized/cpu/qnnpack
scripts/build-android-armv7.sh
adb push ./build/android/armeabi-v7a/{your_binary} /data/qnnpack

3. run tests
adb shell
for t in $(ls /data/qnnpack); do /data/qnnpack/$t; done

4. results: 

failing excerpts:
```
[----------] Global test environment tear-down                          
[==========] 152 tests from 8 test cases ran. (1969 ms total)                                           
[  PASSED  ] 122 tests.                                      
[  FAILED  ] 30 tests, listed below:                                
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_eq_8               
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_eq_8_strided_a             
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_eq_8_strided_c             
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_eq_8_qmin128                       
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_eq_8_qmax128               
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_eq_8_bzp0                          
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_gt_8                           
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_gt_8_strided_a                        
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_gt_8_strided_c                                            
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_gt_8_bzp0               
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_gt_8_subtile               
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_div_8                              
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_div_8_strided_a            
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_div_8_strided_c                    
[  FAILED  ] Q8GEMM_4x8c2_XZP__AARCH32_NEON.k_div_8_subtile                  
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_eq_8                                          
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_eq_8_strided_a                        
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_eq_8_strided_c                                
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_eq_8_qmin128              
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_eq_8_qmax128                                                      
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_eq_8_bzp0                             
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_gt_8                                     
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_gt_8_strided_a                    
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_gt_8_strided_c                           
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_gt_8_bzp0                             
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_gt_8_subtile                                  
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_div_8                              
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_div_8_strided_a                
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_div_8_strided_c             
[  FAILED  ] Q8GEMM_4x8c2_XZP__NEON.k_div_8_subtile                       

```

## Expected behavior

tests pass

## Environment

Please copy and paste the output from our
[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py)
(or fill out the checklist below manually).

Collecting environment information...                                                             
PyTorch version: 1.6.0a0+443369a                                                                  
Is debug build: No                                                                                
CUDA used to build PyTorch: Could not collect                                                     
                                                                                                  
OS: CentOS Linux 7 (Core)                                                                         
GCC version: (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5)                                               
CMake version: version 3.14.0                                                                     
                                                                                                  
Python version: 3.8                                                                               
Is CUDA available: No                                                                             
CUDA runtime version: Could not collect                                                           
GPU models and configuration:                                                                     
GPU 0: Tesla M40                                                                                  
GPU 1: Tesla M40                                                                                  
                                                                                                  
Nvidia driver version: 396.69                                                                     
cuDNN version: Could not collect                                                                  
                                                                                                  
Versions of relevant libraries:                                                                   
[pip] numpy==1.18.1                                                                               
[pip] torch==1.6.0a0+8092465                                                                      
[conda] blas                      1.0                         mkl                                 
[conda] magma-cuda92              2.5.1                         1    pytorch                      
[conda] mkl                       2020.0                      166                                 
[conda] mkl-include               2020.0                      166                                 
[conda] mkl-service               2.3.0            py38he904b0f_0                                 
[conda] mkl_fft                   1.0.15           py38ha843d7b_0                                 
[conda] mkl_random                1.1.0            py38h962f231_0                                 
[conda] numpy                     1.18.1           py38h4f9e942_0                                 
[conda] numpy-base                1.18.1           py38hde5b4d6_1                                 
[conda] torch                     1.5.0a0+15c6d93          pypi_0    pypi                         
(pytorch) [vasiliy@devgpu167.prn2 ~/local/pytorch/aten/src/ATen/native/quantized/cpu/qnnpack]     

## Additional context

<!-- Add any other context about the problem here. -->