quantize_per_tensor get diffrenet results w/o setting OMP_NUM_THREADS=1 #80501
Description
🐛 Describe the bug
For python file below (we call test_quant.py)
import torch
torch.manual_seed(20)
w = torch.randn(39979771, 128)
scales = 0.00187
zp = 0
i8_arg = torch.quantize_per_tensor(w, scales, zp, torch.qint8)
arg = i8_arg.dequantize()
print(w[10622505])
print(i8_arg[10622505])python test_quant.py
OMP_NUM_THREADS=1 python test_quant.py
will get different i8_args.
Versions
Collecting environment information...
PyTorch version: 1.13.0a0+git0922cc0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: CentOS Stream 8 (x86_64)
GCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-10)
Clang version: 13.0.0 (Red Hat 13.0.0-3.module_el8.6.0+1074+380cef3f)
CMake version: version 3.19.6
Libc version: glibc-2.10
Python version: 3.7.7 (default, Mar 26 2020, 15:48:22) [GCC 7.3.0] (64-bit runtime)
Python platform: Linux-4.18.0-365.el8.x86_64-x86_64-with-centos-8
Is CUDA available: False
CUDA runtime version: No CUDA
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Versions of relevant libraries:
[pip3] intel-extension-for-pytorch==1.12.0+cpu
[pip3] numpy==1.21.2
[pip3] torch==1.13.0a0+git0922cc0
[conda] blas 1.0 mkl
[conda] intel-extension-for-pytorch 1.12.0+cpu pypi_0 pypi
[conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-include 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py37h7f8727e_0
[conda] mkl_fft 1.3.1 py37hd3c417c_0
[conda] mkl_random 1.2.2 py37h51133e4_0
[conda] numpy 1.21.2 py37h20f2e39_0
[conda] numpy-base 1.21.2 py37h79a1101_0
[conda] torch 1.13.0a0+git0922cc0 dev_0