Repository: pytorch
Issue: Noticing a Significant Slowdown in Inference Time with PyTorch 2.2 Compared to PyTorch 1.10
Labels: module: performance,module: cuda,triaged

### 🐛 Describe the bug

I encountered an issue when upgrading from PyTorch 1.10 with CUDA 11.3 to PyTorch 2.2 with CUDA 12.1. Both environments run without errors, but I am unsure why there is such a significant difference in their speeds.


```python
import time
import torch
from tqdm import tqdm

steps = 15000

start_time = time.time()
x = torch.rand(1,1024,3072).cuda()
model = torch.nn.Linear(3072, 3073*2).cuda()
for i in tqdm(range(steps)):
    model(x)

end_time = time.time()
execution_time = end_time - start_time
print("Total time: %.2f s" % execution_time)
```
While running inference on the following code snippet using PyTorch 1.10 and CUDA 11.3, I observed a completion time of approximately 15 seconds on an NVIDIA A800 GPU:
```bash
100%|██████████████████████████████████| 15000/15000 [00:15<00:00, 982.22it/s]
```
However, upon switching to PyTorch 2.2 coupled with CUDA 12.1, I noticed a substantial increase in the execution time for the same code, which took around 33 seconds under identical conditions with the same GPU model (NVIDIA A800).
```bash
100%|██████████████████████████████████| 15000/15000 [00:33<00:00, 445.91it/s]
```

### Versions

# Torch1.10
PyTorch version: 1.10.1
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: 10.0.0-4ubuntu1 
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.8.18 (default, Sep 11 2023, 13:40:15)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA A800 80GB PCIe
GPU 1: NVIDIA A800 80GB PCIe

Nvidia driver version: 535.129.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      48 bits physical, 48 bits virtual
CPU(s):                             256
On-line CPU(s) list:                0-254
Off-line CPU(s) list:               255
Thread(s) per core:                 1
Core(s) per socket:                 64
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          AuthenticAMD
CPU family:                         25
Model:                              1
Model name:                         AMD EPYC 7763 64-Core Processor
Stepping:                           1
Frequency boost:                    enabled
CPU MHz:                            1694.999
CPU max MHz:                        2450.0000
CPU min MHz:                        1500.0000
BogoMIPS:                           4899.90
Virtualization:                     AMD-V
L1d cache:                          2 MiB
L1i cache:                          2 MiB
L2 cache:                           32 MiB
L3 cache:                           256 MiB
NUMA node0 CPU(s):                  0-63,128-191
NUMA node1 CPU(s):                  64-127,192-254
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] pytorch-lightning==1.9.0
[pip3] rotary-embedding-torch==0.5.3
[pip3] torch==1.10.1
[pip3] torch-scatter==2.0.9
[pip3] torchaudio==0.10.1
[pip3] torchmetrics==1.3.1
[pip3] torchvision==0.11.2
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1              h9edb442_10    conda-forge
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py38h95df7f1_0    conda-forge
[conda] mkl_fft                   1.3.1            py38h8666266_1    conda-forge
[conda] mkl_random                1.2.2            py38h1abd341_0    conda-forge
[conda] numpy                     1.24.4                   pypi_0    pypi
[conda] numpy-base                1.24.3           py38h31eccc5_0  
[conda] pytorch                   1.10.1          py3.8_cuda11.3_cudnn8.2.0_0    pytorch
[conda] pytorch-lightning         1.9.0                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] pytorch-scatter           2.0.9           py38_torch_1.10.0_cu113    pyg
[conda] torchaudio                0.10.1               py38_cu113    pytorch
[conda] torchmetrics              1.3.1                    pypi_0    pypi
[conda] torchvision               0.11.2               py38_cu113    pytorch


# Torch2.2
PyTorch version: 2.2.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: 10.0.0-4ubuntu1 
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.8.18 (default, Sep 11 2023, 13:40:15)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A800 80GB PCIe
GPU 1: NVIDIA A800 80GB PCIe

Nvidia driver version: 535.129.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      48 bits physical, 48 bits virtual
CPU(s):                             256
On-line CPU(s) list:                0-254
Off-line CPU(s) list:               255
Thread(s) per core:                 1
Core(s) per socket:                 64
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          AuthenticAMD
CPU family:                         25
Model:                              1
Model name:                         AMD EPYC 7763 64-Core Processor
Stepping:                           1
Frequency boost:                    enabled
CPU MHz:                            1505.743
CPU max MHz:                        2450.0000
CPU min MHz:                        1500.0000
BogoMIPS:                           4899.90
Virtualization:                     AMD-V
L1d cache:                          2 MiB
L1i cache:                          2 MiB
L2 cache:                           32 MiB
L3 cache:                           256 MiB
NUMA node0 CPU(s):                  0-63,128-191
NUMA node1 CPU(s):                  64-127,192-254
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] pytorch-lightning==1.9.0
[pip3] torch==2.2.0
[pip3] torch-scatter==2.1.2
[pip3] torchaudio==2.2.0
[pip3] torchmetrics==1.3.1
[pip3] torchvision==0.17.0
[pip3] triton==2.2.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0            py38h5eee18b_1  
[conda] mkl_fft                   1.3.8            py38h5eee18b_0  
[conda] mkl_random                1.2.4            py38hdb19cb5_0  
[conda] numpy                     1.24.4                   pypi_0    pypi
[conda] numpy-base                1.24.3           py38h060ed82_1  
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-lightning         1.9.0                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] pytorch-scatter           2.1.2           py38_torch_2.2.0_cu121    pyg
[conda] torch                     2.2.0                    pypi_0    pypi
[conda] torchaudio                2.2.0                py38_cu121    pytorch
[conda] torchmetrics              1.3.1                    pypi_0    pypi
[conda] torchtriton               2.2.0                      py38    pytorch
[conda] torchvision               0.17.0               py38_cu121    pytorch

cc @ptrblck