Unexpected difference in torchvision.transforms.Normalize results when using DataLoader with num_workers>=1 and pin_memory=True. #9187
Description
🐛 Describe the bug
Bug
Unexpected difference in torchvision.transforms.Normalize results when using DataLoader with num_workers>=1 and pin_memory=True.
Description
I found an unexpected behavior when using torch.utils.data.DataLoader.
- When I set
num_workers >= 1andpin_memory=True, the results oftorchvision.transforms.Normalizebecome slightly different after accessing the DataLoader object. - However, if I run the transforms before accessing the DataLoader, the results remain consistent.
Steps to Reproduce
Here is a minimal reproducible example.
Python script
import argparse
import random
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--workers', help='number of data loading workers', default=0, type=int)
parser.add_argument(
'--pin', help='pin memory for DataLoader', action='store_true')
parser.add_argument(
'--pre_run', help='run transform before accessing DataLoader', action='store_true')
# args, rest = parser.parse_known_args()
args = parser.parse_args()
return args
class ToyDataset(Dataset):
def __init__(self, length=10, transform=None):
self.length = length
self.transform = transform
def __len__(self):
return self.length
def __getitem__(self, idx):
image = np.ones((512, 960, 3), dtype=np.uint8) * idx
if self.transform:
image = self.transform(image)
label = idx
return image, label
def try_transform(description=None):
if description:
print(f"{description}")
image = np.ones((512, 960, 3), dtype=np.uint8)
h, w, c = image.shape
half = h // 2
image[:half+1, :, :] = 1
image[half-1:, :, :] = 0
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
print(f"\tsum before transform: {image.sum()}")
image = transform(image)
print(f"\tsum after transform: {image.sum()}")
if __name__ == "__main__":
args = parse_args()
print(f"workers: {args.workers}, pin_memory: {args.pin}, pre_run: {args.pre_run}")
dataset = ToyDataset(length=1,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
)
toy_loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=args.pin)
if args.pre_run:
try_transform(description="Use transform before start accessing DataLoader")
for images, labels in toy_loader:
pass
# print(f"Batch images shape: {images.shape}, Labels: {labels}")
try_transform(description="Use transform after start accessing DataLoader")
print()Bash script
echo "1"
python test_torchvision.py --workers=0 --pin
echo "2"
python test_torchvision.py --workers=1 --pin
echo "3"
python test_torchvision.py --workers=0
echo "4"
python test_torchvision.py --workers=1
echo "5"
python test_torchvision.py --workers=0 --pin --pre_run
echo "6"
python test_torchvision.py --workers=1 --pin --pre_run
echo "7"
python test_torchvision.py --workers=0 --pre_run
echo "8"
python test_torchvision.py --workers=1 --pre_runObserved Behavior
With workers=1 and pin_memory=True, the output of transforms.Normalize differs slightly (e.g., -2915763.0 vs. -2915762.25).
In all other cases, the results are consistent.
Example output:
Testing torchvision transforms
1
workers: 0, pin_memory: True, pre_run: False
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
2
workers: 1, pin_memory: True, pre_run: False
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915762.25
3
workers: 0, pin_memory: False, pre_run: False
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
4
workers: 1, pin_memory: False, pre_run: False
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
5
workers: 0, pin_memory: True, pre_run: True
Use transform before start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
6
workers: 1, pin_memory: True, pre_run: True
Use transform before start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
7
workers: 0, pin_memory: False, pre_run: True
Use transform before start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
8
workers: 1, pin_memory: False, pre_run: True
Use transform before start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
Use transform after start accessing DataLoader
sum before transform: 734400
sum after transform: -2915763.0
Expected Behavior
The output of torchvision.transforms.Normalize should remain consistent regardless of num_workers or pin_memory settings.
Versions
Collecting environment information...
PyTorch version: 2.8.0+cu129
Is debug build: False
CUDA used to build PyTorch: 12.9
ROCM used to build PyTorch: N/A
OS: Ubuntu 24.04.3 LTS (x86_64)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.39
Python version: 3.12.11 | packaged by conda-forge | (main, Jun 4 2025, 14:45:31) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-6.14.0-27-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 5090
Nvidia driver version: 575.64.03
cuDNN version: Could not collect
Is XPU available: False
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
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Vendor ID: GenuineIntel
Model name: Intel(R) Core(TM) i9-14900K
CPU family: 6
Model: 183
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
Stepping: 1
CPU(s) scaling MHz: 39%
CPU max MHz: 6000.0000
CPU min MHz: 800.0000
BogoMIPS: 6374.40
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 896 KiB (24 instances)
L1i cache: 1.3 MiB (24 instances)
L2 cache: 32 MiB (12 instances)
L3 cache: 36 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Ghostwrite: 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 Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==2.1.2
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] torch==2.8.0+cu129
[pip3] torchvision==0.23.0+cu129
[pip3] triton==3.4.0
[conda] numpy 2.1.2 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.9.1.4 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.9.79 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.9.86 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.9.79 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.4.1.4 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.10.19 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.5.82 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.10.65 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.27.3 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.9.86 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.9.79 pypi_0 pypi
[conda] torch 2.8.0+cu129 pypi_0 pypi
[conda] torchvision 0.23.0+cu129 pypi_0 pypi
[conda] triton 3.4.0 pypi_0 pypi