Description
🐛 Describe the bug
Hi,
I recently noticed a weird behavior in Pytorch. The import order of torch and numpy can have a significant impact on the dataloader performance. In short (and see the complete example script below):
# Setting A: faster. 100 iterations take 91 seconds. Average load time 0.035.
import torch
import numpy as np
# Setting B: slower. 100 iterations take 158 seconds. Average load time ~0.45.
import numpy as np
import torch
It seems this performance is determined by the first time these two packages are imported (e.g., importing these two packages in the main script in B order and the dataloader script in A order, it will end up with B performance).
Reproduction
I used AWS EC2 machines (and more specifically, zone us-west-2 with p4de24xlarge instances). I am not sure if this is reproducible in other places.
Step 1: Dockerfile
FROM nvidia/cuda:11.3.0-devel-ubuntu20.04
# Fix NV docker problem
RUN apt-key adv --fetch-keys https://developer.download.nvidia.cn/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/7fa2af80.pub
# Install handy tools
RUN apt-get update && apt-get install vim htop tmux sudo git wget -y && rm -rf /var/lib/apt/lists/*
# Create user with sudo privilege
RUN addgroup --gid 1000 ubuntu
RUN adduser --disabled-password --gecos '' --uid 1000 --gid 1000 ubuntu
RUN adduser ubuntu sudo
RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
USER ubuntu
USER 1000:1000
RUN sudo chown ubuntu:ubuntu /home/ubuntu/ # Actually, not sure if this is even needed
# Conda
ENV PATH="/home/ubuntu/miniconda3/bin:${PATH}"
ARG PATH="/home/ubuntu/miniconda3/bin:${PATH}"
RUN sudo apt-get install -y wget
RUN cd /home/ubuntu/ \
&& wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
&& bash Miniconda3-latest-Linux-x86_64.sh -b -p /home/ubuntu/miniconda3/ \
&& rm -f Miniconda3-latest-Linux-x86_64.sh
RUN conda install python=3.9 pytorch torchvision torchaudio pytorch-cuda=11.8 xformers -c pytorch -c nvidia -c xformers -y
RUN pip install tqdm
RUN conda clean --all -y
Step 2: After getting into the environment
Add these lines to ~/.bashrc and source ~/.bashrc to init conda
conda_root="/home/ubuntu/miniconda3/"
conda_steup_bin="${conda_root}bin/conda"
__conda_setup="$($conda_steup_bin 'shell.bash' 'hook')"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "${conda_root}etc/profile.d/conda.sh" ]; then
. "${conda_root}etc/profile.d/conda.sh"
else
export PATH="${conda_root}bin:$PATH"
fi
fi
unset __conda_setup
Step 3: Example script
# Switching these two lines will get a different performance
import torch
import numpy as np
from torch.utils.data import Dataset
import time
class MyDataset(Dataset):
def __getitem__(self, i):
st = time.time()
data = {k: np.random.rand(3, 512, 512) for k in range(6)} # it seems only numpy has the issue
print(" [*] Worker load time {:.4f}".format(time.time()-st))
return data
def __len__(self):
return 1000000
if __name__ == "__main__":
from torch.utils.data import DataLoader
from tqdm import tqdm
dataloader = DataLoader(MyDataset())
for data in tqdm(dataloader):
passVersions
Collecting environment information...
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31
Python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.228-132.418.amzn2.x86_64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.3.109
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB
Nvidia driver version: 470.161.03
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: 46 bits physical, 48 bits virtual
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz
Stepping: 7
CPU MHz: 1916.685
BogoMIPS: 5999.99
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 1.5 MiB
L1i cache: 1.5 MiB
L2 cache: 48 MiB
L3 cache: 71.5 MiB
NUMA node0 CPU(s): 0-23,48-71
NUMA node1 CPU(s): 24-47,72-95
Vulnerability Itlb multihit: KVM: Vulnerable
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, 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 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke
Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.0.0
[pip3] torchaudio==2.0.0
[pip3] torchvision==0.15.0
[pip3] triton==2.0.0
[conda] blas 1.0 mkl
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] mkl 2023.1.0 h6d00ec8_46342
[conda] mkl-service 2.4.0 py39h5eee18b_1
[conda] mkl_fft 1.3.6 py39h417a72b_1
[conda] mkl_random 1.2.2 py39h417a72b_1
[conda] numpy 1.24.3 py39hf6e8229_1
[conda] numpy-base 1.24.3 py39h060ed82_1
[conda] pytorch 2.0.0 py3.9_cuda11.8_cudnn8.7.0_0 pytorch
[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torchaudio 2.0.0 py39_cu118 pytorch
[conda] torchtriton 2.0.0 py39 pytorch
[conda] torchvision 0.15.0 py39_cu118 pytorch