Harmony

This repository contains the source code implementation of the following papers:

''Harmony: Overcoming the hurdles of GPU memory capacity to train massive DNN models on commodity servers'', which appeared at VLDB 2022.
''Doing more with less: Training large DNN models on commodity servers for the masses'', which appeared at HotOS 2021.

This work was done as part of Microsoft Research's Project Fiddle. This source code is available under the MIT License.

Directory Structure

harmony: the Harmony source code, with detailed instructions, various example scripts, as well as previous results.
model_lib: the model libary containing model code that is not included in pytorch, such as the transformer library from huggingface.
util_lib: the customized utility libary.

Setup

To run Harmony, the easiest way is to use the standard nvidia's container (nvcr.io/nvidia/pytorch:20.03-py3) which satisfies most dependencies. It can be launched by:

./launch.sh

Once getting into the container, the remaining dependencies can be satisified by running:

./install.sh

Note:

Harmony was developed in the environment of Python 3.6.9, PyTorch 1.5.0a0, CUDA 10.1.243, cuDNN 7.6.3, NCCL 2.4.8, Nvidia driver 418, Ubuntu 18.04.3 LTS.
Harmony was developed with Nivida GPUs.
Harmony does not modfiy PyTorch library and may remain portable to different versions.

Dataset

GLUE (including MRPC): It can be downloaded by running this script and unpacked to a directorary /data/glue/MRPC.
WikiText-2 and WikiText-103: It can be downloaded from here and unpacked to a directorary /data/wikitext-2-tokens and /data/wikitext-103-tokens.
ImageNet: The ImageNet ILSVC 2012 can be downloaded by running this script and unpacked to a directory /data/imagenet/.

End-to-end Workflow

The end-to-end workflow of Harmony can be illustrated by the figure below:

For example, to run a BERT-Large with Harmony, we can go through following steps:

Decompose model into per-layer code

cd harmony/1_decomposer/bert_thomwolf && ./run_bert_large.sh

Profile each layer

cd ../../2_profiler/bert_thomwolf && ./run_bert_large.sh

Search the best schedule

cd ../../3_scheduler && ./run_bert_large.sh

Run the best schedule

cd ../4_runtime/bert_thomwolf && ./run_bert_large.sh

More examples can be found under harmony/1_decomposer, harmony/2_profiler, harmony/3_scheduler, and harmony/4_runtime.

Experiments

To conduct the experiments in the VLDB paper, the scripts are provided as below:

Figure 8

cd harmony/4_runtime/bert_thomwolf && ./run_bert_large__fig8.sh

Figure 10

cd harmony/4_runtime/bert_thomwolf && ./run_bert96__fig10.sh
cd harmony/4_runtime/gpt2_huggingface && ./run_gpt2_xl__fig10_fig12.sh
cd harmony/4_runtime/vgg_resnet_torch && ./run_vgg416__fig10.sh
cd harmony/4_runtime/vgg_resnet_torch && ./run_resnet1026__fig10.sh

Figure 12

cd harmony/4_runtime/gpt2_huggingface && ./run_gpt2_xl__fig10_fig12.sh

Figure 13

cd harmony/4_runtime/bert_thomwolf && ./run_bert_large__fig13.sh

Figure 17 and Figure 18

cd harmony/4_runtime/gpt2_huggingface && ./run_gpt2_billions__fig17_fig18.sh

Figure 21

cd harmony/4_runtime/gpt2_huggingface && ./run_gpt2_medium__fig21.sh

Table 1

cd harmony/3_scheduler && ./run_four_models__tab1.sh

Note

For experiments of Figure 17 and Figure 18, three prerequisits exist to run largest models saturating the CPU memory capacity. (Tested on Ubuntu 18.04.)

Raise the limitation of pinned memory

Step 1: open /etc/security/limits.conf

sudo vim /etc/security/limits.conf

Step 2: make memlock unlimited

#<domain>      <type>  <item>         <value>
#

#*               soft    core            0
#root            hard    core            100000
#*               hard    rss             10000
#@student        hard    nproc           20
#@faculty        soft    nproc           20
#@faculty        hard    nproc           50
#ftp             hard    nproc           0
#ftp             -       chroot          /ftp
#@student        -       maxlogins       4

*              -       memlock         unlimited
root           -       memlock         unlimited

# End of file

Step 3: verify

ulimit -a

Max out shared memory

Step 1: Open /etc/fstab

sudo vim /etc/fstab

Step 2: Locate /dev/shm and use the tmpfs size option to specify max size

# /etc/fstab: static file system information.
#
# Use 'blkid' to print the universally unique identifier for a
# device; this may be used with UUID= as a more robust way to name devices
# that works even if disks are added and removed. See fstab(5).
#
# <file system> <mount point>   <type>  <options>       <dump>  <pass>
# / was on /dev/sda1 during installation
UUID=4e3b7d44-77c9-4cc8-be72-fa2ff836ac2f /               ext4    errors=remount-ro 0       1
/swapfile                                 none            swap    sw              0       0
# resize /dev/shm
tmpfs /dev/shm tmpfs defaults,size=750g 0 0

Step 3: To make change effective immediately, remount the /dev/shm filesystem:

mount -o remount /dev/shm

Step 4: Verify

df -h

Disable swapping to disk

Step 1: Open sysctl.conf

sudo vim /etc/sysctl.conf

Step 2: Add this line vm.swappiness = 0

###################################################################
# Protected links
#
# Protects against creating or following links under certain conditions
# Debian kernels have both set to 1 (restricted) 
# See https://www.kernel.org/doc/Documentation/sysctl/fs.txt
#fs.protected_hardlinks=0
#fs.protected_symlinks=0

vm.swappiness = 0

Step 3: Restart machine

sudo reboot now

After all experiments, restore swapping to disk

# vm.swappiness = 0 # comment out

Setup Container

Finally, we need to unlock the resource limitation of container by setting options in launch.sh as below. Assume that the machine has 750GB CPU memory and 8 GPUs.

nvidia-docker run \
    ...
    --memory=750g \
    --memory-swap=750g \
    --memory-swappiness=0 \
    --memory-reservation=750g \
    --shm-size=750g \
    --ulimit memlock=750000000000:750000000000 \
    --gpus '"device=0,1,2,3,4,5,6,7"' \
    ...

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

Licensed under the MIT License.

Reference

If you find the code helpful, citing our papers would be appreciated : )

@article{VLDB22Harmony,
    title = {{Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers}}, 
    author = {Youjie Li and Amar Phanishayee and Derek Murray and Jakub Tarnawski and Nam Sung Kim},
    journal = {The 48th International Conference on Very Large Databases (VLDB'22)},
    year = {2022},
    address = {Sydney, Australia},
    month = sep
}

@inproceedings{HotOS21Harmony,
    title = {{Doing More with Less: Training Large DNN Models on Commodity Servers for the Masses}},
    author = {Youjie Li and Amar Phanishayee and Derek Murray and Nam Sung Kim},
    booktitle = {Workshop on Hot Topics in Operating Systems (HotOS’21)},
    year = {2021},
    address = {Ann Arbor, MI, USA},
    month = jun
}

pinxuezhao/harmony