This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intention of Apex is to make up-to-date utilities available to users as quickly as possible.
Full API Documentation: https://nvidia.github.io/apex
GTC 2019 and Pytorch DevCon 2019 Slides
apex.amp is a tool to enable mixed precision training by changing only 3 lines of your script.
Users can easily experiment with different pure and mixed precision training modes by supplying
different flags to amp.initialize.
Webinar introducing Amp
(The flag cast_batchnorm has been renamed to keep_batchnorm_fp32).
Comprehensive Imagenet example
Moving to the new Amp API (for users of the deprecated "Amp" and "FP16_Optimizer" APIs)
apex.parallel.DistributedDataParallel is a module wrapper, similar to
torch.nn.parallel.DistributedDataParallel. It enables convenient multiprocess distributed training,
optimized for NVIDIA's NCCL communication library.
The Imagenet example
shows use of apex.parallel.DistributedDataParallel along with apex.amp.
apex.parallel.SyncBatchNorm extends torch.nn.modules.batchnorm._BatchNorm to
support synchronized BN.
It allreduces stats across processes during multiprocess (DistributedDataParallel) training.
Synchronous BN has been used in cases where only a small
local minibatch can fit on each GPU.
Allreduced stats increase the effective batch size for the BN layer to the
global batch size across all processes (which, technically, is the correct
formulation).
Synchronous BN has been observed to improve converged accuracy in some of our research models.
To properly save and load your amp training, we introduce the amp.state_dict(), which contains all loss_scalers and their corresponding unskipped steps,
as well as amp.load_state_dict() to restore these attributes.
In order to get bitwise accuracy, we recommend the following workflow:
# Initialization
opt_level = 'O1'
model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)
# Train your model
...
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
...
# Save checkpoint
checkpoint = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'amp': amp.state_dict()
}
torch.save(checkpoint, 'amp_checkpoint.pt')
...
# Restore
model = ...
optimizer = ...
checkpoint = torch.load('amp_checkpoint.pt')
model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
amp.load_state_dict(checkpoint['amp'])
# Continue training
...Note that we recommend restoring the model using the same opt_level. Also note that we recommend calling the load_state_dict methods after amp.initialize.
Python 3
CUDA 9 or newer
PyTorch 0.4 or newer. The CUDA and C++ extensions require pytorch 1.0 or newer.
We recommend the latest stable release, obtainable from https://pytorch.org/. We also test against the latest master branch, obtainable from https://github.com/pytorch/pytorch.
It's often convenient to use Apex in Docker containers. Compatible options include:
- NVIDIA Pytorch containers from NGC, which come with Apex preinstalled. To use the latest Amp API, you may need to
pip uninstall apexthen reinstall Apex using the Quick Start commands below. - official Pytorch -devel Dockerfiles, e.g.
docker pull pytorch/pytorch:nightly-devel-cuda10.0-cudnn7, in which you can install Apex using the Quick Start commands.
See the Docker example folder for details.
- Python 3.6
- Pytorch 1.5 or newer, The HIPExtensions require 1.5 or newer.
- We recommend follow the instructions from ROCm-Pytorch to install pytorch on ROCm.
- Note: For pytorch versions < 1.8, building from source is no longer supported, please use the release package ROCm-Apex v0.3 .
Apex on ROCm supports both python only build and extension build. Note: Pytorch version recommended is >=1.5 for extension build.
python setup.py install
python setup.py install --cpp_ext --cuda_ext
Note that using --cuda_ext flag to install Apex will also enable all the extensions supported on ROCm including "--distributed_adam", "--distributed_lamb", "--bnp", "--xentropy", "--deprecated_fused_adam", "--deprecated_fused_lamb", and "--fast_multihead_attn".
pip install ninja
pip install -v --install-option="--cpp_ext" --install-option="--cuda_ext" 'git+https://github.com/ROCmSoftwarePlatform/apex.git'
For performance and full functionality, we recommend installing Apex with CUDA and C++ extensions via
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
Apex also supports a Python-only build (required with Pytorch 0.4) via
pip install -v --disable-pip-version-check --no-cache-dir ./
A Python-only build omits:
- Fused kernels required to use
apex.optimizers.FusedAdam. - Fused kernels required to use
apex.normalization.FusedLayerNorm. - Fused kernels that improve the performance and numerical stability of
apex.parallel.SyncBatchNorm. - Fused kernels that improve the performance of
apex.parallel.DistributedDataParallelandapex.amp.DistributedDataParallel,amp, andSyncBatchNormwill still be usable, but they may be slower.
Pyprof support has been moved to its own dedicated repository. The codebase is deprecated in Apex and will be removed soon.
Windows support is experimental, and Linux is recommended. pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . may work if you were able to build Pytorch from source
on your system. pip install -v --no-cache-dir . (without CUDA/C++ extensions) is more likely to work. If you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.