# Introduction

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 intent of Apex is to make up-to-date utilities available to users as quickly as possible.

## Full API Documentation: [https://nvidia.github.io/apex](https://nvidia.github.io/apex)

## [GTC 2019](https://github.com/mcarilli/mixed_precision_references/tree/master/GTC_2019) and [Pytorch DevCon 2019](https://github.com/mcarilli/mixed_precision_references/tree/master/Pytorch_Devcon_2019) Slides

# Contents

## 1. Amp:  Automatic Mixed Precision

`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](https://info.nvidia.com/webinar-mixed-precision-with-pytorch-reg-page.html)
(The flag `cast_batchnorm` has been renamed to `keep_batchnorm_fp32`).

[API Documentation](https://nvidia.github.io/apex/amp.html)

[Comprehensive Imagenet example](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)

[DCGAN example coming soon...](https://github.com/NVIDIA/apex/tree/master/examples/dcgan)

[Moving to the new Amp API](https://nvidia.github.io/apex/amp.html#transition-guide-for-old-api-users) (for users of the deprecated "Amp" and "FP16_Optimizer" APIs)

## 2. Distributed Training

`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.

[API Documentation](https://nvidia.github.io/apex/parallel.html)

[Python Source](https://github.com/NVIDIA/apex/tree/master/apex/parallel)

[Example/Walkthrough](https://github.com/NVIDIA/apex/tree/master/examples/simple/distributed)

The [Imagenet example](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
shows use of `apex.parallel.DistributedDataParallel` along with `apex.amp`.

### Synchronized Batch Normalization

`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.

### Checkpointing

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:
```python
# 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`.

# Installation

## Containers
NVIDIA PyTorch Containers are available on NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch.
The containers come with all the custom extensions available at the moment. 

See [the NGC documentation](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html) for details such as:
- how to pull a container
- how to run a pulled container
- release notes

## From Source

To install Apex from source, we recommend using the nightly Pytorch obtainable from https://github.com/pytorch/pytorch.

The latest stable release obtainable from https://pytorch.org should also work.

### Linux
For performance and full functionality, we recommend installing Apex with
CUDA and C++ extensions via
```bash
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 via
```bash
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` and `apex.normalization.FusedRMSNorm`.
- Fused kernels that improve the performance and numerical stability of `apex.parallel.SyncBatchNorm`.
- Fused kernels that improve the performance of `apex.parallel.DistributedDataParallel` and `apex.amp`.
`DistributedDataParallel`, `amp`, and `SyncBatchNorm` will still be usable, but they may be slower.

Pyprof support has been moved to its own [dedicated repository](https://github.com/NVIDIA/PyProf).
Pyprof is deprecated in Apex and the pyprof directory will be removed by the end of June 2022.


### [Experimental] Windows
`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. A Python-only build via `pip install -v --no-cache-dir .` is more likely to work.  
If you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.
