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https://github.com/NVIDIA/apex

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
https://github.com/NVIDIA/apex

Last synced: 3 months ago
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A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Host: GitHub
URL: https://github.com/NVIDIA/apex
Owner: NVIDIA
License: bsd-3-clause
Created: 2018-04-23T16:28:52.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2024-07-25T12:28:44.000Z (3 months ago)
Last Synced: 2024-07-26T06:21:23.767Z (3 months ago)
Language: Python
Homepage:
Size: 14.9 MB
Stars: 8,208
Watchers: 102
Forks: 1,361
Open Issues: 714
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

Awesome-pytorch-list-CNVersion - apex
awesome-list - Apex - Tools for easy mixed precision and distributed training in Pytorch (Deep Learning Framework / High-Level DL APIs)
Awesome-pytorch-list - apex
awesome - NVIDIA/apex - A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch (Python)
awesome-python-machine-learning-resources - GitHub - 53% open · ⏱️ 25.08.2022): (GPU实用程序)

README

        # 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

**Deprecated. Use [PyTorch AMP](https://pytorch.org/docs/stable/amp.html)**

`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 deprecated. Use [`torch.nn.parallel.DistributedDataParallel`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html?highlight=distributeddataparallel#torch.nn.parallel.DistributedDataParallel)**

`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

**Deprecated. Use [`torch.nn.SyncBatchNorm`](https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html)**

`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

Each [`apex.contrib`](./apex/contrib) module requires one or more install options other than `--cpp_ext` and `--cuda_ext`.

Note that contrib modules do not necessarily support stable PyTorch releases.

## 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.

We recommend installing [`Ninja`](https://ninja-build.org/) to make compilation faster.

### 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

# if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... 

pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./

# otherwise

pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --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-build-isolation --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.

### [Experimental] Windows

`pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-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.

## Custom C++/CUDA Extensions and Install Options

If a requirement of a module is not met, then it will not be built.

|  Module Name  |  Install Option  |  Misc  |

|---------------|------------------|--------|

|  `apex_C`     |  `--cpp_ext`     | |

|  `amp_C`      |  `--cuda_ext`    | |

|  `syncbn`     |  `--cuda_ext`    | |

|  `fused_layer_norm_cuda`  |  `--cuda_ext`  | [`apex.normalization`](./apex/normalization) |

|  `mlp_cuda`   |  `--cuda_ext`    | |

|  `scaled_upper_triang_masked_softmax_cuda`  |  `--cuda_ext`  | |

|  `generic_scaled_masked_softmax_cuda`  |  `--cuda_ext`  | |

|  `scaled_masked_softmax_cuda`  |  `--cuda_ext`  | |

|  `fused_weight_gradient_mlp_cuda`  |  `--cuda_ext`  | Requires CUDA>=11 |

|  `permutation_search_cuda`  |  `--permutation_search`  | [`apex.contrib.sparsity`](./apex/contrib/sparsity)  |

|  `bnp`        |  `--bnp`         |  [`apex.contrib.groupbn`](./apex/contrib/groupbn) |

|  `xentropy`   |  `--xentropy`    |  [`apex.contrib.xentropy`](./apex/contrib/xentropy)  |

|  `focal_loss_cuda`  |  `--focal_loss`  |  [`apex.contrib.focal_loss`](./apex/contrib/focal_loss)  |

|  `fused_index_mul_2d`  |  `--index_mul_2d`  |  [`apex.contrib.index_mul_2d`](./apex/contrib/index_mul_2d)  |

|  `fused_adam_cuda`  |  `--deprecated_fused_adam`  |  [`apex.contrib.optimizers`](./apex/contrib/optimizers)  |

|  `fused_lamb_cuda`  |  `--deprecated_fused_lamb`  |  [`apex.contrib.optimizers`](./apex/contrib/optimizers)  |

|  `fast_layer_norm`  |  `--fast_layer_norm`  |  [`apex.contrib.layer_norm`](./apex/contrib/layer_norm). different from `fused_layer_norm` |

|  `fmhalib`    |  `--fmha`        |  [`apex.contrib.fmha`](./apex/contrib/fmha)  |

|  `fast_multihead_attn`  |  `--fast_multihead_attn`  |  [`apex.contrib.multihead_attn`](./apex/contrib/multihead_attn)  |

|  `transducer_joint_cuda`  |  `--transducer`  |  [`apex.contrib.transducer`](./apex/contrib/transducer)  |

|  `transducer_loss_cuda`   |  `--transducer`  |  [`apex.contrib.transducer`](./apex/contrib/transducer)  |

|  `cudnn_gbn_lib`  |  `--cudnn_gbn`  | Requires cuDNN>=8.5, [`apex.contrib.cudnn_gbn`](./apex/contrib/cudnn_gbn) |

|  `peer_memory_cuda`  |  `--peer_memory`  |  [`apex.contrib.peer_memory`](./apex/contrib/peer_memory)  |

|  `nccl_p2p_cuda`  |  `--nccl_p2p`  | Requires NCCL >= 2.10, [`apex.contrib.nccl_p2p`](./apex/contrib/nccl_p2p)  |

|  `fast_bottleneck`  |  `--fast_bottleneck`  |  Requires `peer_memory_cuda` and `nccl_p2p_cuda`, [`apex.contrib.bottleneck`](./apex/contrib/bottleneck) |

|  `fused_conv_bias_relu`  |  `--fused_conv_bias_relu`  | Requires cuDNN>=8.4, [`apex.contrib.conv_bias_relu`](./apex/contrib/conv_bias_relu) |