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# Change Log
All notable changes to this project will be documented in this file.
## v0.0.2 | 2022-02
### Added
- Unifed distributed layers
- MoE support
- DevOps tools such as github action, code review automation, etc.
- New project official website
### Changes
- refactored the APIs for usability, flexibility and modularity
- adapted PyTorch AMP for tensor parallel
- refactored utilities for tensor parallel and pipeline parallel
- Separated benchmarks and examples as independent repositories
- Updated pipeline parallelism to support non-interleaved and interleaved versions
- refactored installation scripts for convenience
### Fixed
- zero level 3 runtime error
- incorrect calculation in gradient clipping
## v0.0.1 beta | 2021-10
The first beta version of Colossal-AI. Thanks to all contributors for the effort to implement the system.
### Added
- Initial architecture of the system
- Features such as tensor parallelism, gradient clipping, gradient accumulation
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# Contributing
Colossal-AI welcomes any constructive contribution from the community and the team is more than willing to work on problems you have encountered to make it a better project.
## Environment Setup
To contribute to Colossal-AI, we would like to first guide you to set up a proper development environment so that you can better implement your code. It is good to install this system from source with the `editable` flag (`-e`, for development mode) so that your change to the source code will be reflected in runtime without repeated installation and uninstallation. Here are the steps to set up the development environment.
1. Uninstall any existing Colossal-AI distribution.
```shell
pip uninstall colossalai
```
2. Clone the repository to local workspace
```shell
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
```
3. The *Get Started* section of [official documentation](https://colossalai.org) has provided instructions to build from source. Follow to instruction to build from source, **but replace the last `pip install` statement with the command below by adding the `-e` flag.**
```shell
pip install <options> -e .
```
## Coding Standards
### Unit Tests
We use [PyTest](https://docs.pytest.org/en/latest/) to execute tests. You can install pytest by `pip install pytest`. As some of the tests require initialization of the distributed backend, GPUs are needed to execute these tests.
If you only want to run CPU tests, you can run
```bash
python -m cpu tests/
```
If you have 8 GPUs on your machine, you can run the full test
```bash
python tests/
```
If you do not have 8 GPUs on your machine, do not worry. Unit testing will be automatically conducted when you put up a pull request to the main branch.
### Code Style
We have some static checks when you commit your code change, please make sure you can pass all the tests and make sure the coding style meets our requirements. We use pre-commit hook to make sure the code is aligned with the writing standard. To set up the code style checking, you need to follow the steps below.
```shell
# these commands are executed under the Colossal-AI directory
pip install pre-commit
pre-commit install
```
Code format checking will be automatically executed when you commit your changes.
## Contribution Guide
You need to follow these steps below to make contribution to the main repository via pull request. You can learn about the details of pull request [here](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests). We follow the [Gitflow workflow](https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow) during development. Thus, we work on the `develop` branch instead of the `main` branch in most of the time. The `main` branch is mainly served for version release.
### 1. Fork the Official Repository
Firstly, you need to visit the [Colossal-AI repository](https://github.com/hpcaitech/ColossalAI) and fork into your own account. The `fork` button is at the right top corner of the web page alongside with buttons such as `watch` and `star`.
Now, you can clone your own forked repository into your local environment.
```shell
git clone https://github.com/<YOUR-USERNAME>/ColossalAI.git
```
### 2. Configure Git
You need to set the official repository as your upstream so that you can synchronize with the latest update in the official repository. You can learn about upstream [here](https://www.atlassian.com/git/tutorials/git-forks-and-upstreams).
Then add the original repository as upstream
```shell
cd ColossalAI
git remote add upstream https://github.com/hpcaitech/ColossalAI.git
```
you can use the following command to verify that the remote is set. You should see both `origin` and `upstream` in the output.
```shell
git remote -v
```
### 3. Synchronize with Official Repository
Before you make changes to the codebase, it is always good to fetch the latest updates in the official repository. In order to do so, you can use the commands below.
```shell
git fetch upstream
git checkout develop
git merge upstream/develop
git push origin develop
```
Otherwise, you can click the `fetch upstream` button on the github webpage of the main branch of your forked repository. Then, use these commands to sync.
```
git checkout develop
git fetch develop
```
### 4. Choose/Create an Issue for Your Pull Request
Generally, your code change should be only targeted at one problem. Stacking multiple commits for different problems into one pull request will only make the code review such dire suffering and make the system prone to new bugs as the reviewer may not understand the code logic correctly. Thus, you should choose an existing issue or [create your own issue](https://github.com/hpcaitech/ColossalAI/issues) as your pull request target. If you wish to create a new issue, do use appropriate title and description and add related labels.
### 5. Create a New Branch
You should not make changes to the `main` or `develop` branch of your forked repository as this might make upstream synchronization difficult. You can create a new branch with the appropriate name. General branch name format should start with `hotfix/` and `feature/`. `hotfix` is for bug fix and `feature` is for addition of a new feature.
```shell
git checkout -b <NEW-BRANCH-NAME>
```
### 6. Implementation and Code Commit
Now you can implement your code change in the source code. Remember that you installed the system in development, thus you do not need to uninstall and install to make the code take effect. The code change will be reflected in every new PyThon execution.
You can commit and push the changes to your local repository. The changes should be kept logical, modular and atomic.
```shell
git add -A
git commit -m "<COMMIT-MESSAGE>"
git push -u origin <NEW-BRANCH-NAME>
```
### 7. Open a Pull Request
You can now create a pull request on the GitHub webpage of your repository. The source branch is `<NEW-BRANCH-NAME>` of your repository and the target branch should be `develop` of `hpcaitech/ColossalAI`. After creating this pull request, you should be able to see it [here](https://github.com/hpcaitech/ColossalAI/pulls).
Do write clearly the description of your pull request and [link the pull request to your target issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue). This will automatically close the issue when the pull request is approved.
In case of code conflict, you should rebase your branch and resolve the conflicts manually.
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# Colossal-AI
[![logo](./docs/images/Colossal-AI_logo.png)](https://www.colossalai.org/)
<div align="center">
<h3> <a href="https://arxiv.org/abs/2110.14883"> Paper </a> |
<a href="https://www.colossalai.org/"> Documentation </a> |
<a href="https://github.com/hpcaitech/ColossalAI-Examples"> Examples </a> |
<a href="https://github.com/hpcaitech/ColossalAI/discussions"> Forum </a> |
<a href="https://medium.com/@hpcaitech"> Blog </a></h3>
<br/>
[![Build](https://github.com/hpcaitech/ColossalAI/actions/workflows/PR_CI.yml/badge.svg)](https://github.com/hpcaitech/ColossalAI/actions/workflows/PR_CI.yml)
[![Documentation](https://readthedocs.org/projects/colossalai/badge/?version=latest)](https://colossalai.readthedocs.io/en/latest/?badge=latest)
[![codebeat badge](https://codebeat.co/badges/bfe8f98b-5d61-4256-8ad2-ccd34d9cc156)](https://codebeat.co/projects/github-com-hpcaitech-colossalai-main)
</div>
An integrated large-scale model training system with efficient parallelization techniques.
## Installation
### PyPI
```bash
pip install colossalai
```
This command will install CUDA extension if your have installed CUDA, NVCC and torch.
If you don't want to install CUDA extension, you should add `--global-option="--no_cuda_ext"`, like:
```bash
pip install colossalai --global-option="--no_cuda_ext"
```
If you want to use `ZeRO`, you can run:
```bash
pip install colossalai[zero]
```
### Install From Source
> The documentation will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :)
```shell
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# install dependency
pip install -r requirements/requirements.txt
# install colossalai
pip install .
```
If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):
```shell
pip install --global-option="--no_cuda_ext" .
```
## Use Docker
Run the following command to build a docker image from Dockerfile provided.
```bash
cd ColossalAI
docker build -t colossalai ./docker
```
Run the following command to start the docker container in interactive mode.
```bash
docker run -ti --gpus all --rm --ipc=host colossalai bash
```
## Contributing
If you wish to contribute to this project, you can follow the guideline in [Contributing](./CONTRIBUTING.md)
## Quick View
### Start Distributed Training in Lines
```python
import colossalai
from colossalai.utils import get_dataloader
# my_config can be path to config file or a dictionary obj
# 'localhost' is only for single node, you need to specify
# the node name if using multiple nodes
colossalai.launch(
config=my_config,
rank=rank,
world_size=world_size,
backend='nccl',
port=29500,
host='localhost'
)
# build your model
model = ...
# build you dataset, the dataloader will have distributed data
# sampler by default
train_dataset = ...
train_dataloader = get_dataloader(dataset=dataset,
shuffle=True
)
# build your
optimizer = ...
# build your loss function
criterion = ...
# build your lr_scheduler
engine, train_dataloader, _, _ = colossalai.initialize(
model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader
)
# start training
engine.train()
for epoch in range(NUM_EPOCHS):
for data, label in train_dataloader:
engine.zero_grad()
output = engine(data)
loss = engine.criterion(output, label)
engine.backward(loss)
engine.step()
```
### Write a Simple 2D Parallel Model
Let's say we have a huge MLP model and its very large hidden size makes it difficult to fit into a single GPU. We can
then distribute the model weights across GPUs in a 2D mesh while you still write your model in a familiar way.
```python
from colossalai.nn import Linear2D
import torch.nn as nn
class MLP_2D(nn.Module):
def __init__(self):
super().__init__()
self.linear_1 = Linear2D(in_features=1024, out_features=16384)
self.linear_2 = Linear2D(in_features=16384, out_features=1024)
def forward(self, x):
x = self.linear_1(x)
x = self.linear_2(x)
return x
```
## Features
Colossal-AI provides a collection of parallel training components for you. We aim to support you to write your
distributed deep learning models just like how you write your single-GPU model. We provide friendly tools to kickstart
distributed training in a few lines.
- Data Parallelism
- Pipeline Parallelism
- 1D, 2D, 2.5D, 3D and sequence parallelism
- Friendly trainer and engine
- Extensible for new parallelism
- Mixed Precision Training
- Zero Redundancy Optimizer (ZeRO)
Please visit our [documentation and tutorials](https://www.colossalai.org/) for more details.
## Cite Us
```
@article{bian2021colossal,
title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
journal={arXiv preprint arXiv:2110.14883},
year={2021}
}
```
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Metadata-Version: 2.1
Name: colossalai
Version: 0.0.2
Summary: An integrated large-scale model training system with efficient parallelization techniques
Home-page: UNKNOWN
License: UNKNOWN
Description: UNKNOWN
Platform: UNKNOWN
Provides-Extra: zero
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colossalai/context/random/__init__.py
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colossalai/engine/gradient_handler/_data_parallel_gradient_handler.py
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colossalai/kernel/cuda_native/__init__.py
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colossalai/kernel/cuda_native/csrc/layer_norm_cuda_kernel.cu
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colossalai/kernel/cuda_native/csrc/scaled_masked_softmax.h
colossalai/kernel/cuda_native/csrc/scaled_masked_softmax_cuda.cu
colossalai/kernel/cuda_native/csrc/scaled_upper_triang_masked_softmax.cpp
colossalai/kernel/cuda_native/csrc/scaled_upper_triang_masked_softmax.h
colossalai/kernel/cuda_native/csrc/scaled_upper_triang_masked_softmax_cuda.cu
colossalai/kernel/cuda_native/csrc/type_shim.h
colossalai/kernel/cuda_native/csrc/kernels/cross_entropy.cu
colossalai/kernel/cuda_native/csrc/kernels/cublas_wrappers.cu
colossalai/kernel/cuda_native/csrc/kernels/cuda_util.cu
colossalai/kernel/cuda_native/csrc/kernels/dropout_kernels.cu
colossalai/kernel/cuda_native/csrc/kernels/general_kernels.cu
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colossalai/kernel/cuda_native/csrc/kernels/transform_kernels.cu
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colossalai/kernel/cuda_native/csrc/kernels/include/context.h
colossalai/kernel/cuda_native/csrc/kernels/include/cross_entropy_layer.h
colossalai/kernel/cuda_native/csrc/kernels/include/cublas_wrappers.h
colossalai/kernel/cuda_native/csrc/kernels/include/cuda_util.h
colossalai/kernel/cuda_native/csrc/kernels/include/dropout.h
colossalai/kernel/cuda_native/csrc/kernels/include/feed_forward.h
colossalai/kernel/cuda_native/csrc/kernels/include/kernels.h
colossalai/kernel/cuda_native/csrc/kernels/include/ls_cub.cuh
colossalai/kernel/cuda_native/csrc/kernels/include/normalize_layer.h
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colossalai/kernel/cuda_native/csrc/kernels/include/strided_batch_gemm.h
colossalai/kernel/hip_native/csrc/colossal_C_frontend.cpp
colossalai/kernel/hip_native/csrc/compat.h
colossalai/kernel/hip_native/csrc/layer_norm_hip.cpp
colossalai/kernel/hip_native/csrc/multi_tensor_apply.cuh
colossalai/kernel/hip_native/csrc/multihead_attention_1d.cpp
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colossalai/kernel/hip_native/csrc/scaled_masked_softmax.cpp
colossalai/kernel/hip_native/csrc/scaled_masked_softmax.h
colossalai/kernel/hip_native/csrc/scaled_upper_triang_masked_softmax.cpp
colossalai/kernel/hip_native/csrc/scaled_upper_triang_masked_softmax.h
colossalai/kernel/hip_native/csrc/type_shim.h
colossalai/kernel/hip_native/csrc/kernels/include/block_reduce.h
colossalai/kernel/hip_native/csrc/kernels/include/context.h
colossalai/kernel/hip_native/csrc/kernels/include/cross_entropy_layer.h
colossalai/kernel/hip_native/csrc/kernels/include/cublas_wrappers.h
colossalai/kernel/hip_native/csrc/kernels/include/dropout.h
colossalai/kernel/hip_native/csrc/kernels/include/feed_forward.h
colossalai/kernel/hip_native/csrc/kernels/include/hip_util.h
colossalai/kernel/hip_native/csrc/kernels/include/kernels.h
colossalai/kernel/hip_native/csrc/kernels/include/ls_cub.cuh
colossalai/kernel/hip_native/csrc/kernels/include/normalize_layer.h
colossalai/kernel/hip_native/csrc/kernels/include/softmax.h
colossalai/kernel/hip_native/csrc/kernels/include/strided_batch_gemm.h
colossalai/kernel/jit/__init__.py
colossalai/kernel/jit/bias_dropout_add.py
colossalai/kernel/jit/bias_gelu.py
colossalai/kernel/jit/option.py
colossalai/logging/__init__.py
colossalai/logging/logging.py
colossalai/nn/__init__.py
colossalai/nn/init.py
colossalai/nn/layer/__init__.py
colossalai/nn/layer/base_layer.py
colossalai/nn/layer/colossalai_layer/__init__.py
colossalai/nn/layer/colossalai_layer/_utils.py
colossalai/nn/layer/colossalai_layer/dropout.py
colossalai/nn/layer/colossalai_layer/embedding.py
colossalai/nn/layer/colossalai_layer/linear.py
colossalai/nn/layer/colossalai_layer/normalization.py
colossalai/nn/layer/moe/__init__.py
colossalai/nn/layer/moe/_operation.py
colossalai/nn/layer/moe/layers.py
colossalai/nn/layer/parallel_1d/__init__.py
colossalai/nn/layer/parallel_1d/_operation.py
colossalai/nn/layer/parallel_1d/_utils.py
colossalai/nn/layer/parallel_1d/layers.py
colossalai/nn/layer/parallel_2d/__init__.py
colossalai/nn/layer/parallel_2d/_operation.py
colossalai/nn/layer/parallel_2d/_utils.py
colossalai/nn/layer/parallel_2d/layers.py
colossalai/nn/layer/parallel_2p5d/__init__.py
colossalai/nn/layer/parallel_2p5d/_operation.py
colossalai/nn/layer/parallel_2p5d/_utils.py
colossalai/nn/layer/parallel_2p5d/layers.py
colossalai/nn/layer/parallel_3d/__init__.py
colossalai/nn/layer/parallel_3d/_operation.py
colossalai/nn/layer/parallel_3d/_utils.py
colossalai/nn/layer/parallel_3d/layers.py
colossalai/nn/layer/parallel_sequence/__init__.py
colossalai/nn/layer/parallel_sequence/_operation.py
colossalai/nn/layer/parallel_sequence/_utils.py
colossalai/nn/layer/parallel_sequence/layers.py
colossalai/nn/layer/utils/__init__.py
colossalai/nn/layer/utils/common.py
colossalai/nn/layer/vanilla/__init__.py
colossalai/nn/layer/vanilla/layers.py
colossalai/nn/layer/wrapper/__init__.py
colossalai/nn/layer/wrapper/lambda_wrapper.py
colossalai/nn/layer/wrapper/pipeline_wrapper.py
colossalai/nn/loss/__init__.py
colossalai/nn/loss/loss_1d.py
colossalai/nn/loss/loss_2d.py
colossalai/nn/loss/loss_2p5d.py
colossalai/nn/loss/loss_3d.py
colossalai/nn/loss/loss_moe.py
colossalai/nn/lr_scheduler/__init__.py
colossalai/nn/lr_scheduler/cosine.py
colossalai/nn/lr_scheduler/delayed.py
colossalai/nn/lr_scheduler/linear.py
colossalai/nn/lr_scheduler/multistep.py
colossalai/nn/lr_scheduler/onecycle.py
colossalai/nn/lr_scheduler/poly.py
colossalai/nn/lr_scheduler/torch.py
colossalai/nn/metric/__init__.py
colossalai/nn/metric/_utils.py
colossalai/nn/metric/accuracy_2d.py
colossalai/nn/metric/accuracy_2p5d.py
colossalai/nn/metric/accuracy_3d.py
colossalai/nn/model/__init__.py
colossalai/nn/model/model_from_config.py
colossalai/nn/optimizer/__init__.py
colossalai/nn/optimizer/colossalai_optimizer.py
colossalai/nn/optimizer/fused_adam.py
colossalai/nn/optimizer/fused_lamb.py
colossalai/nn/optimizer/fused_sgd.py
colossalai/nn/optimizer/lamb.py
colossalai/nn/optimizer/lars.py
colossalai/registry/__init__.py
colossalai/registry/registry.py
colossalai/trainer/__init__.py
colossalai/trainer/_trainer.py
colossalai/trainer/hooks/__init__.py
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colossalai/trainer/hooks/_checkpoint_hook.py
colossalai/trainer/hooks/_log_hook.py
colossalai/trainer/hooks/_lr_scheduler_hook.py
colossalai/trainer/hooks/_metric_hook.py
colossalai/utils/__init__.py
colossalai/utils/activation_checkpoint.py
colossalai/utils/checkpointing.py
colossalai/utils/common.py
colossalai/utils/cuda.py
colossalai/utils/memory.py
colossalai/utils/timer.py
colossalai/utils/data_sampler/__init__.py
colossalai/utils/data_sampler/base_sampler.py
colossalai/utils/data_sampler/data_parallel_sampler.py
colossalai/utils/gradient_accumulation/__init__.py
colossalai/utils/gradient_accumulation/_gradient_accumulation.py
colossalai/utils/multi_tensor_apply/__init__.py
colossalai/utils/multi_tensor_apply/multi_tensor_apply.py
colossalai/zero/__init__.py
colossalai/zero/loss_scaler.py
colossalai/zero/zero_redundancy_optimizer_level_2.py
colossalai/zero/zero_redundancy_optimizer_level_3.py
model_zoo/__init__.py
model_zoo/helper.py
model_zoo/bert/__init__.py
model_zoo/gpt/__init__.py
model_zoo/gpt/gpt.py
model_zoo/mlp_mixer/__init__.py
model_zoo/mlp_mixer/parallel_3d/__init__.py
model_zoo/mlp_mixer/parallel_3d/mlp_mixer.py
model_zoo/moe/__init__.py
model_zoo/moe/models.py
model_zoo/moe/util.py
model_zoo/vit/__init__.py
model_zoo/vit/vision_transformer_from_config.py
model_zoo/vit/vit.py
requirements/requirements-test.txt
requirements/requirements-zero.txt
requirements/requirements.txt
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colossalai.egg-info/requires.txt deleted 100644 → 0
View file @ c4dd1fd4
torch>=1.8
torchvision>=0.9
numpy
tqdm
psutil
tensorboard
packaging
pre-commit
[zero]
deepspeed
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