Commit da3f0934 authored by zhuwenwen's avatar zhuwenwen
Browse files

delete unused files

parent c4dd1fd4
# 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
\ No newline at end of file
# 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.
\ No newline at end of file
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
include *.txt README.md
recursive-include requirements *.txt
recursive-include colossalai *.cpp *.h *.cu *.tr *.cuh *.cc
\ No newline at end of file
# 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}
}
```
<meta HTTP-EQUIV="REFRESH" content="0; url=http://www.cs.toronto.edu/~kriz/cifar.html">
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
MANIFEST.in
README.md
setup.py
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/colossal_C_frontend.cpp
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/layer_norm_hip.cpp
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/layer_norm_hip_kernel.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/multi_tensor_adam.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/multi_tensor_l2norm_kernel.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/multi_tensor_lamb.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/multi_tensor_scale_kernel.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/multi_tensor_sgd_kernel.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/multihead_attention_1d.cpp
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/scaled_masked_softmax.cpp
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/scaled_masked_softmax_hip.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/scaled_upper_triang_masked_softmax.cpp
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/scaled_upper_triang_masked_softmax_hip.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/kernels/cublas_wrappers.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/kernels/dropout_kernels.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/kernels/general_kernels.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/kernels/hip_util.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/kernels/normalize_kernels.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/kernels/softmax_kernels.hip
/public/home/huchen/colossalAI/ColossalAI/colossalai/kernel/hip_native/csrc/kernels/transform_kernels.hip
colossalai/__init__.py
colossalai/constants.py
colossalai/core.py
colossalai/global_variables.py
colossalai/initialize.py
colossalai.egg-info/PKG-INFO
colossalai.egg-info/SOURCES.txt
colossalai.egg-info/dependency_links.txt
colossalai.egg-info/requires.txt
colossalai.egg-info/top_level.txt
colossalai/amp/__init__.py
colossalai/amp/amp_type.py
colossalai/amp/apex_amp/__init__.py
colossalai/amp/apex_amp/apex_amp.py
colossalai/amp/naive_amp/__init__.py
colossalai/amp/naive_amp/_fp16_optimizer.py
colossalai/amp/naive_amp/naive_amp.py
colossalai/amp/torch_amp/__init__.py
colossalai/amp/torch_amp/_grad_scaler.py
colossalai/amp/torch_amp/torch_amp.py
colossalai/builder/__init__.py
colossalai/builder/builder.py
colossalai/builder/pipeline.py
colossalai/communication/__init__.py
colossalai/communication/collective.py
colossalai/communication/p2p.py
colossalai/communication/ring.py
colossalai/communication/utils.py
colossalai/context/__init__.py
colossalai/context/config.py
colossalai/context/parallel_context.py
colossalai/context/parallel_mode.py
colossalai/context/process_group_initializer/__init__.py
colossalai/context/process_group_initializer/initializer_1d.py
colossalai/context/process_group_initializer/initializer_2d.py
colossalai/context/process_group_initializer/initializer_2p5d.py
colossalai/context/process_group_initializer/initializer_3d.py
colossalai/context/process_group_initializer/initializer_data.py
colossalai/context/process_group_initializer/initializer_model.py
colossalai/context/process_group_initializer/initializer_moe.py
colossalai/context/process_group_initializer/initializer_pipeline.py
colossalai/context/process_group_initializer/initializer_sequence.py
colossalai/context/process_group_initializer/initializer_tensor.py
colossalai/context/process_group_initializer/process_group_initializer.py
colossalai/context/random/__init__.py
colossalai/context/random/_helper.py
colossalai/context/random/seed_manager.py
colossalai/engine/__init__.py
colossalai/engine/_base_engine.py
colossalai/engine/gradient_handler/__init__.py
colossalai/engine/gradient_handler/_base_gradient_handler.py
colossalai/engine/gradient_handler/_data_parallel_gradient_handler.py
colossalai/engine/gradient_handler/_moe_gradient_handler.py
colossalai/engine/gradient_handler/_pipeline_parallel_gradient_handler.py
colossalai/engine/gradient_handler/_sequence_parallel_gradient_handler.py
colossalai/engine/gradient_handler/_zero_gradient_handler.py
colossalai/engine/ophooks/__init__.py
colossalai/engine/ophooks/_base_ophook.py
colossalai/engine/ophooks/_memtracer_ophook.py
colossalai/engine/schedule/__init__.py
colossalai/engine/schedule/_base_schedule.py
colossalai/engine/schedule/_non_pipeline_schedule.py
colossalai/engine/schedule/_pipeline_schedule.py
colossalai/kernel/__init__.py
colossalai/kernel/cuda_native/__init__.py
colossalai/kernel/cuda_native/layer_norm.py
colossalai/kernel/cuda_native/multihead_attention.py
colossalai/kernel/cuda_native/scaled_softmax.py
colossalai/kernel/cuda_native/csrc/colossal_C_frontend.cpp
colossalai/kernel/cuda_native/csrc/compat.h
colossalai/kernel/cuda_native/csrc/layer_norm_cuda.cpp
colossalai/kernel/cuda_native/csrc/layer_norm_cuda_kernel.cu
colossalai/kernel/cuda_native/csrc/multi_tensor_adam.cu
colossalai/kernel/cuda_native/csrc/multi_tensor_apply.cuh
colossalai/kernel/cuda_native/csrc/multi_tensor_l2norm_kernel.cu
colossalai/kernel/cuda_native/csrc/multi_tensor_lamb.cu
colossalai/kernel/cuda_native/csrc/multi_tensor_scale_kernel.cu
colossalai/kernel/cuda_native/csrc/multi_tensor_sgd_kernel.cu
colossalai/kernel/cuda_native/csrc/multihead_attention_1d.cpp
colossalai/kernel/cuda_native/csrc/multihead_attention_1d.h
colossalai/kernel/cuda_native/csrc/scaled_masked_softmax.cpp
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
colossalai/kernel/cuda_native/csrc/kernels/normalize_kernels.cu
colossalai/kernel/cuda_native/csrc/kernels/softmax_kernels.cu
colossalai/kernel/cuda_native/csrc/kernels/transform_kernels.cu
colossalai/kernel/cuda_native/csrc/kernels/include/block_reduce.h
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
colossalai/kernel/cuda_native/csrc/kernels/include/softmax.h
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
colossalai/kernel/hip_native/csrc/multihead_attention_1d.h
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
colossalai/trainer/hooks/_base_hook.py
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
\ No newline at end of file
torch>=1.8
torchvision>=0.9
numpy
tqdm
psutil
tensorboard
packaging
pre-commit
[zero]
deepspeed
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment