Merge branch 'master' into master

Author: loadams

README.md

@@ -1,5 +1,5 @@
 # DeepSpeed Examples
-This repository contains various examples including training, inference, compression, benchmarks, and applications that use [DeepSpeed](https://github.com/microsoft/DeepSpeed).
+This repository contains various examples including training, inference, compression, benchmarks, and applications that use [DeepSpeed](https://github.com/deepspeedai/DeepSpeed).
 
 ## 1. Applications
 This folder contains end-to-end applications that use DeepSpeed to train and use cutting-edge models.
@@ -8,7 +8,7 @@ This folder contains end-to-end applications that use DeepSpeed to train and use
 There are several training and finetuning examples so please see the individual folders for specific instructions.
 
 ## 3. Inference
-- The DeepSpeed-MII inference [README](./inference/mii/README.md) explains how to get started with running model inference with [DeepSpeed-MII](https://github.com/Microsoft/DeepSpeed-MII) and [DeepSpeed-FastGen](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen).
+- The DeepSpeed-MII inference [README](./inference/mii/README.md) explains how to get started with running model inference with [DeepSpeed-MII](https://github.com/deepspeedai/DeepSpeed-MII) and [DeepSpeed-FastGen](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen).
 - The DeepSpeed Huggingface inference [README](./inference/huggingface/README.md) explains how to get started with running DeepSpeed Huggingface inference examples.
 
 ## 4. Compression
@@ -20,7 +20,7 @@ All benchmarks that use the DeepSpeed library are maintained in this folder.
 # Build Pipeline Status
 | Description | Status |
 | ----------- | ------ |
-| Integrations | [![nv-ds-chat](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml) |
+| Integrations | [![nv-ds-chat](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml) |
 
 # Contributing
 

applications/DeepSpeed-Chat/README.md

@@ -62,9 +62,9 @@ A fast, affordable, scalable and open system framework for enabling end-to-end R
 
 ## 📰 Latest News 📰
 
-* ***[2023/08] 🚀 [DeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md)*** 🚀
+* ***[2023/08] 🚀 [DeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md)*** 🚀
 
-* ***[2023/04] [DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)*** [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/japanese/README.md)]
+* ***[2023/04] [DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat)*** [[English](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat/README.md)] [[中文](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat/chinese/README.md)] [[日本語](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat/japanese/README.md)]
 
 To cite DeepSpeed Chat, please cite our [arxiv report](https://arxiv.org/abs/2308.01320):
 
@@ -87,7 +87,7 @@ https://user-images.githubusercontent.com/124002815/230290966-a78ea171-ab65-4fcc
 
 In the spirit of democratizing ChatGPT-style models and their capabilities, DeepSpeed is proud to introduce a general system framework for enabling an end-to-end training experience for ChatGPT-like models, named ***DeepSpeed Chat***. It can automatically take your favorite pre-trained large language models through an OpenAI InstructGPT style three stages to produce your very own high-quality ChatGPT-style model. DeepSpeed Chat makes training for high-quality ChatGPT-style models easy, fast, affordable and scalable.
 
-With just one click, you can train, generate and serve a 1.3 billion parameter ChatGPT model within 1.36 hours on a single consumer-grade NVIDIA A6000 GPU with 48GB memory. On a single DGX node with 8 NVIDIA A100-40G GPUs, DeepSpeed-Chat enables training for a 13 billion parameter ChatGPT model in 13.6 hours. On multi-GPU multi-node systems (cloud scenarios),i.e., 8 DGX nodes with 8 NVIDIA A100 GPUs/node, DeepSpeed-Chat can train a 66 billion parameter ChatGPT model in under 9 hours. Finally, it enables 15X faster training over the existing RLHF systems, and can handle training of ChatGPT-like models with over 200 billion parameters: another impossible feat with the existing systems. For the full range of discussion on various model sizes and low training costs enabled by DeepSpeed-Chat, please refer to the [Release Blog](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat) and [Training Performance Evaluation](#-training-performance-evaluation-).  
+With just one click, you can train, generate and serve a 1.3 billion parameter ChatGPT model within 1.36 hours on a single consumer-grade NVIDIA A6000 GPU with 48GB memory. On a single DGX node with 8 NVIDIA A100-40G GPUs, DeepSpeed-Chat enables training for a 13 billion parameter ChatGPT model in 13.6 hours. On multi-GPU multi-node systems (cloud scenarios),i.e., 8 DGX nodes with 8 NVIDIA A100 GPUs/node, DeepSpeed-Chat can train a 66 billion parameter ChatGPT model in under 9 hours. Finally, it enables 15X faster training over the existing RLHF systems, and can handle training of ChatGPT-like models with over 200 billion parameters: another impossible feat with the existing systems. For the full range of discussion on various model sizes and low training costs enabled by DeepSpeed-Chat, please refer to the [Release Blog](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat) and [Training Performance Evaluation](#-training-performance-evaluation-).  
 
 Beyond this release, DeepSpeed system has been proudly serving as the system backend for accelerating a range of ongoing efforts for fast training/fine-tuning Chat-Style models (e.g., LLaMA). The following are some of the open-source examples that are powered by DeepSpeed:
 
@@ -108,7 +108,7 @@ A summary of DeepSpeed Chat includes:
 + **Easy-breezy Training Experience**: A single script capable of taking a pre-trained Huggingface model and running it through all three steps of the RLHF training.  
 + **A Universal System Support for Today’s ChatGPT-like Model Training**: DeepSpeed Chat can serve as the system backend for not only the 3-step instruct-base RLHF pipeline, but also the current single model finetuning exploration (e.g., LLaMA-centric finetuning) and generic RLHF training for various models and scenarios.
 
-Please check out our [Blog Release](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat) and [Documentation and Tutorial](#-documentation-and-tutorial-) for more details on our training methodology and new system technologies.
+Please check out our [Blog Release](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat) and [Documentation and Tutorial](#-documentation-and-tutorial-) for more details on our training methodology and new system technologies.
 
 
 ## ☕ Quick Start ☕
@@ -120,7 +120,7 @@ Please check out our [Blog Release](https://github.com/microsoft/DeepSpeed/tree/
 ```bash
 pip install deepspeed>=0.9.0
 
-git clone https://github.com/microsoft/DeepSpeedExamples.git
+git clone https://github.com/deepspeedai/DeepSpeedExamples.git
 cd DeepSpeedExamples/applications/DeepSpeed-Chat/
 pip install -r requirements.txt
 pip install -e .
@@ -251,9 +251,9 @@ bash training_scripts/opt/single_gpu/run_1.3b.sh
 
 
 ### 🐼 Adding and using your own datasets in DeepSpeed-Chat
-In addition to the datasets used in our example scripts, you can also add and use your own datasets. To do so, first you need to add a new Class in [dschat/utils/data/raw_datasets.py](https://github.com/microsoft/DeepSpeedExamples/blob/master/applications/DeepSpeed-Chat/dschat/utils/data/raw_datasets.py) to define the format when using your data. You need to make sure to follow the APIs and format defined in the PromptRawDataset class to ensure a consistent data format that DeepSpeed-Chat relies on. You can look at the existing classes to learn how to do so.
+In addition to the datasets used in our example scripts, you can also add and use your own datasets. To do so, first you need to add a new Class in [dschat/utils/data/raw_datasets.py](https://github.com/deepspeedai/DeepSpeedExamples/blob/master/applications/DeepSpeed-Chat/dschat/utils/data/raw_datasets.py) to define the format when using your data. You need to make sure to follow the APIs and format defined in the PromptRawDataset class to ensure a consistent data format that DeepSpeed-Chat relies on. You can look at the existing classes to learn how to do so.
 
-Second, you need to add an if condition in function get_raw_dataset in [dschat/utils/data/data_utils.py](https://github.com/microsoft/DeepSpeedExamples/blob/master/applications/DeepSpeed-Chat/dschat/utils/data/data_utils.py) corresponding to your new dataset. The dataset_name string in the if condition should be the dataset name you will provide as a arg for the training scripts. Last, you need to add your new dataset's dataset_name into your "--data_path" arg in your training scripts.  
+Second, you need to add an if condition in function get_raw_dataset in [dschat/utils/data/data_utils.py](https://github.com/deepspeedai/DeepSpeedExamples/blob/master/applications/DeepSpeed-Chat/dschat/utils/data/data_utils.py) corresponding to your new dataset. The dataset_name string in the if condition should be the dataset name you will provide as a arg for the training scripts. Last, you need to add your new dataset's dataset_name into your "--data_path" arg in your training scripts.  
 If you have downloaded huggingface datasets manually, you can add your local path into "--data_path", such as "--data_path ./relative/Dahoas/rm-static" and "--data_path /absolute/Dahoas/rm-static". Remember you should not make `data/` in your local path, it may cause an exception to `load_dataset`.
 
 One thing to note is that some datasets may only have one response instead of two responses. For those datasets, you can only use them in step 1. And in such case, you should add the dataset_name as part of the "--sft_only_data_path" arg instead of the "--data_path" arg. One thing to note is that: If you plan to only do step 1 SFT, adding more single-response datasets is definitely beneficial. However, if you do plan to do steps 2 and 3, then adding too many single-response datasets during SFT could backfire: these data could be different from the data used for steps 2/3, generating different distributions which could cause training instability/worse model quality during step 2/3. That is part of the reason why we focused on trying the datasets with two responses and the preference, and always split a dataset into all 3 steps.
@@ -375,7 +375,7 @@ Figure 4. Superior generation phase acceleration from DeepSpeed Chat’s Hybrid
 
 </p>
 
-For other detailed results and in-depth analysis, including effective throughput and scalability of DeepSpeed-Chat, please refer to our [Blog Post](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat).
+For other detailed results and in-depth analysis, including effective throughput and scalability of DeepSpeed-Chat, please refer to our [Blog Post](https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat).
 
 ## 😽 Supported Models 😽
 
@@ -400,7 +400,7 @@ model family | size range | details
 
 | Description | Status |
 | ----------- | ------ |
-| Integrations | [![nv-ds-chat](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master)](https://github.com/microsoft/DeepSpeed/actions/workflows/nv-ds-chat.yml) |
+| Integrations | [![nv-ds-chat](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master)](https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml) |
 
 A DeepSpeed CI workflow runs the DeepSpeed-Chat Step 3 pipeline nightly across the following test configurations:
 
@@ -441,7 +441,7 @@ Our future plan includes but not limited to :
 ## 💬 DeepSpeed Chat and DeepSpeed Community 💬
 
 Just like how the success of [the BLOOM model](https://huggingface.co/bigscience/bloom) was supported by both [DeepSpeed Team](https://github.com/bigscience-workshop/Megatron-DeepSpeed) and many [open source contributors](https://huggingface.co/bigscience), we welcome all AI developers/practitioners/researchers to join this on-going effort for DeepSpeed-Chat. To participate:
-- Show your support by leaving a star ⭐ to our [DeepSpeed](https://github.com/microsoft/DeepSpeed) and [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples) GitHub repositories.
+- Show your support by leaving a star ⭐ to our [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) and [DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples) GitHub repositories.
 - Follow us on [twitter](https://twitter.com/MSFTDeepSpeed) to get notified about our latest news. For Chinese users, you can also follow our [Chinese Zhihu account](https://www.zhihu.com/people/deepspeed). For Japanese users, you can also follow our [Japanese twitter account](https://twitter.com/MSFTDeepSpeedJP).
 - Currently we prefer to interact with open source users mainly on GitHub so that it's easier for all users to search for related information. For bug reports, please submit a GitHub issue. For contribution, please submit a pull request (PR). For general question/discussion, please open a new discussion or join any existing discussions.
 - We are open to collaborations with universities, research labs, and companies, such as working together on deep learning research, applying DeepSpeed to empower real-world AI models and applications, and so on. For such requests (and other requests unsuitable for GitHub), please directly email to [email protected].

applications/DeepSpeed-Chat/dschat/utils/data/data_utils.py

@@ -3,7 +3,7 @@
 
 # DeepSpeed Team
 """
-Part of the code was adopted from https://github.com/microsoft/Megatron-DeepSpeed/blob/main/megatron/data/dataset_utils.py
+Part of the code was adopted from https://github.com/deepspeedai/Megatron-DeepSpeed/blob/main/megatron/data/dataset_utils.py
 """
 import torch
 from torch.utils.data import Dataset, Subset, ConcatDataset

applications/DeepSpeed-Chat/e2e_rlhf.py

@@ -41,7 +41,7 @@
     3: "training/step3_rlhf_finetuning",
 }
 model_type = {1: "actor", 2: "reward", 3: "step3"}
-dse_url = "https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/"
+dse_url = "https://github.com/deepspeedai/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/"
 
 
 def parse_args():

applications/DeepSpeed-Chat/setup.py

@@ -16,7 +16,7 @@
     name="deepspeed-chat",
     version="0.1",
     url=
-    "https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat",
+    "https://github.com/deepspeedai/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat",
     include_package_data=True,
     packages=setuptools.find_packages(include=['dschat']),
     install_requires=[