Update GH org references

Author: tjruwase

.github/ISSUE_TEMPLATE/deepspeed_chat_bug_report.md

@@ -32,7 +32,7 @@ If applicable, add screenshots to help explain your problem.
 **System info (please complete the following information):**
  - OS: [e.g. Ubuntu 18.04]
  - GPU count and types [e.g. two machines with x8 A100s each]
- - (if applicable) what [DeepSpeed-MII](https://github.com/microsoft/deepspeed-mii) version are you using
+ - (if applicable) what [DeepSpeed-MII](https://github.com/deepspeedai/deepspeed-mii) version are you using
  - (if applicable) Hugging Face Transformers/Accelerate/etc. versions
  - Python version
  - Any other relevant info about your setup

.github/workflows/nv-a6000.yml

@@ -66,7 +66,7 @@ jobs:
               BRANCH="${{ github.event.inputs.mii_branch }}"
           fi
           echo "Cloning DeepSpeed-MII branch: $BRANCH"
-          git clone -b $BRANCH --depth=1 https://github.com/microsoft/DeepSpeed-MII.git
+          git clone -b $BRANCH --depth=1 https://github.com/deepspeedai/DeepSpeed-MII.git
           cd DeepSpeed-MII
           pip install .[dev]
           cd tests

.github/workflows/nv-ds-chat.yml

@@ -54,7 +54,7 @@ jobs:
               BRANCH="${{ github.event.inputs.dse_branch }}"
           fi
           echo "DeepSpeedExamples Branch: $BRANCH"
-          git clone -b $BRANCH https://github.com/microsoft/DeepSpeedExamples.git
+          git clone -b $BRANCH https://github.com/deepspeedai/DeepSpeedExamples.git
           cd DeepSpeedExamples/applications/DeepSpeed-Chat
           pip install -r requirements.txt
           pip install -e .

.github/workflows/nv-mii.yml

@@ -66,7 +66,7 @@ jobs:
               BRANCH="${{ github.event.inputs.mii_branch }}"
           fi
           echo "Cloning DeepSpeed-MII branch: $BRANCH"
-          git clone -b $BRANCH --depth=1 https://github.com/microsoft/DeepSpeed-MII.git
+          git clone -b $BRANCH --depth=1 https://github.com/deepspeedai/DeepSpeed-MII.git
           cd DeepSpeed-MII
           pip install .[dev]
           unset TORCH_CUDA_ARCH_LIST # only jit compile for current arch

CONTRIBUTING.md

@@ -23,7 +23,7 @@ and then repeat the previous `git commit` command.
 ## Testing
 DeepSpeed tracks two types of tests: unit tests and more costly model convergence tests.
 The model convergence tests train
-[DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/) and measure
+[DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples/) and measure
 end-to-end convergence and related metrics. Unit tests are found in `tests/unit/` and
 the model convergence tests are found in `tests/model/`.
 
@@ -40,7 +40,7 @@ tests. Note that [pytest-forked](https://github.com/pytest-dev/pytest-forked) an
 
 ### Model Tests
 To execute model tests, first [install DeepSpeed](#installation). The
-[DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/) repository is cloned
+[DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples/) repository is cloned
 as part of this process. Next, execute the model test driver:
 ```bash
 cd tests/model/
@@ -85,8 +85,8 @@ Based on the issue we shall discuss the merit of the new feature and decide whet
 ### Step 2: implementation and verification
 Contributor will go ahead and implement the feature, and the DeepSpeed team will provide guidance/helps as needed. The required deliverables include:
 
-* A PR to [microsoft/DeepSpeed](https://github.com/microsoft/DeepSpeed) including (1) the feature implementation (2) unit tests (3) documentation (4) tutorial
-* A PR to [microsoft/DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples) or [microsoft/Megatron-DeepSpeed](https://github.com/microsoft/Megatron-DeepSpeed) including the examples of how to use the feature (this is related to the planned testing experiments in proposal)
+* A PR to [deepspeedai/DeepSpeed](https://github.com/deepspeedai/DeepSpeed) including (1) the feature implementation (2) unit tests (3) documentation (4) tutorial
+* A PR to [deepspeedai/DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples) or [deepspeedai/Megatron-DeepSpeed](https://github.com/deepspeedai/Megatron-DeepSpeed) including the examples of how to use the feature (this is related to the planned testing experiments in proposal)
 * In the implementation (code, documentation, tutorial), we require the feature author to record their GitHub username as a contact method for future questions/maintenance.
 
 After receiving the PRs, we will review them and merge them after necessary tests/fixes.

README.md

@@ -169,7 +169,7 @@ def get_accelerator():
                 # ensuring that we are free from CUDA initialization errors.
                 # While "torch.cuda.device_count() > 0" check ensures that       #ignore-cuda
                 # we won't try to do any CUDA calls when no device is available
-                # For reference: https://github.com/microsoft/DeepSpeed/pull/6810
+                # For reference: https://github.com/deepspeedai/DeepSpeed/pull/6810
                 if torch.cuda.device_count() > 0 and torch.cuda.is_available():  #ignore-cuda
                     accelerator_name = "cuda"
             except (RuntimeError, ImportError) as e:

accelerator/real_accelerator.py

@@ -2,5 +2,5 @@
 
 If you are looking for DeepSpeed benchmarks, please see the following resources:
 
-1. [Communication Benchmarking Suite](https://github.com/microsoft/DeepSpeedExamples/tree/master/benchmarks/communication)
-2. [Inference Benchmarks](https://github.com/microsoft/DeepSpeedExamples/tree/master/benchmarks/inference)
+1. [Communication Benchmarking Suite](https://github.com/deepspeedai/DeepSpeedExamples/tree/master/benchmarks/communication)
+2. [Inference Benchmarks](https://github.com/deepspeedai/DeepSpeedExamples/tree/master/benchmarks/inference)

benchmarks/README.md

@@ -65,7 +65,7 @@ DeepSpeed-RLHF system is capable of unparalleled efficiency at scale, making com
 *Table 2. Multi-Node 64x A100-80GB: Training Time and Corresponding Approximate Cost on Azure.*
 </div>
 
-> ***Very Important Details***: The numbers in both tables above are for Step 3 of the training and are based on actual measured training throughput on DeepSpeed-RLHF curated dataset and training recipe which trains for one epoch on a total of 135M tokens. We have in total 67.5M query tokens (131.9k queries with sequence length 256) and 67.5M generated tokens (131.9k answers with sequence length 256), and a maximum global batch size per step of 0.5M tokens (1024 query-answer pairs). We urge readers to pay attention to these specifications before making any cost and e2e time comparisons with DeepSpeed-RLHF. See our [benchmark settings](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/training/step3_rlhf_finetuning/BenckmarkSetting.md) page for more details.
+> ***Very Important Details***: The numbers in both tables above are for Step 3 of the training and are based on actual measured training throughput on DeepSpeed-RLHF curated dataset and training recipe which trains for one epoch on a total of 135M tokens. We have in total 67.5M query tokens (131.9k queries with sequence length 256) and 67.5M generated tokens (131.9k answers with sequence length 256), and a maximum global batch size per step of 0.5M tokens (1024 query-answer pairs). We urge readers to pay attention to these specifications before making any cost and e2e time comparisons with DeepSpeed-RLHF. See our [benchmark settings](https://github.com/deepspeedai/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/training/step3_rlhf_finetuning/BenckmarkSetting.md) page for more details.
 
 
 ***Democratizing RLHF Training***: With just a single GPU, DeepSpeed-HE supports training models with over 13 billion parameters, enabling data scientists without access to multi-GPU systems to create not just toy RLHF models but large and powerful ones that can be used in real-world scenarios.
@@ -95,7 +95,7 @@ We use an example of pretrained OPT-13B as the actor model and OPT-350M as the r
 ```python
 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
 
@@ -285,7 +285,7 @@ This improvement in efficiency stems from DeepSpeed-HE’s ability to accelerate
 
 ## Effective Throughput and Scalability Analysis
 
-***(I) Effective Throughput Analysis.*** The effective throughput of DeepSpeed-HE during Stage 3 of the RLHF training depends on the throughput that it achieves during the generation and RL training phases. In our RLHF pipeline, the generation phase comprises approximately 20% of the total computation while the RL training phase comprises of remaining 80% (see [benchmark settings](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/training/step3_rlhf_finetuning/BenckmarkSetting.md) page for details). However, despite having a small proportion, the former can take a large portion of the e2e time as it requires running the actor model once for each of the 256 generated tokens with an initial prompt of 256 tokens, making it memory bandwidth bound and difficult to achieve high throughput. In contrast, the RL training phase is compute bound running the reference actor model with just a couple of forward and backward passes with full 512 tokens from both prompt and generation per sample and can achieve good throughput.
+***(I) Effective Throughput Analysis.*** The effective throughput of DeepSpeed-HE during Stage 3 of the RLHF training depends on the throughput that it achieves during the generation and RL training phases. In our RLHF pipeline, the generation phase comprises approximately 20% of the total computation while the RL training phase comprises of remaining 80% (see [benchmark settings](https://github.com/deepspeedai/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/training/step3_rlhf_finetuning/BenckmarkSetting.md) page for details). However, despite having a small proportion, the former can take a large portion of the e2e time as it requires running the actor model once for each of the 256 generated tokens with an initial prompt of 256 tokens, making it memory bandwidth bound and difficult to achieve high throughput. In contrast, the RL training phase is compute bound running the reference actor model with just a couple of forward and backward passes with full 512 tokens from both prompt and generation per sample and can achieve good throughput.
 
 <div align="center">
 
@@ -320,13 +320,13 @@ As a result, for a given max global batch size, DeepSpeed-HE achieves the best t
 
 We are very excited to share that DeepSpeed-Chat is now open-sourced and available to the AI community.
 
-* To get started, please visit our github page for DeepSpeed-Chat: [GitHub Landing Page](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat)
+* To get started, please visit our github page for DeepSpeed-Chat: [GitHub Landing Page](https://github.com/deepspeedai/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat)
 
-* We will continue to improve DeepSpeed-Chat with your feedback and support. Our [roadmap](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/README.md#-deepspeed-chats-roadmap-) shows currently supported features as well as ones that are planned for the future.
+* We will continue to improve DeepSpeed-Chat with your feedback and support. Our [roadmap](https://github.com/deepspeedai/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/README.md#-deepspeed-chats-roadmap-) shows currently supported features as well as ones that are planned for the future.
 
 DeepSpeed-Chat is part of the bigger DeepSpeed ecosystem comprising a multitude of Deep Learning systems and modeling technologies. To learn more,
 
 * Please visit our [website](https://www.deepspeed.ai/) for detailed blog posts, tutorials, and helpful documentation.
 * You can also follow us on our [English Twitter](https://twitter.com/MSFTDeepSpeed), [Japanese Twitter](https://twitter.com/MSFTDeepSpeedJP), and [Chinese Zhihu](https://www.zhihu.com/people/deepspeed) for latest news on DeepSpeed.
 
-DeepSpeed welcomes your contributions! We encourage you to report issues, contribute PRs, and join discussions on the [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/) page. Please see our [contributing guide](https://github.com/microsoft/DeepSpeed/blob/master/CONTRIBUTING.md) for more details. We are open to collaborations with universities, research labs, companies, such as those 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 deepspeed-info@microsoft.com.
+DeepSpeed welcomes your contributions! We encourage you to report issues, contribute PRs, and join discussions on the [DeepSpeed GitHub](https://github.com/deepspeedai/DeepSpeed/) page. Please see our [contributing guide](https://github.com/deepspeedai/DeepSpeed/blob/master/CONTRIBUTING.md) for more details. We are open to collaborations with universities, research labs, companies, such as those 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 deepspeed-info@microsoft.com.