DeepSpeed github repo move sync

.github/workflows/self-scheduled.yml

@@ -366,7 +366,7 @@ jobs:
         run: |
           python3 -m pip uninstall -y deepspeed
           rm -rf DeepSpeed
-          git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build
+          git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build
           DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
 
       - name: NVIDIA-SMI

docker/transformers-past-gpu/Dockerfile

@@ -48,8 +48,8 @@ RUN python3 -m pip uninstall -y torch-tensorrt apex
 # Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout)
 RUN python3 -m pip uninstall -y deepspeed
 # This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.)
-# Issue: https://github.com/microsoft/DeepSpeed/issues/2010
-# RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \
+# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010
+# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \
 #    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
 RUN python3 -m pip install -U "itsdangerous<2.1.0"

docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile

@@ -34,8 +34,8 @@ RUN python3 -m pip uninstall -y torch-tensorrt apex
 # Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout)
 RUN python3 -m pip uninstall -y deepspeed
 # This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.)
-# Issue: https://github.com/microsoft/DeepSpeed/issues/2010
-# RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \
+# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010
+# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \
 #    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
 ## For `torchdynamo` tests

docs/source/en/debugging.md

@@ -30,7 +30,7 @@ DeepSpeed compiles CUDA C++ code and it can be a potential source of errors when
 
 <Tip>
 
-For any other installation issues, please [open an issue](https://github.com/microsoft/DeepSpeed/issues) with the DeepSpeed team.
+For any other installation issues, please [open an issue](https://github.com/deepspeedai/DeepSpeed/issues) with the DeepSpeed team.
 
 </Tip>
 
@@ -89,7 +89,7 @@ sudo ln -s /usr/bin/g++-7  /usr/local/cuda-10.2/bin/g++
 If you're still having issues with installing DeepSpeed or if you're building DeepSpeed at run time, you can try to prebuild the DeepSpeed modules before installing them. To make a local build for DeepSpeed:
 
 ```bash
-git clone https://github.com/microsoft/DeepSpeed/
+git clone https://github.com/deepspeedai/DeepSpeed/
 cd DeepSpeed
 rm -rf build
 TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install . \
@@ -141,7 +141,7 @@ It is also possible to not specify `TORCH_CUDA_ARCH_LIST` and the build program
 For training on multiple machines with the same setup, you'll need to make a binary wheel:
 
 ```bash
-git clone https://github.com/microsoft/DeepSpeed/
+git clone https://github.com/deepspeedai/DeepSpeed/
 cd DeepSpeed
 rm -rf build
 TORCH_CUDA_ARCH_LIST="8.6" DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 \

docs/source/en/deepspeed.md

@@ -28,7 +28,7 @@ This guide will walk you through how to deploy DeepSpeed training, the features
 
 ## Installation
 
-DeepSpeed is available to install from PyPI or Transformers (for more detailed installation options, take a look at the DeepSpeed [installation details](https://www.deepspeed.ai/tutorials/advanced-install/) or the GitHub [README](https://github.com/microsoft/deepspeed#installation)).
+DeepSpeed is available to install from PyPI or Transformers (for more detailed installation options, take a look at the DeepSpeed [installation details](https://www.deepspeed.ai/tutorials/advanced-install/) or the GitHub [README](https://github.com/deepspeedai/DeepSpeed#installation)).
 
 <Tip>
 
@@ -114,10 +114,10 @@ DeepSpeed works with the [`Trainer`] class by way of a config file containing al
 
 <Tip>
 
-Find a complete list of DeepSpeed configuration options on the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference. You can also find more practical examples of various DeepSpeed configuration examples on the [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples) repository or the main [DeepSpeed](https://github.com/microsoft/DeepSpeed) repository. To quickly find specific examples, you can:
+Find a complete list of DeepSpeed configuration options on the [DeepSpeed Configuration JSON](https://www.deepspeed.ai/docs/config-json/) reference. You can also find more practical examples of various DeepSpeed configuration examples on the [DeepSpeedExamples](https://github.com/deepspeedai/DeepSpeedExamples) repository or the main [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) repository. To quickly find specific examples, you can:
 
 ```bash
-git clone https://github.com/microsoft/DeepSpeedExamples
+git clone https://github.com/deepspeedai/DeepSpeedExamples
 cd DeepSpeedExamples
 find . -name '*json'
 # find examples with the Lamb optimizer
@@ -303,7 +303,7 @@ For more information about initializing large models with ZeRO-3 and accessing t
 
 [ZeRO-Infinity](https://hf.co/papers/2104.07857) allows offloading model states to the CPU and/or NVMe to save even more memory. Smart partitioning and tiling algorithms allow each GPU to send and receive very small amounts of data during offloading such that a modern NVMe can fit an even larger total memory pool than is available to your training process. ZeRO-Infinity requires ZeRO-3.
 
-Depending on the CPU and/or NVMe memory available, you can offload both the [optimizer states](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) and [parameters](https://www.deepspeed.ai/docs/config-json/#parameter-offloading), just one of them, or none. You should also make sure the `nvme_path` is pointing to an NVMe device, because while it still works with a normal hard drive or solid state drive, it'll be significantly slower. With a modern NVMe, you can expect peak transfer speeds of ~3.5GB/s for read and ~3GB/s for write operations. Lastly, [run a benchmark](https://github.com/microsoft/DeepSpeed/issues/998) on your training setup to determine the optimal `aio` configuration.
+Depending on the CPU and/or NVMe memory available, you can offload both the [optimizer states](https://www.deepspeed.ai/docs/config-json/#optimizer-offloading) and [parameters](https://www.deepspeed.ai/docs/config-json/#parameter-offloading), just one of them, or none. You should also make sure the `nvme_path` is pointing to an NVMe device, because while it still works with a normal hard drive or solid state drive, it'll be significantly slower. With a modern NVMe, you can expect peak transfer speeds of ~3.5GB/s for read and ~3GB/s for write operations. Lastly, [run a benchmark](https://github.com/deepspeedai/DeepSpeed/issues/998) on your training setup to determine the optimal `aio` configuration.
 
 The example ZeRO-3/Infinity configuration file below sets most of the parameter values to `auto`, but you could also manually add these values.
 
@@ -1157,7 +1157,7 @@ For Transformers>=4.28, if `synced_gpus` is automatically set to `True` if multi
 
 ## Troubleshoot
 
-When you encounter an issue, you should consider whether DeepSpeed is the cause of the problem because often it isn't (unless it's super obviously and you can see DeepSpeed modules in the exception)! The first step should be to retry your setup without DeepSpeed, and if the problem persists, then you can report the issue. If the issue is a core DeepSpeed problem and unrelated to the Transformers integration, open an Issue on the [DeepSpeed repository](https://github.com/microsoft/DeepSpeed).
+When you encounter an issue, you should consider whether DeepSpeed is the cause of the problem because often it isn't (unless it's super obviously and you can see DeepSpeed modules in the exception)! The first step should be to retry your setup without DeepSpeed, and if the problem persists, then you can report the issue. If the issue is a core DeepSpeed problem and unrelated to the Transformers integration, open an Issue on the [DeepSpeed repository](https://github.com/deepspeedai/DeepSpeed).
 
 For issues related to the Transformers integration, please provide the following information:
 
@@ -1227,7 +1227,7 @@ This means the DeepSpeed loss scaler is unable to find a scaling coefficient to
 
 ## Resources
 
-DeepSpeed ZeRO is a powerful technology for training and loading very large models for inference with limited GPU resources, making it more accessible to everyone. To learn more about DeepSpeed, feel free to read the [blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed), [documentation](https://www.deepspeed.ai/getting-started/), and [GitHub repository](https://github.com/microsoft/deepspeed). 
+DeepSpeed ZeRO is a powerful technology for training and loading very large models for inference with limited GPU resources, making it more accessible to everyone. To learn more about DeepSpeed, feel free to read the [blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed), [documentation](https://www.deepspeed.ai/getting-started/), and [GitHub repository](https://github.com/deepspeedai/DeepSpeed). 
 
 The following papers are also a great resource for learning more about ZeRO:
 

docs/source/en/main_classes/deepspeed.md

@@ -16,7 +16,7 @@ rendered properly in your Markdown viewer.
 
 # DeepSpeed
 
-[DeepSpeed](https://github.com/microsoft/DeepSpeed), powered by Zero Redundancy Optimizer (ZeRO), is an optimization library for training and fitting very large models onto a GPU. It is available in several ZeRO stages, where each stage progressively saves more GPU memory by partitioning the optimizer state, gradients, parameters, and enabling offloading to a CPU or NVMe. DeepSpeed is integrated with the [`Trainer`] class and most of the setup is automatically taken care of for you. 
+[DeepSpeed](https://github.com/deepspeedai/DeepSpeed), powered by Zero Redundancy Optimizer (ZeRO), is an optimization library for training and fitting very large models onto a GPU. It is available in several ZeRO stages, where each stage progressively saves more GPU memory by partitioning the optimizer state, gradients, parameters, and enabling offloading to a CPU or NVMe. DeepSpeed is integrated with the [`Trainer`] class and most of the setup is automatically taken care of for you. 
 
 However, if you want to use DeepSpeed without the [`Trainer`], Transformers provides a [`HfDeepSpeedConfig`] class.
 

docs/source/en/perf_train_gpu_many.md

@@ -476,7 +476,7 @@ And GPU1 does the same by enlisting GPU3 to its aid.
 Since each dimension requires at least 2 GPUs, here you'd need at least 4 GPUs.
 
 Implementations:
-- [DeepSpeed](https://github.com/microsoft/DeepSpeed)
+- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed)
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
 - [Varuna](https://github.com/microsoft/varuna)
 - [SageMaker](https://arxiv.org/abs/2111.05972)
@@ -497,7 +497,7 @@ This diagram is from a blog post [3D parallelism: Scaling to trillion-parameter
 Since each dimension requires at least 2 GPUs, here you'd need at least 8 GPUs.
 
 Implementations:
-- [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
+- [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
 - [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
 - [Varuna](https://github.com/microsoft/varuna)
 - [SageMaker](https://arxiv.org/abs/2111.05972)