From 43a7f735943ba12203f09a7991492c9d740f536e Mon Sep 17 00:00:00 2001
From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com>
Date: Wed, 4 Oct 2023 17:34:36 -0700
Subject: [PATCH 01/38] add Japanese blog of DS visual chat (#4454)

---
 .../10-03-2023/README-Japanese.md             | 183 ++++++++++++++++++
 1 file changed, 183 insertions(+)
 create mode 100755 blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md

diff --git a/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md b/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
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+<div align="center">
+
+# DeepSpeed-VisualChat: 複数ラウンド・複数画像の入力が可能なAIチャット体験を実現
+</div>
+
+<div align="center">
+
+<img src="../assets/images/hero-figure.png" width="1000px" alt="DeepSpeed-VisualChat!"/>
+
+</div>
+
+DeepSpeed-VisualChatを引用する場合、[arxiv上のレポート](https://arxiv.org/abs/2309.14327)を引用してください。
+
+```
+@article{yao2023deepspeed-visualchat,
+  title={{DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention}},
+  author={Zhewei Yao and Xiaoxia Wu and Conglong Li and Minjia Zhang and Heyang Qin and Olatunji Ruwase and Ammar Ahmad Awan and Samyam Rajbhandari and Yuxiong He},
+  journal={arXiv preprint arXiv:2309.14327},
+  year={2023}
+}
+```
+
+# 1. 概要
+GPTやLLaMaのような大規模言語モデル（LLM）は、テキスト生成やテキスト理解などの多くのタスクにおいて、Zero-shot/Few-shot学習、あるいはinstructed fine-tuningによって、非常に優れた能力を示してきました。しかし、AIエージェントをより多様なタスクに対応させるには、マルチモーダルを扱う能力が必要です。例えば、AIエージェントは画像を読んだり、音声を聞いたり、ビデオを見たりすることができる必要があります。こうした機能は、テキストベースのLLMにはほとんどありません。
+
+近年、LLMに視覚的な能力を導入することは、研究・実践の両方において広く試みられています。特に、画像をそのまま与えて、LLMが理解できるようにする取り組みが行われています（大規模視覚言語モデル、略してLVLMなどと呼ばれる）。
+
+こうした分野における、既存の研究の主な問題は以下の通りです：
+
+* 視覚に関する質問への回答やキャプション付けのように、単一の画像に関連するタスクや、同時に入力される複数の画像の処理に重点が置かれており、画像とテキストが交互に入力されるような状況には対応していない
+* システムのスケーラビリティは、～10Bのパラメータを持つモデルに限定される
+
+しかし、本来はAIチャットエージェントには、複数のテキストと画像の両方が与えられる可能性があります。また、LLMの生成能力は、モデルサイズが大きくなるにつれて急速に向上することが知られており、～10Bのモデルではその能力が制限されてしまいます。
+
+これらの問題を解決するために、我々は以下の新たな機能を備えたDeepSpeed-VisualChat（詳細は[arxivのレポート](https://arxiv.org/abs/2309.14327)を参照）を開発しました:
+
+* ***完全にオープンソース化され、前例のないスケーラビリティを備えた複数ラウンド・複数画像を処理できるフレームワーク***： DeepSpeed-VisualChatは、完全にオープンソース化された先進的なフレームワークの1つであり、複数ラウンドを通じて画像とテキストが両方与えられる対話を可能にします。また、DeepSpeedを利用することで、比類ないスケーラビリティを実現しており、実際に2Bのビジュアルエンコーダーと70BのLLaMA-2デコーダーモデルで訓練を行えます。
+* ***マルチモーダル因果的注意(MMCA)***: マルチモーダルモデルのための新しいアテンションMMCA（Multi-Modal Causal Attention）を考案し、異なるモダリティ間で独立にアテンションの重みを計算します。MMCAは、従来のcross attentionに類似したものですが、生成タスクのためのcausal attentionを強化しており、追加のモジュールやパラメータが不要になります。また、標準的なcausal attentionと比較して、優れた訓練データ効率を示します。
+* ***順次与えられる画像とテキストを扱うためのデータブレンディング***: DeepSpeed-VisualChatは、既存のデータセットに様々なデータブレンディング技術を採用しています。これにより、順次与えられるテキストと画像の不足という、利用可能なオープンソースデータセットのほとんどに当てはまる課題を克服しています。
+
+# 2 モデルアーキテクチャの概要
+<div align="center">
+  <img src="../assets/images/model.png" alt="model arch" width="400"/>
+
+  *図1: モデルアーキテクチャの概要*
+
+</div>
+
+*図1*に示すように、DeepSpeed-VisualChatのモデルアーキテクチャは、CLIPのような視覚エンコーダー、LLaMa-7Bのような言語デコーダー、特徴アライメントを行う linear projectionレイヤの3つのコンポーネントで構成されています。モデルのほとんどのパラメータは固定されており、言語モデルのembeddingとlinear projectionレイヤのみが学習可能です。その結果、学習可能なパラメータの総数は O(10M) (LLaMa-2-13B) から O(100M) (LLaMa-2-70B) となります。
+
+# 3. DeepSpeed マルチモーダル Causal Attention (MMCA)
+
+マルチモーダルモデルで、画像とテキストをつなぐ一般的なattentionの機構は二つあります。一つはMiniGPTやQWen-VLで使われているようなcausal attentionで、もう一つはOtterやFlamingoで使われているようなcross attentionです。
+
+
+<div align="center">
+  <img src="../assets/images/attention.png" alt="Different attention mehanisms" width="1000"/>
+</div>
+
+*図2: 異なるアテンションの機構: 「ユーザー：画像を説明してください」という入力文と3つの画像トークン（I-token1、I-token2、I-token3）と組み合わせて与えた場合の、それぞれのattention機構の構成を示しています。左側では、標準的なcausal attentionによって、画像トークンをテキストとして扱う様子を示しています。中央は、テキストトークンに対する標準的なcausal attentionを維持しながら、画像に適用されるcross attentionを使用する様子を示しています。右側では、画像トークンはself attentionのみを行い、テキストトークンはテキスト／画像トークンへのアテンションを独立に計算するという、新しいマルチモーダルのためのアテンションの提案を、オレンジ色のマスクで強調して示しています。この仕組みは、Q, Kをクエリとキーとしたとき、 softmax($`QK^T \odot M_1`$)+ softmax($`QK^T \odot M_2`$)として定義されます。M $`\in`$ R<sup>10x10</sup>としたとき、$`M_1`$=[M==1], and $`M_2`$=[M==2] です。*
+
+<b>Causal Attention（CA）</b>：CAに基づく方法は、視覚的特徴（最終的な視覚エンコーダ層の出力からの特徴）を単純にテキストの特徴量に投影し、テキスト埋め込み層以降の通常のテキストの特徴量と組み合わせてLLMに送り込むというものです。CAの利点は、LLMにおける本来のアテンション機構の自然な拡張であり、そのため余分なモジュールやパラメータを導入しないことです。しかし、このアプローチにはいくつかの直感的な問題があります：
+
+* 視覚トークンはすでに双方向に特徴量に変換されており、本来他の視覚トークンやテキストトークンとのアテンションの必要はありませんが、実際には前の視覚またはテキストトークンとのアテンションがあります。。
+* テキストトークンの場合、モデルは前のテキストトークンと画像トークンとの間でどのようにアテンションの重みを配分するかを学習する必要があります。これらの問題により、LVLMにおけるCAのデータ効率にはしばしば問題があることが分かりました。この問題への対処として、LLaVAとQWen-VLは、視覚的特徴とテキストの特徴を完全に対応させるために、視覚言語の事前学習を必要とします。
+
+<b>Cross Attention (CrA)</b>：代替案であるCross Attention (CrA) と CAの組み合わせは、より優れたデータ効率を示しますが、いくつかの欠点もあります：
+
+* モデルに新しいパラメーターを導入する必要があります。例えば、Otterは、Cross Attentionによって導入された新しいパラメータがあるため、LLaVAが数百万個の学習可能なパラメータを持つのに対し、15億個以上のパラメータを必要とします。これにより、学習コストと必要メモリ量が大幅に増加します。
+* 訓練中に会話の途中で画像が与えられた場合、前のテキストトークンは与えられた画像とのアテンションを求められないので、慎重な設計が必要です。
+
+<b>マルチモーダル Causal Attention (MMCA)</b>：これらの問題を解決するために、我々は新しいマルチモーダルCausal Attention (MMCA) を提案します。この機構は、CAと同様のパラメータ効率と、CrAと同様のデータ効率の、両方の利点を持つものです。全体的なアイデアは以下の通りです：
+
+* 視覚トークンは視覚エンコーダによってエンコードされるため、視覚トークンは自分自身とのアテンションのみを利用する。
+* テキストトークンについては、その前のすべてのトークンに注目する。ただし、前のテキストトークンと画像トークンに対して、それぞれ別々のアテンションの重み行列を持つ。
+
+MMCAの2つ目のポイントは、1つのモダリティに対するアテンションの重みが、もう1つのモダリティに影響を与える可能性があるということです。例えば、テキストトークンは、視覚情報よりもテキスト情報により大きなアテンションを持つかもしれません。そのため、アテンションの重み行列を両方のモダリティで正規化すると、視覚トークンのアテンションスコアが非常に小さくなる可能性があります。3つのアテンション機構の視覚化については、*図2*を参照してください。
+
+<b>出力例</b> まず、異なるアテンションの機構を採用した、画像を一つだけ用いた会話におけるDeepSpeed-VisualChatの能力を示す様々な例を紹介します。これらの実験では、LLaMA2-7B言語モデルとQWen-VL視覚エンコーダを視覚エンコーダとして併用します。これら2つのモデルはlinear projection layerを介して接続されています。このモデルは2つのLLaVaデータセットで学習を行いました。*図3*と*図4*で実証されているように、DeepSpeed-VisualChatはMMCAと組み合わされることで、画像内の視覚的な詳細を効果的に識別し、ユーザーのクエリに対して首尾一貫した応答を提供します。さらに、MMCAは、Causal AttentionとCross Attentionの両方から合成されたマスクを使用するような、別のアテンション機構と比べて、より包括的で正確な画像詳細の把握が可能です。また、CrAとCAの組み合わせやMMCAとは対照的に、CA単独では若干エラーが多く（*図3*）、推論能力の程度が低い（*図4*）可能性があることも明らかです。
+
+<div align="center">
+  <img src="../assets/images/cat-chat.png" alt="Small kitten" width="600"/>
+
+  *図3: (1) 標準的なcausal attention (CA) (2) cross attentionと組み合わせた標準的なcausal attention (CA+CrA) (3)DeepSpeed-VisualChatの特別なマルチモーダルCausal Attention (MMCA) の出力比較を示す視覚入力と言語入力の例。*
+</div>
+
+<div align="center">
+  <img src="../assets/images/lake-chat.png" alt="Beautiful lake" width="600"/>
+  *図4：DeepSpeed-VisualChatは、示された場面を美しい湖として正確に識別し、妥当な提案のセットを提示する。対照的に、ベースラインは画像を「ボート乗り場のあるドック」と誤認識している。*
+</div>
+
+# 4. データブレンディング
+
+[arxivのレポート](https://arxiv.org/abs/2309.14327)に記載されているように、訓練には3つのソースから9つのデータセットを使用しました。複数ラウンド・複数画像の入力を可能にするために決定的に欠けている要素は、適切なデータがないことです。我々が見つけた複数ラウンド・複数画像の唯一のデータソースはSparklesDialogueデータセットで、そこにはわずか6520サンプルしか含まれていません。この制限に対処するため、既存の単一画像または単一ラウンドのデータから、複数ラウンド・複数画像のデータを合成するために、単純なデータ連結とLLaVA-Otterデータ混合という2つの方法を採用しました。
+
+## 4.1 単純なデータ連結
+LLaVAモデルで利用する "llava" と "llava_dial" データセットでは、各サンプルは1つの画像に対する単一／複数ラウンドの会話で構成されています。ユーザーが複数の画像について逐次質問するシナリオをシミュレートするため、これら2つのデータセットに対して、簡単なデータ後処理を行いました。具体的には、ランダムな数のサンプルを1つのサンプルとして連結しました。 "llava" の場合は1～3個のサンプルを連結し、"llava_dial" の場合は1～2個のサンプルを連結しました。
+
+## 4.2 LLaVAとOtterのデータブレンディング
+
+LLaVAモデルで使用されているllavaとllava_dialデータセット、およびOtterモデルで使用されているotter_mimicit_cgdデータセットは、すべてCOCO train2017画像を使用しています。llavaデータセットとllava_dialデータセットには、各サンプルに1つの画像に対する単発／複数回の会話が含まれます。otter_mimicit_cgdデータセットでは、各サンプルは画像のペアに対する1ラウンドの会話を含んでいます。そこで、otter_mimicit_cgdデータセットの各サンプルについて、同じ画像を使うllavaとllava_dialのサンプルを探し、「llava/llava_dial会話 -> otter_mimicit_cgd会話」という流れで新しいサンプルを構築しました。
+
+<div align="center">
+  <img src="../assets/images/data-blending.png" alt="Friends" width="600"/>
+  *図5: LLaVA-Otterデータブレンド後のデータサンプル。灰色のダイアログボックスはLLaVAデータセットから、オレンジ色のダイアログボックスはOtterデータセットからのもの*
+</div>
+
+# 5. デモ
+いくつかのオープンソースデータセットで2Bビジュアルエンコーダーと13B LLaMAモデルを使い、DeepSpeed-VisualChat-13Bモデルを訓練しました。DeepSpeed-VisualChat-13Bは、画像キャプション機能（*図6-8*）、計数とテキスト読み取り（*図6*）、著名人の認識（*図7*）、ストーリーテリング（*図8*）などを示しています。
+
+<div align="center">
+  <img src="../assets/images/friends.png" alt="Friends" width="600"/>
+  *図6: DeepSpeed-VisualChatは、画像内の人数を数え、最初の画像のテキストを読み取ることができます。また、複数画像を横断的に理解することも可能です。*
+</div>
+
+
+<div align="center">
+  <img src="../assets/images/ceos.png" alt="CEO" width="600"/>
+  *図7: DeepSpeed-VisualChatは有名人を認識し、その人物の業績と関連付けることができます*
+</div>
+
+
+<div align="center">
+  <img src="../assets/images/zootopia.png" alt="Zootopia" width="600"/>
+  *図8: DeepSpeed-VisualChatは、ストーリーを作ったり、映画を認識したりできます。*
+</div>
+
+
+# 6. DeepSpeed-VisualChatを使い始めるには
+DeepSpeed-VisualChatは使いやすく、かつ優れたスケーラビリティを持つ学習フレームワークで、これまでLLaMa-2-70Bモデルでテストされています。
+すべての実験で統一された命令チューニング形式を採用しており、そのテンプレートを以下に示します。
+
+```
+<System Instruction>      % You are a powerful vision-language assistant.
+
+### Image 1: <image>       % some image, e.g., cat-1.png
+### Question: <question>   % please describe the image.
+### Answer: <answer>       % It's a cute black cat.
+
+### Image 2: <image>       % some image, e.g., cat-2.png
+### Image 3: <image>       % some image, e.g., cat-3.png
+### Question: <question>   % What's the difference between the three cats?
+### Answer: <answer>       % The colors of the three cats are different.
+...
+```
+
+DeepSpeed-VisualChatの訓練は簡単かつ便利に実行できます。ここではCLIPビジュアルエンコーダーとLLaMa-7Bモデルを使用する例を示します：
+
+```
+git clone https://github.com/microsoft/DeepSpeedExamples.git
+cd DeepSpeedExamples/applications/DeepSpeed-VisualChat/
+pip install -r requirements.txt
+cd training
+bash training_scripts/run_7b.sh
+```
+
+訓練されたチェックポイントは自動的にHugging Faceと互換性のある形式で保存され、独自のビジュアルチャットAPIを提供するために使用できます：
+
+```
+cd ../chat
+bash chat_scripts/run.sh # You need to change necessary variables, e.g, ckpt path
+```
+
+より大規模なモデル推論をサポートするために、我々はHugging Faceの大規模モデル推論をDeepSpeed-VisualChat APIに組み込みました。そのため、ユーザーはGPUメモリ容量とモデルサイズに基づいて、異なるGPU数を選択することができます。
+
+詳細は[ランディングページ](https://github.com/microsoft/DeepSpeedExamples/DeepSpeed-VisualChat)をご参照ください。
+
+# 7. 早速使ってみましょう！
+
+DeepSpeed-VisualChatがオープンソース化され、AIコミュニティで利用できるようになったことを大変嬉しく思います。
+
+* まずは、DeepSpeed-VisualChatのGitHubページをご覧ください： [GitHubランディングページ](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-VisualChat)
+
+* DeepSpeed-VisualChatは、皆様からのフィードバックとサポートにより改良を続けていきます。私たちの[ロードマップ](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-VisualChat/README.md#-deepspeed-visualchats-roadmap-)は、現在サポートされている機能と将来的に計画している機能を示しています。
+
+DeepSpeed-VisualChatは、さまざまなDeep Learningシステムやモデリング技術を含む、より大きなDeepSpeedエコシステムの一部です。詳細については、以下をご覧ください。
+
+* 私たちの[ウェブサイト](https://www.deepspeed.ai/)で、詳細なブログ記事、チュートリアル、役立つドキュメントを提供しています。
+* DeepSpeedの最新ニュースは、[English X(Twitter)](https://twitter.com/MSFTDeepSpeed)、[Japanese X(Twitter)](https://twitter.com/MSFTDeepSpeedJP)、[Chinese Zhihu](https://www.zhihu.com/people/deepspeed)をフォローしてください。
+
+DeepSpeedは、皆様の開発への参加を歓迎しています。DeepSpeedのGitHubページで、バグ報告、Pull Request、ディスカッションへの参加が可能です。詳細は[ガイドライン](https://github.com/microsoft/DeepSpeed/blob/master/CONTRIBUTING.md)をご覧ください。また、大学、研究所、企業とのコラボレーションも行っています。こうしたコラボレーションについてのご要望（およびGitHubには適さないその他の話題）については<deepspeed-info@microsoft.com> まで直接メールをお送りください。
+
+* 私たちの[DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/)および[DeepSpeedExamples GitHub](https://github.com/microsoft/DeepSpeedExamples/)リポジトリが気に入ったら、ぜひスターをつけてください！

From 93a6d7a547d29522a063f67cc205f067d78ef73c Mon Sep 17 00:00:00 2001
From: Conglong Li <conglong.li@gmail.com>
Date: Wed, 4 Oct 2023 17:43:41 -0700
Subject: [PATCH 02/38] fix blog format (#4456)

---
 blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md b/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
index 80ffcf1efa32..7520b460ce33 100755
--- a/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
+++ b/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
@@ -55,9 +55,9 @@ GPTやLLaMaのような大規模言語モデル（LLM）は、テキスト生成
 
 <div align="center">
   <img src="../assets/images/attention.png" alt="Different attention mehanisms" width="1000"/>
-</div>
 
-*図2: 異なるアテンションの機構: 「ユーザー：画像を説明してください」という入力文と3つの画像トークン（I-token1、I-token2、I-token3）と組み合わせて与えた場合の、それぞれのattention機構の構成を示しています。左側では、標準的なcausal attentionによって、画像トークンをテキストとして扱う様子を示しています。中央は、テキストトークンに対する標準的なcausal attentionを維持しながら、画像に適用されるcross attentionを使用する様子を示しています。右側では、画像トークンはself attentionのみを行い、テキストトークンはテキスト／画像トークンへのアテンションを独立に計算するという、新しいマルチモーダルのためのアテンションの提案を、オレンジ色のマスクで強調して示しています。この仕組みは、Q, Kをクエリとキーとしたとき、 softmax($`QK^T \odot M_1`$)+ softmax($`QK^T \odot M_2`$)として定義されます。M $`\in`$ R<sup>10x10</sup>としたとき、$`M_1`$=[M==1], and $`M_2`$=[M==2] です。*
+  *図2: 異なるアテンションの機構: 「ユーザー：画像を説明してください」という入力文と3つの画像トークン（I-token1、I-token2、I-token3）と組み合わせて与えた場合の、それぞれのattention機構の構成を示しています。左側では、標準的なcausal attentionによって、画像トークンをテキストとして扱う様子を示しています。中央は、テキストトークンに対する標準的なcausal attentionを維持しながら、画像に適用されるcross attentionを使用する様子を示しています。右側では、画像トークンはself attentionのみを行い、テキストトークンはテキスト／画像トークンへのアテンションを独立に計算するという、新しいマルチモーダルのためのアテンションの提案を、オレンジ色のマスクで強調して示しています。この仕組みは、Q, Kをクエリとキーとしたとき、 softmax($`QK^T \odot M_1`$)+ softmax($`QK^T \odot M_2`$)として定義されます。M $`\in`$ R<sup>10x10</sup>としたとき、$`M_1`$=[M==1], and $`M_2`$=[M==2] です。*
+</div>
 
 <b>Causal Attention（CA）</b>：CAに基づく方法は、視覚的特徴（最終的な視覚エンコーダ層の出力からの特徴）を単純にテキストの特徴量に投影し、テキスト埋め込み層以降の通常のテキストの特徴量と組み合わせてLLMに送り込むというものです。CAの利点は、LLMにおける本来のアテンション機構の自然な拡張であり、そのため余分なモジュールやパラメータを導入しないことです。しかし、このアプローチにはいくつかの直感的な問題があります：
 
@@ -86,6 +86,7 @@ MMCAの2つ目のポイントは、1つのモダリティに対するアテン
 
 <div align="center">
   <img src="../assets/images/lake-chat.png" alt="Beautiful lake" width="600"/>
+
   *図4：DeepSpeed-VisualChatは、示された場面を美しい湖として正確に識別し、妥当な提案のセットを提示する。対照的に、ベースラインは画像を「ボート乗り場のあるドック」と誤認識している。*
 </div>
 
@@ -102,6 +103,7 @@ LLaVAモデルで使用されているllavaとllava_dialデータセット、お
 
 <div align="center">
   <img src="../assets/images/data-blending.png" alt="Friends" width="600"/>
+
   *図5: LLaVA-Otterデータブレンド後のデータサンプル。灰色のダイアログボックスはLLaVAデータセットから、オレンジ色のダイアログボックスはOtterデータセットからのもの*
 </div>
 
@@ -110,18 +112,21 @@ LLaVAモデルで使用されているllavaとllava_dialデータセット、お
 
 <div align="center">
   <img src="../assets/images/friends.png" alt="Friends" width="600"/>
+
   *図6: DeepSpeed-VisualChatは、画像内の人数を数え、最初の画像のテキストを読み取ることができます。また、複数画像を横断的に理解することも可能です。*
 </div>
 
 
 <div align="center">
   <img src="../assets/images/ceos.png" alt="CEO" width="600"/>
+
   *図7: DeepSpeed-VisualChatは有名人を認識し、その人物の業績と関連付けることができます*
 </div>
 
 
 <div align="center">
   <img src="../assets/images/zootopia.png" alt="Zootopia" width="600"/>
+
   *図8: DeepSpeed-VisualChatは、ストーリーを作ったり、映画を認識したりできます。*
 </div>
 

From f63c35b4b31afab3efc0989f9bf4aa22d58d63d6 Mon Sep 17 00:00:00 2001
From: Conglong Li <conglong.li@gmail.com>
Date: Wed, 4 Oct 2023 17:48:58 -0700
Subject: [PATCH 03/38] Update README-Japanese.md (#4457)

---
 blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md b/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
index 7520b460ce33..ec23e005bff6 100755
--- a/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
+++ b/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
@@ -168,7 +168,7 @@ bash chat_scripts/run.sh # You need to change necessary variables, e.g, ckpt pat
 
 より大規模なモデル推論をサポートするために、我々はHugging Faceの大規模モデル推論をDeepSpeed-VisualChat APIに組み込みました。そのため、ユーザーはGPUメモリ容量とモデルサイズに基づいて、異なるGPU数を選択することができます。
 
-詳細は[ランディングページ](https://github.com/microsoft/DeepSpeedExamples/DeepSpeed-VisualChat)をご参照ください。
+詳細は[ランディングページ](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-VisualChat)をご参照ください。
 
 # 7. 早速使ってみましょう！
 

From 2c220d6593772cc00fcb04faa97a56bfd62e4076 Mon Sep 17 00:00:00 2001
From: Conglong Li <conglong.li@gmail.com>
Date: Wed, 4 Oct 2023 21:49:49 -0700
Subject: [PATCH 04/38] DeepSpeed-VisualChat Chinese blog (#4458)

* DS-VChat-blog-clean (#4445)

Co-authored-by: Heyang Qin <heyangqin@microsoft.com>
Co-authored-by: Zhewei Yao <zheweiy@berkeley.edu>
Co-authored-by: Minjia Zhang <33713995+minjiaz@users.noreply.github.com>
Co-authored-by: Xiaoxia (Shirley) Wu <94406484+xiaoxiawu-microsoft@users.noreply.github.com>

* add links to readme

* fix

* update hero-figure

* update title

* Create README_Chinese.md

* Update README_Chinese.md

* Update README_Chinese.md

* unify name format

* improve blog

* blog index

---------

Co-authored-by: Ammar Ahmad Awan <ammar.awan@microsoft.com>
Co-authored-by: Heyang Qin <heyangqin@microsoft.com>
Co-authored-by: Zhewei Yao <zheweiy@berkeley.edu>
Co-authored-by: Minjia Zhang <33713995+minjiaz@users.noreply.github.com>
Co-authored-by: Xiaoxia (Shirley) Wu <94406484+xiaoxiawu-microsoft@users.noreply.github.com>
Co-authored-by: yaozhewei <zheweiyao@gmail.com>
Co-authored-by: xiaoxiawu-microsoft <xiaoxiawu@microsoft.com>
---
 README.md                                     |   2 +-
 .../10-03-2023/README-Chinese.md              | 181 ++++++++++++++++++
 .../deepspeed-visualchat/10-03-2023/README.md |   6 +-
 ...2023-10-04-deepspeed-visualchat-chinese.md |   7 +
 ...023-10-04-deepspeed-visualchat-japanese.md |   7 +
 .../_posts/2023-10-04-deepspeed-visualchat.md |   7 +
 docs/index.md                                 |   2 +-
 7 files changed, 207 insertions(+), 5 deletions(-)
 create mode 100644 blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md
 create mode 100644 docs/_posts/2023-10-04-deepspeed-visualchat-chinese.md
 create mode 100644 docs/_posts/2023-10-04-deepspeed-visualchat-japanese.md
 create mode 100644 docs/_posts/2023-10-04-deepspeed-visualchat.md

diff --git a/README.md b/README.md
index e4560979b124..abf20667882b 100755
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@
 ## Latest News
 <b> <span style="color:orange" > DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)</span>.</b>
 
-* [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)
+* [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)]
 * [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)]
 * [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md)
 * [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)
diff --git a/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md b/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md
new file mode 100644
index 000000000000..e91ff1ecd51e
--- /dev/null
+++ b/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md
@@ -0,0 +1,181 @@
+
+<div align="center">
+
+# DeepSpeed-VisualChat：多轮图像+文字，为你展现不一样的AI聊天魅力
+
+</div>
+
+<div align="center">
+
+<img src="../assets/images/hero-figure.png" width="1000px" alt="DeepSpeed-VisualChat!"/>
+
+</div>
+
+要引用 DeepSpeed-VisualChat，请引用我们的 [arxiv 报告](https://arxiv.org/abs/2309.14327)：
+
+
+```
+@article{yao2023deepspeed-visualchat,
+  title={{DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention}},
+  author={Zhewei Yao and Xiaoxia Wu and Conglong Li and Minjia Zhang and Heyang Qin and Olatunji Ruwase and Ammar Ahmad Awan and Samyam Rajbhandari and Yuxiong He},
+  journal={arXiv preprint arXiv:2309.14327},
+  year={2023}
+}
+```
+
+# 1. 概述
+大型语言模型 (LLMs)，如 GPT 和 LLaMa，在各种文本生成和理解任务中都展现出了卓越的能力，特别是在经过零次/少次学习（zero-/few-shot learning）或微调（instructed fine-tuning）后。然而，要让 AI 模型为多样化的任务做好准备，需要加入的一个关键特性是多模态能力；例如，AI 模型应该能够读取图像、听到声音、观看视频等。这种能力在纯文本基础的 LLMs 中基本上是不存在的。
+
+最近，大量的研究项目开始探索将视觉能力引入到 LLMs 中，特别是通过插入图片输入使 LLMs 来理解图片（简称为大型视觉语言模型或 LVLMs）。
+
+大多数现有工作的主要缺点是：
+* 重点主要放在与单一图像相关的任务上，如视觉问题回答和字幕，或处理需要同时输入的多个图像。两种方法都不太擅长管理交错的图像和文本输入。
+* 系统的可扩展性仅限于具有约 10B 参数的模型，这比最大的开源模型小了一个数量级。
+
+然而，对于一个真正的 AI 聊天模型，输入内容可能是与文本交错的多个图像，这是目前的工作很少涉及的情况。此外，随着模型大小的增加，LLMs 的生成能力增长迅速。因此，将系统能力集中在约 10B 的模型上限制了对 LVLMs 潜力的进一步探索。
+
+为了解决这些问题，我们推出了 DeepSpeed-VisualChat（请参阅 [arxiv 报告](https://arxiv.org/abs/2309.14327) 以获取更多详细信息），带有以下新特性：
+
+* ***全开源多轮多图框架与前所未有的可扩展性***：DeepSpeed-VisualChat，作为开创性的全开源框架之一，支持多轮和多图对话，容纳交错的文本和图像输入。我们利用 DeepSpeed 提高我们的训练效果，使用一个 2B 的视觉编码器和一个 70B 的 LLaMA-2 解码器模型，展示了我们框架的显著可扩展性。
+* ***多模态因果注意力 (MMCA)*** 我们为多模态模型设计了一个新的 MMCA 注意力机制，独立地计算各种模态的注意力权重。MMCA 达到了与传统交叉注意机制类似的目标，但为生成任务提供了增强的因果注意解释，消除了对额外模块或参数的需求。与标准的因果注意力相比，它还提供了更好的训练数据效率。
+* ***交错输入的数据混合*** 为了促进交错模态的对话，DeepSpeed-VisualChat 在现有数据集上采用了各种数据混合技术，克服了大多数现有开源数据集中交错文本和图像输入的短缺。
+
+# 2. 模型架构概述
+<div align="center">
+  <img src="../assets/images/model.png" alt="模型结构" width="400"/>
+
+  *图 1：DeepSpeed-VisualChat 的模型架构示意图。*
+</div>
+
+如 *图 1* 所示，DeepSpeed-VisualChat 的模型架构由三个部分组成：一个视觉编码器，如 CLIP；一个语言解码器，如 LLaMa-7B；和一个特征对齐线性投影层。模型的大部分都是冻结的，只有语言模型的嵌入和线性投影层是可训练的。因此，可训练参数的总数大约在 O(10M) (LLaMa-2-13B) 到 O(100M) (LLaMa-2-70B) 之间。
+
+# 3. DeepSpeed 多模态因果注意力
+
+用于在多模态模型中连接视觉和文本组件的两种常见注意机制是：因果注意力，如在 MiniGPT 和 QWen-VL 中使用的，以及交叉注意力，如在 Otter 和 Flamingo 中使用的。
+
+<div align="center">
+  <img src="../assets/images/attention.png" alt="不同的注意机制" width="1000"/>
+
+  *图 2：不同的注意机制：使用一个输入句子“用户：请描述这个图片。”和三个图像令牌（I-token1、I-token2、I-token3）来比较不同的注意机制。在左边，我们展示了标准的因果注意力，将图像令牌视为文本。在中间，我们展示了应用于图像的交叉注意力，同时保持文本令牌的标准因果注意力。在右边，我们展示了我们的创新 MMCA 注意力机制，其中图像令牌只执行自注意，文本令牌独立地注意文本/图像令牌，橙色为图像部分。这种机制由：softmax($`QK^T \odot M_1`$)+ softmax($`QK^T \odot M_2`$) 定义，其中 Q 和 K 分别为查询和密钥，$`M_1`$=[M==1]，和 $`M_2`$=[M==2]，其中 M $`\in`$ R<sup>10x10</sup>。*
+</div>
+
+<b>因果注意力 (CA)</b>：基于 CA 的方法简单地将视觉特征（即来自最终视觉编码器层输出的特征）投影到文本特征，并将它们与文本嵌入层后的正常文本特征组合，以送入 LLMs。CA 的好处是它是 LLMs 原始注意机制的自然扩展，因此，它不引入任何额外的模块或参数。但是，直觉上这种方法会带来一些问题：
+
+* 每个视觉令牌会关注它之前的视觉和文本令牌。然而视觉令牌已经以双向方式完全编码，不需要进一步关注它之前的视觉和文本令牌。
+* 对于一个文本令牌，模型需要学习如何在其之前的文本和图像令牌之间分配其注意权重。由于这些问题，我们发现 LVLMs 中 CA 的数据效率通常是有问题的。为了解决这个问题，LLaVA 和 QWen-VL 需要视觉-语言预训练来完全对齐视觉特征和文本特征。
+
+<b>交叉注意力 (CrA)</b>：作为替代方案，交叉注意力 (CrA) 与 CA 的结合展示出更好的数据效率，但也带有一些缺点：
+
+* 它为模型引入了新的参数。例如，具有交叉注意力引入的新参数的 Otter 拥有超过 15 亿的可训练参数。和 LLaVA 的百万级可训练参数相比，这大大增加了训练成本和内存需求。
+* 如果在训练过程中中间引入了一个图像，需要仔细设计，因为先前的文本令牌不应该能够注意到图像。
+
+<b>多模态因果注意机制 (MMCA)</b>：为了克服这些问题，我们提出了一种新的多模态因果注意机制 (MMCA)，它既有 CA 的参数效率，又有 CrA 的数据效率。总体思路如下：
+
+* 对于视觉令牌，它们只关注自己，因为视觉令牌是由视觉编码器编码的。
+* 对于文本令牌，它们关注所有以前的令牌。但是，对文本和图像令牌 MMCA 使用两个单独的注意权重矩阵。
+
+MMCA 的第二点背后的直觉是，一个模态的注意权重可能会影响另一个模态。例如，文本令牌可能会比视觉信息更多地关注文本信息。因此，如果注意权重矩阵在两种模态之间进行归一化，那么视觉令牌的注意得分可能会非常小。请参考 *图 2* 以查看三种注意机制的可视化。
+
+<b>演示结果。</b>我们首先通过几个例子展示在不同的注意机制下 DeepSpeed-VisualChat 的单图像视觉语言对话功能。在这些实验中，我们使用 LLaMA2-7B 语言模型和 QWen-VL 视觉编码器作为我们的视觉编码器。这两个模型通过一个简单的线性投影层连接在一起。这个模型在两个 LLaVa 数据集上进行了训练。正如 *图 3* 和 *图 4* 所示，当与 MMCA 配合使用时，DeepSpeed-VisualChat 有效地识别了图像中的视觉细节，对用户的问题提供了准确通顺的回答。
+此外，与其他注意机制（如使用因果注意力和交叉注意力的组合）相比，MMCA 表现出更全面和精确的图像细节把握。与 CrA 和 CA 的组合以及 MMCA 相比，仅使用 CA 可能会显示出稍微多一些的错误（*图 3*）或导致较低的理解能力（*图 4*）。
+
+<div align="center">
+  <img src="../assets/images/cat-chat.png" alt="小猫咪" width="600"/>
+
+  *图 3：示例视觉和语言输入，显示了（1）标准因果注意力 (CA) （2）与交叉注意力组合的标准因果注意力 (CA+ CrA) 和（3）DeepSpeed-VisualChat 中的特殊多模态因果注意力 (MMCA) 之间的输出比较。*
+</div>
+
+<div align="center">
+  <img src="../assets/images/lake-chat.png" alt="美丽的湖泊" width="600"/>
+
+  *图 4：DeepSpeed-VisualChat 准确地识别了场景是一个美丽的湖泊，并提供了一组合理的建议。相比之下，其他的注意力机制误解了图像认为其包含“带船坡的码头”。*
+</div>
+
+# 4. 数据混合
+我们使用了 3 个来源的 9 个数据集，如我们的 [arxiv 报告](https://arxiv.org/abs/2309.14327) 所述。一个实现多轮和多图对话的关键缺失元素是没有足够的数据。我们找到的唯一的多轮多图数据来源是 SparklesDialogue 数据集，它只包含 6520 个样本。为了解决这个问题，我们采用了两种方法，从现有的单图或单轮数据中合成多轮多图数据：简单的数据连接和 LLaVA-Otter 数据混合。
+
+## 4.1 简单数据连接
+对于 LLaVA 模型使用的 "llava" 和 "llava_dial" 数据集，每个样本包括单图像的单轮/多轮对话。为了模拟用户依次询问多个图像的情况，我们对这两个数据集进行了简单的数据后处理。具体来说，我们随机将不同数量的样本连接成一个样本。在 "llava" 的情况下，我们连接了 1 到 3 个样本，而在 "llava_dial" 的情况下，我们连接了 1 到 2 个样本。
+
+## 4.2 LLaVA-Otter 数据混合
+我们注意到，LLaVA 模型使用的 llava 和 llava_dial 数据集以及 Otter 模型使用的 otter_mimicit_cgd 数据集都使用了 COCO train2017 图像。对于 llava 和 llava_dial 数据集，每个样本包括一个图像的单轮/多轮对话。对于 otter_mimicit_cgd 数据集，每个样本包括一对图像的单轮对话。这使我们能够构建一个合成的多轮多图数据 llava_otter_blend 作为更自然的混合：对于 otter_mimicit_cgd 数据集中的每个样本，我们寻找使用相同图像的 llava 和 llava_dial 样本，然后以 "llava/llava_dial 对话然后 otter_mimicit_cgd 对话" 的方式构建一个新样本。
+
+<div align="center">
+  <img src="../assets/images/data-blending.png" alt="朋友们" width="600"/>
+
+  *图 5：经过 LLaVA-Otter 数据混合后的数据样本。灰色对话框来自 LLaVA 数据集，橙色对话框来自 Otter 数据集。*
+</div>
+
+# 5. 演示
+我们在几个开源数据集上训练了我们的 DeepSpeed-VisualChat-13B 模型，该模型使用一个 2B 的视觉编码器和 13B 的 LLaMA 模型。DeepSpeed-VisualChat-13B 展示了图像字幕功能（*图 6--8*），计数和文本阅读（*图 6*），名人识别（*图 7*），讲故事（*图 8*）等。
+
+<div align="center">
+  <img src="../assets/images/friends.png" alt="朋友们" width="600"/>
+
+  *图 6：DeepSpeed-VisualChat 可以计算图像中的人数，并读取第一张图像中的文本。它还展示了跨图像的理解。*
+</div>
+
+<div align="center">
+  <img src="../assets/images/ceos.png" alt="CEO" width="600"/>
+
+  *图 7：DeepSpeed-VisualChat 可以识别名人并将他们与其成就联系起来。*
+</div>
+
+<div align="center">
+  <img src="../assets/images/zootopia.png" alt="疯狂动物城" width="600"/>
+
+  *图 8：DeepSpeed-VisualChat 可以讲故事并识别电影。*
+</div>
+
+# 6. 如何开始使用 DeepSpeed-VisualChat
+DeepSpeed-VisualChat 是一个易于使用的训练框架，具有很好的可扩展性，到目前为止已经在 LLaMa-2-70B 模型上进行了测试。我们为所有实验采用了统一的指令调优格式，模板如下所示。
+```
+<System Instruction>      % You are a powerful vision-language assistant.
+
+### Image 1: <image>       % some image, e.g., cat-1.png
+### Question: <question>   % please describe the image.
+### Answer: <answer>       % It's a cute black cat.
+
+### Image 2: <image>       % some image, e.g., cat-2.png
+### Image 3: <image>       % some image, e.g., cat-3.png
+### Question: <question>   % What's the difference between the three cats?
+### Answer: <answer>       % The colors of the three cats are different.
+...
+```
+
+使用 DeepSpeed-VisualChat 训练模型是简单和方便的。这里我们给出了基于 CLIP 视觉编码器和 LLaMa-7B 模型的一个例子：
+
+```
+git clone https://github.com/microsoft/DeepSpeedExamples.git
+cd DeepSpeedExamples/applications/DeepSpeed-VisualChat/
+pip install -r requirements.txt
+cd training
+bash training_scripts/run_7b.sh
+```
+
+训练后的模型权重将自动保存为 Hugging Face 兼容版本，并且可以用于启动您自己的视觉聊天 API：
+```
+cd ../chat
+bash chat_scripts/run.sh # You need to change necessary variables, e.g, ckpt path
+```
+
+为了支持更大的模型推理，我们已经将 Hugging Face 大模型推理集成到我们的 DeepSpeed-VisualChat API 中。因此，用户可以根据 GPU 内存容量和模型大小选择不同数量的 GPU。
+
+请参考我们的 [GitHub 主页](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-VisualChat) 了解更多细节。
+
+# 7. 发布：今天尝试 DeepSpeed-VisualChat！
+
+我们非常兴奋地分享 DeepSpeed-VisualChat 现已开源并供 AI 社区使用。
+
+* 要开始使用，请访问我们的 DeepSpeed-VisualChat GitHub 页面：[GitHub 主页](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-VisualChat)
+
+* 我们将继续在您的反馈和支持下改进 DeepSpeed-VisualChat。我们的 [路线图](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-VisualChat/README.md#-deepspeed-visualchats-roadmap-) 显示了目前支持的功能以及未来计划支持的功能。
+
+DeepSpeed-VisualChat 是更大的 DeepSpeed 生态系统的一部分，其中包括一系列深度学习系统和建模技术。要了解更多信息，
+
+* 请访问我们的 [网站](https://www.deepspeed.ai/) 了解详细的博客文章、教程和文档。
+* 在我们的 [英文 X(Twitter)](https://twitter.com/MSFTDeepSpeed)、[日语 X(Twitter)](https://twitter.com/MSFTDeepSpeedJP) 和 [中文知乎](https://www.zhihu.com/people/deepspeed) 上关注我们，以获取 DeepSpeed 的最新消息。
+
+我们欢迎您为 DeepSpeed 做出贡献！我们鼓励您报告问题、贡献 PRs、并在 [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/) 页面上参加讨论。有关更多详细信息，请查看我们的 [贡献指南](https://github.com/microsoft/DeepSpeed/blob/master/CONTRIBUTING.md)。我们对与大学、研究实验室、公司等进行合作持开放态度，例如共同进行深度学习研究、应用 DeepSpeed 为现实世界的 AI 模型和应用提供支持等等。对于此类请求（以及其他不适合 GitHub 的请求），请直接发送电子邮件至 deepspeed-info@microsoft.com。
+
+* 如果你喜欢我们的工作，请在 [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/) 和 [DeepSpeedExamples GitHub](https://github.com/microsoft/DeepSpeedExamples/) 上为我们的仓库点“星”。
diff --git a/blogs/deepspeed-visualchat/10-03-2023/README.md b/blogs/deepspeed-visualchat/10-03-2023/README.md
index 06dcd851766c..eac9d06c3cea 100755
--- a/blogs/deepspeed-visualchat/10-03-2023/README.md
+++ b/blogs/deepspeed-visualchat/10-03-2023/README.md
@@ -56,14 +56,14 @@ There are two common attention mechanisms used to connect the visual and textual
 
 <div align="center">
   <img src="../assets/images/attention.png" alt="Different attention mehanisms" width="1000"/>
-</div>
 
-*Figure 2: Different Attention Mechanisms: Examine the differing attention mechanisms using an input sentence "User: Please describe the image." coupled with three Image tokens (I-token1, I-token2, I-token3). On the left, we demonstrate standard causal attention, treating image tokens as text. In the middle, we present cross attention applied to images, while maintaining standard causal attention for text tokens. On the right, we illustrate our innovative multi-modal attention proposal where image tokens only perform self-attention, and text tokens attend to text/image tokens independently, highlighted with an orange mask. This mechanism is defined by: softmax($`QK^T \odot M_1`$)+ softmax($`QK^T \odot M_2`$) with Q and K as query and key, $`M_1`$=[M==1], and $`M_2`$=[M==2], with M $`\in`$ R<sup>10x10</sup> in this case.*
+  *Figure 2: Different Attention Mechanisms: Examine the differing attention mechanisms using an input sentence "User: Please describe the image." coupled with three Image tokens (I-token1, I-token2, I-token3). On the left, we demonstrate standard causal attention, treating image tokens as text. In the middle, we present cross attention applied to images, while maintaining standard causal attention for text tokens. On the right, we illustrate our innovative multi-modal attention proposal where image tokens only perform self-attention, and text tokens attend to text/image tokens independently, highlighted with an orange mask. This mechanism is defined by: softmax($`QK^T \odot M_1`$)+ softmax($`QK^T \odot M_2`$) with Q and K as query and key, $`M_1`$=[M==1], and $`M_2`$=[M==2], with M $`\in`$ R<sup>10x10</sup> in this case.*
+</div>
 
 
 <b>Causal Attention (CA)</b>: The CA-based method simply projects visual features (i.e., the features from the output of the final visual encoder layer) into textual features and combines them with the normal textual features after the textual embedding layer to feed into LLMs. The benefit of CA is that it's a natural extension of the original attention mechanism in LLMs, and as such, it doesn't introduce any extra modules or parameters. However, this approach raises some intuitive problems:
 
-* For a visual token, it attends to previous visual and textual tokens, even though visual tokens are already fully encoded in a bidirectional manner and do not need further attention from other visual tokens or the beginning of textual tokens.
+* For a visual token, it attends to previous visual and textual tokens, even though visual tokens are already fully encoded in a bidirectional manner and do not need further attention to other visual tokens or previous textual tokens.
 * For a textual token, the model needs to learn how to distribute its attention weights between its previous textual and image tokens. Due to these issues, we found that the data efficiency of CA in LVLMs is often problematic. To address this, LLaVA and QWen-VL require visual-language pretraining to fully align visual features with textual features.
 
 <b>Cross Attention (CrA)</b>: The alternative, cross attention (CrA), along with CA, exhibits better data efficiency but also comes with a few drawbacks:
diff --git a/docs/_posts/2023-10-04-deepspeed-visualchat-chinese.md b/docs/_posts/2023-10-04-deepspeed-visualchat-chinese.md
new file mode 100644
index 000000000000..290b8b4b8ba4
--- /dev/null
+++ b/docs/_posts/2023-10-04-deepspeed-visualchat-chinese.md
@@ -0,0 +1,7 @@
+---
+title: "DeepSpeed-VisualChat：多轮图像+文字，为你展现不一样的AI聊天魅力"
+excerpt: ""
+link: https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md
+date: 2023-10-04 00:00:00
+tags: training Chinese
+---
diff --git a/docs/_posts/2023-10-04-deepspeed-visualchat-japanese.md b/docs/_posts/2023-10-04-deepspeed-visualchat-japanese.md
new file mode 100644
index 000000000000..f8b7e20cc2cf
--- /dev/null
+++ b/docs/_posts/2023-10-04-deepspeed-visualchat-japanese.md
@@ -0,0 +1,7 @@
+---
+title: "DeepSpeed-VisualChat: 複数ラウンド・複数画像の入力が可能なAIチャット体験を実現"
+excerpt: ""
+link: https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md
+date: 2023-10-04 00:00:00
+tags: training Japanese
+---
diff --git a/docs/_posts/2023-10-04-deepspeed-visualchat.md b/docs/_posts/2023-10-04-deepspeed-visualchat.md
new file mode 100644
index 000000000000..74a1eb66fd5c
--- /dev/null
+++ b/docs/_posts/2023-10-04-deepspeed-visualchat.md
@@ -0,0 +1,7 @@
+---
+title: "DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs"
+excerpt: ""
+link: https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md
+date: 2023-10-04 00:00:00
+tags: training English
+---
diff --git a/docs/index.md b/docs/index.md
index 79fd6baae250..14c131a9a22d 100755
--- a/docs/index.md
+++ b/docs/index.md
@@ -7,7 +7,7 @@ title: "Latest News"
 ---
 <b> <span style="color:orange" > DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)</span>.</b>
 
-* [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)
+* [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)]
 * [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](/deepspeed4science/)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)]
 * [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md)
 * [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)

From 4294ea172caad1449af3af5e8941825ced4b5404 Mon Sep 17 00:00:00 2001
From: Michael Wyatt <michaelwyatt@microsoft.com>
Date: Thu, 5 Oct 2023 15:31:24 -0700
Subject: [PATCH 05/38] CI fix for torch 2.1 release (#4452)

* Fix for torch 2.1 release
Co-authored-by: Logan Adams <loadams@microsoft.com>
---
 .github/workflows/formatting.yml             | 11 ++++---
 .github/workflows/nv-ds-chat.yml             |  2 +-
 .github/workflows/nv-lightning-v100.yml      |  2 +-
 .github/workflows/nv-mii.yml                 |  2 +-
 .github/workflows/nv-torch-latest-v100.yml   |  6 ++--
 .github/workflows/nv-torch-nightly-v100.yml  |  2 +-
 .github/workflows/nv-transformers-v100.yml   |  2 +-
 deepspeed/comm/torch.py                      | 34 +++++++++++---------
 op_builder/async_io.py                       |  8 ++++-
 tests/unit/checkpoint/test_mics_optimizer.py |  5 ++-
 10 files changed, 44 insertions(+), 30 deletions(-)

diff --git a/.github/workflows/formatting.yml b/.github/workflows/formatting.yml
index ac6266121f54..a168af277fb8 100644
--- a/.github/workflows/formatting.yml
+++ b/.github/workflows/formatting.yml
@@ -27,12 +27,13 @@ jobs:
           which python
           python --version
 
-      - name: Install deepspeed
+      - name: Install dependencies
         run: |
-          pip install .[dev,autotuning,triton]
-          ds_report
+          # Previously we would do pip install .[dev] but this is causing out of
+          # space errors start with torch 2.1.0 release
+          grep -E "clang-format|pre-commit" requirements/requirements-dev.txt | xargs pip install
 
       - name: Formatting checks
         run: |
-           pip show pre-commit clang-format
-           pre-commit run --all-files
+          pip show pre-commit clang-format
+          pre-commit run --all-files
diff --git a/.github/workflows/nv-ds-chat.yml b/.github/workflows/nv-ds-chat.yml
index fce87a849cfe..c90b7c72ac0b 100644
--- a/.github/workflows/nv-ds-chat.yml
+++ b/.github/workflows/nv-ds-chat.yml
@@ -27,7 +27,7 @@ jobs:
 
       - name: Install pytorch
         run: |
-          pip3 install -U --cache-dir $TORCH_CACHE torch
+          pip3 install -U --cache-dir $TORCH_CACHE torch --index-url https://download.pytorch.org/whl/cu118
           python -c "import torch; print('torch:', torch.__version__, torch)"
           python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
 
diff --git a/.github/workflows/nv-lightning-v100.yml b/.github/workflows/nv-lightning-v100.yml
index 341dea57b663..75d2dc732d4d 100644
--- a/.github/workflows/nv-lightning-v100.yml
+++ b/.github/workflows/nv-lightning-v100.yml
@@ -26,7 +26,7 @@ jobs:
 
       - name: Install pytorch
         run: |
-          pip install -U --cache-dir $TORCH_CACHE torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116
+          pip install -U --cache-dir $TORCH_CACHE torch torchvision --index-url https://download.pytorch.org/whl/cu118
           python -c "import torch; print('torch:', torch.__version__, torch)"
           python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
 
diff --git a/.github/workflows/nv-mii.yml b/.github/workflows/nv-mii.yml
index a8c5a9afefcc..2b101b023d67 100644
--- a/.github/workflows/nv-mii.yml
+++ b/.github/workflows/nv-mii.yml
@@ -26,7 +26,7 @@ jobs:
 
       - name: Install pytorch
         run: |
-          pip3 install -U --cache-dir $TORCH_CACHE torch
+          pip3 install -U --cache-dir $TORCH_CACHE torch --index-url https://download.pytorch.org/whl/cu118
           python -c "import torch; print('torch:', torch.__version__, torch)"
           python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
 
diff --git a/.github/workflows/nv-torch-latest-v100.yml b/.github/workflows/nv-torch-latest-v100.yml
index 544fb50acec3..2b91df3ae44c 100644
--- a/.github/workflows/nv-torch-latest-v100.yml
+++ b/.github/workflows/nv-torch-latest-v100.yml
@@ -26,7 +26,7 @@ jobs:
 
       - name: Install pytorch
         run: |
-          pip install -U --cache-dir $TORCH_CACHE torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116
+          pip install -U --cache-dir $TORCH_CACHE torch torchvision --index-url https://download.pytorch.org/whl/cu118
           python -c "import torch; print('torch:', torch.__version__, torch)"
           python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
 
@@ -52,8 +52,8 @@ jobs:
         run: |
           unset TORCH_CUDA_ARCH_LIST # only jit compile for current arch
           cd tests
-          coverage run --concurrency=multiprocessing -m pytest $PYTEST_OPTS --forked -n 4 unit/ --torch_ver="2.0" --cuda_ver="11.7"
-          coverage run --concurrency=multiprocessing -m pytest $PYTEST_OPTS --forked -m 'sequential' unit/ --torch_ver="2.0" --cuda_ver="11.7"
+          coverage run --concurrency=multiprocessing -m pytest $PYTEST_OPTS --forked -n 4 unit/ --torch_ver="2.1" --cuda_ver="11.8"
+          coverage run --concurrency=multiprocessing -m pytest $PYTEST_OPTS --forked -m 'sequential' unit/ --torch_ver="2.1" --cuda_ver="11.8"
 
       - name: Coverage report
         run: |
diff --git a/.github/workflows/nv-torch-nightly-v100.yml b/.github/workflows/nv-torch-nightly-v100.yml
index c2bf5919f20d..d0df6e546982 100644
--- a/.github/workflows/nv-torch-nightly-v100.yml
+++ b/.github/workflows/nv-torch-nightly-v100.yml
@@ -24,7 +24,7 @@ jobs:
 
       - name: Install pytorch
         run: |
-          pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cu116
+          pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu118
           python -c "import torch; print('torch:', torch.__version__, torch)"
           python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
 
diff --git a/.github/workflows/nv-transformers-v100.yml b/.github/workflows/nv-transformers-v100.yml
index 4e5a34365f52..1cc0c6588610 100644
--- a/.github/workflows/nv-transformers-v100.yml
+++ b/.github/workflows/nv-transformers-v100.yml
@@ -27,7 +27,7 @@ jobs:
       - name: Install pytorch
         run: |
           # use the same pytorch version as transformers CI
-            pip install -U --cache-dir $TORCH_CACHE torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
+          pip install -U --cache-dir $TORCH_CACHE torch==2.0.1+cu118 --index-url https://download.pytorch.org/whl/cu118
           python -c "import torch; print('torch:', torch.__version__, torch)"
           python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
 
diff --git a/deepspeed/comm/torch.py b/deepspeed/comm/torch.py
index f7cbc5ed1c15..531f54d2628f 100644
--- a/deepspeed/comm/torch.py
+++ b/deepspeed/comm/torch.py
@@ -17,23 +17,25 @@
 DS_COMM_REDUCE_OFF = False
 
 
-def is_torch_two():
-    TORCH_MAJOR = int(torch.__version__.split('.')[0])
-    if TORCH_MAJOR >= 2:
+def is_torch_ver_eq_2_0():
+    TORCH_MAJOR, TORCH_MINOR = map(int, torch.__version__.split('.')[:2])
+    if TORCH_MAJOR == 2 and TORCH_MINOR == 0:
         return True
-    else:
-        return False
+    return False
 
 
-def torch_ver_ge_1_13():
-    if is_torch_two():
+def is_torch_ver_ge_2_1():
+    TORCH_MAJOR, TORCH_MINOR = map(int, torch.__version__.split('.')[:2])
+    if TORCH_MAJOR >= 2 and TORCH_MINOR >= 1:
         return True
-    else:
-        TORCH_MAJOR = int(torch.__version__.split('.')[0])
-        assert TORCH_MAJOR == 1
+    return False
 
-        TORCH_MINOR = int(torch.__version__.split('.')[1])
-        return TORCH_MINOR >= 13
+
+def torch_ver_ge_1_13():
+    TORCH_MAJOR, TORCH_MINOR = map(int, torch.__version__.split('.')[:2])
+    if TORCH_MAJOR >= 1 and TORCH_MINOR >= 13:
+        return True
+    return False
 
 
 def has_coalescing_manager():
@@ -45,9 +47,11 @@ def has_all_reduce_coalesced():
     return hasattr(torch.distributed, "all_reduce_coalesced") and torch_ver_ge_1_13()
 
 
-def get_coalescing_manager(group, device, reqs):
-    if is_torch_two():
+def get_coalescing_manager(group, device, reqs, async_op):
+    if is_torch_ver_eq_2_0():
         return torch.distributed.distributed_c10d._coalescing_manager(group, device=device, reqs=reqs)
+    elif is_torch_ver_ge_2_1():
+        return torch.distributed.distributed_c10d._coalescing_manager(group, device=device, async_ops=async_op)
     else:
         return torch.distributed.distributed_c10d._coalescing_manager(group, reqs)
 
@@ -234,7 +238,7 @@ def all_gather_coalesced(self, output_tensors, input_tensors, group=None, async_
                                                                                  async_op=async_op)
         elif has_coalescing_manager():
             reqs = []
-            with get_coalescing_manager(group, input_tensors[0].device, reqs):
+            with get_coalescing_manager(group, input_tensors[0].device, reqs, async_op):
                 for output, input in zip(output_tensors, input_tensors):
                     handle = torch.distributed.distributed_c10d.all_gather_into_tensor(output,
                                                                                        input,
diff --git a/op_builder/async_io.py b/op_builder/async_io.py
index 2db18e3629a1..da511a0a8c9d 100644
--- a/op_builder/async_io.py
+++ b/op_builder/async_io.py
@@ -5,6 +5,7 @@
 
 import distutils.spawn
 import subprocess
+import torch
 
 from .builder import OpBuilder
 
@@ -35,11 +36,16 @@ def cxx_args(self):
         # -O0 for improved debugging, since performance is bound by I/O
         CPU_ARCH = self.cpu_arch()
         SIMD_WIDTH = self.simd_width()
+        TORCH_MAJOR, TORCH_MINOR = map(int, torch.__version__.split('.')[0:2])
+        if TORCH_MAJOR >= 2 and TORCH_MINOR >= 1:
+            CPP_STD = '-std=c++17'
+        else:
+            CPP_STD = '-std=c++14'
         return [
             '-g',
             '-Wall',
             '-O0',
-            '-std=c++14',
+            CPP_STD,
             '-shared',
             '-fPIC',
             '-Wno-reorder',
diff --git a/tests/unit/checkpoint/test_mics_optimizer.py b/tests/unit/checkpoint/test_mics_optimizer.py
index 5d0bff3967c4..3f853cd5c13a 100644
--- a/tests/unit/checkpoint/test_mics_optimizer.py
+++ b/tests/unit/checkpoint/test_mics_optimizer.py
@@ -8,13 +8,16 @@
 
 import deepspeed
 
+from deepspeed.runtime.utils import required_torch_version
 from unit.common import DistributedTest
 from unit.simple_model import *
-
 from unit.checkpoint.common import *
 
 import pytest
 
+if not required_torch_version(max_version=2.0):
+    pytest.skip("Skipping until we resolve problems with torch 2.1", allow_module_level=True)
+
 
 class TestMiCSCheckpoint(DistributedTest):
     world_size = 4

From d72edb3b0d1588eee94a6c4fbea07f31aa5fb898 Mon Sep 17 00:00:00 2001
From: "Wang, Yi" <yi.a.wang@intel.com>
Date: Fri, 6 Oct 2023 06:31:57 +0800
Subject: [PATCH 06/38] fix lm head overriden issue, move it from checkpoint
 in-loop loading to out loop (#4206)

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
---
 deepspeed/module_inject/load_checkpoint.py |  7 -------
 deepspeed/module_inject/replace_module.py  | 19 +++++++++++--------
 2 files changed, 11 insertions(+), 15 deletions(-)

diff --git a/deepspeed/module_inject/load_checkpoint.py b/deepspeed/module_inject/load_checkpoint.py
index 12b1799e49f2..a24d36344cdc 100644
--- a/deepspeed/module_inject/load_checkpoint.py
+++ b/deepspeed/module_inject/load_checkpoint.py
@@ -276,13 +276,6 @@ def load_module_recursive(module, prefix='', level=0):
                     level + 1)
 
     load_module_recursive(r_module)
-    embedding_weight = None
-
-    for n, p in r_module.named_parameters():
-        if "word_embeddings." in n or "embed_tokens." in n or "wte." in n:
-            embedding_weight = p
-    if embedding_weight is not None and r_module.lm_head.weight.is_meta:
-        r_module.lm_head.weight = embedding_weight
 
     for sd_ in sd:
         del sd_
diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py
index af3c4f1c885d..f9dd921b9ae1 100644
--- a/deepspeed/module_inject/replace_module.py
+++ b/deepspeed/module_inject/replace_module.py
@@ -296,6 +296,15 @@ def replace_fn(child, _policy, layer_id=0, prefix="", state_dict=None):
 
         return new_module
 
+    def set_lm_head(module):
+        embedding_weight = None
+        for n, p in module.named_parameters():
+            if "word_embeddings." in n or "embed_tokens." in n or "wte." in n:
+                embedding_weight = p
+        if embedding_weight is not None and hasattr(module, "lm_head") and hasattr(
+                module.lm_head, "weight") and module.lm_head.weight.is_meta:
+            module.lm_head.weight = embedding_weight
+
     if checkpoint_dict is not None and not config.replace_with_kernel_inject:
         # AutoTP shard loading
         checkpoint = checkpoint_dict["checkpoints"]
@@ -309,6 +318,7 @@ def replace_fn(child, _policy, layer_id=0, prefix="", state_dict=None):
                                              checkpoint=checkpoint_file)
             pbar.update(1)
             gc.collect()
+        set_lm_head(replaced_module)
     else:
         replaced_module = replace_module(model=model,
                                          orig_class=orig_layer_impl,
@@ -386,6 +396,7 @@ def replace_fn(child, _policy, layer_id=0, prefix="", state_dict=None):
                                                container=container_g)
                     sds = [None for _ in sds]
                     gc.collect()
+        set_lm_head(replaced_module)
         print(f"checkpoint loading time at rank {rank}: {time.time()-start_time} sec")
 
     if config.save_mp_checkpoint_path is not None:
@@ -554,14 +565,6 @@ def replace_module(model, orig_class, replace_fn, _replace_policy, checkpoint=No
         "You can find some samples here: https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/module_inject/replace_policy.py"
 
     replaced_module, _ = _replace_module(model, policy, state_dict=sd)
-    if checkpoint is not None:
-        embedding_weight = None
-        for n, p in replaced_module.named_parameters():
-            if "word_embeddings." in n or "embed_tokens." in n or "wte." in n:
-                embedding_weight = p
-        if embedding_weight is not None and hasattr(replaced_module, "lm_head") and hasattr(
-                replaced_module.lm_head, "weight") and replaced_module.lm_head.weight.is_meta:
-            replaced_module.lm_head.weight = embedding_weight
     return replaced_module
 
 

From 8e64c3b5509c1251fbd0956e28a3079f8bec4aee Mon Sep 17 00:00:00 2001
From: "Hongjiu \"Enneamer\" Zhang"
 <32317100+enneamer@users.noreply.github.com>
Date: Fri, 6 Oct 2023 06:32:14 +0800
Subject: [PATCH 07/38] feat: add Lion optimizer (#4331)

Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
---
 csrc/cpu/lion/fused_lion.cpp             |  43 ++++
 csrc/includes/cpu_lion.h                 | 233 +++++++++++++++++++++
 csrc/includes/simd.h                     |  60 +++++-
 csrc/lion/cpu_lion.cpp                   |  16 ++
 csrc/lion/cpu_lion_impl.cpp              | 255 +++++++++++++++++++++++
 csrc/lion/fused_lion_frontend.cpp        |  22 ++
 csrc/lion/multi_tensor_apply.cuh         | 132 ++++++++++++
 csrc/lion/multi_tensor_lion.cu           | 126 +++++++++++
 deepspeed/ops/__init__.py                |   1 +
 deepspeed/ops/lion/__init__.py           |   7 +
 deepspeed/ops/lion/cpu_lion.py           | 141 +++++++++++++
 deepspeed/ops/lion/fused_lion.py         | 131 ++++++++++++
 deepspeed/ops/lion/multi_tensor_apply.py |  17 ++
 deepspeed/runtime/config.py              |   3 +-
 deepspeed/runtime/engine.py              |  10 +-
 deepspeed/runtime/zero/utils.py          |   4 +-
 docs/_tutorials/advanced-install.md      |   2 +
 op_builder/cpu_lion.py                   |  48 +++++
 op_builder/fused_lion.py                 |  37 ++++
 tests/unit/ops/lion/test_cpu_lion.py     |  96 +++++++++
 tests/unit/ops/lion/test_lion.py         |  59 ++++++
 21 files changed, 1439 insertions(+), 4 deletions(-)
 create mode 100644 csrc/cpu/lion/fused_lion.cpp
 create mode 100644 csrc/includes/cpu_lion.h
 create mode 100644 csrc/lion/cpu_lion.cpp
 create mode 100644 csrc/lion/cpu_lion_impl.cpp
 create mode 100644 csrc/lion/fused_lion_frontend.cpp
 create mode 100644 csrc/lion/multi_tensor_apply.cuh
 create mode 100644 csrc/lion/multi_tensor_lion.cu
 create mode 100755 deepspeed/ops/lion/__init__.py
 create mode 100755 deepspeed/ops/lion/cpu_lion.py
 create mode 100644 deepspeed/ops/lion/fused_lion.py
 create mode 100644 deepspeed/ops/lion/multi_tensor_apply.py
 create mode 100644 op_builder/cpu_lion.py
 create mode 100644 op_builder/fused_lion.py
 create mode 100644 tests/unit/ops/lion/test_cpu_lion.py
 create mode 100644 tests/unit/ops/lion/test_lion.py

diff --git a/csrc/cpu/lion/fused_lion.cpp b/csrc/cpu/lion/fused_lion.cpp
new file mode 100644
index 000000000000..708df7f0146a
--- /dev/null
+++ b/csrc/cpu/lion/fused_lion.cpp
@@ -0,0 +1,43 @@
+// Copyright (c) Microsoft Corporation.
+// SPDX-License-Identifier: Apache-2.0
+
+// DeepSpeed Team
+
+#include "cpu_lion.h"
+
+// C++ interface
+
+void multi_tensor_lion(int chunk_size,
+                       at::Tensor noop_flag,
+                       std::vector<std::vector<at::Tensor>> tensor_lists, /*gpmv*/
+                       const float lr,
+                       const float beta1,
+                       const float beta2,
+                       const int step,
+                       const int mode,
+                       const float weight_decay)
+{
+    static bool initialized = false;
+    if (!initialized) {
+        create_lion_optimizer(0);
+        initialized = true;
+    }
+    for (int i = 0; i < tensor_lists[0].size(); i++) {
+        ds_lion_step(0,
+                     step,
+                     lr,
+                     beta1,
+                     beta2,
+                     weight_decay,
+                     tensor_lists[1][i],
+                     tensor_lists[0][i],
+                     tensor_lists[2][i]);
+    }
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("multi_tensor_lion",
+          &multi_tensor_lion,
+          "Compute and apply gradient update to parameters for Lion optimizer");
+}
diff --git a/csrc/includes/cpu_lion.h b/csrc/includes/cpu_lion.h
new file mode 100644
index 000000000000..76034ceb3459
--- /dev/null
+++ b/csrc/includes/cpu_lion.h
@@ -0,0 +1,233 @@
+// Copyright (c) Microsoft Corporation.
+// SPDX-License-Identifier: Apache-2.0
+
+// DeepSpeed Team
+
+#pragma once
+
+#define NOMINMAX  // Windows idiosyncrasy
+                  // https://stackoverflow.com/questions/4913922/possible-problems-with-nominmax-on-visual-c
+
+#include <stdio.h>
+#include <torch/extension.h>
+#include <cassert>
+#include "simd.h"
+
+#if defined(__ENABLE_CUDA__)
+#include <cuda_fp16.h>
+#include <cuda_runtime_api.h>
+#include "cuda.h"
+#include "custom_cuda_layers.h"
+typedef __half ds_half_precision_t;
+#else
+#include <cmath>
+typedef unsigned short ds_half_precision_t;
+#endif
+
+#define STEP(SPAN)                                             \
+    void Step_##SPAN(float* _params,                           \
+                     float* grads,                             \
+                     float* _exp_avg,                          \
+                     size_t _param_size,                       \
+                     ds_half_precision_t* dev_param = nullptr, \
+                     bool half_precision = false);
+
+class Lion_Optimizer {
+public:
+    Lion_Optimizer(float alpha = 1e-3,
+                   float betta1 = 0.9,
+                   float betta2 = 0.999,
+                   float weight_decay = 0)
+        : _alpha(alpha), _betta1(betta1), _betta2(betta2), _weight_decay(weight_decay), _step(0)
+    {
+#if defined(__ENABLE_CUDA__)
+        cudaMallocHost((void**)_doubled_buffer, TILE * sizeof(float));
+        cudaMallocHost((void**)(_doubled_buffer + 1), TILE * sizeof(float));
+
+        _streams[0] = TrainingContext::Instance().GetCurrentStream();
+        _streams[1] = TrainingContext::Instance().GetNewStream();
+        _buf_index = false;
+#endif
+    }
+    ~Lion_Optimizer()
+    {
+#if defined(__ENABLE_CUDA__)
+        cudaFreeHost(_doubled_buffer[0]);
+        cudaFreeHost(_doubled_buffer[1]);
+#endif
+    }
+
+#if defined(__AVX512__) or defined(__AVX256__)
+    template <int span>
+    void Step_AVX(size_t* rounded_size,
+                  float* _params,
+                  float* grads,
+                  float* _exp_avg,
+                  size_t param_size,
+                  ds_half_precision_t* dev_param = nullptr,
+                  bool half_precision = false);
+#endif
+    STEP(1)
+    STEP(4)
+    STEP(8)
+#if defined(__ENABLE_CUDA__)
+    inline void SynchronizeStreams()
+    {
+        for (int i = 0; i < 2; i++) cudaStreamSynchronize(_streams[i]);
+    }
+#endif
+    inline void IncrementStep(size_t step, float beta1, float beta2)
+    {
+        _step++;
+        if (_step != step || beta1 != _betta1 || beta2 != _betta2) {
+            _step = step;
+            _betta1 = beta1;
+            _betta2 = beta2;
+        }
+    }
+    inline void update_state(float lr, float weight_decay)
+    {
+        _alpha = lr;
+        _weight_decay = weight_decay;
+    }
+
+private:
+    float _alpha;
+    float _betta1;
+    float _betta2;
+    float _weight_decay;
+    size_t _step;
+
+#if defined(__ENABLE_CUDA__)
+    float* _doubled_buffer[2];
+    cudaStream_t _streams[2];
+    bool _buf_index;
+#endif
+};
+
+#if defined(__AVX512__) or defined(__AVX256__)
+template <int span>
+void Lion_Optimizer::Step_AVX(size_t* rounded_size,
+                              float* _params,
+                              float* grads,
+                              float* _exp_avg,
+                              size_t _param_size,
+                              ds_half_precision_t* dev_params,
+                              bool half_precision)
+{
+    size_t new_rounded_size = 0;
+    int rshft = half_precision ? 1 : 0;
+
+    constexpr float neg1 = -1.0f;
+    AVX_Data neg1_4;
+    neg1_4.data = SIMD_SET(neg1);
+
+    AVX_Data betta1_4;
+    betta1_4.data = SIMD_SET(_betta1);
+    AVX_Data betta2_4;
+    betta2_4.data = SIMD_SET(_betta2);
+
+    float betta1_minus1 = 1 - _betta1;
+    float betta2_minus1 = 1 - _betta2;
+    AVX_Data betta1_minus1_4;
+    betta1_minus1_4.data = SIMD_SET(betta1_minus1);
+    AVX_Data betta2_minus1_4;
+    betta2_minus1_4.data = SIMD_SET(betta2_minus1);
+
+    float step_size = -_alpha;
+    AVX_Data step_size_4;
+    step_size_4.data = SIMD_SET(step_size);
+
+    float after_decay = 1.0f - _alpha * _weight_decay;
+    AVX_Data after_decay_4;
+    if (_weight_decay > 0) after_decay_4.data = SIMD_SET(after_decay);
+
+    new_rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH * span);
+    for (size_t t = 0; t < new_rounded_size; t += TILE) {
+        size_t copy_size = TILE;
+        if ((t + TILE) > new_rounded_size) copy_size = new_rounded_size - t;
+        size_t offset = copy_size + t;
+#if defined(__ENABLE_CUDA__)
+        if ((t / TILE) >= 2) { cudaStreamSynchronize(_streams[_buf_index]); }
+#endif
+#pragma omp parallel for
+        for (size_t i = t; i < offset; i += SIMD_WIDTH * span) {
+            AVX_Data grad_4[span];
+            simd_load<span>(grad_4, grads + (i >> rshft), half_precision);
+
+            AVX_Data momentum_4[span];
+            simd_load<span>(momentum_4, _exp_avg + i, false);
+
+            AVX_Data param_4[span];
+            simd_load<span>(param_4, _params + (i >> rshft), half_precision);
+
+            AVX_Data tmp_4[span];
+
+            simd_mul<span>(tmp_4, momentum_4, betta1_4);
+            simd_fma<span>(tmp_4, grad_4, betta1_minus1_4, tmp_4);
+            // We already used intrinsics, so consider the machine representation fixed.
+            simd_and<span>(tmp_4, tmp_4, neg1_4);
+            simd_xor<span>(tmp_4, tmp_4, step_size_4);
+            if (_weight_decay > 0) {
+                simd_fma<span>(param_4, param_4, after_decay_4, tmp_4);
+            } else {
+                simd_add<span>(param_4, param_4, tmp_4);
+            }
+
+            simd_mul<span>(momentum_4, momentum_4, betta2_4);
+            simd_fma<span>(momentum_4, grad_4, betta2_minus1_4, momentum_4);
+
+            simd_store<span>(_params + (i >> rshft), param_4, half_precision);
+#if defined(__ENABLE_CUDA__)
+            if (dev_params) {
+                simd_store<span>(_doubled_buffer[_buf_index] + (i - t), param_4, half_precision);
+            }
+#endif
+            simd_store<span>(_exp_avg + i, momentum_4, false);
+        }
+#if defined(__ENABLE_CUDA__)
+        if (dev_params) {
+            if (half_precision)
+                launch_param_update_half(
+                    _doubled_buffer[_buf_index], dev_params + t, copy_size, _streams[_buf_index]);
+            else
+                launch_param_update(
+                    _doubled_buffer[_buf_index], dev_params + t, copy_size, _streams[_buf_index]);
+
+            _buf_index = !_buf_index;
+        }
+#endif
+    }
+    *rounded_size = new_rounded_size;
+}
+#endif
+
+int create_lion_optimizer(int optimizer_id,
+                          float alpha = 1e-3,
+                          float betta1 = 0.9,
+                          float betta2 = 0.999,
+                          float weight_decay = 0,
+                          bool should_log = false);
+
+int ds_lion_step(int optimizer_id,
+                 size_t step,
+                 float lr,
+                 float beta1,
+                 float beta2,
+                 float weight_decay,
+                 torch::Tensor& params,
+                 torch::Tensor& grads,
+                 torch::Tensor& exp_avg);
+
+int ds_lion_step_plus_copy(int optimizer_id,
+                           size_t step,
+                           float lr,
+                           float beta1,
+                           float beta2,
+                           float weight_decay,
+                           torch::Tensor& params,
+                           torch::Tensor& grads,
+                           torch::Tensor& exp_avg,
+                           torch::Tensor& gpu_params);
+
+int destroy_lion_optimizer(int optimizer_id);
diff --git a/csrc/includes/simd.h b/csrc/includes/simd.h
index 712dd5b32e96..f77568be7835 100644
--- a/csrc/includes/simd.h
+++ b/csrc/includes/simd.h
@@ -24,6 +24,10 @@
 #define SIMD_FMA(x, y, c) _mm512_fmadd_ps(x, y, c)
 #define SIMD_SQRT(x) _mm512_sqrt_ps(x)
 #define SIMD_DIV(x, y) _mm512_div_ps(x, y)
+#define SIMD_AND(x, y) _mm512_and_ps(x, y)
+#define SIMD_ANDNOT(x, y) _mm512_andnot_ps(x, y)
+#define SIMD_OR(x, y) _mm512_or_ps(x, y)
+#define SIMD_XOR(x, y) _mm512_xor_ps(x, y)
 #define SIMD_WIDTH 16
 
 #define SIMD_LOAD2(x, h) \
@@ -42,10 +46,14 @@
 #define SIMD_FMA(x, y, c) _mm256_fmadd_ps(x, y, c)
 #define SIMD_SQRT(x) _mm256_sqrt_ps(x)
 #define SIMD_DIV(x, y) _mm256_div_ps(x, y)
+#define SIMD_AND(x, y) _mm256_and_ps(x, y)
+#define SIMD_ANDNOT(x, y) _mm256_andnot_ps(x, y)
+#define SIMD_OR(x, y) _mm256_or_ps(x, y)
+#define SIMD_XOR(x, y) _mm256_xor_ps(x, y)
 #define SIMD_WIDTH 8
+
 #define SIMD_LOAD2(x, h) \
     ((h) ? _mm256_cvtph_ps(_mm_loadu_si128((const __m128i*)x)) : _mm256_loadu_ps(x))
-
 #define SIMD_STORE2(x, d, h)                                                                \
     ((h) ? _mm_store_ps(x, _mm_castsi128_ps(_mm256_cvtps_ph(d, _MM_FROUND_TO_NEAREST_INT))) \
          : _mm256_storeu_ps(x, d))
@@ -136,5 +144,55 @@ inline void simd_div(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data* src_a_r)
 #pragma unroll
     for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_DIV(src_a_l[i].data, src_a_r[i].data); }
 }
+template <int span>
+inline void simd_and(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_AND(src_a_l[i].data, src_a_r.data); }
+}
+template <int span>
+inline void simd_and(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data* src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_AND(src_a_l[i].data, src_a_r[i].data); }
+}
+template <int span>
+inline void simd_andnot(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_ANDNOT(src_a_l[i].data, src_a_r.data); }
+}
+template <int span>
+inline void simd_andnot(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data* src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) {
+        dst[i].data = SIMD_ANDNOT(src_a_l[i].data, src_a_r[i].data);
+    }
+}
+template <int span>
+inline void simd_or(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_OR(src_a_l[i].data, src_a_r.data); }
+}
+template <int span>
+inline void simd_or(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data* src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_OR(src_a_l[i].data, src_a_r[i].data); }
+}
+template <int span>
+inline void simd_xor(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_XOR(src_a_l[i].data, src_a_r.data); }
+}
+template <int span>
+inline void simd_xor(AVX_Data* dst, AVX_Data* src_a_l, AVX_Data* src_a_r)
+{
+#pragma unroll
+    for (size_t i = 0; i < span; ++i) { dst[i].data = SIMD_XOR(src_a_l[i].data, src_a_r[i].data); }
+}
 
 #endif
diff --git a/csrc/lion/cpu_lion.cpp b/csrc/lion/cpu_lion.cpp
new file mode 100644
index 000000000000..a0562eac9c4a
--- /dev/null
+++ b/csrc/lion/cpu_lion.cpp
@@ -0,0 +1,16 @@
+// Copyright (c) Microsoft Corporation.
+// SPDX-License-Identifier: Apache-2.0
+
+// DeepSpeed Team
+
+#include "cpu_lion.h"
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("lion_update", &ds_lion_step, "DeepSpeed CPU Lion update (C++)");
+    m.def("lion_update_copy",
+          &ds_lion_step_plus_copy,
+          "DeepSpeed CPU Lion update and param copy (C++)");
+    m.def("create_lion", &create_lion_optimizer, "DeepSpeed CPU Lion (C++)");
+    m.def("destroy_lion", &destroy_lion_optimizer, "DeepSpeed CPU Lion destroy (C++)");
+}
diff --git a/csrc/lion/cpu_lion_impl.cpp b/csrc/lion/cpu_lion_impl.cpp
new file mode 100644
index 000000000000..5c24e23b4b20
--- /dev/null
+++ b/csrc/lion/cpu_lion_impl.cpp
@@ -0,0 +1,255 @@
+// Copyright (c) Microsoft Corporation.
+// SPDX-License-Identifier: Apache-2.0
+
+// DeepSpeed Team
+
+#include <torch/extension.h>
+#include <cassert>
+#include <cmath>
+#include <iostream>
+#include <memory>
+#include <type_traits>
+#include <unordered_map>
+#include "cpu_lion.h"
+
+#if defined(__ENABLE_CUDA__)
+#include <cuda_runtime_api.h>
+#include "cublas_v2.h"
+#include "cuda.h"
+#include "curand.h"
+#include "custom_cuda_layers.h"
+#endif
+
+static std::unordered_map<int, std::shared_ptr<void>> s_optimizers;
+
+// C++ interface
+
+void Lion_Optimizer::Step_1(float* _params,
+                            float* grads,
+                            float* _exp_avg,
+                            size_t _param_size,
+                            ds_half_precision_t* dev_params,
+                            bool half_precision)
+{
+    size_t rounded_size = 0;
+#if defined(__AVX512__) or defined(__AVX256__)
+    Step_AVX<1>(&rounded_size, _params, grads, _exp_avg, _param_size, dev_params, half_precision);
+#endif
+    if (_param_size > rounded_size) {
+        float betta1_minus1 = 1 - _betta1;
+        float betta2_minus1 = 1 - _betta2;
+
+        float alpha = _alpha;
+        float after_decay = 1 - alpha * _weight_decay;
+        ds_half_precision_t* grads_cast_h;
+        ds_half_precision_t* params_cast_h;
+        if (half_precision) {
+            grads_cast_h = reinterpret_cast<ds_half_precision_t*>(grads);
+            params_cast_h = reinterpret_cast<ds_half_precision_t*>(_params);
+        }
+
+        for (size_t t = rounded_size; t < _param_size; t += TILE) {
+            size_t copy_size = TILE;
+            if ((t + TILE) > _param_size) copy_size = _param_size - t;
+            size_t offset = copy_size + t;
+#if defined(__ENABLE_CUDA__)
+            if ((t / TILE) >= 2) { cudaStreamSynchronize(_streams[_buf_index]); }
+#endif
+#pragma omp parallel for
+            for (size_t k = t; k < offset; k++) {
+                float grad = half_precision ? (float)grads_cast_h[k] : grads[k];
+                float param = half_precision ? (float)params_cast_h[k] : _params[k];
+                float momentum = _exp_avg[k];
+                float tmp = momentum * _betta1;
+                tmp = grad * betta1_minus1 + tmp;
+                // Rely on portable C++ methods to manipulate the sign bit of a floating-point
+                // number.
+                tmp = -std::copysignf(alpha, tmp);
+                if (_weight_decay > 0) {
+                    param = param * after_decay + tmp;
+                } else {
+                    param = param + tmp;
+                }
+                momentum = momentum * _betta2;
+                momentum = grad * betta2_minus1 + momentum;
+#if defined(__ENABLE_CUDA__)
+                if (dev_params) _doubled_buffer[_buf_index][k - t] = param;
+#endif
+                if (half_precision)
+                    params_cast_h[k] = (ds_half_precision_t)param;
+                else
+                    _params[k] = param;
+                _exp_avg[k] = momentum;
+            }
+#if defined(__ENABLE_CUDA__)
+            if (dev_params) {
+                launch_param_update(
+                    _doubled_buffer[_buf_index], dev_params + t, (copy_size), _streams[_buf_index]);
+
+                _buf_index = !_buf_index;
+            }
+#endif
+        }
+    }
+}
+
+void Lion_Optimizer::Step_4(float* _params,
+                            float* grads,
+                            float* _exp_avg,
+                            size_t _param_size,
+                            ds_half_precision_t* dev_params,
+                            bool half_precision)
+{
+    size_t rounded_size = 0;
+#if defined(__AVX512__) or defined(__AVX256__)
+    Step_AVX<4>(&rounded_size, _params, grads, _exp_avg, _param_size, dev_params, half_precision);
+#endif
+    if (_param_size > rounded_size)
+        Step_1((_params + rounded_size),
+               (grads + rounded_size),
+               (_exp_avg + rounded_size),
+               (_param_size - rounded_size),
+               (dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
+               half_precision);
+}
+
+int create_lion_optimizer(int optimizer_id,
+                          float alpha,
+                          float betta1,
+                          float betta2,
+                          float weight_decay,
+                          bool should_log)
+{
+    auto opt = std::make_shared<Lion_Optimizer>(alpha, betta1, betta2, weight_decay);
+
+    s_optimizers[optimizer_id] = opt;
+
+    if (should_log) {
+        std::string avx_type = "";
+#if defined(__AVX512__)
+        avx_type = "AVX512";
+#else
+#if defined(__AVX256__)
+        avx_type = "AVX2";
+#else
+        avx_type = "scalar";
+#endif
+#endif
+
+        printf("Lion Optimizer #%d is created with %s arithmetic capability.\n",
+               optimizer_id,
+               avx_type.c_str());
+        printf("Config: alpha=%f, betas=(%f, %f), weight_decay=%f\n",
+               alpha,
+               betta1,
+               betta2,
+               weight_decay);
+    }
+
+    return 0;
+}
+
+void Lion_Optimizer::Step_8(float* _params,
+                            float* grads,
+                            float* _exp_avg,
+                            size_t _param_size,
+                            ds_half_precision_t* dev_params,
+                            bool half_precision)
+{
+    size_t rounded_size = 0;
+#if defined(__AVX512__) or defined(__AVX256__)
+    Step_AVX<8>(&rounded_size, _params, grads, _exp_avg, _param_size, dev_params, half_precision);
+#endif
+    if (_param_size > rounded_size)
+        Step_4((_params + rounded_size),
+               (grads + rounded_size),
+               (_exp_avg + rounded_size),
+               (_param_size - rounded_size),
+               (dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
+               half_precision);
+}
+
+int ds_lion_step(int optimizer_id,
+                 size_t step,
+                 float lr,
+                 float beta1,
+                 float beta2,
+                 float weight_decay,
+                 torch::Tensor& params,
+                 torch::Tensor& grads,
+                 torch::Tensor& exp_avg)
+{
+    auto params_c = params.contiguous();
+    auto grads_c = grads.contiguous();
+    auto exp_avg_c = exp_avg.contiguous();
+
+    // assert(params.options().dtype() == grads.options().dtype());
+
+    float* params_ptr = (float*)params_c.data_ptr();
+    float* grads_ptr = (float*)grads_c.data_ptr();
+    float* exp_avg_ptr = (float*)exp_avg_c.data_ptr();
+
+    std::shared_ptr<Lion_Optimizer> opt =
+        std::static_pointer_cast<Lion_Optimizer>(s_optimizers[optimizer_id]);
+    opt->IncrementStep(step, beta1, beta2);
+    opt->update_state(lr, weight_decay);
+
+    opt->Step_8(params_ptr,
+                grads_ptr,
+                exp_avg_ptr,
+                params_c.numel(),
+                nullptr,
+                (params.options().dtype() == at::kHalf));
+
+#if defined(__ENABLE_CUDA__)
+    opt->SynchronizeStreams();
+#endif
+    return 0;
+}
+
+int ds_lion_step_plus_copy(int optimizer_id,
+                           size_t step,
+                           float lr,
+                           float beta1,
+                           float beta2,
+                           float weight_decay,
+                           torch::Tensor& params,
+                           torch::Tensor& grads,
+                           torch::Tensor& exp_avg,
+                           torch::Tensor& gpu_params)
+{
+#if defined(__ENABLE_CUDA__)
+    auto params_c = params.contiguous();
+    auto gpu_params_c = gpu_params.contiguous();
+    auto exp_avg_c = exp_avg.contiguous();
+    auto grads_c = grads.contiguous();
+
+    float* params_ptr = (float*)params_c.data_ptr();
+    float* grads_ptr = (float*)grads_c.data_ptr();
+    ds_half_precision_t* gpu_params_ptr = (ds_half_precision_t*)gpu_params_c.data_ptr();
+    float* exp_avg_ptr = (float*)exp_avg_c.data_ptr();
+
+    std::shared_ptr<Lion_Optimizer> opt =
+        std::static_pointer_cast<Lion_Optimizer>(s_optimizers[optimizer_id]);
+    opt->IncrementStep(step, beta1, beta2);
+    opt->update_state(lr, weight_decay);
+    opt->Step_8(params_ptr,
+                grads_ptr,
+                exp_avg_ptr,
+                params_c.numel(),
+                gpu_params_ptr,
+                (params.options().dtype() == at::kHalf));
+
+    opt->SynchronizeStreams();
+#else
+    assert(false);
+#endif
+    return 0;
+}
+
+int destroy_lion_optimizer(int optimizer_id)
+{
+    s_optimizers.erase(optimizer_id);
+
+    return 0;
+}
diff --git a/csrc/lion/fused_lion_frontend.cpp b/csrc/lion/fused_lion_frontend.cpp
new file mode 100644
index 000000000000..e523f97ca309
--- /dev/null
+++ b/csrc/lion/fused_lion_frontend.cpp
@@ -0,0 +1,22 @@
+// Copyright (c) Microsoft Corporation.
+// SPDX-License-Identifier: Apache-2.0
+
+// DeepSpeed Team
+
+#include <torch/extension.h>
+
+void multi_tensor_lion_cuda(int chunk_size,
+                            at::Tensor noop_flag,
+                            std::vector<std::vector<at::Tensor>> tensor_lists,
+                            const float lr,
+                            const float beta1,
+                            const float beta2,
+                            const int step,
+                            const float weight_decay);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("multi_tensor_lion",
+          &multi_tensor_lion_cuda,
+          "Compute and apply gradient update to parameters for Lion optimizer");
+}
diff --git a/csrc/lion/multi_tensor_apply.cuh b/csrc/lion/multi_tensor_apply.cuh
new file mode 100644
index 000000000000..12f41cb49c6b
--- /dev/null
+++ b/csrc/lion/multi_tensor_apply.cuh
@@ -0,0 +1,132 @@
+// Copyright (c) Microsoft Corporation.
+// SPDX-License-Identifier: Apache-2.0
+
+// DeepSpeed Team
+
+/*
+Copyright NVIDIA/apex
+This file is adapted from fused adam in NVIDIA/apex, commit a109f85
+*/
+
+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/Exceptions.h>
+#include <c10/cuda/CUDAGuard.h>
+#include "compat.h"
+
+#include <assert.h>
+
+// #include <iostream>
+
+// This header is the one-stop shop for all your multi-tensor apply needs.
+
+// TODO:  Kernel arg size limit may be <4KB for some other cards (ie Jetson)
+constexpr int depth_to_max_tensors[5] = {110, 64, 48, 36, 30};
+constexpr int depth_to_max_blocks[5] = {320, 320, 320, 320, 320};
+
+template <int n>
+struct TensorListMetadata {
+    void* addresses[n][depth_to_max_tensors[n - 1]];
+    int sizes[depth_to_max_tensors[n - 1]];
+    unsigned char block_to_tensor[depth_to_max_blocks[n - 1]];
+    int block_to_chunk[depth_to_max_blocks[n - 1]];  // I fear this needs to be a full int.
+    int start_tensor_this_launch;
+};
+
+template <typename T, typename U, typename... ArgTypes>
+__global__ void multi_tensor_apply_kernel(int chunk_size,
+                                          volatile int* noop_flag,
+                                          T tl,
+                                          U callable,
+                                          ArgTypes... args)
+{
+    // Hand the chunk information to the user-supplied functor to process however it likes.
+    callable(chunk_size, noop_flag, tl, args...);
+}
+
+template <int depth, typename T, typename... ArgTypes>
+void multi_tensor_apply(int block_size,
+                        int chunk_size,
+                        const at::Tensor& noop_flag,
+                        const std::vector<std::vector<at::Tensor>>& tensor_lists,
+                        T callable,
+                        ArgTypes... args)
+{
+    TORCH_CHECK(tensor_lists.size() == depth, "tensor_lists.size() != depth");
+    int len0 = tensor_lists[0].size();
+    TORCH_CHECK(len0 > 0, "tensor_lists[0].size() is not > 0");
+    auto ref_device = tensor_lists[0][0].device();
+    TORCH_CHECK(ref_device.type() == at::kCUDA, "expected input to be on cuda");
+    for (int l = 0; l < tensor_lists.size(); l++)  // No range-based for because I need indices
+    {
+        TORCH_CHECK(tensor_lists[l].size() == len0, "Size mismatch among tensor lists");
+        for (int t = 0; t < tensor_lists[l].size(); t++) {
+            // TODO:  Print which tensor fails.
+            bool contiguous_memory = tensor_lists[l][t].is_contiguous();
+#ifdef VERSION_GE_1_5
+            contiguous_memory = (contiguous_memory ||
+                                 tensor_lists[l][t].is_contiguous(at::MemoryFormat::ChannelsLast));
+#endif
+            TORCH_CHECK(contiguous_memory, "A tensor was not contiguous.");
+            TORCH_CHECK(tensor_lists[l][t].device() == ref_device,
+                        "A tensor was not on the same device as the first tensor");
+            TORCH_CHECK(tensor_lists[l][t].numel() == tensor_lists[0][t].numel(), "Size mismatch");
+        }
+    }
+
+    int ntensors = tensor_lists[0].size();
+
+    TensorListMetadata<depth> tl;
+
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(tensor_lists[0][0]));
+    auto stream = at::cuda::getCurrentCUDAStream();
+
+    tl.start_tensor_this_launch = 0;
+    int loc_block_info = 0;
+    int loc_tensor_info = 0;
+    for (int t = 0; t < ntensors; t++) {
+        tl.sizes[loc_tensor_info] = tensor_lists[0][t].numel();
+        for (int d = 0; d < depth; d++)
+            tl.addresses[d][loc_tensor_info] = tensor_lists[d][t].data_ptr();
+        loc_tensor_info++;
+
+        int chunks_this_tensor = (tensor_lists[0][t].numel() + chunk_size - 1) / chunk_size;
+
+        for (int chunk = 0; chunk < chunks_this_tensor; chunk++) {
+            // std::cout << chunks_this_tensor << std::endl;
+            tl.block_to_tensor[loc_block_info] = loc_tensor_info - 1;
+            tl.block_to_chunk[loc_block_info] = chunk;
+            loc_block_info++;
+
+            bool tensors_full = (loc_tensor_info == depth_to_max_tensors[depth - 1] &&
+                                 chunk == chunks_this_tensor - 1);
+            bool blocks_full = (loc_block_info == depth_to_max_blocks[depth - 1]);
+            bool last_chunk = (t == ntensors - 1 && chunk == chunks_this_tensor - 1);
+            if (tensors_full || blocks_full || last_chunk) {
+                // using accscalar_t = acc_type<scalar_t, true>;
+                multi_tensor_apply_kernel<<<loc_block_info, block_size, 0, stream>>>(
+                    chunk_size, noop_flag.DATA_PTR<int>(), tl, callable, args...);
+
+                AT_CUDA_CHECK(cudaGetLastError());
+
+                // Reset.  The control flow possibilities here make my brain hurt.
+                loc_block_info = 0;
+                if (chunk == chunks_this_tensor - 1) {
+                    // std::cout << "Hit case 1 " << cond1 << " " << cond2 << " " << cond3 <<
+                    // std::endl;
+                    loc_tensor_info = 0;
+                    tl.start_tensor_this_launch = t + 1;
+                } else {
+                    // std::cout << "Hit case 2 " << cond1 << " " << cond2 << " " << cond3 <<
+                    // std::endl;
+                    tl.sizes[0] = tl.sizes[loc_tensor_info - 1];
+                    for (int d = 0; d < depth; d++)
+                        tl.addresses[d][0] = tl.addresses[d][loc_tensor_info - 1];
+                    loc_tensor_info = 1;
+                    tl.start_tensor_this_launch = t;
+                }
+            }
+        }
+    }
+}
diff --git a/csrc/lion/multi_tensor_lion.cu b/csrc/lion/multi_tensor_lion.cu
new file mode 100644
index 000000000000..f5fe6dfdd7d0
--- /dev/null
+++ b/csrc/lion/multi_tensor_lion.cu
@@ -0,0 +1,126 @@
+// Copyright (c) Microsoft Corporation.
+// SPDX-License-Identifier: Apache-2.0
+
+// DeepSpeed Team
+
+/*
+Copyright NVIDIA/apex
+This file is adapted from fused adam in NVIDIA/apex, commit a109f85
+*/
+
+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/Exceptions.h>
+// Another possibility:
+// #include <torch/all.h>
+
+#include <assert.h>
+
+#include "multi_tensor_apply.cuh"
+#include "type_shim.h"
+
+#define BLOCK_SIZE 512
+#define ILP 4
+
+using MATH_T = float;
+
+template <typename T>
+struct LionFunctor {
+    __device__ __forceinline__ void operator()(int chunk_size,
+                                               volatile int* noop_gmem,
+                                               TensorListMetadata<3>& tl,
+                                               const float beta1,
+                                               const float beta2,
+                                               const float lr,
+                                               const float decay)
+    {
+        // I'd like this kernel to propagate infs/nans.
+        // if(*noop_gmem == 1)
+        //   return;
+
+        int tensor_loc = tl.block_to_tensor[blockIdx.x];
+
+        // potentially use to pass in list of scalar
+        // int tensor_num = tl.start_tensor_this_launch + tensor_loc;
+
+        int chunk_idx = tl.block_to_chunk[blockIdx.x];
+        int n = tl.sizes[tensor_loc];
+
+        T* g = (T*)tl.addresses[0][tensor_loc];
+        g += chunk_idx * chunk_size;
+
+        T* p = (T*)tl.addresses[1][tensor_loc];
+        p += chunk_idx * chunk_size;
+
+        T* m = (T*)tl.addresses[2][tensor_loc];
+        m += chunk_idx * chunk_size;
+
+        n -= chunk_idx * chunk_size;
+
+        MATH_T after_decay = 1.0f - lr * decay;
+
+        // see note in multi_tensor_scale_kernel.cu
+        for (int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x * ILP) {
+            MATH_T r_g[ILP];
+            MATH_T r_p[ILP];
+            MATH_T r_m[ILP];
+#pragma unroll
+            for (int ii = 0; ii < ILP; ii++) {
+                int i = i_start + threadIdx.x + ii * blockDim.x;
+                if (i < n && i < chunk_size) {
+                    r_g[ii] = g[i];
+                    r_p[ii] = p[i];
+                    r_m[ii] = m[i];
+                } else {
+                    r_g[ii] = MATH_T(0);
+                    r_p[ii] = MATH_T(0);
+                    r_m[ii] = MATH_T(0);
+                }
+            }
+#pragma unroll
+            for (int ii = 0; ii < ILP; ii++) {
+                MATH_T c = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
+                MATH_T update = c > 0 ? (-lr) : lr;
+                r_p[ii] = r_p[ii] * after_decay + update;
+                r_m[ii] = beta2 * r_m[ii] + (1 - beta2) * r_g[ii];
+            }
+#pragma unroll
+            for (int ii = 0; ii < ILP; ii++) {
+                int i = i_start + threadIdx.x + ii * blockDim.x;
+                if (i < n && i < chunk_size) {
+                    p[i] = r_p[ii];
+                    m[i] = r_m[ii];
+                }
+            }
+        }
+    }
+};
+
+void multi_tensor_lion_cuda(int chunk_size,
+                            at::Tensor noop_flag,
+                            std::vector<std::vector<at::Tensor>> tensor_lists,
+                            const float lr,
+                            const float beta1,
+                            const float beta2,
+                            const int step,
+                            const float weight_decay)
+{
+    using namespace at;
+
+    // Assume single type across p,g,m1,m2 now
+    DISPATCH_DOUBLE_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(),
+                                   0,
+                                   "lion",
+                                   multi_tensor_apply<3>(BLOCK_SIZE,
+                                                         chunk_size,
+                                                         noop_flag,
+                                                         tensor_lists,
+                                                         LionFunctor<scalar_t_0>(),
+                                                         beta1,
+                                                         beta2,
+                                                         lr,
+                                                         weight_decay);)
+
+    AT_CUDA_CHECK(cudaGetLastError());
+}
diff --git a/deepspeed/ops/__init__.py b/deepspeed/ops/__init__.py
index b5a03c458a46..ba1c9c1fd9f0 100755
--- a/deepspeed/ops/__init__.py
+++ b/deepspeed/ops/__init__.py
@@ -6,6 +6,7 @@
 from . import adam
 from . import adagrad
 from . import lamb
+from . import lion
 #from ..git_version_info_installed import installed_ops as __installed_ops__
 #if __installed_ops__['sparse_attn']:
 from . import sparse_attention
diff --git a/deepspeed/ops/lion/__init__.py b/deepspeed/ops/lion/__init__.py
new file mode 100755
index 000000000000..2f90e5ec2e80
--- /dev/null
+++ b/deepspeed/ops/lion/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .cpu_lion import DeepSpeedCPULion
+from .fused_lion import FusedLion
diff --git a/deepspeed/ops/lion/cpu_lion.py b/deepspeed/ops/lion/cpu_lion.py
new file mode 100755
index 000000000000..a91a00643873
--- /dev/null
+++ b/deepspeed/ops/lion/cpu_lion.py
@@ -0,0 +1,141 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+from cpuinfo import get_cpu_info
+from deepspeed.utils import logger
+from deepspeed.utils.logging import should_log_le
+from deepspeed.ops.op_builder import CPULionBuilder
+
+
+class DeepSpeedCPULion(torch.optim.Optimizer):
+    optimizer_id = 0
+
+    def __init__(self, model_params, lr=1e-3, betas=(0.9, 0.999), weight_decay=0, fp32_optimizer_states=True):
+        """Fast vectorized implementation of Lion optimizer on CPU:
+
+        See Symbolic Discovery of Optimization Algorithms (https://doi.org/10.48550/arXiv.2302.06675).
+
+        .. note::
+                We recommend using our `config
+                <https://www.deepspeed.ai/docs/config-json/#optimizer-parameters>`_
+                to allow :meth:`deepspeed.initialize` to build this optimizer
+                for you.
+
+
+        Arguments:
+            model_params (iterable): iterable of parameters to optimize or dicts defining
+                parameter groups.
+            lr (float, optional): learning rate. (default: 1e-3)
+            betas (Tuple[float, float], optional): coefficients used for computing
+                running averages of gradient and its square. (default: (0.9, 0.999))
+            weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+            full_precision_optimizer_states: creates momentum and variance in full precision regardless of
+                        the precision of the parameters (default: True)
+        """
+
+        default_args = dict(lr=lr, betas=betas, weight_decay=weight_decay)
+        super(DeepSpeedCPULion, self).__init__(model_params, default_args)
+
+        cpu_info = get_cpu_info()
+        self.cpu_vendor = cpu_info["vendor_id_raw"].lower() if "vendor_id_raw" in cpu_info else "unknown"
+        if "amd" in self.cpu_vendor:
+            for group_id, group in enumerate(self.param_groups):
+                for param_id, p in enumerate(group['params']):
+                    if p.dtype == torch.half:
+                        logger.warning("FP16 params for CPULion may not work on AMD CPUs")
+                        break
+                else:
+                    continue
+                break
+
+        self.opt_id = DeepSpeedCPULion.optimizer_id
+        DeepSpeedCPULion.optimizer_id = DeepSpeedCPULion.optimizer_id + 1
+        self.fp32_optimizer_states = fp32_optimizer_states
+        self.ds_opt_lion = CPULionBuilder().load()
+
+        self.ds_opt_lion.create_lion(self.opt_id, lr, betas[0], betas[1], weight_decay, should_log_le("info"))
+
+    def __del__(self):
+        # need to destroy the C++ object explicitly to avoid a memory leak when deepspeed.initialize
+        # is used multiple times in the same process (notebook or pytest worker)
+        self.ds_opt_lion.destroy_lion(self.opt_id)
+
+    def __setstate__(self, state):
+        super(DeepSpeedCPULion, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('amsgrad', False)
+
+    @torch.no_grad()
+    def step(self, closure=None, fp16_param_groups=None):
+        """Update the model parameters.
+
+        .. note::
+            This method will be called internally by ZeRO-Offload. DeepSpeed
+            users should still use ``engine.step()`` as shown in the
+            `Getting Started
+            <https://www.deepspeed.ai/getting-started/#training>`_ guide.
+
+        Args:
+            closure (callable, optional): closure to compute the loss.
+                Defaults to ``None``.
+            fp16_param_groups: FP16 GPU parameters to update. Performing the
+                copy here reduces communication time. Defaults to ``None``.
+
+        Returns:
+            loss: if ``closure`` is provided. Otherwise ``None``.
+        """
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        # intended device for step
+        device = torch.device('cpu')
+
+        # converting the fp16 params to a group of parameter
+        if type(fp16_param_groups) is list:
+            if type(fp16_param_groups[0]) is not list:
+                fp16_param_groups = [fp16_param_groups]
+        elif fp16_param_groups is not None:
+            fp16_param_groups = [[fp16_param_groups]]
+
+        for group_id, group in enumerate(self.param_groups):
+            for param_id, p in enumerate(group['params']):
+
+                if p.grad is None:
+                    continue
+
+                assert p.device == device, f"CPULion param is on {p.device} and must be 'cpu', make " \
+                        "sure you enabled 'offload_optimizer': 'cpu' in your ZeRO config."
+
+                state = self.state[p]
+                # State initialization
+                if len(state) == 0:
+                    #print(f'group {group_id} param {param_id} = {p.numel()}')
+                    state['step'] = 0
+
+                    #use full precision by default unless self.fp32_optimizer_states is off
+                    state_dtype = torch.float if self.fp32_optimizer_states else p.dtype
+
+                    # gradient momentums
+                    state['exp_avg'] = torch.zeros_like(p.data, dtype=state_dtype, device=device)
+                    #memory_format=torch.preserve_format)
+                    # gradient variances
+                    state['exp_avg_sq'] = torch.zeros_like(p.data, dtype=state_dtype, device=device)
+                    #memory_format=torch.preserve_format)
+
+                state['step'] += 1
+                beta1, beta2 = group['betas']
+
+                if fp16_param_groups is not None:
+                    self.ds_opt_lion.lion_update_copy(self.opt_id, state['step'], group['lr'], beta1, beta2,
+                                                      group['weight_decay'], p.data, p.grad.data, state['exp_avg'],
+                                                      fp16_param_groups[group_id][param_id].data)
+                else:
+                    self.ds_opt_lion.lion_update(self.opt_id, state['step'], group['lr'], beta1, beta2,
+                                                 group['weight_decay'], p.data, p.grad.data, state['exp_avg'])
+        return loss
diff --git a/deepspeed/ops/lion/fused_lion.py b/deepspeed/ops/lion/fused_lion.py
new file mode 100644
index 000000000000..7332a7f96361
--- /dev/null
+++ b/deepspeed/ops/lion/fused_lion.py
@@ -0,0 +1,131 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""
+This file is modified from fused_adam.py
+"""
+
+import torch
+from .multi_tensor_apply import MultiTensorApply
+
+multi_tensor_applier = MultiTensorApply(2048 * 32)
+from deepspeed.accelerator import get_accelerator
+from deepspeed.ops.op_builder import FusedLionBuilder
+
+
+class FusedLion(torch.optim.Optimizer):
+    """Implements Lion algorithm.
+
+    Currently GPU-only.
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups.
+        lr (float, optional): learning rate. (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square. (default: (0.9, 0.999))
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        set_grad_none (bool, optional): whether set grad to None when zero_grad()
+            method is called. (default: True)
+
+    .. _Symbolic Discovery of Optimization Algorithms:
+        https://doi.org/10.48550/arXiv.2302.06675
+    """
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), weight_decay=0., set_grad_none=True):
+
+        defaults = dict(lr=lr, betas=betas, weight_decay=weight_decay)
+        super(FusedLion, self).__init__(params, defaults)
+        self.set_grad_none = set_grad_none
+
+        fused_lion_cuda = FusedLionBuilder().load()
+        # Skip buffer
+        self._dummy_overflow_buf = get_accelerator().IntTensor([0])
+        self.multi_tensor_lion = fused_lion_cuda.multi_tensor_lion
+
+    def zero_grad(self):
+        if self.set_grad_none:
+            for group in self.param_groups:
+                for p in group['params']:
+                    p.grad = None
+        else:
+            super(FusedLion, self).zero_grad()
+
+    def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None, grad_scaler=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+
+        The remaining arguments are deprecated, and are only retained (for the moment) for error-checking purposes.
+        """
+        if any(p is not None for p in [grads, output_params, scale, grad_norms]):
+            raise RuntimeError('FusedLion has been updated.')
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            if len(group['params']) == 0:
+                continue
+            beta1, beta2 = group['betas']
+
+            # assume same step across group now to simplify things
+            # per parameter step can be easily support by making it tensor, or pass list into kernel
+            if 'step' not in group:
+                group['step'] = 0
+
+            # create lists for multi-tensor apply
+            g_16, p_16, m_16 = [], [], []
+            g_bf, p_bf, m_bf = [], [], []
+            g_32, p_32, m_32 = [], [], []
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                if p.grad.data.is_sparse:
+                    raise NotImplementedError('FusedLion does not support sparse gradients')
+
+                state = self.state[p]
+                # State initialization
+                if len(state) == 0:
+                    # DeepSpeed ZeRO 3 processes each subgroup a time, so we need to keep tracking step count for each tensor separately.
+                    # While this is not an issue for ZeRO 1 & 2, since they apply a single optimization step to the whole param group at the same time.
+                    # In order to keep backward compatibility for the existing checkpoints, we use group['state'] to initialize state['step'] if it exists.
+                    state['step'] = group.get('step', 0)
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p.data)
+
+                if p.dtype == torch.float16:
+                    g_16.append(p.grad.data)
+                    p_16.append(p.data)
+                    m_16.append(state['exp_avg'])
+                elif p.dtype == torch.bfloat16:
+                    g_bf.append(p.grad)
+                    p_bf.append(p)
+                    m_bf.append(state['exp_avg'])
+                elif p.dtype == torch.float32:
+                    g_32.append(p.grad.data)
+                    p_32.append(p.data)
+                    m_32.append(state['exp_avg'])
+                else:
+                    raise RuntimeError('FusedLion only support fp16, bf16 and fp32.')
+
+            if len(g_16) > 0:
+                state['step'] += 1
+                multi_tensor_applier(self.multi_tensor_lion, self._dummy_overflow_buf, [g_16, p_16, m_16], group['lr'],
+                                     beta1, beta2, state['step'], group['weight_decay'])
+
+            if len(g_bf) > 0:
+                state['step'] += 1
+                multi_tensor_applier(self.multi_tensor_lion, self._dummy_overflow_buf, [g_bf, p_bf, m_bf], group['lr'],
+                                     beta1, beta2, state['step'], group['weight_decay'])
+
+            if len(g_32) > 0:
+                state['step'] += 1
+                multi_tensor_applier(self.multi_tensor_lion, self._dummy_overflow_buf, [g_32, p_32, m_32], group['lr'],
+                                     beta1, beta2, state['step'], group['weight_decay'])
+
+        return loss
diff --git a/deepspeed/ops/lion/multi_tensor_apply.py b/deepspeed/ops/lion/multi_tensor_apply.py
new file mode 100644
index 000000000000..0ba228505cef
--- /dev/null
+++ b/deepspeed/ops/lion/multi_tensor_apply.py
@@ -0,0 +1,17 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""
+Copyright NVIDIA/apex
+This file is adapted from NVIDIA/apex, commit a109f85
+"""
+
+
+class MultiTensorApply(object):
+
+    def __init__(self, chunk_size):
+        self.chunk_size = chunk_size
+
+    def __call__(self, op, noop_flag_buffer, tensor_lists, *args):
+        return op(self.chunk_size, noop_flag_buffer, tensor_lists, *args)
diff --git a/deepspeed/runtime/config.py b/deepspeed/runtime/config.py
index 77f1cd51b02a..c31b9671296f 100755
--- a/deepspeed/runtime/config.py
+++ b/deepspeed/runtime/config.py
@@ -77,9 +77,10 @@
 MUADAM_OPTIMIZER = 'muadam'
 MUADAMW_OPTIMIZER = 'muadamw'
 MUSGD_OPTIMIZER = 'musgd'
+LION_OPTIMIZER = 'lion'
 DEEPSPEED_OPTIMIZERS = [
     ADAGRAD_OPTIMIZER, ADAM_OPTIMIZER, ADAMW_OPTIMIZER, LAMB_OPTIMIZER, ONEBIT_ADAM_OPTIMIZER, ONEBIT_LAMB_OPTIMIZER,
-    ZERO_ONE_ADAM_OPTIMIZER, MUADAM_OPTIMIZER, MUADAMW_OPTIMIZER, MUSGD_OPTIMIZER
+    ZERO_ONE_ADAM_OPTIMIZER, MUADAM_OPTIMIZER, MUADAMW_OPTIMIZER, MUSGD_OPTIMIZER, LION_OPTIMIZER
 ]
 
 # extra optimizer parameters for adam/adamw
diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py
index 22d7c882eb1a..b4c8ef56c701 100644
--- a/deepspeed/runtime/engine.py
+++ b/deepspeed/runtime/engine.py
@@ -37,7 +37,8 @@
 
 from deepspeed.runtime.config import DEEPSPEED_OPTIMIZERS, \
     ADAGRAD_OPTIMIZER, ADAM_OPTIMIZER, ADAMW_OPTIMIZER, LAMB_OPTIMIZER, ONEBIT_ADAM_OPTIMIZER, ONEBIT_LAMB_OPTIMIZER, \
-    TORCH_ADAM_PARAM, ADAM_W_MODE, ADAM_W_MODE_DEFAULT, ZERO_ONE_ADAM_OPTIMIZER, MUADAM_OPTIMIZER, MUADAMW_OPTIMIZER, MUSGD_OPTIMIZER
+    TORCH_ADAM_PARAM, ADAM_W_MODE, ADAM_W_MODE_DEFAULT, ZERO_ONE_ADAM_OPTIMIZER, MUADAM_OPTIMIZER, MUADAMW_OPTIMIZER, \
+    MUSGD_OPTIMIZER, LION_OPTIMIZER
 
 from deepspeed.runtime.dataloader import DeepSpeedDataLoader
 from deepspeed.runtime.constants import \
@@ -1296,6 +1297,13 @@ def _configure_basic_optimizer(self, model_parameters):
             optimizer = OnebitLamb(model_parameters, self, **optimizer_parameters)
             if not self.fp16_enabled():
                 logger.warning(f"Currently the convergence of 1-bit Lamb is only verified under FP16")
+        elif self.optimizer_name() == LION_OPTIMIZER:
+            if self.zero_use_cpu_optimizer():
+                from deepspeed.ops.lion import DeepSpeedCPULion
+                optimizer = DeepSpeedCPULion(model_parameters, **optimizer_parameters)
+            else:
+                from deepspeed.ops.lion import FusedLion
+                optimizer = FusedLion(model_parameters, **optimizer_parameters)
         elif self.optimizer_name() == MUADAM_OPTIMIZER:
             try:
                 from mup import MuAdam
diff --git a/deepspeed/runtime/zero/utils.py b/deepspeed/runtime/zero/utils.py
index 0250796f793d..0bf1ca4a894d 100755
--- a/deepspeed/runtime/zero/utils.py
+++ b/deepspeed/runtime/zero/utils.py
@@ -12,6 +12,7 @@
 from deepspeed.ops.adam import DeepSpeedCPUAdam
 from deepspeed.ops.adagrad import DeepSpeedCPUAdagrad
 from deepspeed.ops.adam import FusedAdam
+from deepspeed.ops.lion import DeepSpeedCPULion, FusedLion
 from deepspeed.utils.nvtx import instrument_w_nvtx
 from deepspeed.accelerator import get_accelerator
 
@@ -37,7 +38,8 @@ class ZeRORuntimeException(Exception):
 
 
 ZERO_SUPPORTED_OPTIMIZERS = [
-    torch.optim.Adam, torch.optim.AdamW, FusedAdam, DeepSpeedCPUAdam, torch.optim.Adagrad, DeepSpeedCPUAdagrad
+    torch.optim.Adam, torch.optim.AdamW, FusedAdam, DeepSpeedCPUAdam, torch.optim.Adagrad, DeepSpeedCPUAdagrad,
+    DeepSpeedCPULion, FusedLion
 ]
 
 # Add apex FusedAdam to supported list if apex is installed
diff --git a/docs/_tutorials/advanced-install.md b/docs/_tutorials/advanced-install.md
index 0558433994ca..c2b4c04cad1c 100755
--- a/docs/_tutorials/advanced-install.md
+++ b/docs/_tutorials/advanced-install.md
@@ -60,7 +60,9 @@ Available `DS_BUILD` options include:
 * `DS_BUILD_AIO` builds asynchronous (NVMe) I/O op
 * `DS_BUILD_CCL_COMM` builds the communication collective libs
 * `DS_BUILD_CPU_ADAM` builds the CPUAdam op
+* `DS_BUILD_CPU_LION` builds the CPULion op
 * `DS_BUILD_FUSED_ADAM` builds the FusedAdam op (from [apex](https://github.com/NVIDIA/apex))
+* `DS_BUILD_FUSED_LION` builds the FusedLion op
 * `DS_BUILD_CPU_ADAGRAD` builds the CPUAdagrad op
 * `DS_BUILD_FUSED_LAMB` builds the FusedLamb op
 * `DS_BUILD_QUANTIZER` builds the quantizer op
diff --git a/op_builder/cpu_lion.py b/op_builder/cpu_lion.py
new file mode 100644
index 000000000000..5c16d10ebb44
--- /dev/null
+++ b/op_builder/cpu_lion.py
@@ -0,0 +1,48 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import os
+from .builder import TorchCPUOpBuilder
+
+
+class CPULionBuilder(TorchCPUOpBuilder):
+    BUILD_VAR = "DS_BUILD_CPU_LION"
+    NAME = "cpu_lion"
+
+    def __init__(self):
+        super().__init__(name=self.NAME)
+
+    def absolute_name(self):
+        return f'deepspeed.ops.lion.{self.NAME}_op'
+
+    def sources(self):
+        if self.build_for_cpu:
+            return ['csrc/lion/cpu_lion.cpp', 'csrc/lion/cpu_lion_impl.cpp']
+
+        return ['csrc/lion/cpu_lion.cpp', 'csrc/lion/cpu_lion_impl.cpp', 'csrc/common/custom_cuda_kernel.cu']
+
+    def libraries_args(self):
+        args = super().libraries_args()
+        if self.build_for_cpu:
+            return args
+
+        if not self.is_rocm_pytorch():
+            args += ['curand']
+
+        return args
+
+    def include_paths(self):
+        import torch
+        if self.build_for_cpu:
+            CUDA_INCLUDE = []
+        elif not self.is_rocm_pytorch():
+            CUDA_INCLUDE = [os.path.join(torch.utils.cpp_extension.CUDA_HOME, "include")]
+        else:
+            CUDA_INCLUDE = [
+                os.path.join(torch.utils.cpp_extension.ROCM_HOME, "include"),
+                os.path.join(torch.utils.cpp_extension.ROCM_HOME, "include", "rocrand"),
+                os.path.join(torch.utils.cpp_extension.ROCM_HOME, "include", "hiprand"),
+            ]
+        return ['csrc/includes'] + CUDA_INCLUDE
diff --git a/op_builder/fused_lion.py b/op_builder/fused_lion.py
new file mode 100644
index 000000000000..b900a8f2369d
--- /dev/null
+++ b/op_builder/fused_lion.py
@@ -0,0 +1,37 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .builder import CUDAOpBuilder
+
+import sys
+
+
+class FusedLionBuilder(CUDAOpBuilder):
+    BUILD_VAR = "DS_BUILD_FUSED_LION"
+    NAME = "fused_lion"
+
+    def __init__(self):
+        super().__init__(name=self.NAME)
+
+    def absolute_name(self):
+        return f'deepspeed.ops.lion.{self.NAME}_op'
+
+    def sources(self):
+        return ['csrc/lion/fused_lion_frontend.cpp', 'csrc/lion/multi_tensor_lion.cu']
+
+    def include_paths(self):
+        return ['csrc/includes', 'csrc/lion']
+
+    def cxx_args(self):
+        args = super().cxx_args()
+        return args + self.version_dependent_macros()
+
+    def nvcc_args(self):
+        nvcc_flags = ['-O3'] + self.version_dependent_macros()
+        if not self.is_rocm_pytorch():
+            nvcc_flags.extend(
+                ['-allow-unsupported-compiler' if sys.platform == "win32" else '', '-lineinfo', '--use_fast_math'] +
+                self.compute_capability_args())
+        return nvcc_flags
diff --git a/tests/unit/ops/lion/test_cpu_lion.py b/tests/unit/ops/lion/test_cpu_lion.py
new file mode 100644
index 000000000000..61a069af3257
--- /dev/null
+++ b/tests/unit/ops/lion/test_cpu_lion.py
@@ -0,0 +1,96 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+import numpy as np
+import pytest
+from cpuinfo import get_cpu_info
+
+import deepspeed
+from deepspeed.accelerator import get_accelerator
+from deepspeed.ops.lion import FusedLion
+from deepspeed.ops.op_builder import CPULionBuilder
+from unit.common import DistributedTest
+
+if not deepspeed.ops.__compatible_ops__[CPULionBuilder.NAME]:
+    pytest.skip("cpu-lion is not compatible", allow_module_level=True)
+
+pytest.cpu_vendor = get_cpu_info()["vendor_id_raw"].lower()
+
+
+def check_equal(first, second, atol=1e-2, verbose=False):
+    x = first.detach().numpy()
+    y = second.detach().numpy()
+    print("ATOL", atol)
+    if verbose:
+        print("x = {}".format(x.flatten()))
+        print("y = {}".format(y.flatten()))
+        print('-' * 80)
+    np.testing.assert_allclose(x, y, err_msg="param-update mismatch!", atol=atol)
+
+
+def _compare_optimizers(model_size, param1, optimizer1, param2, optimizer2):
+    for i in range(10):
+        param1.grad = torch.randn(model_size, device=param1.device).to(param1.dtype)
+        param2.grad = param1.grad.clone().detach().to(device=param2.device, dtype=param2.dtype)
+
+        optimizer1.step()
+        optimizer2.step()
+
+    tolerance = param1.float().norm().detach().numpy() * 1e-2
+    check_equal(param1.float().norm(), param2.float().cpu().norm(), atol=tolerance, verbose=True)
+
+
+@pytest.mark.parametrize('dtype', [torch.half, torch.float], ids=["fp16", "fp32"])
+@pytest.mark.parametrize('model_size',
+                         [
+                             (64),
+                             (22),
+                             #(55),
+                             (128),
+                             (1024),
+                             (1048576),
+                         ]) # yapf: disable
+class TestCPULion(DistributedTest):
+    world_size = 1
+    reuse_dist_env = True
+    requires_cuda_env = False
+    if not get_accelerator().is_available():
+        init_distributed = False
+        set_dist_env = False
+
+    @pytest.mark.skipif(not get_accelerator().is_available(), reason="only supported in CUDA environments.")
+    def test_fused_lion_equal(self, dtype, model_size):
+        if ("amd" in pytest.cpu_vendor) and (dtype == torch.half):
+            pytest.skip("cpu-lion with half precision not supported on AMD CPUs")
+
+        from deepspeed.ops.lion import DeepSpeedCPULion
+
+        cpu_data = torch.randn(model_size, device='cpu').to(dtype)
+        cpu_param = torch.nn.Parameter(cpu_data)
+        cuda_param = torch.nn.Parameter(cpu_data.to(get_accelerator().device_name()))
+
+        cpu_optimizer = DeepSpeedCPULion([cpu_param])
+        cuda_optimizer = FusedLion([cuda_param])
+
+        _compare_optimizers(model_size=model_size,
+                            param1=cpu_param,
+                            optimizer1=cpu_optimizer,
+                            param2=cuda_param,
+                            optimizer2=cuda_optimizer)
+
+
+class TestCPULionGPUError(DistributedTest):
+
+    def test_cpu_lion_gpu_error(self):
+        model_size = 64
+        from deepspeed.ops.lion import DeepSpeedCPULion
+        device = get_accelerator().device_name(0)  # 'cuda:0' or 'xpu:0'
+        param = torch.nn.Parameter(torch.randn(model_size, device=device))
+        optimizer = DeepSpeedCPULion([param])
+
+        param.grad = torch.randn(model_size, device=device)
+        with pytest.raises(AssertionError):
+            optimizer.step()
diff --git a/tests/unit/ops/lion/test_lion.py b/tests/unit/ops/lion/test_lion.py
new file mode 100644
index 000000000000..b2c3ac2f52df
--- /dev/null
+++ b/tests/unit/ops/lion/test_lion.py
@@ -0,0 +1,59 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import deepspeed
+import torch
+import pytest
+
+from deepspeed.ops.lion import FusedLion
+from deepspeed.ops.lion import DeepSpeedCPULion
+from unit.common import DistributedTest
+from unit.simple_model import SimpleModel
+from deepspeed.accelerator import get_accelerator
+
+if torch.half not in get_accelerator().supported_dtypes():
+    pytest.skip(f"fp16 not supported, valid dtype: {get_accelerator().supported_dtypes()}", allow_module_level=True)
+# yapf: disable
+#'optimizer, zero_offload, resulting_optimizer
+lion_configs = [["Lion",  False, FusedLion],
+                ["Lion",  True,  DeepSpeedCPULion]]
+
+@pytest.mark.parametrize(
+    'optimizer, zero_offload, resulting_optimizer',
+    lion_configs)
+class TestLionConfigs(DistributedTest):
+    world_size = 1
+    reuse_dist_env = True
+
+    def test(self,
+             optimizer,
+             zero_offload,
+             resulting_optimizer):
+        config_dict = {
+            "train_batch_size": 2,
+            "steps_per_print": 1,
+            "optimizer": {
+                "type": optimizer,
+                "params": {
+                    "lr": 0.00015,
+                }
+            },
+            "gradient_clipping": 1.0,
+            "fp16": {
+                "enabled": True
+            },
+            "zero_optimization": {
+                "stage": 2,
+                "cpu_offload": zero_offload
+            }
+        }
+        model = SimpleModel(10)
+        model, _, _, _ = deepspeed.initialize(config=config_dict,
+                                              model=model,
+                                              model_parameters=model.parameters())
+        # get base optimizer under zero
+        ds_optimizer = model.optimizer.optimizer
+        opt_class = resulting_optimizer
+        assert isinstance(ds_optimizer, opt_class)

From f9698c7307d02ce99144ed8941568a4ba9f994ed Mon Sep 17 00:00:00 2001
From: Nadav Elyahu <88962733+nelyahu@users.noreply.github.com>
Date: Fri, 6 Oct 2023 13:26:31 +0300
Subject: [PATCH 08/38] pipe engine eval_batch: add option to disable loss
 broadcast (#4326)

it is sometimes not required to have the loss bcasted to all rank after
evaluation cycle and it is only required by some ranks.
It adds overhead of communication between rank.
by setting bcast_loss=False (default is True and retains the previous behavior),
loss will not be bcasted.
if monitor is enabled loss will be bcasted.

Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
---
 deepspeed/runtime/pipe/engine.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/deepspeed/runtime/pipe/engine.py b/deepspeed/runtime/pipe/engine.py
index 4e8a0faa0ae0..0c7c9f7a1090 100644
--- a/deepspeed/runtime/pipe/engine.py
+++ b/deepspeed/runtime/pipe/engine.py
@@ -386,7 +386,7 @@ def train_batch(self, data_iter=None):
         # TODO: should return precisely what loss returned and allow others to be queried?
         return self.agg_train_loss
 
-    def eval_batch(self, data_iter, return_logits=False, compute_loss=True, reduce_output='avg'):
+    def eval_batch(self, data_iter, return_logits=False, compute_loss=True, reduce_output='avg', bcast_loss=True):
         """Evaluate the pipeline on a batch of data from ``data_iter``. The
         engine will evaluate ``self.train_batch_size()`` total samples
         collectively across all workers.
@@ -449,7 +449,7 @@ def eval_batch(self, data_iter, return_logits=False, compute_loss=True, reduce_o
         if self.is_last_stage():
             eval_output = self._reduce_outputs(self.fwd_outputs, reduce=reduce_output)
 
-        if compute_loss:
+        if compute_loss and (bcast_loss or self.monitor.enabled):
             eval_output = self._bcast_pipe_scalar(eval_output)
 
         if self.global_rank == 0 and self.monitor.enabled:

From 2118c635916120553e9cab66e06abe962db391c2 Mon Sep 17 00:00:00 2001
From: Logan Adams <114770087+loadams@users.noreply.github.com>
Date: Fri, 6 Oct 2023 14:44:27 -0700
Subject: [PATCH 09/38] Add release flow (#4467)

---
 .github/workflows/release.yml | 47 +++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 .github/workflows/release.yml

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 000000000000..093bc98d41e8
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,47 @@
+name: Build and publish DeepSpeed release
+
+on:
+  push:
+    tags:
+      - 'v*.*.*'
+
+jobs:
+  deploy:
+    runs-on: ubuntu-20.04
+    environment: release-env
+
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        ref: "master"
+    - name: Get release version from tag
+      run: |
+        echo "RELEASE_VERSION=${GITHUB_REF#refs/*/v}" >> $GITHUB_ENV
+    - name: Check release version
+      run: |
+        python release/check_release_version.py --release_version ${{ env.RELEASE_VERSION }}
+    - name: Build DeepSpeed
+      run: |
+        DS_BUILD_STRING=" " python setup.py sdist_wheel
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        password: ${{ secrets.PYPI_API_TOKEN }}
+        repository-url: https://upload.pypi.org/legacy/
+    - name: Bump version
+      run: |
+        python release/bump_patch_version.py --current_version ${{ env.RELEASE_VERSION }}
+    - name: Create Pull Request
+      uses: peter-evans/create-pull-request@v4
+      with:
+        token: ${{ secrets.GH_PAT }}
+        add-paths: |
+          version.txt
+        body: |
+          **Auto-generated PR to update version.txt after a DeepSpeed release**
+          Released version - ${{ env.RELEASE_VERSION }}
+          Author           - @${{ github.actor }}
+        branch: AutoPR/${{ env.RELEASE_VERSION }}
+        assignees: ${{ github.actor }}
+        title: "Update version.txt after ${{ env.RELEASE_VERSION }} release"
+        author: ${{ github.actor }} <${{ github.actor }}@users.noreply.github.com>

From 26d0dd927b21a3422973fd5cea9616ec03987823 Mon Sep 17 00:00:00 2001
From: Michael Wyatt <michaelwyatt@microsoft.com>
Date: Fri, 6 Oct 2023 14:57:13 -0700
Subject: [PATCH 10/38] bump to 0.11.0

---
 version.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/version.txt b/version.txt
index 9b40aa6c214f..d9df1bbc0c7b 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.10.4
+0.11.0

From c4d467953320b3f420d2acf3d97ae423ad1f9003 Mon Sep 17 00:00:00 2001
From: Jeff Rasley <jerasley@microsoft.com>
Date: Fri, 6 Oct 2023 15:10:36 -0700
Subject: [PATCH 11/38] bump to 0.11.1

---
 version.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/version.txt b/version.txt
index d9df1bbc0c7b..af88ba824866 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.11.0
+0.11.1

From 7ed952eff1c71f7652fe4786a16c8d917f9c37d6 Mon Sep 17 00:00:00 2001
From: Kazuki Fujii <68278821+okoge-kaz@users.noreply.github.com>
Date: Sun, 8 Oct 2023 04:07:05 +0900
Subject: [PATCH 12/38] Fix bug in bfloat16 optimizer related to checkpointing
 (#4434)

* fix: bf16 optimizer if condition

* fix: unexpected keyword argument 'load_serial'

* fix: add load_serial arg to bf16_optimizer

* style: fix indentation

---------

Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
---
 deepspeed/runtime/bf16_optimizer.py | 3 ++-
 deepspeed/runtime/engine.py         | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/deepspeed/runtime/bf16_optimizer.py b/deepspeed/runtime/bf16_optimizer.py
index 9c7a84d4841e..550af8fac057 100644
--- a/deepspeed/runtime/bf16_optimizer.py
+++ b/deepspeed/runtime/bf16_optimizer.py
@@ -365,7 +365,8 @@ def load_state_dict(self,
                         state_dict_list,
                         checkpoint_folder,
                         load_optimizer_states=True,
-                        load_from_fp32_weights=False):
+                        load_from_fp32_weights=False,
+                        load_serial=None):
         if checkpoint_folder:
             self._load_universal_checkpoint(checkpoint_folder, load_optimizer_states, load_from_fp32_weights)
         else:
diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py
index b4c8ef56c701..8a8193ddd8f5 100644
--- a/deepspeed/runtime/engine.py
+++ b/deepspeed/runtime/engine.py
@@ -3287,7 +3287,7 @@ def _get_zero_param_shapes(self):
         # if we don't use it, we get parameters ordered incorrectly
         if hasattr(self.optimizer, "round_robin_bit16_groups"):
             bit16_groups = self.optimizer.round_robin_bit16_groups
-        elif self.bfloat16_enabled() and not self.zero_optimization():
+        elif self.bfloat16_enabled() and hasattr(self.optimizer, "bf16_groups"):
             bit16_groups = self.optimizer.bf16_groups
         else:
             bit16_groups = self.optimizer.bit16_groups if self.zero_optimization_stage(

From 59d62d614ed4390b82bdb36811fcc55c710510fe Mon Sep 17 00:00:00 2001
From: Max Kovalenko <75629718+deepcharm@users.noreply.github.com>
Date: Mon, 9 Oct 2023 04:32:25 +0300
Subject: [PATCH 13/38] Move tensors to device if mp is not enabled (#4461)

Tensors are not moved to device in gather_partitioned_activations
in case model parallel is not enabled.

Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
---
 deepspeed/runtime/activation_checkpointing/checkpointing.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/deepspeed/runtime/activation_checkpointing/checkpointing.py b/deepspeed/runtime/activation_checkpointing/checkpointing.py
index 77407a52026a..108cb37b57fb 100644
--- a/deepspeed/runtime/activation_checkpointing/checkpointing.py
+++ b/deepspeed/runtime/activation_checkpointing/checkpointing.py
@@ -270,6 +270,8 @@ def gather_partitioned_activations(tensors, device=None):
         # don't need to do all_gather if model parallel is not enabled
         if mp_group is None or mp_size == 1:
             item = item.view(list(size.numpy()))
+            if device is not None:
+                item = item.to(device)
             inputs.append(item)
             continue
 

From 6b634d0e7e7a7059f9fdf8434b7d92d3da2953ff Mon Sep 17 00:00:00 2001
From: Michael Wyatt <michaelwyatt@microsoft.com>
Date: Sun, 8 Oct 2023 18:33:17 -0700
Subject: [PATCH 14/38] move torch import (#4468)

---
 op_builder/async_io.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/op_builder/async_io.py b/op_builder/async_io.py
index da511a0a8c9d..b55c821910b9 100644
--- a/op_builder/async_io.py
+++ b/op_builder/async_io.py
@@ -5,7 +5,6 @@
 
 import distutils.spawn
 import subprocess
-import torch
 
 from .builder import OpBuilder
 
@@ -36,6 +35,7 @@ def cxx_args(self):
         # -O0 for improved debugging, since performance is bound by I/O
         CPU_ARCH = self.cpu_arch()
         SIMD_WIDTH = self.simd_width()
+        import torch  # Keep this import here to avoid errors when building DeepSpeed wheel without torch installed
         TORCH_MAJOR, TORCH_MINOR = map(int, torch.__version__.split('.')[0:2])
         if TORCH_MAJOR >= 2 and TORCH_MINOR >= 1:
             CPP_STD = '-std=c++17'

From 6763e2de610b006c0fafc2c19a31f6508c920d1b Mon Sep 17 00:00:00 2001
From: Yejing-Lai <55339926+Yejing-Lai@users.noreply.github.com>
Date: Mon, 9 Oct 2023 18:24:16 +0800
Subject: [PATCH 15/38] add lm_head and embed_out tensor parallel (#3962)

* add lm_head and embed_out tensor parallel

* fix load lm_head.weight name issue

* replace all_reduce with inference_all_reduce

* refactor lm_head tensor parallel

---------

Co-authored-by: Chen, Zhenhuan <zhenhuan.chen@intel.com>
---
 deepspeed/module_inject/auto_tp.py        | 20 ++++++++++++++-
 deepspeed/module_inject/layers.py         | 30 +++++++++++++++++++++++
 deepspeed/module_inject/replace_module.py | 11 ++++++++-
 3 files changed, 59 insertions(+), 2 deletions(-)

diff --git a/deepspeed/module_inject/auto_tp.py b/deepspeed/module_inject/auto_tp.py
index daf143919558..2e348de63454 100644
--- a/deepspeed/module_inject/auto_tp.py
+++ b/deepspeed/module_inject/auto_tp.py
@@ -11,7 +11,7 @@
 from typing import Optional
 import torch
 from deepspeed import comm as dist
-from .layers import LinearAllreduce, LinearLayer
+from .layers import LinearAllreduce, LinearLayer, LmHeadLinearAllreduce
 from deepspeed.accelerator import get_accelerator
 from .fusedqkv_utils import require_tp_fused_qkvw, prepare_tp_fused_qkvw
 
@@ -318,6 +318,11 @@ def _replace(self, child, name, conv_linear_layer):
             del data
 
             setattr(child, "replaced", True)
+            if name == "lm_head" or name == 'embed_out':
+                return LmHeadLinearAllreduce(
+                    torch.nn.parameter.Parameter(data_dc, requires_grad=False), dist.get_rank(), dist.get_world_size(),
+                    child.bias if child.bias is None else torch.nn.parameter.Parameter(
+                        child.bias.to(get_accelerator().current_device_name())), self.mp_group)
             return LinearAllreduce(torch.nn.parameter.Parameter(data_dc, requires_grad=False), child.bias if child.bias is None else \
                         torch.nn.parameter.Parameter(child.bias.to(get_accelerator().current_device_name())), self.mp_group)
         else:
@@ -436,3 +441,16 @@ def _replace_module(self, r_module, prev_name='', prev_class_name=''):
                 self.update_mp_params(child)
                 self._replace_module(child, name, class_name)
         return r_module
+
+    def _replace_last_linear_module(self, r_module):
+        if hasattr(r_module, "lm_head"):
+            name = "lm_head"
+            child = r_module.lm_head
+        elif hasattr(r_module, "embed_out"):
+            name = "embed_out"
+            child = r_module.embed_out
+        else:
+            return r_module
+        if child.__class__ in self.linear_policies:
+            setattr(r_module, name, self.linear_policies[child.__class__](child, name, self.conv_linear_layer))
+        return r_module
diff --git a/deepspeed/module_inject/layers.py b/deepspeed/module_inject/layers.py
index aa29651ec4cf..7a565560dec9 100644
--- a/deepspeed/module_inject/layers.py
+++ b/deepspeed/module_inject/layers.py
@@ -29,6 +29,36 @@ def forward(self, input):
         return output
 
 
+class LmHeadLinearAllreduce(nn.Module):
+
+    def __init__(
+        self,
+        weight,
+        rank,
+        world_size,
+        bias=None,
+        mp_group=None,
+    ):
+        super(LmHeadLinearAllreduce, self).__init__()
+        self.weight = weight
+        self.bias = bias
+        self.mp_group = mp_group
+        self.rank = rank
+        self.world_size = world_size
+
+    def forward(self, input):
+        assert input.shape[
+            -1] % self.world_size == 0, 'Please ensure that self.world_size is divisible by input.shape[-1]'
+        input_shard = input.shape[-1] // self.world_size
+        output = torch.matmul(input[:, :, self.rank * input_shard:(self.rank + 1) * input_shard],
+                              self.weight.transpose(-1, -2))
+        if self.mp_group is not None:
+            dist.inference_all_reduce(output, group=self.mp_group)
+        if self.bias is not None:
+            output += self.bias
+        return output
+
+
 class LinearLayer(nn.Module):
 
     def __init__(self, weight_shape=None, dtype=torch.half, weight=None, bias=None):
diff --git a/deepspeed/module_inject/replace_module.py b/deepspeed/module_inject/replace_module.py
index f9dd921b9ae1..8666372fa3f4 100644
--- a/deepspeed/module_inject/replace_module.py
+++ b/deepspeed/module_inject/replace_module.py
@@ -275,6 +275,8 @@ def replace_wo_policy(module, all_reduce_linears, prefix="", state_dict=None):
         _autotp.update_linear_policies()
 
         # 4. Replace modules
+        if "lm_head" in all_reduce_linears or "embed_out" in all_reduce_linears:
+            return _autotp._replace_last_linear_module(module)
         return _autotp._replace_module(module)
 
     def replace_fn(child, _policy, layer_id=0, prefix="", state_dict=None):
@@ -304,6 +306,13 @@ def set_lm_head(module):
         if embedding_weight is not None and hasattr(module, "lm_head") and hasattr(
                 module.lm_head, "weight") and module.lm_head.weight.is_meta:
             module.lm_head.weight = embedding_weight
+        # enable tensor parallel for the last linear
+        if hasattr(module, "lm_head") and hasattr(module.lm_head, "weight") and not module.lm_head.weight.is_meta:
+            module = replace_wo_policy(module, ("lm_head", ), 0, "lm_head")
+        elif hasattr(module, "embed_out") and hasattr(module.embed_out,
+                                                      "weight") and not module.embed_out.weight.is_meta:
+            module = replace_wo_policy(module, ("embed_out", ), 0, "embed_out")
+        return module
 
     if checkpoint_dict is not None and not config.replace_with_kernel_inject:
         # AutoTP shard loading
@@ -318,7 +327,7 @@ def set_lm_head(module):
                                              checkpoint=checkpoint_file)
             pbar.update(1)
             gc.collect()
-        set_lm_head(replaced_module)
+        replaced_module = set_lm_head(replaced_module)
     else:
         replaced_module = replace_module(model=model,
                                          orig_class=orig_layer_impl,

From 923f3590ee908d39cfcd3c59d3fc0ae27bbd7e8c Mon Sep 17 00:00:00 2001
From: Michael Wyatt <michaelwyatt@microsoft.com>
Date: Mon, 9 Oct 2023 09:44:39 -0700
Subject: [PATCH 16/38] fix bad build command (#4483)

---
 .github/workflows/release.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 093bc98d41e8..fe6122d04890 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -14,6 +14,8 @@ jobs:
     - uses: actions/checkout@v3
       with:
         ref: "master"
+    - id: setup-venv
+      uses: ./.github/workflows/setup-venv
     - name: Get release version from tag
       run: |
         echo "RELEASE_VERSION=${GITHUB_REF#refs/*/v}" >> $GITHUB_ENV
@@ -22,7 +24,7 @@ jobs:
         python release/check_release_version.py --release_version ${{ env.RELEASE_VERSION }}
     - name: Build DeepSpeed
       run: |
-        DS_BUILD_STRING=" " python setup.py sdist_wheel
+        DS_BUILD_STRING=" " python setup.py sdist
     - name: Publish to PyPI
       uses: pypa/gh-action-pypi-publish@release/v1
       with:

From e9503fe40ee127e74c8b0ea962aac4f0ae957624 Mon Sep 17 00:00:00 2001
From: Michael Wyatt <michaelwyatt@microsoft.com>
Date: Mon, 9 Oct 2023 09:53:41 -0700
Subject: [PATCH 17/38] fix missing package

---
 .github/workflows/release.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index fe6122d04890..8e016b4169cb 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -21,6 +21,7 @@ jobs:
         echo "RELEASE_VERSION=${GITHUB_REF#refs/*/v}" >> $GITHUB_ENV
     - name: Check release version
       run: |
+        pip install packaging
         python release/check_release_version.py --release_version ${{ env.RELEASE_VERSION }}
     - name: Build DeepSpeed
       run: |

From 77a28371630ebb12f9fa058d6adda84e283d6175 Mon Sep 17 00:00:00 2001
From: Michael Wyatt <michaelwyatt@microsoft.com>
Date: Mon, 9 Oct 2023 09:57:15 -0700
Subject: [PATCH 18/38] [create-pull-request] automated change (#4484)

Co-authored-by: mrwyattii <mrwyattii@users.noreply.github.com>
---
 version.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/version.txt b/version.txt
index af88ba824866..bc859cbd6d99 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.11.1
+0.11.2

From e7acee4933a596cf17fed50376823a5625d85f31 Mon Sep 17 00:00:00 2001
From: Logan Adams <114770087+loadams@users.noreply.github.com>
Date: Mon, 9 Oct 2023 10:08:29 -0700
Subject: [PATCH 19/38] Remove DS_BUILD_UTILS reference (#4485)

---
 docs/_tutorials/advanced-install.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_tutorials/advanced-install.md b/docs/_tutorials/advanced-install.md
index c2b4c04cad1c..10197e62f681 100755
--- a/docs/_tutorials/advanced-install.md
+++ b/docs/_tutorials/advanced-install.md
@@ -61,6 +61,7 @@ Available `DS_BUILD` options include:
 * `DS_BUILD_CCL_COMM` builds the communication collective libs
 * `DS_BUILD_CPU_ADAM` builds the CPUAdam op
 * `DS_BUILD_CPU_LION` builds the CPULion op
+* `DS_BUILD_EVOFORMER_ATTN` builds the EvoformerAttn op (from [Alphafold](https://www.deepspeed.ai/tutorials/ds4sci_evoformerattention/))
 * `DS_BUILD_FUSED_ADAM` builds the FusedAdam op (from [apex](https://github.com/NVIDIA/apex))
 * `DS_BUILD_FUSED_LION` builds the FusedLion op
 * `DS_BUILD_CPU_ADAGRAD` builds the CPUAdagrad op
@@ -71,7 +72,6 @@ Available `DS_BUILD` options include:
 * `DS_BUILD_TRANSFORMER` builds the transformer op
 * `DS_BUILD_TRANSFORMER_INFERENCE` builds the transformer-inference op
 * `DS_BUILD_STOCHASTIC_TRANSFORMER` builds the stochastic transformer op
-* `DS_BUILD_UTILS` builds various optimized utilities
 
 To speed up the build-all process, you can parallelize the compilation process with:
 

From 604d701e35548e5407b017c088bdc3760832c9e0 Mon Sep 17 00:00:00 2001
From: Matthew Hoffman <matthew@protopia.ai>
Date: Mon, 9 Oct 2023 10:59:30 -0800
Subject: [PATCH 20/38] Introduce pydantic_v1 compatibility module for
 pydantic>=2.0.0 support (#4407)

* Introduce pydantic_v1 compatibility module for pydantic>=2.0.0 support
---
 deepspeed/comm/config.py                   |  2 +-
 deepspeed/inference/config.py              |  3 +--
 deepspeed/monitor/config.py                |  2 +-
 deepspeed/pydantic_v1.py                   | 16 ++++++++++++++++
 deepspeed/runtime/config_utils.py          |  2 +-
 deepspeed/runtime/zero/config.py           |  2 +-
 deepspeed/runtime/zero/offload_config.py   |  2 +-
 environment.yml                            |  2 +-
 requirements/requirements-readthedocs.txt  |  4 ++--
 requirements/requirements.txt              |  2 +-
 tests/unit/runtime/test_ds_config_model.py |  2 +-
 11 files changed, 27 insertions(+), 12 deletions(-)
 create mode 100644 deepspeed/pydantic_v1.py

diff --git a/deepspeed/comm/config.py b/deepspeed/comm/config.py
index 138badebe5a9..1c441bb6bfe9 100644
--- a/deepspeed/comm/config.py
+++ b/deepspeed/comm/config.py
@@ -3,8 +3,8 @@
 
 # DeepSpeed Team
 
-from pydantic import BaseModel
 from .constants import *
+from ..pydantic_v1 import BaseModel
 
 
 class CommsConfig(BaseModel):
diff --git a/deepspeed/inference/config.py b/deepspeed/inference/config.py
index 70f1c0dbd5b7..1d5018aaa75b 100644
--- a/deepspeed/inference/config.py
+++ b/deepspeed/inference/config.py
@@ -5,10 +5,9 @@
 
 import torch
 import deepspeed
+from deepspeed.pydantic_v1 import Field, validator
 from deepspeed.runtime.config_utils import DeepSpeedConfigModel
 from deepspeed.runtime.zero.config import DeepSpeedZeroConfig
-from pydantic import Field
-from pydantic import validator
 from typing import Dict, Union
 from enum import Enum
 
diff --git a/deepspeed/monitor/config.py b/deepspeed/monitor/config.py
index 2706764290fd..5a8ca6ecf5cd 100644
--- a/deepspeed/monitor/config.py
+++ b/deepspeed/monitor/config.py
@@ -3,7 +3,7 @@
 
 # DeepSpeed Team
 
-from pydantic import root_validator
+from deepspeed.pydantic_v1 import root_validator
 from deepspeed.runtime.config_utils import DeepSpeedConfigModel
 
 
diff --git a/deepspeed/pydantic_v1.py b/deepspeed/pydantic_v1.py
new file mode 100644
index 000000000000..6aba072ad929
--- /dev/null
+++ b/deepspeed/pydantic_v1.py
@@ -0,0 +1,16 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+"""Pydantic v1 compatibility module.
+
+Pydantic v2 introduced breaking changes that hinder its adoption:
+https://docs.pydantic.dev/latest/migration/. To provide deepspeed users the option to
+migrate to pydantic v2 on their own timeline, deepspeed uses this compatibility module
+as a pydantic-version-agnostic alias for pydantic's v1 API.
+"""
+
+try:
+    from pydantic.v1 import *  # noqa: F401
+except ImportError:
+    from pydantic import *  # noqa: F401
diff --git a/deepspeed/runtime/config_utils.py b/deepspeed/runtime/config_utils.py
index 0fb1372deac8..5522a8e79d69 100755
--- a/deepspeed/runtime/config_utils.py
+++ b/deepspeed/runtime/config_utils.py
@@ -9,7 +9,7 @@
 import collections
 import collections.abc
 from functools import reduce
-from pydantic import BaseModel
+from deepspeed.pydantic_v1 import BaseModel
 from deepspeed.utils import logger
 
 
diff --git a/deepspeed/runtime/zero/config.py b/deepspeed/runtime/zero/config.py
index 1fc11f0e46f5..35d60b5b3290 100644
--- a/deepspeed/runtime/zero/config.py
+++ b/deepspeed/runtime/zero/config.py
@@ -3,10 +3,10 @@
 
 # DeepSpeed Team
 
-from pydantic import Field, validator
 import sys
 from typing import Optional
 from enum import Enum
+from deepspeed.pydantic_v1 import Field, validator
 from deepspeed.runtime.config_utils import get_scalar_param, pp_int, DeepSpeedConfigModel
 from deepspeed.utils import logger
 from .offload_config import DeepSpeedZeroOffloadParamConfig, DeepSpeedZeroOffloadOptimizerConfig, OffloadDeviceEnum
diff --git a/deepspeed/runtime/zero/offload_config.py b/deepspeed/runtime/zero/offload_config.py
index c3a6dc7af530..1bd79412d39f 100644
--- a/deepspeed/runtime/zero/offload_config.py
+++ b/deepspeed/runtime/zero/offload_config.py
@@ -3,9 +3,9 @@
 
 # DeepSpeed Team
 
-from pydantic import Field, validator
 from enum import Enum
 from pathlib import Path
+from deepspeed.pydantic_v1 import Field, validator
 from deepspeed.runtime.config_utils import DeepSpeedConfigModel, pp_int
 
 
diff --git a/environment.yml b/environment.yml
index e55fe96e5a5a..28c298717d80 100644
--- a/environment.yml
+++ b/environment.yml
@@ -18,4 +18,4 @@ dependencies:
   - certifi
   - openssl
   - python=3.10
-  - pydantic<2.0.0
+  - pydantic
diff --git a/requirements/requirements-readthedocs.txt b/requirements/requirements-readthedocs.txt
index a6d7915e0ea5..fcd0ec5a9a6a 100644
--- a/requirements/requirements-readthedocs.txt
+++ b/requirements/requirements-readthedocs.txt
@@ -1,9 +1,9 @@
-autodoc_pydantic<2.0.0
+autodoc_pydantic
 docutils<0.18
 hjson
 packaging
 psutil
 py-cpuinfo
-pydantic<2.0.0
+pydantic
 torch
 tqdm
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8c5e76750573..6840d6dbcc98 100755
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,6 +4,6 @@ numpy
 packaging>=20.0
 psutil
 py-cpuinfo
-pydantic<2.0.0
+pydantic
 torch
 tqdm
diff --git a/tests/unit/runtime/test_ds_config_model.py b/tests/unit/runtime/test_ds_config_model.py
index b9c67c9a30dd..87ea747cf423 100644
--- a/tests/unit/runtime/test_ds_config_model.py
+++ b/tests/unit/runtime/test_ds_config_model.py
@@ -6,8 +6,8 @@
 import pytest
 import os
 import json
-from pydantic import Field, ValidationError
 from typing import List
+from deepspeed.pydantic_v1 import Field, ValidationError
 from deepspeed.runtime import config as ds_config
 from deepspeed.runtime.config_utils import DeepSpeedConfigModel
 

From 706a72562ab6a4276fb0e6a7348a04f8e11bdf79 Mon Sep 17 00:00:00 2001
From: Bram Vanroy <2779410+BramVanroy@users.noreply.github.com>
Date: Tue, 10 Oct 2023 17:56:10 +0200
Subject: [PATCH 21/38] Allow env var for timeout (#4405)

Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com>
---
 deepspeed/constants.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/deepspeed/constants.py b/deepspeed/constants.py
index 7ebc8f9983a5..30135f41b7b6 100644
--- a/deepspeed/constants.py
+++ b/deepspeed/constants.py
@@ -3,6 +3,7 @@
 
 # DeepSpeed Team
 
+import os
 from datetime import timedelta
 
 #############################################
@@ -15,6 +16,6 @@
 # (only if NCCL_BLOCKING_WAIT or NCCL_ASYNC_ERROR_HANDLING is set to 1).
 # To make an attempt at backwards compatibility with THD, we use an
 # extraordinarily high default timeout, given that THD did not have timeouts.
-default_pg_timeout = timedelta(minutes=30)
+default_pg_timeout = timedelta(minutes=int(os.getenv("DEEPSPEED_TIMEOUT", default=30)))
 INFERENCE_GENERIC_MODE = 'generic'
 INFERENCE_SPECIALIZED_MODE = 'specialized'

From 427253b94bf425395d48f494270550bac9ac880a Mon Sep 17 00:00:00 2001
From: Logan Adams <114770087+loadams@users.noreply.github.com>
Date: Tue, 10 Oct 2023 10:41:28 -0700
Subject: [PATCH 22/38] Update ROCm verison (#4486)

* Update ROCm verison

* Update .github/workflows/amd-mi200.yml

---------

Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com>
---
 .github/workflows/amd-mi200.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/amd-mi200.yml b/.github/workflows/amd-mi200.yml
index a275225cc5e4..77f33f744ea8 100644
--- a/.github/workflows/amd-mi200.yml
+++ b/.github/workflows/amd-mi200.yml
@@ -28,7 +28,7 @@ jobs:
 
       - name: Install pytorch
         run: |
-          pip install -U --cache-dir $TORCH_CACHE torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+          pip install -U --cache-dir $TORCH_CACHE torch torchvision --index-url https://download.pytorch.org/whl/rocm5.6
           python -c "import torch; print('torch:', torch.__version__, torch)"
           python -c "import torch; print('CUDA available:', torch.cuda.is_available())"
 

From 6c86ff393fbb579a5ca40f104cd70a14d3cb1eda Mon Sep 17 00:00:00 2001
From: stephen youn <13525892+stephen-youn@users.noreply.github.com>
Date: Tue, 10 Oct 2023 20:01:48 -0700
Subject: [PATCH 23/38] adding 8bit dequantization kernel for asym fine-grained
 block quantization in zero-inference (#4450)

* kernels added for asym fine-grained block quantization with 8bits

* formatting

* clean up the code

* rename quantize_int4.cu to quantize_intX.cu

* rename test_int4_quantization.py to test_intX_quantization.py

* "rename test_int4_quantization.py to test_intX_quantization.py"

This reverts commit 2d341405b2ed6cf69e83fcabad1804513ea92122.

* rename

* fix after the pr comments

* increased coverage of QuantLinear test
(w/ and w/o the cuda kernels)

* formatting

---------

Co-authored-by: Stephen Youn <styoun@microsoft.com>
Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
---
 csrc/includes/quantization.h                  |  8 +++
 csrc/quantization/pt_binding.cpp              | 23 ++++++++
 .../{quantize_int4.cu => quantize_intX.cu}    | 51 +++++++++++++++++
 deepspeed/inference/quantization/utils.py     | 18 +++---
 op_builder/quantizer.py                       |  2 +-
 ...ntization.py => test_intX_quantization.py} | 56 +++++++++++--------
 6 files changed, 126 insertions(+), 32 deletions(-)
 rename csrc/quantization/{quantize_int4.cu => quantize_intX.cu} (76%)
 rename tests/unit/inference/quantization/{test_int4_quantization.py => test_intX_quantization.py} (91%)

diff --git a/csrc/includes/quantization.h b/csrc/includes/quantization.h
index d2873abf1839..45828832d8d2 100644
--- a/csrc/includes/quantization.h
+++ b/csrc/includes/quantization.h
@@ -98,3 +98,11 @@ void launch_dequantize_int4_to_half_experimental(uint8_t* data_in,
                                                  int num_group,
                                                  int group_size,
                                                  cudaStream_t stream);
+
+void launch_dequantize_int8_to_half_experimental(uint8_t* data_in,
+                                                 half* data_out,
+                                                 half* scale_buffer,
+                                                 half* min_val_buffer,
+                                                 int num_group,
+                                                 int group_size,
+                                                 cudaStream_t stream);
diff --git a/csrc/quantization/pt_binding.cpp b/csrc/quantization/pt_binding.cpp
index d4c253ee005d..a4210897092d 100644
--- a/csrc/quantization/pt_binding.cpp
+++ b/csrc/quantization/pt_binding.cpp
@@ -156,6 +156,26 @@ at::Tensor dequantize_int4_to_half_experimental(at::Tensor& data_in,
     return output;
 }
 
+at::Tensor dequantize_int8_to_half_experimental(at::Tensor& data_in,
+                                                at::Tensor& scale_buffer,
+                                                at::Tensor& min_val_buffer,
+                                                int num_group,
+                                                int group_size)
+{
+    auto output_options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA);
+    auto output = torch::empty({num_group, group_size}, output_options);
+
+    launch_dequantize_int8_to_half_experimental((uint8_t*)data_in.data_ptr(),
+                                                (half*)output.data_ptr(),
+                                                (half*)scale_buffer.data_ptr(),
+                                                (half*)min_val_buffer.data_ptr(),
+                                                num_group,
+                                                group_size,
+                                                at::cuda::getCurrentCUDAStream());
+
+    return output;
+}
+
 std::vector<at::Tensor> ds_swizzle_quant(at::Tensor& input_vals,
                                          int groups,
                                          int num_bits,
@@ -270,6 +290,9 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
     m.def("dequantize_int4_to_half_experimental",
           &dequantize_int4_to_half_experimental,
           "Dequantize int4 to half (experimental)");
+    m.def("dequantize_int8_to_half_experimental",
+          &dequantize_int8_to_half_experimental,
+          "Dequantize int8 to half (experimental)");
     m.def("swizzle_quant", &ds_swizzle_quant);
     m.def("quantized_reduction", &quantized_reduction);
 }
diff --git a/csrc/quantization/quantize_int4.cu b/csrc/quantization/quantize_intX.cu
similarity index 76%
rename from csrc/quantization/quantize_int4.cu
rename to csrc/quantization/quantize_intX.cu
index fed707c1fa7c..b26151ab5c8c 100644
--- a/csrc/quantization/quantize_int4.cu
+++ b/csrc/quantization/quantize_intX.cu
@@ -228,3 +228,54 @@ void launch_dequantize_int4_to_half_experimental(uint8_t* data_in,
     dequantize_int4_to_half<<<num_block, 256, 0, stream>>>(
         data_in, data_out, scale_buffer, min_val_buffer, num_group, group_size);
 }
+
+template <int N>
+__device__ __forceinline__ AlignedArray<half, N> int8_to_half(const AlignedArray<uint8_t, N>& data)
+{
+    AlignedArray<half, N> ret;
+
+#pragma unroll
+    for (int idx = 0; idx < N; idx += 1) { ret[idx] = half(int(data[idx])); }
+
+    return ret;
+}
+
+__global__ void dequantize_int8_to_half(uint8_t* data_in,
+                                        half* data_out,
+                                        half* scale_buffer,
+                                        half* min_val_buffer,
+                                        int num_group,
+                                        int group_size)
+{
+    using AccessType = AlignedArray<uint8_t, 8>;
+    using AccessTypeOut = AlignedArray<half, 8>;
+
+    for (int idx = threadIdx.x + blockIdx.x * blockDim.x; idx < num_group * group_size / 8;
+         idx += blockDim.x * gridDim.x) {
+        int id_group = idx / (group_size / 8);
+        AccessType value = reinterpret_cast<AccessType*>(data_in)[idx];
+        half scale = scale_buffer[id_group];
+        half min_value = min_val_buffer[id_group];
+
+        AccessTypeOut output = int8_to_half(value);
+        output = divide<half, 8>()(output, scale);
+        output = plus<half, 8>()(output, min_value);
+
+        reinterpret_cast<AccessTypeOut*>(data_out)[idx] = output;
+    }
+}
+
+void launch_dequantize_int8_to_half_experimental(uint8_t* data_in,
+                                                 half* data_out,
+                                                 half* scale_buffer,
+                                                 half* min_val_buffer,
+                                                 int num_group,
+                                                 int group_size,
+                                                 cudaStream_t stream)
+{
+    int num_warp = num_group / 4;
+    int num_block = num_warp / 8;  // 256 trd / block
+
+    dequantize_int8_to_half<<<num_block, 256, 0, stream>>>(
+        data_in, data_out, scale_buffer, min_val_buffer, num_group, group_size);
+}
diff --git a/deepspeed/inference/quantization/utils.py b/deepspeed/inference/quantization/utils.py
index d47eb265c214..712abc384a44 100644
--- a/deepspeed/inference/quantization/utils.py
+++ b/deepspeed/inference/quantization/utils.py
@@ -105,20 +105,24 @@ def __init__(self, config: Dict, dtype: torch.dtype) -> None:
     def dequantize(self, tensor: Tensor, quant_scale: Tensor, quant_min: Tensor) -> Tensor:
         # Use customized CUDA quantization kernel if possible.
         if self.config['group_size'] % 8 == 0 and \
-                self.config['num_bits'] == 4 and \
+                (self.config['num_bits'] == 4 or self.config['num_bits'] == 8) and \
                 self.config['group_dim'] == len(tensor.shape) - 1 and \
                     self.dtype == torch.float16 and device == 'cuda':
 
             last_dimension_size = self.config['group_size']
             if self.config['num_bits'] == 4:
                 last_dimension_size = last_dimension_size // 2
-            quantized_tensor = get_quantizer_cuda_module().dequantize_int4_to_half_experimental(
-                tensor.reshape(-1, last_dimension_size), quant_scale, quant_min,
-                tensor.numel() // last_dimension_size, self.config['group_size'])
-
-            shape = list(tensor.shape)
-            if self.config['num_bits'] == 4:
+                quantized_tensor = get_quantizer_cuda_module().dequantize_int4_to_half_experimental(
+                    tensor.reshape(-1, last_dimension_size), quant_scale, quant_min,
+                    tensor.numel() // last_dimension_size, self.config['group_size'])
+                shape = list(tensor.shape)
                 shape[-1] = shape[-1] * 2
+            elif self.config['num_bits'] == 8:
+                # last_dimension_size = last_dimension_size // 2
+                quantized_tensor = get_quantizer_cuda_module().dequantize_int8_to_half_experimental(
+                    tensor.reshape(-1, last_dimension_size), quant_scale, quant_min,
+                    tensor.numel() // last_dimension_size, self.config['group_size'])
+                shape = list(tensor.shape)
 
             return quantized_tensor.reshape(shape)
 
diff --git a/op_builder/quantizer.py b/op_builder/quantizer.py
index ada80b8f3331..fd765b743de0 100644
--- a/op_builder/quantizer.py
+++ b/op_builder/quantizer.py
@@ -22,7 +22,7 @@ def sources(self):
             'csrc/quantization/pt_binding.cpp',
             'csrc/quantization/fake_quantizer.cu',
             'csrc/quantization/quantize.cu',
-            'csrc/quantization/quantize_int4.cu',
+            'csrc/quantization/quantize_intX.cu',
             'csrc/quantization/dequantize.cu',
             'csrc/quantization/swizzled_quantize.cu',
             'csrc/quantization/quant_reduce.cu',
diff --git a/tests/unit/inference/quantization/test_int4_quantization.py b/tests/unit/inference/quantization/test_intX_quantization.py
similarity index 91%
rename from tests/unit/inference/quantization/test_int4_quantization.py
rename to tests/unit/inference/quantization/test_intX_quantization.py
index 56a5a7d48382..56df2b232d15 100644
--- a/tests/unit/inference/quantization/test_int4_quantization.py
+++ b/tests/unit/inference/quantization/test_intX_quantization.py
@@ -53,12 +53,11 @@ def quantization_test_helper(pre_quant_type: torch.dtype, num_bits: int):
     assert mean_diff < 0.15 and max_diff < 0.5, f'Numeric error exceed threshold, mean diff {mean_diff} (threshold 0.15), max diff {max_diff} (threshold 0.5)'
 
 
-def zero3_post_init_quantization_test_helper(cpu_offload: bool, nvme_offload: bool):
+def zero3_post_init_quantization_test_helper(cpu_offload: bool, nvme_offload: bool, bits: int):
     import deepspeed
     from transformers.deepspeed import HfDeepSpeedConfig
 
-    def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: bool) -> Dict:
-        bits = 4
+    def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: bool, bits: int) -> Dict:
         GB = 1 << 30
 
         ds_config = {
@@ -143,7 +142,7 @@ def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: b
         return ds_config
 
     hf_config = AutoConfig.from_pretrained('facebook/opt-125m')
-    ds_config = get_zero3_ds_config(hf_config=hf_config, cpu_offload=cpu_offload, nvme_offload=nvme_offload)
+    ds_config = get_zero3_ds_config(hf_config=hf_config, cpu_offload=cpu_offload, nvme_offload=nvme_offload, bits=bits)
 
     input_ids = torch.ones(1, 16, dtype=torch.int32, device=device)
     attention_mask = torch.ones(1, 16, dtype=torch.float32, device=device)
@@ -171,12 +170,11 @@ def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: b
     assert mean_diff < 0.4, f'Numeric error exceed threshold, relative error {mean_diff} (threshold 0.4)'
 
 
-def zero3_quantized_initialization_test_helper(cpu_offload: bool, nvme_offload: bool):
+def zero3_quantized_initialization_test_helper(cpu_offload: bool, nvme_offload: bool, bits: int):
     import deepspeed
     from transformers.deepspeed import HfDeepSpeedConfig
 
-    def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: bool) -> Dict:
-        bits = 4
+    def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: bool, bits: int) -> Dict:
         GB = 1 << 30
 
         ds_config = {
@@ -223,7 +221,7 @@ def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: b
         return ds_config
 
     hf_config = AutoConfig.from_pretrained('facebook/opt-125m')
-    ds_config = get_zero3_ds_config(hf_config=hf_config, cpu_offload=cpu_offload, nvme_offload=nvme_offload)
+    ds_config = get_zero3_ds_config(hf_config=hf_config, cpu_offload=cpu_offload, nvme_offload=nvme_offload, bits=bits)
 
     input_ids = torch.ones(1, 16, dtype=torch.int32, device=device)
     attention_mask = torch.ones(1, 16, dtype=torch.float32, device=device)
@@ -249,16 +247,26 @@ def get_zero3_ds_config(hf_config: OPTConfig, cpu_offload: bool, nvme_offload: b
     assert mean_diff < 0.4, f'Numeric error exceed threshold, relative error {mean_diff} (threshold 0.4)'
 
 
-class TestQuantizedInt4(DistributedTest):
+@pytest.fixture(params=[4, 8], ids=["4bits", "8bits"])
+def quantization_bits(request):
+    return request.param
 
-    def test_model_quantization(self):
+
+@pytest.fixture(params=[0, 1], ids=["0", "1"])
+def group_dim(request):
+    return request.param
+
+
+class TestQuantizedInt(DistributedTest):
+
+    def test_model_quantization(self, quantization_bits):
         reset_random()
 
         config = AutoConfig.from_pretrained('facebook/opt-125m')
 
         with torch.no_grad():
             model = OPTDecoderLayer(config).half().to(device)
-            bits = 4
+            bits = quantization_bits
 
             ds_config = {
                 'weight_quantization': {
@@ -307,7 +315,7 @@ def test_model_quantization(self):
             assert type(model.self_attn.out_proj) is QuantizedLinear
 
     @pytest.mark.skipif(device == 'cpu', reason='CPU does support FP16 GEMM')
-    def test_quantized_linear(self):
+    def test_quantized_linear(self, quantization_bits, group_dim):
         reset_random()
 
         layers = []
@@ -326,9 +334,9 @@ def test_quantized_linear(self):
                 'weight_quantization': {
                     'post_init_quant': {
                         'layer': {
-                            'num_bits': 4,
+                            'num_bits': quantization_bits,
                             'group_size': 64,
-                            'group_dim': 0,
+                            'group_dim': group_dim,
                             'symmetric': False
                         }
                     }
@@ -368,31 +376,31 @@ def test_half_int8_quantization(self):
         quantization_test_helper(torch.float16, 8)
 
     @pytest.mark.skipif(device == 'cpu', reason='CPU does support FP16 GEMM')
-    def test_zero3_int4_post_init_quant(self):
+    def test_zero3_int4_post_init_quant(self, quantization_bits):
         reset_random()
-        zero3_post_init_quantization_test_helper(cpu_offload=False, nvme_offload=False)
+        zero3_post_init_quantization_test_helper(cpu_offload=False, nvme_offload=False, bits=quantization_bits)
 
     @pytest.mark.skipif(device == 'cpu', reason='CPU does support FP16 GEMM')
-    def test_zero3_int4_post_init_quant_cpu_offload(self):
+    def test_zero3_int4_post_init_quant_cpu_offload(self, quantization_bits):
         reset_random()
-        zero3_post_init_quantization_test_helper(cpu_offload=True, nvme_offload=False)
+        zero3_post_init_quantization_test_helper(cpu_offload=True, nvme_offload=False, bits=quantization_bits)
 
     @pytest.mark.skipif(device == 'cpu', reason='CPU does support FP16 GEMM')
     def test_zero3_int4_post_init_quant_nvme_offload(self):
         reset_random()
-        zero3_post_init_quantization_test_helper(cpu_offload=False, nvme_offload=True)
+        zero3_post_init_quantization_test_helper(cpu_offload=False, nvme_offload=True, bits=4)
 
     @pytest.mark.skipif(device == 'cpu', reason='CPU does support FP16 GEMM')
-    def test_zero3_int4_quantized_initialization(self):
+    def test_zero3_int4_quantized_initialization(self, quantization_bits):
         reset_random()
-        zero3_quantized_initialization_test_helper(cpu_offload=False, nvme_offload=False)
+        zero3_quantized_initialization_test_helper(cpu_offload=False, nvme_offload=False, bits=quantization_bits)
 
     @pytest.mark.skipif(device == 'cpu', reason='CPU does support FP16 GEMM')
-    def test_zero3_int4_quantized_initialization_cpu_offload(self):
+    def test_zero3_int4_quantized_initialization_cpu_offload(self, quantization_bits):
         reset_random()
-        zero3_quantized_initialization_test_helper(cpu_offload=True, nvme_offload=False)
+        zero3_quantized_initialization_test_helper(cpu_offload=True, nvme_offload=False, bits=quantization_bits)
 
     @pytest.mark.skipif(device == 'cpu', reason='CPU does support FP16 GEMM')
     def test_zero3_int4_quantized_initialization_nvme_offload(self):
         reset_random()
-        zero3_quantized_initialization_test_helper(cpu_offload=False, nvme_offload=True)
+        zero3_quantized_initialization_test_helper(cpu_offload=False, nvme_offload=True, bits=4)

From a25a67a083c8b13fe06735c720f626de34f03409 Mon Sep 17 00:00:00 2001
From: Logan Adams <114770087+loadams@users.noreply.github.com>
Date: Wed, 11 Oct 2023 12:51:26 -0700
Subject: [PATCH 24/38] Fix scale factor on flops profiler (#4500)

---
 deepspeed/profiling/flops_profiler/profiler.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/deepspeed/profiling/flops_profiler/profiler.py b/deepspeed/profiling/flops_profiler/profiler.py
index fe19299fcb60..ddcabf0d29e5 100644
--- a/deepspeed/profiling/flops_profiler/profiler.py
+++ b/deepspeed/profiling/flops_profiler/profiler.py
@@ -722,9 +722,9 @@ def _upsample_flops_compute(*args, **kwargs):
 
     flops = input.numel()
     if isinstance(scale_factor, tuple) and len(scale_factor) == len(input):
-        flops * int(_prod(scale_factor))
+        flops *= int(_prod(scale_factor))
     else:
-        flops * scale_factor**len(input)
+        flops *= scale_factor**len(input)
     return flops, 0
 
 

From 574fbc0d683b9e4b7151bdeb1056fc57d2868bc5 Mon Sep 17 00:00:00 2001
From: Conglong Li <conglong.li@gmail.com>
Date: Wed, 11 Oct 2023 15:36:06 -0700
Subject: [PATCH 25/38] add DeepSpeed4Science white paper (#4502)

---
 README.md                                  |  3 ++-
 blogs/deepspeed4science/README.md          | 11 +++++++++++
 blogs/deepspeed4science/chinese/README.md  | 11 +++++++++++
 blogs/deepspeed4science/japanese/README.md | 11 +++++++++++
 docs/_pages/deepspeed4science.md           | 13 ++++++++++++-
 docs/index.md                              |  3 ++-
 6 files changed, 49 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index abf20667882b..81ac3031457a 100755
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@
 <b> <span style="color:orange" > DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)</span>.</b>
 
 * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)]
-* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)]
+* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)]
 * [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md)
 * [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 Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ulysses) [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md)]
@@ -236,6 +236,7 @@ Conduct](https://opensource.microsoft.com/codeofconduct/). For more information
 25. Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He. (2023) DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales [arXiv:2308.01320](https://arxiv.org/abs/2308.01320).
 26. Xiaoxia Wu, Zhewei Yao, Yuxiong He. (2023) ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats [arXiv:2307.09782](https://arxiv.org/abs/2307.09782)
 27. Zhewei Yao, Xiaoxia Wu, Conglong Li, Minjia Zhang, Heyang Qin, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He. (2023) DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention [arXiv:2309.14327](https://arxiv.org/pdf/2309.14327.pdf)
+28. Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, et al. (2023) DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [arXiv:2310.04610](https://arxiv.org/abs/2310.04610)
 
 
 
diff --git a/blogs/deepspeed4science/README.md b/blogs/deepspeed4science/README.md
index 2a80ea2e749e..a318490329a5 100644
--- a/blogs/deepspeed4science/README.md
+++ b/blogs/deepspeed4science/README.md
@@ -5,3 +5,14 @@
 </div>
 
 [https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)
+
+To cite DeepSpeed4Science, please cite our [white paper](https://arxiv.org/abs/2310.04610):
+
+```
+@article{song2023deepspeed4science,
+  title={DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies},
+  author={Song, Shuaiwen Leon and Kruft, Bonnie and Zhang, Minjia and Li, Conglong and Chen, Shiyang and Zhang, Chengming and Tanaka, Masahiro and Wu, Xiaoxia and Rasley, Jeff and Awan, Ammar Ahmad and others},
+  journal={arXiv preprint arXiv:2310.04610},
+  year={2023}
+}
+```
diff --git a/blogs/deepspeed4science/chinese/README.md b/blogs/deepspeed4science/chinese/README.md
index 07647c767553..dabc4ab077f2 100644
--- a/blogs/deepspeed4science/chinese/README.md
+++ b/blogs/deepspeed4science/chinese/README.md
@@ -12,6 +12,17 @@
 *图1：DeepSpeed4Science方法概述：专为加速科学发现和应对其复杂性而量身定制的AI系统技术开发。*
 </div>
 
+如需引用 DeepSpeed4Science，请引用我们的[white paper](https://arxiv.org/abs/2310.04610):
+
+```
+@article{song2023deepspeed4science,
+  title={DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies},
+  author={Song, Shuaiwen Leon and Kruft, Bonnie and Zhang, Minjia and Li, Conglong and Chen, Shiyang and Zhang, Chengming and Tanaka, Masahiro and Wu, Xiaoxia and Rasley, Jeff and Awan, Ammar Ahmad and others},
+  journal={arXiv preprint arXiv:2310.04610},
+  year={2023}
+}
+```
+
 ## 简介
 
 在接下来的十年中，深度学习可能会彻底改变自然科学，增强我们对自然现象进行建模和预测的能力。这可能预示着科学探索的新时代，为从药物开发到可再生能源的各个领域带来重大进展。为了响应这一机会以及微软“予力全球每一人、每一组织，成就不凡”的使命，[微软DeepSpeed团队](https://www.deepspeed.ai/)启动了一个名为[DeepSpeed4Science](https://deepspeed4science.ai/)的新计划，旨在通过AI系统技术创新帮助领域专家解锁当今最大的科学之谜。
diff --git a/blogs/deepspeed4science/japanese/README.md b/blogs/deepspeed4science/japanese/README.md
index 774ef79a17dc..276528650ab5 100644
--- a/blogs/deepspeed4science/japanese/README.md
+++ b/blogs/deepspeed4science/japanese/README.md
@@ -12,6 +12,17 @@
 *図1：DeepSpeed4Scienceのアプローチ: 汎用の言語モデルのサポートを超え、科学的発見とその複雑さの解決に特化したAI技術を開発*
 </div>
 
+DeepSpeed4Science を引用するには、こちらの[white paper](https://arxiv.org/abs/2310.04610)を引用してください:
+
+```
+@article{song2023deepspeed4science,
+  title={DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies},
+  author={Song, Shuaiwen Leon and Kruft, Bonnie and Zhang, Minjia and Li, Conglong and Chen, Shiyang and Zhang, Chengming and Tanaka, Masahiro and Wu, Xiaoxia and Rasley, Jeff and Awan, Ammar Ahmad and others},
+  journal={arXiv preprint arXiv:2310.04610},
+  year={2023}
+}
+```
+
 ## はじめに
 
 自然の出来事をモデル化し予測する深層学習の能力は急速に高まっており、次の１０年間に、自然科学に革命を起こすかも知れません。薬の開発から再生可能エネルギーまでの各セクターで、大きな進展をもたらす新しい科学的探求の時代が到来するでしょう。「地球上のすべての人と組織がもっと多くのことを成し遂げられるようにする」というMicrosoftのミッションに従い、この機会に、[DeepSpeedチーム](https://www.deepspeed.ai/)では[DeepSpeed4Science](https://deepspeed4science.ai/)という新しいイニシアティブを立ち上げました。これは、AIシステム技術のイノベーションを通じて他に類を見ない技術を構築し、様々な分野の専門家が、科学分野における大きな謎を解き明かす手助けをすることを目指しています。
diff --git a/docs/_pages/deepspeed4science.md b/docs/_pages/deepspeed4science.md
index 6dd87ce996e2..b35351838f22 100755
--- a/docs/_pages/deepspeed4science.md
+++ b/docs/_pages/deepspeed4science.md
@@ -6,7 +6,18 @@ toc_label: "Contents"
 toc_sticky: true
 ---
 
-In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called *DeepSpeed4Science*, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. This page serves as an overview page for all technologies released (or to be released in the future) as part of the DeepSpeed4Science initiative, making it easier for scientists to shop for techniques they need. Details of the DeepSpeed4Science initiative can be found at [our website](https://deepspeed4science.ai/). For each technique we will introduce what is it for, when to use it, links to how to use it, and existing scientific applications of the techniques (we welcome users to contribute more showcases if you apply our techniques in your scientific research):
+In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called *DeepSpeed4Science*, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. This page serves as an overview page for all technologies released (or to be released in the future) as part of the DeepSpeed4Science initiative, making it easier for scientists to shop for techniques they need. Details of the DeepSpeed4Science initiative can be found at [our website](https://deepspeed4science.ai/). For each technique we will introduce what is it for, when to use it, links to how to use it, and existing scientific applications of the techniques (we welcome users to contribute more showcases if you apply our techniques in your scientific research).
+
+To cite DeepSpeed4Science, please cite our [white paper](https://arxiv.org/abs/2310.04610):
+
+```
+@article{song2023deepspeed4science,
+  title={DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies},
+  author={Song, Shuaiwen Leon and Kruft, Bonnie and Zhang, Minjia and Li, Conglong and Chen, Shiyang and Zhang, Chengming and Tanaka, Masahiro and Wu, Xiaoxia and Rasley, Jeff and Awan, Ammar Ahmad and others},
+  journal={arXiv preprint arXiv:2310.04610},
+  year={2023}
+}
+```
 
 * [2023/09] We are releasing two techniques: [DeepSpeed4Science large-scale training framework](#new-megatron-deepspeed-for-large-scale-ai4science-model-training), [DS4Sci_EvoformerAttention](#memory-efficient-evoformerattention-kernels) and their scientific applications in structural biology research.
 
diff --git a/docs/index.md b/docs/index.md
index 14c131a9a22d..f6b3eb18ed1f 100755
--- a/docs/index.md
+++ b/docs/index.md
@@ -8,7 +8,7 @@ title: "Latest News"
 <b> <span style="color:orange" > DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)</span>.</b>
 
 * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)]
-* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](/deepspeed4science/)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)]
+* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)]
 * [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md)
 * [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 Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ulysses) [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md)]
@@ -137,6 +137,7 @@ comments.
 25. Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He. (2023) DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales [arXiv:2308.01320](https://arxiv.org/abs/2308.01320).
 26. Xiaoxia Wu, Zhewei Yao, Yuxiong He. (2023) ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats [arXiv:2307.09782](https://arxiv.org/abs/2307.09782)
 27. Zhewei Yao, Xiaoxia Wu, Conglong Li, Minjia Zhang, Heyang Qin, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He. (2023) DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention [arXiv:2309.14327](https://arxiv.org/pdf/2309.14327.pdf)
+28. Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, et al. (2023) DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [arXiv:2310.04610](https://arxiv.org/abs/2310.04610)
 
 # Videos
 1. DeepSpeed KDD 2020 Tutorial

From 4fc181b01077521ba42379013ce91a1c294e5d8e Mon Sep 17 00:00:00 2001
From: Liangliang-Ma <liangliang.ma@intel.com>
Date: Thu, 12 Oct 2023 22:17:44 +0800
Subject: [PATCH 26/38] [CCLBackend] update API (#4378)

* unify ccl.py for cpu and gpu'

* handle all coll in on func

* add coresponding func in cclbackend

* add coresponding pybind func

* change device setting

---------

Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
---
 csrc/cpu/comm/ccl.cpp |  59 ++++++++++++++++----
 deepspeed/comm/ccl.py | 126 +++++++++++++++++++++++++++++++++++++++---
 2 files changed, 167 insertions(+), 18 deletions(-)

diff --git a/csrc/cpu/comm/ccl.cpp b/csrc/cpu/comm/ccl.cpp
index accf431f6929..9a04b6f873f2 100644
--- a/csrc/cpu/comm/ccl.cpp
+++ b/csrc/cpu/comm/ccl.cpp
@@ -277,10 +277,20 @@ int world_size = -1;
 
 std::set<int> _comm_ids;
 std::set<int> _colors;
-ccl::vector_class<ccl::communicator> _ccl_comms;
+std::vector<ccl::communicator> _ccl_comms;
+ccl::shared_ptr_class<ccl::kvs> sub_kvs;
+std::map<std::vector<int>, int> group_to_comm_id;
 
 ccl::communicator& _get_comm_from_group() { return _ccl_comms[0]; }
 ccl::communicator& _get_comm_from_group(py::object group) { return _ccl_comms[0]; }
+ccl::communicator& _get_comm_from_group(std::vector<int> ranks)
+{
+    if (group_to_comm_id.find(ranks) != group_to_comm_id.end()) {
+        auto id = group_to_comm_id.find(ranks);
+        return _ccl_comms[id->second];
+    }
+    return _ccl_comms[0];
+}
 
 #define CCLCHECK(cmd) \
     do {              \
@@ -394,12 +404,29 @@ int next_unique_val(std::set<int> s)
     }
 }
 
-py::object new_group(std::vector<int> ranks)
+std::vector<uint8_t> get_sub_kvs_addr(bool first)
+{
+    if (first) {
+        sub_kvs = ccl::create_main_kvs();
+        ccl::kvs::address_type main_addr = sub_kvs->get_address();
+        auto ccl_kvs_addr = std::vector<uint8_t>(main_addr.begin(), main_addr.end());
+        return ccl_kvs_addr;
+    } else {
+        ccl::kvs::address_type main_addr;
+        auto ccl_kvs_addr = std::vector<uint8_t>(main_addr.begin(), main_addr.end());
+        return ccl_kvs_addr;
+    }
+}
+
+void initialize_sub_comm(int size, int rank, torch::Tensor& kvs_data, std::vector<int> ranks)
 {
-    int comm_id = next_unique_val(_comm_ids);
-    int color = next_unique_val(_colors);
-    std::cout << "RANK: " << get_rank() << " COMM_ID: " << comm_id << " COLOR: " << color
-              << std::endl;
+    ccl::kvs::address_type main_addr;
+    if (rank != 0) {
+        memcpy(main_addr.data(), kvs_data.data_ptr(), main_addr.size());
+        sub_kvs = ccl::create_kvs(main_addr);
+    }
+    _ccl_comms.push_back(ccl::create_communicator(size, rank, sub_kvs));
+    group_to_comm_id[ranks] = _ccl_comms.size() - 1;
 }
 
 ccl::datatype get_ccl_datatype(c10::ScalarType type)
@@ -452,7 +479,7 @@ ccl::reduction get_ccl_reduce_op(py::object op, at::Tensor& input)
     return ccl_op;
 }
 
-void broadcast(torch::Tensor& data, int src, py::object group, bool async_op)
+void broadcast(torch::Tensor& data, int src, std::vector<int> group, bool async_op)
 {
     CCLCHECK(ccl::broadcast(data.data_ptr(),
                             data.numel(),
@@ -463,7 +490,7 @@ void broadcast(torch::Tensor& data, int src, py::object group, bool async_op)
 }
 
 // TODO: implement torch's async_op behavior, document it.
-void all_reduce(torch::Tensor& data, py::object op, py::object group, bool async_op)
+void all_reduce(torch::Tensor& data, py::object op, std::vector<int> group, bool async_op)
 {
     CCLCHECK(ccl::allreduce(data.data_ptr(),
                             data.data_ptr(),
@@ -477,7 +504,7 @@ void all_reduce(torch::Tensor& data, py::object op, py::object group, bool async
 void all_reduce_caching(torch::Tensor& data,
                         py::object op,
                         std::string match_id,
-                        py::object group,
+                        std::vector<int> group,
                         bool async_op)
 {
     ccl::allreduce_attr attr = ccl::default_allreduce_attr;
@@ -510,7 +537,7 @@ static void parallel_memcpy(void* to, void* from, size_t n_bytes)
     }
 }
 
-void inference_all_reduce(torch::Tensor& data, py::object op, py::object group, bool async_op)
+void inference_all_reduce(torch::Tensor& data, py::object op, std::vector<int> group, bool async_op)
 {
     static py::object ReduceOp = py::module_::import("deepspeed.comm").attr("ReduceOp");
     static auto ReduceOpSum = (int)py::int_(ReduceOp.attr("SUM").attr("value"));
@@ -583,11 +610,18 @@ void inference_all_reduce(torch::Tensor& data, py::object op, py::object group,
     }
 }
 
-void barrier(py::object group, bool async_op)
+void barrier(std::vector<int> group, bool async_op)
 {
     CCLCHECK(ccl::barrier(_get_comm_from_group(group)).wait());
 }
 
+std::vector<std::string> get_available_coll()
+{
+    std::vector<std::string> colls{
+        "broadcast", "all_reduce", "inference_all_reduce", "all_reduce_caching", "barrier"};
+    return colls;
+}
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
 {
     m.def("get_kvs_addr", &get_kvs_addr, "create and get main kvs addr");
@@ -599,4 +633,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
     m.def("inference_all_reduce", &inference_all_reduce, "low latency all_reduce implementation");
     m.def("all_reduce_caching", &all_reduce_caching, "ccl all_reduce with caching");
     m.def("barrier", &barrier, "barrier");
+    m.def("initialize_sub_comm", &initialize_sub_comm, "initialize_sub_comm");
+    m.def("get_sub_kvs_addr", &get_sub_kvs_addr, "get_sub_kvs_addr");
+    m.def("get_available_coll", &get_available_coll, "get_available_coll");
 }
diff --git a/deepspeed/comm/ccl.py b/deepspeed/comm/ccl.py
index 65646f6ba646..859e70c7db3b 100644
--- a/deepspeed/comm/ccl.py
+++ b/deepspeed/comm/ccl.py
@@ -21,6 +21,16 @@ def build_ccl_op():
     return ccl_cpp_module
 
 
+class CCLHandler():
+
+    def __init__(self, ccl_comm_op=None):
+        self.ccl_comm_op = ccl_comm_op
+
+    def wait(self):
+        # backend covered it
+        pass
+
+
 class CCLBackend(TorchBackend):
 
     def __init__(self, name='ccl', rank=-1, world_size=-1, mpu=None, timeout=None, init_method=None):
@@ -39,27 +49,129 @@ def __init__(self, name='ccl', rank=-1, world_size=-1, mpu=None, timeout=None, i
         size = self.get_world_size()
         rank = self.get_rank()
         main_kvs = self.ccl_comm_op.get_kvs_addr(rank)
-        main_kvs = torch.tensor(main_kvs).to(torch.uint8)
+        main_kvs = torch.tensor(main_kvs).to(torch.uint8).to(get_accelerator().current_device_name())
         super(CCLBackend, self).broadcast(main_kvs, 0)
         self.ccl_comm_op.initialize(size, rank, main_kvs)
         self.initialized = True
+        self.groups = [tuple(range(self.get_world_size()))]
+        self.available_coll = self.ccl_comm_op.get_available_coll()
 
     def is_initialized(self):
         return self.initialized
 
-    def broadcast(self, tensor, src, group=None, async_op=False):
-        self.ccl_comm_op.broadcast(tensor, src, group, async_op)
+    def run_collective(self, name, **kwargs):
+        if name in self.available_coll:
+            kwargs['group'] = self.get_all_ranks_from_group(kwargs['group'])
+            if 'dst' in kwargs:
+                kwargs['dst'] = kwargs['group'].index(kwargs['dst'])
+            if 'src' in kwargs:
+                kwargs['src'] = kwargs['group'].index(kwargs['src'])
+            func = "self.ccl_comm_op." + name
+            eval(func)(*(kwargs.values()))
+            return CCLHandler(self.ccl_comm_op)
+        else:
+            func = "super(CCLBackend, self)." + name
+            return eval(func)(*(kwargs.values()))
 
     def all_reduce(self, tensor, op=ReduceOp.SUM, group=None, async_op=False):
         use_caching = False
         if use_caching:
             match_id = f"{tensor.size()}-{op}"
-            self.ccl_comm_op.all_reduce_caching(tensor, op, match_id, group, async_op)
+            return self.run_collective(name="all_reduce_caching",
+                                       tensor=tensor,
+                                       op=op,
+                                       match_id=match_id,
+                                       group=group,
+                                       async_op=async_op)
         else:
-            self.ccl_comm_op.all_reduce(tensor, op, group, async_op)
+            return self.run_collective(name="all_reduce", tensor=tensor, op=op, group=group, async_op=async_op)
 
     def inference_all_reduce(self, tensor, op=ReduceOp.SUM, group=None, async_op=False):
-        self.ccl_comm_op.inference_all_reduce(tensor, op, group, async_op)
+        return self.run_collective(name="inference_all_reduce", tensor=tensor, op=op, group=group, async_op=async_op)
+
+    def broadcast(self, tensor, src, group=None, async_op=False):
+        return self.run_collective(name="broadcast", tensor=tensor, src=src, group=group, async_op=async_op)
+
+    def all_gather(self, tensor_list, tensor, group=None, async_op=False):
+        return self.run_collective(name="all_gather",
+                                   tensor_list=tensor_list,
+                                   tensor=tensor,
+                                   group=group,
+                                   async_op=async_op)
+
+    def reduce_scatter_tensor(self, output_tensor, input_tensor, op, group=None, async_op=False):
+        return self.run_collective(name="reduce_scatter_tensor",
+                                   output_tensor=output_tensor,
+                                   input_tensor=input_tensor,
+                                   op=op,
+                                   group=group)
+
+    def all_gather_into_tensor(self, output_tensor, input_tensor, group=None, async_op=False):
+        return self.run_collective(name="all_gather_into_tensor",
+                                   output_tensor=output_tensor,
+                                   input_tensor=input_tensor,
+                                   group=group)
+
+    def all_to_all_single(self, output, input, output_split_sizes, input_split_sizes, group=None, async_op=False):
+        return self.run_collective(name="all_to_all_single",
+                                   output=output,
+                                   input=input,
+                                   output_split_sizes=output_split_sizes,
+                                   input_split_sizes=input_split_sizes,
+                                   group=group)
+
+    def send(self, tensor, dst, group=None, async_op=False):
+        return self.run_collective(name="send", tensor=tensor, dst=dst, group=group, async_op=async_op)
+
+    def recv(self, tensor, src, group=None, async_op=False):
+        return self.run_collective(name="recv", tensor=tensor, src=src, group=group, async_op=async_op)
+
+    def gather(self, tensor, gather_list, dst, group=None, async_op=False):
+        return self.run_collective(name="gather", tensor=tensor, gather_list=gather_list, dst=dst, group=group)
+
+    def scatter(self, tensor, gather_list, dst, group=None, async_op=False):
+        return self.run_collective(name="scatter", tensor=tensor, gather_list=gather_list, dst=dst, group=group)
 
     def barrier(self, group=None, async_op=False):
-        self.ccl_comm_op.barrier(group, async_op)
+        return self.run_collective(name="barrier", group=group, async_op=async_op)
+
+    def monitored_barrier(self, group=None, timeout=None, wait_all_ranks=False):
+        return self.run_collective(name="monitored_barrier", group=group)
+
+    def reduce_scatter(self, output, input_list, op=ReduceOp.SUM, group=None, async_op=False):
+        return self.run_collective(name="reduce_scatter",
+                                   output=output,
+                                   input_list=input_list,
+                                   op=op,
+                                   group=group,
+                                   async_op=async_op)
+
+    def reduce(self, tensor, dst, op=ReduceOp.SUM, group=None, async_op=False):
+        return self.run_collective(name="reduce", tensor=tensor, dst=dst, op=op, group=group, async_op=async_op)
+
+    def new_group(self, ranks):
+        return super(CCLBackend, self).new_group(ranks)
+
+    def _new_group(self, ranks, group):
+        size = len(ranks)
+        rank = self.get_rank()
+        sub_main_kvs = self.ccl_comm_op.get_sub_kvs_addr(rank == ranks[0])
+        sub_main_kvs = torch.tensor(sub_main_kvs).to(torch.uint8).to(get_accelerator().current_device_name())
+        super(CCLBackend, self).broadcast(sub_main_kvs, ranks[0], group)
+        self.ccl_comm_op.initialize_sub_comm(size, ranks.index(rank), sub_main_kvs, ranks)
+        self.groups.append(tuple(ranks))
+
+    def get_all_ranks_from_group(self, group):
+        if group is None:
+            return list(range(self.get_world_size()))
+        rank = 0
+        results = []
+        try:
+            while True:
+                results.append(super(CCLBackend, self).get_global_rank(group, rank))
+                rank += 1
+        except RuntimeError:
+            pass
+        if tuple(results) not in self.groups:
+            self._new_group(results, group)
+        return results

From 5bbbf41fe501296452368bc3c41577c0e528623f Mon Sep 17 00:00:00 2001
From: Sam Ade Jacobs <samjacobs@microsoft.com>
Date: Mon, 16 Oct 2023 09:40:35 -0700
Subject: [PATCH 27/38] Ulysses: add col-ai evaluation (#4517)

---
 blogs/deepspeed-ulysses/README.md             | 134 +++++++++++-------
 blogs/deepspeed-ulysses/media/convgZ.png      | Bin 0 -> 38034 bytes
 .../media/dense1B1Mscale.png                  | Bin 0 -> 48395 bytes
 blogs/deepspeed-ulysses/media/dense30B.png    | Bin 0 -> 54038 bytes
 blogs/deepspeed-ulysses/media/dense7B.png     | Bin 0 -> 53977 bytes
 blogs/deepspeed-ulysses/media/sparse30B.png   | Bin 0 -> 54013 bytes
 blogs/deepspeed-ulysses/media/sparse7B.png    | Bin 0 -> 52201 bytes
 7 files changed, 84 insertions(+), 50 deletions(-)
 create mode 100644 blogs/deepspeed-ulysses/media/convgZ.png
 create mode 100644 blogs/deepspeed-ulysses/media/dense1B1Mscale.png
 create mode 100644 blogs/deepspeed-ulysses/media/dense30B.png
 create mode 100644 blogs/deepspeed-ulysses/media/dense7B.png
 create mode 100644 blogs/deepspeed-ulysses/media/sparse30B.png
 create mode 100644 blogs/deepspeed-ulysses/media/sparse7B.png

diff --git a/blogs/deepspeed-ulysses/README.md b/blogs/deepspeed-ulysses/README.md
index 6c5cc63cbdfa..314787dc1abe 100644
--- a/blogs/deepspeed-ulysses/README.md
+++ b/blogs/deepspeed-ulysses/README.md
@@ -10,6 +10,17 @@
 
 </div>
 
+To cite DeepSpeed-Ulysses, please cite our [arxiv report](https://arxiv.org/abs/2309.14327):
+
+```
+@article{jacobs2023deepspeed,
+      title={DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models},
+      author={Sam Ade Jacobs and Masahiro Tanaka and Chengming Zhang and Minjia Zhang and Shuaiwen Leon Song and Samyam Rajbhandari and Yuxiong He},
+      journal={arXiv preprint arXiv:2309.14509},
+      year={2023},
+}
+```
+
 ## Introduction
 
 Training large models with long sequences is becoming very important
@@ -193,7 +204,7 @@ scaling not just to large sequence lengths but also to large models.
 
 ## Evaluation
 
-We evaluate DeepSpeed-Ulysses on GPT,
+We evaluate DeepSpeed-Ulysses (Ulysses) on GPT,
 a foundation model for many NLP tasks on up to 64 A100 GPUs with 40GB memory. Our
 evaluations are four-fold: i) sequence length scalability, ii)
 throughput for dense attention and comparison with existing system, and
@@ -212,7 +223,7 @@ maintains similar computation throughput across different sequence
 length at appropriate GPU count.
 
 <div align="center">
-<img src="./media/fig2Ulysses.png" style="width:5in;height:4in" />
+<img src="./media/dense1B1Mscale.png" style="width:5in;height:4in" />
 
 *Figure 2: DeepSpeed sequence parallelism strong scalability evaluation
 at different sequence length and GPU count.*
@@ -220,66 +231,89 @@ at different sequence length and GPU count.*
 
 ### Dense Attention Evaluation
 
-Next, we evaluate DeepSpeed sequence parallelism on 30 billion parameter
-dense attention model and benchmark against Megatron sequence
-parallelism on 64 A100 GPUs. The results of these evaluations are shown
-in Figures 3.
-
-We compare DeepSpeed sequence parallelism with Megatron-LM for a 30B
-model running various sequence lengths. For our evaluation we chose the
-sequence parallelism degree and global batch size that produced the best
-performance (measured as throughput or TFLOPs) for both DeepSpeed
-sequence parallelism and Megatron-LM, this we call optimal (batch
-size-sequence length) configurations. For DeepSpeed sequence
-parallelism, we always use a ZeRO parallelism degree of 64.
-
-Figure 3 shows that DeepSpeed sequence parallelism consistently
-outperforms Megatron-LM for the sequence length that can be run with
-both. In addition, DeepSpeed sequence parallelism can run longer
-sequence than Megatron-LM. DeepSpeed sequence parallelism performance
-advantages are two folds: (1) DeepSpeed sequence parallelism in
-combination with ZeRO-3 fits more sample than Megatron-LM because of
-memory optimization leading to higher throughput (2) DeepSpeed sequence
-parallelism benefits from efficient all-to-all communication relative to
-*all-gather* communication as applied in Megatron-LM sequence
-parallelism.
+Next, we evaluate Ulysses on 7 billion (7B) and 30 billion (30B) parameter
+GPT dense attention models and compare against Megatron-LM's sequence
+parallelism (Megatron LM) and Colosal AI sequence parallelism (ColAI-SP) on
+32 and 64 A100 GPUs respectively. The results of these evaluations are shown
+in Figures 3 and 4.
+
+We compare Ulysses with Megatron-LM and ColAI-SP for 7B and 30B models
+running various sequence lengths. We chose the sequence parallelism
+degree and micro-batch size that produced the best performance
+(measured as TFLOPs) for the three methods, this we call optimal
+(batch size-sequence length) configurations. For Ulysses, we always
+use a ZeRO-3 parallelism degrees of 32 and 64 for 7B and 30B models
+respectively.
+
+
+Figures 3 and 4 show that Ulysses consistently outperforms Megatron-LM
+and ColAI-SP for the sequence length that can be run with them. In addition,
+Ulysses can run longer sequence than the two existing methods. Ulysses
+performance advantages are two folds: (1) Ulysses in combination with ZeRO-3
+parameter sharding across both data and sequence parallel groups fits more
+samples than Megatron-LM and ColAI-SP because of the memory optimization
+leading to higher throughput (2) Ulysses benefits from efficient *all-to-all*
+communication relative to *all-gather* *reduce-scatter* and *ring-style* P2P
+communication as applied in Megatron-LM and ColAI-SP sequence parallelism.
+However, for dense attention at long sequence length, the throughput is
+primarily determined by local attention computation due to quadratic
+computation complexity of attention, therefore performance gap between Ulysses
+and the two existing methods closes for sequence length that can be run with them.
 
 <div align="center">
-<img src="./media/fig3Ulysses.png" style="width:5in;height:4in" />
+<img src="./media/dense7B.png" style="width:5in;height:4in" />
 
-*Figure 3: Evaluation of DeepSpeed and Megatron LM sequence parallelism on 30B
-parameter model with dense attention.*
+*Figure 3: Evaluation of Ulysses vs Megatron LM vs ColAI-SP on GPT-7B parameter
+ model with dense attention (32 GPUs).*
+</div>
+
+<div align="center">
+<img src="./media/dense30B.png" style="width:5in;height:4in" />
+
+*Figure 4:  Evaluation of Ulysses vs Megatron LM vs ColAI-SP on GPT-30B parameter
+ model with dense attention (64 GPUs).*
 </div>
 
 ### Sparse Attention Evaluation
 
-Similarly, we evaluate DeepSpeed sequence parallelism on 30 billion
-parameter sparse attention model and benchmark against Megatron sequence
-parallelism. Results of our evaluation are shown in Figure 4. We observe
-similar trends with sparse attention as dense attention experiments. We
-observe more than 2X throughput performance of DeepSpeed sequence
-parallelism compared to Megatron-LM. For memory saving, DeepSpeed
-sequence parallelism leveraging ZeRO-3 scales to 4X longer sequence
-lengths than Megatron-LM.
-
-DeepSpeed sequence parallelism outperforms Megatron-LM for sequence
-length that can be run with both. In fact, the current DeepSpeed
-throughput is bottlenecked by the local sparse attention implementation,
-and as a result DeepSpeed throughput decreases as the sequence length
-increases. We expect this gap in performance between DeepSpeed and
-Megatron to increase further for larger sequence lengths as we improve
-the performance of the local sparse attention implementation in future.
+Similarly, we evaluate Ulysses on 7 billion and 30 billion parameter sparse
+attention models and benchmark against Megatron-LM sequence parallelism.
+There is no public implementation of block sparse attention for ColAI-SP,
+therefore, evaluation of sparse attention is in comparison with Megatron-LM.
+Results of our evaluation are shown in Figures 5 and 6. We observe similar
+trends with sparse attention as dense attention experiments. We observe more
+than 2x throughput performance of Ulysses compared to Megatron-LM. For memory
+saving, Ulysses leveraging ZeRO-3 scales to 4x longer sequence lengths
+than Megatron-LM.
+
+Ulysses outperforms Megatron-LM for sequence length that can be run with both.
+In fact, the current Ulysses throughput is bottle-necked by the local sparse
+attention implementation, and as a result Ulysses throughput decreases as
+the sequence length increases. We expect this gap in performance between our
+method and Megatron-LM to increase further for larger sequence lengths as we
+improve the performance of the local sparse attention implementation in future.
+A noteworthy observation is that the decreasing performance gap between Ulysses
+and Megatron-LM observed in dense attention evaluation is less pronounced in
+sparse attention evaluation, because the attention computation in sparse attention
+is less dominant compared to dense attention.
+
+<div align="center">
+<img src="./media/sparse7B.png" style="width:5in;height:4in" />
+
+*Figure 5: Evaluation of Ulysses and Megatron LM sequence parallelism on GPT-7B
+parameter model with block sparse attention (32 GPUs).*
+</div>
 
 <div align="center">
-<img src="./media/fig4Ulysses.png" style="width:5in;height:4in" />
+<img src="./media/sparse30B.png" style="width:5in;height:4in" />
 
-*Figure 4: Evaluation of DeepSpeed and Megatron LM sequence parallelism on 30B
-parameter model with block sparse attention.*
+*Figure 6: Evaluation of Ulysses and Megatron LM sequence parallelism on GPT-30B
+parameter model with block sparse attention (64 GPUs).*
 </div>
 
 ### Convergence Study
 
-Lastly, Figure 5 shows convergence of a 1.3 billion GPT model at 32K
+Lastly, Figure 7 shows convergence of a 1.3 billion GPT model at 32K
 sequence length on 8 A100 GPUs with sequence parallelism degree set at 4
 for both DeepSpeed and Megatron-LM sequence parallelism. For DeepSpeed
 sequence parallelism, we evaluate convergence with different ZeRO
@@ -289,9 +323,9 @@ there is no (negative) impact on quality of trained models, this assertion is
 validated through experiments and is shown in Figure 5.
 
 <div align="center">
-<img src="./media/convg.png" width="500px" />
+<img src="./media/convgZ.png" width="500px" />
 
-*Figure 5: Convergence evaluation of DeepSpeed sequence parallelism with different
+*Figure 7: Convergence evaluation of DeepSpeed sequence parallelism with different
 ZeRO memory optimization stages.*
 </div>
 
diff --git a/blogs/deepspeed-ulysses/media/convgZ.png b/blogs/deepspeed-ulysses/media/convgZ.png
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From 78c518ed979b4cf1df1816bdbe52b0d21531bee4 Mon Sep 17 00:00:00 2001
From: Sam Ade Jacobs <samjacobs@microsoft.com>
Date: Mon, 16 Oct 2023 11:16:15 -0700
Subject: [PATCH 28/38] Update README.md (#4518)

Fix arxiv link
---
 blogs/deepspeed-ulysses/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/blogs/deepspeed-ulysses/README.md b/blogs/deepspeed-ulysses/README.md
index 314787dc1abe..7ea7a4535e90 100644
--- a/blogs/deepspeed-ulysses/README.md
+++ b/blogs/deepspeed-ulysses/README.md
@@ -10,7 +10,7 @@
 
 </div>
 
-To cite DeepSpeed-Ulysses, please cite our [arxiv report](https://arxiv.org/abs/2309.14327):
+To cite DeepSpeed-Ulysses, please cite our [arxiv report](https://arxiv.org/abs/2309.14509):
 
 ```
 @article{jacobs2023deepspeed,

From 12aedac6ce8b25c95e5808b9b0b9ba27f3111ee0 Mon Sep 17 00:00:00 2001
From: Jeff Rasley <jerasley@microsoft.com>
Date: Mon, 16 Oct 2023 05:23:28 -0700
Subject: [PATCH 29/38] add available memory check to accelerators (#4508)

* add available memory check to accelerator

* catch case where nvmlInit fails

* add pynvml to reqs

* fix for cpu systems

* Update accelerator/cuda_accelerator.py

Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com>

* simplify

---------

Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com>
---
 accelerator/abstract_accelerator.py |  4 ++++
 accelerator/cpu_accelerator.py      |  3 +++
 accelerator/cuda_accelerator.py     | 25 +++++++++++++++++++++++++
 accelerator/mps_accelerator.py      |  3 +++
 accelerator/npu_accelerator.py      |  3 +++
 requirements/requirements.txt       |  1 +
 6 files changed, 39 insertions(+)

diff --git a/accelerator/abstract_accelerator.py b/accelerator/abstract_accelerator.py
index b6ef453464a2..0c7ee3212fad 100644
--- a/accelerator/abstract_accelerator.py
+++ b/accelerator/abstract_accelerator.py
@@ -147,6 +147,10 @@ def max_memory_reserved(self, device_index=None):
     def total_memory(self, device_index=None):
         ...
 
+    @abc.abstractmethod
+    def available_memory(self, device_index=None):
+        ...
+
     # Data types
     @abc.abstractmethod
     def is_bf16_supported(self):
diff --git a/accelerator/cpu_accelerator.py b/accelerator/cpu_accelerator.py
index 6570a2bdac67..fdbbd33c07a2 100644
--- a/accelerator/cpu_accelerator.py
+++ b/accelerator/cpu_accelerator.py
@@ -159,6 +159,9 @@ def max_memory_reserved(self, device_index=None):
     def total_memory(self, device_index=None):
         return psutil.virtual_memory().total
 
+    def available_memory(self, device_index=None):
+        return psutil.virtual_memory().available
+
     # Misc
     def amp(self):
         return torch.cpu.amp
diff --git a/accelerator/cuda_accelerator.py b/accelerator/cuda_accelerator.py
index b0aa34970a98..4715e28b192c 100644
--- a/accelerator/cuda_accelerator.py
+++ b/accelerator/cuda_accelerator.py
@@ -15,12 +15,29 @@
 except ImportError:
     pass
 
+# Delay import pynvml to avoid import error when CUDA is not available
+pynvml = None
+
 
 class CUDA_Accelerator(DeepSpeedAccelerator):
 
     def __init__(self):
         self._name = 'cuda'
         self._communication_backend_name = 'nccl'
+        if pynvml is None:
+            self._init_pynvml()
+
+    def _init_pynvml(self):
+        global pynvml
+        try:
+            import pynvml
+        except ImportError:
+            return
+        try:
+            pynvml.nvmlInit()
+        except pynvml.NVMLError:
+            pynvml = None
+            return
 
     def is_synchronized_device(self):
         return False
@@ -136,6 +153,14 @@ def max_memory_reserved(self, device_index=None):
     def total_memory(self, device_index=None):
         return torch.cuda.get_device_properties(device_index).total_memory
 
+    def available_memory(self, device_index=None):
+        if pynvml:
+            handle = pynvml.nvmlDeviceGetHandleByIndex(device_index)
+            info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+            return info.free
+        else:
+            return self.total_memory(device_index) - self.memory_allocated(device_index)
+
     # Data types
     def is_bf16_supported(self):
         return torch.cuda.is_bf16_supported()
diff --git a/accelerator/mps_accelerator.py b/accelerator/mps_accelerator.py
index b5488dcdf8ca..77595f6b636c 100644
--- a/accelerator/mps_accelerator.py
+++ b/accelerator/mps_accelerator.py
@@ -131,6 +131,9 @@ def max_memory_reserved(self, device_index=None):
     def total_memory(self, device_index=None):
         return
 
+    def available_memory(self, device_index=None):
+        return
+
     # Data types
     def is_bf16_supported(self):
         return False
diff --git a/accelerator/npu_accelerator.py b/accelerator/npu_accelerator.py
index 912a6c89e228..9776c74bc422 100644
--- a/accelerator/npu_accelerator.py
+++ b/accelerator/npu_accelerator.py
@@ -127,6 +127,9 @@ def max_memory_reserved(self, device_index=None):
     def total_memory(self, device_index=None):
         return torch.npu.get_device_properties(device_index).total_memory
 
+    def available_memory(self, device_index=None):
+        return self.total_memory(device_index) - self.memory_allocated(device_index)
+
     # Data types
     def is_bf16_supported(self):
         return torch.npu.is_bf16_supported()
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6840d6dbcc98..80c9f9b3287a 100755
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,6 @@ packaging>=20.0
 psutil
 py-cpuinfo
 pydantic
+pynvml
 torch
 tqdm

From 2cbfb89ab2f45d7fc3a4b81bdf754710bb400b90 Mon Sep 17 00:00:00 2001
From: mzl <mingzhi.liu@intel.com>
Date: Wed, 18 Oct 2023 05:48:14 +0800
Subject: [PATCH 30/38] clear redundant parameters in zero3 bwd hook (#4520)

---
 deepspeed/runtime/zero/stage3.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py
index ed44af08a686..42bcc5da38b1 100644
--- a/deepspeed/runtime/zero/stage3.py
+++ b/deepspeed/runtime/zero/stage3.py
@@ -1065,19 +1065,19 @@ def create_reduce_and_remove_grad_hooks(self):
                     param.all_gather()
 
                     #print(f"After all gather {param.device}, {param.shape}")
-                    def wrapper(param, i):
+                    def wrapper(param):
                         param_tmp = param.expand_as(param)
                         grad_acc = param_tmp.grad_fn.next_functions[0][0]
 
                         @instrument_w_nvtx
                         def reduce_partition_and_remove_grads(*notneeded):
-                            self.reduce_ready_partitions_and_remove_grads(param, i)
+                            self.reduce_ready_partitions_and_remove_grads(param)
 
                         grad_acc.register_hook(reduce_partition_and_remove_grads)
                         self.grad_accs.append(grad_acc)
 
                     #print(f"param grad fn {param.expand_as(param).grad_fn}")
-                    wrapper(param, i)
+                    wrapper(param)
 
                     # Partition the parameter after creating the hook
                     param.partition()
@@ -1095,7 +1095,7 @@ def report_ipg_memory_usage(self, tag, param_elems):
             force=False)
 
     ###############Independent Partition Gradient ########################
-    def reduce_independent_p_g_buckets_and_remove_grads(self, param, i):
+    def reduce_independent_p_g_buckets_and_remove_grads(self, param):
         #print_rank_0(f"Inside reduce ipg buckets. {debug_param2name_id_shape(param)}, ipg elements {self.elements_in_ipg_bucket}, reduce bucket size {self.reduce_bucket_size}", force=True)
 
         # Because the ipg bucket is initialized with a random place holder tensor, we must
@@ -1361,9 +1361,9 @@ def partition_grads(self, params_to_release: List[Parameter], grad_partitions: L
                                                           gradient_tensors=offload_fp32_gradients[i])
         return buffers
 
-    def reduce_ready_partitions_and_remove_grads(self, param, i):
+    def reduce_ready_partitions_and_remove_grads(self, param):
         #print_rank_0(f"Backward {debug_param2name_id_shape(param)}", force=True)
-        self.reduce_independent_p_g_buckets_and_remove_grads(param, i)
+        self.reduce_independent_p_g_buckets_and_remove_grads(param)
 
     def zero_reduced_gradients(self, partition_id, i):
 

From 3e70a8871537e32e15925e446e9adfebbc7fe679 Mon Sep 17 00:00:00 2001
From: CurryRice233 <nmeia@qq.com>
Date: Wed, 18 Oct 2023 11:57:51 +0800
Subject: [PATCH 31/38] Add NPU FusedAdam support (#4343)

* add npu support dtypes

* add npu fused_adam support

* add license

* Update accelerator/npu_accelerator.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update op_builder/npu/fused_adam.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update op_builder/npu/fused_adam.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update op_builder/npu/fused_adam.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update op_builder/npu/fused_adam.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update op_builder/npu/fused_adam.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update op_builder/npu/fused_adam.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update op_builder/npu/fused_adam.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update accelerator/npu_accelerator.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

* Update accelerator/npu_accelerator.py

Co-authored-by: Hz, Ji <hzji210@gmail.com>

---------

Co-authored-by: jializheng <jializheng@huawei.com>
Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
Co-authored-by: Hz, Ji <hzji210@gmail.com>
---
 accelerator/npu_accelerator.py | 44 +++++++++++++-------
 op_builder/npu/__init__.py     |  2 +-
 op_builder/npu/fused_adam.py   | 74 ++++++++++++++++++++++++++++++++++
 3 files changed, 105 insertions(+), 15 deletions(-)
 create mode 100644 op_builder/npu/fused_adam.py

diff --git a/accelerator/npu_accelerator.py b/accelerator/npu_accelerator.py
index 9776c74bc422..7ebbd320bb15 100644
--- a/accelerator/npu_accelerator.py
+++ b/accelerator/npu_accelerator.py
@@ -2,16 +2,28 @@
 # SPDX-License-Identifier: Apache-2.0
 
 # DeepSpeed Team
+import importlib
+import inspect
 
-import torch
 from .abstract_accelerator import DeepSpeedAccelerator
+# During setup stage torch may not be installed, pass on no torch will
+# allow op builder related API to be executed.
+try:
+    import torch.npu
+except ImportError:
+    pass
 
 
 class NPU_Accelerator(DeepSpeedAccelerator):
 
     def __init__(self):
+        super().__init__()
         self._name = 'npu'
         self._communication_backend_name = 'hccl'
+        # dict that holds class name <--> class type mapping i.e.
+        # 'AsyncIOBuilder': <class 'op_builder.async_io.AsyncIOBuilder'>
+        # this dict will be filled at init stage
+        self.class_dict = None
 
     def is_synchronized_device(self):
         return False
@@ -216,25 +228,29 @@ def op_builder_dir(self):
         except ImportError:
             return "deepspeed.ops.op_builder.npu"
 
+    def _lazy_init_class_dict(self):
+        if self.class_dict:
+            return
+
+        op_builder_module = importlib.import_module(self.op_builder_dir())
+
+        # get op builder class from op_builder/npu/__init__.py
+        self.class_dict = {}
+        for class_name, class_obj in inspect.getmembers(op_builder_module, inspect.isclass):
+            self.class_dict[class_name] = class_obj
+
     # create an instance of op builder and return, name specified by class_name
     def create_op_builder(self, class_name):
         builder_class = self.get_op_builder(class_name)
-        if builder_class != None:
-            return builder_class()
-        return None
+        return None if builder_class is None else builder_class()
 
     # return an op builder class, name specified by class_name
     def get_op_builder(self, class_name):
-        try:
-            # is op_builder from deepspeed or a 3p version? this should only succeed if it's deepspeed
-            # if successful this also means we're doing a local install and not JIT compile path
-            from op_builder import __deepspeed__  # noqa: F401 # type: ignore
-            from op_builder.npu import NotImplementedBuilder
-        except ImportError:
-            from deepspeed.ops.op_builder.npu import NotImplementedBuilder
-
-        # return a NPUNotImplementedBuilder to avoid get NoneType[Name] in unit tests
-        return NotImplementedBuilder
+        self._lazy_init_class_dict()
+        if class_name in self.class_dict:
+            return self.class_dict[class_name]
+        else:
+            return self.class_dict['NotImplementedBuilder'] if 'NotImplementedBuilder' in self.class_dict else None
 
     def build_extension(self):
         from torch.utils.cpp_extension import BuildExtension
diff --git a/op_builder/npu/__init__.py b/op_builder/npu/__init__.py
index 3efcc859aaa8..0d9e76b3903f 100644
--- a/op_builder/npu/__init__.py
+++ b/op_builder/npu/__init__.py
@@ -5,5 +5,5 @@
 '''Copyright The Microsoft DeepSpeed Team'''
 
 # NPU related operators will be added in the future.
-
+from .fused_adam import FusedAdamBuilder
 from .no_impl import NotImplementedBuilder
diff --git a/op_builder/npu/fused_adam.py b/op_builder/npu/fused_adam.py
new file mode 100644
index 000000000000..fc1bc83c7cc7
--- /dev/null
+++ b/op_builder/npu/fused_adam.py
@@ -0,0 +1,74 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .builder import NPUOpBuilder
+
+try:
+    import torch_npu
+except ImportError as e:
+    pass
+
+
+class NPUFusedAdam:
+
+    @staticmethod
+    def multi_tensor_adam(chunk_size, noop_flag_buffer, tensor_lists, lr, beta1, beta2, epsilon, step, adam_w_mode,
+                          bias_correction, weight_decay, *args):
+        bias_correction1 = beta1**step
+        bias_correction2 = beta2**step
+
+        # iteration group['params']
+        for i in range(len(tensor_lists[0])):
+            grad_flat = tensor_lists[0][i]
+            param_flat = tensor_lists[1][i]
+            m_flat = tensor_lists[2][i]
+            v_flat = tensor_lists[3][i]
+
+            if adam_w_mode:
+                param_flat.data, m_flat, v_flat = torch_npu.npu_apply_adam_w(
+                    bias_correction1,
+                    bias_correction2,
+                    lr,
+                    weight_decay,
+                    beta1,
+                    beta2,
+                    epsilon,
+                    grad_flat,
+                    None,  # max_grad_norm
+                    False,  # amsgrad
+                    False,  # maximize
+                    out=(param_flat.data, m_flat, v_flat))
+            else:
+                param_flat.data, m_flat, v_flat = torch_npu.npu_apply_adam(
+                    bias_correction1,
+                    bias_correction2,
+                    lr,
+                    beta1,
+                    beta2,
+                    epsilon,
+                    grad_flat,
+                    False,  # use_locking
+                    False,  # use_nesterov
+                    out=(param_flat.data, m_flat, v_flat))
+
+
+class FusedAdamBuilder(NPUOpBuilder):
+    BUILD_VAR = "DS_BUILD_FUSED_ADAM"
+    NAME = "fused_adam"
+
+    def __init__(self):
+        super().__init__(name=self.NAME)
+
+    def absolute_name(self):
+        return f'deepspeed.ops.adam.{self.NAME}_op'
+
+    def sources(self):
+        return []
+
+    def include_paths(self):
+        return []
+
+    def load(self, verbose=True):
+        return NPUFusedAdam

From a7358817f59c528ea567902ab8958d5ebb44f803 Mon Sep 17 00:00:00 2001
From: Liangliang-Ma <liangliang.ma@intel.com>
Date: Thu, 19 Oct 2023 01:04:57 +0800
Subject: [PATCH 32/38] fix error type in ccl.py (#4521)

Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
---
 deepspeed/comm/ccl.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/deepspeed/comm/ccl.py b/deepspeed/comm/ccl.py
index 859e70c7db3b..6e915d0ca430 100644
--- a/deepspeed/comm/ccl.py
+++ b/deepspeed/comm/ccl.py
@@ -170,7 +170,7 @@ def get_all_ranks_from_group(self, group):
             while True:
                 results.append(super(CCLBackend, self).get_global_rank(group, rank))
                 rank += 1
-        except RuntimeError:
+        except ValueError:
             pass
         if tuple(results) not in self.groups:
             self._new_group(results, group)

From 8a93ded874469d148b8459e5c892188821c49def Mon Sep 17 00:00:00 2001
From: Quentin Anthony <qganthony@yahoo.com>
Date: Wed, 18 Oct 2023 19:36:20 -0400
Subject: [PATCH 33/38] Fixed deepspeed.comm.monitored_barrier call (#4496)

* Fixed deepspeed.comm.monitored_barrier call

* Formatting

---------

Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
---
 deepspeed/comm/comm.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/deepspeed/comm/comm.py b/deepspeed/comm/comm.py
index 765cc91287ea..568211645f40 100644
--- a/deepspeed/comm/comm.py
+++ b/deepspeed/comm/comm.py
@@ -416,7 +416,7 @@ def monitored_barrier(group=None,
                       log_name='monitored_barrier',
                       debug=get_caller_func()):
     global cdb
-    return cdb.barrier(group=group, timeout=timeout, wait_all_ranks=wait_all_ranks)
+    return cdb.monitored_barrier(group=group, timeout=timeout, wait_all_ranks=wait_all_ranks)
 
 
 def log_summary(show_straggler=False):

From beed962c252cab1a5f32f0d272ecf6a03df9d26e Mon Sep 17 00:00:00 2001
From: Ilya Vologin <34806644+cupertank@users.noreply.github.com>
Date: Thu, 19 Oct 2023 18:48:29 +0200
Subject: [PATCH 34/38] [Bug fix] Add rope_theta for llama config (#4480)

* Add rope_theta for llama config

* Add rope_theta to bias_add_transform_0213

* Fix CI problems

* Add rope_theta to linear layer

---------

Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com>
Co-authored-by: Lev Kurilenko <113481193+lekurile@users.noreply.github.com>
---
 .../inference/csrc/apply_rotary_pos_emb.cu    | 20 ++++++++++++---
 .../transformer/inference/csrc/pt_binding.cpp | 21 +++++++++++-----
 csrc/transformer/inference/csrc/transform.cu  | 25 ++++++++++++-------
 .../includes/inference_cuda_layers.h          |  4 ++-
 deepspeed/module_inject/containers/llama.py   |  1 +
 deepspeed/ops/transformer/inference/config.py |  4 ++-
 .../inference/op_binding/linear.py            |  4 +--
 .../inference/op_binding/softmax_context.py   |  9 ++++---
 8 files changed, 61 insertions(+), 27 deletions(-)

diff --git a/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu b/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu
index e743ffc3f64f..e326c762c0f3 100644
--- a/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu
+++ b/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu
@@ -32,6 +32,7 @@ __global__ void apply_rotary_pos_half(T* mixed_query,
                                       unsigned num_heads,
                                       unsigned head_size,
                                       unsigned total_count,
+                                      float rope_theta,
                                       int max_out_tokens)
 {
     constexpr int T_per_thread = granularity / sizeof(T);
@@ -61,7 +62,7 @@ __global__ void apply_rotary_pos_half(T* mixed_query,
             const int neuron_idx = base_neuron_idx + i;
             if (neuron_idx < rotary_dim) {
                 float inv_freq = (float)((neuron_idx % half_dim) * 2) / (float)rotary_dim;
-                inv_freq = 1.0 / powf(10000.0, inv_freq) * (float)seq_idx;
+                inv_freq = 1.0 / powf(rope_theta, inv_freq) * (float)seq_idx;
 
                 float rotary_sign = (neuron_idx > (half_dim - 1) ? -1.0 : 1.0);
                 float q_rot = conversion::to<float>(q[i]) * rotary_sign;
@@ -95,6 +96,7 @@ __global__ void apply_rotary_pos_half(T* mixed_query,
                                                                                   num_heads,   \
                                                                                   head_size,   \
                                                                                   total_count, \
+                                                                                  rope_theta,  \
                                                                                   max_out_tokens);
 
 #ifdef __HIP_PLATFORM_HCC__
@@ -136,6 +138,7 @@ void launch_apply_rotary_pos_emb(T* mixed_query,
                                  unsigned offset,
                                  unsigned num_heads,
                                  unsigned batch,
+                                 float rope_theta,
                                  cudaStream_t stream,
                                  int max_out_tokens)
 {
@@ -176,9 +179,18 @@ void launch_apply_rotary_pos_emb(T* mixed_query,
     }
 }
 
-#define INSTANTIATE_LAUNCH_ROTARY_POS_EMB(T)      \
-    template void launch_apply_rotary_pos_emb<T>( \
-        T*, T*, unsigned, unsigned, unsigned, unsigned, unsigned, unsigned, cudaStream_t, int);
+#define INSTANTIATE_LAUNCH_ROTARY_POS_EMB(T)                   \
+    template void launch_apply_rotary_pos_emb<T>(T*,           \
+                                                 T*,           \
+                                                 unsigned,     \
+                                                 unsigned,     \
+                                                 unsigned,     \
+                                                 unsigned,     \
+                                                 unsigned,     \
+                                                 unsigned,     \
+                                                 float,        \
+                                                 cudaStream_t, \
+                                                 int);
 
 INSTANTIATE_LAUNCH_ROTARY_POS_EMB(float);
 #ifdef BF16_AVAILABLE
diff --git a/csrc/transformer/inference/csrc/pt_binding.cpp b/csrc/transformer/inference/csrc/pt_binding.cpp
index 634b6e3adbbb..4fd64112e148 100644
--- a/csrc/transformer/inference/csrc/pt_binding.cpp
+++ b/csrc/transformer/inference/csrc/pt_binding.cpp
@@ -445,7 +445,8 @@ std::vector<at::Tensor> ds_softmax_context(at::Tensor& query_key_value,
                                            bool no_masking,
                                            unsigned layer_id,
                                            unsigned num_layers,
-                                           at::Tensor& alibi)
+                                           at::Tensor& alibi,
+                                           float rope_theta)
 {
     unsigned bsz = query_key_value.size(0);
     unsigned seq_len = query_key_value.size(1);
@@ -493,7 +494,8 @@ std::vector<at::Tensor> ds_softmax_context(at::Tensor& query_key_value,
                                       rotate_every_two,
                                       InferenceContext::Instance().GetCurrentStream(),
                                       3,
-                                      InferenceContext::Instance().GetMaxTokenLength());
+                                      InferenceContext::Instance().GetMaxTokenLength(),
+                                      rope_theta);
     if (rotary_dim > 0 && rotate_half)
         launch_apply_rotary_pos_emb(query_cont,
                                     kv_cache,
@@ -503,6 +505,7 @@ std::vector<at::Tensor> ds_softmax_context(at::Tensor& query_key_value,
                                     (is_prompt ? 0 : soft_len - 1),
                                     heads,
                                     bsz,
+                                    rope_theta,
                                     InferenceContext::Instance().GetCurrentStream(),
                                     InferenceContext::Instance().GetMaxTokenLength());
 
@@ -1100,7 +1103,8 @@ at::Tensor ds_linear_layer(at::Tensor& input,
                            bool add_bias,
                            bool do_flash_attn,
                            int num_heads,
-                           bool transposed_mode)
+                           bool transposed_mode,
+                           float rope_theta)
 {
     auto input_cont = input.contiguous();
     auto options = at::TensorOptions()
@@ -1174,7 +1178,8 @@ at::Tensor ds_linear_layer(at::Tensor& input,
                 false,
                 InferenceContext::Instance().GetCurrentStream(),
                 3,
-                input.size(1));
+                input.size(1),
+                rope_theta);
             return at::from_blob(final_output,
                                  {3, input.size(0), num_heads, input.size(1), padded_head_size},
                                  options);
@@ -1200,7 +1205,8 @@ at::Tensor ds_linear_layer(at::Tensor& input,
                 false,
                 InferenceContext::Instance().GetCurrentStream(),
                 3,
-                input.size(1));
+                input.size(1),
+                rope_theta);
             return at::from_blob(
                 final_output, {3, input.size(0), num_heads, input.size(1), head_size}, options);
             // return at::from_blob(workspace, {input.size(0) * input.size(1), 3, num_heads,
@@ -1847,7 +1853,8 @@ std::vector<at::Tensor> apply_rotary_pos_emb(at::Tensor& mixed_query,
                                              unsigned rotary_dim,
                                              unsigned offset,
                                              unsigned num_heads,
-                                             bool rotate_half)
+                                             bool rotate_half,
+                                             float rope_theta)
 {
     auto query_cont = mixed_query.contiguous();
     auto key_cont = key_layer.contiguous();
@@ -1865,6 +1872,7 @@ std::vector<at::Tensor> apply_rotary_pos_emb(at::Tensor& mixed_query,
                                            offset,
                                            num_heads,
                                            bsz,
+                                           rope_theta,
                                            InferenceContext::Instance().GetCurrentStream(),
                                            InferenceContext::Instance().GetMaxTokenLength());
     else
@@ -1876,6 +1884,7 @@ std::vector<at::Tensor> apply_rotary_pos_emb(at::Tensor& mixed_query,
                                             offset,
                                             num_heads,
                                             bsz,
+                                            rope_theta,
                                             InferenceContext::Instance().GetCurrentStream(),
                                             InferenceContext::Instance().GetMaxTokenLength());
     return {query_cont, key_cont};
diff --git a/csrc/transformer/inference/csrc/transform.cu b/csrc/transformer/inference/csrc/transform.cu
index 0b8bffa643c6..06b29647ab2a 100644
--- a/csrc/transformer/inference/csrc/transform.cu
+++ b/csrc/transformer/inference/csrc/transform.cu
@@ -32,7 +32,8 @@ __global__ void bias_add_transform_0213(float* output,
                                         bool rotate_half,
                                         bool rotate_every_two,
                                         int head_ext,
-                                        int max_out_tokens)
+                                        int max_out_tokens,
+                                        float rope_theta)
 {
     int d0_stride = hidden_dim * seq_length;
     int d1_stride = hidden_dim;
@@ -70,7 +71,7 @@ __global__ void bias_add_transform_0213(float* output,
 #pragma unroll
             for (int o = 0; o < 2; o++) {
                 float inv_freq = (float)(((d3 << 1) + o) * 2) / (float)(rotary_dim << 2);
-                inv_freq = 1.0 / powf(10000.0, inv_freq) * (float)seq_id;
+                inv_freq = 1.0 / powf(rope_theta, inv_freq) * (float)seq_id;
                 q_f[o].x = (-1.0 * q_f[o].y * sinf(inv_freq) + q_f[o].x * cosf(inv_freq));
                 q_f[o].y = (q_f[o].x * sinf(inv_freq) + q_f[o].y * cosf(inv_freq));
             }
@@ -100,7 +101,8 @@ __global__ void bias_add_transform_0213(T* output,  // q
                                         bool rotate_half,
                                         bool rotate_every_two,
                                         int head_ext,
-                                        int max_out_tokens)
+                                        int max_out_tokens,
+                                        float rope_theta)
 {
     using T2 =
         typename std::conditional<std::is_same<T, __half>::value, __half2, __nv_bfloat162>::type;
@@ -147,7 +149,7 @@ __global__ void bias_add_transform_0213(T* output,  // q
 #pragma unroll
             for (int o = 0; o < 4; o++) {
                 float inv_freq = (float)(((d3 << 2) + o) * 2) / (float)(rotary_dim << 3);
-                inv_freq = 1.0 / powf(10000.0, inv_freq) * (float)seq_id;
+                inv_freq = 1.0 / powf(rope_theta, inv_freq) * (float)seq_id;
                 float q_data[2];
                 q_data[0] = conversion::to<float>(q_h[o].x);
                 q_data[1] = conversion::to<float>(q_h[o].y);
@@ -181,7 +183,8 @@ void launch_bias_add_transform_0213<float>(float* output,
                                            bool rotate_every_two,
                                            cudaStream_t stream,
                                            int trans_count,
-                                           int max_out_tokens)
+                                           int max_out_tokens,
+                                           float rope_theta)
 {
     hidden_dim >>= 2;
     int head_ext = (hidden_dim - 1) / MAX_THREADS + 1;
@@ -204,7 +207,8 @@ void launch_bias_add_transform_0213<float>(float* output,
                                                                 rotate_half,
                                                                 rotate_every_two,
                                                                 head_ext,
-                                                                max_out_tokens);
+                                                                max_out_tokens,
+                                                                rope_theta);
 }
 
 template <typename T>
@@ -225,7 +229,8 @@ void launch_bias_add_transform_0213(T* output,
                                     bool rotate_every_two,
                                     cudaStream_t stream,
                                     int trans_count,
-                                    int max_out_tokens)
+                                    int max_out_tokens,
+                                    float rope_theta)
 {
     hidden_dim >>= 3;
     int head_ext = 1;  // (hidden_dim - 1) / MAX_THREADS + 1;
@@ -247,7 +252,8 @@ void launch_bias_add_transform_0213(T* output,
                                                                 rotate_half,
                                                                 rotate_every_two,
                                                                 head_ext,
-                                                                max_out_tokens);
+                                                                max_out_tokens,
+                                                                rope_theta);
 }
 
 #define INSTANTIATE_LAUNCH_BIAS_ADD_TRANSFORM_0213(T)             \
@@ -268,7 +274,8 @@ void launch_bias_add_transform_0213(T* output,
                                                     bool,         \
                                                     cudaStream_t, \
                                                     int,          \
-                                                    int)
+                                                    int,          \
+                                                    float)
 
 #ifdef BF16_AVAILABLE
 INSTANTIATE_LAUNCH_BIAS_ADD_TRANSFORM_0213(__nv_bfloat16);
diff --git a/csrc/transformer/inference/includes/inference_cuda_layers.h b/csrc/transformer/inference/includes/inference_cuda_layers.h
index 5240ebb1d524..dcc020483687 100644
--- a/csrc/transformer/inference/includes/inference_cuda_layers.h
+++ b/csrc/transformer/inference/includes/inference_cuda_layers.h
@@ -168,6 +168,7 @@ void launch_apply_rotary_pos_emb(T* mixed_query,
                                  unsigned offset,
                                  unsigned num_heads,
                                  unsigned batch,
+                                 float rope_theta,
                                  cudaStream_t stream,
                                  int max_out_tokens);
 
@@ -207,7 +208,8 @@ void launch_bias_add_transform_0213(T* outputs,
                                     bool rotate_every_two,
                                     cudaStream_t stream,
                                     int trans_count,
-                                    int max_out_tokens);
+                                    int max_out_tokens,
+                                    float rope_theta);
 template <typename T>
 void pad_data(T* padded_output,
               T* output,
diff --git a/deepspeed/module_inject/containers/llama.py b/deepspeed/module_inject/containers/llama.py
index af99d658017c..f6157e5cdfed 100644
--- a/deepspeed/module_inject/containers/llama.py
+++ b/deepspeed/module_inject/containers/llama.py
@@ -34,6 +34,7 @@ def create_module(self, config=None):
         _config.rotate_half = True
         _config.rotate_every_two = False
         _config.rotary_dim = self.hidden_size // self.num_attention_heads
+        _config.rope_theta = self.policy.client_module.self_attn.rope_theta
         self.module = DeepSpeedGPTInference(_config, mp_group=self.mp_group)
 
         return self.module
diff --git a/deepspeed/ops/transformer/inference/config.py b/deepspeed/ops/transformer/inference/config.py
index 4e29a2137c64..d5aff4f541f7 100644
--- a/deepspeed/ops/transformer/inference/config.py
+++ b/deepspeed/ops/transformer/inference/config.py
@@ -79,7 +79,8 @@ def __init__(self,
                  transposed_mode=False,
                  use_triton=False,
                  triton_autotune=False,
-                 num_kv=-1):
+                 num_kv=-1,
+                 rope_theta=10000):
         super(DeepSpeedInferenceConfig,
               self).__init__(hidden_size, (intermediate_size if intermediate_size > 0 else 4 * hidden_size), heads,
                              num_hidden_layers)
@@ -114,6 +115,7 @@ def __init__(self,
         self.use_triton = use_triton
         self.triton_autotune = triton_autotune
         self.num_kv = num_kv
+        self.rope_theta = rope_theta
 
     @classmethod
     def from_dict(cls, json_object):
diff --git a/deepspeed/ops/transformer/inference/op_binding/linear.py b/deepspeed/ops/transformer/inference/op_binding/linear.py
index e970b562c6d6..b8decb6dc5ea 100644
--- a/deepspeed/ops/transformer/inference/op_binding/linear.py
+++ b/deepspeed/ops/transformer/inference/op_binding/linear.py
@@ -31,7 +31,7 @@ def __init__(self, config: DeepSpeedInferenceConfig):
         except AttributeError:
             self.linear_func = self.linear_fallback
 
-    def linear_fallback(self, input, weight, bias, add_bias, do_flash_attn, num_heads, transpose):
+    def linear_fallback(self, input, weight, bias, add_bias, do_flash_attn, num_heads, transpose, rope_theta):
         raise NotImplementedError
 
     def forward(self,
@@ -44,7 +44,7 @@ def forward(self,
                 external_cache: bool = None,
                 num_layers: int = None):
         qkv_out = self.linear_func(input, weight, bias, add_bias, do_flash_attn, num_heads,
-                                   self.config.transposed_mode)
+                                   self.config.transposed_mode, self.config.rope_theta)
         return qkv_out
 
     @staticmethod
diff --git a/deepspeed/ops/transformer/inference/op_binding/softmax_context.py b/deepspeed/ops/transformer/inference/op_binding/softmax_context.py
index 012399ea1ef3..0dc4e08a3633 100644
--- a/deepspeed/ops/transformer/inference/op_binding/softmax_context.py
+++ b/deepspeed/ops/transformer/inference/op_binding/softmax_context.py
@@ -23,9 +23,9 @@ def __init__(self, config: DeepSpeedInferenceConfig):
         except AttributeError:
             self.softmax_context_func = self.softmax_context_fallback
 
-    def softmax_context_fallback(self, query_key_value, attn_mask, rotary_dim, rotate_half, roteate_every_two, heads,
-                                 norm_factor, triangular_masking, local_attention, window_size, no_masking, layer_id,
-                                 num_layers, alibi):
+    def softmax_context_fallback(self, query_key_value, attn_mask, rotary_dim, rotate_half, rotate_every_two, heads,
+                                 num_kv, norm_factor, triangular_masking, local_attention, window_size, no_masking,
+                                 layer_id, num_layers, alibi, rope_theta):
         raise NotImplementedError
 
     def forward(self, query_key_value: torch.Tensor, attn_mask: torch.Tensor, heads: int, num_kv: int,
@@ -41,6 +41,7 @@ def forward(self, query_key_value: torch.Tensor, attn_mask: torch.Tensor, heads:
         output = self.softmax_context_func(query_key_value, attn_mask, self.config.rotary_dim, self.config.rotate_half,
                                            self.config.rotate_every_two, heads, num_kv, norm_factor,
                                            self.config.triangular_masking, self.config.local_attention,
-                                           self.config.window_size, no_masking, layer_id, num_layers, alibi)
+                                           self.config.window_size, no_masking, layer_id, num_layers, alibi,
+                                           self.config.rope_theta)
 
         return output

From 3e4a587135447f4ba0f64010870acc297d333198 Mon Sep 17 00:00:00 2001
From: Ramya Ramineni <62723901+rraminen@users.noreply.github.com>
Date: Thu, 19 Oct 2023 13:14:01 -0500
Subject: [PATCH 35/38] Added rocblas header (#4538)

Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
---
 csrc/includes/gemm_test.h | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/csrc/includes/gemm_test.h b/csrc/includes/gemm_test.h
index cd9fbb5a4e17..6d04921c2e81 100644
--- a/csrc/includes/gemm_test.h
+++ b/csrc/includes/gemm_test.h
@@ -9,6 +9,9 @@
 #ifndef __HIP_PLATFORM_HCC__
 #include <cuda_profiler_api.h>
 #endif
+#ifdef __HIP_PLATFORM_HCC__
+#include <rocblas/rocblas.h>
+#endif
 #include <array>
 #include <cstdio>
 #include <cstdlib>

From a5b1cb1eb510233ba1f35ee04bb1c3c0404051b4 Mon Sep 17 00:00:00 2001
From: Jeff Rasley <jerasley@microsoft.com>
Date: Thu, 19 Oct 2023 13:52:13 -0700
Subject: [PATCH 36/38] [docs] ZeRO infinity slides and blog (#4542)

---
 README.md                                |   2 +-
 docs/assets/files/SC21-ZeRO-Infinity.pdf | Bin 0 -> 1481232 bytes
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100755 docs/assets/files/SC21-ZeRO-Infinity.pdf

diff --git a/README.md b/README.md
index 81ac3031457a..2e4279d08540 100755
--- a/README.md
+++ b/README.md
@@ -214,7 +214,7 @@ Conduct](https://opensource.microsoft.com/codeofconduct/). For more information
 3. Minjia Zhang, Yuxiong He. (2020) Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. [arXiv:2010.13369](https://arxiv.org/abs/2010.13369) and [NeurIPS 2020](https://proceedings.neurips.cc/paper/2020/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html).
 4. Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He. (2021) ZeRO-Offload: Democratizing Billion-Scale Model Training. [arXiv:2101.06840](https://arxiv.org/abs/2101.06840) and [USENIX ATC 2021](https://www.usenix.org/conference/atc21/presentation/ren-jie).
 5. Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He. (2021) 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. [arXiv:2102.02888](https://arxiv.org/abs/2102.02888) and [ICML 2021](http://proceedings.mlr.press/v139/tang21a.html).
-6. Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He. (2021) ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. [arXiv:2104.07857](https://arxiv.org/abs/2104.07857) and [SC 2021](https://dl.acm.org/doi/abs/10.1145/3458817.3476205).
+6. Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He. (2021) ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. [arXiv:2104.07857](https://arxiv.org/abs/2104.07857) and [SC 2021](https://dl.acm.org/doi/abs/10.1145/3458817.3476205). [[paper]](https://arxiv.org/abs/2104.07857) [[slides]](docs/assets/files/SC21-ZeRO-Infinity.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/)
 7. Conglong Li, Ammar Ahmad Awan, Hanlin Tang, Samyam Rajbhandari, Yuxiong He. (2021) 1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed. [arXiv:2104.06069](https://arxiv.org/abs/2104.06069) and [HiPC 2022](https://hipc.org/advance-program/).
 8. Conglong Li, Minjia Zhang, Yuxiong He. (2021) The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models. [arXiv:2108.06084](https://arxiv.org/abs/2108.06084) and [NeurIPS 2022](https://openreview.net/forum?id=JpZ5du_Kdh).
 9. Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He. (2022) Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. [arXiv:2202.06009](https://arxiv.org/abs/2202.06009).
diff --git a/docs/assets/files/SC21-ZeRO-Infinity.pdf b/docs/assets/files/SC21-ZeRO-Infinity.pdf
new file mode 100755
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HcmV?d00001


From c7724c61815a7c9c7827f2a82e853af3c21fd5b7 Mon Sep 17 00:00:00 2001
From: Logan Adams <114770087+loadams@users.noreply.github.com>
Date: Thu, 19 Oct 2023 14:01:48 -0700
Subject: [PATCH 37/38] Switch from HIP_PLATFORM_HCC to HIP_PLATFORM_AMD
 (#4539)

* Switch from HIP_PLATFORM_HCC to HIP_PLATFORM_AMD

* Merge changes and fix from #4528
---
 csrc/includes/conversion_utils.h              |  2 +-
 csrc/includes/cublas_wrappers.h               | 10 +++---
 csrc/includes/ds_kernel_utils.h               |  6 ++--
 csrc/includes/feed_forward.h                  |  6 ++--
 csrc/includes/gemm_test.h                     | 20 +++++------
 csrc/includes/general_kernels.h               |  2 +-
 csrc/includes/quantizer.h                     |  2 +-
 csrc/includes/reduction_utils.h               |  8 ++---
 csrc/includes/strided_batch_gemm.h            |  8 ++---
 csrc/lamb/fused_lamb_cuda_kernel.cu           |  4 +--
 csrc/quantization/fake_quantizer.cu           |  8 ++---
 csrc/random_ltd/token_sort.cu                 |  2 +-
 csrc/spatial/includes/spatial_cuda_layers.h   |  2 +-
 csrc/transformer/cublas_wrappers.cu           | 24 ++++++-------
 csrc/transformer/ds_transformer_cuda.cpp      |  2 +-
 .../inference/csrc/apply_rotary_pos_emb.cu    |  6 ++--
 .../transformer/inference/csrc/pt_binding.cpp | 34 +++++++++----------
 csrc/transformer/inference/csrc/softmax.cu    |  2 +-
 csrc/transformer/inference/csrc/transform.cu  |  2 +-
 .../inference/includes/inference_context.h    |  2 +-
 .../includes/inference_cublas_wrappers.h      | 26 +++++++-------
 21 files changed, 89 insertions(+), 89 deletions(-)

diff --git a/csrc/includes/conversion_utils.h b/csrc/includes/conversion_utils.h
index 1c2d3b4b7a47..3a90a3e91ddf 100644
--- a/csrc/includes/conversion_utils.h
+++ b/csrc/includes/conversion_utils.h
@@ -265,7 +265,7 @@ DS_D_INLINE float2 to(__nv_bfloat162 val)
 template <>
 DS_D_INLINE __half to(double val)
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     float val_f = __double2float_rn(val);
     return __float2half(val_f);
 #else
diff --git a/csrc/includes/cublas_wrappers.h b/csrc/includes/cublas_wrappers.h
index b016832dc9b3..1f359fcccf1b 100644
--- a/csrc/includes/cublas_wrappers.h
+++ b/csrc/includes/cublas_wrappers.h
@@ -10,7 +10,7 @@
 #include <cuda.h>
 #include <cuda_fp16.h>
 #include <cuda_runtime.h>
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
 #include <mma.h>
 #endif
 #include <stdio.h>
@@ -26,7 +26,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                    const float* A,
                    const float* B,
                    float* C,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                    rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
 #else
                    cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT);
@@ -43,7 +43,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                    const __half* A,
                    const __half* B,
                    __half* C,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                    rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
 #else
                    cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -64,7 +64,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                 int stride_B,
                                 int stride_C,
                                 int batch,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                 rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
 #else
                                 cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT);
@@ -85,7 +85,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                 int stride_B,
                                 int stride_C,
                                 int batch,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                 rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
 #else
                                 cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP);
diff --git a/csrc/includes/ds_kernel_utils.h b/csrc/includes/ds_kernel_utils.h
index dfcf7dff7f29..a3af561adfe5 100644
--- a/csrc/includes/ds_kernel_utils.h
+++ b/csrc/includes/ds_kernel_utils.h
@@ -15,7 +15,7 @@ used throughout the codebase.
 #define DS_HD_INLINE __host__ __device__ __forceinline__
 #define DS_D_INLINE __device__ __forceinline__
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 
 // constexpr variant of warpSize for templating
 constexpr int hw_warp_size = 64;
@@ -23,7 +23,7 @@ constexpr int hw_warp_size = 64;
 #include <hip/hip_cooperative_groups.h>
 #include <hip/hip_fp16.h>
 
-#else  // !__HIP_PLATFORM_HCC__
+#else  // !__HIP_PLATFORM_AMD__
 
 // constexpr variant of warpSize for templating
 constexpr int hw_warp_size = 32;
@@ -40,7 +40,7 @@ constexpr int hw_warp_size = 32;
 #include <cooperative_groups.h>
 #include <cuda_fp16.h>
 
-#endif  //__HIP_PLATFORM_HCC__
+#endif  //__HIP_PLATFORM_AMD__
 
 inline int next_pow2(const int val)
 {
diff --git a/csrc/includes/feed_forward.h b/csrc/includes/feed_forward.h
index 8cf9ee9ef594..46e3ba748d52 100644
--- a/csrc/includes/feed_forward.h
+++ b/csrc/includes/feed_forward.h
@@ -48,7 +48,7 @@ class FeedForward {
                        weights,
                        input_ptr,
                        out,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo(config_.gemm_algos[0]));
 #else
                        cublasGemmAlgo_t(config_.gemm_algos[0]));
@@ -77,7 +77,7 @@ class FeedForward {
                        input_ptr,
                        out_grad,
                        weights_grad,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo(config_.gemm_algos[1]));
 #else
                        cublasGemmAlgo_t(config_.gemm_algos[1]));
@@ -94,7 +94,7 @@ class FeedForward {
                        weights,
                        out_grad,
                        inp_grad_out,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo(config_.gemm_algos[2]));
 #else
                        cublasGemmAlgo_t(config_.gemm_algos[2]));
diff --git a/csrc/includes/gemm_test.h b/csrc/includes/gemm_test.h
index 6d04921c2e81..278515174523 100644
--- a/csrc/includes/gemm_test.h
+++ b/csrc/includes/gemm_test.h
@@ -6,10 +6,10 @@
 #pragma once
 
 #include <cuda_fp16.h>
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
 #include <cuda_profiler_api.h>
 #endif
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 #include <rocblas/rocblas.h>
 #endif
 #include <array>
@@ -67,7 +67,7 @@ class GemmTest {
                            B,
                            A,
                            C,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                            static_cast<rocblas_gemm_algo>(algo));
 #else
                            static_cast<cublasGemmAlgo_t>(algo));
@@ -86,7 +86,7 @@ class GemmTest {
                            A,
                            C,
                            B,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                            static_cast<rocblas_gemm_algo>(algo));
 #else
                            static_cast<cublasGemmAlgo_t>(algo));
@@ -105,7 +105,7 @@ class GemmTest {
                            B,
                            C,
                            A,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                            static_cast<rocblas_gemm_algo>(algo));
 #else
                            static_cast<cublasGemmAlgo_t>(algo));
@@ -121,7 +121,7 @@ class GemmTest {
         float fast_latency = (std::numeric_limits<float>::max)();
         int fast_algo = 0;
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
         for (int algo = (int)rocblas_gemm_algo_standard; algo <= (int)rocblas_gemm_algo_standard;
 #else
         for (int algo = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP;
@@ -211,7 +211,7 @@ class StridedGemmTest {
                                         stride_b,
                                         stride_c,
                                         bsz,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                         static_cast<rocblas_gemm_algo>(algo));
 #else
                                         static_cast<cublasGemmAlgo_t>(algo));
@@ -245,7 +245,7 @@ class StridedGemmTest {
                                         stride_b,
                                         stride_c,
                                         bsz,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                         static_cast<rocblas_gemm_algo>(algo));
 #else
                                         static_cast<cublasGemmAlgo_t>(algo));
@@ -276,7 +276,7 @@ class StridedGemmTest {
                                         stride_b,
                                         stride_c,
                                         bsz,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                         static_cast<rocblas_gemm_algo>(algo));
 #else
                                         static_cast<cublasGemmAlgo_t>(algo));
@@ -292,7 +292,7 @@ class StridedGemmTest {
         float fast_latency = (std::numeric_limits<float>::max)();
         int fast_algo = 0;
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
         for (int algo = (int)rocblas_gemm_algo_standard; algo <= (int)rocblas_gemm_algo_standard;
 #else
         for (int algo = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP;
diff --git a/csrc/includes/general_kernels.h b/csrc/includes/general_kernels.h
index 28e2cbf2984f..bd621d3c4329 100644
--- a/csrc/includes/general_kernels.h
+++ b/csrc/includes/general_kernels.h
@@ -8,7 +8,7 @@
 #include <stdio.h>
 #include <stdlib.h>
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 #include <hip/hip_cooperative_groups.h>
 #else
 #include <cooperative_groups.h>
diff --git a/csrc/includes/quantizer.h b/csrc/includes/quantizer.h
index 2ae10bad3527..f4f63160d79b 100644
--- a/csrc/includes/quantizer.h
+++ b/csrc/includes/quantizer.h
@@ -5,7 +5,7 @@
 
 #pragma once
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 #include <hip/hip_cooperative_groups.h>
 #else
 #include <cooperative_groups.h>
diff --git a/csrc/includes/reduction_utils.h b/csrc/includes/reduction_utils.h
index bb55377b1dcf..8d0c2d6986a1 100644
--- a/csrc/includes/reduction_utils.h
+++ b/csrc/includes/reduction_utils.h
@@ -280,7 +280,7 @@ DS_D_INLINE __half init<ROpType::Max>()
 template <>
 DS_D_INLINE __half2 init<ROpType::Add>()
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     return __half2{_Float16_2{0x0000, 0x0000}};
 #else
     constexpr __half2_raw zero = {0x0000, 0x0000};
@@ -291,7 +291,7 @@ DS_D_INLINE __half2 init<ROpType::Add>()
 template <>
 DS_D_INLINE __half2 init<ROpType::Min>()
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     return __half2{_Float16_2{0x7C00, 0x7C00}};
 #else
     constexpr __half2_raw inf = {0x7C00, 0x7C00};
@@ -302,7 +302,7 @@ DS_D_INLINE __half2 init<ROpType::Min>()
 template <>
 DS_D_INLINE __half2 init<ROpType::Max>()
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     return __half2{_Float16_2{0xFC00, 0xFC00}};
 #else
     constexpr __half2_raw neg_inf = {0xFC00, 0xFC00};
@@ -414,7 +414,7 @@ DS_D_INLINE void _block(cg::thread_block& tb,
     // Unused when `partition_size == 1` or total_warps == 1
     __shared__ float reduce_buffer[max_warps * elems];
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     const int total_threads = blockDim.x * blockDim.y * blockDim.z;
     const int running_warps = total_threads / hw_warp_size;
 #else
diff --git a/csrc/includes/strided_batch_gemm.h b/csrc/includes/strided_batch_gemm.h
index cd126f4b0584..86d1e3dea11a 100644
--- a/csrc/includes/strided_batch_gemm.h
+++ b/csrc/includes/strided_batch_gemm.h
@@ -77,7 +77,7 @@ class StridedBatchGemm {
                                     stride_b,
                                     stride_c,
                                     bsz,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                     rocblas_gemm_algo(_config.gemm_algos[0]));
 #else
                                     cublasGemmAlgo_t(_config.gemm_algos[0]));
@@ -105,7 +105,7 @@ class StridedBatchGemm {
                                     stride_b,
                                     stride_c,
                                     _config.batch_size,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                     rocblas_gemm_algo(_config.gemm_algos[0]));
 #else
                                     cublasGemmAlgo_t(_config.gemm_algos[0]));
@@ -149,7 +149,7 @@ class StridedBatchGemm {
                                     stride_b,
                                     stride_c,
                                     bsz,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                     rocblas_gemm_algo(_config.gemm_algos[1]));
 #else
                                     cublasGemmAlgo_t(_config.gemm_algos[1]));
@@ -178,7 +178,7 @@ class StridedBatchGemm {
                                     stride_b,
                                     stride_c,
                                     bsz,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                     rocblas_gemm_algo(_config.gemm_algos[2]));
 #else
                                     cublasGemmAlgo_t(_config.gemm_algos[2]));
diff --git a/csrc/lamb/fused_lamb_cuda_kernel.cu b/csrc/lamb/fused_lamb_cuda_kernel.cu
index ca94a8e5ec2c..d9bacae73457 100644
--- a/csrc/lamb/fused_lamb_cuda_kernel.cu
+++ b/csrc/lamb/fused_lamb_cuda_kernel.cu
@@ -17,7 +17,7 @@
 #include <iostream>
 
 // #include <helper_functions.h>
-#if defined(__HIP_PLATFORM_HCC__) && HIP_VERSION > 305
+#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION > 305
 #include <hip/hip_cooperative_groups.h>
 #else
 #include <cooperative_groups.h>
@@ -109,7 +109,7 @@ __device__ void reduce_block_in_shared_memory(T* s_a, T* s_b, T* g_a, T* g_b)
 
     cg::sync(cta);
 
-#if (__CUDA_ARCH__ >= 300) || (defined(__HIP_PLATFORM_HCC__) && HIP_VERSION >= 502)
+#if (__CUDA_ARCH__ >= 300) || (defined(__HIP_PLATFORM_AMD__) && HIP_VERSION >= 502)
     if (tid < 32) {
         cg::coalesced_group active = cg::coalesced_threads();
 
diff --git a/csrc/quantization/fake_quantizer.cu b/csrc/quantization/fake_quantizer.cu
index 0f53e5235921..4c08cd4cc3d2 100644
--- a/csrc/quantization/fake_quantizer.cu
+++ b/csrc/quantization/fake_quantizer.cu
@@ -11,7 +11,7 @@ namespace cg = cooperative_groups;
 
 __global__ void fake_quantize_kernel(__half* vals, int group_size, int num_bits)
 {
-#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_HCC__)
+#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_AMD__)
 
     cg::thread_block b = cg::this_thread_block();  // tb
     cg::thread_block_tile<32> g =
@@ -197,7 +197,7 @@ __global__ void sr_fake_quantize_kernel(__half* vals,
                                         int num_bits,
                                         std::pair<uint64_t, uint64_t> seed)
 {
-#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_HCC__)
+#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_AMD__)
 
     cg::thread_block b = cg::this_thread_block();
     cg::thread_block_tile<32> g = cg::tiled_partition<32>(b);
@@ -475,7 +475,7 @@ template void launch_sr_fake_quantize_kernel(__half* vals,
 
 __global__ void fake_quantize_kernel_asym(__half* vals, int group_size, int num_bits)
 {
-#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_HCC__)
+#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_AMD__)
 
     cg::thread_block b = cg::this_thread_block();
     cg::thread_block_tile<32> g = cg::tiled_partition<32>(b);
@@ -720,7 +720,7 @@ __global__ void sr_fake_quantize_kernel_asym(__half* vals,
                                              int num_bits,
                                              std::pair<uint64_t, uint64_t> seed)
 {
-#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_HCC__)
+#if __CUDA_ARCH__ >= 700 || defined(__HIP_PLATFORM_AMD__)
 
     cg::thread_block b = cg::this_thread_block();
     cg::thread_block_tile<32> g = cg::tiled_partition<32>(b);
diff --git a/csrc/random_ltd/token_sort.cu b/csrc/random_ltd/token_sort.cu
index aae1b93e1c24..3049471cfe34 100644
--- a/csrc/random_ltd/token_sort.cu
+++ b/csrc/random_ltd/token_sort.cu
@@ -15,7 +15,7 @@ constexpr int granularity = 16;
 constexpr int mem_vals = granularity / sizeof(int32_t);
 constexpr int max_buffer_size = (threads + 1) * mem_vals;
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 constexpr int warp_size = 64;
 #else
 constexpr int warp_size = 32;
diff --git a/csrc/spatial/includes/spatial_cuda_layers.h b/csrc/spatial/includes/spatial_cuda_layers.h
index bf6ac0cf18c2..4f56f89f16cd 100644
--- a/csrc/spatial/includes/spatial_cuda_layers.h
+++ b/csrc/spatial/includes/spatial_cuda_layers.h
@@ -9,7 +9,7 @@
 #define HALF_PRECISION_AVAILABLE = 1
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 #include <hip/hip_cooperative_groups.h>
 #else
 #include <cooperative_groups.h>
diff --git a/csrc/transformer/cublas_wrappers.cu b/csrc/transformer/cublas_wrappers.cu
index b48b7086921a..7821a8759ab0 100644
--- a/csrc/transformer/cublas_wrappers.cu
+++ b/csrc/transformer/cublas_wrappers.cu
@@ -5,7 +5,7 @@
 
 #include "cublas_wrappers.h"
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_gemm_ex(rocblas_handle handle,
                    rocblas_operation transa,
                    rocblas_operation transb,
@@ -33,7 +33,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                    cublasGemmAlgo_t algo)
 #endif
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     rocblas_status status = rocblas_gemm_ex(handle,
                                             transa,
                                             transb,
@@ -80,7 +80,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                                          algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {
@@ -96,7 +96,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
     return 0;
 }
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_gemm_ex(rocblas_handle handle,
                    rocblas_operation transa,
                    rocblas_operation transb,
@@ -124,7 +124,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                    cublasGemmAlgo_t algo)
 #endif
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     rocblas_status status = rocblas_gemm_ex(handle,
                                             transa,
                                             transb,
@@ -171,7 +171,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                                          algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {
@@ -187,7 +187,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
     return 0;
 }
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_strided_batched_gemm(rocblas_handle handle,
                                 int m,
                                 int n,
@@ -223,7 +223,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                 cublasGemmAlgo_t algo)
 #endif
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     rocblas_status status =
         rocblas_gemm_strided_batched_ex(handle,
                                         op_A,
@@ -280,7 +280,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                                        algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {
@@ -297,7 +297,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
     return 0;
 }
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_strided_batched_gemm(rocblas_handle handle,
                                 int m,
                                 int n,
@@ -333,7 +333,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                 cublasGemmAlgo_t algo)
 #endif
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     rocblas_status status =
         rocblas_gemm_strided_batched_ex(handle,
                                         op_A,
@@ -390,7 +390,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                                        algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {
diff --git a/csrc/transformer/ds_transformer_cuda.cpp b/csrc/transformer/ds_transformer_cuda.cpp
index 11afa74fa008..b637bb710c67 100644
--- a/csrc/transformer/ds_transformer_cuda.cpp
+++ b/csrc/transformer/ds_transformer_cuda.cpp
@@ -145,7 +145,7 @@ BertTransformerLayer<T>::~BertTransformerLayer()
 template <typename T>
 void BertTransformerLayer<T>::Initialize()
 {
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
     if (std::is_same<T, __half>::value) cublasSetMathMode(_cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
 }
diff --git a/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu b/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu
index e326c762c0f3..a06dbb48fd33 100644
--- a/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu
+++ b/csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu
@@ -4,7 +4,7 @@
 // DeepSpeed Team
 
 #include "conversion_utils.h"
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 #include "hip/hip_cooperative_groups.h"
 #else
 #include "cooperative_groups.h"
@@ -13,7 +13,7 @@
 #include "inference_cuda_layers.h"
 #include "memory_access_utils.h"
 
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
 #include <cuda_profiler_api.h>
 #endif
 
@@ -99,7 +99,7 @@ __global__ void apply_rotary_pos_half(T* mixed_query,
                                                                                   rope_theta,  \
                                                                                   max_out_tokens);
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 #define LAUNCH_FOR_ALIGNMENT(ALIGNMENT)         \
     if (threads_per_head == 4) {                \
         LAUNCH_ROT_POS_EMB_HALF(4, ALIGNMENT);  \
diff --git a/csrc/transformer/inference/csrc/pt_binding.cpp b/csrc/transformer/inference/csrc/pt_binding.cpp
index 4fd64112e148..b7277d1e1678 100644
--- a/csrc/transformer/inference/csrc/pt_binding.cpp
+++ b/csrc/transformer/inference/csrc/pt_binding.cpp
@@ -163,7 +163,7 @@ at::Tensor einsum_sec_sm_ecm(at::Tensor& Q, at::Tensor& W)
                    (T*)W.data_ptr(),
                    (T*)Q.data_ptr(),
                    (T*)O.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                    rocblas_gemm_algo_standard);
 #else
                    CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -216,7 +216,7 @@ void attention_unfused(at::Tensor& prev_key_cont,
                                 seq_len * k,
                                 seq_len * soft_len,
                                 bsz * heads,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                 rocblas_gemm_algo_standard);
 #else
                                 CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -253,7 +253,7 @@ void attention_unfused(at::Tensor& prev_key_cont,
                                 seq_len * soft_len,
                                 seq_len * k,
                                 bsz * heads,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                 rocblas_gemm_algo_standard);
 #else
                                 CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -388,7 +388,7 @@ void attention_unfused(T* prev_key_cont,
                                 seq_len * k,
                                 seq_len * soft_len,
                                 bsz * heads,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                 rocblas_gemm_algo_standard);
 #else
                                 CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -421,7 +421,7 @@ void attention_unfused(T* prev_key_cont,
                                 seq_len * soft_len,
                                 seq_len * k,
                                 bsz * heads,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                                 rocblas_gemm_algo_standard);
 #else
                                 CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -886,7 +886,7 @@ void quantized_gemm(void* output,
                    weight16,
                    (T*)input,
                    (T*)output,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                    rocblas_gemm_algo_standard);
 #else
                    CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -931,7 +931,7 @@ at::Tensor qkv_unfused_cublas(at::Tensor& output,
                        (T*)weight.data_ptr(),
                        workspace,
                        (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1003,7 +1003,7 @@ std::vector<at::Tensor> ds_rms_qkv(at::Tensor& input,
                        (T*)weight.data_ptr(),
                        (T*)rms_norm.data_ptr(),
                        (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1089,7 +1089,7 @@ void quantized_gemm(at::Tensor& output,
                    (T*)weight16.data_ptr(),
                    (T*)input.data_ptr(),
                    (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                    rocblas_gemm_algo_standard);
 #else
                    CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1135,7 +1135,7 @@ at::Tensor ds_linear_layer(at::Tensor& input,
                    (T*)weight.data_ptr(),
                    (T*)input_cont.data_ptr(),
                    (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                    rocblas_gemm_algo_standard);
 #else
                    CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1353,7 +1353,7 @@ at::Tensor ds_vector_matmul(at::Tensor& input,
                        (T*)weight.data_ptr(),
                        (T*)input.data_ptr(),
                        (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1439,7 +1439,7 @@ at::Tensor mlp_unfused_cublas(at::Tensor& output,
                        (T*)weight.data_ptr(),
                        inp_norm,
                        intermediate,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1483,7 +1483,7 @@ at::Tensor mlp_unfused_cublas(at::Tensor& output,
                        (T*)weight1.data_ptr(),
                        intermediate,
                        (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1617,7 +1617,7 @@ std::vector<at::Tensor> ds_rms_mlp_gemm(at::Tensor& input,
                        (T*)weight_interm.data_ptr(),
                        (T*)inp_norm.data_ptr(),
                        intermediate_ptr,
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1680,7 +1680,7 @@ std::vector<at::Tensor> ds_rms_mlp_gemm(at::Tensor& input,
                        (T*)weight_out.data_ptr(),
                        intermediate_ptr,
                        (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard,
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP,
@@ -1742,7 +1742,7 @@ at::Tensor fused_gemm_gelu(at::Tensor& input,
                        (T*)weight.data_ptr(),
                        (T*)input.data_ptr(),
                        (T*)intermediate.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
@@ -1776,7 +1776,7 @@ at::Tensor fused_gemm_gelu(at::Tensor& input,
                        (T*)weight_out.data_ptr(),
                        (T*)intermediate.data_ptr(),
                        (T*)output.data_ptr(),
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
                        rocblas_gemm_algo_standard);
 #else
                        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
diff --git a/csrc/transformer/inference/csrc/softmax.cu b/csrc/transformer/inference/csrc/softmax.cu
index c0e98e10b5ad..bb06cc149ef4 100644
--- a/csrc/transformer/inference/csrc/softmax.cu
+++ b/csrc/transformer/inference/csrc/softmax.cu
@@ -7,7 +7,7 @@
 #include "conversion_utils.h"
 #include "inference_cuda_layers.h"
 
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
 #include <cuda_profiler_api.h>
 #endif
 #include <cstdio>
diff --git a/csrc/transformer/inference/csrc/transform.cu b/csrc/transformer/inference/csrc/transform.cu
index 06b29647ab2a..8bc5a94e16ee 100644
--- a/csrc/transformer/inference/csrc/transform.cu
+++ b/csrc/transformer/inference/csrc/transform.cu
@@ -3,7 +3,7 @@
 
 // DeepSpeed Team
 
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
 #include <cuda_profiler_api.h>
 #endif
 #include "conversion_utils.h"
diff --git a/csrc/transformer/inference/includes/inference_context.h b/csrc/transformer/inference/includes/inference_context.h
index aaf568553560..378fd4e5e990 100644
--- a/csrc/transformer/inference/includes/inference_context.h
+++ b/csrc/transformer/inference/includes/inference_context.h
@@ -70,7 +70,7 @@ class InferenceContext {
             std::cerr << message << std::endl;
             throw std::runtime_error(message);
         }
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
         cublasSetMathMode(_cublasHandle, CUBLAS_TENSOR_OP_MATH);
 #endif
         cudaEventCreate(&_comp1_event);
diff --git a/csrc/transformer/inference/includes/inference_cublas_wrappers.h b/csrc/transformer/inference/includes/inference_cublas_wrappers.h
index 3471ba04254f..640751b12c8f 100644
--- a/csrc/transformer/inference/includes/inference_cublas_wrappers.h
+++ b/csrc/transformer/inference/includes/inference_cublas_wrappers.h
@@ -13,12 +13,12 @@
 #endif
 #include <cuda_fp16.h>
 #include <cuda_runtime.h>
-#ifndef __HIP_PLATFORM_HCC__
+#ifndef __HIP_PLATFORM_AMD__
 #include <mma.h>
 #endif
 #include <stdio.h>
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_gemm_ex(rocblas_handle handle,
                    rocblas_operation transa,
                    rocblas_operation transb,
@@ -49,7 +49,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
 #endif
 {
     const int ldb = (b_stride == -1) ? ((transb == CUBLAS_OP_N) ? k : n) : b_stride;
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     rocblas_status status = rocblas_gemm_ex(handle,
                                             transa,
                                             transb,
@@ -96,7 +96,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                                          algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {
@@ -113,7 +113,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
 }
 
 template <typename T>
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_gemm_ex(rocblas_handle handle,
                    rocblas_operation transa,
                    rocblas_operation transb,
@@ -144,7 +144,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
 #endif
 {
     const int ldb = (b_stride == -1) ? ((transb == CUBLAS_OP_N) ? k : n) : b_stride;
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     constexpr auto rocblas_dtype_16 = std::is_same<T, __half>::value ? rocblas_datatype_f16_r
                                                                      : rocblas_datatype_bf16_r;
     rocblas_status status = rocblas_gemm_ex(handle,
@@ -194,7 +194,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
                                          algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {
@@ -210,7 +210,7 @@ int cublas_gemm_ex(cublasHandle_t handle,
     return 0;
 }
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_strided_batched_gemm(rocblas_handle handle,
                                 int m,
                                 int n,
@@ -246,7 +246,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                 cublasGemmAlgo_t algo)
 #endif
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     rocblas_status status =
         rocblas_gemm_strided_batched_ex(handle,
                                         op_A,
@@ -303,7 +303,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                                        algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {
@@ -321,7 +321,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
 }
 
 template <typename T>
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
 int cublas_strided_batched_gemm(rocblas_handle handle,
                                 int m,
                                 int n,
@@ -357,7 +357,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                 cublasGemmAlgo_t algo)
 #endif
 {
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     constexpr auto rocblas_dtype_16 = std::is_same<T, __half>::value ? rocblas_datatype_f16_r
                                                                      : rocblas_datatype_bf16_r;
     rocblas_status status =
@@ -417,7 +417,7 @@ int cublas_strided_batched_gemm(cublasHandle_t handle,
                                                        algo);
 #endif
 
-#ifdef __HIP_PLATFORM_HCC__
+#ifdef __HIP_PLATFORM_AMD__
     if (status != rocblas_status_success) {
 #else
     if (status != CUBLAS_STATUS_SUCCESS) {

From 04cd6af130d954c5337675ae8f2f45aee1db40db Mon Sep 17 00:00:00 2001
From: "Ma, Guokai" <guokai.ma@gmail.com>
Date: Fri, 20 Oct 2023 06:17:25 +0800
Subject: [PATCH 38/38] turn off I_MPI_PIN for impi launcher (#4531)

Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
---
 deepspeed/launcher/multinode_runner.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/deepspeed/launcher/multinode_runner.py b/deepspeed/launcher/multinode_runner.py
index 5b879efee469..730146f5bcd2 100644
--- a/deepspeed/launcher/multinode_runner.py
+++ b/deepspeed/launcher/multinode_runner.py
@@ -279,6 +279,9 @@ def get_cmd(self, environment, active_resources):
         export_cmd += ['-genv', 'WORLD_SIZE', str(total_process_count)]
         export_cmd += ['-genv', 'LOCAL_SIZE', str(process_per_node)]
 
+        # turn off IMPI core binding, use deepspeed's own core binding
+        export_cmd += ['-genv', 'I_MPI_PIN', '0']
+
         export_cmd += ['-hosts']
         hosts = ""
         for i, host in enumerate(self.resource_pool.keys()):