[docs] paper updates (#4584)

jeffra · web-flow · commit 45b07bf9444c · 2023-10-30T14:17:18.000-07:00
diff --git a/README.md b/README.md
@@ -221,7 +221,7 @@ Conduct](https://opensource.microsoft.com/codeofconduct/). For more information
 10. Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He. (2022) DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale [arXiv:2201.05596](https://arxiv.org/abs/2201.05596) and [ICML 2022](https://proceedings.mlr.press/v162/rajbhandari22a.html). [[pdf]](https://arxiv.org/abs/2201.05596) [[slides]](docs/assets/files/ICML-5mins.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/)
 11. Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, Bryan Catanzaro. (2022) Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model [arXiv:2201.11990](https://arxiv.org/abs/2201.11990).
 12. Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He. (2022) Extreme Compression for Pre-trained Transformers Made Simple and Efficient. [arXiv:2206.01859](https://arxiv.org/abs/2206.01859) and [NeurIPS 2022](https://openreview.net/forum?id=xNeAhc2CNAl).
-13. Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. [arXiv:2206.01861](https://arxiv.org/abs/2206.01861) and [NeurIPS 2022](https://openreview.net/forum?id=f-fVCElZ-G1).
+13. Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. [arXiv:2206.01861](https://arxiv.org/abs/2206.01861) and [NeurIPS 2022](https://openreview.net/forum?id=f-fVCElZ-G1) [[slides]](docs/assets/files/zeroquant_series.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/)
 14. Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. (2022) DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. [arXiv:2207.00032](https://arxiv.org/abs/2207.00032) and [SC 2022](https://dl.acm.org/doi/abs/10.5555/3571885.3571946). [[paper]](https://arxiv.org/abs/2207.00032) [[slides]](docs/assets/files/sc22-ds-inference.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/)
 15. Zhewei Yao, Xiaoxia Wu, Conglong Li, Connor Holmes, Minjia Zhang, Cheng Li, Yuxiong He. (2022) Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. [arXiv:2211.11586](https://arxiv.org/abs/2211.11586).
 16. Conglong Li, Zhewei Yao, Xiaoxia Wu, Minjia Zhang, Yuxiong He. (2022) DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. [arXiv:2212.03597](https://arxiv.org/abs/2212.03597) [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/)
@@ -230,13 +230,13 @@ Conduct](https://opensource.microsoft.com/codeofconduct/). For more information
 19. Sheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell, Kurt Keutzer, Yuxiong He. (2023) Scaling Vision-Language Models with Sparse Mixture of Experts. [arXiv:2303.07226](https://arxiv.org/abs/2303.07226) and [Finding at EMNLP2023](https://2023.emnlp.org/).
 20. Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, Aamir Shafi, Mustafa Abduljabbar, Hari Subramoni, Dhabaleswar Panda. (2023) MCR-DL: Mix-and-Match Communication Runtime for Deep Learning [arXiv:2303.08374](https://arxiv.org/abs/2303.08374) and will appear at IPDPS 2023.
 21. Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He, Abhinav Bhatele. (2023) A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training [arXiv:2303.06318](https://arxiv.org/abs/2303.06318) and will appear at ICS 2023.
-22. Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He. (2023) ZeRO++: Extremely Efficient Collective Communication for Giant Model Training [arXiv:2306.10209](https://arxiv.org/abs/2306.10209) and [ML for Sys Workshop at NeurIPS2023](http://mlforsystems.org/)
-23. Zhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn, Yuxiong He. (2023) ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation [arXiv:2303.08302](https://arxiv.org/abs/2303.08302) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/)
+22. Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He. (2023) ZeRO++: Extremely Efficient Collective Communication for Giant Model Training [arXiv:2306.10209](https://arxiv.org/abs/2306.10209) and [ML for Sys Workshop at NeurIPS2023](http://mlforsystems.org/) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)
+23. Zhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn, Yuxiong He. (2023) ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation [arXiv:2303.08302](https://arxiv.org/abs/2303.08302) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/) [[slides]](docs/assets/files/zeroquant_series.pdf)
 24. Pareesa Ameneh Golnari, Zhewei Yao, Yuxiong He. (2023) Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important? [arXiv:2305.09847](https://arxiv.org/abs/2305.09847)
 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) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/)
+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) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/) [[slides]](docs/assets/files/zeroquant_series.pdf)
 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)
+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) [[blog]](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)
 29. Zhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu, Elton Zheng, Yuxiong He. (2023) ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers [arXiv:2310.17723](https://arxiv.org/abs/2310.17723)
 
 
diff --git a/docs/assets/files/zeroquant_series.pdf b/docs/assets/files/zeroquant_series.pdf
diff --git a/docs/index.md b/docs/index.md
@@ -122,7 +122,7 @@ comments.
 10. Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He. (2022) DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale [arXiv:2201.05596](https://arxiv.org/abs/2201.05596) and [ICML 2022](https://proceedings.mlr.press/v162/rajbhandari22a.html). [[pdf]](https://arxiv.org/abs/2201.05596) [[slides]](docs/assets/files/ICML-5mins.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/)
 11. Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, Bryan Catanzaro. (2022) Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model [arXiv:2201.11990](https://arxiv.org/abs/2201.11990).
 12. Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He. (2022) Extreme Compression for Pre-trained Transformers Made Simple and Efficient. [arXiv:2206.01859](https://arxiv.org/abs/2206.01859) and [NeurIPS 2022](https://openreview.net/forum?id=xNeAhc2CNAl).
-13. Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. [arXiv:2206.01861](https://arxiv.org/abs/2206.01861) and [NeurIPS 2022](https://openreview.net/forum?id=f-fVCElZ-G1).
+13. Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. [arXiv:2206.01861](https://arxiv.org/abs/2206.01861) and [NeurIPS 2022](https://openreview.net/forum?id=f-fVCElZ-G1) [[slides]](docs/assets/files/zeroquant_series.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/)
 14. Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. (2022) DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. [arXiv:2207.00032](https://arxiv.org/abs/2207.00032) and [SC 2022](https://dl.acm.org/doi/abs/10.5555/3571885.3571946). [[paper]](https://arxiv.org/abs/2207.00032) [[slides]](docs/assets/files/sc22-ds-inference.pdf) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/)
 15. Zhewei Yao, Xiaoxia Wu, Conglong Li, Connor Holmes, Minjia Zhang, Cheng Li, Yuxiong He. (2022) Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. [arXiv:2211.11586](https://arxiv.org/abs/2211.11586).
 16. Conglong Li, Zhewei Yao, Xiaoxia Wu, Minjia Zhang, Yuxiong He. (2022) DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. [arXiv:2212.03597](https://arxiv.org/abs/2212.03597) [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/)
@@ -131,13 +131,13 @@ comments.
 19. Sheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell, Kurt Keutzer, Yuxiong He. (2023) Scaling Vision-Language Models with Sparse Mixture of Experts. [arXiv:2303.07226](https://arxiv.org/abs/2303.07226) and [Finding at EMNLP2023](https://2023.emnlp.org/).
 20. Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, Aamir Shafi, Mustafa Abduljabbar, Hari Subramoni, Dhabaleswar Panda. (2023) MCR-DL: Mix-and-Match Communication Runtime for Deep Learning [arXiv:2303.08374](https://arxiv.org/abs/2303.08374) and will appear at IPDPS 2023.
 21. Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He, Abhinav Bhatele. (2023) A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training [arXiv:2303.06318](https://arxiv.org/abs/2303.06318) and will appear at ICS 2023.
-22. Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He. (2023) ZeRO++: Extremely Efficient Collective Communication for Giant Model Training [arXiv:2306.10209](https://arxiv.org/abs/2306.10209) and [ML for Sys Workshop at NeurIPS2023](http://mlforsystems.org/)
-23. Zhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn, Yuxiong He. (2023) ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation [arXiv:2303.08302](https://arxiv.org/abs/2303.08302) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/)
+22. Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He. (2023) ZeRO++: Extremely Efficient Collective Communication for Giant Model Training [arXiv:2306.10209](https://arxiv.org/abs/2306.10209) and [ML for Sys Workshop at NeurIPS2023](http://mlforsystems.org/) [[blog]](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)
+23. Zhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn, Yuxiong He. (2023) ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation [arXiv:2303.08302](https://arxiv.org/abs/2303.08302) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/) [[slides]](docs/assets/files/zeroquant_series.pdf)
 24. Pareesa Ameneh Golnari, Zhewei Yao, Yuxiong He. (2023) Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important? [arXiv:2305.09847](https://arxiv.org/abs/2305.09847)
 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) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/)
+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) and [ENLSP2023 Workshop at NeurIPS2023](https://neurips2023-enlsp.github.io/) [[slides]](docs/assets/files/zeroquant_series.pdf)
 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)
+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) [[blog]](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)
 29. Zhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu, Elton Zheng, Yuxiong He. (2023) ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers [arXiv:2310.17723](https://arxiv.org/abs/2310.17723)
 
 # Videos
@@ -152,7 +152,8 @@ comments.
     * Registration is free and all videos are available on-demand.
     * [ZeRO & Fastest BERT: Increasing the scale and speed of deep learning training in DeepSpeed](https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html).
 3. [DeepSpeed on AzureML](https://youtu.be/yBVXR8G8Bg8)
-4. Community Tutorials
+4. [Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference](https://www.youtube.com/watch?v=cntxC3g22oU) [[slides]](docs/assets/files/presentation-mlops.pdf)
+5. Community Tutorials
     * [DeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim)](https://www.youtube.com/watch?v=pDGI668pNg0)
     * [Turing-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher)](https://www.youtube.com/watch?v=tC01FRB0M7w)
     * [Ultimate Guide To Scaling ML Models (The AI Epiphany)](https://www.youtube.com/watch?v=hc0u4avAkuM)
