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1. HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs

Author

Zhang, Chengming, Smith, Shaden, Sun, Baixi, Tian, Jiannan, Soifer, Jonathan, Yu, Xiaodong, Song, Shuaiwen Leon, He, Yuxiong, and Tao, Dingwen

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation. Among all CF approaches, SimpleX is the state-of-the-art method that adopts a novel loss function and a proper number of negative samples. However, there is no work that optimizes SimpleX on multi-core CPUs, leading to limited performance. To this end, we perform an in-depth profiling and analysis of existing SimpleX implementations and identify their performance bottlenecks including (1) irregular memory accesses, (2) unnecessary memory copies, and (3) redundant computations. To address these issues, we propose an efficient CF training system (called HEAT) that fully enables the multi-level caching and multi-threading capabilities of modern CPUs. Specifically, the optimization of HEAT is threefold: (1) It tiles the embedding matrix to increase data locality and reduce cache misses (thus reduces read latency); (2) It optimizes stochastic gradient descent (SGD) with sampling by parallelizing vector products instead of matrix-matrix multiplications, in particular the similarity computation therein, to avoid memory copies for matrix data preparation; and (3) It aggressively reuses intermediate results from the forward phase in the backward phase to alleviate redundant computation. Evaluation on five widely used datasets with both x86- and ARM-architecture processors shows that HEAT achieves up to 45.2X speedup over existing CPU solution and 4.5X speedup and 7.9X cost reduction in Cloud over existing GPU solution with NVIDIA V100 GPU., Comment: 12 pages, 14 figures, 7 tables, accepted by ACM ICS '23

Published
2023
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2. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Author

Workshop, BigScience, Scao, Teven Le, Fan, Angela, Akiki, Christopher, Pavlick, Ellie, Ilić, Suzana, Hesslow, Daniel, Castagné, Roman, Luccioni, Alexandra Sasha, Yvon, François, Gallé, Matthias, Tow, Jonathan, Rush, Alexander M., Biderman, Stella, Webson, Albert, Ammanamanchi, Pawan Sasanka, Wang, Thomas, Sagot, Benoît, Muennighoff, Niklas, del Moral, Albert Villanova, Ruwase, Olatunji, Bawden, Rachel, Bekman, Stas, McMillan-Major, Angelina, Beltagy, Iz, Nguyen, Huu, Saulnier, Lucile, Tan, Samson, Suarez, Pedro Ortiz, Sanh, Victor, Laurençon, Hugo, Jernite, Yacine, Launay, Julien, Mitchell, Margaret, Raffel, Colin, Gokaslan, Aaron, Simhi, Adi, Soroa, Aitor, Aji, Alham Fikri, Alfassy, Amit, Rogers, Anna, Nitzav, Ariel Kreisberg, Xu, Canwen, Mou, Chenghao, Emezue, Chris, Klamm, Christopher, Leong, Colin, van Strien, Daniel, Adelani, David Ifeoluwa, Radev, Dragomir, Ponferrada, Eduardo González, Levkovizh, Efrat, Kim, Ethan, Natan, Eyal Bar, De Toni, Francesco, Dupont, Gérard, Kruszewski, Germán, Pistilli, Giada, Elsahar, Hady, Benyamina, Hamza, Tran, Hieu, Yu, Ian, Abdulmumin, Idris, Johnson, Isaac, Gonzalez-Dios, Itziar, de la Rosa, Javier, Chim, Jenny, Dodge, Jesse, Zhu, Jian, Chang, Jonathan, Frohberg, Jörg, Tobing, Joseph, Bhattacharjee, Joydeep, Almubarak, Khalid, Chen, Kimbo, Lo, Kyle, Von Werra, Leandro, Weber, Leon, Phan, Long, allal, Loubna Ben, Tanguy, Ludovic, Dey, Manan, Muñoz, Manuel Romero, Masoud, Maraim, Grandury, María, Šaško, Mario, Huang, Max, Coavoux, Maximin, Singh, Mayank, Jiang, Mike Tian-Jian, Vu, Minh Chien, Jauhar, Mohammad A., Ghaleb, Mustafa, Subramani, Nishant, Kassner, Nora, Khamis, Nurulaqilla, Nguyen, Olivier, Espejel, Omar, de Gibert, Ona, Villegas, Paulo, Henderson, Peter, Colombo, Pierre, Amuok, Priscilla, Lhoest, Quentin, Harliman, Rheza, Bommasani, Rishi, López, Roberto Luis, Ribeiro, Rui, Osei, Salomey, Pyysalo, Sampo, Nagel, Sebastian, Bose, Shamik, Muhammad, Shamsuddeen Hassan, Sharma, Shanya, Longpre, Shayne, Nikpoor, Somaieh, Silberberg, Stanislav, Pai, Suhas, Zink, Sydney, Torrent, Tiago Timponi, Schick, Timo, Thrush, Tristan, Danchev, Valentin, Nikoulina, Vassilina, Laippala, Veronika, Lepercq, Violette, Prabhu, Vrinda, Alyafeai, Zaid, Talat, Zeerak, Raja, Arun, Heinzerling, Benjamin, Si, Chenglei, Taşar, Davut Emre, Salesky, Elizabeth, Mielke, Sabrina J., Lee, Wilson Y., Sharma, Abheesht, Santilli, Andrea, Chaffin, Antoine, Stiegler, Arnaud, Datta, Debajyoti, Szczechla, Eliza, Chhablani, Gunjan, Wang, Han, Pandey, Harshit, Strobelt, Hendrik, Fries, Jason Alan, Rozen, Jos, Gao, Leo, Sutawika, Lintang, Bari, M Saiful, Al-shaibani, Maged S., Manica, Matteo, Nayak, Nihal, Teehan, Ryan, Albanie, Samuel, Shen, Sheng, Ben-David, Srulik, Bach, Stephen H., Kim, Taewoon, Bers, Tali, Fevry, Thibault, Neeraj, Trishala, Thakker, Urmish, Raunak, Vikas, Tang, Xiangru, Yong, Zheng-Xin, Sun, Zhiqing, Brody, Shaked, Uri, Yallow, Tojarieh, Hadar, Roberts, Adam, Chung, Hyung Won, Tae, Jaesung, Phang, Jason, Press, Ofir, Li, Conglong, Narayanan, Deepak, Bourfoune, Hatim, Casper, Jared, Rasley, Jeff, Ryabinin, Max, Mishra, Mayank, Zhang, Minjia, Shoeybi, Mohammad, Peyrounette, Myriam, Patry, Nicolas, Tazi, Nouamane, Sanseviero, Omar, von Platen, Patrick, Cornette, Pierre, Lavallée, Pierre François, Lacroix, Rémi, Rajbhandari, Samyam, Gandhi, Sanchit, Smith, Shaden, Requena, Stéphane, Patil, Suraj, Dettmers, Tim, Baruwa, Ahmed, Singh, Amanpreet, Cheveleva, Anastasia, Ligozat, Anne-Laure, Subramonian, Arjun, Névéol, Aurélie, Lovering, Charles, Garrette, Dan, Tunuguntla, Deepak, Reiter, Ehud, Taktasheva, Ekaterina, Voloshina, Ekaterina, Bogdanov, Eli, Winata, Genta Indra, Schoelkopf, Hailey, Kalo, Jan-Christoph, Novikova, Jekaterina, Forde, Jessica Zosa, Clive, Jordan, Kasai, Jungo, Kawamura, Ken, Hazan, Liam, Carpuat, Marine, Clinciu, Miruna, Kim, Najoung, Cheng, Newton, Serikov, Oleg, Antverg, Omer, van der Wal, Oskar, Zhang, Rui, Zhang, Ruochen, Gehrmann, Sebastian, Mirkin, Shachar, Pais, Shani, Shavrina, Tatiana, Scialom, Thomas, Yun, Tian, Limisiewicz, Tomasz, Rieser, Verena, Protasov, Vitaly, Mikhailov, Vladislav, Pruksachatkun, Yada, Belinkov, Yonatan, Bamberger, Zachary, Kasner, Zdeněk, Rueda, Alice, Pestana, Amanda, Feizpour, Amir, Khan, Ammar, Faranak, Amy, Santos, Ana, Hevia, Anthony, Unldreaj, Antigona, Aghagol, Arash, Abdollahi, Arezoo, Tammour, Aycha, HajiHosseini, Azadeh, Behroozi, Bahareh, Ajibade, Benjamin, Saxena, Bharat, Ferrandis, Carlos Muñoz, McDuff, Daniel, Contractor, Danish, Lansky, David, David, Davis, Kiela, Douwe, Nguyen, Duong A., Tan, Edward, Baylor, Emi, Ozoani, Ezinwanne, Mirza, Fatima, Ononiwu, Frankline, Rezanejad, Habib, Jones, Hessie, Bhattacharya, Indrani, Solaiman, Irene, Sedenko, Irina, Nejadgholi, Isar, Passmore, Jesse, Seltzer, Josh, Sanz, Julio Bonis, Dutra, Livia, Samagaio, Mairon, Elbadri, Maraim, Mieskes, Margot, Gerchick, Marissa, Akinlolu, Martha, McKenna, Michael, Qiu, Mike, Ghauri, Muhammed, Burynok, Mykola, Abrar, Nafis, Rajani, Nazneen, Elkott, Nour, Fahmy, Nour, Samuel, Olanrewaju, An, Ran, Kromann, Rasmus, Hao, Ryan, Alizadeh, Samira, Shubber, Sarmad, Wang, Silas, Roy, Sourav, Viguier, Sylvain, Le, Thanh, Oyebade, Tobi, Le, Trieu, Yang, Yoyo, Nguyen, Zach, Kashyap, Abhinav Ramesh, Palasciano, Alfredo, Callahan, Alison, Shukla, Anima, Miranda-Escalada, Antonio, Singh, Ayush, Beilharz, Benjamin, Wang, Bo, Brito, Caio, Zhou, Chenxi, Jain, Chirag, Xu, Chuxin, Fourrier, Clémentine, Periñán, Daniel León, Molano, Daniel, Yu, Dian, Manjavacas, Enrique, Barth, Fabio, Fuhrimann, Florian, Altay, Gabriel, Bayrak, Giyaseddin, Burns, Gully, Vrabec, Helena U., Bello, Imane, Dash, Ishani, Kang, Jihyun, Giorgi, John, Golde, Jonas, Posada, Jose David, Sivaraman, Karthik Rangasai, Bulchandani, Lokesh, Liu, Lu, Shinzato, Luisa, de Bykhovetz, Madeleine Hahn, Takeuchi, Maiko, Pàmies, Marc, Castillo, Maria A, Nezhurina, Marianna, Sänger, Mario, Samwald, Matthias, Cullan, Michael, Weinberg, Michael, De Wolf, Michiel, Mihaljcic, Mina, Liu, Minna, Freidank, Moritz, Kang, Myungsun, Seelam, Natasha, Dahlberg, Nathan, Broad, Nicholas Michio, Muellner, Nikolaus, Fung, Pascale, Haller, Patrick, Chandrasekhar, Ramya, Eisenberg, Renata, Martin, Robert, Canalli, Rodrigo, Su, Rosaline, Su, Ruisi, Cahyawijaya, Samuel, Garda, Samuele, Deshmukh, Shlok S, Mishra, Shubhanshu, Kiblawi, Sid, Ott, Simon, Sang-aroonsiri, Sinee, Kumar, Srishti, Schweter, Stefan, Bharati, Sushil, Laud, Tanmay, Gigant, Théo, Kainuma, Tomoya, Kusa, Wojciech, Labrak, Yanis, Bajaj, Yash Shailesh, Venkatraman, Yash, Xu, Yifan, Xu, Yingxin, Xu, Yu, Tan, Zhe, Xie, Zhongli, Ye, Zifan, Bras, Mathilde, Belkada, Younes, and Wolf, Thomas

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Published
2022

3. DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale

Author

Aminabadi, Reza Yazdani, Rajbhandari, Samyam, Zhang, Minjia, Awan, Ammar Ahmad, Li, Cheng, Li, Du, Zheng, Elton, Rasley, Jeff, Smith, Shaden, Ruwase, Olatunji, and He, Yuxiong

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance
Abstract

The past several years have witnessed the success of transformer-based models, and their scale and application scenarios continue to grow aggressively. The current landscape of transformer models is increasingly diverse: the model size varies drastically with the largest being of hundred-billion parameters; the model characteristics differ due to the sparsity introduced by the Mixture-of-Experts; the target application scenarios can be latency-critical or throughput-oriented; the deployment hardware could be single- or multi-GPU systems with different types of memory and storage, etc. With such increasing diversity and the fast-evolving pace of transformer models, designing a highly performant and efficient inference system is extremely challenging. In this paper, we present DeepSpeed Inference, a comprehensive system solution for transformer model inference to address the above-mentioned challenges. DeepSpeed Inference consists of (1) a multi-GPU inference solution to minimize latency while maximizing the throughput of both dense and sparse transformer models when they fit in aggregate GPU memory, and (2) a heterogeneous inference solution that leverages CPU and NVMe memory in addition to the GPU memory and compute to enable high inference throughput with large models which do not fit in aggregate GPU memory. DeepSpeed Inference reduces latency by up to 7.3X over the state-of-the-art for latency-oriented scenarios and increases throughput by over 1.5x for throughput-oriented scenarios. Moreover, it enables trillion parameter scale inference under real-time latency constraints by leveraging hundreds of GPUs, an unprecedented scale for inference. It can inference 25x larger models than with GPU-only solutions, while delivering a high throughput of 84 TFLOPS (over $50\%$ of A6000 peak).

Published
2022

4. Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Author

Smith, Shaden, Patwary, Mostofa, Norick, Brandon, LeGresley, Patrick, Rajbhandari, Samyam, Casper, Jared, Liu, Zhun, Prabhumoye, Shrimai, Zerveas, George, Korthikanti, Vijay, Zhang, Elton, Child, Rewon, Aminabadi, Reza Yazdani, Bernauer, Julie, Song, Xia, Shoeybi, Mohammad, He, Yuxiong, Houston, Michael, Tiwary, Saurabh, and Catanzaro, Bryan

Subjects
Computer Science - Computation and Language
Abstract

Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of their success, the size of these models has increased rapidly, requiring high-performance hardware, software, and algorithmic techniques to enable training such large models. As the result of a joint effort between Microsoft and NVIDIA, we present details on the training of the largest monolithic transformer based language model, Megatron-Turing NLG 530B (MT-NLG), with 530 billion parameters. In this paper, we first focus on the infrastructure as well as the 3D parallelism methodology used to train this model using DeepSpeed and Megatron. Next, we detail the training process, the design of our training corpus, and our data curation techniques, which we believe is a key ingredient to the success of the model. Finally, we discuss various evaluation results, as well as other interesting observations and new properties exhibited by MT-NLG. We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning accuracies on several NLP benchmarks and establishes new state-of-the-art results. We believe that our contributions will help further the development of large-scale training infrastructures, large-scale language models, and natural language generations., Comment: Shaden Smith and Mostofa Patwary contributed equally

Published
2022

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