These options correspond to previously described parallelism techniques: 2D parameter partitioning is also known as ZeRO-3 (Rajbhandari et al., 2020) or fully sharded data parallelism; 1D activation partitioning is also known as Megatron (Shoeybi et al., 2019) and is the default in the Mesh TensorFlow Transformer (Shazeer et al., 2018); and 2D activation partitioning is the fully sharded case described in Xu et al. We also provide a model configuration (without any checkpoints) for a decoder-only architecture that is compatible with LaMDA(Thoppilan et al., 2022). This paper shows that a standard Transformer architecture can be used with minimal modifications to process byte sequences, characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and shows that byte-level models are competitive with their token-level counterparts. JAX(Bradbury et al., 2018; Frostig et al., 2018) is uniquely positioned to provide such benefits; its NumPy-like(Harris et al., 2020) API makes it easy to understand and develop, while the jax.pjit API backed by XLA GSPMD (Xu et al., 2021) provides a powerful and efficient compiler-based programming model for parallelism. View 8 excerpts, references methods and background. $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively. Furthermore, downstream usage and evaluation of these models requires either finetuning or prompting, which must be applied consistently across competing models. View 4 excerpts, cites background and methods. External adopters include academic and commercial users of Cloud TPUs, such as portions of the the Big Science project (Wang et al., 2022). James Bradbury implemented partitioning in t5x and co-wrote the paper. It is essentially a new and improved implementation of the T5 codebase (based on Mesh TensorFlow) in JAX and Flax. Additionally, models trained with the legacy T5 codebase444https://github.com/google-research/text-to-text-transfer-transformer based on Mesh TensorFlow can be read directly by t5x. Marvin Ritter advised on deterministic pipelines and the use of CLU Metrics. However, training models at these sizes is challenging and often demands specialized and hand-tuned software systems, making it difficult to quickly iterate over experimental research ideas. In (tensor) model parallelism, the model computation for a single example, and the model parameters themselves, are split across devices. At runtime, the user provides a mapping from each logical axis name to one of the two hardware axes (model and data). 1. They closely mimic the pedagogical Flax examples777https://github.com/google/flax/tree/main/examples. We also provide a model configuration (without any checkpoints) for a decoder-only architecture that is compatible with LaMDA(Thoppilan et al., 2022). What language model architecture and pretraining objective work best for zero-shot generalization? Thanks to the many other contributors to the project: Ian Simon, Reiner Pope, Vincent Zhao, Pierre Ruyssen, Linting Xue, Junwhan Ahn, Barret Zoph, David Dohan, Masumi Parekh, Chang Lan, Frederick Liu, Julien Amelot, Luheng He, Fede Lebron, Rebecca Chen, Anosh Raj, Mandy Guo, Ethan Dyer, Mihai Tiuca, Hongkun Yu, Kevin Brooks, David Soergel, Kelvin Guu, Joshua Ainslie, Luyao Xu, Ji Ma, Josh Gardner, Daphne Ippolito, Peter Hawkins, Bo Pang, Marc Rasi, Wei Li, Wenhu Chen, Iulia Turc, John Wieting, Alex Passos, Zonglin Li, Katie Everett, Marvin Ritter, Olivier Bachem, Francesco Piccinno, Jakub Adamek, Jonathan Heek, Parker Schuh, Hexiang Hu, Du Phan, Max Moroz, David Miller, Ryan Doherty, David Elworthy, Alfonso Castao, Julian Eisenschlos, Vlad-Doru Ion, Lucas Dixon, Ron Shapiro, Dinghua Li, Aaron Parisi, Xi Chen, Nan Ding, Chung-ching Chang, Timothy Dozat, Natalia Ponomareva, Delesley Hutchins, Ankush Garg, Yu-Han Liu, Mehrdad Khatir, Costanza Conforti, Philipp Keck, Raphal Marinier, Marie Pellat, Raghuram Vadapalli, Joshua Maynez, Yi Tay, Xihui Wu, David Belanger, Luke Metz, Dan Zheng, Deepti Bhatia, Hariharan Shanmugavadivel, Rewon Child, Rigel Swavely, Mihir Sanjay Kale, Arash Afkanpour, Roberto Rama, Juro Gottweis, Jonathan Herzig, Yilei Yang, Elias Mizan, Pedram Pejman, Jiayu Ye, Smit Sanghavi, Rahul Joshi, Ziqiang Feng, Charles Sutton, Weikang Zhou, Liam Fedus, Shanqing Cai, Ginger Perng, Yash Katariya, Urvashi Khandelwal, Sebastian Gehrmann, Edward Loper, Tianze Shi, Luke Vilnis, Amelia Archer, Tom Weingarten, David Zats, Murtaza Dhuliawala, Xin Xie, Sahil Dua, Andr Susano Pinto, Piotr Padlewski, Sascha Rothe, Erik Aas, Felix Stahlberg, Ken Durden, Christina Sorokin, Jaehoon Lee, Roy Frostig, Jacob Devlin, Jorge Gonzalez Mendez, Deepak Ramachandran, Santiago Ontanon, Karthik Raman, Yi Sun, Ali Elqursh, Reuben La Haye, Adam Fahrenkopf, Alex Polozov, Vinay Ramasesh, Ian Tenney. Le (2022), LaMDA: Language Models for Dialog Applications. It uses tf.data.Dataset to create scalable data pipelines but requires minimal use of TensorFlow. By combining the benefits of on-demand content, coaching, community, and practical exercises on a robust Scaling Up performance platform, we give leaders like you the surest pathway to evolve your company and yourself. Thanks to Douglas Eck and Zoubin Ghahramani for sponsoring the project. A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. Alex Salcianu's 5 research works with 31 citations and 153 reads, including: Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio} TensorFlow: large-scale machine learning on heterogeneous systems, PyTorch: an imperative style, high-performance deep learning library, Exploring the limits of transfer learning with a unified text-to-text transformer, Tensor2tensor for neural machine translation, 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Gaurav Mishra leads seqio, implemented deterministic pipelines, and co-authored the paper. Figure 1 illustrates the modular structure of t5x, in particular how t5x uses open-source libraries to implement different functionalities. Please note that the y-axis is in log scale . To achieve these requirements, once a Deterministic Task/Mixture is defined, a distributed caching job (implemented in Apache Beam555https://beam.apache.org) loads the raw data, preprocesses and shuffles the examples, assigns ordered indices, and writes the data to sharded files. Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the need to distribute computation on supercomputer clusters (e.g., TPUs), prevent bottlenecks when infeeding data . Figure 2: Percentage of nonzero entries across different layers of trained Transformers (a) for both language data with T5 and vision data with ViT, (b) on both training and evaluation data, (c) for ViT trained on two ImageNet of different scales (21k vs 1k classes), (d) on ViT of varying configurations, and (e, f) on T5 of varying configurations. seqio is a library for processing data to be fed into models for training, inference, and evaluation. rea Communication becomes a bottleneck in various distributed Machine Learni Owing to the remarkable photometric precision of space observatories lik flax.partitioning.with_sharding_constraint, M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. As a typical example, users can inject hyperparameters or a custom model object as function arguments for training. Thanks to the many other contributors to the project: Ian Simon, Reiner Pope, Vincent Zhao, Pierre Ruyssen, Linting Xue, Junwhan Ahn, Barret Zoph, David Dohan, Masumi Parekh, Chang Lan, Frederick Liu, Julien Amelot, Luheng He, Fede Lebron, Rebecca Chen, Anosh Raj, Mandy Guo, Ethan Dyer, Mihai Tiuca, Hongkun Yu, Kevin Brooks, David Soergel, Kelvin Guu, Joshua Ainslie, Luyao Xu, Ji Ma, Josh Gardner, Daphne Ippolito, Peter Hawkins, Bo Pang, Marc Rasi, Wei Li, Wenhu Chen, Iulia Turc, John Wieting, Alex Passos, Zonglin Li, Katie Everett, Marvin Ritter, Olivier Bachem, Francesco Piccinno, Jakub Adamek, Jonathan Heek, Parker Schuh, Hexiang Hu, Du Phan, Max Moroz, David Miller, Ryan Doherty, David Elworthy, Alfonso Castao, Julian Eisenschlos, Vlad-Doru Ion, Lucas Dixon, Ron Shapiro, Dinghua Li, Aaron Parisi, Xi Chen, Nan Ding, Chung-ching Chang, Timothy Dozat, Natalia Ponomareva, Delesley Hutchins, Ankush Garg, Yu-Han Liu, Mehrdad Khatir, Costanza Conforti, Philipp Keck, Raphal Marinier, Marie Pellat, Raghuram Vadapalli, Joshua Maynez, Yi Tay, Xihui Wu, David Belanger, Luke Metz, Dan Zheng, Deepti Bhatia, Hariharan Shanmugavadivel, Rewon Child, Rigel Swavely, Mihir Sanjay Kale, Arash Afkanpour, Roberto Rama, Juro Gottweis, Jonathan Herzig, Yilei Yang, Elias Mizan, Pedram Pejman, Jiayu Ye, Smit Sanghavi, Rahul Joshi, Ziqiang Feng, Charles Sutton, Weikang Zhou, Liam Fedus, Shanqing Cai, Ginger Perng, Yash Katariya, Urvashi Khandelwal, Sebastian Gehrmann, Edward Loper, Tianze Shi, Luke Vilnis, Amelia Archer, Tom Weingarten, David Zats, Murtaza Dhuliawala, Xin Xie, Sahil Dua, Andr Susano Pinto, Piotr Padlewski, Sascha Rothe, Erik Aas, Felix Stahlberg, Ken Durden, Christina Sorokin, Jaehoon Lee, Roy Frostig, Jacob Devlin, Jorge Gonzalez Mendez, Deepak Ramachandran, Santiago Ontanon, Karthik Raman, Yi Sun, Ali Elqursh, Reuben La Haye, Adam Fahrenkopf, Alex Polozov, Vinay Ramasesh, Ian Tenney. Partitioning - One of the key features of t5x is its ability to parallelize over data, parameters, and activations. By clicking accept or continuing to use the site, you agree to the terms outlined in our. These two kinds of parallelism are orthogonal, in that a system with N=MD devices can use M-way model parallelism and D-way data parallelism at the same time. He (2020), ZeRO: memory optimizations toward training trillion parameter models, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, J. Rasley, S. Rajbhandari, O. Ruwase, and Y. respectively. In the following subsections, we discuss the design of t5x including how it wraps jax.pjit to provide a high-level interface to XLA GSPMD for simple yet efficient scaling via parameter, activation, and data partitioning. Scalable T5 is an implementation of T5.1.1 using jax.scan to significantly reduce compilation time and provide finer-grained control over activation memory. With its modular design, the model implementations in t5x can be flexible. We are continuing to actively develop both libraries, prioritizing future work based on researcher needs and feedback. t5x simplifies the process of building and training large language models at scale while maintaining ease of use, and seqio provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. These two kinds of parallelism are orthogonal, in that a system with N=MD devices can use M-way model parallelism and D-way data parallelism at the same time. A. Vaswani, S. Bengio, E. Brevdo, F. Chollet, A. N. Gomez, S. Gouws, L. Jones, . Kaiser, N. Kalchbrenner, N. Parmar, Tensor2tensor for neural machine translation, A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, . Kaiser, and I. Polosukhin (2017), Advances in neural information processing systems, T. Wang, A. Roberts, D. Hesslow, T. L. Scao, H. W. Chung, I. Beltagy, J. Launay, and C. Raffel (2022). He (2020), ZeRO: memory optimizations toward training trillion parameter models, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, J. Rasley, S. Rajbhandari, O. Ruwase, and Y. A key differentiator of seqio from most other dataset frameworks is its use of a task-based API, which is illustrated in Figure 2. . This way the same task can be made compatible with various architectures (e.g., encoder-decoder or decoder-only). ), C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu (2020), Exploring the limits of transfer learning with a unified text-to-text transformer, S. Rajbhandari, J. Rasley, O. Ruwase, and Y. Previous Google-released systems based on TensorFlow include Tensor2Tensor(Vaswani et al., 2018), Lingvo(Shen et al., 2019), and the Mesh TensorFlow(Shazeer et al., 2018)-based T5 (Raffel et al., 2020). Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. In This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. We have found these four features to be beneficial when training extremely large models. However, training models at these sizes is challenging and often demands specialized and hand-tuned software systems, making it difficult to quickly iterate over experimental research ideas. While XLA GSPMD will automatically select matching partitions for the intermediate activations produced by these parameters, users may also provide overrides by naming their axes as well via flax.partitioning.with_sharding_constraint to better optimize memory usage and between-device communication. Flaxformer). In this paper, we present t5x, a JAX-based open-source library that is focused on building Transformer models at a wide range of scales. As a typical example, users can inject hyperparameters or a custom model object as function arguments for training. In particular, researchers should be able to control function arguments and even use custom components without needing to modify the core library code. Recoverability - A deterministic dataset can be continued from an arbitrary point in training. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. In Transformers, it involves partitioning parameters and some intermediate activations along axes like the MLP hidden dimension and the heads dimension. t5x is compatible with Flax-based model implementations with some minor caveats. This is online education built for real-time ROI. ), C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu (2020), S. Rajbhandari, J. Rasley, O. Ruwase, and Y. arXiv as responsive web pages so you Dependency injection with Gin allows users to easily swap the module implementation in their configuration. James Bradbury implemented partitioning in t5x and co-wrote the paper. Section 2.3 discusses a few specialized features in Flax that are required by t5x to enable model parallelism. This avoids repeating data on intentional or unintentional (e.g., due to preemption) training restarts, which can lead to reduced performance and memorization (Lee et al., 2022). These open-source libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data. In this work, we present two software libraries that ease these issues: t5x simplifies the process of building and training large language models at scale while maintaining ease of use, and seqio provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. With t5x, we provide well-tested666We validated these models by reproducing the T5 models fromRaffel et al. $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively. We started the project in the fall of 2020 and open sourced the library code in October 2021. This is particularly important when examples are correlated (e.g., they are based on the same source document) or multiple epochs are used. We use the XLA GSPMD partitioner(Xu et al., 2021) to automatically shard the computation graph and use jax.pjit as a frontend to interact with GSPMD, providing our own high-level API to simplify configuration. Scaling can be complicated due to various factors including the need to distribute computation on supercomputer clusters (e.g., TPUs), prevent bottlenecks when infeeding data, and ensure reproducible results. Sharding - Data can be arbitrarily sharded across any number of readers to enable efficient distributed reads from data-parallel workers. Anselm Levskaya built the initial prototype for t5x and wrote much of the code. Given a flax.nn.module module implemented as described above, one must simply wrap it in a subclass of t5x.BaseModel to define its loss, evaluation, and inference methods to make it compatible with the core t5x interface. t5x is a library for training, evaluating, and inferring with JAX models across many scales, with a focus on Transformer-based language models. Jeremy Maitin-Shepard advised on the use of TensorStore. Datasets and Evaluation - By default, we use seqio to create reproducible tasks, which we cover in detail in Section 3. A key differentiator of seqio from most other dataset frameworks is its use of a task-based API, which is illustrated in Figure 2. Introduction Scaling Up Models and Data with t5x and seqio Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, and 39 others including Colin Raffel arXiv preprint arXiv:2203.17189, 2022. By scaling the model up to 20B parameters, this paper achieves SOTA performance on 50 well-established supervised NLP tasks ranging from language generation, language understanding, text classication, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval. Some major differentiators of t5x are its use of JAX and Flax for model expression, its support for TPU (including TPU v4), and its Gin-based configuration system that allows uses to modify nearly everything about the model and training procedure. Y. Xu, H. Lee, D. Chen, B. Hechtman, Y. Huang, R. Joshi, M. Krikun, D. Lepikhin, A. Ly, M. Maggioni, R. Pang, N. Shazeer, S. Wang, T. Wang, Y. Wu, and Z. Chen (2021), GSPMD: General and Scalable Parallelization for ML Computation Graphs, L. Xue, A. Barua, N. Constant, R. Al-Rfou, S. Narang, M. Kale, A. Roberts, and C. Raffel (2022), ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models, Transactions of the Association for Computational Linguistics, L. Xue, N. Constant, A. Roberts, M. Kale, R. Al-Rfou, A. Siddhant, A. Barua, and C. Raffel (2021), MT5: a massively multilingual pre-trained text-to-text transformer, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, https://github.com/google-research/text-to-text-transfer-transformer, https://github.com/google/flax/tree/main/examples, https://github.com/facebookresearch/fairscale, https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00461/2004058/tacl_a_00461.pdf. Layers and modules can be written directly with Flax (e.g., the Minimal implementations discussed in Section 4) or using a higher-level library such as Flaxformer333https://github.com/google/flaxformer. Model parallelism involves partitioning model computation over axes other than the batch dimension. R ANK G EN is presented, a 1.2B parameter encoder model for English that scores model generations given a prex that signif-icantly outperforms decoding algorithms like nucleus, top- k, and typical sampling on both automatic metrics and human evaluations with English writers. Noah Fiedel is a member of the leadership team, contributed to the high level design and roadmap, and co-wrote the paper.
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