scaling up models and data with t5x and seqio

Feature converters are used to convert task features into the raw values that will be fed into the model itself. In (tensor) model parallelism, the model computation for a single example, and the model parameters themselves, are split across devices. Introduction ), 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. 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. We have found these four features to be beneficial when training extremely large models. Fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks and supports distributed training across multiple GPUs and machines. At runtime, the user provides a mapping from each logical axis name to one of the two hardware axes (model and data). We have also found it useful for manually skipping batches of a dataset that produce instabilities during training. Request PDF | Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio} | Recent neural network-based language models have benefited greatly from scaling up the size of training datasets . 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. SeqIO is a library for processing sequential data to be fed into downstream sequence models. 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. Model parallelism involves partitioning model computation over axes other than the batch dimension. These logical axes are used to group tensor dimensions that one would expect to always partition in the same way in various settings, for example batch (for partitioning across examples in a batch), kv (for partitioning across the dimensions of key-value matrices in Transformer self-attention layers), or head (for partitioning across heads in multi-headed attention). 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. More advanced users can replace entire modules (e.g., a custom checkpointer) in a similar manner. Scaling Up Models and Data with $\\texttt{t5x}$ and $\\texttt{seqio}$. 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. Teams are using these libraries for research projects (from small-scale research to the largest language models trained at Google) and user-facing products. 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 is particularly important when examples are correlated (e.g., they are based on the same source document) or multiple epochs are used. Even when implemented in different libraries, the model checkpoints can be made compatible. 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. flax.partitioning.with_sharding_constraint, M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. T5-like encoder-decoder models as well as GPT-like decoder-only architectures. the need to distribute computation on supercomputer clusters (e.g., TPUs), The former is also termed 1D parameter partitioning, since parameters are only subject to model parallel partitioning over one array axis, while the latter is 2D parameter partitioning since a second array axis in each parameter is also partitioned. These model implementations are minimal in the sense that they only use Flax with limited abstractions as opposed to using higher-level libraries built on top of Flax (e.g. He (2020), DeepSpeed: system optimizations enable training deep learning models with over 100 billion parameters, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, N. Shazeer, Y. Cheng, N. Parmar, D. Tran, A. Vaswani, P. Koanantakool, P. Hawkins, H. Lee, M. Hong, C. Young, R. Sepassi, and B. Hechtman (2018), Mesh-tensorflow: deep learning for supercomputers, Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). prevent bottlenecks when infeeding data, and ensure reproducible results. 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. 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. He, M. Houston, S. Tiwary, and B. Catanzaro (2022), Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model, R. Thoppilan, D. De Freitas, J. T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of sequence models (starting with language) at many scales. (2020), originally implemented in Mesh TensorFlow Transformer. In particular, with one line of code, the returned dataset can be transformed to a numpy iterator and hence it is fully . View 8 excerpts, references methods and background. 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. Year. 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. It is essentially a new and improved implementation of the T5 codebase (based on Mesh TensorFlow) in JAX and Flax. 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. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. These open-source libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data. 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. Neural network models are highly scalable. It uses tensorflow.data to create scalable data pipelines but requires minimal use of TensorFlow. UL2 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. It uses tf.data.Dataset to create scalable data pipelines but requires minimal use of TensorFlow. Greater New York City Area. 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. Checkpointing - For large models, straightforward tasks like checkpointing can be challenging, especially when using parameter and optimizer partitioning. Dependency injection with Gin allows users to easily swap the module implementation in their configuration. Click To Get Model/Code. ByT5: Towards a token-free future with pre-trained byte-to-byte models Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. They increase throughput, protect against overfitting, ease debugging, and provide fine-grained control over the examples seen during training in order to avoid instabilities. 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. (2020), originally implemented in Mesh TensorFlow Transformer. We started the project in the fall of 2020 and open sourced the library code in October 2021. Models - To actually implement the modeling layers, we use Flax(Heek et al., 2020), a high-level library built on JAX. 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. This enables each set of data parallel hosts to sequentially read an interleave an exclusive set of files at train time, helping to optimize throughput and greatly reducing the chance of an input bottleneck. Former undergrad @IITBombay and intern @GoogleAI, @TTIC_Connect, @mozilla. More advanced users can replace entire modules (e.g., a custom checkpointer) in a similar manner. J. Shen, P. Nguyen, Y. Wu, Z. Chen, M. X. Chen, Y. Jia, A. Kannan, T. Sainath, Y. Cao, C. Chiu, Lingvo: a modular and scalable framework for sequence-to-sequence modeling, M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro (2019), Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism, S. Smith, M. Patwary, B. Norick, P. LeGresley, S. Rajbhandari, J. Casper, Z. Liu, S. Prabhumoye, G. Zerveas, V. Korthikanti, E. Zhang, R. Child, R. Yazdani Aminabadi, J. Bernauer, X. Colin Raffel and Noam Shazeer helped design seqio. Minimal model implementations with checkpoints: T5 fromRaffel et al. Please note that the y-axis is in log scale . Neural network models are highly scalable. Maarten Bosma helped design deterministic pipelines. It uses tf.data.Dataset to create scalable data pipelines but requires minimal use of TensorFlow. Recoverability - A deterministic dataset can be continued from an arbitrary point in training. Analysis and optimization of droop controller for microgrid system based on small-signal dynamic model. Other intermediate activations (those with an embedding/model axis but not hidden/heads) can either be replicated over the model parallel axis (1D activation partitioning) or sharded (2D activation partitioning). We expect this control to also be useful for researchers interested in understanding how specific aspects of the dataset (e.g., order and repeats) might affect the models ability to generalize or memorize. Alex Salcianu's 5 research works with 31 citations and 153 reads, including: Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio} $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. 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. (2021). With t5x, we provide well-tested666We validated these models by reproducing the T5 models fromRaffel et al. A well-read pre-trained language model for Chinese that is able to seamlessly perform different types of tasks with zero or few-shot demonstrations, and has basic skills at explaining and calibrating the decisions from itself, which can be promising directions for future research. A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. Le (2022), LaMDA: Language Models for Dialog Applications. Importantly, the examples are sharded by the modulo of their index to the number of files. Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, and James Lee-Thorp made substantial code contributions. Many different kinds of parallelism are useful for scaling large models. 2019. 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). A key differentiator of seqio from most other dataset frameworks is its use of a task-based API, which is illustrated in Figure 2. . Anselm Levskaya built the initial prototype for t5x and wrote much of the code. 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. [PDF] Scaling Up Models and Data with t5x and seqio | Semantic Scholar Recent neural network-based language models have beneted greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Get our free extension to see links to code for papers anywhere online! terabytes of training data. These open-source libraries have been used to train Keywords: Large language models, data parallelism, model parallelism, data processing 1 arXiv:2203.17189v1 [cs.LG] 31 Mar 2022. It can also involve either replicating the parameters and optimizer state or sharding them over the data parallel axis. A. Vaswani, S. Bengio, E. Brevdo, F. Chollet, A. N. Gomez, S. Gouws, L. Jones, . Kaiser, N. Kalchbrenner, N. Parmar, 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). These model implementations are minimal in the sense that they only use Flax with limited abstractions as opposed to using higher-level libraries built on top of Flax (e.g. t5x also doesnt support pipeline parallelism, a major component of systems like DeepSpeed. SeqIO is a library for processing sequential data to be fed into downstream sequence models. K Yu, Q Ai, S Wang, J Ni, T Lv. T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of sequence models (starting with language) at many scales. Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$ 2 code implementations . 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. Marc van Zee's 3 research works with 209 reads, including: Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio} With its modular design, the model implementations in t5x can be flexible. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Vigas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng (2015), M. Baines, S. Bhosale, V. Caggiano, N. Goyal, S. Goyal, M. Ott, B. Lefaudeux, V. Liptchinsky, M. Rabbat, S. Sheiffer, A. Sridhar, and M. Xu (2021), FairScale: a general purpose modular pytorch library for high performance and large scale training, J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang (2018), JAX: composable transformations of Python+NumPy programs, R. Frostig, M. Johnson, and C. Leary (2018), Compiling machine learning programs via high-level tracing, C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Ro, M. Wiebe, P. Peterson, P. Grard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant (2020), J. Heek, A. Levskaya, A. Oliver, M. Ritter, B. Rondepierre, A. Steiner, and M. van Zee (2020), Flax: a neural network library and ecosystem for JAX, K. Lee, D. Ippolito, A. Nystrom, C. Zhang, D. Eck, C. Callison-Burch, and N. Carlini (2022), Deduplicating training data makes language models better, M. Ott, S. Edunov, A. Baevski, A. - Deterministic datasets are prepared by an offline job that ensures data is well-shuffled. Alexandre Passos and Ryan Sepassi advised on overall technical design. Recoverability - A deterministic dataset can be continued from an arbitrary point in training. Figure 1 illustrates the modular structure of t5x, in particular how t5x uses open-source libraries to implement different functionalities. There are many open source libraries for training sequence models. Adam Roberts founded and leads the project, designed and wrote much of seqio and t5x, and co-authored the paper. t5x is compatible with Flax-based model implementations with some minor caveats. arXiv Vanity renders academic papers from T5X. Marvin Ritter advised on deterministic pipelines and the use of CLU Metrics. We have found these four features to be beneficial when training extremely large models. In Transformers, it involves partitioning parameters and some intermediate activations along axes like the MLP hidden dimension and the heads dimension. 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. For everything else, email us at [emailprotected]. (2021). The Task object associates raw data sources with preprocessing stepsto define the inputs and targetsand evaluation metricsto create consistent benchmarks. Jeremy Maitin-Shepard advised on the use of TensorStore. Sharding - Data can be arbitrarily sharded across any number of readers to enable efficient distributed reads from data-parallel workers. Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, and James Lee-Thorp made substantial code contributions. This is online education built for real-time ROI. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. J Ni, J Li, J McAuley. 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. 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. 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). Gaurav Mishra leads seqio, implemented deterministic pipelines, and co-authored the paper. In this work, we present two software libraries that ease these issues: $\texttt{t5x}$ simplifies the process of building and training large language models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. 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. (2020) and T5.1.1 (introduced after the paper). Datasets and Evaluation - By default, we use seqio to create reproducible tasks, which we cover in detail in Section 3. To add evaluation results you first need to, Papers With Code is a free resource with all data licensed under, add a task GROOT is a simple yet effective framework for Generative Reward Optimization Of Text sequences by training a generative sequential labeling model to match the decoder output distribution with that of the (black-box) reward function. This way the same task can be made compatible with various architectures (e.g., encoder-decoder or decoder-only). He (2020), DeepSpeed: system optimizations enable training deep learning models with over 100 billion parameters, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, N. Shazeer, Y. Cheng, N. Parmar, D. Tran, A. Vaswani, P. Koanantakool, P. Hawkins, H. Lee, M. Hong, C. Young, R. Sepassi, and B. Hechtman (2018), Mesh-tensorflow: deep learning for supercomputers, Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Hyung Won Chung designed and wrote much of t5x, led its open sourcing, and co-authored the paper. Get model/code for Scaling Up Models and Data with $\\texttt{t5x}$ and $\\texttt{seqio}$ Users of t5x and seqio cite the usability and research-friendliness of the libraries as reasons for adoption. Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov (Product Manager), and Josh Newlan (Technical Program Manager) are members of the leadership team and co-wrote the paper. The typical usage involves either pretraining from scratch or finetuning an existing language model implemented in Flaxa JAX-based neural network library (Heek et al., 2020)and then running inference for evaluations and/or downstream applications. Linear constrained optimization techniques have been applied to many Du, Y. Li, H. Lee, H. S. Zheng, A. Ghafouri, M. Menegali, Y. Huang, M. Krikun, D. Lepikhin, J. Qin, D. Chen, Y. Xu, Z. Chen, A. Roberts, M. Bosma, V. Zhao, Y. Zhou, C. Chang, I. Krivokon, W. Rusch, M. Pickett, P. Srinivasan, L. Man, K. Meier-Hellstern, M. Ringel Morris, T. Doshi, R. Delos Santos, T. Duke, J. Soraker, B. Zevenbergen, V. Prabhakaran, M. Diaz, B. Hutchinson, K. Olson, A. Molina, E. Hoffman-John, J. Lee, L. Aroyo, R. Rajakumar, A. Butryna, M. Lamm, V. Kuzmina, J. Fenton, A. Cohen, R. Bernstein, R. Kurzweil, B. Aguera-Arcas, C. Cui, M. Croak, E. Chi, and Q. These logical axes are used to group tensor dimensions that one would expect to always partition in the same way in various settings, for example batch (for partitioning across examples in a batch), kv (for partitioning across the dimensions of key-value matrices in Transformer self-attention layers), or head (for partitioning across heads in multi-headed attention). 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. GPU and CPU acceleration are supported, but t5x is optimized for TPU.

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