A Novel Tensor-Expert Hybrid Parallelism Approach to Scale Mixture-of-Experts Training

A new neural network architecture called Mixture-of-Experts (MoE) has been proposed recently that increases the parameters of a neural network (the base model) by adding sparsely activated expert blocks, without changing the total number of floating point operations for training or inference. In theory, this architecture allows us to train arbitrarily large models while keeping the computational costs same as that of the base model. However, beyond 64 to 128 experts blocks, prior work has observed diminishing returns in the test accuracies of these MoE models. Thus, training high quality MoE models requires us to scale the size of the base models, along with the number of expert blocks. In this work, we propose a novel, three-dimensional, hybrid parallel algorithm that combines tensor, expert, and data parallelism to enable the training of MoE models with 4-8x larger base models than the current state-of-the-art -- DeepSpeed-MoE. We propose memory optimizations in the optimizer step, and communication optimizations that eliminate redundant movement of data. Removing these redundancies provides a speedup of nearly 21%. When training a 40 billion parameter MoE model (6.7 billion base model with 16 experts) on 128 V100 GPUs, our optimizations significantly improve the peak half precision flop/s from 20% to 27%

DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention

Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms. To address this, we present the DeepSpeed-VisualChat framework, designed to optimize Large Language Models (LLMs) by incorporating multi-modal capabilities, with a focus on enhancing the proficiency of Large Vision and Language Models in handling interleaved inputs. Our framework is notable for (1) its open-source support for multi-round and multi-image dialogues, (2) introducing an innovative multi-modal causal attention mechanism, and (3) utilizing data blending techniques on existing datasets to assure seamless interactions in multi-round, multi-image conversations. Compared to existing frameworks, DeepSpeed-VisualChat shows superior scalability up to 70B parameter language model size, representing a significant advancement in multi-modal language models and setting a solid foundation for future explorations

ZeRO++: Extremely Efficient Collective Communication for Giant Model Training

Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, or at scale which forces batch size per GPU to be small, ZeRO's effective throughput is limited because of high communication volume from gathering weights in forward pass, backward pass, and averaging gradients. This paper introduces three communication volume reduction techniques, which we collectively refer to as ZeRO++, targeting each of the communication collectives in ZeRO. First is block-quantization based all-gather. Second is data remapping that trades-off communication for more memory. Third is a novel all-to-all based quantized gradient averaging paradigm as replacement of reduce-scatter collective, which preserves accuracy despite communicating low precision data. Collectively, ZeRO++ reduces communication volume of ZeRO by 4x, enabling up to 2.16x better throughput at 384 GPU scale.Comment: 12 page

LSTM-Sharp: An Adaptable, Energy-Efficient Hardware Accelerator for Long Short-Term Memory

The effectiveness of LSTM neural networks for popular tasks such as Automatic Speech Recognition has fostered an increasing interest in LSTM inference acceleration. Due to the recurrent nature and data dependencies of LSTM computations, designing a customized architecture specifically tailored to its computation pattern is crucial for efficiency. Since LSTMs are used for a variety of tasks, generalizing this efficiency to diverse configurations, i.e., adaptiveness, is another key feature of these accelerators. In this work, we first show the problem of low resource-utilization and adaptiveness for the state-of-the-art LSTM implementations on GPU, FPGA and ASIC architectures. To solve these issues, we propose an intelligent tiled-based dispatching mechanism that efficiently handles the data dependencies and increases the adaptiveness of LSTM computation. To do so, we propose LSTM-Sharp as a hardware accelerator, which pipelines LSTM computation using an effective scheduling scheme to hide most of the dependent serialization. Furthermore, LSTM-Sharp employs dynamic reconfigurable architecture to adapt to the model's characteristics. LSTM-Sharp achieves 1.5x, 2.86x, and 82x speedups on average over the state-of-the-art ASIC, FPGA, and GPU implementations respectively, for different LSTM models and resource budgets. Furthermore, we provide significant energy-reduction with respect to the previous solutions, due to the low power dissipation of LSTM-Sharp (383 GFLOPs/Watt)

Flexible hardware acceleration for instruction-grain program monitoring

Instruction-grain program monitoring tools, which check and analyze executing programs at the granularity of individual instructions, are invaluable for quickly detecting bugs and security attacks and then limiting their damage (via containment and/or recovery). Unfortunately, their fine-grain nature implies very high monitoring overheads for software-only tools, which are typically based on dynamic binary instrumentation. Previous hardware proposals either focus on mechanisms that target specific bugs or address only the cost of binary instrumentation. In this paper, we propose a flexible hardware solution for accelerating a wide range of instruction-grain monitoring tools. By examining a number of diverse tools (for memory checking, security tracking, and data race detection), we identify three significant common sources of overheads and then propose three novel hardware techniques for addressing these overheads; Inheritance Tracking, Idempotent Filters, and Metadata-TLBs. Together, these constitute a general-purpose hardware acceleration framework. Experimental results show our framework reduces overheads by 2-3X over the previous state-of-the-art, while supporting the needed flexibility. © 2008 IEEE

DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales

ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance. However, the current landscape lacks an accessible, efficient, and cost-effective end-to-end RLHF (Reinforcement Learning with Human Feedback) training pipeline for these powerful models, particularly when training at the scale of billions of parameters. This paper introduces DeepSpeed-Chat, a novel system that democratizes RLHF training, making it accessible to the AI community. DeepSpeed-Chat offers three key capabilities: an easy-to-use training and inference experience for ChatGPT-like models, a DeepSpeed-RLHF pipeline that replicates the training pipeline from InstructGPT, and a robust DeepSpeed-RLHF system that combines various optimizations for training and inference in a unified way. The system delivers unparalleled efficiency and scalability, enabling training of models with hundreds of billions of parameters in record time and at a fraction of the cost. With this development, DeepSpeed-Chat paves the way for broader access to advanced RLHF training, even for data scientists with limited resources, thereby fostering innovation and further development in the field of AI.Comment: 14 pages, 7 figure

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

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