Search Results for author:
Found 26 papers, 8 papers with code
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding
Furthermore, we present and inexpensive, heuristic-driven search algorithm that identifies promising heterogeneous compression configurations that meet a compression ratio constraint.
DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale
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.
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements.
Extreme Compression for Pre-trained Transformers Made Simple and Efficient
Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices.
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs
DNN models across many domains continue to grow in size, resulting in high resource requirements for effective training, and unpalatable (and often unaffordable) costs for organizations and research labs across scales.
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam
1-bit gradient compression and local steps are two representative techniques that enable drastic communication reduction in distributed SGD.
ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise
In recent years, large pre-trained Transformer-based language models have led to dramatic improvements in many natural language understanding tasks.
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
As the training of giant dense models hits the boundary on the availability and capability of the hardware resources today, Mixture-of-Experts (MoE) models become one of the most promising model architectures due to their significant training cost reduction compared to a quality-equivalent dense model.
A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities
With both scaling trends, new problems and challenges emerge in DL inference serving systems, which gradually trends towards Large-scale Deep learning Serving systems (LDS).
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM
In particular, we propose to formulate the NxM sparsity as a constrained optimization problem and use Alternating Direction Method of Multipliers (ADMM) to optimize the downstream tasks while taking the underlying hardware constraints into consideration.
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
In particular, in mobile and IoT devices, real-time data can be stored not just in high-speed RAMs but in internal storage devices as well, which offer significantly larger capacity than the RAMs.
Demystifying Hyperparameter Optimization in Federated Learning
Such data heterogeneity and privacy requirements bring unique challenges for learning hyperparameter optimization as the training dynamics change across clients even within the same training round and they are difficult to measure due to privacy constraints.
HoloFormer: Deep Compression of Pre-Trained Transforms via Unified Optimization of N:M Sparsity and Integer Quantization
In this work, we propose a unified, systematic approach to learning N:M sparsity and integer quantization for pre-trained Transformers using the Alternating Directions Method of Multipliers (ADMM).
ScaLA: Speeding-Up Fine-tuning of Pre-trained Transformer Networks via Efficient and Scalable Adversarial Perturbation
To address this challenge, we propose ScaLA, a scalable and robust method for large-batch optimization of transformer networks via adversarial perturbation.
Curriculum Learning: A Regularization Method for Efficient and Stable Billion-Scale GPT Model Pre-Training
To reduce their expensive training cost, practitioners attempt to increase the batch sizes and learning rates.
Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search
Our experiments give guidance on how to approximate and generalize MRNG to build proximity graphs on a large scale.
ZeRO-Offload: Democratizing Billion-Scale Model Training
By combining compute and memory efficiency with ease-of-use, ZeRO-Offload democratizes large-scale model training making it accessible to even data scientists with access to just a single GPU.
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation
Recently, the DL compiler, together with Learning to Compile has proven to be a powerful technique for optimizing deep learning models.
AdaTune: Adaptive Tensor Program Compilation Made Efficient
Deep learning models are computationally intense, and implementations often have to be highly optimized by experts or hardware vendors to be usable in practice.
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory
The emergence of heterogeneous memory (HM) brings a solution to significantly increase memory capacity and break the above tradeoff: Using HM, billions of data points can be placed in the main memory on a single machine without using any data compression.
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping
Recently, Transformer-based language models have demonstrated remarkable performance across many NLP domains.
SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Network
To solve these issues, we propose an intelligent tiled-based dispatching mechanism for increasing the adaptiveness of RNN computation, in order to efficiently handle the data dependencies.
Learning to Anneal and Prune Proximity Graphs for Similarity Search
This paper studies similarity search, which is a crucial enabler of many feature vector--based applications.
Zoom: SSD-based Vector Search for Optimizing Accuracy, Latency and Memory
With the advancement of machine learning and deep learning, vector search becomes instrumental to many information retrieval systems, to search and find best matches to user queries based on their semantic similarities. These online services require the search architecture to be both effective with high accuracy and efficient with low latency and memory footprint, which existing work fails to offer.
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks.
Learning Intrinsic Sparse Structures within Long Short-Term Memory
This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs.
