Search Results for author:
Found 20 papers, 15 papers with code
Mojito: Motion Trajectory and Intensity Control for Video Generation
Recent advancements in diffusion models have shown great promise in producing high-quality video content.
Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping
Given the popularity of generative AI, Large Language Models (LLMs) often consume hundreds or thousands of GPUs for parallelizing and accelerating the training process.
Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer
Large Language Models (LLMs) with long context capabilities are integral to complex tasks in natural language processing and computational biology, such as text generation and protein sequence analysis.
Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training
First, the universal checkpoint format, which consists of a consolidated representation of each model parameter and metadata for mapping parameter fragments into training ranks of arbitrary model-parallelism configuration.
FastPersist: Accelerating Model Checkpointing in Deep Learning
Model checkpoints are critical Deep Learning (DL) artifacts that enable fault tolerance for training and downstream applications, such as inference.
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
To address this problem, this paper introduces Multi-scale Positional Encoding (Ms-PoE) which is a simple yet effective plug-and-play approach to enhance the capacity of LLMs to handle the relevant information located in the middle of the context, without fine-tuning or introducing any additional overhead.
FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
However, existing systems do not provide Tensor Core support for FP6 quantization and struggle to achieve practical performance improvements during LLM inference.
ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks
With our design, FP6 can become a promising solution to the current 4-bit quantization methods used in LLMs.
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.
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.
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.
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computational costs.
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.
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.
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement
In this paper, we propose a fully automated and lightweight adaptive batching methodology to enable fine-grained batch size adaption (e. g., at a mini-batch level) that can achieve state-of-the-art performance with record breaking batch sizes.
ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning
It requires 800 NVIDIA V100 GPUs just to fit a trillion parameter model for training, and such clusters are simply out of reach for most data scientists.
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.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Large deep learning models offer significant accuracy gains, but training billions to trillions of parameters is challenging.
