Efficiently Scaling Transformer Inference

• URL: http://arxiv.org/abs/2211.05102v1
• Date: Wed, 9 Nov 2022 18:50:38 GMT
• Title: Efficiently Scaling Transformer Inference
• Authors: Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Levskaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, Jeff Dean
• Abstract summary: We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings. We develop a simple analytical model for inference efficiency to select the best multi-dimensional partitioning techniques optimized for TPU v4 slices. We achieve a low-batch-size latency of 29ms per token during generation (using int8 weight quantization) and a 76% MFU during large-batch-size processing of input tokens.
• Score: 8.196193683641582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long sequence lengths. Better understanding of the engineering tradeoffs for inference for large Transformer-based models is important as use cases of these models are growing rapidly throughout application areas. We develop a simple analytical model for inference efficiency to select the best multi-dimensional partitioning techniques optimized for TPU v4 slices based on the application requirements. We combine these with a suite of low-level optimizations to achieve a new Pareto frontier on the latency and model FLOPS utilization (MFU) tradeoffs on 500B+ parameter models that outperforms the FasterTransformer suite of benchmarks. We further show that with appropriate partitioning, the lower memory requirements of multiquery attention (i.e. multiple query heads share single key/value head) enables scaling up to 32x larger context lengths. Finally, we achieve a low-batch-size latency of 29ms per token during generation (using int8 weight quantization) and a 76% MFU during large-batch-size processing of input tokens, while supporting a long 2048-token context length on the PaLM 540B parameter model.

Related papers

• Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters [0.0]
  emphQuantum-Inspired Adapters, a PEFT approach inspired by Hamming-weight quantum circuits from quantum machine learning literature. We test our proposed adapters by adapting large language models and large vision transformers on benchmark datasets.
  arXiv  Detail & Related papers  (2025-02-10T13:06:56Z)
• MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference [0.8388591755871735]
  Homomorphic Encryption (HE) enables us to perform machine learning tasks over encrypted data. We propose MOFHEI, a framework that optimize the model to make HE-based neural network inference, fast and efficient. Our framework achieves up to 98% pruning ratio on LeNet, eliminating up to 93% of the required HE operations for performing PI.
  arXiv  Detail & Related papers  (2024-12-10T22:44:54Z)
• Puzzle: Distillation-Based NAS for Inference-Optimized LLMs [17.72841008597783]
  Large language models (LLMs) have demonstrated remarkable capabilities, but their adoption is limited by high computational costs during inference. We present Puzzle, a framework to accelerate LLM inference on specific hardware while preserving their capabilities. Nemotron-51B stands as the most accurate language model capable of inference on a single GPU with large batch sizes.
  arXiv  Detail & Related papers  (2024-11-28T13:45:42Z)
• FluidML: Fast and Memory Efficient Inference Optimization [3.7676096626244986]
  We present FluidML, a generic runtime memory management and optimization framework. We show that FluidML can consistently reduce the end-to-end inference latency by up to 25.38% for popular language models. We also show that FluidML can reduce peak memory usage by up to 41.47%, compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-11-14T07:16:23Z)
• EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference [49.94169109038806]
  This paper introduces EPS-MoE, a novel expert pipeline scheduler for MoE that surpasses the existing parallelism schemes. Our results demonstrate at most 52.4% improvement in prefill throughput compared to existing parallel inference methods.
  arXiv  Detail & Related papers  (2024-10-16T05:17:49Z)
• SPT: Fine-Tuning Transformer-based Language Models Efficiently with Sparsification [14.559316921646356]
  Fine-tuning Transformer-based models for downstream tasks has long running time and high memory consumption. We propose the SPT system to fine-tune Transformer-based models efficiently by introducing sparsity. SPT consistently outperforms well-optimized baselines, reducing the peak memory consumption by up to 50% and accelerating fine-tuning by up to 2.2x.
  arXiv  Detail & Related papers  (2023-12-16T07:44:52Z)
• MatFormer: Nested Transformer for Elastic Inference [91.45687988953435]
  MatFormer is a novel Transformer architecture designed to provide elastic inference across diverse deployment constraints. MatFormer achieves this by incorporating a nested Feed Forward Network (FFN) block structure within a standard Transformer model. We show that a 850M decoder-only MatFormer language model (MatLM) allows us to extract multiple smaller models spanning from 582M to 850M parameters.
  arXiv  Detail & Related papers  (2023-10-11T17:57:14Z)
• E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning [55.50908600818483]
  Fine-tuning large-scale pretrained vision models for new tasks has become increasingly parameter-intensive. We propose an Effective and Efficient Visual Prompt Tuning (E2VPT) approach for large-scale transformer-based model adaptation. Our approach outperforms several state-of-the-art baselines on two benchmarks.
  arXiv  Detail & Related papers  (2023-07-25T19:03:21Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering [75.86788916930377]
  bilaterally slimmable Transformer (BST) integrated into arbitrary Transformer-based VQA models. One slimmed MCAN-BST submodel achieves comparable accuracy on VQA-v2. Smallest MCAN-BST submodel has 9M parameters and 0.16G FLOPs during inference.
  arXiv  Detail & Related papers  (2022-03-24T02:26:04Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.