Characterizing the Accuracy - Efficiency Trade-off of Low-rank Decomposition in Language Models
- URL: http://arxiv.org/abs/2405.06626v1
- Date: Fri, 10 May 2024 17:40:02 GMT
- Title: Characterizing the Accuracy - Efficiency Trade-off of Low-rank Decomposition in Language Models
- Authors: Chakshu Moar, Michael Pellauer, Hyoukjun Kwon,
- Abstract summary: Large language models (LLMs) have emerged and presented their general problem-solving capabilities with one model.
We formalize the low-rank decomposition design space and show that the decomposition design space is enormous.
Results show that we can achieve a 9% model size reduction with minimal accuracy drops.
- Score: 1.530997923234786
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have emerged and presented their general problem-solving capabilities with one model. However, the model size has increased dramatically with billions of parameters to enable such broad problem-solving capabilities. In addition, due to the dominance of matrix-matrix and matrix-vector multiplications in LLMs, the compute-to-model size ratio is significantly lower than that of CNNs. This shift pushes LLMs from a computation-bound regime to a memory-bound regime. Therefore, optimizing the memory footprint and traffic is an important optimization direction for LLMs today. Model compression methods such as quantization and parameter pruning have been actively explored for achieving the memory footprint and traffic optimization. However, the accuracy-efficiency trade-off of rank pruning for LLMs is not well-understood yet. Therefore, we characterize the accuracy-efficiency trade-off of a low-rank decomposition method, specifically Tucker decomposition, on recent language models, including an open-source LLM, Llama 2. We formalize the low-rank decomposition design space and show that the decomposition design space is enormous (e.g., O($2^{37}$) for Llama2-7B). To navigate such a vast design space, we formulate the design space and perform thorough case studies of accuracy-efficiency trade-offs using six widely used LLM benchmarks on BERT and Llama 2 models. Our results show that we can achieve a 9\% model size reduction with minimal accuracy drops, which range from 4\%p to 10\%p, depending on the difficulty of the benchmark, without any retraining to recover accuracy after decomposition. The results show that low-rank decomposition can be a promising direction for LLM-based applications that require real-time service in scale (e.g., AI agent assist and real-time coding assistant), where the latency is as important as the model accuracy.
Related papers
- Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization [0.6445087473595953]
Large language models (LLMs) demonstrate outstanding performance in various tasks in machine learning.
deploying LLM inference poses challenges due to the high compute and memory requirements.
We present Tender, an algorithm-hardware co-design solution that enables efficient deployment of LLM inference at low precision.
arXiv Detail & Related papers (2024-06-16T09:51:55Z) - Optimization-based Structural Pruning for Large Language Models without Back-Propagation [57.9629676017527]
We propose an optimization-based structural pruning on Large-Language Models (LLMs)
Our method learns the pruning masks in a probabilistic space directly by optimizing the loss of the pruned model.
Our method operates for 2.7 hours with around 35GB memory for the 13B models on a single A100 GPU, and our pruned models outperform the state-of-the-arts w.r.t. perplexity.
arXiv Detail & Related papers (2024-06-15T09:31:03Z) - Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models [79.46938238953916]
Fine-tuning large language models (LLMs) to diverse applications is crucial to meet complex demands.
Recent studies suggest decomposing a fine-tuned LLM into a base model and corresponding delta weights, which are then compressed using low-rank or low-bit approaches to reduce costs.
In this work, we observe that existing low-rank and low-bit compression methods can significantly harm the model performance for task-specific fine-tuned LLMs.
arXiv Detail & Related papers (2024-06-13T07:57:27Z) - ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization [13.622268474310918]
ShiftAddLLM is an efficient multiplication-free model for large language models.
It achieves perplexity improvements of 5.6 and 22.7 points at comparable or lower latency.
Experiments on five LLM families and eight tasks consistently validate the effectiveness of ShiftAddLLM.
arXiv Detail & Related papers (2024-06-10T02:47:55Z) - DB-LLM: Accurate Dual-Binarization for Efficient LLMs [83.70686728471547]
Large language models (LLMs) have significantly advanced the field of natural language processing.
Existing ultra-low-bit quantization always causes severe accuracy drops.
We propose a novel Dual-Binarization method for LLMs, namely DB-LLM.
arXiv Detail & Related papers (2024-02-19T09:04:30Z) - BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [53.31402059062365]
BiLLM is a groundbreaking 1-bit post-training quantization scheme tailored for pretrained large language models.
It achieves for the first time high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLMs families.
arXiv Detail & Related papers (2024-02-06T09:26:34Z) - Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs [67.38165028487242]
We introduce Dynamic Sparse No Training (DSnoT), a training-free fine-tuning approach to fine-tune large language models (LLMs)
Inspired by the Dynamic Sparse Training, DSnoT minimizes the reconstruction error between the dense and sparse LLMs.
Our paper offers fresh insights into how to fine-tune sparse LLMs in an efficient training-free manner and open new venues to scale the great potential of sparsity to LLMs.
arXiv Detail & Related papers (2023-10-13T07:38:52Z) - FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only
Quantization for LLMs [9.072821427818557]
Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment.
We propose an efficient weight-only quantization method that reduces memory consumption and accelerates inference for LLMs.
We evaluate our approach on large-scale open source models such as OPT-175B and internal MoE models, showcasing minimal accuracy loss while achieving up to 3.65 times higher throughput.
arXiv Detail & Related papers (2023-08-16T23:57:41Z) - Scaling Relationship on Learning Mathematical Reasoning with Large
Language Models [75.29595679428105]
We investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM.
We find that rejection samples from multiple models push LLaMA-7B to an accuracy of 49.3% on GSM8K which outperforms the supervised fine-tuning (SFT) accuracy of 35.9% significantly.
arXiv Detail & Related papers (2023-08-03T15:34:01Z) - INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error
Correction through Low-Rank Adaptation [5.837035655563323]
We introduce a method that dramatically reduces fine-tuning VRAM requirements and rectifies quantization errors in quantized Large Language Models.
Our method reduces the memory requirements by up to 5.6 times, which enables fine-tuning a 7 billion parameter Large Language Model (LLM) on consumer laptops.
arXiv Detail & Related papers (2023-06-13T22:25:35Z)
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.