MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations

• URL: http://arxiv.org/abs/2407.02238v1
• Date: Tue, 2 Jul 2024 13:00:19 GMT
• Title: MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations
• Authors: Akash Dutta, Ali Jannesari,
• Abstract summary: In this paper, we propose MIREncoder, a Multi-modal IR-based Auto-Encoder that can be pre-trained to generate a learned embedding space. A multi-modal approach enables us to better extract features from compilable programs. Our evaluations will show that our proposed approach can outperform the state of the art while reducing overhead.
• Score: 6.919817502555546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the primary areas of interest in High Performance Computing is the improvement of performance of parallel workloads. Nowadays, compilable source code-based optimization tasks that employ deep learning often exploit LLVM Intermediate Representations (IRs) for extracting features from source code. Most such works target specific tasks, or are designed with a pre-defined set of heuristics. So far, pre-trained models are rare in this domain, but the possibilities have been widely discussed. Especially approaches mimicking large-language models (LLMs) have been proposed. But these have prohibitively large training costs. In this paper, we propose MIREncoder, a M}ulti-modal IR-based Auto-Encoder that can be pre-trained to generate a learned embedding space to be used for downstream tasks by machine learning-based approaches. A multi-modal approach enables us to better extract features from compilable programs. It allows us to better model code syntax, semantics and structure. For code-based performance optimizations, these features are very important while making optimization decisions. A pre-trained model/embedding implicitly enables the usage of transfer learning, and helps move away from task-specific trained models. Additionally, a pre-trained model used for downstream performance optimization should itself have reduced overhead, and be easily usable. These considerations have led us to propose a modeling approach that i) understands code semantics and structure, ii) enables use of transfer learning, and iii) is small and simple enough to be easily re-purposed or reused even with low resource availability. Our evaluations will show that our proposed approach can outperform the state of the art while reducing overhead.

Related papers

• Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning [0.08192907805418582]
  Current approaches either yield subpar results when using pretrained models without task-specific adaptation, or require substantial computational resources for fine-tuning. We propose a novel method for adapting foundational, multimodal embeddings to downstream tasks, without the need of expensive fine-tuning processes.
  arXiv  Detail & Related papers  (2025-02-04T06:30:12Z)
• Deriving Coding-Specific Sub-Models from LLMs using Resource-Efficient Pruning [4.762390044282733]
  Large Language Models (LLMs) have demonstrated their exceptional performance in various complex code generation tasks. To mitigate such requirements, model pruning techniques are used to create more compact models with significantly fewer parameters. In this work, we explore the idea of efficiently deriving coding-specific sub-models through unstructured pruning.
  arXiv  Detail & Related papers  (2025-01-09T14:00:01Z)
• EmbedLLM: Learning Compact Representations of Large Language Models [28.49433308281983]
  We propose EmbedLLM, a framework designed to learn compact vector representations of Large Language Models. We introduce an encoder-decoder approach for learning such embeddings, along with a systematic framework to evaluate their effectiveness. Empirical results show that EmbedLLM outperforms prior methods in model routing both in accuracy and latency.
  arXiv  Detail & Related papers  (2024-10-03T05:43:24Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler [0.10923877073891444]
  We introduce the first RL environment for the MLIR compiler, dedicated to facilitating MLIR compiler research. We also propose a novel formulation of the action space as a product of simpler action subspaces, enabling more efficient and effective optimizations.
  arXiv  Detail & Related papers  (2024-09-17T10:49:45Z)
• ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling [15.67321902882617]
  We introduce OR-Instruct, a semi-automated data synthesis framework for optimization modeling. We also introduce IndustryOR, the first industrial benchmark for evaluating LLMs in solving practical OR problems.
  arXiv  Detail & Related papers  (2024-05-28T01:55:35Z)
• CoLLiE: Collaborative Training of Large Language Models in an Efficient Way [59.09824823710863]
  CoLLiE is an efficient library that facilitates collaborative training of large language models. With its modular design and comprehensive functionality, CoLLiE offers a balanced blend of efficiency, ease of use, and customization.
  arXiv  Detail & Related papers  (2023-12-01T08:02:16Z)
• Self-Supervised Learning via Maximum Entropy Coding [57.56570417545023]
  We propose Maximum Entropy Coding (MEC) as a principled objective that explicitly optimize on the structure of the representation. MEC learns a more generalizable representation than previous methods based on specific pretext tasks. It achieves state-of-the-art performance consistently on various downstream tasks, including not only ImageNet linear probe, but also semi-supervised classification, object detection, instance segmentation, and object tracking.
  arXiv  Detail & Related papers  (2022-10-20T17:58:30Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)
• Conservative Objective Models for Effective Offline Model-Based Optimization [78.19085445065845]
  Computational design problems arise in a number of settings, from synthetic biology to computer architectures. We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
  arXiv  Detail & Related papers  (2021-07-14T17:55:28Z)
• Top-KAST: Top-K Always Sparse Training [50.05611544535801]
  We propose Top-KAST, a method that preserves constant sparsity throughout training. We show that it performs comparably to or better than previous works when training models on the established ImageNet benchmark. In addition to our ImageNet results, we also demonstrate our approach in the domain of language modeling.
  arXiv  Detail & Related papers  (2021-06-07T11:13:05Z)

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.