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COMET: Towards Practical W4A4KV4 LLMs Serving 25

COMET: Towards Practical W4A4KV4 LLMs Serving

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30th International conference on Architectural Support for programming languages and Operating Systems-ASPLOS

作者： Liu, Lian Cheng, Long Ren, Haimeng Xu, Zhaohui Pan, Yudong Wang, Mengdi Li, Xiaowei Han, Yinhe Wang, Ying Univ Chinese Acad Sci CAS Inst Comp Technol Beijing Peoples R China North China Elect Power Univ Beijing Peoples R China ShanghaiTech Univ Shanghai Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798400710797

Quantization is a widely-used compression technology to reduce the overhead of serving large language models (LLMs) on terminal devices and in cloud data centers. However, prevalent quantization methods, such as 8-bitweight-activation or 4-bit weight-only quantization, achieve limited performance improvements due to poor support for low-precision (e.g., 4-bit) activation. This work, for the first time, realizes practical W4A4KV4 serving for LLMs, fully utilizing the INT4 tensor cores on modern GPUs and reducing the memory bottleneck caused by the KV cache. Specifically, we propose a novel fine-grained mixed-precision quantization algorithm (FMPQ) that compresses most activations into 4-bit with negligible accuracy loss. To support mixed-precision matrix multiplication for W4A4 and W4A8, we develop a highly optimized W4Ax kernel. Our approach introduces a novel mixed-precision data layout to facilitate access and fast dequantization for activation and weight tensors, utilizing the GPU's software pipeline to hide the overhead of data loading and conversion. Additionally, we propose fine-grained streaming multiprocessor (SM) scheduling to achieve load balance across different SMs. We integrate the optimized W4Ax kernel into our inference framework, COMET, and provide efficient management to support popular LLMs such as LLaMA-3-70B. Extensive evaluations demonstrate that, when running LLaMA family models on a single A100-80GSMX4, COMET achieves a kernel-level speedup of 2.88x over cuBLAS and a 2.02x throughput improvement compared to TensorRT-LLM from an end-to-end framework perspective.

关键词： Large language Models (LLM) Serving LLM Quantization Algorithm-System Co-design

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Proceedings - 2024 32nd IEEE/acm International conference on Program Comprehension, ICPC 2024

Proceedings - 2024 32nd IEEE/ACM International Conference on...

引用

32nd IEEE/acm International conference on Program Comprehension, ICPC 2024

ISBN: (纸本)9798400705861

The proceedings contain 46 papers. The topics discussed include: exploring and improving code completion for test code;on the generalizability of deep learning-based code completion across programming language versions;ESGen: commit message generation based on edit sequence of code change;capturing and understanding the drift between design, implementation, and documentation;towards summarizing code snippets using pre-trained transformers;improving AST-level code completion with graph retrieval and multi-field attention;HyperCRX: a browser extension for insights into GitHub projects and developers;innovating coding: evaluating the impact of innovative thinking in programming;and MESIA: understanding and leveraging supplementary nature of method-level comments for automatic comment generation.

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SPPL: Probabilistic programming with Fast Exact Symbolic Inference 2021

SPPL: Probabilistic Programming with Fast Exact Symbolic Inf...

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42nd acm sigplan International conference on programming language design and implementation (PLDI)

作者： Saad, Feras A. Rinard, Martin C. Mansinghka, Vikash K. MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781450383912

We present the Sum-Product Probabilistic language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inference queries. SPPL translates probabilistic programs into sum-product expressions, a new symbolic representation and associated semantic domain that extends standard sum-product networks to support mixed-type distributions, numeric transformations, logical formulas, and pointwise and set-valued constraints. We formalize SPPL via a novel translation strategy from probabilistic programs to sum-product expressions and give sound exact algorithms for conditioning on and computing probabilities of events. SPPL imposes a collection of restrictions on probabilistic programs to ensure they can be translated into sum-product expressions, which allow the system to leverage new techniques for improving the scalability of translation and inference by automatically exploiting probabilistic structure. We implement a prototype of SPPL with a modular architecture and evaluate it on benchmarks the system targets, showing that it obtains up to 3500x speedups over state-of-the-art symbolic systems on tasks such as verifying the fairness of decision tree classifiers, smoothing hidden Markov models, conditioning transformed random variables, and computing rare event probabilities.

关键词： probabilistic programming symbolic execution

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3CPEs: Concrete Computational Concepts programming Environments for Elementary Computer Science Education

3CPEs: Concrete Computational Concepts Programming Environme...

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CHI conference on Human Factors in Computing Sytems (CHI)

作者： Sung, Ching-Ying Cherng, Fu-Yin Chiu, Yi-Lun Chen, Peng-Hsi Chen, Bing-Yu Univ Washington Seattle WA 98195 USA Natl Chung Cheng Univ Chiayi Chiayi County Taiwan Natl Taiwan Univ Taipei Taiwan Keio Univ Tokyo Japan

ISBN: (纸本)9798400703317

This study introduces the Concrete Computational Concepts programming Environments (3CPEs), an innovative approach designed to help elementary students grasp abstract computational concepts through concrete metaphors using tangible and everyday objects. Traditional programming environments for children focus on concrete programming language syntax and semantics yet often fall short in conveying advanced computational concepts. To address this gap, 3CPEs combine the hands-on appeal of computer-science unplugged activities with the versatility of programming environments, using familiar objects and gamified elements to visualize abstract computational and algorithmic ideas. An example is CT (Computational Thinking) Chef, a 3CPEs implementation to enhance elementary students' understanding of programming concepts. Through design workshops with five elementary programming teachers, we preliminarily identified teaching challenges and evaluated 3CPEs' efficacy as an educational tool. The feedback underscores CT Chef's potential in facilitating the teaching of intricate computational concepts for young learners.

关键词： programming Environment Concrete Metaphor design Computational Thinking Elementary Education

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An Efficient Interpreter for Datalog by De-specializing Relations 2021

An Efficient Interpreter for Datalog by De-specializing Rela...

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42nd acm sigplan International conference on programming language design and implementation (PLDI)

作者： Hu, Xiaowen Zhao, David Jordan, Herbert Scholz, Bernhard Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Innsbruck Innsbruck Austria

ISBN: (纸本)9781450383912

Datalog is becoming increasingly popular as a standard tool for a variety of use cases. Modern Datalog engines can achieve high performance by specializing data structures for relational operations. For example, the Datalog engine Souffle achieves high performance with a synthesizer that specializes data structures for relations. However, the synthesizer cannot always be deployed, and a fast interpreter is required. This work introduces the design and implementation of the Souffle Tree Interpreter (STI). Key for the performance of the STI is the support for fast operations on relations. We obtain fast operations by de-specializing data structures so that they can work in a virtual execution environment. Our new interpreter achieves a competitive performance slowdown between 1.32 and 5.67x when compared to synthesized code. If compile time overheads of the synthesizer are also considered, the interpreter can be 6.46x faster on average for the first run.

关键词： Interpreter implementation Datalog Engine Static Data Structure

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An End-To-End programming Model for AI Engine Architectures 14

An End-To-End Programming Model for AI Engine Architectures

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14th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies, HEART 2024

作者： Levental, Maksim Khan, Arham Chard, Ryan Chard, Kyle Neuendorffer, Stephen Foster, Ian University of Chicago United States Argonne National Laboratory United States AMD United States

ISBN: (纸本)9798400717277

Coarse-Grained Reconfigurable Architectures (CGRAs) are becoming a promising alternative to conventional computing architectures such as CPUs, GPUs, and FPGAs when energy efficiency and high performance are required. Like CPUs and GPUs, CGRAs have processing elements (PEs) that can perform complex operations, such as vectorized arithmetic, and like FPGAs, they support a reconfigurable topology of components. Because of their coarser grain reconfigurability, they are less challenging to program than FPGAs but more challenging than CPUs and GPUs. This paper presents an end-To-end programming model for AMD AI Engine CGRAs, which enables programming simultaneously at a high level and a very implementation-specific level, all in the same language, all in the same "flow". Our programming model allows users to specify, implement, and test on-device, enabling the productive design of dataflow programs for streaming applications. The programming model is open source and includes a language frontend (Python eDSL), an MLIR based compiler, export paths to target codegen compilers, and runtime infrastructure. We show that our approach to language and compiler design enables users to program with much less friction and ceremony while preserving access to all features and device APIs necessary for achieving performance competitive with existing AI Engine programming models. © 2024 Owner/Author.

关键词： Field programmable gate arrays (FPGA)

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Ansible Lightspeed: A Code Generation Service for IT Automation 24

Ansible Lightspeed: A Code Generation Service for IT Automat...

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39th acm/IEEE International conference on Automated Software Engineering (ASE)

作者： Sahoo, Priyam Pujar, Saurabh Nalawade, Ganesh Gebhardt, Richard Mandel, Louis Buratti, Luca Red Hat Raleigh NC USA IBM Res Yorktown Hts NY 10598 USA

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

The availability of Large language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been released, almost all of them focus on general-purpose programming languages. Domain-specific languages, such as those crucial for Information Technology (IT) automation, have not received much attention. Ansible is one such YAML-based IT automation-specific language. Ansible Lightspeed is an LLM-based service designed explicitly to generate Ansible YAML, given natural language prompt. In this paper, we present the design and implementation of the Ansible Lightspeed service. We then evaluate its utility to developers using diverse indicators, including extended utilization, analysis of user edited suggestions, as well as user sentiments analysis. The evaluation is based on data collected for 10,696 real users including 3,910 returning users. The code for Ansible Lightspeed service and the analysis framework is made available for others to use. To our knowledge, our study is the first to involve thousands of users of code assistants for domain-specific languages. We are also the first code completion tool to present N-Day user retention figures, which is 13.66% on Day 30. We propose an improved version of user acceptance rate, called Strong Acceptance rate, where a suggestion is considered accepted only if less than 50% of it is edited and these edits do not change critical parts of the suggestion. By focusing on Ansible, Lightspeed is able to achieve a strong acceptance rate of 49.08% for multi-line Ansible task suggestions. With our findings we provide insights into the effectiveness of small, dedicated models in a domain-specific context. We hope this work serves as a reference for software engineering and machine learning researchers exploring code c

关键词： Productivity Sentiment analysis Codes Automation Large language models Natural languages Machine learning Software Software engineering Domain specific languages

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CompCertO: Compiling Certified Open C Components 2021

CompCertO: Compiling Certified Open C Components

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42nd acm sigplan International conference on programming language design and implementation (PLDI)

作者： Koenig, Jeremie Shao, Zhong Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781450383912

Since the introduction of CompCert, researchers have been refining its language semantics and correctness theorem, and used them as components in software verification efforts. Meanwhile, artifacts ranging from CPU designs to network protocols have been successfully verified, and there is interest in making them interoperable to tackle end-to-end verification at an even larger scale. Recent work shows that a synthesis of game semantics, refinement-based methods, and abstraction layers has the potential to serve as a common theory of certified components. Integrating certified compilers to such a theory is a critical goal. However, none of the existing variants of CompCert meets the requirements we have identified for this task. CompCertO extends the correctness theorem of CompCert to characterize compiled program components directly in terms of their interaction with each other. Through a careful and compositional treatment of calling conventions, this is achieved with minimal effort.

关键词： Compositional Compiler Correctness Game Semantics Simulation Convention language Interface

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Example-Guided Synthesis of Relational Queries 2021

Example-Guided Synthesis of Relational Queries

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42nd acm sigplan International conference on programming language design and implementation (PLDI)

作者： Thakkar, Aalok Naik, Aaditya Sands, Nathaniel Alur, Rajeev Naik, Mayur Raghothaman, Mukund Univ Penn Philadelphia PA 19104 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450383912

Program synthesis tasks are commonly specified via input-output examples. Existing enumerative techniques for such tasks are primarily guided by program syntax and only make indirect use of the examples. We identify a class of synthesis algorithms for programming-by-examples, which we call Example-Guided Synthesis (EGS), that exploits latent structure in the provided examples while generating candidate programs. We present an instance of EGS for the synthesis of relational queries and evaluate it on 86 tasks from three application domains: knowledge discovery, program analysis, and database querying. Our evaluation shows that EGS outperforms state-of-the-art synthesizers based on enumerative search, constraint solving, and hybrid techniques in terms of synthesis time, quality of synthesized programs, and ability to prove unrealizability.

关键词： programming by example Example-Guided Synthesis

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Towards an Automatic Extracting UML Class Diagram from System's Textual Specification 24

Towards an Automatic Extracting UML Class Diagram from Syste...

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7th International conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Babaalla, Zakaria Jakimi, Abdeslam Oualla, Mohamed Saadane, Rachid Chehri, Abdellah GL-ISI Team Department of Informatics Faculty of Sciences and Techniques Errachidia Morocco Electrical Engineering Department Hassania School of Public Works Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada

ISBN: (纸本)9798400709296

Developing a software system from natural language requirements is a complex and delicate task that requires a high level of design and programming expertise. Increasing the level of abstraction used to describe these requirements is the most natural solution. Model-Driven Engineering (MDE) also takes this route, using abstract models as primary entities to generate source code automatically or semi-automatically. Among these models, the UML class diagram occupies a privileged place in object-oriented systems because it not only serves as a basis for communication between developers but also provides a closely aligned static representation of the system implementation. However, creating a UML class diagram from a textual system specification poses a significant challenge due to the inherent imprecision and ambiguity commonly found in natural language expressions. In this paper, we propose a model-centric approach based on deep learning for the automatic extraction of UML class diagrams from textual requirements. © 2024 acm.

关键词： Object oriented programming

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