Forklift: A Neural Lifter for Translating Assembly Code to LLVM IR

Alapfogalmak

Forklift, a neural network model, can automatically translate assembly code from various instruction set architectures (ISAs) like x86, ARM, and RISC-V into the LLVM compiler's intermediate representation (IR), enabling efficient porting of legacy software to new hardware.

Kivonat

The paper presents Forklift, a neural lifter that can translate assembly code from different ISAs (x86, ARM, RISC-V) into the LLVM compiler's intermediate representation (IR). This allows for efficient porting of legacy software to new hardware by leveraging the LLVM ecosystem for recompilation and optimization.
Key highlights:

The escalating demand to migrate legacy software across different ISAs has driven the development of assembly-to-assembly translators, but these require substantial engineering effort.
Lifting, a technique where source assembly is translated to an architecture-independent IR (e.g., LLVM IR), can reuse existing compiler infrastructure, but existing lifters still require significant manual engineering.
Forklift is the first neural lifter that learns how to translate assembly to LLVM IR using a token-level encoder-decoder Transformer model.
Forklift can incrementally add support for new ISAs by fine-tuning the assembly encoder and freezing the IR decoder, improving accuracy and efficiency.
Forklift is evaluated on two challenging benchmark suites, outperforming a state-of-the-art hand-written lifter (Lasagne) and a large language model (GPT-4) on x86 code translation.
Forklift's modular design allows it to be extended to new ISAs without requiring manual engineering effort for each new architecture.

Statisztikák

Forklift outperforms Lasagne by 2.5x and GPT-4 by 4.4x on optimized x86 code translation to LLVM IR on the ExeBench benchmark.
On the Synth benchmark, Forklift achieves 51.46% accuracy on x86 code, 67.96% on ARM, and 67.42% on RISC-V, compared to Lasagne's 37.86% on x86 and GPT-4's 0.95-3.37% across ISAs.

Idézetek

"Forklift outperforms existing approaches without requiring manual engineering effort, automatically learning the intricacies of each ISA, compiler, and optimization level."
"Once Forklift lifts code to LLVM IR, we can leverage the existing power of LLVM to re–optimize and recompile to the desired target, directly benefiting from the LLVM ecosystem."

Főbb Kivonatok

Forklift: An Extensible Neural Lifter

by Jord... : arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16041.pdf

Mélyebb kérdések

How can Forklift's performance be further improved, especially on more complex or longer assembly functions?

Forklift's performance can be enhanced in several ways to handle more complex or longer assembly functions effectively:

Model Architecture Refinement: Fine-tuning the encoder-decoder Transformer architecture used in Forklift can help improve its ability to handle longer sequences and more intricate assembly code structures. This could involve experimenting with different Transformer variants or incorporating additional attention mechanisms to capture more nuanced relationships within the code.

Data Augmentation: Increasing the diversity and volume of training data can help the model generalize better to complex scenarios. Augmenting the dataset with a wider range of assembly functions, especially those that are more intricate or lengthy, can expose the model to a broader spectrum of patterns and variations, leading to improved performance on complex inputs.

Regularization Techniques: Implementing regularization techniques such as dropout or weight decay can prevent overfitting, especially when dealing with longer sequences. Regularization helps the model generalize better and reduces the risk of memorizing specific patterns in the training data that may not generalize well to unseen complex assembly functions.

Ensemble Learning: Employing ensemble learning by combining predictions from multiple Forklift models trained with different hyperparameters or initializations can enhance performance. Ensemble methods often lead to more robust and accurate predictions, especially in handling complex and diverse data.

Fine-tuning Strategies: Exploring advanced fine-tuning strategies, such as curriculum learning or reinforcement learning, can help the model adapt better to complex assembly functions over time. By gradually increasing the complexity of the training data or providing rewards for accurate translations, Forklift can learn to handle more intricate code structures effectively.

How could Forklift's incremental learning approach be applied to other domains beyond assembly-to-IR translation, such as cross-language code translation or program synthesis?

Forklift's incremental learning approach, where new encoders are incrementally added while keeping the decoder frozen, can be adapted to various domains beyond assembly-to-IR translation:

Cross-Language Code Translation: In the context of translating code between different programming languages, Forklift's incremental learning strategy can be applied by freezing the translation decoder and fine-tuning the encoder for each source-target language pair. This approach allows the model to leverage knowledge from previously learned languages and incrementally adapt to new language structures, improving translation accuracy and efficiency.

Program Synthesis: For program synthesis tasks, where the goal is to automatically generate code based on high-level specifications, Forklift's incremental learning can be utilized to enhance the model's ability to synthesize programs in diverse domains. By freezing the synthesis logic and incrementally fine-tuning the encoder for new input-output pairs, the model can learn to generate accurate programs across a wide range of problem domains.

Natural Language Processing: The incremental learning approach can also be extended to natural language processing tasks such as machine translation or text generation. By freezing the language decoder and fine-tuning the encoder for new languages or text styles, the model can adapt to different linguistic patterns and improve its language generation capabilities over time.

By applying Forklift's incremental learning methodology to these domains, it is possible to build more robust and adaptable AI systems that can effectively handle diverse data and tasks with improved accuracy and efficiency.

What are the potential limitations of using LLVM IR as the target representation, and how could alternative intermediate representations be explored?

While LLVM IR offers several advantages as a target representation for Forklift, there are some potential limitations to consider:

Complexity: LLVM IR is a low-level intermediate representation that may contain intricate details and optimizations specific to the LLVM compiler infrastructure. This complexity can make it challenging for the model to learn accurate translations, especially for more complex assembly functions with advanced features or optimizations.

Portability: LLVM IR is closely tied to the LLVM compiler framework, which may limit the portability of the translations generated by Forklift to other compiler ecosystems. Translating to a compiler-specific IR like LLVM IR may restrict the applicability of the model to environments outside the LLVM ecosystem.

Semantic Gap: LLVM IR may have a significant semantic gap with certain assembly languages, leading to potential information loss or inaccuracies in the translation process. This gap can make it harder for the model to capture the full semantics of the original assembly code, especially in cases where the assembly language features are not directly translatable to LLVM IR constructs.

To explore alternative intermediate representations, the following approaches could be considered:

Domain-Specific IR: Developing domain-specific intermediate representations tailored to the characteristics of the assembly languages being translated could improve the model's ability to capture language-specific nuances and optimize the translation process for specific use cases.

Simpler IR: Using a simpler and more abstract intermediate representation that captures the essential semantics of the assembly code without the complexity of LLVM IR could enhance the model's interpretability and generalization capabilities. A less verbose and more intuitive IR could facilitate better learning and translation performance.

Multi-Level IR: Implementing a multi-level intermediate representation system that combines high-level and low-level representations could provide a more comprehensive view of the code semantics. By incorporating multiple levels of abstraction, the model can capture both the detailed instructions and the higher-level program structure, leading to more accurate translations.

Exploring these alternative representations can help address the limitations of using LLVM IR as the target representation and enhance Forklift's performance in translating assembly code to a more suitable and effective intermediate form.