Hardware and Software Platform Inference (2411.05197v2)

Abstract: It is now a common business practice to buy access to LLM inference rather than self-host, because of significant upfront hardware infrastructure and energy costs. However, as a buyer, there is no mechanism to verify the authenticity of the advertised service including the serving hardware platform, e.g. that it is actually being served using an NVIDIA H100. Furthermore, there are reports suggesting that model providers may deliver models that differ slightly from the advertised ones, often to make them run on less expensive hardware. That way, a client pays premium for a capable model access on more expensive hardware, yet ends up being served by a (potentially less capable) cheaper model on cheaper hardware. In this paper we introduce hardware and software platform inference (HSPI) -- a method for identifying the underlying GPU architecture and software stack of a (black-box) machine learning model solely based on its input-output behavior. Our method leverages the inherent differences of various GPU architectures and compilers to distinguish between different GPU types and software stacks. By analyzing the numerical patterns in the model's outputs, we propose a classification framework capable of accurately identifying the GPU used for model inference as well as the underlying software configuration. Our findings demonstrate the feasibility of inferring GPU type from black-box models. We evaluate HSPI against models served on different real hardware and find that in a white-box setting we can distinguish between different GPUs with between $83.9\%$ and $100\%$ accuracy. Even in a black-box setting we achieve results that are up to 3x higher than random guess accuracy. Our code is available at https://github.com/ChengZhang-98/HSPI.

• The paper introduces HSPI, a novel black-box method employing Border Inputs and Logits Distributions to infer GPU and software configurations.
• It achieves white-box accuracy between 83.9% and 100%, demonstrating robust performance in distinguishing hardware setups.
• The study lays the groundwork for enhancing ML service transparency and security, proposing avenues for industry standardization and future hardware integration.

Hardware and Software Platform Inference

The paper, "Hardware and Software Platform Inference (HSPI)," embarks on a meticulous exploration of an emergent problem domain in machine learning—deducing the underlying hardware and software setups used in serving machine learning models. The motivation stems from recognizing the prevalent industry practice of opting for third-party model inference services over self-hosting, driven by prohibitive infrastructure costs. However, transparency in these services remains a nebulous issue, potentially impacting service operation and security.

Core Contributions

The research identifies and tackles the problem of determining GPU architecture and software configurations using a purely black-box method, HSPI. This involves analyzing the input-output behaviors of served machine learning models. The authors introduce two innovative methodologies: HSPI with Border Inputs (HSPI-BI) and HSPI with Logits Distributions (HSPI-LD), applicable across both vision and language tasks. Through these methods, they achieve impressively high accuracy rates in identifying hardware platforms, with results in the white-box setting ranging from 83.9% to 100% accuracy, and significant performance in black-box scenarios as well.

Technical Mechanisms

HSPI operates by exploiting the inherent computational discrepancies arising from different configurations. The concept of Equivalence Classes (EQCs) is central to this methodology. Variations in quantization levels, GPU architectures, and arithmetic operations often shift computation into different EQCs, allowing the authors to differentiate between hardware and software environments. The paper’s approach to classifying hardware configurations inspires an informed dialogue on the feasibility and practical implications of such an undertaking, given the variations in arithmetic precision consistency due to different machine setups.

Implications and Discussion

The implications of this research are multifold. Practically, HSPI can serve as a potent tool for verifying the authenticity of provided model services, significantly enhancing transparency and trust in machine learning operations. Theoretically, it postulates an exciting intersection between hardware architecture and software execution paradigms, encouraging further research into optimizing model performance across different platforms.

Developments in AI and machine learning could find enhanced reliability and security through the awareness and insights HSPI provides. This introduces a potential paradigm for ML governance, empowering users with verifiable insights into their deployed models' hardware and software provenance. However, the paper notes some limitations. The methods sometimes face difficulties in distinguishing between closely-related hardware platforms or subtle software variations, suggesting future avenues for refinement.

Future Directions

The paper encourages expanding this research to encompass more diverse hardware environments, particularly with the continuous advancements in AI-dedicated hardware like Groq and Cerebras processors. Understanding how these newer architectures might impact EQC dynamics could be groundbreaking in hardware fingerprinting.

The potential for establishing industry standards through HSPI methodologies is especially notable. Ensuring consistent ML model quality and deploying reliable behavioral benchmarks can revolutionize how machine learning models are managed and governed in complex software and hardware ecosystems.

Conclusion

In summary, this work marks significant forward strides in deciphering the complexities of machine learning deployment environments. HSPI offers a robust analytical framework for discerning the often opaque inference setups, laying down essential groundwork for transparency and assurance in AI service operations. This critical understanding aids stakeholders in maintaining trust and efficacy in the rapidly evolving landscape of machine learning services.