刚刚，Thinking Machines Lab首次发长文，揭开LLM推理不确定性真相

Core Viewpoint - The article discusses the challenges of achieving reproducibility in large language models (LLMs) due to the lack of batch invariance, which leads to nondeterministic outputs even under controlled conditions [10][41][46]. Group 1: Introduction to the Issue - Thinking Machines Lab, founded by former OpenAI CTO Mira Murati, published its first article addressing nondeterminism in LLM inference [1][3]. - The blog aims to cover a wide range of topics related to their research, including numerical computation and prompt engineering [3]. Group 2: Understanding Nondeterminism - Reproducibility is a cornerstone of scientific progress, yet obtaining consistent results from LLMs is challenging [10]. - Even with the temperature parameter set to 0, LLM APIs can still produce nondeterministic outputs [11]. - The nondeterminism is attributed to floating-point non-associativity and concurrency, which affects the order of operations in GPU computations [13][30]. Group 3: The Root Cause of Nondeterminism - The article argues that the common assumption linking concurrency and floating-point operations to nondeterminism does not fully explain the issue [14][30]. - Floating-point non-associativity leads to different results based on the order of operations, especially in parallel computations [19][26]. - The actual implementation of kernel functions in LLMs contributes to the nondeterministic behavior observed [27][30]. Group 4: Batch Invariance - The lack of batch invariance is identified as a key factor causing nondeterminism in LLM outputs [41][46]. - Batch size changes can lead to different results for the same input, which is counterintuitive for mathematical functions [43]. - The article emphasizes that ensuring kernel functions are batch invariant is crucial for achieving consistent outputs in LLM inference [46]. Group 5: Solutions for Achieving Determinism - The article outlines strategies to implement batch invariance in key operations such as RMSNorm, matrix multiplication, and attention mechanisms [49][60][71]. - By ensuring that the operations do not depend on batch size, the LLM inference can produce consistent results [46][81]. - The authors provide a demonstration of deterministic inference using their batch-invariant kernel function library [82]. Group 6: Performance Considerations - Initial performance tests indicate that while the batch-invariant kernel functions may not be fully optimized, they do not lead to catastrophic performance declines [89]. - The article highlights the importance of maintaining performance while achieving deterministic outputs in LLMs [88]. Group 7: Implications for Reinforcement Learning - The article discusses how achieving deterministic inference can facilitate true on-policy reinforcement learning by ensuring consistent outputs between training and inference [90]. - This consistency is essential for effective training and sampling processes in reinforcement learning environments [90]. Group 8: Conclusion - The article advocates for a proactive approach to understanding and addressing the sources of nondeterminism in LLMs, encouraging the community to strive for reproducibility in AI systems [93].