VQV Signal
CoRun identifies batch-dependent GPU execution as a key cause of nondeterministic outputs in LLM inference, due to dynamic input shapes affecting kernel tiling and floating-point operations. The approach uses padding to standardize input shapes, improving determinism without sacrificing efficiency.
CoRun identifies batch-dependent GPU execution as a key cause of nondeterministic outputs in LLM inference, due to dynamic input shapes affecting kernel tiling and floating-point operations. The approach uses padding to standardize input shapes, improving determinism without sacrificing efficiency.
AI-assisted summary based on the listed source.
Despite fixed sampling parameters and random seeds, Large Language Model (LLM) inference exhibits output inconsistency, which undermines downstream tasks such as model evaluation and reinforcement learning. A major source of this nondeterminism is batch-dependent GPU execution: dynamic input shapes change kernel...
Deterministic LLM inference is crucial for reliable model evaluation and reinforcement learning, where output consistency impacts downstream task performance. CoRun's padding method offers a simple and efficient solution to reduce nondeterminism caused by batch variability.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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