thinking-machines-lab/batch_invariant_ops

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  A PyTorch library providing batch-invariant kernels and tools to achieve deterministic LLM inference, especially when dealing with varying batch sizes.

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• highreadme#2

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  # Batch Invariant Ops
  
  A companion library release to https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/. This library contains some batch-invariant kernels as well as an example of achieving deterministic vLLM inference.
  
  ## Overview
  
  This library primarily leverages torch.Library to sub out existing PyTorch kernels with "batch-invariant" ones. This allows many existing PyTorch models to use the batch-invariant ops with low overhead and non-intrusive code changes.

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  # Batch Invariant Ops
  
  This library provides essential tools and batch-invariant PyTorch kernels to achieve *deterministic LLM inference*, specifically addressing nondeterminism that can arise from varying batch sizes. It is a companion to our blog post on defeating nondeterminism in LLM inference. By leveraging `torch.Library`, it allows existing PyTorch models to use these batch-invariant operations with minimal code changes and low overhead.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

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  Seeking a library for deterministic PyTorch model inference, consistent across batch sizes.

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