SNLP: Layer-Parallel Inference via Structured Newton Corrections

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layer-parallel inference structured-newton transformers parallelism deep-learning research-article

Summary

This paper introduces SNLP, a framework that enables layer-parallel inference for transformers by replacing exact Newton corrections with structured approximations, achieving up to 2.3x speedup on a 0.5B model while improving perplexity.

Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipeline parallelism. We study whether this layerwise dependency can be relaxed by treating the hidden-state trace across layers as the solution of a nonlinear residual equation and solving it with parallel Newton-style updates. While this view is principled, exact Newton corrections require expensive Jacobian-vector products and naive fixed-point iterations are unstable on trained Transformers. We introduce Structured Newton Layer Parallelism (SNLP), a training and inference framework that replaces exact layer Jacobians with cheap architecture-induced surrogate dynamics. In residual Transformers, this yields Identity Newton (IDN), where the correction reduces to a prefix-sum-like update; in mHC-style architectures, HC Newton (HCN) uses the model's residual mixing matrix. We further introduce SNLP-aware regularization, which trains models to make one or a few structured Newton iterations accurately approximate the sequential forward. Experiments on nanochat-scale Transformers show that SNLP regularization improves layer-parallel compatibility and can also improve standard sequential perplexity, reducing baseline PPL by 4.7%-23.4%. At inference time, SNLP combined with layer fusion and chunkwise decomposition achieves practical wall-clock speedups: on a 0.5B Nanochat model, it reaches 2.3x speedup while still improving PPL by 6.1%. These results suggest that layer-parallel inference is not merely a numerical approximation to sequential execution, but can act as a useful solver-induced inference bias. We also characterize limitations: off-the-shelf pretrained models are less amenable to this procedure, and exact convergence recovers the sequential computation rather than providing monotonic inference-time scaling.
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Cached at: 05/19/26, 02:29 AM

Paper page - SNLP: Layer-Parallel Inference via Structured Newton Corrections

Source: https://huggingface.co/papers/2605.17842

Abstract

Transformer models can achieve faster inference through parallel Newton-style updates that approximate sequential computations using structured Jacobian approximations and specialized regularization techniques.

Autoregressive language modelsexecuteTransformer layerssequentially, creating a latency bottleneck that is not removed by conventional tensor orpipeline parallelism. We study whether this layerwise dependency can be relaxed by treating the hidden-state trace across layers as the solution of anonlinear residual equationand solving it with parallelNewton-style updates. While this view is principled, exact Newton corrections require expensiveJacobian-vector productsand naivefixed-point iterationsare unstable on trained Transformers. We introduceStructured Newton Layer Parallelism(SNLP), a training and inference framework that replaces exact layer Jacobians with cheap architecture-induced surrogate dynamics. Inresidual Transformers, this yieldsIdentity Newton(IDN), where the correction reduces to a prefix-sum-like update; inmHC-style architectures,HC Newton(HCN) uses the model’s residual mixing matrix. We further introduce SNLP-aware regularization, which trains models to make one or a few structured Newton iterations accurately approximate the sequential forward. Experiments on nanochat-scale Transformers show that SNLP regularization improves layer-parallel compatibility and can also improve standard sequentialperplexity, reducing baseline PPL by 4.7%-23.4%. At inference time, SNLP combined withlayer fusionandchunkwise decompositionachieves practicalwall-clock speedups: on a 0.5B Nanochat model, it reaches 2.3x speedup while still improving PPL by 6.1%. These results suggest that layer-parallel inference is not merely a numerical approximation tosequential execution, but can act as a useful solver-inducedinference bias. We also characterize limitations: off-the-shelf pretrained models are less amenable to this procedure, and exact convergence recovers the sequential computation rather than providing monotonic inference-time scaling.

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