Gaussian distributions are invariant under the mean-field Transformer flow, reducing infinite-dimensional dynamics to a bilinear control system on mean and covariance with explicit reachability and stability results.
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A unified perspective on the dynamics of deep transformers.arXiv preprint arXiv:2501.18322
13 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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abstract
Transformers, which are state-of-the-art in most machine learning tasks, represent the data as sequences of vectors called tokens. This representation is then exploited by the attention function, which learns dependencies between tokens and is key to the success of Transformers. However, the iterative application of attention across layers induces complex dynamics that remain to be fully understood. To analyze these dynamics, we identify each input sequence with a probability measure and model its evolution as a Vlasov equation called Transformer PDE, whose velocity field is non-linear in the probability measure. Our first set of contributions focuses on compactly supported initial data. We show the Transformer PDE is well-posed and is the mean-field limit of an interacting particle system, thus generalizing and extending previous analysis to several variants of self-attention: multi-head attention, L2 attention, Sinkhorn attention, Sigmoid attention, and masked attention--leveraging a conditional Wasserstein framework. In a second set of contributions, we are the first to study non-compactly supported initial conditions, by focusing on Gaussian initial data. Again for different types of attention, we show that the Transformer PDE preserves the space of Gaussian measures, which allows us to analyze the Gaussian case theoretically and numerically to identify typical behaviors. This Gaussian analysis captures the evolution of data anisotropy through a deep Transformer. In particular, we highlight a clustering phenomenon that parallels previous results in the non-normalized discrete case.
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A mean-field kinetic theory derivation produces a closed-form U-shaped token retrieval profile that explains the lost-in-the-middle phenomenon in Transformers.
Derives transformer-like dual-filter inference layers from first-principles optimal control on nonlinear discrete and linear Gaussian sequence models.
AdamW-trained transformer hidden states and backpropagated variables converge uniformly in L2 to a forward-backward ODE system (McKean-Vlasov when non-causal) at rate O(L^{-1}+L^{-1/3}H^{-1/2}) as depth L and heads H increase, with bounds independent of token number.
Transformers converge pathwise to a stochastic particle system and SPDE in the scaling limit, exhibiting synchronization by noise and exponential energy dissipation when common noise is coercive relative to self-attention drift.
Symmetric self-attention dynamics select the dominant eigendirection of V, producing homogeneous alignment when one positive eigenvalue dominates or sign-split polarization when V is negative definite.
A preconditioned regularized Wasserstein proximal sampling algorithm is introduced for particle-based approximation of Gibbs distributions, featuring a PDE-derived kernel formulation and non-asymptotic convergence analysis for quadratic potentials.
Derives forward and backward propagation-of-chaos bounds for finite vs. infinite-context transformers modeled as contextual flow maps, achieving Wasserstein rate n^{-1/d} generally and n^{-1/2} for transformer-like cases.
Models multi-head transformer data flow as time-dependent Wasserstein gradient flows of an attention-capturing interaction energy, with proofs on omega-limit stationary points and stability under weight and input perturbations.
In the low-temperature regime, the token distribution in mean-field transformers concentrates onto the push-forward under a key-query-value projection with Wasserstein distance scaling as √(log(β+1)/β) exp(Ct) + exp(-ct).
Formalizes nonlinear M2M regression and introduces transformer architectures as static maps and dynamic velocity fields between probability measures, tested on synthetic, particle, and organoid datasets.
Mean-field transformer models synchronize to a Dirac point mass exponentially fast with explicit quantitative rates under suitable parameter assumptions.
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A mean-field kinetic theory derivation produces a closed-form U-shaped token retrieval profile that explains the lost-in-the-middle phenomenon in Transformers.
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Derives transformer-like dual-filter inference layers from first-principles optimal control on nonlinear discrete and linear Gaussian sequence models.
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Uniform Scaling Limits in AdamW-Trained Transformers
AdamW-trained transformer hidden states and backpropagated variables converge uniformly in L2 to a forward-backward ODE system (McKean-Vlasov when non-causal) at rate O(L^{-1}+L^{-1/3}H^{-1/2}) as depth L and heads H increase, with bounds independent of token number.
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Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models
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Symmetric self-attention dynamics select the dominant eigendirection of V, producing homogeneous alignment when one positive eigenvalue dominates or sign-split polarization when V is negative definite.
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A preconditioned regularized Wasserstein proximal sampling algorithm is introduced for particle-based approximation of Gibbs distributions, featuring a PDE-derived kernel formulation and non-asymptotic convergence analysis for quadratic potentials.
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Multi-Headed Transformer Architectures as Time-dependent Wasserstein Gradient Flows
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Quantifying Concentration Phenomena of Mean-Field Transformers in the Low-Temperature Regime
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Measure-to-measure Regression with Transformers
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Quantitative Clustering in Mean-Field Transformer Models
Mean-field transformer models synchronize to a Dirac point mass exponentially fast with explicit quantitative rates under suitable parameter assumptions.
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