Edouard Oyallon

Model Parallelism With Subnetwork Data Parallelism

Pre-training large neural networks at scale imposes heavy memory demands on accelerators and often requires costly communication. We introdu… (voir plus)ce Subnetwork Data Parallelism (SDP), a distributed training framework that partitions a model into structured subnetworks trained across workers without exchanging activations. We study two complementary masking regimes: backward masking, which applies sparsity only in the backward step to retain unbiased gradients, and forward masking, which also removes parameters in the forward pass to deliver stronger efficiency gains while providing additional regularization. We further explore two subnetwork construction strategies: neuron level and block level, applied across both CNNs and transformers. In experiments spanning CNNs and transformers on CIFAR and ImageNet, as well as LLM pre-training on FineWeb, SDP reduces per-device memory usage by 30%-75% while maintaining or improving performance. Notably, in FLOP-matched settings, forward masking can sometimes achieve better performance.

2025-07-11

ArXiv (prépublication)

Model Parallelism With Subnetwork Data Parallelism

Distributed pre-training of large models at scale often imposes heavy memory demands on individual nodes and incurs significant intra-node c… (voir plus)ommunication costs. We propose a novel alternative approach that reduces the memory requirements by training small, structured subnetworks of the model on separate workers. Unlike pipelining, our method avoids inter-node activation communication and maintains bandwidth requirements that are comparable to or lower than standard data parallel communication schemes based on all-reduce. We evaluate two subnetwork construction strategies guided by the principle of ensuring uniform representation of each parameter across the distributed training setup. Our results show that the stochastic block dropping technique consistently outperforms the width-wise subnetwork construction previously explored in federated learning. We empirically attribute this superior performance to stronger gradient alignment in subnetworks that retain blocks having skip connections. Preliminary experiments highlight the promise of our approach, achieving a

2025-06-11

ICML.cc/2025/Workshop/ES-FoMo-III (publié)

PETRA: Parallel End-to-end Training with Reversible Architectures

Stephane Rivaud

Thomas Pumir

Michael Eickenberg

Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (voir plus)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.

2025-01-01

ICLR (publié)

ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training

Pierre ERBACHER

Louis Serrano

2024-10-10

NeurIPS.cc/2024/Workshop/OPT (publié)

$\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers

Benjamin Therien

Charles-Etienne Joseph

2024-10-10

NeurIPS.cc/2024/Workshop/OPT (publié)

WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average

Masih Aminbeidokhti

The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at … (voir plus)an increased cost at inference. Weight averaging methods aim at balancing the generalization of ensembling and the inference speed of a single model by averaging the parameters of an ensemble of models. Yet, naive averaging results in poor performance as models converge to different loss basins, and aligning the models to improve the performance of the average is challenging. Alternatively, inspired by distributed training, methods like DART and PAPA have been proposed to train several models in parallel such that they will end up in the same basin, resulting in good averaging accuracy. However, these methods either compromise ensembling accuracy or demand significant communication between models during training. In this paper, we introduce WASH, a novel distributed method for training model ensembles for weight averaging that achieves state-of-the-art image classification accuracy. WASH maintains models within the same basin by randomly shuffling a small percentage of weights during training, resulting in diverse models and lower communication costs compared to standard parameter averaging methods.

2024-10-10

NeurIPS.cc/2024/Workshop/OPT (publié)

PETRA: Parallel End-to-end Training with Reversible Architectures

Stephane Rivaud

Thomas Pumir

Michael Eickenberg

Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (voir plus)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.

2024-06-04

ArXiv (prépublication)

ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training

Pierre ERBACHER

Louis Serrano

2024-06-03

ArXiv (prépublication)

ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training

Pierre ERBACHER

Louis Serrano

Training LLMs relies on distributed implementations using multiple GPUs to compute gradients in parallel with sharded optimizers. However, s… (voir plus)ynchronizing gradients in data parallel setups introduces communication overhead that grows with the number of workers, limiting parallelization efficiency. Local optimization algorithms reduce communications but incur high memory costs as they prevent optimizer state sharding, hindering scalability. To address this, we propose \textbf{AC}cumulate while \textbf{CO}mmunicate (\acco), a memory-efficient optimization algorithm for distributed LLM training. By synchronizing delayed gradients while computing new ones, \acco~reduces GPU idle time and supports heterogeneous hardware. To mitigate the convergence issues caused by delayed updates, we introduce a novel technique ensuring training dynamics align with standard distributed optimization. Compared to ZeRO-1, our approach is significantly faster and scales effectively across heterogeneous hardware.

2024-06-03

ArXiv (prépublication)

ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training

Pierre ERBACHER

Louis Serrano

Training Large Language Models (LLMs) relies heavily on distributed implementations, employing multiple GPUs to compute stochastic gradients… (voir plus) on model replicas in parallel. However, synchronizing gradients in data parallel settings induces a communication overhead increasing with the number of distributed workers, which can impede the efficiency gains of parallelization. To address this challenge, optimization algorithms reducing inter-worker communication have emerged, such as local optimization methods used in Federated Learning. While effective in minimizing communication overhead, these methods incur significant memory costs, hindering scalability: in addition to extra momentum variables, if communications are only allowed between multiple local optimization steps, then the optimizer's states cannot be sharded among workers. In response, we propose

2024-06-03

ArXiv (prépublication)