SWARM Parallelism: Training Large Models Can Be Surprisingly
Communication-Efficient
- URL: http://arxiv.org/abs/2301.11913v2
- Date: Thu, 29 Jun 2023 17:11:08 GMT
- Title: SWARM Parallelism: Training Large Models Can Be Surprisingly
Communication-Efficient
- Authors: Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov
- Abstract summary: Deep learning applications benefit from using large models with billions of parameters.
Training these models is notoriously expensive due to the need for specialized HPC clusters.
We consider alternative setups for training large models: using cheap "preemptible" instances or pooling existing resources from multiple regions.
- Score: 69.61083127540776
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many deep learning applications benefit from using large models with billions
of parameters. Training these models is notoriously expensive due to the need
for specialized HPC clusters. In this work, we consider alternative setups for
training large models: using cheap "preemptible" instances or pooling existing
resources from multiple regions. We analyze the performance of existing
model-parallel algorithms in these conditions and find configurations where
training larger models becomes less communication-intensive. Based on these
findings, we propose SWARM parallelism, a model-parallel training algorithm
designed for poorly connected, heterogeneous and unreliable devices. SWARM
creates temporary randomized pipelines between nodes that are rebalanced in
case of failure. We empirically validate our findings and compare SWARM
parallelism with existing large-scale training approaches. Finally, we combine
our insights with compression strategies to train a large Transformer language
model with 1B shared parameters (approximately 13B before sharing) on
preemptible T4 GPUs with less than 200Mb/s network.
Related papers
- Partitioned Neural Network Training via Synthetic Intermediate Labels [0.0]
GPU memory constraints have become a notable bottleneck in training such sizable models.
This study advocates partitioning the model across GPU and generating synthetic intermediate labels to train individual segments.
This approach results in a more efficient training process that minimizes data communication while maintaining model accuracy.
arXiv Detail & Related papers (2024-03-17T13:06:29Z) - ATOM: Asynchronous Training of Massive Models for Deep Learning in a Decentralized Environment [7.916080032572087]
atom is a resilient distributed training framework designed for asynchronous training of vast models in a decentralized setting.
atom aims to accommodate a complete LLM on one host (peer) through seamlessly model swapping and concurrently trains multiple copies across various peers to optimize training throughput.
Our experiments using different GPT-3 model configurations reveal that, in scenarios with suboptimal network connections, atom can enhance training efficiency up to $20 times$ when juxtaposed with the state-of-the-art decentralized pipeline parallelism approaches.
arXiv Detail & Related papers (2024-03-15T17:43:43Z) - Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
We take a closer theoretical look at Independent Subnetwork Training (IST)
IST is a recently proposed and highly effective technique for solving the aforementioned problems.
We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
arXiv Detail & Related papers (2023-06-28T18:14:22Z) - Does compressing activations help model parallel training? [64.59298055364336]
We present the first empirical study on the effectiveness of compression methods for model parallelism.
We implement and evaluate three common classes of compression algorithms.
We evaluate these methods across more than 160 settings and 8 popular datasets.
arXiv Detail & Related papers (2023-01-06T18:58:09Z) - Decentralized Training of Foundation Models in Heterogeneous
Environments [77.47261769795992]
Training foundation models, such as GPT-3 and PaLM, can be extremely expensive.
We present the first study of training large foundation models with model parallelism in a decentralized regime over a heterogeneous network.
arXiv Detail & Related papers (2022-06-02T20:19:51Z) - Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel
Training [23.633810934134065]
Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.
System supports parallel training methods such as data, pipeline, tensor, and sequence parallelism.
arXiv Detail & Related papers (2021-10-28T04:45:55Z) - TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale
Language Models [60.23234205219347]
TeraPipe is a high-performance token-level pipeline parallel algorithm for synchronous model-parallel training of Transformer-based language models.
We show that TeraPipe can speed up the training by 5.0x for the largest GPT-3 model with 175 billion parameters on an AWS cluster.
arXiv Detail & Related papers (2021-02-16T07:34:32Z) - Scaling Distributed Deep Learning Workloads beyond the Memory Capacity
with KARMA [58.040931661693925]
We propose a strategy that combines redundant recomputing and out-of-core methods.
We achieve an average of 1.52x speedup in six different models over the state-of-the-art out-of-core methods.
Our data parallel out-of-core solution can outperform complex hybrid model parallelism in training large models, e.g. Megatron-LM and Turning-NLG.
arXiv Detail & Related papers (2020-08-26T07:24:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.