Tensor Parallelism
Tensor parallelism is a model parallelism technique in deep learning that splits individual tensors (e.g., weight matrices or activations) across multiple devices, such as GPUs, to distribute memory and computational load. It involves partitioning large tensors along specific dimensions and performing operations in parallel, with communication between devices to combine results. This approach is crucial for training and inference of extremely large models that exceed the memory capacity of a single device.
Developers should learn and use tensor parallelism when working with massive neural network models, such as large language models (LLMs) or vision transformers, that have billions or trillions of parameters and cannot fit into the memory of a single GPU. It is essential for scaling model size beyond hardware limits in distributed training setups, enabling efficient parallel computation and reducing memory bottlenecks. Use cases include training models like GPT-3 or BERT variants in research and production environments.