🧮Tensor Parallelism

NEW

A model-parallel technique that partitions large tensors across devices to execute operations concurrently.

DESCRIPTION

Tensor parallelism splits layer parameters and activations across multiple devices, enabling training of models that exceed single-device memory. It requires collective operations like all-reduce for synchronization. Frameworks such as Megatron-LM and DeepSpeed implement tensor parallel strategies for transformers. Trade-offs include communication overhead and implementation complexity.

ALTERNATIVE DEFINITIONS

NVIDIA Developer

Tensor-model parallelism splits parameters of each layer across multiple devices.

Clearly distinguishes tensor-level partitioning from other parallelism forms.

Source

DeepSpeed

Tensor parallelism partitions large weight matrices and compute across devices to enable training very large models.

Focuses on partitioning weight matrices and enabling scale.

Source

RELATED TERMS

Model Parallelism ZeRO Megatron-LM DeepSpeed Sharded Tensors Distributed Training All-Reduce

COMPETING TERMS

Data Parallelism Pipeline Parallelism

RELATIONS

• Broader: Parallelism

KNOWLEDGE AREAS

AI MLOps Infrastructure

TARGET AUDIENCE

AI Engineer MLOps Engineer Data Scientist Cloud Architect

EXTERNAL SOURCES

• Wikipedia - Model parallelism
• NVIDIA Megatron-LM - Implementation of tensor-model parallelism
• DeepSpeed - Distributed training framework with TP support
• Hugging Face - Distributed training toolkit overview

TOP QUESTIONS

Why use Tensor Parallelism for large models?

It allows training models that exceed single-device memory. Splitting tensors spreads compute and memory across devices.

Category: Relevancy

How does Tensor Parallelism differ from Data Parallelism?

Data parallelism replicates models across devices and splits batches. Tensor parallelism shards model tensors within layers.

Category: Alternatives

What frameworks support Tensor Parallelism?

Megatron-LM and DeepSpeed provide robust TP for transformers. Some libraries integrate TP into hybrid strategies.

Category: Platform

What communication is needed in Tensor Parallelism?

Collective ops like all-reduce synchronize partial results. Efficient kernels and networks are critical.

Category: Requirements

What are common mistakes when applying Tensor Parallelism?

Ignoring communication overhead and topology can negate gains. Mismatched tensor partitioning harms performance.

Category: Mistakes

How does Tensor Parallelism interact with Pipeline Parallelism?

They can be combined in hybrid schemes. TP shards layers; pipeline parallel splits layer sequences.

Category: Example

What is a typical Tensor Parallelism topology?

Shard attention and feed-forward matrices across GPUs in a node. Use NVLink or fast interconnects.

Category: Example

What’s the biggest risk in Tensor Parallelism?

Network bottlenecks and synchronization overhead. Inadequate profiling can mask inefficiencies.

Category: Risks

How to validate Tensor Parallelism benefits?

Profile throughput and time-to-accuracy versus baselines. Measure communication overhead and memory utilization.

Category: Testing