Training Large Models using Model/Pipeline Parallelism

As deep learning models continue to grow in size, from millions to trillions of parameters, training them efficiently has become a major engineering challenge. A single GPU can no longer hold the entire model or handle its compute demands. To overcome these hardware limitations, the deep learning community employs various forms of parallelism: data parallelism, model parallelism, and pipeline parallelism.

While data parallelism distributes the dataset across devices, model and pipeline parallelism focus on splitting the model itself, enabling massive neural networks to train efficiently across multiple GPUs or even multiple nodes.

Model Parallelism involves splitting a single neural network’s parameters across multiple devices. Instead of each GPU holding a full copy of the model, different GPUs are responsible for different parts of it.

For example, consider a deep neural network with four layers:

• GPU 1 handles layers 1 and 2
• GPU 2 handles layers 3 and 4

During forward propagation, the output of GPU 1 is sent to GPU 2 for the next set of computations. The same happens in reverse during backpropagation.

Advantages

• Allows training models larger than a single GPU’s memory.
• Enables efficient use of multiple GPUs without redundant model copies.

Challenges

• Requires careful balancing: if one GPU does much more work than another, others sit idle.
• High communication overhead can occur when passing activations between devices.
• Implementation complexity, partitioning the model effectively is non-trivial.

Example Use Case

Model parallelism is often used in large transformer architectures (like GPT or BERT variants), where the weight matrices are massive and can be split across GPUs.

 

Pipeline Parallelism extends the idea of model parallelism by organizing model layers into stages that process data like an assembly line.

Suppose you have 4 GPUs and a model split into 4 sequential stages:

• Each GPU holds one stage.
• Mini-batches are divided into micro-batches that flow through the pipeline.

While GPU 1 processes micro-batch 2, GPU 2 can already process micro-batch 1’s output, and so on, ensuring all GPUs work concurrently.

Advantages

• Greatly improves GPU utilization compared to pure model parallelism.
• Reduces idle time through pipeline scheduling (e.g., “1F1B” schedule: one forward, one backward).

Challenges

• Requires careful pipeline scheduling to minimize “pipeline bubbles” (idle time when the pipeline isn’t full).
• Communication latency can still be a bottleneck.
• Micro-batch tuning is critical, too small causes overhead, too large increases bubble time.

Example Systems

• GPipe (Google): early system introducing efficient pipeline parallelism.
• DeepSpeed (Microsoft) and Megatron-LM (NVIDIA): combine pipeline, model, and data parallelism for trillion-parameter-scale training.

 

Combining Approaches: 3D Parallelism is State-of-the-art large-scale training frameworks (e.g., DeepSpeed, Megatron-Deepspeed) combine:

a)       Data Parallelism (split data),

b)       Model Parallelism (split weights), and

c)       Pipeline Parallelism (split layers).

This hybrid, known as 3D Parallelism, enables scaling models efficiently across thousands of GPUs.

Let’s explore further with an example, when training a 1-trillion parameter transformer:

• Each GPU might store only a fraction of the model’s layers (pipeline parallelism).
• Each layer’s large weight matrices are split across multiple GPUs (model parallelism).
• The dataset is sharded across multiple GPU groups (data parallelism).

The combination enables high utilization and distributed memory efficiency.

In Conclusion, training large-scale models efficiently is as much a systems problem as a mathematical one. Model and pipeline parallelism are foundational techniques enabling the deep learning revolution at scale, from GPT models to large vision transformers.

As models grow even larger, frameworks that seamlessly combine these parallelism strategies will define the next generation of AI infrastructure.

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