Model parallelism

See also: Machine learning terms

Model Parallelism in Machine Learning

Model parallelism is an approach in machine learning that addresses the computational challenges posed by the increasing size and complexity of modern neural network models. It involves the concurrent execution of different parts of a single model across multiple processing units, often in parallel to other parts of the model. This article discusses the motivation behind model parallelism, its implementation strategies, and its advantages and disadvantages.

Motivation

As machine learning models grow larger and more complex, they require increasing amounts of memory and computation resources. This growth in demand is driven by the pursuit of improved performance, as larger models tend to have higher representational capacity and can better capture complex patterns in the data. However, this increased size and complexity can lead to several challenges, including:

Memory limitations: The memory capacity of a single processing unit, such as a Graphics Processing Unit (GPU) or Tensor Processing Unit (TPU), may not be sufficient to store the entire model and its associated data.
Computational bottlenecks: The sheer number of parameters and operations in large models can cause significant delays in training and inference, which may be unacceptable for certain applications or time-sensitive tasks.

Model parallelism offers a solution to these challenges by dividing the model's computation across multiple processing units, thereby allowing for the efficient execution of larger and more complex models.

Implementation Strategies

There are several strategies for implementing model parallelism, each with its own trade-offs and suitability for different types of models and hardware architectures. Some common strategies include:

Data parallelism: This approach involves splitting the input data across multiple processing units, each of which trains a complete copy of the model. Gradients are then aggregated and synchronized across the units before updating the model parameters. Data parallelism is more suitable for models that fit within the memory of a single processing unit.
Layer-wise parallelism: In this approach, different layers of the neural network are assigned to different processing units. Each unit computes the output of its assigned layer and passes it to the next unit in the pipeline. Layer-wise parallelism is particularly useful for models with a sequential structure, such as Recurrent Neural Networks (RNNs) or Transformers.
Pipeline parallelism: This strategy involves dividing the model into several stages, where each stage processes a portion of the input data and passes its output to the next stage. Pipeline parallelism can be applied to both feedforward and recurrent architectures and is well-suited for models with a large number of layers.
Tensor slicing: In tensor slicing, model parameters are divided across multiple processing units along specific dimensions of the tensors. This approach is especially effective for models with large, dense weight matrices, such as Fully Connected (FC) layers or Convolutional Neural Networks (CNNs).

Advantages and Disadvantages

Model parallelism offers several benefits, including:

Scalability: Model parallelism allows for the efficient execution of larger models that would otherwise be infeasible due to memory or computation constraints.
Faster training and inference: By distributing the model's computation across multiple processing units, model parallelism can potentially reduce training and inference times.

However, model parallelism also has some drawbacks, such as:

Communication overhead: Model parallelism often requires frequent communication between processing units to exchange intermediate results, gradients, and updates. This communication can introduce latency and consume valuable bandwidth.
Implementation complexity: Implementing model parallelism can be more complex than other approaches, such as data parallelism, and may require careful consideration of hardware and software constraints.

Explain Like I'm 5 (ELI5)

Imagine you're trying to build