Neural networks, which mimic human brains, have moved to the forefront of scientific research. Their one main issue? They require a ton of data processing and storage power—more than the average device can provide. That’s where model parallelism comes in.
Model parallelism distributes the machine learning models that feed neural networks across multiple devices, allowing for more efficient use of available memory and enabling the training of larger models that might exceed the capacity of individual devices.
Let’s dig into what model parallelism is, its benefits, and how to implement it. We’ll also look at some real-world examples.
What Is Model Parallelism?
Model parallelism is a technique in machine learning where the computational workload of a neural network is distributed across multiple devices or processors. Unlike data parallelism, in which different batches of data independently train model copies, model parallelism involves splitting a single neural network across many devices, each responsible for computing a portion of the model's operations. Think of it as attacking a problem from five different angles or multiple teams, each with its own strengths and capabilities, for the sake of resolving the problem as efficiently as possible.
Benefits of Model Parallelism
In a nutshell, model parallelism accelerates machine learning at scale. On a slightly more granular level, it also:
Provides Flexibility in Model Design
With model parallelism, researchers have more flexibility in designing complex neural network architectures. This includes architectures with intricate layers and structures, as well as models that involve different types of operations.
Reduces Bottlenecks
By distributing the workload, model parallelism helps mitigate computational bottlenecks that may arise during training. This is particularly important when dealing with large data sets or models with intricate architectures.
But in the end, the benefits of model parallelism boil down to “divide and conquer.”
Implementing Model Parallelism
Here are some of the fundamental steps of implementing model parallelism:
- Identify the model components: Examine the neural network architecture and identify components that can be split across devices. This might include layers, subnetworks, or specific operations.
- Divide the model: Partition the identified components into segments that can be allocated to different devices. Consider the computational load of each segment to ensure a balanced distribution.
- Allocate devices: Assign each segment to a specific device. This may involve utilizing multiple GPUs, TPUs, or other accelerators. Frameworks like TensorFlow and PyTorch provide APIs for device placement.
- Manage data flow: Implement mechanisms for managing data flow between devices. Ensure that input data is appropriately partitioned and distributed to the devices handling different segments of the model.
- Fine-tune the processes: Modify the training process to perform operations in parallel on different devices. This may include parallelizing forward and backward passes, gradient updates, and weight synchronization.
- Optimize: Implement optimization techniques specific to model parallelism, such as gradient accumulation, to ensure efficient training. These techniques help manage the flow of gradients across devices.
- Update parameters: Synchronize model parameters across devices after each training step. This involves updating the weights of the entire model based on the aggregated gradients.
Also, be sure to keep in mind common challenges with implementing model parallelism, including:
- Load balancing: Ensuring a balanced distribution of computational load across devices can be tough. Be sure to monitor and adjust the partitioning of model components to maintain load balance.
- Communication overhead: There can be overhead associated with communication between devices. Optimize communication patterns, explore techniques like asynchronous updates, and minimize unnecessary data transfers.
- Data dependency: Handling dependencies between data batches and model segments can be a challenge. Implement mechanisms for managing data dependencies, such as overlapping computation and communication.
- Debugging and profiling: Use debugging and profiling tools provided by the framework, and monitor performance metrics to identify bottlenecks.
- Framework support: There can be framework-specific differences in supporting model parallelism. Choose a framework with good support for model parallelism, and stay updated on new features and improvements.
- Compatibility with optimizers: Compatibility issues with certain optimizers in a parallelized setup are common. Choose optimizers that are compatible with parallel training or modify existing ones to accommodate model parallelism.
Examples of Model Parallelism in Action
Let’s look at some successful real-world applications of model parallelism. All of the examples below use model parallelism to distribute the machine learning models across multiple GPUs to efficiently handle a massive computational load.
GPT-3 by OpenAI
By now, most people have heard of, if not used, ChatGPT. GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language model designed for natural language processing tasks. GPT-3 is a massive model with 175 billion parameters.
Facebook AI's wav2vec 2.0
Wav2vec 2.0 is a speech recognition model developed by Facebook AI for converting spoken language into written text.
DeepSpeech 2 by Baidu
DeepSpeech 2 is a deep learning model for automatic speech recognition developed by Baidu Research. It uses model parallelism to distribute the workload across multiple GPUs, facilitating the training of large-scale models for speech recognition.
Vision Transformers (ViTs)
Vision transformers have gained popularity for image classification tasks, replacing traditional convolutional neural networks in some cases.
Megatron by NVIDIA
Megatron is a deep learning model parallelism library developed by NVIDIA, designed to scale the training of massive language models.
All of these examples showcase how model parallelism is instrumental in handling the training of large and complex models, leading to improved performance, scalability, and efficiency across various machine learning applications.
Conclusion
Model parallelism is a “divide and conquer” technique to make it easier for systems to apply huge machine learning models. But for model parallelism to work, you still need a powerful, flexible, and efficient data storage infrastructure.
Pure Storage offers AIRI®, a certified NVIDIA DGX BasePOD full-stack solution that simplifies AI deployment and scales quickly and efficiently to keep your data teams focused on delivering valuable insights, not managing IT. Check it out and see for yourself how well it will support your machine learning endeavors.
