Training a Model on Multiple GPUs with Data Parallelism
Training a large language model is slow. If you have multiple GPUs, you can accelerate training by distributing the workload across them to run in parallel. In this article, you will learn about data parallelism techniques. In particular, you will learn about training data parallelism. If you have multiple GPUs, you can combine them to operate as a single GPU with greater memory capacity. This technique is called data parallelism.
Data Parallelism Techniques
Data parallelism is to share the same model with multiple processors to work on different data. This is not focused on speed. In fact, switching to data parallelism may slow down training due to extra communication overhead. Data parallelism is useful when a model still fits on a single GPU but cannot be trained with a large batch size due to memory constraints. In this case, you can use gradient accumulation. This is equivalent to running small batches on multiple GPUs and then aggregating the gradients, as in data parallelism.
Implementation with PyTorch
Running a PyTorch model with data parallelism is easy. All you need to do is wrap the model with nn.DataParallel. The result is a new model that can distribute and aggregate data across all local GPUs. Consider the training loop from the previous article, you just need to wrap the model right after you create it:
model = nn.DataParallel(model)
You can see that nothing has changed in the training loop. But when you created the model, you wrapped it with nn.DataParallel. The wrapped model is a proxy for the original model but distributes data across multiple GPUs. Every GPU has an identical copy of the model. When you run the model with a batched tensor, the tensor is split across GPUs, and each GPU processes a micro-batch. The results are then aggregated to produce the output tensor. Similarly, for the backward pass, each GPU computes the gradient for its micro-batch, and the final gradient is aggregated across all GPUs to update the model parameters.
Best Practices
From the user’s perspective, a model trained in data parallelism is no different from a single-GPU model. However, when you save the model, you should save the underlying model, accessible as model.module. When loading the model, load the original model first, then wrap it with nn.DataParallel again. Note that when you run the training loop as above, the first GPU will consume most of the memory because it holds the master copy of the model parameters and gradients, as well as the optimizer and scheduler state. If you require precise control, you can specify the list of GPUs to use and the device on which to store the master copy of the model parameters. For more information, check out PyTorch’s documentation on Distributed Data Parallel (DDP).
Moreover, when using PyTorch DataParallel, you should be aware that it runs as a multithreaded program. This can be problematic because Python multithreading performance is limited. Therefore, PyTorch recommends using Distributed Data Parallel (DDP) instead, which is designed to work with multiple GPUs and is more efficient.
Consequently, you can achieve faster training times by using data parallelism with multiple GPUs. However, it is essential to consider the trade-offs, such as extra communication overhead and the need for precise control over the training process.
However, with the right approach and best practices, you can unlock the full potential of data parallelism and train your models much faster.
Therefore, if you’re looking to accelerate your model training with multiple GPUs, data parallelism is definitely worth considering.
In conclusion, training a model on multiple GPUs with data parallelism is a powerful technique for accelerating model training. By understanding the basics of data parallelism and implementing it correctly with PyTorch, you can achieve faster training times and improve your model’s performance.
