Parallelism in GenAI Models

The rapid growth in data volume and the increasing complexity of machine learning models have made distributed machine learning essential. Traditional single-node trainingIt is where the model and the data are processed on a single device. often fails to handle large-scale datasets and massive models.

Distributed machine learning (DML) addresses this challenge using multiple servers or nodes, enabling faster, more efficient training. DML divides the training workload across multiple GPUs or machines at its core, significantly reducing training times and improving scalability. This approach accelerates model development and enables training on datasets that would otherwise exceed a single device’s memory and computational limits.

However, this approach introduces inherent complexities:

• Communication overhead: Exchanging data and model updates between multiple nodes can be time-consuming and resource-intensive.

• Synchronization challenges: Ensuring all nodes have the latest model parameters and updates requires careful coordination.

• Fault tolerance: Dealing with node failures or network issues is crucial to prevent disruptions in the training process.

DML includes techniques like data and model parallelism that are crucial in optimizing different aspects of the training process. Let’s look at them in detail.

Data parallelism

In data parallelism, the dataset is partitioned and distributed to multiple GPUs containing the same model, each of which processes a subset of the data. After individual training on each node, the model updates/gradients are synchronized across all nodes to maintain consistency. This dramatically lowers the training time for large models.

The dataset splitting is quite simple:

• We can split the data equally if all nodes are computationally equal (homogenous cluster).

• If nodes are computationally different, i.e., some are more powerful than others (heterogenous cluster), we can split the data using some bias.

There are various model synchronization techniques, each with pros and cons.

Parameter server

This technique uses a separate server to manage the model’s weights. The server aggregates and updates the model weights by pulling individual gradients from each training server. We can call this approach centralized due to a central point of truth. However, this solution presents the classic System Design problem: a single point of failure.

Peer-to-peer synchronization

Peer-to-peer (P2P) synchronization is a decentralized approach where servers (nodes) work collaboratively to synchronize the model. Each server communicates with its peers to gather and share updates, ensuring everyone stays on the same page. There are many sub-types of P2P model synchronization, including:

• AllReduce: The simplest approach in P2P data parallelism is where every node contributes its local gradients to its peers, and a global average is calculated. This guarantees that all workers operate on identical updated gradients after synchronization. This is efficient for small clusters and eliminates the single point of failure, but its communication and complexity overhead quickly adds up.

• Ring AllReduce: This is a specialized implementation of AllReduce, which organizes workers in a virtual ring topology to reduce communication overhead. Each worker communicates only with its two neighbors, passing gradients sequentially. This scales efficiently with more training servers and has lower bandwidth requirements than basic AllReduce. However, this sequential information passing can sometimes be slower, especially if one server lags.

• Herirarchical AllReduce: When scaling to thousands of GPUs, the communication demands of basic AllReduce or ring AllReduce become overwhelming. Hierarchical AllReduce introduces intermediate aggregation steps by grouping workers into smaller subgroups.
  
  Reduce introduces a more organized approach:

  • Cluster coordinators: The GPUs are divided into smaller groups, each managed by a coordinator. Think of these coordinators as team leaders.

  • Within-cluster aggregation: A simpler method like AllReduce or ring AllReduce combines updates inside each cluster. It’s easier to manage things within smaller teams.

  • Coordinator communication: The coordinators then use AllReduce to communicate and combine the aggregated updates from each group, creating a streamlined flow of information.

Note that after the aggregation within the cluster is wrapped up, each server in the cluster possesses that information. So, even if one fails, this information can be communicated to the coordinators. Due to the redundancy in each step, we can scale this architecture to thousands of servers, but the same is its con: We are calculating the same information multiple times, increasing the complexity.

Here’s a summary of the different architectural concepts we discussed in data parallelism:

Model parallelism

Model parallelism splits the model across multiple servers, enabling the training of large models that cannot fit on a single device. We can also use model parallelism in inferencing. With the advent of high-power, state-of-the-art GPUs with enough RAM to store the models, the need for model parallelism in training has diminished. However, it can still be useful in inferencing to reduce the time it takes for an input to feed forward through the model.

There are different ways to partition a model, each with its trade-offs:

1. Layer-wise partitioning: This strategy divides the model into distinct layers, assigning each layer to a different device. For example, the input, hidden, and output layers could be placed on separate GPUs in a neural network. This straightforward approach can lead to communication bottlenecks if layers have strong dependencies.

2. Operator-wise partitioning: This finer-grained strategy breaks down individual operations within a layer across multiple devices. For example, a matrix multiplication operation within a layer could be split across several GPUs. This can improve efficiency for computationally intensive operations but requires more careful management of data flow and synchronization.

Let’s see how it works.

Hybrid parallelism

In hybrid parallelism, data and model parallelism are combined to leverage both benefits. The dataset is split across nodes (data parallelism), and the model within each node is further split across GPUs (model parallelism). This way, we can handle large datasets and models effectively and efficiently, utilizing computational resources across multiple nodes.

Note: In our design problems, we exclusively use data parallelism due to its convenience and the ability of new GPUs to fit the models we use. We should use model or hybrid parallelism in extremely large models (which do not fit on single GPUs, e.g., GPT) or in inferencing.

Challenges in parallelizing GenAI models

Parallelizing generative AI models successfully requires careful consideration of various factors. While the potential benefits are significant, there are challenges to address. Let’s explore these challenges and their solutions:

Fault tolerance

In large distributed systems, the risk of node failure or communication errors increases, potentially leading to training interruptions.

We can alleviate these issues by:

• Checkpointing: Save intermediate states periodically to recover from failures.

• Redundancy: Use backup workers or mirrored model replicas to handle failures.

• Monitoring: Set up monitoring among the servers to ensure any error is reported and handled gracefully.

Hardware heterogeneity

Not all GPUs or servers in a distributed setup may have the same compute power, memory, or architecture, leading to inefficiencies and bottlenecks.

We can incorporate the following to ensure training stability:

• Device-specific workloads: Allocate workloads tailored to the computational capabilities of each device.

• Unified architecture: Use homogeneous hardware clusters (i.e., the same class of GPUs) or optimize the software stack to handle heterogeneity.

Load imbalance

If certain GPUs handle more work than others, this results in idle time for some devices and reduces overall efficiency. This is the phenomenon of load imbalance.

We can balance the load by:

• Dynamic work allocation: We can adaptively adjust the workload distribution based on each GPU’s computational capacity to ensure lower waiting times between processing.

• Partition optimization: By carefully dividing the model’s layers among the devices, i.e., assigning layers according to computational capacity, we ensure no single device is overloaded, leading to more efficient computation.

Conclusion

DML has emerged as a cornerstone of modern AI, enabling researchers and practitioners to scale their models and datasets to unprecedented levels. DML overcomes the limitations of single-node training by leveraging powerful concurrency techniques, such as data, model, and hybrid parallelism. Centralized and decentralized synchronization methods offer flexibility in communication, ensuring compatibility with diverse infrastructure and application requirements.

As AI models grow increasingly complex, the importance of efficient distribution strategies will only intensify. Innovations in DML frameworks and hardware acceleration continue to push the boundaries, making it possible to train models like GPT-4 and beyond with billions of parameters in a fraction of the time once thought necessary.