#aihypercomputers

Google AI Hypercomputer

Updates to the AI hypercomputer software include a new resource center, quicker training and inference, and more.

AI has more promise than ever before, and infrastructure is essential to its advancement. Google Cloud’s supercomputing architecture, AI Hypercomputer, is built on open software, performance-optimized hardware, and adaptable consumption models. When combined, they provide outstanding performance and efficiency, scalability and resilience, and the freedom to select products at each tier according to your requirements.

A unified hub for AI Hypercomputer resources, enhanced resiliency at scale, and significant improvements to training and inference performance are all being announced today.

Github resources for AI hypercomputers

The open software layer of AI Hypercomputer offers reference implementations and workload optimizations to enhance the time-to-value for your particular use case, in addition to supporting top ML Frameworks and orchestration options. Google Cloud is launching the AI Hypercomputer GitHub organization to make the advancements in its open software stack easily accessible to developers and practitioners. This is a central location where you can find reference implementations like MaxText and MaxDiffusion, orchestration tools like xpk (the Accelerated Processing Kit for workload management and cluster creation), and GPU performance recipes on Google Cloud. It urges you to join us as it expand this list and modify these resources to reflect a quickly changing environment.

A3 Mega VMs are now supported by MaxText

MaxText is an open-source reference implementation for large language models (LLMs) that offers excellent speed and scalability. Performance-optimized LLM training examples are now available for A3 Mega VMs, which provide a 2X increase in GPU-to-GPU network capacity over A3 VMs and are powered by NVIDIA H100 Tensor Core GPUs. To make it possible for collaborative communication and computing on GPUs to overlap, Google Cloud collaborated closely with NVIDIA to enhance JAX and XLA. It has included example scripts and improved model settings for GPUs with XLA flags enabled.

As the number of VMs in the cluster increases, MaxText with A3 Mega VMs can provide training performance that scales almost linearly, as seen below using Llama2-70b pre-training.

Moreover, FP8 mixed-precision training on A3 Mega VMs can be used to increase hardware utilization and acceleration. Accurate Quantized Training (AQT), the quantization library that drives INT8 mixed-precision training on Cloud TPUs, is how it added FP8 capability to MaxText.

Its results on dense models show that FP8 training with AQT can achieve up to 55% more effective model flop use (EMFU) than bf16.

Reference implementations and kernels for MoEs

Consistent resource usage of a small number of experts is beneficial for the majority of mixture of experts (MoE) use cases. But for some applications, it is more crucial to be able to leverage more experts to create richer solutions. Google Cloud has now added both “capped” and “no-cap” MoE implementations to MaxText to give you this flexibility, allowing you to select the one that best suits your model architecture. While no-cap models dynamically distribute resources for maximum efficiency, capped MoE models provide predictable performance.

Pallas kernels, which are optimized for block-sparse matrix multiplication on Cloud TPUs, have been made publicly available to speed up MoE training even more. Pallas is an extension to JAX that gives fine-grained control over code created for XLA devices like GPUs and TPUs; at the moment, block-sparse matrix multiplication is only available for TPUs. These kernels offer high-performance building pieces for training your MoE models and are compatible with both PyTorch and JAX.

With a fixed batch size per device, our testing using the no-cap MoE model (Mixtral-8x7b) shows nearly linear scalability. When it raised the number of experts in the base setup with the number of accelerators, it also saw almost linear scaling, which is suggestive of performance on models with larger sparsity.

Monitoring large-scale training

MLOps can be made more difficult by having sizable groups of accelerators that are supposed to collaborate on a training task. “Why is this one device in a segfault?” is a question you may have. “Did host transfer latencies spike for a reason?” is an alternative. However, monitoring extensive training operations with the right KPIs is necessary to maximize your resource use and increase overall ML Goodput.

Google has provided a reference monitoring recipe to make this important component of your MLOps charter easier to understand. In order to detect anomalies in the configuration and take remedial action, this recipe assists you in creating a Cloud Monitoring dashboard within your Google Cloud project that displays helpful statistical metrics like average or maximum CPU consumption.

Cloud TPU v5p SparseCore is now GA

High-performance random memory access is necessary for recommender models and embedding-based models to utilize the embeddings. The TPU’s hardware embedding accelerator, SparseCore, lets you create recommendation systems that are more potent and effective. With four dedicated SparseCores per Cloud TPU v5p chip, DLRM-V2 can perform up to 2.5 times faster than its predecessor.

Enhancing the performance of LLM inference

Lastly, it implemented ragged attention kernels and KV cache quantization in JetStream, an open-source throughput-and-memory-optimized engine for LLM inference, to enhance LLM inference performance. When combined, these improvements can increase inference performance on Cloud TPU v5e by up to 2X.

Boosting your AI adventure

Each part of the AI Hypercomputer serves as a foundation for the upcoming AI generation, from expanding the possibilities of model training and inference to improving accessibility through a central resource repository.