AI BriefingIntel Sapphire Rapids Accelerates PyTorch
Key point
Introduces how to accelerate PyTorch Transformer training using the AMX technology of Intel Sapphire Rapids.
Details
Intel's 4th generation Xeon CPU, the Sapphire Rapids architecture, introduces a new instruction set to accelerate deep learning workloads.
The core technology, Intel Advanced Matrix Extensions (AMX), accelerates matrix multiplication, the core operation of deep learning models. AMX supports BF16 and INT8 formats, and increases computational efficiency through new 2D CPU registers called tile registers.
By leveraging Intel Extension for PyTorch (IPEX) and Intel oneAPI CCL, this acceleration feature can be applied immediately without modifying Hugging Face Transformers code.
CPU-based training offers the following benefits:
- Cost savings: Cheaper than GPUs, and costs can be significantly lowered by using AWS spot instances.
- Flexibility and availability: Instances are easier to secure than GPUs, and it's good for reusing existing infrastructure.
This guide walks through building a Sapphire Rapids cluster using AWS's R7iz bare metal instances and setting up a distributed training environment.
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