Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking
NVIDIA
This tutorial explores how NVIDIA Transformer Engine accelerates transformer workloads by combining fused GPU kernels, BF16 computation, and hardware-aware FP8 execution. It covers installation, GPU detection, module tour, FP8 recipe configuration, model training, benchmarking, and autoregressive generation.
The tutorial begins by installing NVIDIA Transformer Engine and initializing the PyTorch environment. It detects the GPU architecture to determine if TE kernels and FP8 tensor cores are supported, with a pure-PyTorch fallback for unsupported hardware. Core fused components such as te.Linear, te.LayerNorm, te.LayerNormLinear, te.LayerNormMLP, and te.TransformerLayer are examined. A delayed-scaling FP8 recipe is configured with hybrid E4M3/E5M2 formats and amax history. A compact GPT-style causal language model is built using fused te.TransformerLayer blocks, and an equivalent pure-PyTorch model is implemented for comparison. The model is trained on deterministic synthetic arithmetic-pattern sequences, with conditional FP8 autocast when supported. Benchmarking measures average step latency and peak GPU memory for BF16 and FP8 modes. FP8 metadata, including scaling factors and amax history, is inspected. Finally, greedy autoregressive generation validates that the model learns the arithmetic stride, and extensions like larger models and alternative FP8 formats are suggested.
- Сокращения
- BF16 = Brain Floating Point 16 — 16-битное число с плавающей точкой, усечённая точность
- FP8 = Floating Point 8 — 8-битное число с плавающей точкой
- TE = Transformer Engine — Движок трансформеров
- GPU = Graphics Processing Unit — Графический процессор
- GPT = Generative Pre-trained Transformer — Генеративный предобученный трансформер
- FP32 = Floating Point 32 — 32-битное число с плавающей точкой
Source: MarkTechPost —
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