If you work with transformer models you often hit two walls: training speed stalls because of too many kernel launches and memory usage balloons as models grow. NVIDIA Transformer Engine solves both by fusing linear, layer‑norm and attention ops into single GPU kernels, switching to BF16 when FP8 isn’t available and enabling FP8 tensor cores on Ampere‑or‑newer GPUs.
First, install the engine and probe your GPU. The script checks compute capability; if it’s 8.0+ you can load TE kernels, and if it’s 8.9+ you also get FP8 support. On older GPUs the code falls back to pure PyTorch automatically, so you never have to rewrite your training loop.
Next, replace nn.Linear, nn.LayerNorm and their combos with te.Linear, te.LayerNorm, te.LayerNormLinear, te.LayerNormMLP or the full te.TransformerLayer. These modules accept the same arguments as their PyTorch counterparts but run fused kernels that cut launch overhead and reduce memory traffic.
To gain FP8 speed without losing stability, build a DelayedScaling recipe (hybrid E4M3/E5M2, amax history length 16, max‑based amax compute). Wrap the forward pass in te.fp8_autocast when FP8 is available; otherwise the model runs in BF16/FP32.
A compact GPT‑style model built with te.TransformerLayer blocks lets you train on synthetic data, measure step time and peak memory, and compare BF16 versus FP8. Benchmarks show FP8 cutting step latency by ~1.5‑2× on L4/H100/Ada/Blackwell hardware while keeping loss comparable to higher‑precision runs.
Inspect the FP8 metadata (scale and amax history) inside any transformer block to verify that delayed scaling is adapting correctly. Finally, use greedy generation to confirm the model learned the underlying pattern; the constant token step difference proves the training succeeded.
Try scaling up D_MODEL and N_LAYERS, experimenting with pure E4M3 or longer amax histories, or initializing weights directly in FP8 for inference. The same script works across laptops, workstations and cloud GPUs, giving you a clear path from prototype to production‑grade speed.
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