Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking

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In this tutorial, we explore how NVIDIA Transformer Engine accelerates transformer workloads by combining fused GPU kernels, BF16 computation, and hardware-aware FP8 execution. We begin by installing Transformer Engine and detecting the active GPU architecture so that we can determine whether the runtime supports TE kernels, FP8 tensor cores, or only the pure-PyTorch fallback path. We then examine core fused components such as te.Linear, te.LayerNorm, te.LayerNormLinear, te.LayerNormMLP, and te.TransformerLayer, while also configuring a delayed-scaling FP8 recipe that manages tensor scaling, amax history, and hybrid E4M3/E5M2 formats. Using these components, we construct a compact GPT-style causal language model, train it on deterministic synthetic sequences, compare higher-precision and FP8 execution, measure runtime and peak GPU memory, inspect FP8 metadata, and validate the trained model through autoregressive generation.

Copy CodeCopiedUse a different Browserimport subprocess, sys, os def pip_install(*pkgs): subprocess.run([sys.executable, "-m", "pip", "install", "-q", "--no-build-isolation", *pkgs], check=False) print(">> Installing transformer_engine[pytorch] (this can take a few minutes)...") pip_install("transformer_engine[pytorch]") import time, math, gc import torch import torch.nn as nn import torch.nn.functional as F assert torch.cuda.is_available(), "Enable a GPU runtime in Colab first!" DEVICE = "cuda" props = torch.cuda.get_device_properties(0) CC = (props.major, props.minor) GPU_NAME = props.name print(f">> GPU: {GPU_NAME} | compute capability …

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