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

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 {CC[0]}.{CC[1]} | " f"{props.total_memory/1e9:.1f} GB") TE_CAPABLE = CC >= (8, 0) FP8_CAPABLE = CC >= (8, 9) te = None if TE_CAPABLE: try: import transformer_engine.pytorch as te from transformer_engine.common import recipe print(">> Transformer Engine imported OK:", getattr(te, "__version__", "unknown version")) except Exception as e: print(f">> TE import failed ({e}); using pure-PyTorch fallback.") TE_CAPABLE = FP8_CAPABLE = False else: print(">> GPU is pre-Ampere (e.g. T4): TE kernels unsupported -> fallback mode.") if TE_CAPABLE and FP8_CAPABLE and te is not None: try: ok, reason = te.fp8.check_fp8_support() FP8_CAPABLE = bool(ok) if not ok: print(">> TE reports FP8 unsupported:", reason) except Exception: pass print(f">> Mode: TE={'ON' if TE_CAPABLE else 'OFF'} | " f"FP8={'ON' if FP8_CAPABLE else 'OFF (will use BF16)'}") torch.manual_seed(1234) if TE_CAPABLE: H = 768 x_demo = torch.randn(8, 32, H, device=DEVICE, dtype=torch.bfloat16) lin = te.Linear(H, H, bias=True, params_dtype=torch.bfloat16).to(DEVICE) ln = te.LayerNorm(H, params_dtype=torch.bfloat16).to(DEVICE) ln_lin = te.LayerNormLinear(H, 3 * H, params_dtype=torch.bfloat16).to(DEVICE) ln_mlp = te.LayerNormMLP(H, 4 * H, params_dtype=torch.bfloat16).to(DEVICE) with torch.no_grad(): print("\n>> Module tour (shapes):") print(" te.Linear ", tuple(lin(x_demo).shape)) print(" te.LayerNorm ", tuple(ln(x_demo).shape)) print(" te.LayerNormLinear", tuple(ln_lin(x_demo).shape)) print(" te.LayerNormMLP ", tuple(ln_mlp(x_demo).shape)) del lin, ln, ln_lin, ln_mlp, x_demo gc.collect(); torch.cuda.empty_cache() fp8_recipe = None if FP8_CAPABLE: fp8_recipe = recipe.DelayedScaling( fp8_format=recipe.Format.HYBRID, amax_history_len=16, amax_compute_algo="max", ) print("\n>> FP8 recipe:", fp8_recipe) We install NVIDIA Transformer Engine and initialize the PyTorch environment required for GPU-accelerated execution. We inspect the active GPU, compute capability, and memory capacity to determine whether fused TE kernels and FP8 tensor cores are available. We also validate the core fused modules and configure a delayed-scaling FP8 recipe while preserving an automatic