On this tutorial, we implement a complicated, sensible implementation of the NVIDIA Transformer Engine in Python, specializing in how mixed-precision acceleration might be explored in a practical deep studying workflow. We arrange the atmosphere, confirm GPU and CUDA readiness, try to put in the required Transformer Engine parts, and deal with compatibility points gracefully in order that the pocket book stays runnable even when the complete extension can’t be constructed. As we transfer by way of every step, we construct trainer and scholar networks, evaluate a baseline PyTorch path with a Transformer Engine-enabled path, practice each fashions, benchmark their pace and reminiscence utilization, and visualize the outcomes, giving us a transparent hands-on understanding of how performance-oriented coaching workflows are structured in observe.
import os
import sys
import json
import time
import math
import random
import shutil
import platform
import subprocess
import statistics
def run(cmd, verify=True):
print("n[RUN]", " ".be a part of(cmd))
consequence = subprocess.run(cmd, textual content=True, capture_output=True)
if consequence.stdout.strip():
print(consequence.stdout[-4000:])
if consequence.returncode != 0 and consequence.stderr.strip():
print(consequence.stderr[-4000:])
if verify and consequence.returncode != 0:
elevate subprocess.CalledProcessError(consequence.returncode, cmd)
return consequence
def has_cmd(identify):
return shutil.which(identify) shouldn't be None
run([sys.executable, "-m", "pip", "install", "-q", "--upgrade", "pip"])
run([sys.executable, "-m", "pip", "install", "-q", "ninja", "packaging", "matplotlib"])
import torch
import torch.nn as nn
import torch.nn.practical as F
import matplotlib.pyplot as plt
assert torch.cuda.is_available(), "This pocket book wants a GPU runtime in Colab."
gpu_name = torch.cuda.get_device_name(0)
cc_major, cc_minor = torch.cuda.get_device_capability(0)
cuda_runtime = torch.model.cuda
python_version = sys.model.cut up()[0]
torch_version = torch.__version__
cuda_home = os.environ.get("CUDA_HOME", "/usr/native/cuda")
nvcc_path = shutil.which("nvcc") or os.path.be a part of(cuda_home, "bin", "nvcc")
cudnn_header_candidates = [
os.path.join(cuda_home, "include", "cudnn.h"),
"/usr/include/cudnn.h",
"/usr/local/include/cudnn.h",
]
nvcc_exists = os.path.exists(nvcc_path)
cudnn_header_exists = any(os.path.exists(p) for p in cudnn_header_candidates)
print("=" * 120)
print("ENVIRONMENT CHECK")
print("=" * 120)
print(json.dumps({
"python": python_version,
"platform": platform.platform(),
"torch": torch_version,
"torch_cuda": cuda_runtime,
"gpu_name": gpu_name,
"compute_capability": f"{cc_major}.{cc_minor}",
"cuda_home": cuda_home,
"nvcc_exists": nvcc_exists,
"nvcc_path": nvcc_path if nvcc_exists else None,
"cudnn_header_exists": cudnn_header_exists,
}, indent=2))
print("=" * 120)We put together the Colab atmosphere by importing the required Python libraries, defining a helper operate for executing shell instructions, and putting in the core dependencies for the tutorial. We then import PyTorch and Matplotlib, confirm {that a} GPU is accessible, and gather key atmosphere particulars, together with the GPU identify, CUDA model, Python model, and toolkit paths. This offers us a transparent view of the system state earlier than we try any Transformer Engine set up or mannequin execution.
te_available = False
te_mode = "fallback"
te_import_error = None
attempt:
run([sys.executable, "-m", "pip", "install", "-q", "transformer_engine[core_cu12]"])
besides Exception as e:
print("Core wheel set up failed:", repr(e))
can_try_te_torch = nvcc_exists and cudnn_header_exists
if can_try_te_torch:
env = os.environ.copy()
env["NVTE_FRAMEWORK"] = "pytorch"
env["MAX_JOBS"] = "1"
env["NVTE_BUILD_THREADS_PER_JOB"] = "1"
env["CUDA_PATH"] = cuda_home
env["CUDA_HOME"] = cuda_home
attempt:
print("nAttempting to construct the PyTorch extension for Transformer Engine...")
consequence = subprocess.run(
[sys.executable, "-m", "pip", "install", "-q", "--no-build-isolation", "transformer_engine[pytorch]"],
textual content=True,
capture_output=True,
env=env,
)
if consequence.stdout.strip():
print(consequence.stdout[-4000:])
if consequence.returncode != 0 and consequence.stderr.strip():
print(consequence.stderr[-4000:])
if consequence.returncode == 0:
import transformer_engine.pytorch as te
from transformer_engine.widespread import recipe
te_available = True
te_mode = "transformer_engine"
else:
te_import_error = consequence.stderr[-4000:] if consequence.stderr else "Unknown pip construct error"
besides Exception as e:
te_import_error = repr(e)
else:
te_import_error = "Lacking nvcc or cuDNN headers on this Colab runtime, so TE PyTorch extension can't be constructed right here."
if te_available:
attempt:
fp8_available, fp8_reason = te.is_fp8_available(return_reason=True)
besides Exception as e:
fp8_available, fp8_reason = False, f"Couldn't question FP8 availability: {e}"
attempt:
bf16_available = te.is_bf16_available()
besides Exception:
bf16_available = torch.cuda.is_bf16_supported()
else:
fp8_available = False
fp8_reason = "Transformer Engine not put in; utilizing fallback PyTorch path."
bf16_available = torch.cuda.is_bf16_supported()
amp_dtype = torch.bfloat16 if bf16_available else torch.float16
print("n" + "=" * 120)
print("INSTALL STATUS")
print("=" * 120)
print(json.dumps({
"te_available": te_available,
"te_mode": te_mode,
"fp8_available": fp8_available,
"fp8_reason": fp8_reason,
"te_import_error": te_import_error,
"amp_dtype": str(amp_dtype),
}, indent=2))
print("=" * 120)
system = "cuda"
random.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)
if te_available:
fp8_recipe = recipe.DelayedScaling(margin=0, fp8_format=recipe.Format.E4M3)
def baseline_autocast():
return torch.autocast(device_type="cuda", dtype=amp_dtype)
def te_forward_context(use_fp8):
if te_available and use_fp8:
return te.autocast(enabled=True, recipe=fp8_recipe)
return baseline_autocast()We try to put in the Transformer Engine core bundle after which verify whether or not the Colab runtime can construct the PyTorch extension by verifying the presence of nvcc and cuDNN headers. If the atmosphere helps it, we attempt to set up the Transformer Engine PyTorch backend after which examine whether or not FP8 and BF16 can be found on the present {hardware}. We additionally configure the precision mode and outline the autocast contexts that later enable us to change between commonplace blended precision and Transformer Engine execution.
class TeacherNet(nn.Module):
def __init__(self, hidden_size=512, intermediate_size=2048, num_layers=3, vocab_size=4096):
tremendous().__init__()
self.embed = nn.Embedding(vocab_size, hidden_size)
self.layers = nn.ModuleList([
nn.Sequential(
nn.LayerNorm(hidden_size),
nn.Linear(hidden_size, intermediate_size),
nn.GELU(),
nn.Linear(intermediate_size, hidden_size),
) for _ in range(num_layers)
])
self.head = nn.Linear(hidden_size, hidden_size)
def ahead(self, token_ids):
x = self.embed(token_ids)
for layer in self.layers:
x = x + layer(x)
return self.head(x)
class BaselineStudent(nn.Module):
def __init__(self, hidden_size=512, intermediate_size=2048, num_layers=3, vocab_size=4096):
tremendous().__init__()
self.embed = nn.Embedding(vocab_size, hidden_size)
self.norms = nn.ModuleList([nn.LayerNorm(hidden_size) for _ in range(num_layers)])
self.fc1 = nn.ModuleList([nn.Linear(hidden_size, intermediate_size) for _ in range(num_layers)])
self.fc2 = nn.ModuleList([nn.Linear(intermediate_size, hidden_size) for _ in range(num_layers)])
self.head = nn.Linear(hidden_size, hidden_size)
def ahead(self, token_ids):
x = self.embed(token_ids)
for ln, fc1, fc2 in zip(self.norms, self.fc1, self.fc2):
residual = x
x = ln(x)
x = fc1(x)
x = F.gelu(x, approximate="tanh")
x = fc2(x)
x = x + residual
return self.head(x)
if te_available:
class TEStudent(nn.Module):
def __init__(self, hidden_size=512, intermediate_size=2048, num_layers=3, vocab_size=4096):
tremendous().__init__()
self.embed = nn.Embedding(vocab_size, hidden_size)
self.norms = nn.ModuleList([te.LayerNorm(hidden_size) for _ in range(num_layers)])
self.fc1 = nn.ModuleList([te.Linear(hidden_size, intermediate_size, bias=True) for _ in range(num_layers)])
self.fc2 = nn.ModuleList([te.Linear(intermediate_size, hidden_size, bias=True) for _ in range(num_layers)])
self.head = te.Linear(hidden_size, hidden_size, bias=True)
def ahead(self, token_ids, use_fp8=False):
x = self.embed(token_ids)
with te_forward_context(use_fp8):
for ln, fc1, fc2 in zip(self.norms, self.fc1, self.fc2):
residual = x
x = ln(x)
x = fc1(x)
x = F.gelu(x, approximate="tanh")
x = fc2(x)
x = x + residual
x = self.head(x)
return x
else:
class TEStudent(nn.Module):
def __init__(self, hidden_size=512, intermediate_size=2048, num_layers=3, vocab_size=4096):
tremendous().__init__()
self.embed = nn.Embedding(vocab_size, hidden_size)
self.norms = nn.ModuleList([nn.LayerNorm(hidden_size) for _ in range(num_layers)])
self.fc1 = nn.ModuleList([nn.Linear(hidden_size, intermediate_size) for _ in range(num_layers)])
self.fc2 = nn.ModuleList([nn.Linear(intermediate_size, hidden_size) for _ in range(num_layers)])
self.head = nn.Linear(hidden_size, hidden_size)
def ahead(self, token_ids, use_fp8=False):
x = self.embed(token_ids)
with baseline_autocast():
for ln, fc1, fc2 in zip(self.norms, self.fc1, self.fc2):
residual = x
x = ln(x)
x = fc1(x)
x = F.gelu(x, approximate="tanh")
x = fc2(x)
x = x + residual
x = self.head(x)
return x
def count_params(mannequin):
return sum(p.numel() for p in mannequin.parameters() if p.requires_grad)
def format_millions(n):
return f"{n / 1e6:.2f}M"We outline the neural community architectures used all through the tutorial, together with the trainer mannequin, the baseline scholar mannequin, and the Transformer Engine scholar path. We maintain the mannequin constructions aligned in order that the comparability stays significant whereas permitting the TE path to swap in Transformer Engine layers when the extension is accessible. We additionally outline small utility features for counting parameters and formatting mannequin dimension, which assist us examine the size of the fashions earlier than coaching begins.
hidden_size = 512
intermediate_size = 2048
num_layers = 3
vocab_size = 4096
seq_len = 128
batch_size = 8
steps = 25
benchmark_iters = 20
lr = 2e-4
weight_decay = 1e-2
trainer = TeacherNet(hidden_size, intermediate_size, num_layers, vocab_size).to(system).eval()
baseline_model = BaselineStudent(hidden_size, intermediate_size, num_layers, vocab_size).to(system)
te_model = TEStudent(hidden_size, intermediate_size, num_layers, vocab_size).to(system)
optimizer_baseline = torch.optim.AdamW(baseline_model.parameters(), lr=lr, weight_decay=weight_decay)
optimizer_te = torch.optim.AdamW(te_model.parameters(), lr=lr, weight_decay=weight_decay)
print("Trainer params :", format_millions(count_params(trainer)))
print("Baseline params:", format_millions(count_params(baseline_model)))
print("TE-path params :", format_millions(count_params(te_model)))
def make_batch(batch_size, seq_len, vocab_size, system):
tokens = torch.randint(0, vocab_size, (batch_size, seq_len), system=system)
with torch.no_grad():
goal = trainer(tokens)
return tokens, goal
def peak_mem_mb():
return torch.cuda.max_memory_allocated() / (1024 ** 2)
def train_baseline_step():
baseline_model.practice()
optimizer_baseline.zero_grad(set_to_none=True)
tokens, goal = make_batch(batch_size, seq_len, vocab_size, system)
with baseline_autocast():
pred = baseline_model(tokens)
loss = F.mse_loss(pred, goal)
loss.backward()
optimizer_baseline.step()
return float(loss.detach().merchandise())
def train_te_step(use_fp8):
te_model.practice()
optimizer_te.zero_grad(set_to_none=True)
tokens, goal = make_batch(batch_size, seq_len, vocab_size, system)
pred = te_model(tokens, use_fp8=use_fp8)
loss = F.mse_loss(pred, goal)
loss.backward()
optimizer_te.step()
return float(loss.detach().merchandise())We set the primary experiment hyperparameters, instantiate all fashions on the GPU, and create the optimizers that shall be used throughout coaching. We additionally print the parameter counts to verify that the baseline and TE paths are comparable when it comes to mannequin dimension. As well as, we outline the batch-generation logic, reminiscence monitoring operate, and the person training-step features that execute one optimization step for every mannequin path.
baseline_losses = []
te_losses = []
mode_name = "TE-FP8" if (te_available and fp8_available) else ("TE-BF16/FP16" if te_available else "Fallback-PyTorch")
print("n" + "=" * 120)
print("TRAINING")
print("=" * 120)
for step in vary(1, steps + 1):
b_loss = train_baseline_step()
t_loss = train_te_step(use_fp8=fp8_available)
baseline_losses.append(b_loss)
te_losses.append(t_loss)
if step == 1 or step % 5 == 0 or step == steps:
print(f"step={step:02d} | baseline_loss={b_loss:.6f} | te_path_loss={t_loss:.6f} | mode={mode_name}")
@torch.no_grad()
def evaluate_model(mannequin, is_te=False, use_fp8=False, eval_batches=8):
mannequin.eval()
vals = []
for _ in vary(eval_batches):
tokens, goal = make_batch(batch_size, seq_len, vocab_size, system)
if is_te:
pred = mannequin(tokens, use_fp8=use_fp8)
else:
with baseline_autocast():
pred = mannequin(tokens)
vals.append(float(F.mse_loss(pred, goal).merchandise()))
return sum(vals) / len(vals)
baseline_eval = evaluate_model(baseline_model, is_te=False)
te_eval = evaluate_model(te_model, is_te=True, use_fp8=fp8_available)
def benchmark_train_step(mannequin, optimizer, is_te=False, use_fp8=False, warmup=5, iters=20):
times_ms = []
mems_mb = []
for _ in vary(warmup):
optimizer.zero_grad(set_to_none=True)
tokens, goal = make_batch(batch_size, seq_len, vocab_size, system)
if is_te:
pred = mannequin(tokens, use_fp8=use_fp8)
else:
with baseline_autocast():
pred = mannequin(tokens)
loss = F.mse_loss(pred, goal)
loss.backward()
optimizer.step()
torch.cuda.synchronize()
for _ in vary(iters):
torch.cuda.reset_peak_memory_stats()
optimizer.zero_grad(set_to_none=True)
tokens, goal = make_batch(batch_size, seq_len, vocab_size, system)
begin = time.perf_counter()
if is_te:
pred = mannequin(tokens, use_fp8=use_fp8)
else:
with baseline_autocast():
pred = mannequin(tokens)
loss = F.mse_loss(pred, goal)
loss.backward()
optimizer.step()
torch.cuda.synchronize()
finish = time.perf_counter()
times_ms.append((finish - begin) * 1000.0)
mems_mb.append(peak_mem_mb())
return {
"mean_ms": statistics.imply(times_ms),
"median_ms": statistics.median(times_ms),
"max_memory_mb": max(mems_mb),
}
baseline_bench = benchmark_train_step(baseline_model, optimizer_baseline, is_te=False, use_fp8=False, iters=benchmark_iters)
te_bench = benchmark_train_step(te_model, optimizer_te, is_te=True, use_fp8=fp8_available, iters=benchmark_iters)We run the primary coaching loop for each the baseline mannequin and the TE path, monitoring their losses over a number of steps. We then outline and execute the analysis operate to measure how effectively every mannequin matches the trainer’s outputs after coaching. Lastly, we implement the benchmarking routine to measure per-step runtime and peak CUDA reminiscence utilization, enabling quantitative comparability of efficiency traits.
abstract = {
"gpu_name": gpu_name,
"compute_capability": f"{cc_major}.{cc_minor}",
"te_available": te_available,
"fp8_available": fp8_available,
"fp8_reason": fp8_reason,
"mode": mode_name,
"baseline_eval_mse": baseline_eval,
"te_path_eval_mse": te_eval,
"baseline_mean_step_ms": baseline_bench["mean_ms"],
"te_path_mean_step_ms": te_bench["mean_ms"],
"baseline_peak_mem_mb": baseline_bench["max_memory_mb"],
"te_path_peak_mem_mb": te_bench["max_memory_mb"],
}
print("n" + "=" * 120)
print("SUMMARY")
print("=" * 120)
print(json.dumps(abstract, indent=2))
plt.determine(figsize=(10, 5))
plt.plot(baseline_losses, label="Baseline loss")
plt.plot(te_losses, label=f"{mode_name} loss")
plt.xlabel("Coaching step")
plt.ylabel("MSE loss")
plt.title("Coaching Loss Comparability")
plt.legend()
plt.grid(True)
plt.present()
plt.determine(figsize=(8, 5))
plt.bar(["Baseline", mode_name], [baseline_bench["mean_ms"], te_bench["mean_ms"]])
plt.ylabel("Imply practice step time (ms)")
plt.title("Velocity Comparability")
plt.grid(True, axis="y")
plt.present()
plt.determine(figsize=(8, 5))
plt.bar(["Baseline", mode_name], [baseline_bench["max_memory_mb"], te_bench["max_memory_mb"]])
plt.ylabel("Peak reminiscence (MB)")
plt.title("Peak CUDA Reminiscence Comparability")
plt.grid(True, axis="y")
plt.present()We collect all ultimate metrics right into a abstract dictionary and print the experiment’s consolidated ends in a structured format. We then generate visualizations of coaching loss, imply training-step time, and peak reminiscence utilization to extra intuitively interpret the variations between the baseline and TE paths. This ultimate part helps us transfer from uncooked numbers to sensible insights by displaying how the 2 implementations behave throughout accuracy, pace, and reminiscence.
In conclusion, we constructed excess of a easy set up walkthrough; we created an entire experimental pipeline that helps us perceive how the NVIDIA Transformer Engine suits into trendy GPU-accelerated mannequin coaching. We examined the runtime atmosphere, tailored to Colab limitations, preserved a working fallback path, after which skilled, evaluated, and benchmarked two implementations facet by facet to look at sensible variations in effectivity, precision habits, and useful resource utilization. On the finish, we understood learn how to use the Transformer Engine in a Colab-friendly setting and gained a reusable basis that we will lengthen to bigger transformer architectures, richer benchmarking situations, and extra production-oriented optimization workflows.
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