(beta) Compiling the optimizer with torch.compile¶
Created On: Jan 24, 2024 | Last Updated: Jan 29, 2024 | Last Verified: Nov 05, 2024
Author: Michael Lazos
The optimizer is a key algorithm for training any deep learning model.
Since it is responsible for updating every model parameter, it can often
become the bottleneck in training performance for large models. In this recipe,
we will apply torch.compile to the optimizer to observe the GPU performance
improvement.
Note
This tutorial requires PyTorch 2.2.0 or later.
Model Setup¶
For this example, we’ll use a simple sequence of linear layers. Since we are only benchmarking the optimizer, the choice of model doesn’t matter because optimizer performance is a function of the number of parameters.
Depending on what machine you are using, your exact results may vary.
import torch
model = torch.nn.Sequential(
*[torch.nn.Linear(1024, 1024, False, device="cuda") for _ in range(10)]
)
input = torch.rand(1024, device="cuda")
output = model(input)
output.sum().backward()
Setting up and running the optimizer benchmark¶
In this example, we’ll use the Adam optimizer
and create a helper function to wrap the step()
in torch.compile().
Note
torch.compile is only supported on cuda devices with compute capability >= 7.0
# exit cleanly if we are on a device that doesn't support torch.compile
if torch.cuda.get_device_capability() < (7, 0):
print("Exiting because torch.compile is not supported on this device.")
import sys
sys.exit(0)
opt = torch.optim.Adam(model.parameters(), lr=0.01)
@torch.compile(fullgraph=False)
def fn():
opt.step()
# Let's define a helpful benchmarking function:
import torch.utils.benchmark as benchmark
def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
t0 = benchmark.Timer(
stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
)
return t0.blocked_autorange().mean * 1e6
# Warmup runs to compile the function
for _ in range(5):
fn()
eager_runtime = benchmark_torch_function_in_microseconds(opt.step)
compiled_runtime = benchmark_torch_function_in_microseconds(fn)
assert eager_runtime > compiled_runtime
print(f"eager runtime: {eager_runtime}us")
print(f"compiled runtime: {compiled_runtime}us")
Sample Results:
Eager runtime: 747.2437149845064us
Compiled runtime: 392.07384741178us
