torch-simple-timing 0.1.5

A simple package to time CPU/GPU/Multi-GPU ops

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Torch Simple Timing

A simple yet versatile package to time CPU/GPU/Multi-GPU ops.

1. "I want to time operations once"
   1. That's what a Clock is for
2. "I want to time the same operations multiple times"
   1. That's what a Timer is for

In simple terms:

• A Clock is an object (and context-manager) that will compute the ellapsed time between its start() (or __enter__) and stop() (or __exit__)
• A Timer will internally manage clocks so that you can focus on readability and not data structures

Installation

pip install torch_simple_timing

How to use

A Clock

from torch_simple_parsing import Clock
import torch

t = torch.rand(2000, 2000)
gpu = torch.cuda.is_available()

with Clock(gpu=gpu) as context_clock:
    torch.inverse(t @ t.T)

clock = Clock(gpu=gpu).start()
torch.inverse(t @ t.T)
clock.stop()

print(context_clock.duration) # 0.29688501358032227
print(clock.duration)         # 0.292896032333374

More examples, including bout how to easily share data structures using a store can be found in the documentation.

A Timer

from torch_simple_timing import Timer
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

X = torch.rand(5000, 5000, device=device)
Y = torch.rand(5000, 100, device=device)
model = torch.nn.Linear(5000, 100).to(device)
optimizer = torch.optim.Adam(model.parameters())

gpu = device.type == "cuda"
timer = Timer(gpu=gpu)

for epoch in range(10):
    timer.clock("epoch").start()
    for b in range(50):
        x = X[b*100: (b+1)*100]
        y = Y[b*100: (b+1)*100]
        optimizer.zero_grad()
        with timer.clock("forward", ignore=epoch>0):
            p = model(x)
        loss = torch.nn.functional.cross_entropy(p, y)
        with timer.clock("backward", ignore=epoch>0):
            loss.backward()
        optimizer.step()
    timer.clock("epoch").stop()

stats = timer.stats()
# use stats for display and/or logging
# wandb.summary.update(stats)
print(timer.display(stats=stats, precision=5))

epoch    : 0.25064 ± 0.02728 (n=10)
forward  : 0.00226 ± 0.00526 (n=50)
backward : 0.00209 ± 0.00387 (n=50)

A decorator

You can also use a decorator to time functions without much overhead in your code:

from torch_simple_timing import timeit, get_global_timer, reset_global_timer
import torch

# Use the function name as the timer name
@timeit(gpu=True)
def train():
    x = torch.rand(1000, 1000, device="cuda" if torch.cuda.is_available() else "cpu")
    return torch.inverse(x @ x)

# Use a custom name
@timeit("test")
def test_cpu():
    return torch.inverse(torch.rand(1000, 1000) @ torch.rand(1000, 1000))

if __name__ == "__main__":
    for _ in range((epochs := 10)):
        train()

    test_cpu()

    timer = get_global_timer()
    print(timer.display())

    reset_global_timer()

Prints:

train : 0.045 ± 0.007 (n=10)
test  : 0.046         (n= 1)

By default the @timeit decodrator takes at least a name, will use gpu=False and use the global timer (torch_simple_timing.TIMER). You can pass your own timer with @timeit(name, timer=timer).

See in the docs.