Compute Flop Utilization in PyTorch

mfu_compute.py

import torch
from torch.utils.flop_counter import FlopCounterMode
from triton.testing import do_bench

def get_flops_achieved(f):
    flop_counter = FlopCounterMode(display=False)
    with flop_counter:
        f()
    total_flops = flop_counter.get_total_flops()
    ms_per_iter = do_bench(f)
    iters_per_second = 1e3/ms_per_iter
    print(f"{iters_per_second * total_flops / 1e12} TF/s")

from torchvision.models import resnet18
model = resnet18().cuda().half()
inp = torch.randn(128, 3, 224, 224, device='cuda', dtype=torch.half)
get_flops_achieved(lambda: model(inp).sum().backward())

compiled_model = torch.compile(model)
get_flops_achieved(lambda: compiled_model(inp).sum().backward())

as its docs are missing so we users don't know what is kosher to use it for and what not

I was gonna do it for PyTorch 2.3 release but I didn't end up getting around to it 😭

Yeah please do open an issue :)