timing.py

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from pytictac import Timer, CpuTimer
import time


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output


def main():
    # Training settings
    parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
    parser.add_argument(
        "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
    )
    parser.add_argument(
        "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
    )
    parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training")
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(0)

    if use_cuda:
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    train_kwargs = {"batch_size": args.batch_size}
    test_kwargs = {"batch_size": args.test_batch_size}
    if use_cuda:
        cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
        train_kwargs.update(cuda_kwargs)
        test_kwargs.update(cuda_kwargs)

    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
    dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
    data, target = next(iter(train_loader))
    data, target = data.to(device), target.to(device)

    model = Net().to(device)

    with CpuTimer("cpu timer before warm up"):
        model(data)

    with Timer("gpu timer before warum up"):
        model(data)

    for i in range(100):
        model(data)

    with Timer("gpu timer after warm up"):
        model(data)

    with CpuTimer("cpu timer after warm up"):
        model(data)

    with Timer("100 x gpu timer after warm up"):
        for i in range(100):
            noise = torch.rand_like(data) * 0.0001
            model(data + noise)

    with CpuTimer("100 x cpu timer after warm up"):
        for i in range(100):
            noise = torch.rand_like(data) * 0.0001
            model(data + noise)

    with Timer("100 x gpu timer after warm up - with sync"):
        for i in range(100):
            noise = torch.rand_like(data) * 0.0001
            model(data + noise)
            torch.cuda.synchronize()


if __name__ == "__main__":
    main()