| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| |
| """A deep MNIST classifier using convolutional layers. |
| |
| This example was adapted from |
| https://pytorch.org/docs/master/distributed.html |
| https://pytorch.org/tutorials/intermediate/dist_tuto.html |
| https://github.com/narumiruna/pytorch-distributed-example/blob/master/mnist/main.py |
| |
| Each worker reads the full MNIST dataset and asynchronously trains a CNN with dropout and |
| using the Adam optimizer, updating the model parameters on shared parameter servers. |
| |
| The current training accuracy is printed out after every 100 steps. |
| """ |
| |
| |
| import argparse |
| import os |
| |
| import torch |
| import torch.nn.functional as F |
| from torch import distributed, nn |
| from torch.utils.data import DataLoader |
| from torch.utils.data.distributed import DistributedSampler |
| from torchvision import datasets, transforms |
| |
| |
| class AverageMeter: |
| def __init__(self): |
| self.sum = 0 |
| self.count = 0 |
| |
| def update(self, value, number): |
| self.sum += value * number |
| self.count += number |
| |
| @property |
| def average(self): |
| return self.sum / self.count |
| |
| |
| class AccuracyMeter: |
| def __init__(self): |
| self.correct = 0 |
| self.count = 0 |
| |
| def update(self, output, label): |
| predictions = output.data.argmax(dim=1) |
| correct = predictions.eq(label.data).sum().item() |
| |
| self.correct += correct |
| self.count += output.size(0) |
| |
| @property |
| def accuracy(self): |
| return self.correct / self.count |
| |
| |
| class Trainer: |
| def __init__(self, net, optimizer, train_loader, test_loader, device): |
| self.net = net |
| self.optimizer = optimizer |
| self.train_loader = train_loader |
| self.test_loader = test_loader |
| self.device = device |
| |
| def train(self): |
| train_loss = AverageMeter() |
| train_acc = AccuracyMeter() |
| |
| self.net.train() |
| |
| for data, label in self.train_loader: |
| data = data.to(self.device) |
| label = label.to(self.device) |
| |
| output = self.net(data) |
| loss = F.cross_entropy(output, label) |
| |
| self.optimizer.zero_grad() |
| loss.backward() |
| # average the gradients |
| self.average_gradients() |
| self.optimizer.step() |
| |
| train_loss.update(loss.item(), data.size(0)) |
| train_acc.update(output, label) |
| |
| return train_loss.average, train_acc.accuracy |
| |
| def evaluate(self): |
| test_loss = AverageMeter() |
| test_acc = AccuracyMeter() |
| |
| self.net.eval() |
| |
| with torch.no_grad(): |
| for data, label in self.test_loader: |
| data = data.to(self.device) |
| label = label.to(self.device) |
| |
| output = self.net(data) |
| loss = F.cross_entropy(output, label) |
| |
| test_loss.update(loss.item(), data.size(0)) |
| test_acc.update(output, label) |
| |
| return test_loss.average, test_acc.accuracy |
| |
| def average_gradients(self): |
| world_size = distributed.get_world_size() |
| |
| for p in self.net.parameters(): |
| group = distributed.new_group(ranks=list(range(world_size))) |
| |
| tensor = p.grad.data.cpu() |
| |
| distributed.all_reduce(tensor, op=distributed.reduce_op.SUM, group=group) |
| |
| tensor /= float(world_size) |
| |
| p.grad.data = tensor.to(self.device) |
| |
| |
| class Net(nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.fc = nn.Linear(784, 10) |
| |
| def forward(self, x): |
| return self.fc(x.view(x.size(0), -1)) |
| |
| |
| def get_dataloader(root, batch_size): |
| transform = transforms.Compose( |
| # https://github.com/psf/black/issues/2434 |
| # fmt: off |
| [transforms.ToTensor(), |
| transforms.Normalize((0.13066047740239478,), (0.3081078087569972,))] |
| # fmt: on |
| ) |
| |
| train_set = datasets.MNIST(root, train=True, transform=transform, download=True) |
| sampler = DistributedSampler(train_set) |
| |
| train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=(sampler is None), sampler=sampler) |
| |
| test_loader = DataLoader( |
| datasets.MNIST(root, train=False, transform=transform, download=True), |
| batch_size=batch_size, |
| shuffle=False, |
| ) |
| |
| return train_loader, test_loader |
| |
| |
| def solve(args): |
| device = torch.device("cuda" if args.cuda else "cpu") |
| |
| net = Net().to(device) |
| |
| optimizer = torch.optim.Adam(net.parameters(), lr=args.learning_rate) |
| |
| train_loader, test_loader = get_dataloader(args.root, args.batch_size) |
| |
| trainer = Trainer(net, optimizer, train_loader, test_loader, device) |
| |
| for epoch in range(1, args.epochs + 1): |
| train_loss, train_acc = trainer.train() |
| test_loss, test_acc = trainer.evaluate() |
| |
| print( |
| f"Epoch: {epoch}/{args.epochs},", |
| "train loss: {:.6f}, train acc: {:.6f}, test loss: {:.6f}, test acc: {:.6f}.".format( |
| train_loss, train_acc, test_loss, test_acc |
| ), |
| ) |
| |
| |
| def init_process(args): |
| distributed.init_process_group( |
| backend=args.backend, |
| init_method=args.init_method, |
| rank=args.rank, |
| world_size=args.world_size, |
| ) |
| |
| |
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--backend", type=str, default="tcp", help="Name of the backend to use.") |
| parser.add_argument( |
| "--init-method", |
| "-i", |
| type=str, |
| default=os.environ.get("INIT_METHOD", "tcp://127.0.0.1:23456"), |
| help="URL specifying how to initialize the package.", |
| ) |
| parser.add_argument( |
| "--rank", |
| "-r", |
| type=int, |
| default=int(os.environ.get("RANK")), |
| help="Rank of the current process.", |
| ) |
| parser.add_argument( |
| "--world-size", |
| "-s", |
| type=int, |
| default=int(os.environ.get("WORLD")), |
| help="Number of processes participating in the job.", |
| ) |
| parser.add_argument("--epochs", type=int, default=20) |
| parser.add_argument("--no-cuda", action="store_true") |
| parser.add_argument("--learning-rate", "-lr", type=float, default=1e-3) |
| parser.add_argument("--root", type=str, default="data") |
| parser.add_argument("--batch-size", type=int, default=128) |
| args = parser.parse_args() |
| args.cuda = torch.cuda.is_available() and not args.no_cuda |
| print(args) |
| |
| init_process(args) |
| solve(args) |
| |
| |
| if __name__ == "__main__": |
| main() |