[examples] copy resnet example to image (#4090)

* copy resnet example

* add pytest package

* skip test_ci

* skip test_ci

* skip test_ci

examples/images/resnet/.gitignore

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+data
+checkpoint
+ckpt-fp16
+ckpt-fp32

examples/images/resnet/README.md

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+# Train ResNet on CIFAR-10 from scratch
+
+## 🚀 Quick Start
+
+This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch.
+
+- Training Arguments
+  - `-p`, `--plugin`: Plugin to use. Choices: `torch_ddp`, `torch_ddp_fp16`, `low_level_zero`. Defaults to `torch_ddp`.
+  - `-r`, `--resume`: Resume from checkpoint file path. Defaults to `-1`, which means not resuming.
+  - `-c`, `--checkpoint`: The folder to save checkpoints. Defaults to `./checkpoint`.
+  - `-i`, `--interval`: Epoch interval to save checkpoints. Defaults to `5`. If set to `0`, no checkpoint will be saved.
+  - `--target_acc`: Target accuracy. Raise exception if not reached. Defaults to `None`.
+
+- Eval Arguments
+  - `-e`, `--epoch`: select the epoch to evaluate
+  - `-c`, `--checkpoint`: the folder where checkpoints are found
+
+### Install requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+### Train
+The folders will be created automatically.
+```bash
+# train with torch DDP with fp32
+colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp32
+
+# train with torch DDP with mixed precision training
+colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp16 -p torch_ddp_fp16
+
+# train with low level zero
+colossalai run --nproc_per_node 2 train.py -c ./ckpt-low_level_zero -p low_level_zero
+```
+
+### Eval
+
+```bash
+# evaluate fp32 training
+python eval.py -c ./ckpt-fp32 -e 80
+
+# evaluate fp16 mixed precision training
+python eval.py -c ./ckpt-fp16 -e 80
+
+# evaluate low level zero training
+python eval.py -c ./ckpt-low_level_zero -e 80
+```
+
+Expected accuracy performance will be:
+
+| Model     | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | Booster Low Level Zero |
+| --------- | ------------------------ | --------------------- | --------------------- | ---------------------- |
+| ResNet-18 | 85.85%                   | 84.91%                | 85.46%                | 84.50%                 |
+
+**Note: the baseline is adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`**

examples/images/resnet/eval.py

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+import argparse
+
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+# ==============================
+# Parse Arguments
+# ==============================
+parser = argparse.ArgumentParser()
+parser.add_argument('-e', '--epoch', type=int, default=80, help="resume from the epoch's checkpoint")
+parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory")
+args = parser.parse_args()
+
+# ==============================
+# Prepare Test Dataset
+# ==============================
+# CIFAR-10 dataset
+test_dataset = torchvision.datasets.CIFAR10(root='./data/', train=False, transform=transforms.ToTensor())
+
+# Data loader
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=128, shuffle=False)
+
+# ==============================
+# Load Model
+# ==============================
+model = torchvision.models.resnet18(num_classes=10).cuda()
+state_dict = torch.load(f'{args.checkpoint}/model_{args.epoch}.pth')
+model.load_state_dict(state_dict)
+
+# ==============================
+# Run Evaluation
+# ==============================
+model.eval()
+
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.cuda()
+        labels = labels.cuda()
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))

examples/images/resnet/requirements.txt

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+colossalai
+torch
+torchvision
+tqdm
+pytest

examples/images/resnet/test_ci.sh

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+#!/bin/bash
+set -xe
+
+export DATA=/data/scratch/cifar-10
+
+pip install -r requirements.txt
+
+# TODO: skip ci test due to time limits, train.py needs to be rewritten.
+
+# for plugin in "torch_ddp" "torch_ddp_fp16" "low_level_zero"; do
+#     colossalai run --nproc_per_node 4 train.py --interval 0 --target_acc 0.84 --plugin $plugin
+# done

examples/images/resnet/train.py

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+import argparse
+import os
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import MultiStepLR
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+import colossalai
+from colossalai.booster import Booster
+from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
+from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
+from colossalai.cluster import DistCoordinator
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.utils import get_current_device
+
+# ==============================
+# Prepare Hyperparameters
+# ==============================
+NUM_EPOCHS = 80
+LEARNING_RATE = 1e-3
+
+
+def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase):
+    # transform
+    transform_train = transforms.Compose(
+        [transforms.Pad(4),
+         transforms.RandomHorizontalFlip(),
+         transforms.RandomCrop(32),
+         transforms.ToTensor()])
+    transform_test = transforms.ToTensor()
+
+    # CIFAR-10 dataset
+    data_path = os.environ.get('DATA', './data')
+    with coordinator.priority_execution():
+        train_dataset = torchvision.datasets.CIFAR10(root=data_path,
+                                                     train=True,
+                                                     transform=transform_train,
+                                                     download=True)
+        test_dataset = torchvision.datasets.CIFAR10(root=data_path,
+                                                    train=False,
+                                                    transform=transform_test,
+                                                    download=True)
+
+    # Data loader
+    train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
+    test_dataloader = plugin.prepare_dataloader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False)
+    return train_dataloader, test_dataloader
+
+
+@torch.no_grad()
+def evaluate(model: nn.Module, test_dataloader: DataLoader, coordinator: DistCoordinator) -> float:
+    model.eval()
+    correct = torch.zeros(1, dtype=torch.int64, device=get_current_device())
+    total = torch.zeros(1, dtype=torch.int64, device=get_current_device())
+    for images, labels in test_dataloader:
+        images = images.cuda()
+        labels = labels.cuda()
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+    dist.all_reduce(correct)
+    dist.all_reduce(total)
+    accuracy = correct.item() / total.item()
+    if coordinator.is_master():
+        print(f'Accuracy of the model on the test images: {accuracy * 100:.2f} %')
+    return accuracy
+
+
+def train_epoch(epoch: int, model: nn.Module, optimizer: Optimizer, criterion: nn.Module, train_dataloader: DataLoader,
+                booster: Booster, coordinator: DistCoordinator):
+    model.train()
+    with tqdm(train_dataloader, desc=f'Epoch [{epoch + 1}/{NUM_EPOCHS}]', disable=not coordinator.is_master()) as pbar:
+        for images, labels in pbar:
+            images = images.cuda()
+            labels = labels.cuda()
+            # Forward pass
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+
+            # Backward and optimize
+            booster.backward(loss, optimizer)
+            optimizer.step()
+            optimizer.zero_grad()
+
+            # Print log info
+            pbar.set_postfix({'loss': loss.item()})
+
+
+def main():
+    # ==============================
+    # Parse Arguments
+    # ==============================
+    parser = argparse.ArgumentParser()
+    # FIXME(ver217): gemini is not supported resnet now
+    parser.add_argument('-p',
+                        '--plugin',
+                        type=str,
+                        default='torch_ddp',
+                        choices=['torch_ddp', 'torch_ddp_fp16', 'low_level_zero'],
+                        help="plugin to use")
+    parser.add_argument('-r', '--resume', type=int, default=-1, help="resume from the epoch's checkpoint")
+    parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory")
+    parser.add_argument('-i', '--interval', type=int, default=5, help="interval of saving checkpoint")
+    parser.add_argument('--target_acc',
+                        type=float,
+                        default=None,
+                        help="target accuracy. Raise exception if not reached")
+    args = parser.parse_args()
+
+    # ==============================
+    # Prepare Checkpoint Directory
+    # ==============================
+    if args.interval > 0:
+        Path(args.checkpoint).mkdir(parents=True, exist_ok=True)
+
+    # ==============================
+    # Launch Distributed Environment
+    # ==============================
+    colossalai.launch_from_torch(config={})
+    coordinator = DistCoordinator()
+
+    # update the learning rate with linear scaling
+    # old_gpu_num / old_lr = new_gpu_num / new_lr
+    global LEARNING_RATE
+    LEARNING_RATE *= coordinator.world_size
+
+    # ==============================
+    # Instantiate Plugin and Booster
+    # ==============================
+    booster_kwargs = {}
+    if args.plugin == 'torch_ddp_fp16':
+        booster_kwargs['mixed_precision'] = 'fp16'
+    if args.plugin.startswith('torch_ddp'):
+        plugin = TorchDDPPlugin()
+    elif args.plugin == 'gemini':
+        plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5)
+    elif args.plugin == 'low_level_zero':
+        plugin = LowLevelZeroPlugin(initial_scale=2**5)
+
+    booster = Booster(plugin=plugin, **booster_kwargs)
+
+    # ==============================
+    # Prepare Dataloader
+    # ==============================
+    train_dataloader, test_dataloader = build_dataloader(100, coordinator, plugin)
+
+    # ====================================
+    # Prepare model, optimizer, criterion
+    # ====================================
+    # resent50
+    model = torchvision.models.resnet18(num_classes=10)
+
+    # Loss and optimizer
+    criterion = nn.CrossEntropyLoss()
+    optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE)
+
+    # lr scheduler
+    lr_scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60, 80], gamma=1 / 3)
+
+    # ==============================
+    # Boost with ColossalAI
+    # ==============================
+    model, optimizer, criterion, _, lr_scheduler = booster.boost(model,
+                                                                 optimizer,
+                                                                 criterion=criterion,
+                                                                 lr_scheduler=lr_scheduler)
+
+    # ==============================
+    # Resume from checkpoint
+    # ==============================
+    if args.resume >= 0:
+        booster.load_model(model, f'{args.checkpoint}/model_{args.resume}.pth')
+        booster.load_optimizer(optimizer, f'{args.checkpoint}/optimizer_{args.resume}.pth')
+        booster.load_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{args.resume}.pth')
+
+    # ==============================
+    # Train model
+    # ==============================
+    start_epoch = args.resume if args.resume >= 0 else 0
+    for epoch in range(start_epoch, NUM_EPOCHS):
+        train_epoch(epoch, model, optimizer, criterion, train_dataloader, booster, coordinator)
+        lr_scheduler.step()
+
+        # save checkpoint
+        if args.interval > 0 and (epoch + 1) % args.interval == 0:
+            booster.save_model(model, f'{args.checkpoint}/model_{epoch + 1}.pth')
+            booster.save_optimizer(optimizer, f'{args.checkpoint}/optimizer_{epoch + 1}.pth')
+            booster.save_lr_scheduler(lr_scheduler, f'{args.checkpoint}/lr_scheduler_{epoch + 1}.pth')
+
+    accuracy = evaluate(model, test_dataloader, coordinator)
+    if args.target_acc is not None:
+        assert accuracy >= args.target_acc, f'Accuracy {accuracy} is lower than target accuracy {args.target_acc}'
+
+
+if __name__ == '__main__':
+    main()