/pytorch-patterns

name: pytorch-patterns
description: PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.
metadata:
  origin: ECC

PyTorch Development Patterns

Idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications.

When to Activate

• Writing new PyTorch models or training scripts
• Reviewing deep learning code
• Debugging training loops or data pipelines
• Optimizing GPU memory usage or training speed
• Setting up reproducible experiments

Core Principles

1. Device-Agnostic Code

Always write code that works on both CPU and GPU without hardcoding devices.

# Good: Device-agnostic
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel().to(device)
data = data.to(device)

# Bad: Hardcoded device
model = MyModel().cuda()  # Crashes if no GPU
data = data.cuda()

2. Reproducibility First

Set all random seeds for reproducible results.

# Good: Full reproducibility setup
def set_seed(seed: int = 42) -> None:
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# Bad: No seed control
model = MyModel()  # Different weights every run

3. Explicit Shape Management

Always document and verify tensor shapes.

# Good: Shape-annotated forward pass
def forward(self, x: torch.Tensor) -> torch.Tensor:
    # x: (batch_size, channels, height, width)
    x = self.conv1(x)    # -> (batch_size, 32, H, W)
    x = self.pool(x)     # -> (batch_size, 32, H//2, W//2)
    x = x.view(x.size(0), -1)  # -> (batch_size, 32*H//2*W//2)
    return self.fc(x)    # -> (batch_size, num_classes)

# Bad: No shape tracking
def forward(self, x):
    x = self.conv1(x)
    x = self.pool(x)
    x = x.view(x.size(0), -1)  # What size is this?
    return self.fc(x)           # Will this even work?

Model Architecture Patterns

Clean nn.Module Structure

# Good: Well-organized module
class ImageClassifier(nn.Module):
    def __init__(self, num_classes: int, dropout: float = 0.5) -> None:
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(dropout),
            nn.Linear(64 * 16 * 16, num_classes),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.features(x)
        x = x.view(x.size(0), -1)
        return self.classifier(x)

# Bad: Everything in forward
class ImageClassifier(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        x = F.conv2d(x, weight=self.make_weight())  # Creates weight each call!
        return x

Proper Weight Initialization

# Good: Explicit initialization
def _init_weights(self, module: nn.Module) -> None:
    if isinstance(module, nn.Linear):
        nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
        if module.bias is not None:
            nn.init.zeros_(module.bias)
    elif isinstance(module, nn.Conv2d):
        nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
    elif isinstance(module, nn.BatchNorm2d):
        nn.init.ones_(module.weight)
        nn.init.zeros_(module.bias)

model = MyModel()
model.apply(model._init_weights)

Training Loop Patterns

Standard Training Loop

# Good: Complete training loop with best practices
def train_one_epoch(
    model: nn.Module,
    dataloader: DataLoader,
    optimizer: torch.optim.Optimizer,
    criterion: nn.Module,
    device: torch.device,
    scaler: torch.amp.GradScaler | None = None,
) -> float:
    model.train()  # Always set train mode
    total_loss = 0.0

    for batch_idx, (data, target) in enumerate(dataloader):
        data, target = data.to(device), target.to(device)

        optimizer.zero_grad(set_to_none=True)  # More efficient than zero_grad()

        # Mixed precision training
        with torch.amp.autocast("cuda", enabled=scaler is not None):
            output = model(data)
            loss = criterion(output, target)

        if scaler is not None:
            scaler.scale(loss).backward()
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            scaler.step(optimizer)
            scaler.update()
        else:
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            optimizer.step()

        total_loss += loss.item()

    return total_loss / len(dataloader)

Validation Loop

# Good: Proper evaluation
@torch.no_grad()  # More efficient than wrapping in torch.no_grad() block
def evaluate(
    model: nn.Module,
    dataloader: DataLoader,
    criterion: nn.Module,
    device: torch.device,
) -> tuple[float, float]:
    model.eval()  # Always set eval mode — disables dropout, uses running BN stats
    total_loss = 0.0
    correct = 0
    total = 0

    for data, target in dataloader:
        data, target = data.to(device), target.to(device)
        output = model(data)
        total_loss += criterion(output, target).item()
        correct += (output.argmax(1) == target).sum().item()
        total += target.size(0)

    return total_loss / len(dataloader), correct / total

Data Pipeline Patterns

Custom Dataset

# Good: Clean Dataset with type hints
class ImageDataset(Dataset):
    def __init__(
        self,
        image_dir: str,