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Kodelyth ECC
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pytorch-patterns

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

Invoke via:use pytorch-patterns
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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,
        labels: dict[str, int],
        transform: transforms.Compose | None = None,
    ) -> None:
        self.image_paths = list(Path(image_dir).glob("*.jpg"))
        self.labels = labels
        self.transform = transform
    def __len__(self) -> int:
        return len(self.image_paths)
    def __getitem__(self, idx: int) -> tuple[torch.Tensor, int]:
        img = Image.open(self.image_paths[idx]).convert("RGB")
        label = self.labels[self.image_paths[idx].stem]
        if self.transform:
            img = self.transform(img)
        return img, label

Efficient DataLoader Configuration

# Good: Optimized DataLoader
dataloader = DataLoader(
    dataset,
    batch_size=32,
    shuffle=True,            # Shuffle for training
    num_workers=4,           # Parallel data loading
    pin_memory=True,         # Faster CPU->GPU transfer
    persistent_workers=True, # Keep workers alive between epochs
    drop_last=True,          # Consistent batch sizes for BatchNorm
)
Bad: Slow defaults

dataloader = DataLoader(dataset, batch_size=32)  # num_workers=0, no pin_memory

Custom Collate for Variable-Length Data

# Good: Pad sequences in collate_fn
def collate_fn(batch: list[tuple[torch.Tensor, int]]) -> tuple[torch.Tensor, torch.Tensor]:
    sequences, labels = zip(*batch)
    # Pad to max length in batch
    padded = nn.utils.rnn.pad_sequence(sequences, batch_first=True, padding_value=0)
    return padded, torch.tensor(labels)
dataloader = DataLoader(dataset, batch_size=32, collate_fn=collate_fn)

Checkpointing Patterns

Save and Load Checkpoints

# Good: Complete checkpoint with all training state
def save_checkpoint(
    model: nn.Module,
    optimizer: torch.optim.Optimizer,
    epoch: int,
    loss: float,
    path: str,
) -> None:
    torch.save({
        "epoch": epoch,
        "model_state_dict": model.state_dict(),
        "optimizer_state_dict": optimizer.state_dict(),
        "loss": loss,
    }, path)
def load_checkpoint(
    path: str,
    model: nn.Module,
    optimizer: torch.optim.Optimizer | None = None,
) -> dict:
    checkpoint = torch.load(path, map_location="cpu", weights_only=True)
    model.load_state_dict(checkpoint["model_state_dict"])
    if optimizer:
        optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
    return checkpoint
Bad: Only saving model weights (can't resume training)

torch.save(model.state_dict(), "model.pt")

Performance Optimization

Mixed Precision Training

# Good: AMP with GradScaler
scaler = torch.amp.GradScaler("cuda")
for data, target in dataloader:
    with torch.amp.autocast("cuda"):
        output = model(data)
        loss = criterion(output, target)
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()
    optimizer.zero_grad(set_to_none=True)

Gradient Checkpointing for Large Models

# Good: Trade compute for memory
from torch.utils.checkpoint import checkpoint
class LargeModel(nn.Module):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Recompute activations during backward to save memory
        x = checkpoint(self.block1, x, use_reentrant=False)
        x = checkpoint(self.block2, x, use_reentrant=False)
        return self.head(x)

torch.compile for Speed

# Good: Compile the model for faster execution (PyTorch 2.0+)
model = MyModel().to(device)
model = torch.compile(model, mode="reduce-overhead")
Modes: "default" (safe), "reduce-overhead" (faster), "max-autotune" (fastest)

Quick Reference: PyTorch Idioms

| Idiom | Description | |-------|-------------| | model.train() / model.eval() | Always set mode before train/eval | | torch.no_grad() | Disable gradients for inference | | optimizer.zero_grad(set_to_none=True) | More efficient gradient clearing | | .to(device) | Device-agnostic tensor/model placement | | torch.amp.autocast | Mixed precision for 2x speed | | pin_memory=True | Faster CPU→GPU data transfer | | torch.compile | JIT compilation for speed (2.0+) | | weights_only=True | Secure model loading | | torch.manual_seed | Reproducible experiments | | gradient_checkpointing | Trade compute for memory |

Anti-Patterns to Avoid

# Bad: Forgetting model.eval() during validation
model.train()
with torch.no_grad():
    output = model(val_data)  # Dropout still active! BatchNorm uses batch stats!
Good: Always set eval mode

model.eval()
with torch.no_grad():
    output = model(val_data)
Bad: In-place operations breaking autograd

x = F.relu(x, inplace=True)  # Can break gradient computation
x += residual                  # In-place add breaks autograd graph
Good: Out-of-place operations

x = F.relu(x)
x = x + residual
Bad: Moving data to GPU inside the training loop repeatedly

for data, target in dataloader:
    model = model.cuda()  # Moves model EVERY iteration!
Good: Move model once before the loop

model = model.to(device)
for data, target in dataloader:
    data, target = data.to(device), target.to(device)
Bad: Using .item() before backward

loss = criterion(output, target).item()  # Detaches from graph!
loss.backward()  # Error: can't backprop through .item()
Good: Call .item() only for logging

loss = criterion(output, target)
loss.backward()
print(f"Loss: {loss.item():.4f}")  # .item() after backward is fine
Bad: Not using torch.save properly

torch.save(model, "model.pt")  # Saves entire model (fragile, not portable)
Good: Save state_dict

torch.save(model.state_dict(), "model.pt")

__Remember__: PyTorch code should be device-agnostic, reproducible, and memory-conscious. When in doubt, profile with torch.profiler and check GPU memory with torch.cuda.memory_summary().

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