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feat(skills): add pytorch-patterns skill (#550)
Adds pytorch-patterns skill covering model architecture, training loops, data loading, and GPU optimization patterns.
This commit is contained in:
Muhammad Idrees
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skills/pytorch-patterns/SKILL.md
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396
skills/pytorch-patterns/SKILL.md
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---
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name: pytorch-patterns
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description: PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
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origin: ECC
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---
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# PyTorch Development Patterns
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Idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications.
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## When to Activate
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- Writing new PyTorch models or training scripts
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- Reviewing deep learning code
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- Debugging training loops or data pipelines
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- Optimizing GPU memory usage or training speed
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- Setting up reproducible experiments
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## Core Principles
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### 1. Device-Agnostic Code
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Always write code that works on both CPU and GPU without hardcoding devices.
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```python
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# Good: Device-agnostic
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = MyModel().to(device)
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data = data.to(device)
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# Bad: Hardcoded device
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model = MyModel().cuda() # Crashes if no GPU
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data = data.cuda()
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```
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### 2. Reproducibility First
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Set all random seeds for reproducible results.
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```python
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# Good: Full reproducibility setup
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def set_seed(seed: int = 42) -> None:
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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# Bad: No seed control
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model = MyModel() # Different weights every run
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```
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### 3. Explicit Shape Management
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Always document and verify tensor shapes.
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```python
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# Good: Shape-annotated forward pass
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# x: (batch_size, channels, height, width)
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x = self.conv1(x) # -> (batch_size, 32, H, W)
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x = self.pool(x) # -> (batch_size, 32, H//2, W//2)
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x = x.view(x.size(0), -1) # -> (batch_size, 32*H//2*W//2)
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return self.fc(x) # -> (batch_size, num_classes)
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# Bad: No shape tracking
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def forward(self, x):
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x = self.conv1(x)
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x = self.pool(x)
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x = x.view(x.size(0), -1) # What size is this?
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return self.fc(x) # Will this even work?
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```
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## Model Architecture Patterns
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### Clean nn.Module Structure
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```python
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# Good: Well-organized module
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class ImageClassifier(nn.Module):
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def __init__(self, num_classes: int, dropout: float = 0.5) -> None:
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super().__init__()
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self.features = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, padding=1),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(2),
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)
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self.classifier = nn.Sequential(
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nn.Dropout(dropout),
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nn.Linear(64 * 16 * 16, num_classes),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.features(x)
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x = x.view(x.size(0), -1)
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return self.classifier(x)
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# Bad: Everything in forward
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class ImageClassifier(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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x = F.conv2d(x, weight=self.make_weight()) # Creates weight each call!
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return x
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```
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### Proper Weight Initialization
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```python
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# Good: Explicit initialization
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def _init_weights(self, module: nn.Module) -> None:
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if isinstance(module, nn.Linear):
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nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Conv2d):
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nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
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elif isinstance(module, nn.BatchNorm2d):
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nn.init.ones_(module.weight)
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nn.init.zeros_(module.bias)
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model = MyModel()
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model.apply(model._init_weights)
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```
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## Training Loop Patterns
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### Standard Training Loop
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```python
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# Good: Complete training loop with best practices
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def train_one_epoch(
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model: nn.Module,
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dataloader: DataLoader,
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optimizer: torch.optim.Optimizer,
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criterion: nn.Module,
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device: torch.device,
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scaler: torch.amp.GradScaler | None = None,
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) -> float:
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model.train() # Always set train mode
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total_loss = 0.0
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for batch_idx, (data, target) in enumerate(dataloader):
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad(set_to_none=True) # More efficient than zero_grad()
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# Mixed precision training
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with torch.amp.autocast("cuda", enabled=scaler is not None):
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output = model(data)
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loss = criterion(output, target)
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if scaler is not None:
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scaler.scale(loss).backward()
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
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scaler.step(optimizer)
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scaler.update()
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else:
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
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optimizer.step()
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total_loss += loss.item()
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return total_loss / len(dataloader)
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```
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### Validation Loop
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```python
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# Good: Proper evaluation
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@torch.no_grad() # More efficient than wrapping in torch.no_grad() block
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def evaluate(
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model: nn.Module,
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dataloader: DataLoader,
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criterion: nn.Module,
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device: torch.device,
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) -> tuple[float, float]:
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model.eval() # Always set eval mode — disables dropout, uses running BN stats
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total_loss = 0.0
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correct = 0
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total = 0
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for data, target in dataloader:
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data, target = data.to(device), target.to(device)
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output = model(data)
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total_loss += criterion(output, target).item()
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correct += (output.argmax(1) == target).sum().item()
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total += target.size(0)
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return total_loss / len(dataloader), correct / total
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```
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## Data Pipeline Patterns
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### Custom Dataset
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```python
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# Good: Clean Dataset with type hints
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class ImageDataset(Dataset):
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def __init__(
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self,
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image_dir: str,
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labels: dict[str, int],
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transform: transforms.Compose | None = None,
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) -> None:
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self.image_paths = list(Path(image_dir).glob("*.jpg"))
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self.labels = labels
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self.transform = transform
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def __len__(self) -> int:
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return len(self.image_paths)
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def __getitem__(self, idx: int) -> tuple[torch.Tensor, int]:
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img = Image.open(self.image_paths[idx]).convert("RGB")
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label = self.labels[self.image_paths[idx].stem]
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if self.transform:
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img = self.transform(img)
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return img, label
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```
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### Efficient DataLoader Configuration
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```python
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# Good: Optimized DataLoader
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dataloader = DataLoader(
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dataset,
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batch_size=32,
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shuffle=True, # Shuffle for training
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num_workers=4, # Parallel data loading
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pin_memory=True, # Faster CPU->GPU transfer
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persistent_workers=True, # Keep workers alive between epochs
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drop_last=True, # Consistent batch sizes for BatchNorm
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)
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# Bad: Slow defaults
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dataloader = DataLoader(dataset, batch_size=32) # num_workers=0, no pin_memory
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```
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### Custom Collate for Variable-Length Data
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```python
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# Good: Pad sequences in collate_fn
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def collate_fn(batch: list[tuple[torch.Tensor, int]]) -> tuple[torch.Tensor, torch.Tensor]:
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sequences, labels = zip(*batch)
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# Pad to max length in batch
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padded = nn.utils.rnn.pad_sequence(sequences, batch_first=True, padding_value=0)
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return padded, torch.tensor(labels)
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dataloader = DataLoader(dataset, batch_size=32, collate_fn=collate_fn)
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```
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## Checkpointing Patterns
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### Save and Load Checkpoints
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```python
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# Good: Complete checkpoint with all training state
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def save_checkpoint(
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model: nn.Module,
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optimizer: torch.optim.Optimizer,
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epoch: int,
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loss: float,
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path: str,
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) -> None:
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torch.save({
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"epoch": epoch,
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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"loss": loss,
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}, path)
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def load_checkpoint(
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path: str,
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model: nn.Module,
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optimizer: torch.optim.Optimizer | None = None,
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) -> dict:
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checkpoint = torch.load(path, map_location="cpu", weights_only=True)
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model.load_state_dict(checkpoint["model_state_dict"])
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if optimizer:
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optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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return checkpoint
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# Bad: Only saving model weights (can't resume training)
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torch.save(model.state_dict(), "model.pt")
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```
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## Performance Optimization
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### Mixed Precision Training
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```python
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# Good: AMP with GradScaler
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scaler = torch.amp.GradScaler("cuda")
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for data, target in dataloader:
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with torch.amp.autocast("cuda"):
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output = model(data)
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loss = criterion(output, target)
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scaler.scale(loss).backward()
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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```
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### Gradient Checkpointing for Large Models
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```python
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# Good: Trade compute for memory
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from torch.utils.checkpoint import checkpoint
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class LargeModel(nn.Module):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# Recompute activations during backward to save memory
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x = checkpoint(self.block1, x, use_reentrant=False)
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x = checkpoint(self.block2, x, use_reentrant=False)
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return self.head(x)
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```
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### torch.compile for Speed
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```python
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# Good: Compile the model for faster execution (PyTorch 2.0+)
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model = MyModel().to(device)
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model = torch.compile(model, mode="reduce-overhead")
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# Modes: "default" (safe), "reduce-overhead" (faster), "max-autotune" (fastest)
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```
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## Quick Reference: PyTorch Idioms
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| Idiom | Description |
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|-------|-------------|
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| `model.train()` / `model.eval()` | Always set mode before train/eval |
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| `torch.no_grad()` | Disable gradients for inference |
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| `optimizer.zero_grad(set_to_none=True)` | More efficient gradient clearing |
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| `.to(device)` | Device-agnostic tensor/model placement |
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| `torch.amp.autocast` | Mixed precision for 2x speed |
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| `pin_memory=True` | Faster CPU→GPU data transfer |
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| `torch.compile` | JIT compilation for speed (2.0+) |
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| `weights_only=True` | Secure model loading |
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| `torch.manual_seed` | Reproducible experiments |
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| `gradient_checkpointing` | Trade compute for memory |
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## Anti-Patterns to Avoid
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```python
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# Bad: Forgetting model.eval() during validation
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model.train()
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with torch.no_grad():
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output = model(val_data) # Dropout still active! BatchNorm uses batch stats!
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# Good: Always set eval mode
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model.eval()
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with torch.no_grad():
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output = model(val_data)
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# Bad: In-place operations breaking autograd
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x = F.relu(x, inplace=True) # Can break gradient computation
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x += residual # In-place add breaks autograd graph
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# Good: Out-of-place operations
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x = F.relu(x)
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x = x + residual
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# Bad: Moving data to GPU inside the training loop repeatedly
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for data, target in dataloader:
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model = model.cuda() # Moves model EVERY iteration!
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# Good: Move model once before the loop
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model = model.to(device)
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for data, target in dataloader:
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data, target = data.to(device), target.to(device)
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# Bad: Using .item() before backward
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loss = criterion(output, target).item() # Detaches from graph!
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loss.backward() # Error: can't backprop through .item()
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# Good: Call .item() only for logging
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loss = criterion(output, target)
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loss.backward()
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print(f"Loss: {loss.item():.4f}") # .item() after backward is fine
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# Bad: Not using torch.save properly
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torch.save(model, "model.pt") # Saves entire model (fragile, not portable)
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# Good: Save state_dict
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torch.save(model.state_dict(), "model.pt")
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```
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__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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