### Install pytorch-custom-utils

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Install the library using pip.

```bash
pip install pytorch-custom-utils
```

--------------------------------

### Install Pytorch Custom Utils

Source: https://github.com/lucidrains/pytorch-custom-utils/blob/main/README.md

Install the package via pip.

```bash
$ pip install pytorch-custom-utils
```

--------------------------------

### Integrate W&B Tracking via Context Manager

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Inject a Weights & Biases tracking context manager into a training class to simplify experiment logging.

```python
import torch
from torch import nn
from accelerate import Accelerator
from pytorch_custom_utils import add_wandb_tracker_contextmanager

@add_wandb_tracker_contextmanager(
    accelerator_instance_name='accelerator',
    tracker_hps_instance_name='hparams'
)
class Trainer:
    def __init__(self, model, lr=1e-4, batch_size=32):
        self.accelerator = Accelerator(log_with='wandb')
        self.model = model
        self.hparams = {'lr': lr, 'batch_size': batch_size}

    def train(self):
        # Training logic here
        for step in range(100):
            loss = torch.tensor(1.0 / (step + 1))
            self.accelerator.log({'loss': loss.item()}, step=step)

trainer = Trainer(nn.Linear(10, 10))

# Use the injected context manager
with trainer.wandb_tracking(project='my-project', run='experiment-1'):
    trainer.train()
```

--------------------------------

### Combine Optimizer with Warmup and Scheduler

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Wrap an optimizer to handle learning rate scheduling, warmup, and gradient clipping within an Accelerate training loop.

```python
import torch
from torch import nn
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR
from accelerate import Accelerator
from pytorch_custom_utils import OptimizerWithWarmupSchedule

accelerator = Accelerator()

model = nn.Linear(512, 512)
base_optimizer = AdamW(model.parameters(), lr=1e-4)

# Create optimizer with warmup and cosine scheduling
optimizer = OptimizerWithWarmupSchedule(
    accelerator=accelerator,
    optimizer=base_optimizer,
    scheduler=CosineAnnealingLR,
    scheduler_kwargs={'T_max': 10000},
    warmup_steps=1000,
    max_grad_norm=1.0
)

# Training loop
for batch in dataloader:
    optimizer.zero_grad()
    loss = model(batch).mean()
    accelerator.backward(loss)
    optimizer.step()  # Handles gradient clipping, warmup, and scheduling

# Save and load optimizer state
state = optimizer.state_dict()
optimizer.load_state_dict(state)
```

--------------------------------

### Configure Adam Optimizer with Weight Decay

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Create Adam or AdamW optimizers with built-in support for excluding biases and layer norms from weight decay.

```python
import torch
from torch import nn
from pytorch_custom_utils import get_adam_optimizer

class Model(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.linear = nn.Linear(dim, dim)
        self.norm = nn.LayerNorm(dim)

    def forward(self, x):
        return self.norm(self.linear(x))

model = Model(dim=512)

# Standard AdamW with weight decay (excludes biases/gammas from WD)
optimizer = get_adam_optimizer(
    model.parameters(),
    lr=1e-4,
    wd=0.01,
    betas=(0.9, 0.99),
    eps=1e-8,
    filter_by_requires_grad=True,
    omit_gammas_and_betas_from_wd=True
)

# Without weight decay (returns Adam instead of AdamW)
optimizer_no_wd = get_adam_optimizer(
    model.parameters(),
    lr=3e-4,
    wd=0.0
)

# Apply weight decay to all parameters
optimizer_full_wd = get_adam_optimizer(
    model.parameters(),
    lr=1e-4,
    wd=0.01,
    omit_gammas_and_betas_from_wd=False
)
```

--------------------------------

### Gradient Accumulation Helper

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Use this context manager for efficient gradient accumulation with Accelerate. It automatically handles `no_sync` for distributed training, ensuring gradients are only synchronized on the last accumulation step.

```python
import torch
from torch import nn
from accelerate import Accelerator
from pytorch_custom_utils.accelerate_utils import model_forward_contexts

accelerator = Accelerator()
model = nn.Linear(512, 512)
model = accelerator.prepare(model)
optimizer = torch.optim.Adam(model.parameters())

grad_accum_steps = 4
batches = [torch.randn(8, 512) for _ in range(grad_accum_steps)]

# Efficient gradient accumulation with automatic no_sync
for context, batch in zip(
    model_forward_contexts(accelerator, model, grad_accum_steps),
    batches
):
    with context():  # Handles autocast and no_sync automatically
        loss = model(batch).mean() / grad_accum_steps
        accelerator.backward(loss)

optimizer.step()
optimizer.zero_grad()
```

--------------------------------

### Tensor Padding and Slicing Utilities

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

These functions allow for flexible padding and slicing of tensors along specified dimensions. `pad_at_dim` adds values, `slice_at_dim` extracts a portion, and `pad_or_slice_to` adjusts the tensor to an exact length.

```python
import torch
from pytorch_custom_utils.utils import pad_at_dim, slice_at_dim, pad_or_slice_to

# Pad tensor at specific dimension
tensor = torch.randn(2, 10, 64)

# Pad sequence dimension to length 15 (add 5 zeros at the end)
padded = pad_at_dim(tensor, pad=(0, 5), dim=1, value=0.)
print(padded.shape)  # torch.Size([2, 15, 64])

# Slice to get first 8 elements at dimension 1
 sliced = slice_at_dim(tensor, slice(0, 8), dim=1)
print(sliced.shape)  # torch.Size([2, 8, 64])

# Pad or slice to exact length
exact_12 = pad_or_slice_to(tensor, length=12, dim=1, pad_value=0.)
print(exact_12.shape)  # torch.Size([2, 12, 64])

exact_5 = pad_or_slice_to(tensor, length=5, dim=1)
print(exact_5.shape)  # torch.Size([2, 5, 64])
```

--------------------------------

### Track Device on Pytorch Modules

Source: https://github.com/lucidrains/pytorch-custom-utils/blob/main/README.md

Use the module_device decorator to add a convenient .device property to a nn.Module.

```python
import torch
from torch import nn

from pytorch_custom_utils import module_device

# decorate the class with `module_device` class decorator

@module_device()
class MLP(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.net = nn.Linear(dim, dim)

    def forward(self, x):
        return self.net(x)

# instantiated mlp

mlp = MLP(dim = 512)
mlp.to(torch.device('mps'))

# now you have a convenient .device

mlp.device # mps:0
```

--------------------------------

### Add Save and Load Methods to Modules

Source: https://github.com/lucidrains/pytorch-custom-utils/blob/main/README.md

Use the save_load decorator to add persistence methods to a nn.Module class.

```python
import torch
from torch import nn

from pytorch_custom_utils import save_load

# decorate the entire class with `save_load` class decorator

@save_load()
class MLP(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.net = nn.Sequential(nn.Linear(dim, dim), nn.SiLU(), nn.Linear(dim, dim))

    def forward(self, x):
        return self.net(x)

# instantiated mlp

mlp = MLP(dim = 512)

# now you have a save and load method

mlp.save('./mlp.pt')
mlp.load('./mlp.pt')

# you can also directly initialize from the checkpoint, without having to save the corresponding hyperparameters (in this case, dim = 512)

mlp = MLP.init_and_load('./mlp.pt')
```

--------------------------------

### Count Trainable Parameters with Decorator

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Use the total_parameters decorator to automatically track and expose the count of trainable parameters in a module.

```python
@total_parameters(count_only_requires_grad=True, total_parameters_property_name='trainable_params')
class FrozenEncoder(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.frozen = nn.Linear(dim, dim)
        self.frozen.requires_grad_(False)
        self.trainable = nn.Linear(dim, dim)

    def forward(self, x):
        return self.trainable(self.frozen(x))

encoder = FrozenEncoder(256)
print(f"Trainable: {encoder.trainable_params:,}")  # Only counts trainable layer
```

--------------------------------

### Automatic Input Device Casting with autocast_device

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

The autocast_device decorator automatically moves tensor inputs to the model's device before the forward pass, preventing device mismatch errors. Ensure the module also has device tracking enabled via module_device.

```python
import torch
from torch import nn
from pytorch_custom_utils import module_device
from pytorch_custom_utils.module_device import autocast_device

@module_device()
@autocast_device(methods=['forward', 'encode'])
class Encoder(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.linear = nn.Linear(dim, dim)

    def forward(self, x):
        return self.linear(x)

    def encode(self, x):
        return self.forward(x)

encoder = Encoder(dim=128).cuda()

# Input on CPU automatically moved to CUDA
cpu_tensor = torch.randn(4, 128)  # on CPU
output = encoder(cpu_tensor)  # works without manual .cuda()
print(output.device)  # cuda:0
```

--------------------------------

### Track Module Device with module_device Decorator

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

The module_device decorator adds a .device property to nn.Module subclasses, simplifying device tracking. It automatically returns the current device of the model's parameters, eliminating manual device placement checks.

```python
import torch
from torch import nn
from pytorch_custom_utils import module_device

@module_device(device_property_name='device')
class MLP(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, dim * 4),
            nn.GELU(),
            nn.Linear(dim * 4, dim)
        )

    def forward(self, x):
        return self.net(x)

mlp = MLP(dim=256)
print(mlp.device)  # cpu

mlp.to('cuda')
print(mlp.device)  # cuda:0

mlp.to('mps')
print(mlp.device)  # mps:0
```

--------------------------------

### Save and Load PyTorch Models with save_load Decorator

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

The save_load decorator adds save(), load(), and init_and_load() methods to nn.Module subclasses. It automatically captures constructor arguments for easy model serialization and loading without manual hyperparameter specification.

```python
import torch
from torch import nn
from pytorch_custom_utils import save_load

@save_load(version='1.0.0')
class Transformer(nn.Module):
    def __init__(self, dim, depth, heads):
        super().__init__()
        self.dim = dim
        self.layers = nn.ModuleList([
            nn.TransformerEncoderLayer(d_model=dim, nhead=heads)
            for _ in range(depth)
        ])

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x

# Create and train model
model = Transformer(dim=512, depth=6, heads=8)

# Save model with all config
model.save('./transformer.pt')

# Load weights into existing model
model.load('./transformer.pt', strict=True)

# Initialize and load from checkpoint (no need to know original hyperparameters)
restored_model = Transformer.init_and_load('./transformer.pt')
print(restored_model.dim)  # 512
```

--------------------------------

### Unwrap DDP Models Automatically

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Use the auto_unwrap_model decorator to access model attributes directly even when the model is wrapped by Accelerate's DDP.

```python
import torch
from torch import nn
from accelerate import Accelerator
from pytorch_custom_utils import auto_unwrap_model

@auto_unwrap_model(
    accelerator_instance_name='accelerator',
    model_instance_name='model'
)
class Trainer:
    def __init__(self, dim):
        self.accelerator = Accelerator()
        self.model = nn.Linear(dim, dim)
        self.model = self.accelerator.prepare(self.model)
        # model is now wrapped, but custom_attr is accessible

    def get_weight_shape(self):
        # Accesses unwrapped model automatically
        return self.model.weight.shape

trainer = Trainer(dim=512)
print(trainer.get_weight_shape())  # torch.Size([512, 512])
```

--------------------------------

### Count Model Parameters with total_parameters Decorator

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

The total_parameters decorator adds a property to count all parameters in an nn.Module subclass. It can optionally filter to count only trainable parameters by setting count_only_requires_grad=True.

```python
import torch
from torch import nn
from pytorch_custom_utils import total_parameters

@total_parameters(count_only_requires_grad=False)
class ResNet(nn.Module):
    def __init__(self, dim, num_blocks):
        super().__init__()
        self.blocks = nn.ModuleList([
            nn.Sequential(
                nn.Linear(dim, dim),
                nn.ReLU(),
                nn.Linear(dim, dim)
            ) for _ in range(num_blocks)
        ])

    def forward(self, x):
        for block in self.blocks:
            x = x + block(x)
        return x

model = ResNet(dim=512, num_blocks=4)
print(f"Total parameters: {model.total_parameters:,}")  # Total parameters: 4,198,400
```

--------------------------------

### Masked Mean and Mask Combination

Source: https://context7.com/lucidrains/pytorch-custom-utils/llms.txt

Compute the mean of tensor elements only where a mask is true. `maybe_and_mask` safely combines multiple boolean masks, returning `None` if all inputs are `None`.

```python
import torch
from pytorch_custom_utils.utils import masked_mean, maybe_and_mask

# Masked mean calculation
values = torch.tensor([[1., 2., 3., 4.], [5., 6., 7., 8.]])
mask = torch.tensor([[True, True, False, False], [True, True, True, False]])

# Mean only over masked positions
result = masked_mean(values, mask, dim=-1)
print(result)  # tensor([1.5000, 6.0000])

# Combine multiple masks (handles None safely)
mask1 = torch.tensor([True, True, False, True])
mask2 = torch.tensor([True, False, False, True])
combined = maybe_and_mask(mask1, mask2)
print(combined)  # tensor([True, False, False, True])

# Returns None if all inputs are None
result = maybe_and_mask(None, None)
print(result)  # None
```

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