### Install grokfast-pytorch

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

Install the grokfast-pytorch package using pip.

```bash
pip install grokfast-pytorch
```

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

### Grokfast-PyTorch Usage Example

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

Demonstrates how to use GrokFastAdamW optimizer with a PyTorch model. Ensure GrokFastAdamW is imported before instantiation.

```python
import torch
from torch import nn

# toy model

model = nn.Linear(10, 1)

# import GrokFastAdamW and instantiate with parameters

from grokfast_pytorch import GrokFastAdamW

opt = GrokFastAdamW(
    model.parameters(),
    lr = 1e-4,
    weight_decay = 1e-2
)

# forward and backwards

loss = model(torch.randn(10))
loss.backward()

# optimizer step

opt.step()
opt.zero_grad()
```

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

### Initialize and Use GrokFastAdamW Optimizer

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

Initialize GrokFastAdamW with grokfast enabled and use it in a standard PyTorch training loop. The learning rate is automatically normalized when grokfast is true.

```python
import torch
from torch import nn
from grokfast_pytorch import GrokFastAdamW

# Create a simple neural network
model = nn.Sequential(
    nn.Linear(64, 128),
    nn.ReLU(),
    nn.Linear(128, 64),
    nn.Linear(64, 10)
)

# Initialize GrokFastAdamW with default grokfast settings
optimizer = GrokFastAdamW(
    model.parameters(),
    lr=1e-3,                    # Learning rate (auto-normalized when grokfast=True)
    betas=(0.9, 0.99),          # AdamW momentum coefficients
    weight_decay=1e-2,          # Decoupled weight decay
    eps=1e-8,                   # Numerical stability epsilon
    grokfast=True,              # Enable grokfast gradient amplification
    grokfast_alpha=0.98,        # EMA decay rate for slow gradient detection
    grokfast_lamb=2.0,          # Amplification factor for slow gradients
    grokfast_after_step=0,      # Start grokfast after N steps
    normalize_lr=True           # Normalize LR by (1 + lambda) for fair comparison
)

# Training loop
for epoch in range(100):
    inputs = torch.randn(32, 64)
    targets = torch.randint(0, 10, (32,))

    # Forward pass
    outputs = model(inputs)
    loss = nn.functional.cross_entropy(outputs, targets)

    # Backward pass
    loss.backward()

    # Optimizer step
    optimizer.step()
    optimizer.zero_grad()

    if epoch % 10 == 0:
        print(f"Epoch {epoch}, Loss: {loss.item():.4f}")
```

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

### Enable spectral entropy regularization

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

Configures GrokFastAdamW to compute and backpropagate spectral entropy loss via a pre-step hook.

```python
import torch
from torch import nn
from grokfast_pytorch import GrokFastAdamW

model = nn.Sequential(
    nn.Linear(64, 128),
    nn.Linear(128, 64),
    nn.Linear(64, 10)
)

# Enable spectral entropy regularization
optimizer = GrokFastAdamW(
    model.parameters(),
    lr=1e-3,
    weight_decay=1e-2,
    grokfast=True,
    grokfast_lamb=2.0,
    add_spectral_entropy_reg=True,       # Enable spectral entropy
    spectral_entropy_reg_weight=0.1,     # Weight for entropy loss
    use_svd_lowrank=False,               # Use full SVD (set True for large matrices)
    svd_lowrank_kwargs=dict(q=6, niter=2) # kwargs for torch.svd_lowrank if enabled
)

# Training with spectral entropy regularization
for step in range(200):
    inputs = torch.randn(32, 64)
    targets = torch.randint(0, 10, (32,))

    outputs = model(inputs)
    loss = nn.functional.cross_entropy(outputs, targets)
    loss.backward()

    # Spectral entropy is computed and backpropagated automatically via hook
    optimizer.step()
    optimizer.zero_grad()
```

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

### Initialize GrokFastAdamW with Regenerative Regularization

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

Configure GrokFastAdamW to use regenerative regularization instead of weight decay. Ensure weight decay is set to 0 when using regen_reg_rate.

```python
import torch
from torch import nn
from grokfast_pytorch import GrokFastAdamW

model = nn.Sequential(
    nn.Linear(64, 128),
    nn.ReLU(),
    nn.Linear(128, 10)
)

# Use regenerative regularization instead of weight decay
optimizer = GrokFastAdamW(
    model.parameters(),
    lr=1e-3,
    weight_decay=0.,            # Must be 0 when using regen_reg
    regen_reg_rate=0.01,        # Regularize toward initial parameters
    grokfast=True,
    grokfast_alpha=0.98,
    grokfast_lamb=2.0
)
```

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

### Train on multiple tasks in a continual learning scenario

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

Standard training loop structure for sequential task learning.

```python
for task_id in range(3):
    print(f"Training on task {task_id}")
    for step in range(100):
        inputs = torch.randn(16, 64)
        targets = torch.randint(0, 10, (16,))

        outputs = model(inputs)
        loss = nn.functional.cross_entropy(outputs, targets)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
```

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

### Delay Grokfast activation

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

Uses the grokfast_after_step parameter to defer gradient amplification until a specific training step.

```python
import torch
from torch import nn
from grokfast_pytorch import GrokFastAdamW

model = nn.Linear(256, 10)

# Grokfast activates automatically after step 500
optimizer = GrokFastAdamW(
    model.parameters(),
    lr=1e-3,
    weight_decay=1e-2,
    grokfast=True,
    grokfast_alpha=0.98,
    grokfast_lamb=2.0,
    grokfast_after_step=500    # Start amplifying slow gradients after step 500
)

# Training loop - grokfast kicks in automatically at step 500
for step in range(1000):
    inputs = torch.randn(32, 256)
    loss = model(inputs).sum()
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

    if step == 500:
        print("Grokfast now active!")
```

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

### Control Grokfast Mechanism During Training

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

Dynamically enable, disable, or reset the grokfast mechanism using optimizer methods. This is useful for experiments or enabling grokfast after a warmup period.

```python
import torch
from torch import nn
from grokfast_pytorch import GrokFastAdamW

model = nn.Linear(128, 10)

# Initialize with grokfast enabled
optimizer = GrokFastAdamW(
    model.parameters(),
    lr=1e-3,
    grokfast=True,
    grokfast_alpha=0.98,
    grokfast_lamb=2.0
)

# Training with warmup: disable grokfast initially
optimizer.turn_off_grokfast()

for step in range(1000):
    inputs = torch.randn(16, 128)
    loss = model(inputs).sum()
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

    # Enable grokfast after 100 warmup steps
    if step == 100:
        optimizer.turn_on_grokfast()
        print("Grokfast enabled at step 100")

    # Optionally reset grokfast EMA state
    if step == 500:
        optimizer.clear_grokfast_state()
        print("Grokfast state cleared at step 500")
```

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