### Install ponder-transformer

Source: https://github.com/lucidrains/ponder-transformer/blob/main/README.md

Install the ponder-transformer package using pip.

```bash
pip install ponder-transformer
```

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

### Complete training loop for Ponder Transformer

Source: https://context7.com/lucidrains/ponder-transformer/llms.txt

A full example showing model configuration, training loop with loss optimization, and inference mode usage.

```python
import torch
from torch.optim import AdamW
from ponder_transformer import PonderTransformer

# Model configuration
model = PonderTransformer(
    num_tokens=10000,
    dim=256,
    max_seq_len=128,
    causal=True,
    heads=4,
    dim_head=64,
    ponder_kl_div_loss_weight=0.01,
    ponder_lambda_p=0.2
)

optimizer = AdamW(model.parameters(), lr=1e-4)

# Training loop
model.train()
for step in range(100):
    # Simulate batch data
    x = torch.randint(0, 10000, (16, 128))
    labels = torch.randint(0, 10000, (16, 128))
    mask = torch.ones(16, 128).bool()

    optimizer.zero_grad()
    loss = model(x, labels=labels, mask=mask)
    loss.backward()
    optimizer.step()

    if step % 10 == 0:
        print(f"Step {step}, Loss: {loss.item():.4f}")

# Inference with adaptive computation
model.eval()
with torch.no_grad():
    test_input = torch.randint(0, 10000, (4, 128))
    test_mask = torch.ones(4, 128).bool()
    logits, halt_layers = model(test_input, mask=test_mask)

    print(f"Inference complete - halt layers: {halt_layers.tolist()}")
    # Example: [2, 4, 3, 5] - different samples used different computation depths
```

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

### Initialize and run a single Ponder Transformer block

Source: https://context7.com/lucidrains/ponder-transformer/llms.txt

Demonstrates the instantiation of a single Block and performing a forward pass to obtain transformed states and halt logits.

```python
block = Block(
    dim=512,          # Model dimension
    dim_head=64,      # Dimension per attention head
    heads=8,          # Number of attention heads
    causal=True,      # Causal masking for autoregressive
    ff_mult=4         # Feedforward hidden dimension multiplier
)

# Input hidden states
x = torch.randn(2, 128, 512)  # (batch, seq_len, dim)
mask = torch.ones(2, 128).bool()

# Forward pass returns transformed states and halt logits
output, halt_logits = block(x, mask=mask)

print(f"Output shape: {output.shape}")       # (2, 128, 512)
print(f"Halt logits shape: {halt_logits.shape}")  # (2, 128) for causal, (2,) for non-causal
```

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

### Initialize and Train PonderTransformer

Source: https://github.com/lucidrains/ponder-transformer/blob/main/README.md

Initialize the PonderTransformer model and compute a loss for training. Ensure labels are provided for loss calculation.

```python
import torch
from ponder_transformer import PonderTransformer

model = PonderTransformer(
    num_tokens = 20000,
    dim = 512,
    max_seq_len = 512
)

mask = torch.ones(1, 512).bool()

x = torch.randint(0, 20000, (1, 512))
y = torch.randint(0, 20000, (1, 512))

loss = model(x, labels = y, mask = mask)
loss.backward()
```

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

### Initialize and Train PonderTransformer

Source: https://context7.com/lucidrains/ponder-transformer/llms.txt

Initialize the PonderTransformer model and use it in training mode. Returns a combined loss including KL divergence for regularization. Ensure input token IDs, target token IDs, and attention mask are correctly formatted.

```python
import torch
from ponder_transformer import PonderTransformer

# Initialize the model with vocabulary and architecture settings
model = PonderTransformer(
    num_tokens=20000,           # Vocabulary size
    dim=512,                    # Model dimension
    max_seq_len=512,            # Maximum sequence length
    causal=True,                # Enable causal masking for autoregressive tasks
    dim_head=64,                # Dimension per attention head
    heads=8,                    # Number of attention heads
    ponder_kl_div_loss_weight=0.01,  # Weight for KL divergence regularization
    ponder_lambda_p=0.2,        # Geometric prior probability parameter
    ponder_epsilon=0.05         # Threshold for computing max training steps
)

# Training mode - returns combined loss (weighted cross-entropy + KL divergence)
model.train()
x = torch.randint(0, 20000, (4, 512))      # Input token IDs: (batch, seq_len)
labels = torch.randint(0, 20000, (4, 512)) # Target token IDs: (batch, seq_len)
mask = torch.ones(4, 512).bool()           # Attention mask: (batch, seq_len)

loss = model(x, labels=labels, mask=mask)
loss.backward()
# Output: scalar tensor with combined pondering loss
```

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

### Initialize and Evaluate Causal PonderTransformer

Source: https://github.com/lucidrains/ponder-transformer/blob/main/README.md

Initialize a causal PonderTransformer and set it to evaluation mode. In eval mode, the model returns logits and halting indices, enabling early termination.

```python
import torch
from ponder_transformer import PonderTransformer

model = PonderTransformer(
    num_tokens = 20000,
    dim = 512,
    max_seq_len = 512,
    causal = True
)

x = torch.randint(0, 20000, (2, 512))
mask = torch.ones(2, 512).bool()

model.eval() # setting to eval makes it return the logits as well as the halting indices

logits, layer_indices = model(x,  mask = mask) # (2, 512, 20000), (2)
```

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

### Initialize Non-Causal PonderTransformer for Bidirectional Tasks

Source: https://context7.com/lucidrains/ponder-transformer/llms.txt

Initialize the PonderTransformer for non-autoregressive tasks by setting causal=False to enable bidirectional attention. This mode is suitable for classification or encoding tasks.

```python
import torch
from ponder_transformer import PonderTransformer

# Non-causal model for bidirectional tasks
model = PonderTransformer(
    num_tokens=20000,
    dim=512,
    max_seq_len=256,
    causal=False,  # Bidirectional attention
    heads=8,
    dim_head=64
)

# Training with bidirectional attention
model.train()
x = torch.randint(0, 20000, (8, 256))
labels = torch.randint(0, 20000, (8, 256))
mask = torch.ones(8, 256).bool()

loss = model(x, labels=labels, mask=mask)
loss.backward()

# Evaluation returns logits and per-batch halt indices
model.eval()
logits, halt_indices = model(x, mask=mask)
print(f"Bidirectional logits: {logits.shape}")  # (8, 256, 20000)
```

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

### Evaluate PonderTransformer with Early Stopping

Source: https://context7.com/lucidrains/ponder-transformer/llms.txt

Use the PonderTransformer in evaluation mode for inference. The model automatically halts computation when all batch samples have emitted halting signals, returning logits and the layer indices where each sample halted.

```python
import torch
from ponder_transformer import PonderTransformer

model = PonderTransformer(
    num_tokens=20000,
    dim=512,
    max_seq_len=512,
    causal=True
)

# Switch to evaluation mode for inference with early halting
model.eval()

x = torch.randint(0, 20000, (2, 512))  # Batch of 2 sequences
mask = torch.ones(2, 512).bool()

# Returns logits and layer indices where each batch element halted
logits, halt_layer_indices = model(x, mask=mask)

print(f"Logits shape: {logits.shape}")        # (2, 512, 20000) - predictions
print(f"Halt indices: {halt_layer_indices}")  # (2,) - layer where each sample exited

# Example output:
# Logits shape: torch.Size([2, 512, 20000])
# Halt indices: tensor([3, 5])  # First sample halted at layer 3, second at layer 5
```

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

### PonderTransformer Block Class

Source: https://context7.com/lucidrains/ponder-transformer/llms.txt

The internal Block class represents a single transformer layer with attention, feedforward, and halting probability computation. It outputs transformed hidden states and halt logits for adaptive computation.

```python
import torch
from ponder_transformer.ponder_transformer import Block
```

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