### Run Enwik8 Training Script

Source: https://github.com/lucidrains/block-recurrent-transformer-pytorch/blob/main/README.md

Execute the training script after installing the required dependencies.

```bash
$ python train.py
```

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

### Install Block Recurrent Transformer

Source: https://github.com/lucidrains/block-recurrent-transformer-pytorch/blob/main/README.md

Install the package via pip.

```bash
$ pip install block-recurrent-transformer-pytorch
```

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

### Full training pipeline with Accelerate

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

A complete training script using the RecurrentTrainerWrapper and Hugging Face Accelerate for distributed training on the enwik8 dataset.

```python
import gzip
import random
import numpy as np
import torch
from torch.optim import Adam
from torch.utils.data import DataLoader, Dataset
from accelerate import Accelerator
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer, RecurrentTrainerWrapper

# Hyperparameters
NUM_BATCHES = 100000
BATCH_SIZE = 4
GRADIENT_ACCUMULATE_EVERY = 4
LEARNING_RATE = 1e-4
SEQ_LEN = 2048

# Setup accelerator for distributed training
accelerator = Accelerator()
device = accelerator.device

# Initialize model
model = BlockRecurrentTransformer(
    num_tokens=256,               # byte-level for enwik8
    dim=512,
    depth=6,
    dim_head=64,
    heads=8,
    max_seq_len=1024,
    block_width=512,
    num_state_vectors=512,
    recurrent_layers=(4,),
    use_flash_attn=True
)

train_wrapper = RecurrentTrainerWrapper(
    model,
    xl_memories_dropout=0.1,
    state_dropout=0.1
)

# Dataset class for text data
class TextSamplerDataset(Dataset):
    def __init__(self, data, seq_len):
        self.data = data
        self.seq_len = seq_len

    def __getitem__(self, index):
        rand_start = torch.randint(0, self.data.size(0) - self.seq_len, (1,))
        full_seq = self.data[rand_start : rand_start + self.seq_len + 1].long()
        return full_seq

    def __len__(self):
        return self.data.size(0) // self.seq_len

# Load enwik8 data
with gzip.open("./data/enwik8.gz") as file:
    data = np.frombuffer(file.read(int(95e6)), dtype=np.uint8).copy()
    np_train, np_valid = np.split(data, [int(90e6)])
    data_train = torch.from_numpy(np_train)
    data_val = torch.from_numpy(np_valid)

train_dataset = TextSamplerDataset(data_train, SEQ_LEN)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)

# Optimizer
optimizer = Adam(model.parameters(), lr=LEARNING_RATE)

# Prepare for distributed training
model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader)

# Training loop
model.train()
for batch_idx, batch in enumerate(train_loader):
    if batch_idx >= NUM_BATCHES:
        break

    loss = train_wrapper(batch.to(device))
    accelerator.backward(loss / GRADIENT_ACCUMULATE_EVERY)

    if (batch_idx + 1) % GRADIENT_ACCUMULATE_EVERY == 0:
        accelerator.clip_grad_norm_(model.parameters(), 0.5)
        optimizer.step()
        optimizer.zero_grad()

    if batch_idx % 100 == 0:
        accelerator.print(f"Step {batch_idx}, Loss: {loss.item():.4f}")
```

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

### Configure multiple recurrent layers

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Set up multiple recurrent layers at different depths to facilitate complex state propagation.

```python
import torch
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer
```

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

### Configure compressed memories

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Enable compressed memories to extend effective context length using learned convolutions.

```python
import torch
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer

# Model with compressed memories enabled
model = BlockRecurrentTransformer(
    num_tokens=20000,
    dim=512,
    depth=6,
    dim_head=64,
    heads=8,
    max_seq_len=1024,
    block_width=512,
    num_state_vectors=512,
    recurrent_layers=(4,),
    use_compressed_mem=True,       # Enable compressed memories
    compressed_mem_factor=4,       # Compress by 4x (512 tokens -> 128 compressed)
    use_flash_attn=True
)

# With compression enabled, effective memory extends further back:
# - Recent: 512 tokens (uncompressed)
# - Older: 512 tokens compressed to 128 (representing 512 original tokens)
# Total context: ~1024 tokens per memory layer

seq = torch.randint(0, 20000, (1, 1024))
logits, memories, states = model(seq)

# Process multiple segments
for _ in range(5):
    logits, memories, states = model(seq, xl_memories=memories, states=states)
    print(f"Processing with {len(memories)} memory layers")
```

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

### Initialize and run inference with BlockRecurrentTransformer

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Configure the transformer with recurrent layers and pass state tensors between forward calls to maintain context.

```python
model = BlockRecurrentTransformer(
    num_tokens=20000,
    dim=512,
    depth=8,                       # 8 layers total
    dim_head=64,
    heads=8,
    max_seq_len=1024,
    block_width=512,
    num_state_vectors=256,         # State vectors per recurrent layer
    recurrent_layers=(3, 6),       # Recurrence at layers 3 and 6 (1-indexed)
    read_recurrent_layers=(3, 6),  # Where to read states (must be <= write layer)
    use_flash_attn=True
)

seq = torch.randint(0, 20000, (1, 1024))
logits, memories, states = model(seq)

# Each recurrent layer maintains its own state
print(f"Number of state tensors: {len(states)}")  # 2 (one per recurrent layer)

# Continue processing with accumulated states
logits, memories, states = model(seq, xl_memories=memories, states=states)
```

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

### Initialize and Use Block Recurrent Transformer

Source: https://github.com/lucidrains/block-recurrent-transformer-pytorch/blob/main/README.md

Configure the model architecture and perform forward passes with recurrent memory states.

```python
import torch
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer

model = BlockRecurrentTransformer(
    num_tokens = 20000,             # vocab size
    dim = 512,                      # model dimensions
    depth = 6,                      # depth
    dim_head = 64,                  # attention head dimensions
    heads = 8,                      # number of attention heads
    max_seq_len = 1024,             # the total receptive field of the transformer, in the paper this was 2 * block size
    block_width = 512,              # block size - total receptive field is max_seq_len, 2 * block size in paper. the block furthest forwards becomes the new cached xl memories, which is a block size of 1 (please open an issue if i am wrong)
    num_state_vectors = 512,        # number of state vectors, i believe this was a single block size in the paper, but can be any amount
    recurrent_layers = (4,),        # where to place the recurrent layer(s) for states with fixed simple gating
    use_compressed_mem = False,     # whether to use compressed memories of a single block width, from https://arxiv.org/abs/1911.05507
    compressed_mem_factor = 4,      # compression factor of compressed memories
    use_flash_attn = True           # use flash attention, if on pytorch 2.0
)

seq = torch.randint(0, 2000, (1, 1024))

out, mems1, states1 = model(seq)
out, mems2, states2 = model(seq, xl_memories = mems1, states = states1)
out, mems3, states3 = model(seq, xl_memories = mems2, states = states2)
```

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

### Initialize BlockRecurrentTransformer Model

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Initialize the main transformer model class with a full configuration. This model processes sequences in blocks while maintaining recurrent states and transformer-XL memories for long-range context.

```python
import torch
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer

# Initialize the model with full configuration
model = BlockRecurrentTransformer(
    num_tokens=20000,              # Vocabulary size
    dim=512,                        # Model dimension
    depth=6,                        # Number of transformer layers
    dim_head=64,                    # Dimension per attention head
    heads=8,                        # Number of attention heads
    max_seq_len=1024,              # Total receptive field (typically 2 * block_width)
    block_width=512,               # Block size for processing
    num_state_vectors=512,         # Number of recurrent state vectors
    recurrent_layers=(4,),         # Layers with recurrence (1-indexed)
    use_compressed_mem=False,      # Enable compressed memories
    compressed_mem_factor=4,       # Compression ratio for memories
    use_flash_attn=True            # Use Flash Attention (PyTorch 2.0+)
)

# Forward pass with sequence
seq = torch.randint(0, 20000, (1, 1024))

# First segment - no prior memories or states
logits, memories1, states1 = model(seq)
# logits: (batch, seq_len, num_tokens)
# memories1: list of tensors for XL memories
# states1: list of tensors for recurrent states

# Subsequent segments - pass prior memories and states for continuity
logits, memories2, states2 = model(seq, xl_memories=memories1, states=states1)
logits, memories3, states3 = model(seq, xl_memories=memories2, states=states2)

print(f"Output shape: {logits.shape}")  # torch.Size([1, 1024, 20000])
```

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

### Train with RecurrentTrainerWrapper

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Use the training wrapper to handle segmented training over long sequences with automatic state and memory propagation.

```python
import torch
from torch.optim import Adam
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer, RecurrentTrainerWrapper

# Create the base model
model = BlockRecurrentTransformer(
    num_tokens=256,
    dim=512,
    depth=6,
    dim_head=64,
    heads=8,
    max_seq_len=1024,
    block_width=512,
    num_state_vectors=512,
    recurrent_layers=(4,),
    use_flash_attn=True
)

# Wrap with training wrapper
train_wrapper = RecurrentTrainerWrapper(
    model,
    xl_memories_dropout=0.1,   # Probability of dropping XL memories during training
    state_dropout=0.1          # Probability of dropping states during training
)

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

# Training loop with long sequence
# Sequence length must be (n * max_seq_len) + 1 for proper segmentation
seq_len = 2048  # 2 segments of 1024 each
batch = torch.randint(0, 256, (4, seq_len + 1))  # batch=4, seq_len=2049

# Forward pass computes loss over all segments
model.train()
loss = train_wrapper(batch)

print(f"Training loss: {loss.item():.4f}")

# Backward pass and optimization
loss.backward()
optimizer.step()
optimizer.zero_grad()
```

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

### Text Generation with Autoregressive Model

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Generate sequences autoregressively using the model's `generate` method. Supports top-k sampling and temperature control for diverse text generation. Ensure the model is in evaluation mode (`model.eval()`) before generation.

```python
import torch
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer

model = BlockRecurrentTransformer(
    num_tokens=256,
    dim=512,
    depth=6,
    dim_head=64,
    heads=8,
    max_seq_len=1024,
    block_width=512,
    num_state_vectors=512,
    recurrent_layers=(4,),
    use_flash_attn=True
)

model.eval()

# Prime sequence to start generation
prime = torch.randint(0, 256, (1, 128))  # batch=1, prime_length=128


```

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

### Generate tokens with BlockRecurrentTransformer

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Perform token generation using the base model, optionally providing prior memories and states.

```python
with torch.no_grad():
    generated = model.generate(
        prime,
        length=512,              # Total length to generate (must be <= max_seq_len+1)
        temperature=0.9,         # Sampling temperature (lower = more deterministic)
        filter_thres=0.9,        # Top-k filtering threshold
        xl_memories=[],          # Optional prior memories
        states=[],               # Optional prior states
        return_memories_and_states=False  # Return memories/states for continued generation
    )

print(f"Generated shape: {generated.shape}")  # torch.Size([1, 384])
```

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

### Generate long sequences with RecurrentTrainerWrapper

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Generate sequences exceeding the model's max_seq_len by leveraging the wrapper's automatic segmentation.

```python
import torch
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer, RecurrentTrainerWrapper

model = BlockRecurrentTransformer(
    num_tokens=256,
    dim=512,
    depth=6,
    dim_head=64,
    heads=8,
    max_seq_len=1024,
    block_width=512,
    num_state_vectors=512,
    recurrent_layers=(4,),
    use_flash_attn=True
)

train_wrapper = RecurrentTrainerWrapper(model)
model.eval()

# Start with a prime sequence
prime = torch.randint(0, 256, (1, 64))

# Generate much longer than max_seq_len (handles multiple segments automatically)
with torch.no_grad():
    generated = train_wrapper.generate(
        prime,
        length=4096,           # Can exceed max_seq_len - wrapper handles segmentation
        temperature=0.9,
        filter_thres=0.9
    )

print(f"Generated {generated.shape[-1]} tokens")  # 4096 tokens
```

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

### Forward Pass with Loss Computation

Source: https://context7.com/lucidrains/block-recurrent-transformer-pytorch/llms.txt

Compute cross-entropy loss directly during the forward pass by setting `return_loss=True`. The model automatically handles target offset for next-token prediction and allows specifying an `ignore_index` for loss calculation.

```python
import torch
from block_recurrent_transformer_pytorch import BlockRecurrentTransformer

model = BlockRecurrentTransformer(
    num_tokens=256,
    dim=512,
    depth=6,
    dim_head=64,
    heads=8,
    max_seq_len=1024,
    block_width=512,
    num_state_vectors=512,
    recurrent_layers=(4,),
    ignore_index=-100,   # Token index to ignore in loss computation
    use_flash_attn=True
)

# Input sequence (includes target token at the end)
seq = torch.randint(0, 256, (2, 1025))  # batch=2, seq_len+1 for loss

# Forward pass with loss computation
loss, memories, states = model(
    seq,
    return_loss=True,      # Compute cross-entropy loss
    xl_memories=[],        # No prior memories
    states=[]              # No prior states
)

print(f"Loss: {loss.item():.4f}")

# Backpropagation
loss.backward()
```

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