### Complete Training Example Setup

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

Sets up a complete training pipeline for a FLASH Transformer on the enwik8 dataset. Includes model instantiation, AutoregressiveWrapper, optimizer, and dataset/dataloader configuration for character-level language modeling.

```python
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from flash_pytorch import FLASHTransformer
from flash_pytorch.autoregressive_wrapper import AutoregressiveWrapper
import gzip
import numpy as np

# Configuration
SEQ_LEN = 1024
BATCH_SIZE = 4
GRADIENT_ACCUMULATE_EVERY = 4
LEARNING_RATE = 2e-4

# Create model
model = FLASHTransformer(
    num_tokens=256,
    dim=512,
    depth=8,
    causal=True,
    group_size=256,
    shift_tokens=True,
    laplace_attn_fn=True
)
model = AutoregressiveWrapper(model)
model.cuda()
```

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

### Install FLASH-pytorch

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

Install the package via pip.

```bash
$ pip install FLASH-pytorch
```

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

### AutoregressiveWrapper Text Generation Example

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

Generate new text sequences using the AutoregressiveWrapper. Provide start tokens (prompt) and specify generation length, temperature for sampling, and optionally an end-of-sequence token. Top-k filtering can be applied.

```python
start_tokens = torch.randint(0, 256, (1, 64)).cuda()  # Prompt tokens
generated = model.generate(
    start_tokens,
    seq_len=512,              # Generate 512 new tokens
    temperature=0.8,          # Sampling temperature
    filter_thres=0.9,         # Top-k filtering threshold
    eos_token=None            # Optional early stopping token
)

print(f"Generated shape: {generated.shape}")
```

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

### AutoregressiveWrapper Training Example

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

Compute cross-entropy loss for training using the AutoregressiveWrapper. The wrapper automatically handles the next-token prediction shift. Ensure input tensors are on the correct device (e.g., CUDA).

```python
x = torch.randint(0, 256, (4, 1025)).cuda()  # (batch, seq_len+1)
loss = model(x)  # Automatically shifts for next-token prediction
loss.backward()
print(f"Training loss: {loss.item()}")
```

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

### Decode Generated Tokens

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

A helper function to decode generated token IDs back into human-readable text. This example assumes character-level encoding where token IDs map to ASCII characters.

```python
def decode_tokens(tokens):
    return ''.join([chr(max(32, t)) for t in tokens])

output_text = decode_tokens(generated[0].cpu().tolist())
print(f"Generated text: {output_text[:100]}...")
```

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

### Initialize FLASHTransformer

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

Import the FLASHTransformer class for building complete transformer architectures.

```python
import torch
from flash_pytorch import FLASHTransformer
```

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

### Initialize and use FLASH attention layer

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

Configure the FLASH module for hybrid quadratic-linear attention and process sequences with padding masks.

```python
import torch
from flash_pytorch import FLASH

# Create FLASH attention layer
flash = FLASH(
    dim=512,                          # Model dimension
    group_size=256,                   # Size of attention groups
    causal=True,                      # Autoregressive mode
    query_key_dim=128,                # Query/key dimension
    expansion_factor=2.,              # Hidden dimension multiplier
    laplace_attn_fn=True,             # Use Laplacian attention function
    shift_tokens=True,                # Enable token shifting for better convergence
    reduce_group_non_causal_attn=True # Reduce groups in non-causal mode
)

# Process sequences - auto-pads to nearest group size
x = torch.randn(1, 1111, 512)  # Arbitrary sequence length
output = flash(x)               # (1, 1111, 512) - maintains original length

# With padding mask for variable-length sequences
batch_size, seq_len = 4, 1000
x_batch = torch.randn(batch_size, seq_len, 512)
lengths = [800, 950, 1000, 750]  # Actual lengths per sequence
mask = torch.zeros(batch_size, seq_len, dtype=torch.bool)
for i, length in enumerate(lengths):
    mask[i, :length] = True

output_batch = flash(x_batch, mask=mask)
print(f"Batch output shape: {output_batch.shape}")
# Batch output shape: torch.Size([4, 1000, 512])
```

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

### Initialize FLASH

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

Combine GAU with grouped linear attention to overcome issues with autoregressive linear attention.

```python
import torch
from flash_pytorch import FLASH

flash = FLASH(
    dim = 512,
    group_size = 256,             # group size
    causal = True,                # autoregressive or not
    query_key_dim = 128,          # query / key dimension
    expansion_factor = 2.,        # hidden dimension = dim * expansion_factor
    laplace_attn_fn = True   # new Mega paper claims this is more stable than relu squared as attention function
)

x = torch.randn(1, 1111, 512)     # sequence will be auto-padded to nearest group size
out = flash(x) # (1, 1111, 512)
```

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

### Load and Prepare enwik8 Dataset

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

Loads the enwik8 dataset from a gzipped file, splits it into training and validation sets, and creates PyTorch DataLoaders for training.

```python
# Load and prepare enwik8 data
with gzip.open('./data/enwik8.gz') as file:
    X = np.frombuffer(file.read(int(95e6)), dtype=np.uint8)
    trX, vaX = np.split(X, [int(90e6)])
    data_train, data_val = torch.from_numpy(trX), torch.from_numpy(vaX)

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

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

### Initialize FLASHTransformer

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

Use the full FLASH transformer architecture with support for positional embeddings and RoPE.

```python
import torch
from flash_pytorch import FLASHTransformer

model = FLASHTransformer(
    num_tokens = 20000,          # number of tokens
    dim = 512,                   # model dimension
    depth = 12,                  # depth
    causal = True,               # autoregressive or not
    group_size = 256,            # size of the groups
    query_key_dim = 128,         # dimension of queries / keys
    expansion_factor = 2.,       # hidden dimension = dim * expansion_factor
    norm_type = 'scalenorm',     # in the paper, they claimed scalenorm led to faster training at no performance hit. the other option is 'layernorm' (also default)
    shift_tokens = True          # discovered by an independent researcher in Shenzhen @BlinkDL, this simply shifts half of the feature space forward one step along the sequence dimension - greatly improved convergence even more in my local experiments
)

x = torch.randint(0, 20000, (1, 1024))
logits = model(x) # (1, 1024, 20000)
```

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

### Instantiate FLASH Transformer for Language Modeling

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

Instantiate a FLASHTransformer for causal language modeling. Configure vocabulary size, model dimensions, depth, attention parameters, and normalization types. Ensure 'causal=True' for autoregressive tasks.

```python
model = FLASHTransformer(
    num_tokens=20000,           # Vocabulary size
    dim=512,                    # Model dimension
    depth=12,                   # Number of FLASH layers
    causal=True,                # Autoregressive mode
    group_size=256,             # Attention group size
    query_key_dim=128,          # Query/key dimension
    expansion_factor=2.,        # Hidden dimension multiplier
    norm_type='scalenorm',      # 'scalenorm' (faster) or 'layernorm'
    shift_tokens=True,          # Token shifting for improved convergence
    laplace_attn_fn=True,       # Laplacian attention function
    attn_dropout=0.1            # Attention dropout
)
```

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

### Initialize and use Gated Attention Unit (GAU)

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

Instantiate a GAU for autoregressive modeling and process sequences with optional masking.

```python
import torch
from flash_pytorch import GAU

# Create a Gated Attention Unit for autoregressive modeling
gau = GAU(
    dim=512,                  # Model dimension
    query_key_dim=128,        # Query/key projection dimension
    causal=True,              # Enable causal masking for autoregressive
    expansion_factor=2,       # Hidden dim = dim * expansion_factor
    laplace_attn_fn=True,     # Use Laplacian attention (more stable than ReLU squared)
    dropout=0.1,              # Dropout rate
    add_residual=True         # Add residual connection
)

# Process a batch of sequences
x = torch.randn(1, 1024, 512)  # (batch, seq_len, dim)
output = gau(x)                 # (1, 1024, 512)

# With optional attention mask
mask = torch.ones(1, 1024, dtype=torch.bool)
mask[:, 512:] = False  # Mask out second half
output_masked = gau(x, mask=mask)

print(f"Input shape: {x.shape}")
print(f"Output shape: {output.shape}")
# Input shape: torch.Size([1, 1024, 512])
# Output shape: torch.Size([1, 1024, 512])
```

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

### Run Autoregressive Enwik8 Test

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

Execute the training script for the Enwik8 benchmark.

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

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

### Initialize Gated Attention Unit (GAU)

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

Use GAU to replace multi-headed attention with a single head using ReLU squared activation.

```python
import torch
from flash_pytorch import GAU

gau = GAU(
    dim = 512,
    query_key_dim = 128,     # query / key dimension
    causal = True,           # autoregressive or not
    expansion_factor = 2,    # hidden dimension = dim * expansion_factor
    laplace_attn_fn = True   # new Mega paper claims this is more stable than relu squared as attention function
)

x = torch.randn(1, 1024, 512)
out = gau(x) # (1, 1024, 512)
```

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

### Implement Gradient Accumulation Training Loop

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

Uses standard PyTorch optimization patterns with gradient accumulation to scale training across multiple batches.

```python
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)

for batch_idx, batch in enumerate(train_loader):
    model.train()
    loss = model(batch)
    loss = loss / GRADIENT_ACCUMULATE_EVERY
    loss.backward()

    if (batch_idx + 1) % GRADIENT_ACCUMULATE_EVERY == 0:
        torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
        optimizer.step()
        optimizer.zero_grad()
        print(f"Step {batch_idx + 1}, Loss: {loss.item() * GRADIENT_ACCUMULATE_EVERY:.4f}")

    if batch_idx >= 100:  # Demo: stop after 100 batches
        break
```

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

### Wrap FLASH Transformer with AutoregressiveWrapper

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

Wrap a FLASHTransformer with AutoregressiveWrapper for convenient training and generation. The wrapper handles padding and shifting for autoregressive tasks. Move the model to CUDA if available.

```python
base_model = FLASHTransformer(
    num_tokens=256,             # Character-level vocabulary
    dim=512,
    depth=8,
    causal=True,
    group_size=256,
    shift_tokens=True,
    laplace_attn_fn=True
)

model = AutoregressiveWrapper(base_model, pad_value=0)
model.cuda()
```

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

### FLASH Transformer Forward Pass for Training

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

Perform a forward pass with the FLASHTransformer for training. Input should be token IDs. The output logits can be used for calculating cross-entropy loss.

```python
x = torch.randint(0, 20000, (1, 1024))  # (batch, seq_len)
logits = model(x)                        # (1, 1024, 20000)

print(f"Input shape: {x.shape}")
print(f"Output logits shape: {logits.shape}")
```

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

### Citations

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

BibTeX entries for the relevant research papers and software.

```bibtex
@article{Hua2022TransformerQI,
    title   = {Transformer Quality in Linear Time},
    author  = {Weizhe Hua and Zihang Dai and Hanxiao Liu and Quoc V. Le},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2202.10447}
}
```

```bibtex
@software{peng_bo_2021_5196578,
    author    = {PENG Bo},
    title     = {BlinkDL/RWKV-LM: 0.01},
    month     = {aug},
    year      = {2021},
    publisher = {Zenodo},
    version   = {0.01},
    doi       = {10.5281/zenodo.5196578},
    url       = {https://doi.org/10.5281/zenodo.5196578}
}
```

```bibtex
@inproceedings{Ma2022MegaMA,
    title   = {Mega: Moving Average Equipped Gated Attention},
    author  = {Xuezhe Ma and Chunting Zhou and Xiang Kong and Junxian He and Liangke Gui and Graham Neubig and Jonathan May and Luke Zettlemoyer},
    year    = {2022}
}
```

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

### FLASH Transformer Forward Pass with Attention Mask

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

Perform a forward pass with attention masking for padded batches. The mask should be a boolean tensor where 'False' indicates masked positions.

```python
batch_tokens = torch.randint(0, 20000, (4, 512))
mask = torch.ones(4, 512, dtype=torch.bool)
mask[0, 400:] = False  # First sequence is shorter
logits_masked = model(batch_tokens, mask=mask)
```

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

### TextSamplerDataset for Character-Level Data

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

A PyTorch Dataset class for loading and sampling character-level text data. It reads sequences of a specified length from a numpy array and prepares them for training.

```python
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.cuda()

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

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