### Install Package

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

Install the library using pip.

```bash
$ pip install ring-attention-pytorch
```

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

### Install Requirements

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

Install necessary dependencies for testing.

```bash
$ pip install -r requirements.txt
```

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

### Configure Attention Variants

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

Examples for applying padding masks, grouped query attention, and numerical stability via softclamping.

```python
mask = torch.ones(batch, seq_len).bool()
mask[0, 256:] = False
out = default_attention(q, k, v, mask=mask, causal=False)
```

```python
kv_heads = 2
k_gqa = torch.randn(batch, seq_len, kv_heads, dim_head)
v_gqa = torch.randn(batch, seq_len, kv_heads, dim_head)
out = default_attention(q, k_gqa, v_gqa, causal=True)
```

```python
out = default_attention(
    q, k, v,
    causal=True,
    softclamp_qk_sim=True,
    softclamp_value=50.0
)
```

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

### Implement Distributed Ring Attention Training

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

Full setup for distributed training using PyTorch DDP and the RingTransformer module.

```python
import os
import torch
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from ring_attention_pytorch import RingTransformer

def setup(rank, world_size, use_cuda):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'
    backend = "nccl" if use_cuda else "gloo"
    dist.init_process_group(backend, rank=rank, world_size=world_size)
    if use_cuda:
        torch.cuda.set_device(rank)

def cleanup():
    dist.destroy_process_group()

def train_step(rank, world_size, use_cuda=True):
    setup(rank, world_size, use_cuda)

    # Create model with ring attention
    model = RingTransformer(
        num_tokens=256,
        dim=128,
        depth=2,
        heads=8,
        causal=True,
        ring_attn=True,
        striped_ring_attn=True,
        ring_seq_size=512,
        bucket_size=256
    )

    if use_cuda:
        model = model.cuda(rank)

    # Wrap with DDP
    model = DDP(model)

    # Training loop
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

    # Long sequence training
    seq = torch.randint(0, 256, (2, 4096))
    if use_cuda:
        seq = seq.cuda(rank)

    # Forward and backward with ring attention
    loss = model(seq, return_loss=True)
    loss.backward()
    optimizer.step()

    if rank == 0:
        print(f"Loss: {loss.item():.4f}")

    cleanup()

# Launch distributed training
if __name__ == '__main__':
    world_size = torch.cuda.device_count()  # Use all available GPUs
    mp.spawn(train_step, args=(world_size, True), nprocs=world_size, join=True)
```

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

### Execute low-level ring_flash_attn

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

Implements the core ring reduce algorithm using pre-computed query, key, and value tensors.

```python
import torch
from ring_attention_pytorch import ring_flash_attn

# Prepare Q, K, V tensors
# Shape: (batch, seq_len, heads, dim_head)
batch, seq_len, heads, dim_head = 2, 1024, 8, 64

q = torch.randn(batch, seq_len, heads, dim_head)
k = torch.randn(batch, seq_len, heads, dim_head)
v = torch.randn(batch, seq_len, heads, dim_head)
```

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

### Run Tests

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

Execute test scripts for specific attention implementations.

```bash
$ python assert.py --use-cuda --causal --striped-ring-attn
```

```bash
$ python assert_tree_attn.py --use-cuda --seq-len 8192
```

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

### Initialize RingAttention

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

Configure and use the RingAttention module for sequence processing.

```python
import torch
from ring_attention_pytorch import RingAttention

attn = RingAttention(
    dim = 512,
    dim_head = 64,
    heads = 8,
    causal = True,
    auto_shard_seq = True,
    ring_attn = True,
    ring_seq_size = 512
)

tokens = torch.randn(1, 1024, 512)
attended = attn(tokens)

assert attended.shape == tokens.shape
```

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

### Initialize and use RingAttention module

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

Configures the core attention module with sequence sharding and causal masking. Supports both standard and grouped query attention.

```python
import torch
from ring_attention_pytorch import RingAttention

# Create ring attention module with automatic sequence sharding
attn = RingAttention(
    dim=512,                    # Model dimension
    dim_head=64,                # Dimension per attention head
    heads=8,                    # Number of attention heads
    num_grouped_query_heads=2,  # For grouped query attention (kv_heads = heads // this)
    causal=True,                # Enable causal masking for autoregressive models
    auto_shard_seq=True,        # Automatically shard sequence across machines
    ring_attn=True,             # Enable ring attention
    ring_seq_size=512,          # Sequence size per machine
    bucket_size=256,            # Flash attention bucket size
    striped_ring_attn=False,    # Enable striped attention for better load balancing
    max_lookback_seq_len=None,  # Optional: limit attention lookback
    rotary_embed=True,          # Enable rotary positional embeddings
    use_cuda_kernel=True        # Use optimized CUDA kernels when available
)

# Forward pass with token embeddings
tokens = torch.randn(1, 1024, 512)  # (batch, seq_len, dim)
attended = attn(tokens)

# With optional mask
mask = torch.ones(1, 1024).bool()
attended = attn(tokens, mask=mask)

assert attended.shape == tokens.shape
```

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

### Initialize and train RingTransformer model

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

Sets up a full transformer model for long-context training and inference. Handles token embeddings, layers, and output projection.

```python
import torch
from ring_attention_pytorch import RingTransformer

# Initialize ring attention transformer
model = RingTransformer(
    num_tokens=50000,           # Vocabulary size
    dim=512,                    # Model dimension
    depth=6,                    # Number of transformer layers
    causal=True,                # Autoregressive model
    dim_head=64,                # Dimension per head
    heads=8,                    # Number of attention heads
    ff_mult=4,                  # Feed-forward multiplier
    num_grouped_query_heads=2,  # Grouped query attention groups
    bucket_size=512,            # Flash attention bucket size
    ring_attn=True,             # Enable ring attention
    striped_ring_attn=True,     # Enable striped attention for autoregressive
    ring_seq_size=512,          # Sequence size per machine
    auto_shard_seq=True,        # Auto-shard across machines
    max_lookback_seq_len=2048,  # Optional: limit lookback to save compute
    rotary_embed_theta=10000,   # Rotary embedding base frequency
    use_cuda_kernel=True        # Use CUDA kernels
)

# Training: compute loss directly
token_ids = torch.randint(0, 50000, (2, 8192))  # (batch, seq_len)
loss = model(token_ids, return_loss=True)
loss.backward()

# Inference: get logits
with torch.no_grad():
    logits = model(token_ids)  # (batch, seq_len, num_tokens)

# With explicit labels
labels = token_ids[:, 1:]
input_ids = token_ids[:, :-1]
loss = model(input_ids, labels=labels, return_loss=True)
```

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

### ring_flash_attn_cuda

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

CUDA-optimized ring flash attention using Triton kernels.

```APIDOC
## ring_flash_attn_cuda

### Description
CUDA-optimized ring flash attention using Triton kernels for forward pass and Tri Dao's flash attention for backward pass.

### Parameters
- **q, k, v** (Tensor) - Input tensors on CUDA device
- **mask** (Tensor, optional) - Padding mask
- **causal** (bool) - Causal masking
- **bucket_size** (int) - Flash attention bucket size
- **ring_reduce_col** (bool) - Enable ring reduce
- **striped_ring_attn** (bool) - Striped attention pattern
- **max_lookback_seq_len** (int, optional) - Limit context window
- **ring_size** (int, optional) - Ring size
```

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

### Use Tree Attention Decoding

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

Import and execute tree attention decoding across distributed machines.

```python
from ring_attention_pytorch import tree_attn_decode

out = tree_attn_decode(q, k, v) # where q, k, v exists across all machines
```

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

### default_attention

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

Standard attention implementation for reference or fallback.

```APIDOC
## default_attention

### Description
Standard attention implementation with support for grouped query attention and optional softclamping.

### Parameters
- **q, k, v** (Tensor) - Input tensors
- **causal** (bool) - Causal masking
```

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

### Default Attention Implementation

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

Standard multi-head attention with optional causal masking. Serves as a reference or fallback when specialized attention is not required.

```python
import torch
from ring_attention_pytorch import default_attention

# Standard multi-head attention
batch, seq_len, heads, dim_head = 2, 512, 8, 64

q = torch.randn(batch, seq_len, heads, dim_head)
k = torch.randn(batch, seq_len, heads, dim_head)
v = torch.randn(batch, seq_len, heads, dim_head)

# Basic attention
out = default_attention(q, k, v)

# Causal attention for autoregressive models
out = default_attention(q, k, v, causal=True)
```

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

### ring_flash_attn

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

Standard ring flash attention implementation for distributed sequence processing.

```APIDOC
## ring_flash_attn

### Description
Performs ring flash attention across distributed machines.

### Parameters
- **q** (Tensor) - Query tensor
- **k** (Tensor) - Key tensor
- **v** (Tensor) - Value tensor
- **mask** (Tensor, optional) - Padding mask
- **causal** (bool) - Causal masking
- **bucket_size** (int) - Flash attention bucket size
- **ring_reduce_col** (bool) - Enable ring reduce across machines
- **striped_ring_attn** (bool) - Striped attention pattern
- **max_lookback_seq_len** (int, optional) - Limit attention span
- **ring_size** (int, optional) - Ring size (defaults to world_size)
```

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

### Standard Ring Flash Attention

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

Use for standard attention with optional causal masking and distributed ring reduction. Configure bucket size and ring reduction parameters as needed.

```python
out = ring_flash_attn(
    q, k, v,
    mask=None,                  # Optional padding mask
    causal=True,                # Causal masking
    bucket_size=512,            # Flash attention bucket size
    ring_reduce_col=True,       # Enable ring reduce across machines
    striped_ring_attn=False,    # Striped attention pattern
    max_lookback_seq_len=None,  # Limit attention span
    ring_size=None              # Ring size (defaults to world_size)
)

assert out.shape == q.shape

# With key padding mask for variable length sequences
mask = torch.ones(batch, seq_len).bool()
mask[0, 512:] = False  # Mask out padding

out = ring_flash_attn(q, k, v, mask=mask, causal=False)
```

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

### CUDA Optimized Ring Flash Attention

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

Leverages CUDA and Triton kernels for significant speedups on GPU clusters. Requires CUDA tensors and supports mixed precision.

```python
import torch
from ring_attention_pytorch import ring_flash_attn_cuda

# Requires CUDA tensors
device = torch.device('cuda')
batch, seq_len, heads, dim_head = 2, 2048, 8, 64

q = torch.randn(batch, seq_len, heads, dim_head, device=device)
k = torch.randn(batch, seq_len, heads, dim_head, device=device)
v = torch.randn(batch, seq_len, heads, dim_head, device=device)

# CUDA ring flash attention with striped pattern
out = ring_flash_attn_cuda(
    q, k, v,
    mask=None,
    causal=True,
    bucket_size=1024,
    ring_reduce_col=True,
    striped_ring_attn=True,     # Better load balancing for causal
    max_lookback_seq_len=4096,  # Limit context window
    ring_size=None
)

# Supports mixed precision
q_half = q.half()
k_half = k.half()
v_half = v.half()

out_half = ring_flash_attn_cuda(q_half, k_half, v_half, causal=True)
```

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

### tree_attn_decode

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

Tree Attention Decoding for efficient distributed inference.

```APIDOC
## tree_attn_decode

### Description
Tree Attention Decoding implementation for efficient distributed inference during autoregressive generation.

### Parameters
- **q, k, v** (Tensor) - Query and KV cache tensors
- **eps** (float) - Numerical stability
- **shard_kv_seq** (bool) - Auto-shard KV across world
- **use_triton** (bool, optional) - Auto-detect CUDA for Triton
```

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

### Tree Attention Decoding

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

Efficient distributed inference for autoregressive generation using a tree-reduction pattern. Supports automatic KV cache sharding or pre-sharded KV caches.

```python
import torch
from ring_attention_pytorch import tree_attn_decode

# During inference on a distributed system
# Each machine has a shard of the KV cache
batch, heads, kv_seq_len, dim = 1, 8, 8192, 64

# Query for the current decoding step (single position)
q = torch.randn(batch, heads, 1, dim)

# Full KV cache (will be automatically sharded across machines)
k = torch.randn(batch, heads, kv_seq_len, dim)
v = torch.randn(batch, heads, kv_seq_len, dim)

# Tree attention decoding with automatic KV sharding
out = tree_attn_decode(
    q, k, v,
    eps=1e-8,                   # Numerical stability
    shard_kv_seq=True,          # Auto-shard KV across world
    use_triton=None             # Auto-detect CUDA for Triton
)

assert out.shape == (batch, heads, 1, dim)

# When KV is pre-sharded (shard_kv_seq=False)
# Each machine only passes its local KV shard
# Useful when KV cache is already distributed
local_k = k[:, :, :1024, :]  # Rank's portion of KV
local_v = v[:, :, :1024, :]

out = tree_attn_decode(q, local_k, local_v, shard_kv_seq=False)
```

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

### Ring Rotary Embedding Module

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

Rotary positional embeddings adapted for ring attention, supporting striped patterns and automatic position offsets based on rank. Generates frequencies for a given sequence length or custom positions.

```python
import torch
from ring_attention_pytorch import RingRotaryEmbedding, apply_rotary_pos_emb

# Create rotary embeddings for ring attention
rotary_emb = RingRotaryEmbedding(
    dim=64,           # Head dimension (must match dim_head)
    ring=True,        # Enable ring-aware position calculation
    striped=True,     # Support striped attention pattern
    buckets=2,        # Number of buckets per machine
    theta=10000       # Base frequency
)

# Generate rotary embeddings for sequence length
seq_len = 512
freqs = rotary_emb(seq_len)  # Shape: (seq_len, dim)

# Apply to query and key tensors
# Shape: (batch, seq_len, heads, dim_head)
q = torch.randn(2, seq_len, 8, 64)
k = torch.randn(2, seq_len, 8, 64)

q_rotated = apply_rotary_pos_emb(freqs, q)
k_rotated = apply_rotary_pos_emb(freqs, k)

# Custom positions (for variable-length or special patterns)
custom_positions = torch.arange(seq_len) * 2  # Every other position
freqs_custom = rotary_emb(custom_positions)
```

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

### RingRotaryEmbedding

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

Rotary positional embeddings adapted for ring attention.

```APIDOC
## RingRotaryEmbedding

### Description
Rotary positional embeddings adapted for ring attention with support for striped attention patterns.

### Parameters
- **dim** (int) - Head dimension
- **ring** (bool) - Enable ring-aware position calculation
- **striped** (bool) - Support striped attention pattern
- **buckets** (int) - Number of buckets per machine
- **theta** (int) - Base frequency
```

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