# How to Implement Ring Attention for Long Sequences in Nanotron

> Implement ring attention for long sequences in Nanotron using ring_flash_attn_varlen_func Achieve efficient memory scaling for ultra-long sequences by sharding KV tensors across your GPU ring.

- Repository: [Hugging Face/nanotron](https://github.com/huggingface/nanotron)
- Tags: how-to-guide
- Published: 2026-03-03

---

**Nanotron enables ring attention through `ring_flash_attn_varlen_func` in [`src/nanotron/nn/ring_attention.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/ring_attention.py), which shards KV tensors across a GPU ring, executes local Flash-Attention computations, and fuses partial outputs via log-sum-exp operations to achieve O(1/world-size) memory scaling for ultra-long sequences.**

Nanotron provides a native implementation of ring attention that extends Flash-Attention to sequences far exceeding the memory capacity of a single device. The approach partitions keys and values across a pipeline of GPUs, passes tensor slices asynchronously around the ring, and accumulates attention outputs using numerically-stable log-sum-exp fusion. This architecture allows transformers to process billions of tokens while maintaining constant memory overhead per device.

## Architecture of Ring Attention in Nanotron

The implementation centers on three core components: the communication primitive `RingComm`, the forward/backward pass schedulers, and the fusion utilities that merge partial attention blocks.

### Ring Communication via `RingComm`

The `RingComm` class in [`src/nanotron/nn/ring_attention.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/ring_attention.py) (lines 27-45) manages asynchronous point-to-point transfers of KV tensors. It wraps `torch.distributed.P2POp` and `batch_isend_irecv` to pipeline tensor slices between neighboring ranks without blocking the host.

```python
from nanotron.nn.ring_attention import RingComm

# Inside the ring loop

comm = RingComm(ring_pg)
next_k, next_v = comm.send_recv_kv(k, v)  # Non-blocking send/recv

comm.commit()

# ... compute flash attention on current slice ...

comm.wait()  # Ensure receive completed before next iteration

```

### Forward Pass Implementation

The forward logic resides in `ring_flash_attn_varlen_forward` (lines 10-86). For each ring step, the rank:
1. Sends its current KV slice to the next rank and receives the previous rank's slice via `RingComm`.
2. Computes Flash-Attention between its local queries (Q) and the received KV slice using `_flash_attn_varlen_forward`.
3. Merges the block output and log-sum-exp (LSE) statistics via `update_out_and_lse`.

The causal masking rule `if not causal or step <= rank` ensures that autoregressive models only attend to previous positions in the ring sequence.

### Backward Pass and Gradient Accumulation

The backward pass in `ring_flash_attn_varlen_backward` (lines 88-177) mirrors the forward schedule using two communicators: `kv_comm` for activations and `d_kv_comm` for gradients. It accumulates gradients for Q, K, and V across all ring steps, communicating gradient KV slices asynchronously to minimize idle time.

```python

# Simplified backward flow

for step in range(world_size):
    next_k, next_v = kv_comm.send_recv_kv(k, v)
    if step <= rank or not causal:
        flash_attn_backward(dout, q, k, v, out, lse, ..., 
                           dq=block_dq, dk=block_dk, dv=block_dv)
        dq += block_dq
        d_kv_comm.wait()
        dk = block_dk + next_dk  # Accumulate gradients from neighbors

        dv = block_dv + next_dv

```

### Numerically-Stable Output Fusion

The `update_out_and_lse` and `_update_out_and_lse` functions (lines 86-107) implement single-pass log-sum-exp addition to merge `block_out` with the cumulative `out` tensor. This ensures numerical stability when fusing partial attention contributions from up to `world_size` blocks.

## Step-by-Step Implementation Guide

To use ring attention in your training pipeline, you must initialize a distributed process group and invoke the ring attention function directly or through the high-level `MultiHeadAttention` API.

### Setting Up the Distributed Process Group

Ring attention requires a process group spanning the participating GPUs. Initialize NCCL (or Gloo for CPU testing) and pass the group handle to the attention function.

```python
import torch
import torch.distributed as dist

dist.init_process_group(backend="nccl")
ring_pg = dist.group.WORLD  # Or a specific subgroup for ring parallelism

```

### Calling the Ring Attention Function Directly

For fine-grained control, use `ring_flash_attn_varlen_func` from [`src/nanotron/nn/ring_attention.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/ring_attention.py). This is the same entry point used by the attention registry.

```python
from nanotron.nn.ring_attention import ring_flash_attn_varlen_func

batch, n_heads, head_dim = 2, 8, 64
seq_lengths = torch.tensor([512, 384], dtype=torch.int32, device="cuda")
max_seq = seq_lengths.max().item()

# Create packed QKV tensors with padding

q = torch.randn(batch, max_seq, n_heads, head_dim, device="cuda", dtype=torch.float16)
k = torch.randn(batch, max_seq, n_heads, head_dim, device="cuda", dtype=torch.float16)
v = torch.randn(batch, max_seq, n_heads, head_dim, device="cuda", dtype=torch.float16)

# Cumulative sequence lengths for variable-length inputs

cu_seqlens = torch.zeros(batch + 1, dtype=torch.int32, device="cuda")
cu_seqlens[1:] = torch.cumsum(seq_lengths, dim=0)

out, softmax_lse, _ = ring_flash_attn_varlen_func(
    module=None,                # Unused by the low-level kernel

    q=q, k=k, v=v,
    cu_seqlens=cu_seqlens,
    max_seqlen=max_seq,
    dropout=0.0,
    scaling=None,               # Defaults to 1/sqrt(head_dim)

    causal=False,               # Set True for autoregressive decoding

    window_size=(-1, -1),       # Infinite context window

    alibi_slopes=None,
    deterministic=False,
    return_attn_probs=False,
    ring_pg=ring_pg,            # Critical: passes the process group to RingComm

)

```

### Integrating with `MultiHeadAttention`

For most use cases, configure the attention type through the registry. In [`src/nanotron/nn/attention.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/attention.py) (lines 215-222), the string `"ring"` maps to `ring_flash_attn_varlen_func`.

```python
from nanotron.nn import MultiHeadAttention

attn = MultiHeadAttention(
    dim=1024,
    n_heads=16,
    attention_type="ring",      # Activates ring attention via registry lookup

    causal=True,
    process_group=ring_pg,      # Distributed group for ring communication

)

```

## Alternative: Lucidrains CUDA Kernel Implementation

Nanotron provides a second implementation in [`src/nanotron/nn/ring_attention_lucidrain.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/ring_attention_lucidrain.py) that directly calls the original Lucidrains ring-flash-attention CUDA kernel (`ring_flash_attn_cuda`). This version follows the same API as the standard Flash-Attention implementation and can be swapped by configuring the registry to use the lucidrains module.

## Summary

- **Memory Scaling**: Ring attention achieves O(1/world-size) memory per GPU by partitioning KV tensors across the ring and processing one block at a time.
- **Core Files**: The implementation lives in [`src/nanotron/nn/ring_attention.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/ring_attention.py), with `RingComm` handling communication and `ring_flash_attn_varlen_forward/backward` managing the compute schedule.
- **Public API**: Access ring attention via `ring_flash_attn_varlen_func` or by setting `attention_type="ring"` in `MultiHeadAttention`, which queries the registry defined in [`src/nanotron/nn/attention.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/attention.py).
- **Numerical Stability**: Partial outputs fuse via `update_out_and_lse` using log-sum-exp addition to prevent numerical overflow when combining blocks.
- **Distributed Requirement**: You must provide a valid `ring_pg` (ProcessGroup) to enable the point-to-point communication required by the ring algorithm.

## Frequently Asked Questions

### How does ring attention reduce memory usage compared to standard Flash Attention?

Standard Flash Attention materializes the full sequence length on a single device, requiring O(N) memory for the KV cache. Ring attention in Nanotron partitions the sequence across `world_size` GPUs, so each device holds only O(N/world_size) KV tensor elements. By communicating slices around the ring and computing partial attention outputs incrementally, the per-GPU memory footprint becomes independent of total sequence length, scaling inversely with the number of participating devices.

### Can I use ring attention with causal (autoregressive) masking?

Yes. The forward pass in `ring_flash_attn_varlen_forward` applies a causal masking rule: `if not causal or step <= rank`. This ensures that each rank only attends to KV slices from previous ranks in the ring (and its own slice), preserving the autoregressive property. The backward pass respects the same constraint, accumulating gradients only for valid causal dependencies.

### What is the difference between [`ring_attention.py`](https://github.com/huggingface/nanotron/blob/main/ring_attention.py) and [`ring_attention_lucidrain.py`](https://github.com/huggingface/nanotron/blob/main/ring_attention_lucidrain.py)?

[`src/nanotron/nn/ring_attention.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/ring_attention.py) implements ring attention using native PyTorch operations and standard Flash-Attention kernels, offering flexibility and easier debugging. [`src/nanotron/nn/ring_attention_lucidrain.py`](https://github.com/huggingface/nanotron/blob/main/src/nanotron/nn/ring_attention_lucidrain.py) provides a thin wrapper around the specialized CUDA kernels from the Lucidrains ring-flash-attention repository, potentially offering lower latency for specific hardware configurations. Both expose the same functional interface and integrate with the Nanotron attention registry.

### Do I need a special process group configuration for ring attention?

You must pass a valid `torch.distributed.ProcessGroup` (typically `dist.group.WORLD` or a subgroup) to the `ring_pg` parameter. The `RingComm` class uses this group to determine neighboring ranks for `send_recv_kv` operations. Ensure all ranks in the group are on different GPUs, as the implementation relies on peer-to-peer NCCL communication between physical devices.