How to Implement Ring Attention for Long Sequences in Nanotron

Nanotron enables ring attention through ring_flash_attn_varlen_func in 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 (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.

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.

# 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.

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. This is the same entry point used by the attention registry.

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 (lines 215-222), the string "ring" maps to ring_flash_attn_varlen_func.

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 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, 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.
• 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 and ring_attention_lucidrain.py?

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 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.

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