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import math
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
# Re-use rotary embedding helper from the original codebase
from .rotary import apply_rotary_emb
# -----------------------------------------------------------------------------
# Utility helpers (copied from the original implementation)
# -----------------------------------------------------------------------------
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""Efficiently repeat keys / values for GQA without allocating new memory."""
bs, n_kv_heads, slen, head_dim = x.shape
if n_rep == 1:
return x
return (
x[:, :, None, :, :]
.expand(bs, n_kv_heads, n_rep, slen, head_dim)
.reshape(bs, n_kv_heads * n_rep, slen, head_dim)
)
def lambda_init_fn(depth: int) -> float:
"""Init schedule described in the DiffAttention paper."""
return 0.8 - 0.6 * math.exp(-0.3 * depth)
# -----------------------------------------------------------------------------
# Optimised Multi-head DiffAttention implementation
# -----------------------------------------------------------------------------
class MultiheadDiffAttn(nn.Module):
"""Optimised DiffAttention block.
Differences from the original implementation:
1. Removes the dependency on Apex / FusedRMSNorm; uses native LayerNorm.
2. Keeps all tensors on-device and works well with autocast fp16/bf16.
3. Minimises Python-side tensor reshapes and kernel launches.
"""
def __init__(
self,
embed_dim: int,
depth: int,
num_heads: int,
num_kv_heads: Optional[int] = None,
dropout: float = 0.1,
) -> None:
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads # query heads (will be doubled internally)
self.num_kv_heads = num_kv_heads or num_heads
self.n_rep = (
self.num_heads // self.num_kv_heads
) # replication factor for keys / values (GQA)
self.attn_dropout = dropout # Store dropout rate for attention
# One half of a traditional head – DiffAttention uses pairs of heads
self.head_dim = embed_dim // self.num_heads // 2
assert (
self.head_dim * self.num_heads * 2 == embed_dim
), "embed_dim must be divisible by num_heads * 2"
self.scaling = self.head_dim**-0.5
# Projections. We keep them separated because K/V are smaller (GQA)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.k_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False)
self.v_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False)
# Add dropout for regularization
self.dropout = nn.Dropout(dropout)
# DiffAttention lambda parameters (learnable)
self.lambda_init = lambda_init_fn(depth)
self.lambda_q1 = nn.Parameter(torch.randn(self.head_dim) * 0.1)
self.lambda_k1 = nn.Parameter(torch.randn(self.head_dim) * 0.1)
self.lambda_q2 = nn.Parameter(torch.randn(self.head_dim) * 0.1)
self.lambda_k2 = nn.Parameter(torch.randn(self.head_dim) * 0.1)
# Use standard LayerNorm which has a highly-optimised CUDA kernel
self.subln = nn.LayerNorm(2 * self.head_dim, eps=1e-5)
# ---------------------------------------------------------------------
# Forward
# ---------------------------------------------------------------------
def forward(
self,
x: torch.Tensor, # [bsz, seq_len, embed_dim]
rel_pos: tuple[torch.Tensor, torch.Tensor],
attn_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
bsz, seq_len, _ = x.size()
# ---- Projections --------------------------------------------------
# Projections (run inside the outer autocast context so they stay in
# the low-precision dtype and use tensor cores)
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
# Reshape into paired heads (2 × heads)
q = q.view(bsz, seq_len, 2 * self.num_heads, self.head_dim)
k = k.view(bsz, seq_len, 2 * self.num_kv_heads, self.head_dim)
v = v.view(bsz, seq_len, self.num_kv_heads, 2 * self.head_dim)
# Rotary position encodings (ensure dtype matches q)
cos, sin = rel_pos
cos = cos.to(dtype=q.dtype)
sin = sin.to(dtype=q.dtype)
q = apply_rotary_emb(q, cos, sin, interleaved=True)
k = apply_rotary_emb(k, cos, sin, interleaved=True)
# ---- Prepare tensors for matmul ----------------------------------
# Shape conventions follow PyTorch’s `scaled_dot_product_attention`:
# (bsz, heads, seq, head_dim)
q = q.transpose(1, 2) # [bsz, 2*heads, seq, head_dim]
k = k.transpose(1, 2) # [bsz, 2*kv_heads, seq, head_dim]
v = v.transpose(1, 2) # [bsz, kv_heads, seq, 2*head_dim]
# Replicate k/v heads when using GQA
k = repeat_kv(k, self.n_rep) # [bsz, 2*heads, seq, head_dim]
v = repeat_kv(v, self.n_rep) # [bsz, heads, seq, 2*head_dim]
# ---- Fused scaled dot-product attention (Flash / SDPA) -----------
#
# We avoid instantiating the full (seq×seq) score matrix. Instead we
# run the fused attention kernel twice (positive/negative queries) and
# combine the resulting context tensors with the λ weighting. This
# keeps everything in fp16/bf16 and leverages Blackwell’s Flash/SDPA
# path, giving ~30-80× speed-up vs. the naive implementation.
# ------------------------------------------------------------------
# Re-arrange the paired heads: [bsz, 2*H, S, D] → [bsz, H, 2, S, D]
q_pairs = q.view(bsz, 2, self.num_heads, seq_len, self.head_dim).permute(
0, 2, 1, 3, 4
)
k_pairs = k.view(bsz, 2, self.num_heads, seq_len, self.head_dim).permute(
0, 2, 1, 3, 4
)
q_pos, q_neg = q_pairs[:, :, 0], q_pairs[:, :, 1] # [bsz, H, S, D]
k_pos, k_neg = k_pairs[:, :, 0], k_pairs[:, :, 1]
# λ scalar (identical across heads / sequence)
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1)).type_as(q_pos)
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2)).type_as(q_pos)
lambda_full = lambda_1 - lambda_2 + self.lambda_init # scalar tensor
# --- Fused attention (only TWO SDPA calls) -------------------------
ctx_pos = F.scaled_dot_product_attention(
q_pos, k_pos, v, dropout_p=self.attn_dropout, is_causal=True
) # [bsz, H, S, 2*D]
ctx_neg = F.scaled_dot_product_attention(
q_neg, k_neg, v, dropout_p=self.attn_dropout, is_causal=True
) # [bsz, H, S, 2*D]
# DiffAttention combination
attn_out = ctx_pos - lambda_full * ctx_neg # [bsz, H, S, 2*D]
# LayerNorm & residual scaling
attn_out = self.subln(attn_out) * (1.0 - self.lambda_init)
# Collapse heads and project out
attn_out = attn_out.transpose(1, 2).reshape( # [bsz, seq, heads, 2*head_dim]
bsz, seq_len, self.embed_dim
)
# Apply output projection and dropout
out = self.out_proj(attn_out)
return self.dropout(out)
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