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import torch
import torch.nn as nn
from torch import Tensor
from typing import Optional, Dict, Tuple
import math
from .rotary import apply_rotary_emb
def lambda_init_fn(depth):
return 0.8 - 0.6 * math.exp(-0.3 * depth)
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""torch.repeat_interleave(x, dim=1, repeats=n_rep)"""
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 modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True, memory_efficient=False):
super().__init__()
self.dim = dim
self.eps = eps
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = nn.Parameter(torch.ones(dim))
else:
self.register_parameter('weight', None)
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
if self.weight is not None:
output = output * self.weight
return output
def extra_repr(self) -> str:
return f'dim={self.dim}, eps={self.eps}, elementwise_affine={self.elementwise_affine}'
class MultiheadDiffAttn_Origin(nn.Module):
def __init__(
self,
embed_dim,
depth, # current layer index
num_heads,
num_kv_heads=None,
):
super().__init__()
self.embed_dim = embed_dim
# arg num_heads set to half of baseline Transformer's num_heads
# for e.g., to compare with a baseline Transformer with 16 heads, pass in num_heads=8 for DIFF Transformer
self.num_heads = num_heads
# arg num_kv_heads set to half of baseline Transformer's num_kv_heads if use GQA
# for e.g., to compare with a baseline Transformer with 16 heads and 8 kv_heads,
# pass in num_heads=8, num_kv_heads=4 for DIFF Transformer
# if use MHA, pass in num_kv_heads=None
self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
self.n_rep = self.num_heads // self.num_kv_heads
self.head_dim = embed_dim // num_heads // 2
self.scaling = self.head_dim ** -0.5
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)
# depth means current layer index
self.lambda_init = lambda_init_fn(depth)
self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.subln = RMSNorm(2 * self.head_dim, eps=1e-5, elementwise_affine=True)
def forward(
self,
x,
rel_pos,
attn_mask=None,
):
bsz, tgt_len, embed_dim = x.size()
src_len = tgt_len
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
q = q.view(bsz, tgt_len, 2 * self.num_heads, self.head_dim)
k = k.view(bsz, src_len, 2 * self.num_kv_heads, self.head_dim)
v = v.view(bsz, src_len, self.num_kv_heads, 2 * self.head_dim)
q = apply_rotary_emb(q, *rel_pos, interleaved=True)
k = apply_rotary_emb(k, *rel_pos, interleaved=True)
offset = src_len - tgt_len
q = q.transpose(1, 2)
k = repeat_kv(k.transpose(1, 2), self.n_rep)
v = repeat_kv(v.transpose(1, 2), self.n_rep)
q *= self.scaling
attn_weights = torch.matmul(q, k.transpose(-1, -2))
attn_weights = torch.nan_to_num(attn_weights)
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).type_as(
attn_weights
)
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q)
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q)
lambda_full = lambda_1 - lambda_2 + self.lambda_init
attn_weights = attn_weights.view(bsz, self.num_heads, 2, tgt_len, src_len)
attn_weights = attn_weights[:, :, 0] - lambda_full * attn_weights[:, :, 1]
attn = torch.matmul(attn_weights, v)
attn = self.subln(attn)
attn = attn * (1 - self.lambda_init)
attn = attn.transpose(1, 2).reshape(bsz, tgt_len, self.num_heads * 2 * self.head_dim)
attn = self.out_proj(attn)
return attn
class MultiheadDiffAttn(nn.Module):
def __init__(
self,
embed_dim,
num_heads,
depth, # current layer index
cross=False,
):
super().__init__()
self.embed_dim = embed_dim
self.cross = cross
# The code `pdb.set_trace()` is setting a breakpoint in the code using the Python debugger
# (pdb). When the code is executed, it will pause the program's execution at that point and
# allow you to interactively debug the program.
# pdb.set_trace()
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.scaling = self.head_dim ** -0.5
self.q_proj_1 = nn.Linear(embed_dim, embed_dim, bias=False)
self.k_proj_1 = nn.Linear(embed_dim, embed_dim, bias=False)
self.q_proj_2 = nn.Linear(embed_dim, embed_dim, bias=False)
self.k_proj_2 = nn.Linear(embed_dim, embed_dim, bias=False)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False)
# depth means current layer index
self.lambda_init = lambda_init_fn(depth)
self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1))
self.subln = RMSNorm(self.head_dim, eps=1e-5, elementwise_affine=True)
def forward(
self,
noisy_y,
x,
):
bsz, tgt_len, embed_dim = x.size()
src_len = tgt_len
if self.cross:
q_1 = self.q_proj_1(noisy_y)
k_1 = self.k_proj_1(x)
q_2 = self.q_proj_2(noisy_y)
k_2 = self.k_proj_2(x)
else:
q_1 = self.q_proj_1(noisy_y)
k_1 = self.k_proj_1(noisy_y)
q_2 = self.q_proj_2(x)
k_2 = self.k_proj_2(x)
v = self.v_proj(noisy_y)
q_1 = q_1.view(bsz, tgt_len, self.num_heads, self.head_dim)
k_1 = k_1.view(bsz, src_len, self.num_heads, self.head_dim)
q_2 = q_2.view(bsz, tgt_len, self.num_heads, self.head_dim)
k_2 = k_2.view(bsz, src_len, self.num_heads, self.head_dim)
v = v.view(bsz, src_len, self.num_heads, self.head_dim)
q_1 = q_1.transpose(1, 2)
q_2 = q_2.transpose(1, 2)
k_1 = k_1.transpose(1, 2)
k_2 = k_2.transpose(1, 2)
q_1 *= self.scaling
q_2 *= self.scaling
attn_weights_1 = torch.matmul(q_1, k_1.transpose(-1, -2))
attn_weights_2 = torch.matmul(q_2, k_2.transpose(-1, -2))
with torch.amp.autocast('cuda', enabled=False):
attn_weights_1 = torch.nn.functional.softmax(attn_weights_1, dim=-1)
attn_weights_2 = torch.nn.functional.softmax(attn_weights_2, dim=-1)
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q_1)
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q_1)
lambda_full = lambda_1 - lambda_2 + self.lambda_init
# Fuse differential subtraction with value projection via distributive law:
# (A - λB) @ v = A@v - λ * (B@v)
# Avoids materializing the combined (B, H, N, N) attention matrix.
vt = v.transpose(1, 2)
attn = torch.matmul(attn_weights_1, vt) - lambda_full * torch.matmul(attn_weights_2, vt)
# attn: (bsz, num_heads, tgt_len, head_dim)
attn = self.subln(attn)
attn = attn * (1 - self.lambda_init)
attn = attn.transpose(1, 2).reshape(bsz, tgt_len, self.num_heads * self.head_dim)
attn = self.out_proj(attn)
return attn
class CrossAttentionTransformerLayer(nn.Module):
"""
One-layer Transformer block with ONLY cross-attention:
- Pre-norm -> Multihead cross-attention (Q=tgt, K/V=memory)
- Residual
- Pre-norm -> FeedForward (MLP)
- Residual
Shapes (batch_first=True):
tgt: (B, T_tgt, d_model)
memory: (B, T_mem, d_model)
"""
def __init__(
self,
d_model: int,
nhead: int,
mlp_ratio: int = 4,
dropout: float = 0.1,
activation: str = "gelu",
batch_first: bool = True
):
super().__init__()
self.batch_first = batch_first
# Cross attention: Q from tgt, K/V from memory
self.cross_attn = nn.MultiheadAttention(
embed_dim=d_model, num_heads=nhead, dropout=dropout, batch_first=batch_first
)
self.cross_attn_dropout = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model)
# Feedforward
self.linear1 = nn.Linear(d_model, int(mlp_ratio * d_model))
self.linear2 = nn.Linear(int(mlp_ratio * d_model), d_model)
self.ffn_dropout = nn.Dropout(dropout)
self.norm2 = nn.LayerNorm(d_model)
if activation == "relu":
self.activation = nn.ReLU()
elif activation == "gelu":
self.activation = nn.GELU()
else:
raise ValueError(f"Unsupported activation: {activation}")
def forward(
self,
tgt: torch.Tensor,
memory: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
memory_key_padding_mask: Optional[torch.Tensor] = None,
need_weights: bool = False,
average_attn_weights: bool = True,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""
Args:
tgt: (B, T_tgt, d_model)
memory: (B, T_mem, d_model)
attn_mask: optional mask applied to (tgt_len, mem_len) or (B*nhead, tgt_len, mem_len)
memory_key_padding_mask: (B, T_mem) with True for positions to mask
need_weights: return attention weights
average_attn_weights: average heads if True
Returns:
out: (B, T_tgt, d_model)
attn: (B, T_tgt, T_mem) or (B, nhead, T_tgt, T_mem) if need_weights else None
"""
# --- Cross Attention (pre-norm) ---
x = self.norm1(tgt)
attn_out, attn_w = self.cross_attn(
query=x,
key=memory,
value=memory,
attn_mask=attn_mask,
key_padding_mask=memory_key_padding_mask,
need_weights=need_weights,
average_attn_weights=average_attn_weights,
)
x = tgt + self.cross_attn_dropout(attn_out)
# --- Feedforward (pre-norm) ---
y = self.norm2(x)
y = self.linear2(self.ffn_dropout(self.activation(self.linear1(y))))
out = x + self.ffn_dropout(y)
return out, attn_w if need_weights else None
# class PerceiverDiffTransBlock(nn.Module):
# def __init__(self, d_in, d_latent, heads=8, mlp_ratio=4):
# super().__init__()
# self.ln_z1 = nn.LayerNorm(d_latent)
# self.q = nn.Linear(d_latent, d_latent)
# self.k = nn.Linear(d_in, d_latent)
# self.v = nn.Linear(d_in, d_latent)
# self.q2 = nn.Linear(d_latent, d_latent)
# self.k2 = nn.Linear(d_latent, d_latent)
# self.v2 = nn.Linear(d_latent, d_latent)
# self.cross = nn.MultiheadAttention(d_latent, heads, dropout=0.1, batch_first=True)
# self.ln_z2 = nn.LayerNorm(d_latent)
# self.self_attn = nn.MultiheadAttention(d_latent, heads, dropout=0.1, batch_first=True)
# self.ln_z3 = nn.LayerNorm(d_latent)
# self.mlp = nn.Sequential(
# nn.Linear(d_latent, int(mlp_ratio * d_latent)), nn.GELU(),
# nn.Dropout(0.1),
# nn.Linear(int(mlp_ratio * d_latent), d_latent),
# nn.Dropout(0.1),
# )
# self.adaLN_modulation = nn.Sequential(
# nn.SiLU(),
# nn.Linear(d_latent, 6 * d_latent, bias=True)
# )
# def forward(self, z, x, t):
# shift_self, scale_self, gate_self, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(t).chunk(6, dim=1)
# z = z + self.cross(self.q(self.ln_z1(z)),
# self.k(x), self.v(x))[0]
# z = modulate(self.ln_z2(z), shift_self, scale_self)
# z = z + gate_self.unsqueeze(1) * self.self_attn(self.q2(z), self.k2(z), self.v2(z))[0]
# z = z + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.ln_z3(z), shift_mlp, scale_mlp))
# return z |