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from .multihead_custom_attention import MultiheadCustomAttention
class AdaLN(nn.Module):
"""Adaptive LayerNorm - signal-modulated linear transformation."""
def __init__(self, d_model):
super().__init__()
self.modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(d_model, 2 * d_model)
)
# Initialize as 0 (no scale/shift)
nn.init.constant_(self.modulation[-1].weight, 0)
nn.init.constant_(self.modulation[-1].bias, 0)
def forward(self, x, t):
"""
Args:
x: tensor (B, S, C)
t: tensor (B, C)
Returns:
tensor (B, S, C)
"""
scale, shift = self.modulation(t).chunk(2, dim=-1) # (B, C), (B, C)
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class DummyLayer(nn.Module):
"""Implement adaptive normalization wrappers, pre-/post-norm, pos embed."""
def __init__(self, pre_norm=False):
super().__init__()
self.pre_norm = pre_norm
def _norm(self, x, layer, normalize=True):
if normalize and layer is not None:
return layer(x)
return x
def with_pos_embed(self, tensor, pos=None):
return tensor if pos is None else tensor + pos
def _adaln(self, x, layer, ada_sgnl):
if layer is not None and ada_sgnl is not None:
return layer(x, ada_sgnl)
return x
def forward(self):
pass
class FFWLayer(DummyLayer):
"""Feed-forward layer for Transformers."""
def __init__(self, d_model, dim_fw=None, dropout=0.1, use_adaln=False,
pre_norm=False):
super().__init__(pre_norm=pre_norm)
# Initialize MLP and normalization
dim_fw = 4 * d_model if dim_fw is None else dim_fw
self.ffn = nn.Sequential(
nn.Linear(d_model, dim_fw),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(dim_fw, d_model),
nn.Dropout(dropout)
)
self.norm = nn.LayerNorm(d_model)
# Initialize those with Xavier
self._reset_parameters()
# Initialize adaptive normalization separately
self.adaln = None
if use_adaln:
self.adaln = AdaLN(d_model)
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, x, ada_sgnl=None):
"""
Args:
x: tensor (B, S, C)
ada_sgnl: tensor (B, C)
Returns:
tensor (B, S, C)
"""
# Normalize if pre-norm
x = self._norm(x, self.norm, self.pre_norm)
# Adaptive normalization if applicable
x = self._adaln(x, self.adaln, ada_sgnl)
# Main FFW
x = x + self.ffn(x)
# Normalize if post-norm
x = self._norm(x, self.norm, not self.pre_norm)
return x
class AttentionLayer(DummyLayer):
"""Attention layer, for self-/cross-attention."""
def __init__(self, d_model=256, dropout=0.1, n_heads=8, pre_norm=False,
rotary_pe=False, use_adaln=False, is_self=False):
"""Initialize layers, d_model is the encoder dimension."""
super().__init__(pre_norm=pre_norm)
self.rotary_pe = rotary_pe
self.is_self = is_self # self-attention, different normalization
# Normalization and attention layers
self.adaln = None
if use_adaln:
self.adaln = AdaLN(d_model)
self.attention = MultiheadCustomAttention(
d_model, n_heads, dropout=dropout
)
self.dropout = nn.Dropout(dropout)
self.norm_q = nn.LayerNorm(d_model)
self.norm_kv = None
if pre_norm:
self.norm_kv = self.norm_q if is_self else nn.LayerNorm(d_model)
def forward(self, seq1, seq2,
seq2_key_padding_mask=None,
seq1_pos=None, seq2_pos=None,
seq1_sem_pos=None, seq2_sem_pos=None,
ada_sgnl=None):
"""
Args:
seq1: tensor (B, S1, C)
seq1_pos: (B, S1, C) if not rotary, else (B, S1, C, 2)
seq1_sem_pos: (B, S1, C), semantic embedding
seq2: tensor (B, S2, C)
seq2_key_padding_mask: tensor (B, S2)
seq2_pos: (B, S2, C) if not rotary, else (B, S2, C, 2)
seq2_sem_pos: (B, S2, C), semantic embedding
ada_sgnl: tensor (B, C)
Returns:
tensor (B, S, C)
"""
# Normalize if pre-norm
q1 = self._norm(seq1, self.norm_q, self.pre_norm)
if self.is_self:
k2 = v2 = self._norm(seq2, self.norm_q, self.pre_norm)
else:
k2 = v2 = self._norm(seq2, self.norm_kv, self.pre_norm)
# Add positional embeddings if not rotary - rotary are handled later
if not self.rotary_pe:
q1 = self.with_pos_embed(seq1, seq1_pos)
k2 = self.with_pos_embed(seq2, seq2_pos)
# Add semantic embeddings, e.g. ids of each token
q1 = self.with_pos_embed(q1, seq1_sem_pos)
k2 = self.with_pos_embed(k2, seq2_sem_pos)
# Adaptive normalization if applicable
q1 = self._adaln(q1, self.adaln, ada_sgnl)
k2 = self._adaln(k2, self.adaln if self.is_self else None, ada_sgnl)
v2 = self._adaln(v2, self.adaln if self.is_self else None, ada_sgnl)
# Main attention code
seq1b = self.attention(
query=q1.transpose(0, 1),
key=k2.transpose(0, 1),
value=v2.transpose(0, 1),
attn_mask=None,
key_padding_mask=seq2_key_padding_mask, # (B, S2)
rotary_pe=(seq1_pos, seq2_pos) if self.rotary_pe else None
)[0].transpose(0, 1)
seq1 = seq1 + self.dropout(seq1b)
# Normalize if post-norm
seq1 = self._norm(seq1, self.norm_q, not self.pre_norm)
return seq1
class AttentionModule(nn.Module):
"""Stacking of attention and feed-forward layers."""
def __init__(self, num_layers, d_model=256, dim_fw=None,
dropout=0.1, n_heads=8, pre_norm=False,
rotary_pe=False, use_adaln=False, is_self=False):
super().__init__()
self.num_layers = num_layers
self.is_self = is_self
self.attn_layers = nn.ModuleList()
self.ffw_layers = nn.ModuleList()
for _ in range(num_layers):
self.attn_layers.append(AttentionLayer(
d_model, dropout, n_heads, pre_norm,
rotary_pe, use_adaln, is_self
))
self.ffw_layers.append(FFWLayer(
d_model, dim_fw, dropout, use_adaln, pre_norm=False
))
def forward(self, seq1, seq2,
seq2_key_padding_mask=None,
seq1_pos=None, seq2_pos=None,
seq1_sem_pos=None, seq2_sem_pos=None,
ada_sgnl=None):
"""
Args:
seq1: tensor (B, S1, C)
seq2: tensor (B, S2, C)
seq2_key_padding_mask: tensor (B, S2)
seq1_pos: (B, S1, C) if not rotary, else (B, S1, C, 2)
seq2_pos: (B, S2, C) if not rotary, else (B, S2, C, 2)
seq1_sem_pos: (B, S1, C), semantic embedding
seq2_sem_pos: (B, S2, C), semantic embedding
ada_sgnl: tensor (B, C)
Returns:
tensor (B, S1, C)
"""
output = []
for i in range(self.num_layers):
if self.is_self:
seq2 = seq1
seq1 = self.attn_layers[i](
seq1, seq2,
seq2_key_padding_mask,
seq1_pos, seq2_pos,
seq1_sem_pos, seq2_sem_pos,
ada_sgnl
)
seq1 = self.ffw_layers[i](seq1, ada_sgnl)
output.append(seq1)
return output
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