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from typing import Optional
import torch
import torch.nn as nn
from .utils import get_clone, get_clones, get_activation_fn
class SkipTransformerEncoder(nn.Module):
def __init__(self, encoder_layer: nn.Module, num_layers: int, norm: Optional[nn.Module] = None,
act: Optional[str] = None, is_controlnet: bool = False, is_moe: bool = False) -> None:
super().__init__()
self.d_model = encoder_layer.d_model
self.num_layers = num_layers
self.norm = norm
self.act = get_activation_fn(act)
self.is_controlnet = is_controlnet
self.is_moe = is_moe
assert num_layers % 2 == 1
num_block = (num_layers - 1) // 2
self.input_blocks = get_clones(encoder_layer, num_block)
self.middle_block = get_clone(encoder_layer)
self.output_blocks = get_clones(encoder_layer, num_block)
self.linear_blocks = get_clones(nn.Linear(2 * self.d_model, self.d_model), num_block)
self._reset_parameters()
def _reset_parameters(self) -> None:
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def maybe_controlnet_moe(
self, x: torch.Tensor, controlnet_residuals: Optional[list[torch.Tensor]] = None,
all_intermediates: Optional[tuple] = None, all_router_logits: Optional[tuple] = None
) -> tuple:
if self.is_moe:
all_router_logits += (x[1],)
x = x[0]
if controlnet_residuals is not None:
x = x + controlnet_residuals.pop()
if self.is_controlnet:
all_intermediates += (x,)
return x, controlnet_residuals, all_intermediates, all_router_logits
def forward(self, src: torch.Tensor,
mask: Optional[torch.Tensor] = None,
src_key_padding_mask: Optional[torch.Tensor] = None,
controlnet_residuals: Optional[list[torch.Tensor]] = None) -> tuple:
x = src
xs = []
all_intermediates = () if self.is_controlnet else None
all_router_logits = () if self.is_moe else None
if controlnet_residuals is not None:
controlnet_residuals.reverse()
for module in self.input_blocks:
x = module(x, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
x, controlnet_residuals, all_intermediates, all_router_logits = self.maybe_controlnet_moe(
x, controlnet_residuals, all_intermediates, all_router_logits)
xs.append(x)
x = self.middle_block(x, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
x, controlnet_residuals, all_intermediates, all_router_logits = self.maybe_controlnet_moe(
x, controlnet_residuals, all_intermediates, all_router_logits)
for (module, linear) in zip(self.output_blocks, self.linear_blocks):
x = torch.cat([x, xs.pop()], dim=-1)
x = linear(x)
x = module(x, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
x, controlnet_residuals, all_intermediates, all_router_logits = self.maybe_controlnet_moe(
x, controlnet_residuals, all_intermediates, all_router_logits)
if self.norm:
x = self.act(self.norm(x))
return x, all_intermediates, all_router_logits
class SkipTransformerDecoder(nn.Module):
def __init__(self, decoder_layer: nn.Module, num_layers: int, norm: Optional[nn.Module] = None,
act: Optional[str] = None, is_controlnet: bool = False, is_moe: bool = False) -> None:
super().__init__()
self.d_model = decoder_layer.d_model
self.num_layers = num_layers
self.norm = norm
self.act = get_activation_fn(act)
self.is_controlnet = is_controlnet
self.is_moe = is_moe
assert num_layers % 2 == 1
num_block = (num_layers - 1) // 2
self.input_blocks = get_clones(decoder_layer, num_block)
self.middle_block = get_clone(decoder_layer)
self.output_blocks = get_clones(decoder_layer, num_block)
self.linear_blocks = get_clones(nn.Linear(2 * self.d_model, self.d_model), num_block)
self._reset_parameters()
def _reset_parameters(self) -> None:
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def maybe_controlnet_moe(
self, x: torch.Tensor, controlnet_residuals: Optional[list[torch.Tensor]] = None,
all_intermediates: Optional[tuple] = None, all_router_logits: Optional[tuple] = None
) -> tuple:
if self.is_moe:
all_router_logits += (x[1],)
x = x[0]
if self.is_controlnet:
x = x + controlnet_residuals.pop()
all_intermediates += (x,)
return x, controlnet_residuals, all_intermediates, all_router_logits
def forward(self,
tgt: torch.Tensor,
memory: torch.Tensor,
tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None,
tgt_key_padding_mask: Optional[torch.Tensor] = None,
memory_key_padding_mask: Optional[torch.Tensor] = None,
controlnet_residuals: Optional[list[torch.Tensor]] = None) -> tuple:
x = tgt
xs = []
all_intermediates = () if self.is_controlnet else None
all_router_logits = () if self.is_moe else None
if controlnet_residuals is not None:
controlnet_residuals.reverse()
for module in self.input_blocks:
x = module(x, memory, tgt_mask=tgt_mask,
memory_mask=memory_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
x, controlnet_residuals, all_intermediates, all_router_logits = self.maybe_controlnet_moe(
x, controlnet_residuals, all_intermediates, all_router_logits)
xs.append(x)
x = self.middle_block(x, memory, tgt_mask=tgt_mask,
memory_mask=memory_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
x, controlnet_residuals, all_intermediates, all_router_logits = self.maybe_controlnet_moe(
x, controlnet_residuals, all_intermediates, all_router_logits)
for (module, linear) in zip(self.output_blocks, self.linear_blocks):
x = torch.cat([x, xs.pop()], dim=-1)
x = linear(x)
x = module(x, memory, tgt_mask=tgt_mask,
memory_mask=memory_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
x, controlnet_residuals, all_intermediates, all_router_logits = self.maybe_controlnet_moe(
x, controlnet_residuals, all_intermediates, all_router_logits)
if self.norm:
x = self.act(self.norm(x))
return x, all_intermediates, all_router_logits
class TransformerEncoder(nn.Module):
def __init__(self, encoder_layer: nn.Module, num_layers: int, norm: Optional[nn.Module] = None,
act: Optional[str] = None, return_intermediate: bool = False) -> None:
super().__init__()
self.layers = get_clones(encoder_layer, num_layers)
self.num_layers = num_layers
self.return_intermediate = return_intermediate
self.norm = norm
self.act = get_activation_fn(act)
def forward(self, src: torch.Tensor,
mask: Optional[torch.Tensor] = None,
src_key_padding_mask: Optional[torch.Tensor] = None,
controlnet_residuals: Optional[list[torch.Tensor]] = None) -> torch.Tensor:
output = src
intermediate = []
index = 0
for layer in self.layers:
output = layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
if controlnet_residuals is not None:
output = output + controlnet_residuals[index]
index += 1
if self.return_intermediate:
intermediate.append(output)
if self.norm:
output = self.act(self.norm(output))
if self.return_intermediate:
return torch.stack(intermediate)
return output
class TransformerDecoder(nn.Module):
def __init__(self, decoder_layer: nn.Module, num_layers: int, norm: Optional[nn.Module] = None,
act: Optional[str] = None, return_intermediate: bool = False) -> None:
super().__init__()
self.layers = get_clones(decoder_layer, num_layers)
self.num_layers = num_layers
self.return_intermediate = return_intermediate
self.norm = norm
self.act = get_activation_fn(act)
def forward(self,
tgt: torch.Tensor,
memory: torch.Tensor,
tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None,
tgt_key_padding_mask: Optional[torch.Tensor] = None,
memory_key_padding_mask: Optional[torch.Tensor] = None,
controlnet_residuals: Optional[list[torch.Tensor]] = None) -> torch.Tensor:
output = tgt
intermediate = []
index = 0
for layer in self.layers:
output = layer(output, memory, tgt_mask=tgt_mask,
memory_mask=memory_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
if controlnet_residuals is not None:
output = output + controlnet_residuals[index]
index += 1
if self.return_intermediate:
intermediate.append(output)
if self.norm:
output = self.act(self.norm(output))
if self.return_intermediate:
return torch.stack(intermediate)
return output
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1,
activation: str = "relu", normalize_before: bool = False, norm_eps: float = 1e-5) -> None:
super(TransformerEncoderLayer, self).__init__()
self.d_model = d_model
self.activation_name = activation
self.normalize_before = normalize_before
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward if activation != 'geglu' else dim_feedforward * 2)
self.activation = get_activation_fn(activation) if activation != 'geglu' else nn.GELU()
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model, eps=norm_eps)
self.norm2 = nn.LayerNorm(d_model, eps=norm_eps)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
def forward_post(self,
src: torch.Tensor,
src_mask: Optional[torch.Tensor] = None,
src_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
src2 = self.self_attn(src, src, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src = self.norm1(src)
if self.activation_name == 'geglu':
src2, gate = self.linear1(src).chunk(2, dim=-1)
src2 = src2 * self.activation(gate)
else:
src2 = self.activation(self.linear1(src))
src2 = self.linear2(self.dropout(src2))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
def forward_pre(self,
src: torch.Tensor,
src_mask: Optional[torch.Tensor] = None,
src_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
src2 = self.norm1(src)
src2 = self.self_attn(src2, src2, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src2 = self.norm2(src)
if self.activation_name == 'geglu':
src2, gate = self.linear1(src2).chunk(2, dim=-1)
src2 = src2 * self.activation(gate)
else:
src2 = self.activation(self.linear1(src2))
src2 = self.linear2(self.dropout(src2))
src = src + self.dropout2(src2)
return src
def forward(self,
src: torch.Tensor,
src_mask: Optional[torch.Tensor] = None,
src_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
if self.normalize_before:
return self.forward_pre(src, src_mask, src_key_padding_mask)
return self.forward_post(src, src_mask, src_key_padding_mask)
class TransformerDecoderLayer(nn.Module):
def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, dropout: float = 0.1,
activation: str = "relu", normalize_before: bool = False, norm_eps: float = 1e-5) -> None:
super(TransformerDecoderLayer, self).__init__()
self.d_model = d_model
self.activation_name = activation
self.normalize_before = normalize_before
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward if activation != 'geglu' else dim_feedforward * 2)
self.activation = get_activation_fn(activation) if activation != 'geglu' else nn.GELU()
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model, eps=norm_eps)
self.norm2 = nn.LayerNorm(d_model, eps=norm_eps)
self.norm3 = nn.LayerNorm(d_model, eps=norm_eps)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
def forward_post(self,
tgt: torch.Tensor,
memory: torch.Tensor,
tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None,
tgt_key_padding_mask: Optional[torch.Tensor] = None,
memory_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
tgt2 = self.self_attn(tgt, tgt, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(query=tgt, key=memory, value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
if self.activation_name == 'geglu':
tgt2, gate = self.linear1(tgt).chunk(2, dim=-1)
tgt2 = tgt2 * self.activation(gate)
else:
tgt2 = self.activation(self.linear1(tgt))
tgt2 = self.linear2(self.dropout(tgt2))
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
def forward_pre(self,
tgt: torch.Tensor,
memory: torch.Tensor,
tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None,
tgt_key_padding_mask: Optional[torch.Tensor] = None,
memory_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
tgt2 = self.norm1(tgt)
tgt2 = self.self_attn(tgt2, tgt2, value=tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout1(tgt2)
tgt2 = self.norm2(tgt)
tgt2 = self.multihead_attn(query=tgt2, key=memory, value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout2(tgt2)
tgt2 = self.norm3(tgt)
if self.activation_name == 'geglu':
tgt2, gate = self.linear1(tgt2).chunk(2, dim=-1)
tgt2 = tgt2 * self.activation(gate)
else:
tgt2 = self.activation(self.linear1(tgt2))
tgt2 = self.linear2(self.dropout(tgt2))
tgt = tgt + self.dropout3(tgt2)
return tgt
def forward(self,
tgt: torch.Tensor,
memory: torch.Tensor,
tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None,
tgt_key_padding_mask: Optional[torch.Tensor] = None,
memory_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
if self.normalize_before:
return self.forward_pre(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask)
return self.forward_post(tgt, memory, tgt_mask, memory_mask, tgt_key_padding_mask, memory_key_padding_mask)
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