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import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Dict, Any
from ..builder import ATTENTIONS
from ..utils.stylization_block import StylizationBlock
@ATTENTIONS.register_module()
class EfficientSelfAttention(nn.Module):
"""
Efficient Self-Attention mechanism for motion generation tasks.
Args:
latent_dim (int): Dimension of the latent space.
num_heads (int): Number of attention heads.
dropout (float): Dropout probability.
time_embed_dim (Optional[int]): Dimension of the time embedding (optional).
"""
def __init__(self,
latent_dim: int,
num_heads: int,
dropout: float,
time_embed_dim: Optional[int] = None):
super().__init__()
self.num_heads = num_heads
self.norm = nn.LayerNorm(latent_dim)
self.query = nn.Linear(latent_dim, latent_dim)
self.key = nn.Linear(latent_dim, latent_dim)
self.value = nn.Linear(latent_dim, latent_dim)
self.dropout = nn.Dropout(dropout)
self.time_embed_dim = time_embed_dim
if time_embed_dim is not None:
self.proj_out = StylizationBlock(latent_dim, time_embed_dim, dropout)
def forward(self,
x: torch.Tensor,
src_mask: Optional[torch.Tensor] = None,
emb: Optional[torch.Tensor] = None,
**kwargs: Dict[str, Any]) -> torch.Tensor:
"""
Forward pass of Efficient Self-Attention.
Args:
x (torch.Tensor): Input tensor of shape [B, T, D].
src_mask (Optional[torch.Tensor]): Source mask of shape [B, T] (optional).
emb (Optional[torch.Tensor]): Time embedding tensor of shape [B, D] (optional).
Returns:
torch.Tensor: Output of the self-attention module.
"""
B, T, D = x.shape
H = self.num_heads
query = self.query(self.norm(x))
if src_mask is None:
key = self.key(self.norm(x))
else:
key = self.key(self.norm(x)) + (1 - src_mask) * -1000000
query = F.softmax(query.view(B, T, H, -1), dim=-1)
key = F.softmax(key.view(B, T, H, -1), dim=1)
if src_mask is None:
value = self.value(self.norm(x)).view(B, T, H, -1)
else:
value = (self.value(self.norm(x)) * src_mask).view(B, T, H, -1)
attention = torch.einsum('bnhd,bnhl->bhdl', key, value)
y = torch.einsum('bnhd,bhdl->bnhl', query, attention).reshape(B, T, D)
if self.time_embed_dim is None:
y = x + y
else:
y = x + self.proj_out(y, emb)
return y
@ATTENTIONS.register_module()
class EfficientCrossAttention(nn.Module):
"""
Efficient Cross-Attention mechanism, attending to text and motion inputs.
Args:
latent_dim (int): Dimension of the latent space for motion input.
text_latent_dim (int): Dimension of the latent space for text input.
num_heads (int): Number of attention heads.
dropout (float): Dropout probability.
time_embed_dim (int): Dimension of the time embedding.
"""
def __init__(self,
latent_dim: int,
text_latent_dim: int,
num_heads: int,
dropout: float,
time_embed_dim: Optional[int] = None):
super().__init__()
self.num_heads = num_heads
self.norm = nn.LayerNorm(latent_dim)
self.text_norm = nn.LayerNorm(text_latent_dim)
self.query = nn.Linear(latent_dim, latent_dim)
self.key = nn.Linear(text_latent_dim, latent_dim)
self.value = nn.Linear(text_latent_dim, latent_dim)
self.dropout = nn.Dropout(dropout)
if time_embed_dim is not None:
self.proj_out = StylizationBlock(latent_dim, time_embed_dim, dropout)
else:
self.proj_out = None
def forward(self,
x: torch.Tensor,
xf: torch.Tensor,
emb: Optional[torch.Tensor] = None,
cond_type: Optional[torch.Tensor] = None,
**kwargs: Dict[str, Any]) -> torch.Tensor:
"""
Forward pass of Efficient Cross-Attention.
Args:
x (torch.Tensor): Input motion tensor of shape [B, T, D].
xf (torch.Tensor): Input text tensor of shape [B, N, L].
emb (torch.Tensor): Time embedding tensor of shape [B, D].
cond_type (Optional[torch.Tensor]): Conditioning type tensor (optional).
Returns:
torch.Tensor: Output of the cross-attention module.
"""
B, T, D = x.shape
N = xf.shape[1]
H = self.num_heads
query = self.query(self.norm(x))
key = self.key(self.text_norm(xf))
query = F.softmax(query.view(B, T, H, -1), dim=-1)
if cond_type is None:
key = F.softmax(key.view(B, N, H, -1), dim=1)
value = self.value(self.text_norm(xf)).view(B, N, H, -1)
else:
text_cond_type = ((cond_type % 10) > 0).float().view(B, 1, 1)
text_cond_type = text_cond_type.repeat(1, xf.shape[1], 1)
key = key + (1 - text_cond_type) * -1000000
key = F.softmax(key.view(B, N, H, -1), dim=1)
value = self.value(self.text_norm(xf) * text_cond_type).view(B, N, H, -1)
attention = torch.einsum('bnhd,bnhl->bhdl', key, value)
y = torch.einsum('bnhd,bhdl->bnhl', query, attention).reshape(B, T, D)
if self.proj_out is not None:
y = x + self.proj_out(y, emb)
else:
y = x + y
return y
@ATTENTIONS.register_module()
class EfficientMixedAttention(nn.Module):
"""
Efficient Mixed Attention, combining text and motion attention.
Args:
latent_dim (int): Dimension of the latent space for motion input.
text_latent_dim (int): Dimension of the latent space for text input.
num_heads (int): Number of attention heads.
dropout (float): Dropout probability.
time_embed_dim (int): Dimension of the time embedding.
"""
def __init__(self,
latent_dim: int,
text_latent_dim: int,
num_heads: int,
dropout: float,
time_embed_dim: Optional[int] = None):
super().__init__()
self.num_heads = num_heads
self.norm = nn.LayerNorm(latent_dim)
self.text_norm = nn.LayerNorm(text_latent_dim)
self.query = nn.Linear(latent_dim, latent_dim)
self.key_text = nn.Linear(text_latent_dim, latent_dim)
self.value_text = nn.Linear(text_latent_dim, latent_dim)
self.key_motion = nn.Linear(latent_dim, latent_dim)
self.value_motion = nn.Linear(latent_dim, latent_dim)
self.dropout = nn.Dropout(dropout)
if time_embed_dim is not None:
self.proj_out = StylizationBlock(latent_dim, time_embed_dim, dropout)
else:
self.proj_out = None
def forward(self,
x: torch.Tensor,
xf: torch.Tensor,
src_mask: torch.Tensor,
emb: Optional[torch.Tensor] = None,
cond_type: Optional[torch.Tensor] = None,
**kwargs: Dict[str, Any]) -> torch.Tensor:
"""
Forward pass of Efficient Mixed Attention.
Args:
x (torch.Tensor): Input motion tensor of shape [B, T, D].
xf (torch.Tensor): Input text tensor of shape [B, N, L].
emb (torch.Tensor): Time embedding tensor of shape [B, D].
src_mask (torch.Tensor): Source mask tensor of shape [B, T].
cond_type (torch.Tensor): Conditioning type tensor.
Returns:
torch.Tensor: Output of the mixed attention module.
"""
B, T, D = x.shape
N = xf.shape[1] + x.shape[1]
H = self.num_heads
query = self.query(self.norm(x)).view(B, T, H, -1)
text_cond_type = (cond_type % 10 > 0).float()
src_mask = src_mask.view(B, T, 1)
key_text = self.key_text(self.text_norm(xf))
key_text = key_text + (1 - text_cond_type) * -1000000
key_motion = self.key_motion(self.norm(x)) + (1 - src_mask) * -1000000
key = torch.cat((key_text, key_motion), dim=1)
query = F.softmax(query.view(B, T, H, -1), dim=-1)
key = self.dropout(F.softmax(key.view(B, N, H, -1), dim=1))
value = torch.cat(
(self.value_text(self.text_norm(xf)) * text_cond_type, self.value_motion(self.norm(x)) * src_mask),
dim=1
).view(B, N, H, -1)
attention = torch.einsum('bnhd,bnhl->bhdl', key, value)
y = torch.einsum('bnhd,bhdl->bnhl', query, attention).reshape(B, T, D)
if self.proj_out is not None:
y = x + self.proj_out(y, emb)
else:
y = x + y
return y
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