103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
|
#original code from https://github.com/genmoai/models under apache 2.0 license
|
||
|
|
#adapted to ComfyUI
|
||
|
|
|
||
|
|
from typing import Optional, Tuple
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
|
||
|
|
def modulate(x, shift, scale):
|
||
|
|
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
||
|
|
|
||
|
|
|
||
|
|
def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor:
|
||
|
|
"""
|
||
|
|
Pool tokens in x using mask.
|
||
|
|
|
||
|
|
NOTE: We assume x does not require gradients.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: (B, L, D) tensor of tokens.
|
||
|
|
mask: (B, L) boolean tensor indicating which tokens are not padding.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
pooled: (B, D) tensor of pooled tokens.
|
||
|
|
"""
|
||
|
|
assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens.
|
||
|
|
assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens.
|
||
|
|
mask = mask[:, :, None].to(dtype=x.dtype)
|
||
|
|
mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1)
|
||
|
|
pooled = (x * mask).sum(dim=1, keepdim=keepdim)
|
||
|
|
return pooled
|
||
|
|
|
||
|
|
|
||
|
|
class AttentionPool(nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
embed_dim: int,
|
||
|
|
num_heads: int,
|
||
|
|
output_dim: int = None,
|
||
|
|
device: Optional[torch.device] = None,
|
||
|
|
dtype=None,
|
||
|
|
operations=None,
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
spatial_dim (int): Number of tokens in sequence length.
|
||
|
|
embed_dim (int): Dimensionality of input tokens.
|
||
|
|
num_heads (int): Number of attention heads.
|
||
|
|
output_dim (int): Dimensionality of output tokens. Defaults to embed_dim.
|
||
|
|
"""
|
||
|
|
super().__init__()
|
||
|
|
self.num_heads = num_heads
|
||
|
|
self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype)
|
||
|
|
self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
|
||
|
|
self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype)
|
||
|
|
|
||
|
|
def forward(self, x, mask):
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
x (torch.Tensor): (B, L, D) tensor of input tokens.
|
||
|
|
mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding.
|
||
|
|
|
||
|
|
NOTE: We assume x does not require gradients.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
x (torch.Tensor): (B, D) tensor of pooled tokens.
|
||
|
|
"""
|
||
|
|
D = x.size(2)
|
||
|
|
|
||
|
|
# Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L).
|
||
|
|
attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L).
|
||
|
|
attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L).
|
||
|
|
|
||
|
|
# Average non-padding token features. These will be used as the query.
|
||
|
|
x_pool = pool_tokens(x, mask, keepdim=True) # (B, 1, D)
|
||
|
|
|
||
|
|
# Concat pooled features to input sequence.
|
||
|
|
x = torch.cat([x_pool, x], dim=1) # (B, L+1, D)
|
||
|
|
|
||
|
|
# Compute queries, keys, values. Only the mean token is used to create a query.
|
||
|
|
kv = self.to_kv(x) # (B, L+1, 2 * D)
|
||
|
|
q = self.to_q(x[:, 0]) # (B, D)
|
||
|
|
|
||
|
|
# Extract heads.
|
||
|
|
head_dim = D // self.num_heads
|
||
|
|
kv = kv.unflatten(2, (2, self.num_heads, head_dim)) # (B, 1+L, 2, H, head_dim)
|
||
|
|
kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim)
|
||
|
|
k, v = kv.unbind(2) # (B, H, 1+L, head_dim)
|
||
|
|
q = q.unflatten(1, (self.num_heads, head_dim)) # (B, H, head_dim)
|
||
|
|
q = q.unsqueeze(2) # (B, H, 1, head_dim)
|
||
|
|
|
||
|
|
# Compute attention.
|
||
|
|
x = F.scaled_dot_product_attention(
|
||
|
|
q, k, v, attn_mask=attn_mask, dropout_p=0.0
|
||
|
|
) # (B, H, 1, head_dim)
|
||
|
|
|
||
|
|
# Concatenate heads and run output.
|
||
|
|
x = x.squeeze(2).flatten(1, 2) # (B, D = H * head_dim)
|
||
|
|
x = self.to_out(x)
|
||
|
|
return x
|