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Based on https://github.com/buoyancy99/diffusion-forcing/blob/main/algorithms/diffusion_forcing/models/attention.py
"""
from typing import Optional
from collections import namedtuple
import torch
from torch import nn
from torch.nn import functional as F
from einops import rearrange
from .rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
import numpy as np
class TemporalAxialAttention(nn.Module):
def __init__(
self,
dim: int,
heads: int,
dim_head: int,
reference_length: int,
rotary_emb: RotaryEmbedding,
is_causal: bool = True,
is_temporal_independent: bool = False,
use_domain_adapter = False
):
super().__init__()
self.inner_dim = dim_head * heads
self.heads = heads
self.head_dim = dim_head
self.inner_dim = dim_head * heads
self.to_qkv = nn.Linear(dim, self.inner_dim * 3, bias=False)
self.use_domain_adapter = use_domain_adapter
if self.use_domain_adapter:
lora_rank = 8
self.lora_A = nn.Linear(dim, lora_rank, bias=False)
self.lora_B = nn.Linear(lora_rank, self.inner_dim * 3, bias=False)
self.to_out = nn.Linear(self.inner_dim, dim)
self.rotary_emb = rotary_emb
self.is_causal = is_causal
self.is_temporal_independent = is_temporal_independent
self.reference_length = reference_length
def forward(self, x: torch.Tensor):
B, T, H, W, D = x.shape
# if T>=9:
# try:
# # x = torch.cat([x[:,:-1],x[:,16-T:17-T],x[:,-1:]], dim=1)
# x = torch.cat([x[:,16-T:17-T],x], dim=1)
# except:
# import pdb;pdb.set_trace()
# print("="*50)
# print(x.shape)
B, T, H, W, D = x.shape
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
if self.use_domain_adapter:
q_lora, k_lora, v_lora = self.lora_B(self.lora_A(x)).chunk(3, dim=-1)
q = q+q_lora
k = k+k_lora
v = v+v_lora
q = rearrange(q, "B T H W (h d) -> (B H W) h T d", h=self.heads)
k = rearrange(k, "B T H W (h d) -> (B H W) h T d", h=self.heads)
v = rearrange(v, "B T H W (h d) -> (B H W) h T d", h=self.heads)
q = self.rotary_emb.rotate_queries_or_keys(q, self.rotary_emb.freqs)
k = self.rotary_emb.rotate_queries_or_keys(k, self.rotary_emb.freqs)
q, k, v = map(lambda t: t.contiguous(), (q, k, v))
if self.is_temporal_independent:
attn_bias = torch.ones((T, T), dtype=q.dtype, device=q.device)
attn_bias = attn_bias.masked_fill(attn_bias == 1, float('-inf'))
attn_bias[range(T), range(T)] = 0
elif self.is_causal:
attn_bias = torch.triu(torch.ones((T, T), dtype=q.dtype, device=q.device), diagonal=1)
attn_bias = attn_bias.masked_fill(attn_bias == 1, float('-inf'))
attn_bias[(T-self.reference_length):] = float('-inf')
attn_bias[range(T), range(T)] = 0
else:
attn_bias = None
try:
x = F.scaled_dot_product_attention(query=q, key=k, value=v, attn_mask=attn_bias)
except:
import pdb;pdb.set_trace()
x = rearrange(x, "(B H W) h T d -> B T H W (h d)", B=B, H=H, W=W)
x = x.to(q.dtype)
# linear proj
x = self.to_out(x)
# if T>=10:
# try:
# # x = torch.cat([x[:,:-2],x[:,-1:]], dim=1)
# x = x[:,1:]
# except:
# import pdb;pdb.set_trace()
# print(x.shape)
return x
class SpatialAxialAttention(nn.Module):
def __init__(
self,
dim: int,
heads: int,
dim_head: int,
rotary_emb: RotaryEmbedding,
use_domain_adapter = False
):
super().__init__()
self.inner_dim = dim_head * heads
self.heads = heads
self.head_dim = dim_head
self.inner_dim = dim_head * heads
self.to_qkv = nn.Linear(dim, self.inner_dim * 3, bias=False)
self.use_domain_adapter = use_domain_adapter
if self.use_domain_adapter:
lora_rank = 8
self.lora_A = nn.Linear(dim, lora_rank, bias=False)
self.lora_B = nn.Linear(lora_rank, self.inner_dim * 3, bias=False)
self.to_out = nn.Linear(self.inner_dim, dim)
self.rotary_emb = rotary_emb
def forward(self, x: torch.Tensor):
B, T, H, W, D = x.shape
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
if self.use_domain_adapter:
q_lora, k_lora, v_lora = self.lora_B(self.lora_A(x)).chunk(3, dim=-1)
q = q+q_lora
k = k+k_lora
v = v+v_lora
q = rearrange(q, "B T H W (h d) -> (B T) h H W d", h=self.heads)
k = rearrange(k, "B T H W (h d) -> (B T) h H W d", h=self.heads)
v = rearrange(v, "B T H W (h d) -> (B T) h H W d", h=self.heads)
freqs = self.rotary_emb.get_axial_freqs(H, W)
q = apply_rotary_emb(freqs, q)
k = apply_rotary_emb(freqs, k)
# prepare for attn
q = rearrange(q, "(B T) h H W d -> (B T) h (H W) d", B=B, T=T, h=self.heads)
k = rearrange(k, "(B T) h H W d -> (B T) h (H W) d", B=B, T=T, h=self.heads)
v = rearrange(v, "(B T) h H W d -> (B T) h (H W) d", B=B, T=T, h=self.heads)
x = F.scaled_dot_product_attention(query=q, key=k, value=v, is_causal=False)
x = rearrange(x, "(B T) h (H W) d -> B T H W (h d)", B=B, H=H, W=W)
x = x.to(q.dtype)
# linear proj
x = self.to_out(x)
return x
class MemTemporalAxialAttention(nn.Module):
def __init__(
self,
dim: int,
heads: int,
dim_head: int,
rotary_emb: RotaryEmbedding,
is_causal: bool = True,
):
super().__init__()
self.inner_dim = dim_head * heads
self.heads = heads
self.head_dim = dim_head
self.inner_dim = dim_head * heads
self.to_qkv = nn.Linear(dim, self.inner_dim * 3, bias=False)
self.to_out = nn.Linear(self.inner_dim, dim)
self.rotary_emb = rotary_emb
self.is_causal = is_causal
self.reference_length = 3
def forward(self, x: torch.Tensor):
B, T, H, W, D = x.shape
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
q = rearrange(q, "B T H W (h d) -> (B H W) h T d", h=self.heads)
k = rearrange(k, "B T H W (h d) -> (B H W) h T d", h=self.heads)
v = rearrange(v, "B T H W (h d) -> (B H W) h T d", h=self.heads)
# q = self.rotary_emb.rotate_queries_or_keys(q, self.rotary_emb.freqs)
# k = self.rotary_emb.rotate_queries_or_keys(k, self.rotary_emb.freqs)
q, k, v = map(lambda t: t.contiguous(), (q, k, v))
# if T == 21000:
# # 手动计算缩放点积分数
# _, _, _, d_k = q.shape
# scores = torch.einsum("b h n d, b h m d -> b h n m", q, k) / (d_k ** 0.5) # Shape: (B, T_q, T_k)
# # 计算注意力图 (Attention Map)
# attention_map = F.softmax(scores, dim=-1) # Shape: (B, T_q, T_k)
# b_, h_, n_, m_ = attention_map.shape
# attention_map = attention_map.reshape(1, int(np.sqrt(b_/1)), int(np.sqrt(b_/1)), h_, n_, m_)
# attention_map = attention_map.mean(3)
# attn_bias = torch.zeros((T, T), dtype=q.dtype, device=q.device)
# T_origin = T - self.reference_length
# attn_bias[:T_origin, T_origin:] = 1
# attn_bias[range(T), range(T)] = 1
# attention_map = attention_map * attn_bias
# # print 注意力图
# import matplotlib.pyplot as plt
# fig, axes = plt.subplots(21000, 21000, figsize=(9, 9)) # 调整figsize以适配图像大小
# # 遍历3*3维度
# for i in range(21000):
# for j in range(21000):
# # 取出第(i, j)个子图像
# img = attention_map[0, :, :, i, j].cpu().numpy()
# axes[i, j].imshow(img, cmap='viridis') # 可以自定义cmap
# axes[i, j].axis('off') # 隐藏坐标轴
# # 调整子图间距
# plt.tight_layout()
# plt.savefig('attention_map.png')
# import pdb; pdb.set_trace()
# plt.close()
attn_bias = torch.zeros((T, T), dtype=q.dtype, device=q.device)
attn_bias = attn_bias.masked_fill(attn_bias == 0, float('-inf'))
T_origin = T - self.reference_length
attn_bias[:T_origin, T_origin:] = 0
attn_bias[range(T), range(T)] = 0
# if T==121000:
# import pdb;pdb.set_trace()
try:
x = F.scaled_dot_product_attention(query=q, key=k, value=v, attn_mask=attn_bias)
except:
import pdb;pdb.set_trace()
x = rearrange(x, "(B H W) h T d -> B T H W (h d)", B=B, H=H, W=W)
x = x.to(q.dtype)
# linear proj
x = self.to_out(x)
return x
class MemFullAttention(nn.Module):
def __init__(
self,
dim: int,
heads: int,
dim_head: int,
reference_length: int,
rotary_emb: RotaryEmbedding,
is_causal: bool = True
):
super().__init__()
self.inner_dim = dim_head * heads
self.heads = heads
self.head_dim = dim_head
self.inner_dim = dim_head * heads
self.to_qkv = nn.Linear(dim, self.inner_dim * 3, bias=False)
self.to_out = nn.Linear(self.inner_dim, dim)
self.rotary_emb = rotary_emb
self.is_causal = is_causal
self.reference_length = reference_length
self.store = None
def forward(self, x: torch.Tensor, relative_embedding=False,
extra_condition=None,
state_embed_only_on_qk=False,
reference_length=None):
B, T, H, W, D = x.shape
if state_embed_only_on_qk:
q, k, _ = self.to_qkv(x+extra_condition).chunk(3, dim=-1)
_, _, v = self.to_qkv(x).chunk(3, dim=-1)
else:
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
if relative_embedding:
length = reference_length+1
n_frames = T // length
x = x.reshape(B, n_frames, length, H, W, D)
x_list = []
for i in range(n_frames):
if i == n_frames-1:
q_i = rearrange(q[:, i*length:], "B T H W (h d) -> B h (T H W) d", h=self.heads)
k_i = rearrange(k[:, i*length+1:(i+1)*length], "B T H W (h d) -> B h (T H W) d", h=self.heads)
v_i = rearrange(v[:, i*length+1:(i+1)*length], "B T H W (h d) -> B h (T H W) d", h=self.heads)
else:
q_i = rearrange(q[:, i*length:i*length+1], "B T H W (h d) -> B h (T H W) d", h=self.heads)
k_i = rearrange(k[:, i*length+1:(i+1)*length], "B T H W (h d) -> B h (T H W) d", h=self.heads)
v_i = rearrange(v[:, i*length+1:(i+1)*length], "B T H W (h d) -> B h (T H W) d", h=self.heads)
q_i, k_i, v_i = map(lambda t: t.contiguous(), (q_i, k_i, v_i))
x_i = F.scaled_dot_product_attention(query=q_i, key=k_i, value=v_i)
x_i = rearrange(x_i, "B h (T H W) d -> B T H W (h d)", B=B, H=H, W=W)
x_i = x_i.to(q.dtype)
x_list.append(x_i)
x = torch.cat(x_list, dim=1)
else:
T_ = T - reference_length
q = rearrange(q, "B T H W (h d) -> B h (T H W) d", h=self.heads)
k = rearrange(k[:, T_:], "B T H W (h d) -> B h (T H W) d", h=self.heads)
v = rearrange(v[:, T_:], "B T H W (h d) -> B h (T H W) d", h=self.heads)
q, k, v = map(lambda t: t.contiguous(), (q, k, v))
x = F.scaled_dot_product_attention(query=q, key=k, value=v)
x = rearrange(x, "B h (T H W) d -> B T H W (h d)", B=B, H=H, W=W)
x = x.to(q.dtype)
# linear proj
x = self.to_out(x)
return x
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