996 lines
43 KiB
Python
996 lines
43 KiB
Python
import math
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from inspect import isfunction
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import torch
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import torch as th
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange, repeat
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try:
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILBLE = True
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except:
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XFORMERS_IS_AVAILBLE = False
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from ...lvdm.common import (
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checkpoint,
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exists,
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uniq,
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default,
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max_neg_value,
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init_
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)
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from ...lvdm.basics import (
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conv_nd,
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zero_module,
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normalization
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)
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out):
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super().__init__()
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self.proj = nn.Linear(dim_in, dim_out * 2)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * F.gelu(gate)
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = nn.Sequential(
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nn.Linear(dim, inner_dim),
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nn.GELU()
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) if not glu else GEGLU(dim, inner_dim)
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self.net = nn.Sequential(
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project_in,
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nn.Dropout(dropout),
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nn.Linear(inner_dim, dim_out)
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)
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def forward(self, x):
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return self.net(x)
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def Normalize(in_channels):
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return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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# ---------------------------------------------------------------------------------------------------
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class RelativePosition(nn.Module):
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""" https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """
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def __init__(self, num_units, max_relative_position):
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super().__init__()
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self.num_units = num_units
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self.max_relative_position = max_relative_position
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self.embeddings_table = nn.Parameter(th.Tensor(max_relative_position * 2 + 1, num_units))
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nn.init.xavier_uniform_(self.embeddings_table)
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def forward(self, length_q, length_k):
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device = self.embeddings_table.device
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range_vec_q = th.arange(length_q, device=device)
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range_vec_k = th.arange(length_k, device=device)
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distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
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distance_mat_clipped = th.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
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final_mat = distance_mat_clipped + self.max_relative_position
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# final_mat = th.LongTensor(final_mat).to(self.embeddings_table.device)
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# final_mat = th.tensor(final_mat, device=self.embeddings_table.device, dtype=torch.long)
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final_mat = final_mat.long()
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embeddings = self.embeddings_table[final_mat]
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return embeddings
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class TemporalCrossAttention(nn.Module):
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def __init__(self,
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query_dim,
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context_dim=None,
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heads=8,
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dim_head=64,
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dropout=0.,
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temporal_length=None, # For relative positional representation and image-video joint training.
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image_length=None, # For image-video joint training.
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use_relative_position=False, # whether use relative positional representation in temporal attention.
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img_video_joint_train=False, # For image-video joint training.
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use_tempoal_causal_attn=False,
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bidirectional_causal_attn=False,
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tempoal_attn_type=None,
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joint_train_mode="same_batch",
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**kwargs,
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):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.context_dim = context_dim
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.temporal_length = temporal_length
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self.use_relative_position = use_relative_position
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self.img_video_joint_train = img_video_joint_train
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self.bidirectional_causal_attn = bidirectional_causal_attn
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self.joint_train_mode = joint_train_mode
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assert(joint_train_mode in ["same_batch", "diff_batch"])
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self.tempoal_attn_type = tempoal_attn_type
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if bidirectional_causal_attn:
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assert use_tempoal_causal_attn
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if tempoal_attn_type:
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assert(tempoal_attn_type in ['sparse_causal', 'sparse_causal_first'])
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assert(not use_tempoal_causal_attn)
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assert(not (img_video_joint_train and (self.joint_train_mode == "same_batch")))
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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assert(not (img_video_joint_train and (self.joint_train_mode == "same_batch") and use_tempoal_causal_attn))
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if img_video_joint_train:
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if self.joint_train_mode == "same_batch":
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mask = torch.ones([1, temporal_length+image_length, temporal_length+image_length])
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# mask[:, image_length:, :] = 0
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# mask[:, :, image_length:] = 0
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mask[:, temporal_length:, :] = 0
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mask[:, :, temporal_length:] = 0
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self.mask = mask
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else:
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self.mask = None
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elif use_tempoal_causal_attn:
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# normal causal attn
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self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))
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elif tempoal_attn_type == 'sparse_causal':
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# all frames interact with only the `prev` & self frame
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mask1 = torch.tril(torch.ones([1, temporal_length, temporal_length])).bool() # true indicates keeping
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mask2 = torch.zeros([1, temporal_length, temporal_length]) # initialize to same shape with mask1
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mask2[:,2:temporal_length, :temporal_length-2] = torch.tril(torch.ones([1,temporal_length-2, temporal_length-2]))
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mask2=(1-mask2).bool() # false indicates masking
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self.mask = mask1 & mask2
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elif tempoal_attn_type == 'sparse_causal_first':
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# all frames interact with only the `first` & self frame
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mask1 = torch.tril(torch.ones([1, temporal_length, temporal_length])).bool() # true indicates keeping
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mask2 = torch.zeros([1, temporal_length, temporal_length])
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mask2[:,2:temporal_length, 1:temporal_length-1] = torch.tril(torch.ones([1,temporal_length-2, temporal_length-2]))
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mask2=(1-mask2).bool() # false indicates masking
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self.mask = mask1 & mask2
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else:
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self.mask = None
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if use_relative_position:
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assert(temporal_length is not None)
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self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
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self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim),
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nn.Dropout(dropout)
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)
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nn.init.constant_(self.to_q.weight, 0)
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nn.init.constant_(self.to_k.weight, 0)
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nn.init.constant_(self.to_v.weight, 0)
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nn.init.constant_(self.to_out[0].weight, 0)
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nn.init.constant_(self.to_out[0].bias, 0)
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def forward(self, x, context=None, mask=None):
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# if context is None:
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# print(f'[Temp Attn] x={x.shape},context=None')
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# else:
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# print(f'[Temp Attn] x={x.shape},context={context.shape}')
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nh = self.heads
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out = x
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q = self.to_q(out)
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# if context is not None:
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# print(f'temporal context 1 ={context.shape}')
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# print(f'x={x.shape}')
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context = default(context, x)
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# print(f'temporal context 2 ={context.shape}')
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k = self.to_k(context)
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v = self.to_v(context)
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# print(f'q ={q.shape},k={k.shape}')
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=nh), (q, k, v))
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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if self.use_relative_position:
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len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
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k2 = self.relative_position_k(len_q, len_k)
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sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check
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sim += sim2
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# print('mask',mask)
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if exists(self.mask):
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if mask is None:
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mask = self.mask.to(sim.device)
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else:
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mask = self.mask.to(sim.device).bool() & mask #.to(sim.device)
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else:
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mask = mask
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# if self.img_video_joint_train:
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# # process mask (make mask same shape with sim)
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# c, h, w = mask.shape
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# c, t, s = sim.shape
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# # assert(h == w and t == s),f"mask={mask.shape}, sim={sim.shape}, h={h}, w={w}, t={t}, s={s}"
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# if h > t:
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# mask = mask[:, :t, :]
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# elif h < t: # pad zeros to mask (no attention) only initial mask =1 area compute weights
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# mask_ = torch.zeros([c,t,w]).to(mask.device)
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# mask_[:, :h, :] = mask
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# mask = mask_
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# c, h, w = mask.shape
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# if w > s:
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# mask = mask[:, :, :s]
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# elif w < s: # pad zeros to mask
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# mask_ = torch.zeros([c,h,s]).to(mask.device)
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# mask_[:, :, :w] = mask
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# mask = mask_
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# max_neg_value = -torch.finfo(sim.dtype).max
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# sim = sim.float().masked_fill(mask == 0, max_neg_value)
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if mask is not None:
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max_neg_value = -1e9
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sim = sim + (1-mask.float()) * max_neg_value # 1=masking,0=no masking
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# print('sim after masking: ', sim)
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# if torch.isnan(sim).any() or torch.isinf(sim).any() or (not sim.any()):
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# print(f'sim [after masking], isnan={torch.isnan(sim).any()}, isinf={torch.isinf(sim).any()}, allzero={not sim.any()}')
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attn = sim.softmax(dim=-1)
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# print('attn after softmax: ', attn)
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# if torch.isnan(attn).any() or torch.isinf(attn).any() or (not attn.any()):
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# print(f'attn [after softmax], isnan={torch.isnan(attn).any()}, isinf={torch.isinf(attn).any()}, allzero={not attn.any()}')
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# attn = torch.where(torch.isnan(attn), torch.full_like(attn,0), attn)
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# if torch.isinf(attn.detach()).any():
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# import pdb;pdb.set_trace()
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# if torch.isnan(attn.detach()).any():
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# import pdb;pdb.set_trace()
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out = einsum('b i j, b j d -> b i d', attn, v)
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if self.bidirectional_causal_attn:
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mask_reverse = torch.triu(torch.ones([1, self.temporal_length, self.temporal_length], device=sim.device))
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sim_reverse = sim.float().masked_fill(mask_reverse == 0, max_neg_value)
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attn_reverse = sim_reverse.softmax(dim=-1)
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out_reverse = einsum('b i j, b j d -> b i d', attn_reverse, v)
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out += out_reverse
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if self.use_relative_position:
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v2 = self.relative_position_v(len_q, len_v)
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out2 = einsum('b t s, t s d -> b t d', attn, v2) # TODO check
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out += out2 # TODO check：先add还是先merge head？先计算rpr，on split head之后的数据，然后再merge。
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out = rearrange(out, '(b h) n d -> b n (h d)', h=nh) # merge head
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return self.to_out(out)
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class CrossAttention(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.,
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sa_shared_kv=False, shared_type='only_first', **kwargs,):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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self.sa_shared_kv = sa_shared_kv
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assert(shared_type in ['only_first', 'all_frames', 'first_and_prev', 'only_prev', 'full', 'causal', 'full_qkv'])
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self.shared_type = shared_type
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self.dim_head = dim_head
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
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self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
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self.to_out = nn.Sequential(
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nn.Linear(inner_dim, query_dim),
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nn.Dropout(dropout)
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)
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if XFORMERS_IS_AVAILBLE:
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self.forward = self.efficient_forward
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def forward(self, x, context=None, mask=None):
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h = self.heads
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b = x.shape[0]
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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if self.sa_shared_kv:
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if self.shared_type == 'only_first':
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k,v = map(lambda xx: rearrange(xx[0].unsqueeze(0), 'b n c -> (b n) c').unsqueeze(0).repeat(b,1,1),
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(k,v))
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else:
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raise NotImplementedError
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
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if exists(mask):
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# attention, what we cannot get enough of
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attn = sim.softmax(dim=-1)
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out = einsum('b i j, b j d -> b i d', attn, v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return self.to_out(out)
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def efficient_forward(self, x, context=None, mask=None):
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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v = self.to_v(context)
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b, _, _ = q.shape
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, t.shape[1], self.heads, self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * self.heads, t.shape[1], self.dim_head)
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.contiguous(),
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(q, k, v),
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)
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# actually compute the attention, what we cannot get enough of
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out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
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if exists(mask):
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raise NotImplementedError
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out = (
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out.unsqueeze(0)
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.reshape(b, self.heads, out.shape[1], self.dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b, out.shape[1], self.heads * self.dim_head)
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)
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return self.to_out(out)
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class VideoSpatialCrossAttention(CrossAttention):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0):
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super().__init__(query_dim, context_dim, heads, dim_head, dropout)
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def forward(self, x, context=None, mask=None):
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b, c, t, h, w = x.shape
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if context is not None:
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context = context.repeat(t, 1, 1)
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x = super.forward(spatial_attn_reshape(x), context=context) + x
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return spatial_attn_reshape_back(x,b,h)
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class BasicTransformerBlockST(nn.Module):
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def __init__(self,
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# Spatial Stuff
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dim,
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n_heads,
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d_head,
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dropout=0.,
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context_dim=None,
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gated_ff=True,
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checkpoint=True,
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# Temporal Stuff
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temporal_length=None,
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image_length=None,
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use_relative_position=True,
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img_video_joint_train=False,
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cross_attn_on_tempoal=False,
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temporal_crossattn_type="selfattn",
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order="stst",
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temporalcrossfirst=False,
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temporal_context_dim=None,
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split_stcontext=False,
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local_spatial_temporal_attn=False,
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window_size=2,
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**kwargs,
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):
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super().__init__()
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# Self attention
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self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, **kwargs,)
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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# cross attention if context is not None
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self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
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heads=n_heads, dim_head=d_head, dropout=dropout, **kwargs,)
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self.norm1 = nn.LayerNorm(dim)
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self.norm2 = nn.LayerNorm(dim)
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self.norm3 = nn.LayerNorm(dim)
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self.checkpoint = checkpoint
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self.order = order
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assert(self.order in ["stst", "sstt", "st_parallel"])
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self.temporalcrossfirst = temporalcrossfirst
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self.split_stcontext = split_stcontext
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self.local_spatial_temporal_attn = local_spatial_temporal_attn
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if self.local_spatial_temporal_attn:
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assert(self.order == 'stst')
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assert(self.order == 'stst')
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self.window_size = window_size
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if not split_stcontext:
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temporal_context_dim = context_dim
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# Temporal attention
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assert(temporal_crossattn_type in ["selfattn", "crossattn", "skip"])
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self.temporal_crossattn_type = temporal_crossattn_type
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self.attn1_tmp = TemporalCrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
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temporal_length=temporal_length,
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image_length=image_length,
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use_relative_position=use_relative_position,
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img_video_joint_train=img_video_joint_train,
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**kwargs,
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)
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self.attn2_tmp = TemporalCrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
|
||
# cross attn
|
||
context_dim=temporal_context_dim if temporal_crossattn_type == "crossattn" else None,
|
||
# temporal attn
|
||
temporal_length=temporal_length,
|
||
image_length=image_length,
|
||
use_relative_position=use_relative_position,
|
||
img_video_joint_train=img_video_joint_train,
|
||
**kwargs,
|
||
)
|
||
self.norm4 = nn.LayerNorm(dim)
|
||
self.norm5 = nn.LayerNorm(dim)
|
||
# self.norm1_tmp = nn.LayerNorm(dim)
|
||
# self.norm2_tmp = nn.LayerNorm(dim)
|
||
|
||
##############################################################################################################################################
|
||
def forward(self, x, context=None, temporal_context=None, no_temporal_attn=None, attn_mask=None, **kwargs):
|
||
# print(f'no_temporal_attn={no_temporal_attn}')
|
||
|
||
if not self.split_stcontext:
|
||
# st cross attention use the same context vector
|
||
temporal_context = context.detach().clone()
|
||
|
||
if context is None and temporal_context is None:
|
||
# self-attention models
|
||
if no_temporal_attn:
|
||
raise NotImplementedError
|
||
return checkpoint(self._forward_nocontext, (x), self.parameters(), self.checkpoint)
|
||
else:
|
||
# cross-attention models
|
||
if no_temporal_attn:
|
||
forward_func = self._forward_no_temporal_attn
|
||
else:
|
||
forward_func = self._forward
|
||
inputs = (x, context, temporal_context) if temporal_context is not None else (x, context)
|
||
return checkpoint(forward_func, inputs, self.parameters(), self.checkpoint)
|
||
# if attn_mask is not None:
|
||
# return checkpoint(self._forward, (x, context, temporal_context, attn_mask), self.parameters(), self.checkpoint)
|
||
# return checkpoint(self._forward, (x, context, temporal_context), self.parameters(), self.checkpoint)
|
||
|
||
def _forward(self, x, context=None, temporal_context=None, mask=None, no_temporal_attn=None, ):
|
||
assert(x.dim() == 5), f"x shape = {x.shape}"
|
||
b, c, t, h, w = x.shape
|
||
|
||
if self.order in ["stst", "sstt"]:
|
||
x = self._st_cross_attn(x, context, temporal_context=temporal_context, order=self.order, mask=mask,)#no_temporal_attn=no_temporal_attn,
|
||
elif self.order == "st_parallel":
|
||
x = self._st_cross_attn_parallel(x, context, temporal_context=temporal_context, order=self.order,)#no_temporal_attn=no_temporal_attn,
|
||
else:
|
||
raise NotImplementedError
|
||
|
||
x = self.ff(self.norm3(x)) + x
|
||
if (no_temporal_attn is None) or (not no_temporal_attn):
|
||
x = rearrange(x, '(b h w) t c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d
|
||
elif no_temporal_attn:
|
||
x = rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d
|
||
return x
|
||
|
||
def _forward_no_temporal_attn(self, x, context=None, temporal_context=None, ):
|
||
# temporary implementation :(
|
||
# because checkpoint does not support non-tensor inputs currently.
|
||
assert(x.dim() == 5), f"x shape = {x.shape}"
|
||
b, c, t, h, w = x.shape
|
||
|
||
if self.order in ["stst", "sstt"]:
|
||
# x = self._st_cross_attn(x, context, temporal_context=temporal_context, order=self.order, no_temporal_attn=True,)
|
||
# mask = torch.zeros([1, t, t], device=x.device).bool() if context is None else torch.zeros([1, context.shape[1], t], device=x.device).bool()
|
||
mask = torch.zeros([1, t, t], device=x.device).bool()
|
||
x = self._st_cross_attn(x, context, temporal_context=temporal_context, order=self.order, mask=mask,)
|
||
elif self.order == "st_parallel":
|
||
x = self._st_cross_attn_parallel(x, context, temporal_context=temporal_context, order=self.order, no_temporal_attn=True,)
|
||
else:
|
||
raise NotImplementedError
|
||
|
||
x = self.ff(self.norm3(x)) + x
|
||
x = rearrange(x, '(b h w) t c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d
|
||
# x = rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d
|
||
return x
|
||
|
||
def _forward_nocontext(self, x, no_temporal_attn=None):
|
||
assert(x.dim() == 5), f"x shape = {x.shape}"
|
||
b, c, t, h, w = x.shape
|
||
|
||
if self.order in ["stst", "sstt"]:
|
||
x = self._st_cross_attn(x, order=self.order, no_temporal_attn=no_temporal_attn)
|
||
elif self.order == "st_parallel":
|
||
x = self._st_cross_attn_parallel(x, order=self.order, no_temporal_attn=no_temporal_attn)
|
||
else:
|
||
raise NotImplementedError
|
||
|
||
x = self.ff(self.norm3(x)) + x
|
||
x = rearrange(x, '(b h w) t c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d
|
||
|
||
return x
|
||
##############################################################################################################################################
|
||
|
||
def _st_cross_attn(self, x, context=None, temporal_context=None, order="stst", mask=None): #no_temporal_attn=None,
|
||
b, c, t, h, w = x.shape
|
||
# print(f'[_st_cross_attn input] x={x.shape}, context={context.shape}')
|
||
|
||
if order == "stst":
|
||
# spatial self attention
|
||
x = rearrange(x, 'b c t h w -> (b t) (h w) c')
|
||
x = self.attn1(self.norm1(x)) + x
|
||
x = rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h)
|
||
|
||
# temporal self attention
|
||
# if (no_temporal_attn is None) or (not no_temporal_attn):
|
||
if self.local_spatial_temporal_attn:
|
||
x = local_spatial_temporal_attn_reshape(x,window_size=self.window_size)
|
||
else:
|
||
x = rearrange(x, 'b c t h w -> (b h w) t c')
|
||
x = self.attn1_tmp(self.norm4(x), mask=mask) + x
|
||
|
||
if self.local_spatial_temporal_attn:
|
||
x = local_spatial_temporal_attn_reshape_back(x, window_size=self.window_size,
|
||
b=b, h=h, w=w, t=t)
|
||
else:
|
||
x = rearrange(x, '(b h w) t c -> b c t h w', b=b,h=h,w=w) # 3d -> 5d
|
||
|
||
# spatial cross attention
|
||
x = rearrange(x, 'b c t h w -> (b t) (h w) c')
|
||
# context_ = context.repeat(t, 1, 1) if context is not None else None
|
||
# print(f'[before spatial cross] context={context.shape}')
|
||
if context is not None:
|
||
if context.shape[0] == t: # img captions no_temporal_attn or
|
||
context_ = context
|
||
else:
|
||
context_ = []
|
||
for i in range(context.shape[0]):
|
||
context_.append(context[i].unsqueeze(0).repeat(t, 1, 1))
|
||
context_ = torch.cat(context_,dim=0)
|
||
else:
|
||
context_ = None
|
||
# print(f'[before spatial cross] x={x.shape}, context_={context_.shape}')
|
||
x = self.attn2(self.norm2(x), context=context_) + x
|
||
|
||
# temporal cross attention
|
||
# if (no_temporal_attn is None) or (not no_temporal_attn):
|
||
x = rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h)
|
||
x = rearrange(x, 'b c t h w -> (b h w) t c')
|
||
if self.temporal_crossattn_type == "crossattn":
|
||
# tmporal cross attention
|
||
if temporal_context is not None:
|
||
# print(f'STATTN context={context.shape}, temporal_context={temporal_context.shape}')
|
||
temporal_context = torch.cat([context, temporal_context], dim=1) # blc
|
||
# print(f'STATTN after concat temporal_context={temporal_context.shape}')
|
||
temporal_context = temporal_context.repeat(h*w, 1,1)
|
||
# print(f'after repeat temporal_context={temporal_context.shape}')
|
||
else:
|
||
temporal_context = context[0:1,...].repeat(h*w, 1, 1)
|
||
# print(f'STATTN after concat x={x.shape}')
|
||
x = self.attn2_tmp(self.norm5(x), context=temporal_context, mask=mask) + x
|
||
elif self.temporal_crossattn_type == "selfattn":
|
||
# temporal self attention
|
||
x = self.attn2_tmp(self.norm5(x), context=None, mask=mask) + x
|
||
elif self.temporal_crossattn_type == "skip":
|
||
# no temporal cross and self attention
|
||
pass
|
||
else:
|
||
raise NotImplementedError
|
||
|
||
elif order == "sstt":
|
||
# spatial self attention
|
||
x = rearrange(x, 'b c t h w -> (b t) (h w) c')
|
||
x = self.attn1(self.norm1(x)) + x
|
||
|
||
# spatial cross attention
|
||
context_ = context.repeat(t, 1, 1) if context is not None else None
|
||
x = self.attn2(self.norm2(x), context=context_) + x
|
||
x = rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h)
|
||
|
||
if (no_temporal_attn is None) or (not no_temporal_attn):
|
||
if self.temporalcrossfirst:
|
||
# temporal cross attention
|
||
if self.temporal_crossattn_type == "crossattn":
|
||
# if temporal_context is not None:
|
||
temporal_context = context.repeat(h*w, 1, 1)
|
||
x = self.attn2_tmp(self.norm5(x), context=temporal_context, mask=mask) + x
|
||
elif self.temporal_crossattn_type == "selfattn":
|
||
x = self.attn2_tmp(self.norm5(x), context=None, mask=mask) + x
|
||
elif self.temporal_crossattn_type == "skip":
|
||
pass
|
||
else:
|
||
raise NotImplementedError
|
||
# temporal self attention
|
||
x = rearrange(x, 'b c t h w -> (b h w) t c')
|
||
x = self.attn1_tmp(self.norm4(x), mask=mask) + x
|
||
else:
|
||
# temporal self attention
|
||
x = rearrange(x, 'b c t h w -> (b h w) t c')
|
||
x = self.attn1_tmp(self.norm4(x), mask=mask) + x
|
||
# temporal cross attention
|
||
if self.temporal_crossattn_type == "crossattn":
|
||
if temporal_context is not None:
|
||
temporal_context = context.repeat(h*w, 1, 1)
|
||
x = self.attn2_tmp(self.norm5(x), context=temporal_context, mask=mask) + x
|
||
elif self.temporal_crossattn_type == "selfattn":
|
||
x = self.attn2_tmp(self.norm5(x), context=None, mask=mask) + x
|
||
elif self.temporal_crossattn_type == "skip":
|
||
pass
|
||
else:
|
||
raise NotImplementedError
|
||
else:
|
||
raise NotImplementedError
|
||
|
||
return x
|
||
|
||
def _st_cross_attn_parallel(self, x, context=None, temporal_context=None, order="sst", no_temporal_attn=None):
|
||
""" order: x -> Self Attn -> Cross Attn -> attn_s
|
||
x -> Temp Self Attn -> attn_t
|
||
x' = x + attn_s + attn_t
|
||
"""
|
||
if no_temporal_attn is not None:
|
||
raise NotImplementedError
|
||
|
||
B, C, T, H, W = x.shape
|
||
# spatial self attention
|
||
h = x
|
||
h = rearrange(h, 'b c t h w -> (b t) (h w) c')
|
||
h = self.attn1(self.norm1(h)) + h
|
||
# spatial cross
|
||
# context_ = context.repeat(T, 1, 1) if context is not None else None
|
||
if context is not None:
|
||
context_ = []
|
||
for i in range(context.shape[0]):
|
||
context_.append(context[i].unsqueeze(0).repeat(T, 1, 1))
|
||
context_ = torch.cat(context_,dim=0)
|
||
else:
|
||
context_ = None
|
||
|
||
h = self.attn2(self.norm2(h), context=context_) + h
|
||
h = rearrange(h, '(b t) (h w) c -> b c t h w', b=B, h=H)
|
||
|
||
# temporal self
|
||
h2 = x
|
||
h2 = rearrange(h2, 'b c t h w -> (b h w) t c')
|
||
h2 = self.attn1_tmp(self.norm4(h2))# + h2
|
||
h2 = rearrange(h2, '(b h w) t c -> b c t h w', b=B, h=H, w=W)
|
||
out = h + h2
|
||
return rearrange(out, 'b c t h w -> (b h w) t c')
|
||
|
||
##############################################################################################################################################
|
||
|
||
def spatial_attn_reshape(x):
|
||
return rearrange(x, 'b c t h w -> (b t) (h w) c')
|
||
def spatial_attn_reshape_back(x,b,h):
|
||
return rearrange(x, '(b t) (h w) c -> b c t h w', b=b,h=h)
|
||
def temporal_attn_reshape(x):
|
||
return rearrange(x, 'b c t h w -> (b h w) t c')
|
||
def temporal_attn_reshape_back(x, b,h,w):
|
||
return rearrange(x, '(b h w) t c -> b c t h w', b=b, h=h, w=w)
|
||
def local_spatial_temporal_attn_reshape(x, window_size):
|
||
B, C, T, H, W = x.shape
|
||
NH = H // window_size
|
||
NW = W // window_size
|
||
# x = x.view(B, C, T, NH, window_size, NW, window_size)
|
||
# tokens = x.permute(0, 1, 2, 3, 5, 4, 6).contiguous()
|
||
# tokens = tokens.view(-1, window_size, window_size, C)
|
||
x = rearrange(x, 'b c t (nh wh) (nw ww) -> b c t nh wh nw ww', nh=NH, nw=NW, wh=window_size, ww=window_size).contiguous() # # B, C, T, NH, NW, window_size, window_size
|
||
x = rearrange(x, 'b c t nh wh nw ww -> (b nh nw) (t wh ww) c') # (B, NH, NW) (T, window_size, window_size) C
|
||
return x
|
||
def local_spatial_temporal_attn_reshape_back(x, window_size, b, h, w, t):
|
||
B, L, C = x.shape
|
||
NH = h // window_size
|
||
NW = w // window_size
|
||
x = rearrange(x, '(b nh nw) (t wh ww) c -> b c t nh wh nw ww', b=b, nh=NH, nw=NW, t=t, wh=window_size, ww=window_size)
|
||
x = rearrange(x, 'b c t nh wh nw ww -> b c t (nh wh) (nw ww)')
|
||
return x
|
||
|
||
|
||
class SpatialTemporalTransformer(nn.Module):
|
||
"""
|
||
Transformer block for video-like data (5D tensor).
|
||
First, project the input (aka embedding) with NO reshape.
|
||
Then apply standard transformer action.
|
||
The 5D -> 3D reshape operation will be done in the specific attention module.
|
||
"""
|
||
def __init__(
|
||
self,
|
||
in_channels, n_heads, d_head,
|
||
depth=1, dropout=0.,
|
||
context_dim=None,
|
||
# Temporal stuff
|
||
temporal_length=None,
|
||
image_length=None,
|
||
use_relative_position=True,
|
||
img_video_joint_train=False,
|
||
cross_attn_on_tempoal=False,
|
||
temporal_crossattn_type=False,
|
||
order="stst",
|
||
temporalcrossfirst=False,
|
||
split_stcontext=False,
|
||
temporal_context_dim=None,
|
||
**kwargs,
|
||
):
|
||
super().__init__()
|
||
|
||
self.in_channels = in_channels
|
||
inner_dim = n_heads * d_head
|
||
|
||
self.norm = Normalize(in_channels)
|
||
self.proj_in = nn.Conv3d(in_channels,
|
||
inner_dim,
|
||
kernel_size=1,
|
||
stride=1,
|
||
padding=0)
|
||
|
||
self.transformer_blocks = nn.ModuleList(
|
||
[BasicTransformerBlockST(
|
||
inner_dim, n_heads, d_head, dropout=dropout,
|
||
# cross attn
|
||
context_dim=context_dim,
|
||
# temporal attn
|
||
temporal_length=temporal_length,
|
||
image_length=image_length,
|
||
use_relative_position=use_relative_position,
|
||
img_video_joint_train=img_video_joint_train,
|
||
temporal_crossattn_type=temporal_crossattn_type,
|
||
order=order,
|
||
temporalcrossfirst=temporalcrossfirst,
|
||
split_stcontext=split_stcontext,
|
||
temporal_context_dim=temporal_context_dim,
|
||
**kwargs
|
||
) for d in range(depth)]
|
||
)
|
||
|
||
self.proj_out = zero_module(nn.Conv3d(inner_dim,
|
||
in_channels,
|
||
kernel_size=1,
|
||
stride=1,
|
||
padding=0))
|
||
|
||
def forward(self, x, context=None, temporal_context=None, **kwargs):
|
||
# note: if no context is given, cross-attention defaults to self-attention
|
||
assert(x.dim() == 5), f"x shape = {x.shape}"
|
||
b, c, t, h, w = x.shape
|
||
x_in = x
|
||
|
||
x = self.norm(x)
|
||
x = self.proj_in(x)
|
||
|
||
for block in self.transformer_blocks:
|
||
x = block(x, context=context, temporal_context=temporal_context, **kwargs)
|
||
|
||
x = self.proj_out(x)
|
||
return x + x_in
|
||
|
||
# ---------------------------------------------------------------------------------------------------
|
||
|
||
class STAttentionBlock2(nn.Module):
|
||
def __init__(
|
||
self,
|
||
channels,
|
||
num_heads=1,
|
||
num_head_channels=-1,
|
||
use_checkpoint=False, # not used, only used in ResBlock
|
||
use_new_attention_order=False, # QKVAttention or QKVAttentionLegacy
|
||
temporal_length=16, # used in relative positional representation.
|
||
image_length=8, # used for image-video joint training.
|
||
use_relative_position=False, # whether use relative positional representation in temporal attention.
|
||
img_video_joint_train=False,
|
||
# norm_type="groupnorm",
|
||
attn_norm_type="group",
|
||
use_tempoal_causal_attn=False,
|
||
):
|
||
"""
|
||
version 1: guided_diffusion implemented version
|
||
version 2: remove args input argument
|
||
"""
|
||
super().__init__()
|
||
|
||
if num_head_channels == -1:
|
||
self.num_heads = num_heads
|
||
else:
|
||
assert (
|
||
channels % num_head_channels == 0
|
||
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
|
||
self.num_heads = channels // num_head_channels
|
||
self.use_checkpoint = use_checkpoint
|
||
|
||
self.temporal_length = temporal_length
|
||
self.image_length = image_length
|
||
self.use_relative_position = use_relative_position
|
||
self.img_video_joint_train = img_video_joint_train
|
||
self.attn_norm_type = attn_norm_type
|
||
assert(self.attn_norm_type in ["group", "no_norm"])
|
||
self.use_tempoal_causal_attn = use_tempoal_causal_attn
|
||
|
||
if self.attn_norm_type == "group":
|
||
self.norm_s = normalization(channels)
|
||
self.norm_t = normalization(channels)
|
||
|
||
self.qkv_s = conv_nd(1, channels, channels * 3, 1)
|
||
self.qkv_t = conv_nd(1, channels, channels * 3, 1)
|
||
|
||
if self.img_video_joint_train:
|
||
mask = th.ones([1, temporal_length+image_length, temporal_length+image_length])
|
||
mask[:, temporal_length:, :] = 0
|
||
mask[:, :, temporal_length:] = 0
|
||
self.register_buffer("mask", mask)
|
||
else:
|
||
self.mask = None
|
||
|
||
if use_new_attention_order:
|
||
# split qkv before split heads
|
||
self.attention_s = QKVAttention(self.num_heads)
|
||
self.attention_t = QKVAttention(self.num_heads)
|
||
else:
|
||
# split heads before split qkv
|
||
self.attention_s = QKVAttentionLegacy(self.num_heads)
|
||
self.attention_t = QKVAttentionLegacy(self.num_heads)
|
||
|
||
if use_relative_position:
|
||
self.relative_position_k = RelativePosition(num_units=channels // self.num_heads, max_relative_position=temporal_length)
|
||
self.relative_position_v = RelativePosition(num_units=channels // self.num_heads, max_relative_position=temporal_length)
|
||
|
||
self.proj_out_s = zero_module(conv_nd(1, channels, channels, 1)) # conv_dim, in_channels, out_channels, kernel_size
|
||
self.proj_out_t = zero_module(conv_nd(1, channels, channels, 1)) # conv_dim, in_channels, out_channels, kernel_size
|
||
|
||
def forward(self, x, mask=None):
|
||
b, c, t, h, w = x.shape
|
||
|
||
# spatial
|
||
out = rearrange(x, 'b c t h w -> (b t) c (h w)')
|
||
if self.attn_norm_type == "no_norm":
|
||
qkv = self.qkv_s(out)
|
||
else:
|
||
qkv = self.qkv_s(self.norm_s(out))
|
||
out = self.attention_s(qkv)
|
||
out = self.proj_out_s(out)
|
||
out = rearrange(out, '(b t) c (h w) -> b c t h w', b=b,h=h)
|
||
x += out
|
||
|
||
# temporal
|
||
out = rearrange(x, 'b c t h w -> (b h w) c t')
|
||
if self.attn_norm_type == "no_norm":
|
||
qkv = self.qkv_t(out)
|
||
else:
|
||
qkv = self.qkv_t(self.norm_t(out))
|
||
|
||
# relative positional embedding
|
||
if self.use_relative_position:
|
||
len_q = qkv.size()[-1]
|
||
len_k, len_v = len_q, len_q
|
||
k_rp = self.relative_position_k(len_q, len_k)
|
||
v_rp = self.relative_position_v(len_q, len_v) #[T,T,head_dim]
|
||
out = self.attention_t(qkv, rp=(k_rp, v_rp), mask=self.mask, use_tempoal_causal_attn=self.use_tempoal_causal_attn)
|
||
else:
|
||
out = self.attention_t(qkv, rp=None, mask=self.mask, use_tempoal_causal_attn=self.use_tempoal_causal_attn)
|
||
|
||
out = self.proj_out_t(out)
|
||
out = rearrange(out, '(b h w) c t -> b c t h w', b=b,h=h,w=w)
|
||
|
||
return (x + out)
|
||
|
||
# ---------------------------------------------------------------------------------------------------------------
|
||
|
||
class QKVAttentionLegacy(nn.Module):
|
||
"""
|
||
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
|
||
"""
|
||
|
||
def __init__(self, n_heads):
|
||
super().__init__()
|
||
self.n_heads = n_heads
|
||
|
||
def forward(self, qkv, rp=None, mask=None):
|
||
"""
|
||
Apply QKV attention.
|
||
|
||
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
|
||
:return: an [N x (H * C) x T] tensor after attention.
|
||
"""
|
||
if rp is not None or mask is not None:
|
||
raise NotImplementedError
|
||
bs, width, length = qkv.shape
|
||
assert width % (3 * self.n_heads) == 0
|
||
ch = width // (3 * self.n_heads)
|
||
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
|
||
scale = 1 / math.sqrt(math.sqrt(ch))
|
||
weight = th.einsum(
|
||
"bct,bcs->bts", q * scale, k * scale
|
||
) # More stable with f16 than dividing afterwards
|
||
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
|
||
a = th.einsum("bts,bcs->bct", weight, v)
|
||
return a.reshape(bs, -1, length)
|
||
|
||
@staticmethod
|
||
def count_flops(model, _x, y):
|
||
return count_flops_attn(model, _x, y)
|
||
|
||
# ---------------------------------------------------------------------------------------------------------------
|
||
|
||
class QKVAttention(nn.Module):
|
||
"""
|
||
A module which performs QKV attention and splits in a different order.
|
||
"""
|
||
|
||
def __init__(self, n_heads):
|
||
super().__init__()
|
||
self.n_heads = n_heads
|
||
|
||
def forward(self, qkv, rp=None, mask=None, use_tempoal_causal_attn=False):
|
||
"""
|
||
Apply QKV attention.
|
||
|
||
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
|
||
:return: an [N x (H * C) x T] tensor after attention.
|
||
"""
|
||
bs, width, length = qkv.shape
|
||
assert width % (3 * self.n_heads) == 0
|
||
ch = width // (3 * self.n_heads)
|
||
# print('qkv', qkv.size())
|
||
q, k, v = qkv.chunk(3, dim=1)
|
||
scale = 1 / math.sqrt(math.sqrt(ch))
|
||
# print('bs, self.n_heads, ch, length', bs, self.n_heads, ch, length)
|
||
|
||
weight = th.einsum(
|
||
"bct,bcs->bts",
|
||
(q * scale).view(bs * self.n_heads, ch, length),
|
||
(k * scale).view(bs * self.n_heads, ch, length),
|
||
) # More stable with f16 than dividing afterwards
|
||
# weight:[b,t,s] b=bs*n_heads*T
|
||
|
||
if rp is not None:
|
||
k_rp, v_rp = rp # [length, length, head_dim] [8, 8, 48]
|
||
weight2 = th.einsum(
|
||
'bct,tsc->bst',
|
||
(q * scale).view(bs * self.n_heads, ch, length),
|
||
k_rp
|
||
)
|
||
weight += weight2
|
||
|
||
if use_tempoal_causal_attn:
|
||
# weight = torch.tril(weight)
|
||
assert(mask is None), f'Not implemented for merging two masks!'
|
||
mask = torch.tril(torch.ones(weight.shape))
|
||
else:
|
||
if mask is not None: # only keep upper-left matrix
|
||
# process mask
|
||
c, t, _ = weight.shape
|
||
|
||
if mask.shape[-1] > t:
|
||
mask = mask[:, :t, :t]
|
||
elif mask.shape[-1] < t: # pad ones
|
||
mask_ = th.zeros([c,t,t]).to(mask.device)
|
||
t_ = mask.shape[-1]
|
||
mask_[:, :t_, :t_] = mask
|
||
mask = mask_
|
||
else:
|
||
assert(weight.shape[-1] == mask.shape[-1]), f'weight={weight.shape}, mask={mask.shape}'
|
||
|
||
if mask is not None:
|
||
INF = -1e8 #float('-inf')
|
||
weight = weight.float().masked_fill(mask == 0, INF)
|
||
|
||
weight = F.softmax(weight.float(), dim=-1).type(weight.dtype) #[256, 8, 8] [b, t, t] b=bs*n_heads*h*w,t=nframes
|
||
# weight = F.softmax(weight, dim=-1)#[256, 8, 8] [b, t, t] b=bs*n_heads*h*w,t=nframes
|
||
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) #[256, 48, 8] [b, head_dim, t]
|
||
|
||
if rp is not None:
|
||
a2 = th.einsum(
|
||
"bts,tsc->btc",
|
||
weight,
|
||
v_rp
|
||
).transpose(1,2) # btc->bct
|
||
a += a2
|
||
|
||
return a.reshape(bs, -1, length)
|
||
|
||
# ---------------------------------------------------------------------------------------------------------------
|
||
|
||
# ---------------------------------------------------------------------------------------------------------------
|