import torch
from torch import nn
from typing import Optional
from dataclasses import dataclass
from diffusers.utils import BaseOutput
from diffusers.utils.import_utils import is_xformers_available
import torch.nn.functional as F
from einops import rearrange, repeat
import math
@dataclass
class Transformer3DModelOutput(BaseOutput):
sample: torch.FloatTensor
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
def exists(x):
return x is not None
class CrossAttention(nn.Module):
r"""
copy from diffuser 0.11.1
A cross attention layer.
Parameters:
query_dim (`int`): The number of channels in the query.
cross_attention_dim (`int`, *optional*):
The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
heads (`int`, *optional*, defaults to 8): The number of heads to use for multi-head attention.
dim_head (`int`, *optional*, defaults to 64): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
bias (`bool`, *optional*, defaults to False):
Set to `True` for the query, key, and value linear layers to contain a bias parameter.
"""
def __init__(
self,
query_dim: int,
cross_attention_dim: Optional[int] = None,
heads: int = 8,
dim_head: int = 64,
dropout: float = 0.0,
bias=False,
upcast_attention: bool = False,
upcast_softmax: bool = False,
added_kv_proj_dim: Optional[int] = None,
norm_num_groups: Optional[int] = None,
use_relative_position: bool = False,
):
super().__init__()
# print('num head', heads)
inner_dim = dim_head * heads
cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
self.upcast_attention = upcast_attention
self.upcast_softmax = upcast_softmax
self.scale = dim_head**-0.5
self.heads = heads
self.dim_head = dim_head
# for slice_size > 0 the attention score computation
# is split across the batch axis to save memory
# You can set slice_size with `set_attention_slice`
self.sliceable_head_dim = heads
self._slice_size = None
self._use_memory_efficient_attention_xformers = False # No use xformers for temporal attention
self.added_kv_proj_dim = added_kv_proj_dim
if norm_num_groups is not None:
self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True)
else:
self.group_norm = None
self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
if self.added_kv_proj_dim is not None:
self.add_k_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
self.add_v_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
self.to_out = nn.ModuleList([])
self.to_out.append(nn.Linear(inner_dim, query_dim))
self.to_out.append(nn.Dropout(dropout))
def reshape_heads_to_batch_dim(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
return tensor
def reshape_batch_dim_to_heads(self, tensor):
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor
def reshape_for_scores(self, tensor):
# split heads and dims
# tensor should be [b (h w)] f (d nd)
batch_size, seq_len, dim = tensor.shape
head_size = self.heads
tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3).contiguous()
return tensor
def same_batch_dim_to_heads(self, tensor):
batch_size, head_size, seq_len, dim = tensor.shape # [b (h w)] nd f d
tensor = tensor.reshape(batch_size, seq_len, dim * head_size)
return tensor
def set_attention_slice(self, slice_size):
if slice_size is not None and slice_size > self.sliceable_head_dim:
raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
self._slice_size = slice_size
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, use_image_num=None):
batch_size, sequence_length, _ = hidden_states.shape
encoder_hidden_states = encoder_hidden_states
if self.group_norm is not None:
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = self.to_q(hidden_states) # [b (h w)] f (nd * d)
# print('before reshpape query shape', query.shape)
dim = query.shape[-1]
query = self.reshape_heads_to_batch_dim(query) # [b (h w) nd] f d
# print('after reshape query shape', query.shape)
if self.added_kv_proj_dim is not None:
key = self.to_k(hidden_states)
value = self.to_v(hidden_states)
encoder_hidden_states_key_proj = self.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = self.add_v_proj(encoder_hidden_states)
key = self.reshape_heads_to_batch_dim(key)
value = self.reshape_heads_to_batch_dim(value)
encoder_hidden_states_key_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_key_proj)
encoder_hidden_states_value_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_value_proj)
key = torch.concat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.concat([encoder_hidden_states_value_proj, value], dim=1)
else:
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = self.to_k(encoder_hidden_states)
value = self.to_v(encoder_hidden_states)
key = self.reshape_heads_to_batch_dim(key)
value = self.reshape_heads_to_batch_dim(value)
if attention_mask is not None:
if attention_mask.shape[-1] != query.shape[1]:
target_length = query.shape[1]
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
# do not use xformers for temporal attention
# # attention, what we cannot get enough of
# if self._use_memory_efficient_attention_xformers:
# hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
# # Some versions of xformers return output in fp32, cast it back to the dtype of the input
# hidden_states = hidden_states.to(query.dtype)
# else:
# if self._slice_size is None or query.shape[0] // self._slice_size == 1:
# hidden_states = self._attention(query, key, value, attention_mask)
# else:
# hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
hidden_states = self._attention(query, key, value, attention_mask)
# linear proj
hidden_states = self.to_out[0](hidden_states)
# dropout
hidden_states = self.to_out[1](hidden_states)
return hidden_states
def _attention(self, query, key, value, attention_mask=None):
if self.upcast_attention:
query = query.float()
key = key.float()
attention_scores = torch.baddbmm(
torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
query,
key.transpose(-1, -2),
beta=0,
alpha=self.scale,
)
# print('query shape', query.shape)
# print('key shape', key.shape)
# print('value shape', value.shape)
if attention_mask is not None:
# print('attention_mask', attention_mask.shape)
# print('attention_scores', attention_scores.shape)
# exit()
attention_scores = attention_scores + attention_mask
if self.upcast_softmax:
attention_scores = attention_scores.float()
attention_probs = attention_scores.softmax(dim=-1)
# print(attention_probs.shape)
# cast back to the original dtype
attention_probs = attention_probs.to(value.dtype)
# print(attention_probs.shape)
# compute attention output
hidden_states = torch.bmm(attention_probs, value)
# print(hidden_states.shape)
# reshape hidden_states
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
# print(hidden_states.shape)
# exit()
return hidden_states
def _sliced_attention(self, query, key, value, sequence_length, dim, attention_mask):
batch_size_attention = query.shape[0]
hidden_states = torch.zeros(
(batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
)
slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
for i in range(hidden_states.shape[0] // slice_size):
start_idx = i * slice_size
end_idx = (i + 1) * slice_size
query_slice = query[start_idx:end_idx]
key_slice = key[start_idx:end_idx]
if self.upcast_attention:
query_slice = query_slice.float()
key_slice = key_slice.float()
attn_slice = torch.baddbmm(
torch.empty(slice_size, query.shape[1], key.shape[1], dtype=query_slice.dtype, device=query.device),
query_slice,
key_slice.transpose(-1, -2),
beta=0,
alpha=self.scale,
)
if attention_mask is not None:
attn_slice = attn_slice + attention_mask[start_idx:end_idx]
if self.upcast_softmax:
attn_slice = attn_slice.float()
attn_slice = attn_slice.softmax(dim=-1)
# cast back to the original dtype
attn_slice = attn_slice.to(value.dtype)
attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
hidden_states[start_idx:end_idx] = attn_slice
# reshape hidden_states
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
return hidden_states
def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
# TODO attention_mask
query = query.contiguous()
key = key.contiguous()
value = value.contiguous()
# print(query.shape)
# print(key.shape)
# print(value.shape)
hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
# print(hidden_states.shape)
hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
# print(hidden_states.shape)
# exit()
return hidden_states
class TemporalAttention(CrossAttention):
def __init__(self,
query_dim: int,
cross_attention_dim: Optional[int] = None,
heads: int = 8,
dim_head: int = 64,
dropout: float = 0.0,
bias=False,
upcast_attention: bool = False,
upcast_softmax: bool = False,
added_kv_proj_dim: Optional[int] = None,
norm_num_groups: Optional[int] = None,
rotary_emb=None):
super().__init__(query_dim, cross_attention_dim, heads, dim_head, dropout, bias, upcast_attention, upcast_softmax, added_kv_proj_dim, norm_num_groups)
# relative time positional embeddings
self.time_rel_pos_bias = RelativePositionBias(heads=heads, max_distance=32) # realistically will not be able to generate that many frames of video... yet
self.rotary_emb = rotary_emb
# self.rotary_emb = RotaryEmbedding(32)
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
time_rel_pos_bias = self.time_rel_pos_bias(hidden_states.shape[1], device=hidden_states.device)
batch_size, sequence_length, _ = hidden_states.shape
encoder_hidden_states = encoder_hidden_states
if self.group_norm is not None:
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = self.to_q(hidden_states) # [b (h w)] f (nd * d)
dim = query.shape[-1]
if self.added_kv_proj_dim is not None:
key = self.to_k(hidden_states)
value = self.to_v(hidden_states)
encoder_hidden_states_key_proj = self.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = self.add_v_proj(encoder_hidden_states)
key = self.reshape_heads_to_batch_dim(key)
value = self.reshape_heads_to_batch_dim(value)
encoder_hidden_states_key_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_key_proj)
encoder_hidden_states_value_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_value_proj)
key = torch.concat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.concat([encoder_hidden_states_value_proj, value], dim=1)
else:
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = self.to_k(encoder_hidden_states)
value = self.to_v(encoder_hidden_states)
if attention_mask is not None:
if attention_mask.shape[-1] != query.shape[1]:
target_length = query.shape[1]
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
# Do not use xformers for temporal attention
# attention, what we cannot get enough of
# if self._use_memory_efficient_attention_xformers:
# hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
# # Some versions of xformers return output in fp32, cast it back to the dtype of the input
# hidden_states = hidden_states.to(query.dtype)
# else:
# if self._slice_size is None or query.shape[0] // self._slice_size == 1:
# hidden_states = self._attention(query, key, value, attention_mask, time_rel_pos_bias)
# else:
# hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
hidden_states = self._attention(query, key, value, attention_mask, time_rel_pos_bias)
else:
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
# linear proj
hidden_states = self.to_out[0](hidden_states)
# dropout
hidden_states = self.to_out[1](hidden_states)
return hidden_states
def _attention(self, query, key, value, attention_mask=None, time_rel_pos_bias=None):
if self.upcast_attention:
query = query.float()
key = key.float()
# print('query shape', query.shape)
# print('key shape', key.shape)
# print('value shape', value.shape)
# reshape for adding time positional bais
query = self.scale * rearrange(query, 'b f (h d) -> b h f d', h=self.heads) # d: dim_head; n: heads
key = rearrange(key, 'b f (h d) -> b h f d', h=self.heads) # d: dim_head; n: heads
value = rearrange(value, 'b f (h d) -> b h f d', h=self.heads) # d: dim_head; n: heads
# print('query shape', query.shape)
# print('key shape', key.shape)
# print('value shape', value.shape)
# torch.baddbmm only accepte 3-D tensor
# https://runebook.dev/zh/docs/pytorch/generated/torch.baddbmm
# attention_scores = self.scale * torch.matmul(query, key.transpose(-1, -2))
if exists(self.rotary_emb):
query = self.rotary_emb.rotate_queries_or_keys(query)
key = self.rotary_emb.rotate_queries_or_keys(key)
attention_scores = torch.einsum('... h i d, ... h j d -> ... h i j', query, key)
# print('attention_scores shape', attention_scores.shape)
# print('time_rel_pos_bias shape', time_rel_pos_bias.shape)
# print('attention_mask shape', attention_mask.shape)
attention_scores = attention_scores + time_rel_pos_bias
# print(attention_scores.shape)
# bert from huggin face
# attention_scores = attention_scores / math.sqrt(self.dim_head)
# # Normalize the attention scores to probabilities.
# attention_probs = nn.functional.softmax(attention_scores, dim=-1)
if attention_mask is not None:
# add attention mask
attention_scores = attention_scores + attention_mask
# vdm
attention_scores = attention_scores - attention_scores.amax(dim = -1, keepdim = True).detach()
# # Mask out future positions (causal mask)
# mask = torch.triu(torch.ones(16, 16), diagonal=1).to(device=attention_scores.device, dtype=attention_scores.dtype) #
# attention_scores.masked_fill_(mask == 1, float('-inf'))
# # # disable the fisrt frame
# mask = torch.zeros(16, 16).to(device=attention_scores.device, dtype=attention_scores.dtype)
# mask[:, :1] = 1
# mask[0, 0] = 0
# attention_scores.masked_fill_(mask == 1, float('-inf'))
# only enable the first frame to internact with others frames
# mask = torch.zeros(16, 16).to(device=attention_scores.device, dtype=attention_scores.dtype)
# mask[:1, 1:] = 1
# attention_scores.masked_fill_(mask == 1, float('-inf'))
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# print(attention_probs[0][0])
# cast back to the original dtype
attention_probs = attention_probs.to(value.dtype)
# compute attention output
# hidden_states = torch.matmul(attention_probs, value)
hidden_states = torch.einsum('... h i j, ... h j d -> ... h i d', attention_probs, value)
# print(hidden_states.shape)
# hidden_states = self.same_batch_dim_to_heads(hidden_states)
hidden_states = rearrange(hidden_states, 'b h f d -> b f (h d)')
# print(hidden_states.shape)
# exit()
return hidden_states
class RelativePositionBias(nn.Module):
def __init__(
self,
heads=8,
num_buckets=32,
max_distance=128,
):
super().__init__()
self.num_buckets = num_buckets
self.max_distance = max_distance
self.relative_attention_bias = nn.Embedding(num_buckets, heads)
@staticmethod
def _relative_position_bucket(relative_position, num_buckets=32, max_distance=128):
ret = 0
n = -relative_position
num_buckets //= 2
ret += (n < 0).long() * num_buckets
n = torch.abs(n)
max_exact = num_buckets // 2
is_small = n < max_exact
val_if_large = max_exact + (
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
).long()
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
ret += torch.where(is_small, n, val_if_large)
return ret
def forward(self, n, device):
q_pos = torch.arange(n, dtype = torch.long, device = device)
k_pos = torch.arange(n, dtype = torch.long, device = device)
rel_pos = rearrange(k_pos, 'j -> 1 j') - rearrange(q_pos, 'i -> i 1')
rp_bucket = self._relative_position_bucket(rel_pos, num_buckets = self.num_buckets, max_distance = self.max_distance)
values = self.relative_attention_bias(rp_bucket)
return rearrange(values, 'i j h -> h i j') # num_heads, num_frames, num_frames
class PseudoCrossAttention(CrossAttention):
def forward(self, hidden_states, encoder_hidden_states=None, base_content=None, attention_mask=None, video_length=None):
batch_size, sequence_length, _ = hidden_states.shape
video_length = 17
if self.group_norm is not None:
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = self.to_q(hidden_states)
dim = query.shape[-1]
query = self.reshape_heads_to_batch_dim(query)
if self.added_kv_proj_dim is not None:
raise NotImplementedError
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = self.to_k(encoder_hidden_states)
value = self.to_v(encoder_hidden_states)
key = rearrange(key, "(b f) d c -> b f d c", f=video_length).contiguous()
key[:, 1:] = key[:, 1:] + key[:, :1]
key = rearrange(key, "b f d c -> (b f) d c").contiguous()
value = rearrange(value, "(b f) d c -> b f d c", f=video_length).contiguous()
value[:, 1:] = value[:, 1:] + value[:, :1]
value = rearrange(value, "b f d c -> (b f) d c").contiguous()
key = self.reshape_heads_to_batch_dim(key)
value = self.reshape_heads_to_batch_dim(value)
if attention_mask is not None:
if attention_mask.shape[-1] != query.shape[1]:
target_length = query.shape[1]
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
# attention, what we cannot get enough of
if self._use_memory_efficient_attention_xformers:
hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
hidden_states = hidden_states.to(query.dtype)
else:
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
hidden_states = self._attention(query, key, value, attention_mask)
else:
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
# hidden_states = rearrange(hidden_states, '(b f) d c -> b f d c', f=video_length).contiguous()
# hidden_states[:, :1, ...] = base_content
# hidden_states = rearrange(hidden_states, 'b f d c -> (b f) d c')
# linear proj
hidden_states = self.to_out[0](hidden_states)
# dropout
hidden_states = self.to_out[1](hidden_states)
return hidden_states