import torch
from typing import Tuple, Callable
from diffusers.models.attention_processor import XFormersAttnProcessor, Attention
import xformers, xformers.ops
from typing import Optional
import math
import torch.nn.functional as F
from diffusers.utils import USE_PEFT_BACKEND
from diffusers.utils.import_utils import is_xformers_available
if is_xformers_available():
import xformers
import xformers.ops
xformers_is_available = True
else:
xformers_is_available = False
if hasattr(F, "scaled_dot_product_attention"):
torch2_is_available = True
else:
torch2_is_available = False
def init_generator(device: torch.device, fallback: torch.Generator = None):
"""
Forks the current default random generator given device.
"""
if device.type == "cpu":
return torch.Generator(device="cpu").set_state(torch.get_rng_state())
elif device.type == "cuda":
return torch.Generator(device=device).set_state(torch.cuda.get_rng_state())
else:
if fallback is None:
return init_generator(torch.device("cpu"))
else:
return fallback
def do_nothing(x: torch.Tensor, mode: str = None):
return x
def mps_gather_workaround(input, dim, index):
if input.shape[-1] == 1:
return torch.gather(
input.unsqueeze(-1),
dim - 1 if dim < 0 else dim,
index.unsqueeze(-1)
).squeeze(-1)
else:
return torch.gather(input, dim, index)
def up_or_downsample(item, cur_w, cur_h, new_w, new_h, method):
batch_size = item.shape[0]
item = item.reshape(batch_size, cur_h, cur_w, -1)
item = item.permute(0, 3, 1, 2)
df = cur_h // new_h
if method in "max_pool":
item = F.max_pool2d(item, kernel_size=df, stride=df, padding=0)
elif method in "avg_pool":
item = F.avg_pool2d(item, kernel_size=df, stride=df, padding=0)
else:
item = F.interpolate(item, size=(new_h, new_w), mode=method)
item = item.permute(0, 2, 3, 1)
item = item.reshape(batch_size, new_h * new_w, -1)
return item
def compute_merge(x: torch.Tensor, tome_info):
original_h, original_w = tome_info["size"]
original_tokens = original_h * original_w
downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1])))
dim = x.shape[-1]
if dim == 320:
cur_level = "level_1"
downsample_factor = tome_info['args']['downsample_factor']
ratio = tome_info['args']['ratio']
elif dim == 640:
cur_level = "level_2"
downsample_factor = tome_info['args']['downsample_factor_level_2']
ratio = tome_info['args']['ratio_level_2']
else:
cur_level = "other"
downsample_factor = 1
ratio = 0.0
args = tome_info["args"]
cur_h, cur_w = original_h // downsample, original_w // downsample
new_h, new_w = cur_h // downsample_factor, cur_w // downsample_factor
if tome_info['timestep'] / 1000 > tome_info['args']['timestep_threshold_switch']:
merge_method = args["merge_method"]
else:
merge_method = args["secondary_merge_method"]
if cur_level != "other" and tome_info['timestep'] / 1000 > tome_info['args']['timestep_threshold_stop']:
if merge_method == "downsample" and downsample_factor > 1:
m = lambda x: up_or_downsample(x, cur_w, cur_h, new_w, new_h, args["downsample_method"])
u = lambda x: up_or_downsample(x, new_w, new_h, cur_w, cur_h, args["downsample_method"])
elif merge_method == "similarity" and ratio > 0.0:
w = int(math.ceil(original_w / downsample))
h = int(math.ceil(original_h / downsample))
r = int(x.shape[1] * ratio)
# Re-init the generator if it hasn't already been initialized or device has changed.
if args["generator"] is None:
args["generator"] = init_generator(x.device)
elif args["generator"].device != x.device:
args["generator"] = init_generator(x.device, fallback=args["generator"])
# If the batch size is odd, then it's not possible for prompted and unprompted images to be in the same
# batch, which causes artifacts with use_rand, so force it to be off.
use_rand = False if x.shape[0] % 2 == 1 else args["use_rand"]
m, u = bipartite_soft_matching_random2d(x, w, h, args["sx"], args["sy"], r,
no_rand=not use_rand, generator=args["generator"])
else:
m, u = (do_nothing, do_nothing)
else:
m, u = (do_nothing, do_nothing)
merge_fn, unmerge_fn = (m, u)
return merge_fn, unmerge_fn
def bipartite_soft_matching_random2d(metric: torch.Tensor,
w: int,
h: int,
sx: int,
sy: int,
r: int,
no_rand: bool = False,
generator: torch.Generator = None) -> Tuple[Callable, Callable]:
"""
Partitions the tokens into src and dst and merges r tokens from src to dst.
Dst tokens are partitioned by choosing one randomy in each (sx, sy) region.
Args:
- metric [B, N, C]: metric to use for similarity
- w: image width in tokens
- h: image height in tokens
- sx: stride in the x dimension for dst, must divide w
- sy: stride in the y dimension for dst, must divide h
- r: number of tokens to remove (by merging)
- no_rand: if true, disable randomness (use top left corner only)
- rand_seed: if no_rand is false, and if not None, sets random seed.
"""
B, N, _ = metric.shape
if r <= 0:
return do_nothing, do_nothing
with torch.no_grad():
hsy, wsx = h // sy, w // sx
# For each sy by sx kernel, randomly assign one token to be dst and the rest src
if no_rand:
rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64)
else:
rand_idx = torch.randint(sy * sx, size=(hsy, wsx, 1), device=generator.device, generator=generator).to(
metric.device)
# The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead
idx_buffer_view = torch.zeros(hsy, wsx, sy * sx, device=metric.device, dtype=torch.int64)
idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype))
idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx)
# Image is not divisible by sx or sy so we need to move it into a new buffer
if (hsy * sy) < h or (wsx * sx) < w:
idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64)
idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view
else:
idx_buffer = idx_buffer_view
# We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices
rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1)
# We're finished with these
del idx_buffer, idx_buffer_view
# rand_idx is currently dst|src, so split them
num_dst = hsy * wsx
a_idx = rand_idx[:, num_dst:, :] # src
b_idx = rand_idx[:, :num_dst, :] # dst
def split(x):
C = x.shape[-1]
src = torch.gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C))
dst = torch.gather(x, dim=1, index=b_idx.expand(B, num_dst, C))
return src, dst
# Cosine similarity between A and B
metric = metric / metric.norm(dim=-1, keepdim=True)
a, b = split(metric)
scores = a @ b.transpose(-1, -2)
# Can't reduce more than the # tokens in src
r = min(a.shape[1], r)
# Find the most similar greedily
node_max, node_idx = scores.max(dim=-1)
edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]
unm_idx = edge_idx[..., r:, :] # Unmerged Tokens
src_idx = edge_idx[..., :r, :] # Merged Tokens
dst_idx = torch.gather(node_idx[..., None], dim=-2, index=src_idx)
def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
src, dst = split(x)
n, t1, c = src.shape
unm = torch.gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))
src = torch.gather(src, dim=-2, index=src_idx.expand(n, r, c))
dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)
return torch.cat([unm, dst], dim=1)
def unmerge(x: torch.Tensor) -> torch.Tensor:
unm_len = unm_idx.shape[1]
unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]
_, _, c = unm.shape
src = torch.gather(dst, dim=-2, index=dst_idx.expand(B, r, c))
# Combine back to the original shape
out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype)
out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst)
out.scatter_(dim=-2,
index=torch.gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c),
src=unm)
out.scatter_(dim=-2,
index=torch.gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c),
src=src)
return out
return merge, unmerge
class TokenMergeAttentionProcessor:
def __init__(self):
# priortize torch2's flash attention, if not fall back to xformers then regular attention
if torch2_is_available:
self.attn_method = "torch2"
elif xformers_is_available:
self.attn_method = "xformers"
else:
self.attn_method = "regular"
def torch2_attention(self, attn, query, key, value, attention_mask, batch_size):
inner_dim=key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
return hidden_states
def xformers_attention(self, attn, query, key, value, attention_mask, batch_size):
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
if attention_mask is not None:
attention_mask = attention_mask.reshape(batch_size * attn.heads, -1, attention_mask.shape[-1])
hidden_states = xformers.ops.memory_efficient_attention(
query, key, value, attn_bias=attention_mask, scale=attn.scale
)
hidden_states = attn.batch_to_head_dim(hidden_states)
return hidden_states
def regular_attention(self, attn, query, key, value, attention_mask, batch_size):
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
if attention_mask is not None:
attention_mask = attention_mask.reshape(batch_size * attn.heads, -1, attention_mask.shape[-1])
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
return hidden_states
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
temb: Optional[torch.FloatTensor] = None,
scale: float = 1.0,
) -> torch.FloatTensor:
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
args = () if USE_PEFT_BACKEND else (scale,)
if self._tome_info['args']['merge_tokens'] == "all":
merge_fn, unmerge_fn = compute_merge(hidden_states, self._tome_info)
hidden_states = merge_fn(hidden_states)
query = attn.to_q(hidden_states, *args)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
if self._tome_info['args']['merge_tokens'] == "keys/values":
merge_fn, _ = compute_merge(encoder_hidden_states, self._tome_info)
encoder_hidden_states = merge_fn(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states, *args)
value = attn.to_v(encoder_hidden_states, *args)
if self.attn_method == "torch2":
hidden_states = self.torch2_attention(attn, query, key, value, attention_mask, batch_size)
elif self.attn_method == "xformers":
hidden_states = self.xformers_attention(attn, query, key, value, attention_mask, batch_size)
else:
hidden_states = self.regular_attention(attn, query, key, value, attention_mask, batch_size)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states, *args)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if self._tome_info['args']['merge_tokens'] == "all":
hidden_states = unmerge_fn(hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states