Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,335 Bytes
b197ccc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 |
# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def my_scaled_dot_product_attention(
query,
key,
value,
attn_mask=None,
dropout_p=0.0,
is_causal=False,
scale=None,
special_token_weight=1.0,
special_token_indices=None,
) -> torch.Tensor:
"""
Computes the scaled dot-product attention with additional control over specific tokens.
This function is a re-implementation of the scaled dot-product attention mechanism,
designed to return both the attention map and the output of the attention operation.
It also provides additional control via a scalar that modifies the attention map
for specific tokens.
"""
L, S = query.size(-2), key.size(-2)
scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
attn_bias = torch.zeros(L, S, dtype=query.dtype).cuda()
if is_causal:
assert attn_mask is None
temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
attn_bias.to(query.dtype)
if attn_mask is not None:
if attn_mask.dtype == torch.bool:
attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
else:
attn_bias += attn_mask
attn_weight = query @ key.transpose(-2, -1) * scale_factor
attn_weight += attn_bias
if special_token_indices is not None and special_token_weight != 1.0:
bs = attn_weight.shape[0]
attn_weight[torch.arange(bs), :, :, special_token_indices] = torch.max(
attn_weight[torch.arange(bs), :, :, special_token_indices],
attn_weight[torch.arange(bs), :, :, special_token_indices]
* special_token_weight,
)
attn_weight = torch.softmax(attn_weight, dim=-1)
attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
return attn_weight @ value, attn_weight
class AttnProcessor(torch.nn.Module):
r"""
Processor for implementing scaled dot-product attention.
"""
def __init__(
self,
hidden_size=None,
cross_attention_dim=None,
):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"AttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn,
hidden_states,
qformer_tokens_out=None,
special_token_indices=None,
inference_mode=None,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
special_token_weight=None,
):
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
)
query = attn.to_q(hidden_states)
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
)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
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)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
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
)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](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
class NestedAttnProcessor(torch.nn.Module):
r"""
Nested Attention processor for IP-Adapater for PyTorch 2.0.
"""
def __init__(self, hidden_size, cross_attention_dim=None, normalize_factor=1.0):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"NestedAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.normalize_factor = normalize_factor
self.nested_to_k = nn.Linear(
cross_attention_dim or hidden_size, hidden_size, bias=False
)
self.nested_to_v = nn.Linear(
cross_attention_dim or hidden_size, hidden_size, bias=False
)
def __call__(
self,
attn,
hidden_states,
qformer_tokens_out,
special_token_indices,
inference_mode=False,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
special_token_weight=1.0,
):
assert (
special_token_indices.shape[0] > 0
), "special_token_indices should not be empty"
# if inference mode is set to True, the code assumes that CFG is used and the first half
# of the batch is used for the null prompt and the second half is used for the prompt
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
bs = hidden_states.shape[0]
if input_ndim == 4:
bs, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(bs, channel, height * width).transpose(
1, 2
)
bs, 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, bs
)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(
bs, 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
)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
else:
if attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(
encoder_hidden_states
)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(bs, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(bs, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(bs, -1, attn.heads, head_dim).transpose(1, 2)
# nested attention
nested_key = self.nested_to_k(qformer_tokens_out)
nested_value = self.nested_to_v(qformer_tokens_out)
nested_key = nested_key.view(bs, -1, attn.heads, head_dim).transpose(1, 2)
nested_value = nested_value.view(bs, -1, attn.heads, head_dim).transpose(1, 2)
nested_hidden_states = F.scaled_dot_product_attention(
query,
nested_key,
nested_value,
attn_mask=None,
dropout_p=0.0,
is_causal=False,
)
# normalize V_q
textual_values_norms = torch.norm(
value[torch.arange(bs), :, special_token_indices], dim=-1
)
nested_hidden_states = (
torch.nn.functional.normalize(nested_hidden_states, p=2, dim=-1)
* self.normalize_factor
)
nested_hidden_states = (
textual_values_norms.view(bs, -1, 1, 1) * nested_hidden_states
)
# outer attention
value_without_special_tokens = value.clone()
if inference_mode:
value_without_special_tokens[bs // 2 : bs, :, special_token_indices, :] = (
0.0
)
else:
value_without_special_tokens[
torch.arange(bs), :, special_token_indices, :
] = 0.0
hidden_states_without_special_tokens, attn_weight = (
my_scaled_dot_product_attention(
query,
key,
value_without_special_tokens,
attn_mask=None,
dropout_p=0.0,
is_causal=False,
special_token_weight=special_token_weight,
special_token_indices=special_token_indices,
)
)
# add the special token values
if inference_mode:
special_token_attn_weight = attn_weight[
bs // 2 : bs, :, :, special_token_indices
]
else:
special_token_attn_weight = attn_weight[
torch.arange(bs), :, :, special_token_indices
]
if inference_mode:
special_token_weighted_values = (
special_token_attn_weight * nested_hidden_states[bs // 2 : bs]
)
else:
special_token_weighted_values = (
special_token_attn_weight.unsqueeze(-1) * nested_hidden_states
)
if inference_mode:
hidden_states = hidden_states_without_special_tokens
hidden_states[bs // 2 : bs] += special_token_weighted_values
else:
hidden_states = (
hidden_states_without_special_tokens + special_token_weighted_values
)
# arrange hidden states
hidden_states = hidden_states.transpose(1, 2).reshape(
bs, -1, attn.heads * head_dim
)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(
bs, channel, height, width
)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
|