File size: 14,438 Bytes
66bfb92 |
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 |
import ipdb
import torch
from typing import List, Optional, Dict, Any
from torch import FloatTensor, Tensor
from diffusers.models.attention_processor import Attention, F
from .lora_switching_module import enable_lora,module_active_adapters
from diffusers.models.embeddings import apply_rotary_emb
def attn_forward(
attn: Attention,
hidden_states: torch.FloatTensor,
condition_types: List[str],
encoder_hidden_states: torch.FloatTensor = None,
condition_latents: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
cond_rotary_embs: Optional[List[torch.Tensor]] = None,
model_config: Optional[Dict[str, Any]] = {},
) -> tuple[Any, Any, list[FloatTensor] | None] | tuple[Any, Any] | tuple[Tensor, Tensor] | Tensor:
batch_size, seq_len, _ = hidden_states.shape
# base_key / base_value: [text,noise] if encoder_hidden_states is not None else [noise]
with enable_lora([attn.to_q, attn.to_k, attn.to_v], [item for item in module_active_adapters(attn.to_q) if item not in condition_types]):
base_key = attn.to_k(hidden_states)
base_value = attn.to_v(hidden_states)
query = attn.to_q(hidden_states)
inner_dim = query.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
base_key = base_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
base_value = base_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
base_key = attn.norm_k(base_key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
seq_len = seq_len + encoder_hidden_states.shape[1]
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(
encoder_hidden_states_query_proj
)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(
encoder_hidden_states_key_proj
)
# concat the text embedding and noise embedding
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
base_key = torch.cat([encoder_hidden_states_key_proj, base_key], dim=2)
base_value = torch.cat([encoder_hidden_states_value_proj, base_value], dim=2)
if image_rotary_emb is not None:
query = apply_rotary_emb(query, image_rotary_emb)
base_key = apply_rotary_emb(base_key, image_rotary_emb)
condition_latents_output_list = []
key = base_key
value = base_value
if condition_latents is not None and len(condition_latents) > 0:
for i, cond_type in enumerate(condition_types):
with enable_lora([attn.to_q,attn.to_k, attn.to_v], [cond_type]):
cond_query = attn.to_q(condition_latents[i])
cond_key = attn.to_k(condition_latents[i])
cond_value = attn.to_v(condition_latents[i])
cond_query = cond_query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
cond_key = cond_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
cond_value = cond_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
cond_query = attn.norm_q(cond_query)
if attn.norm_k is not None:
cond_key = attn.norm_k(cond_key)
if cond_rotary_embs is not None:
cond_query = apply_rotary_emb(cond_query, cond_rotary_embs[i])
cond_key = apply_rotary_emb(cond_key, cond_rotary_embs[i])
key = torch.cat([key, cond_key], dim=2)
value = torch.cat([value, cond_value], dim=2)
mix_cond_key = torch.cat([base_key, cond_key], dim=2)
mix_cond_value = torch.cat([base_value, cond_value], dim=2)
condition_latents_output = F.scaled_dot_product_attention(
cond_query, mix_cond_key, mix_cond_value, dropout_p=0.0, is_causal=False, attn_mask=attention_mask
)
condition_latents_output = condition_latents_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
condition_latents_output_list.append(condition_latents_output)
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, is_causal=False, attn_mask=attention_mask
)
hidden_states = hidden_states.transpose(1, 2).reshape(
batch_size, -1, attn.heads * head_dim
)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
with enable_lora([attn.to_out[0]], [item for item in module_active_adapters(attn.to_out[0]) if item not in condition_types]):
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if condition_latents is not None and len(condition_latents) > 0:
for i, cond_type in enumerate(condition_types):
with enable_lora([attn.to_out[0]], [cond_type]):
condition_latents_output_list[i] = attn.to_out[0](condition_latents_output_list[i])
condition_latents_output_list[i] = attn.to_out[1](condition_latents_output_list[i])
return hidden_states, encoder_hidden_states, condition_latents_output_list
else:
return hidden_states, condition_latents_output_list
def block_forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
condition_latents: List[torch.Tensor] = None,
temb: torch.Tensor = None,
cond_temb: List[torch.Tensor] = None,
cond_rotary_embs=None,
image_rotary_emb=None,
condition_types: List[str]=None,
model_config: Optional[Dict[str, Any]] = {},
):
use_cond = condition_latents is not None and len(condition_latents) > 0
# norm : hidden_states->norm_hidden_states, encoder_hidden_states->norm_encoder_hidden_states, condition_latents->norm_condition_latent_list
with enable_lora([self.norm1.linear], [item for item in module_active_adapters(self.norm1.linear) if item not in condition_types]):
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
hidden_states, emb=temb
)
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = (
self.norm1_context(encoder_hidden_states, emb=temb)
)
norm_condition_latent_list = []
cond_gate_msa_list = []
cond_shift_mlp_list = []
cond_scale_mlp_list = []
cond_gate_mlp_list = []
if use_cond:
for i, cond_type in enumerate(condition_types):
with enable_lora([self.norm1.linear],[cond_type]):
norm_condition_latent,cond_gate_msa,cond_shift_mlp,cond_scale_mlp,cond_gate_mlp,= self.norm1(
condition_latents[i], emb=cond_temb
)
norm_condition_latent_list.append(norm_condition_latent)
cond_gate_msa_list.append(cond_gate_msa)
cond_shift_mlp_list.append(cond_shift_mlp)
cond_scale_mlp_list.append(cond_scale_mlp)
cond_gate_mlp_list.append(cond_gate_mlp)
# Attention. attn_output, context_attn_output, cond_attn_output_list
attn_output, context_attn_output,cond_attn_output_list = attn_forward(
self.attn,
model_config=model_config,
hidden_states=norm_hidden_states,
condition_types=condition_types,
encoder_hidden_states=norm_encoder_hidden_states,
condition_latents=norm_condition_latent_list if use_cond else None,
image_rotary_emb=image_rotary_emb,
cond_rotary_embs=cond_rotary_embs if use_cond else None,
)
# Process attention outputs. Gate + Resnet. hidden_states, encoder_hidden_states, condition_latents
# 1. hidden_states
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = hidden_states + attn_output
# 2. encoder_hidden_states
context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
encoder_hidden_states = encoder_hidden_states + context_attn_output
# 3. condition_latents
if use_cond:
for i, cond_type in enumerate(condition_types):
cond_attn_output_list[i] = cond_gate_msa_list[i].unsqueeze(1) * cond_attn_output_list[i]
condition_latents[i] = condition_latents[i] + cond_attn_output_list[i]
if model_config.get("add_cond_attn", False):
hidden_states += cond_attn_output_list[i]
# LayerNorm + Scaling + Shift.
# hidden_states->norm_hidden_states, encoder_hidden_states->norm_encoder_hidden_states, condition_latents->norm_condition_latent_list
# 1. hidden_states
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
# 2. encoder_hidden_states
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
# 3. condition_latents
if use_cond:
for i, cond_type in enumerate(condition_types):
norm_condition_latent_list[i] = self.norm2(condition_latents[i])
norm_condition_latent_list[i] = norm_condition_latent_list[i] * (1 + cond_scale_mlp_list[i][:, None]) + cond_shift_mlp_list[i][:, None]
# MLP Feed-forward + Gate
# 1. hidden_states
with enable_lora([self.ff.net[2]], [item for item in module_active_adapters(self.ff.net[2]) if item not in condition_types]):
hidden_states = hidden_states + gate_mlp.unsqueeze(1) * self.ff(norm_hidden_states)
# 2. encoder_hidden_states
encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * self.ff_context(norm_encoder_hidden_states)
# 3. condition_latents
if use_cond:
for i, cond_type in enumerate(condition_types):
with enable_lora([self.ff.net[2]], [cond_type]):
condition_latents[i] = condition_latents[i] + cond_gate_mlp_list[i].unsqueeze(1) * self.ff(norm_condition_latent_list[i])
# Clip to avoid overflow.
if encoder_hidden_states.dtype == torch.float16:
encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
return encoder_hidden_states, hidden_states, condition_latents if use_cond else None
def single_block_forward(
self,
hidden_states: torch.Tensor,
condition_latents: List[torch.Tensor] = None,
temb: torch.Tensor = None,
cond_temb: List[torch.Tensor] = None,
image_rotary_emb=None,
cond_rotary_embs=None,
condition_types: List[str] = None,
model_config: Optional[Dict[str, Any]] = {},
):
using_cond = condition_latents is not None and len(condition_latents) > 0
with enable_lora([self.norm.linear,self.proj_mlp],[item for item in module_active_adapters(self.norm.linear) if item not in condition_types]):
norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
norm_condition_latent_list = []
mlp_condition_latent_list = []
cond_gate_list = []
if using_cond:
for i, cond_type in enumerate(condition_types):
with enable_lora([self.norm.linear, self.proj_mlp],[cond_type]):
norm_condition_latents, cond_gate = self.norm(condition_latents[i], emb=cond_temb)
mlp_condition_latents = self.act_mlp(self.proj_mlp(norm_condition_latents))
norm_condition_latent_list.append(norm_condition_latents)
mlp_condition_latent_list.append(mlp_condition_latents)
cond_gate_list.append(cond_gate)
attn_output, cond_attn_output = attn_forward(
self.attn,
model_config=model_config,
hidden_states=norm_hidden_states,
condition_types= condition_types,
image_rotary_emb=image_rotary_emb,
**(
{
"condition_latents": norm_condition_latent_list,
"cond_rotary_embs": cond_rotary_embs if using_cond else None,
}
if using_cond
else {}
),
)
with enable_lora([self.proj_out], [item for item in module_active_adapters(self.proj_out) if item not in condition_types]):
hidden_states = hidden_states + gate.unsqueeze(1) * self.proj_out(torch.cat([attn_output, mlp_hidden_states], dim=2))
if using_cond:
for i, cond_type in enumerate(condition_types):
with enable_lora([self.proj_out],[cond_type]):
attn_mlp_condition_latents = torch.cat([cond_attn_output[i], mlp_condition_latent_list[i]], dim=2)
attn_mlp_condition_latents = cond_gate_list[i].unsqueeze(1) * self.proj_out(attn_mlp_condition_latents)
condition_latents[i] = condition_latents[i] + attn_mlp_condition_latents
if hidden_states.dtype == torch.float16:
hidden_states = hidden_states.clip(-65504, 65504)
return (hidden_states,None) if not using_cond else (hidden_states, condition_latents)
|