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ec0a9aa | 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 | from __future__ import annotations
import types
from typing import List, Optional, Tuple, Union
import torch
import torch.amp as amp
from methods.cache_strategy.common import (
DiCacheConfig,
_maybe_concat_condition_mask,
_maybe_embed_action,
initialize_dicache_state,
prepare_cache_runtime_model,
)
try:
try:
from cosmos_predict2.conditioner import DataType
except ImportError:
from cosmos_predict2._src.predict2.conditioner import DataType
except Exception: # pragma: no cover - test fallback for minimal environments
from enum import Enum
class DataType(Enum):
VIDEO = "video"
IMAGE = "image"
try:
try:
from imaginaire.utils import log
except ImportError:
from cosmos_predict2._src.imaginaire.utils import log
except Exception: # pragma: no cover - test fallback for minimal environments
class _FallbackLog:
@staticmethod
def info(*args, **kwargs):
pass
log = _FallbackLog()
def dicache_mini_train_dit_forward(
self,
x_B_C_T_H_W: torch.Tensor,
timesteps_B_T: torch.Tensor,
crossattn_emb: torch.Tensor,
fps: Optional[torch.Tensor] = None,
padding_mask: Optional[torch.Tensor] = None,
data_type: Optional[DataType] = DataType.VIDEO,
intermediate_feature_ids: Optional[List[int]] = None,
img_context_emb: Optional[torch.Tensor] = None,
condition_video_input_mask_B_C_T_H_W: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
del intermediate_feature_ids
assert isinstance(data_type, DataType), f"Expected DataType, got {type(data_type)}."
if kwargs.get("timestep_scale") is None and hasattr(self, "timestep_scale"):
timesteps_B_T = timesteps_B_T * self.timestep_scale
x_B_C_T_H_W = _maybe_concat_condition_mask(
self,
x_B_C_T_H_W,
is_video=data_type == DataType.VIDEO,
condition_video_input_mask_B_C_T_H_W=condition_video_input_mask_B_C_T_H_W,
)
x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb = self.prepare_embedded_sequence(
x_B_C_T_H_W,
fps=fps,
padding_mask=padding_mask,
)
if self.crossattn_proj is not None:
crossattn_emb = self.crossattn_proj(crossattn_emb)
if img_context_emb is not None:
assert self.extra_image_context_dim is not None
img_context_emb = self.img_context_proj(img_context_emb)
context_input = (crossattn_emb, img_context_emb)
else:
context_input = crossattn_emb
with amp.autocast("cuda", enabled=getattr(self, "use_wan_fp32_strategy", False), dtype=torch.float32):
if timesteps_B_T.ndim == 1:
timesteps_B_T = timesteps_B_T.unsqueeze(1)
t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder(timesteps_B_T)
t_embedding_B_T_D, adaln_lora_B_T_3D = _maybe_embed_action(
self,
t_embedding_B_T_D,
adaln_lora_B_T_3D,
kwargs,
)
t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D)
self.affline_scale_log_info = {"t_embedding_B_T_D": t_embedding_B_T_D.detach()}
self.affline_emb = t_embedding_B_T_D
self.crossattn_emb = crossattn_emb
if extra_pos_emb is not None:
assert x_B_T_H_W_D.shape == extra_pos_emb.shape
block_kwargs = {
"emb_B_T_D": t_embedding_B_T_D,
"crossattn_emb": context_input,
"rope_emb_L_1_1_D": rope_emb_L_1_1_D,
"adaln_lora_B_T_3D": adaln_lora_B_T_3D,
"extra_per_block_pos_emb": extra_pos_emb,
}
skip_forward = False
ori_x = x_B_T_H_W_D
residual_x = None
current_idx = self.cnt % 2
test_x = x_B_T_H_W_D.clone()
if self.cnt >= int(self.dicache_num_steps * self.dicache_ret_ratio):
for blk in self.blocks[: self.dicache_probe_depth]:
test_x = blk(test_x, **block_kwargs)
if self.previous_input[current_idx] is not None and self.previous_internal_states[current_idx] is not None:
delta_y = (test_x - self.previous_internal_states[current_idx]).abs().mean() / (
self.previous_internal_states[current_idx].abs().mean() + 1e-8
)
self.accumulated_rel_l1_distance[current_idx] += delta_y
if self.accumulated_rel_l1_distance[current_idx] < self.dicache_rel_l1_thresh:
skip_forward = True
self.resume_flag[current_idx] = False
residual_x = self.residual_cache[current_idx]
else:
self.resume_flag[current_idx] = True
self.accumulated_rel_l1_distance[current_idx] = 0
self.previous_internal_states[current_idx] = test_x.clone()
if skip_forward:
if len(self.residual_window[current_idx]) >= 2:
current_residual = test_x - x_B_T_H_W_D
numer = (current_residual - self.probe_residual_window[current_idx][-2]).abs().mean()
denom = (
self.probe_residual_window[current_idx][-1]
- self.probe_residual_window[current_idx][-2]
).abs().mean()
gamma = (numer / denom).clip(1, 2) if denom > 1e-6 else 1.0
x_B_T_H_W_D = x_B_T_H_W_D + self.residual_window[current_idx][-2] + gamma * (
self.residual_window[current_idx][-1] - self.residual_window[current_idx][-2]
)
else:
x_B_T_H_W_D = x_B_T_H_W_D + residual_x
self.previous_internal_states[current_idx] = test_x
self.previous_input[current_idx] = ori_x
else:
if self.resume_flag[current_idx]:
x_B_T_H_W_D = test_x
remaining = self.blocks[self.dicache_probe_depth :]
else:
remaining = self.blocks
for i, blk in enumerate(remaining):
x_B_T_H_W_D = blk(x_B_T_H_W_D, **block_kwargs)
real_idx = i if not self.resume_flag[current_idx] else i + self.dicache_probe_depth
if real_idx == self.dicache_probe_depth - 1:
self.previous_internal_states[current_idx] = x_B_T_H_W_D.clone()
residual_x = x_B_T_H_W_D - ori_x
self.residual_cache[current_idx] = residual_x
probe_residual = (
self.previous_internal_states[current_idx] - ori_x
if self.previous_internal_states[current_idx] is not None
else residual_x
)
self.previous_input[current_idx] = ori_x
if len(self.residual_window[current_idx]) <= 2:
self.residual_window[current_idx].append(residual_x)
self.probe_residual_window[current_idx].append(probe_residual)
else:
self.residual_window[current_idx][-2] = self.residual_window[current_idx][-1]
self.residual_window[current_idx][-1] = residual_x
self.probe_residual_window[current_idx][-2] = self.probe_residual_window[current_idx][-1]
self.probe_residual_window[current_idx][-1] = probe_residual
x_out = self.final_layer(
x_B_T_H_W_D,
t_embedding_B_T_D,
adaln_lora_B_T_3D=adaln_lora_B_T_3D,
)
x_B_C_Tt_Hp_Wp = self.unpatchify(x_out)
self.cnt += 1
if self.cnt >= self.dicache_num_steps:
initialize_dicache_state(self, num_steps=self.dicache_num_steps)
return x_B_C_Tt_Hp_Wp
def apply_dicache(model, config: DiCacheConfig):
model.dicache_enabled = True
model.dicache_num_steps = config.num_steps
model.dicache_rel_l1_thresh = config.rel_l1_thresh
model.dicache_ret_ratio = config.ret_ratio
model.dicache_probe_depth = config.probe_depth
initialize_dicache_state(model, num_steps=config.num_steps)
prepare_cache_runtime_model(model)
model.forward = types.MethodType(dicache_mini_train_dit_forward, model)
log.info(
f"[DiCache] Applied: steps={config.num_steps} thresh={config.rel_l1_thresh} "
f"ret_ratio={config.ret_ratio} probe_depth={config.probe_depth}"
)
return model
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