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3e936b2 | 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 | from utils.wan_wrapper import WanDiffusionWrapper
from utils.scheduler import SchedulerInterface
from typing import List, Optional, Tuple
import torch
import torch.distributed as dist
class SelfForcingTrainingPipeline:
def __init__(self, denoising_step_list: List[int], scheduler: SchedulerInterface, generator: WanDiffusionWrapper, num_frame_per_block=3, independent_first_frame: bool=False, same_step_across_blocks: bool=False, last_step_only: bool=False, num_max_frames: int=21, context_noise: int=0, **kwargs):
super().__init__()
self.scheduler = scheduler
self.generator = generator
self.denoising_step_list = denoising_step_list
if self.denoising_step_list[-1] == 0:
self.denoising_step_list = self.denoising_step_list[:-1]
self.num_transformer_blocks = 30
self.frame_seq_length = 1560
self.num_frame_per_block = num_frame_per_block
self.context_noise = context_noise
self.i2v = False
self.kv_cache1 = None
self.kv_cache2 = None
self.crossattn_cache = None
self.independent_first_frame = independent_first_frame
self.same_step_across_blocks = same_step_across_blocks
self.last_step_only = last_step_only
self.local_attn_size = kwargs.get('local_attn_size', -1)
if not isinstance(self.local_attn_size, int) and hasattr(self.local_attn_size, '__iter__'):
self.local_attn_size = list(self.local_attn_size)
if isinstance(self.local_attn_size, (list, tuple)):
assert len(self.local_attn_size) == len(self.denoising_step_list), f'local_attn_size length ({len(self.local_attn_size)}) must match denoising_step_list length ({len(self.denoising_step_list)}).'
num_training_frames: Optional[int] = kwargs.get('num_training_frames', 21)
slice_last_frames: int = int(kwargs.get('slice_last_frames', 21))
def _resolve_kv_frames(local_cfg):
if isinstance(local_cfg, (list, tuple)):
base = int(max(local_cfg)) if len(local_cfg) > 0 else -1
return min(base + slice_last_frames, num_training_frames)
else:
base = int(local_cfg)
return min(base + slice_last_frames, num_training_frames)
kv_frames = _resolve_kv_frames(self.local_attn_size)
self.kv_cache_size = int(kv_frames) * self.frame_seq_length
def generate_and_sync_list(self, num_blocks, num_denoising_steps, device):
rank = dist.get_rank() if dist.is_initialized() else 0
if rank == 0:
indices = torch.randint(low=0, high=num_denoising_steps, size=(num_blocks,), device=device)
if self.last_step_only:
indices = torch.ones_like(indices) * (num_denoising_steps - 1)
else:
indices = torch.empty(num_blocks, dtype=torch.long, device=device)
if dist.is_initialized():
dist.broadcast(indices, src=0)
return indices.tolist()
def generate_chunk_with_cache(self, noise: torch.Tensor, conditional_dict: dict, *, current_start_frame: int=0, requires_grad: bool=True, return_sim_step: bool=False) -> Tuple[torch.Tensor, Optional[int], Optional[int]]:
batch_size, chunk_frames, num_channels, height, width = noise.shape
if not self.independent_first_frame or chunk_frames % self.num_frame_per_block == 0:
assert chunk_frames % self.num_frame_per_block == 0
num_blocks = chunk_frames // self.num_frame_per_block
all_num_frames = [self.num_frame_per_block] * num_blocks
else:
assert (chunk_frames - 1) % self.num_frame_per_block == 0
num_blocks = (chunk_frames - 1) // self.num_frame_per_block
all_num_frames = [1] + [self.num_frame_per_block] * num_blocks
output = torch.zeros_like(noise)
num_denoising_steps = len(self.denoising_step_list)
exit_flags = self.generate_and_sync_list(len(all_num_frames), num_denoising_steps, device=noise.device)
if not requires_grad:
start_gradient_frame_index = chunk_frames
else:
start_gradient_frame_index = 0
local_start_frame = 0
if not (isinstance(self.local_attn_size, (list, tuple)) or (hasattr(self.local_attn_size, '__iter__') and (not isinstance(self.local_attn_size, (str, bytes))))):
self.generator.model.local_attn_size = int(self.local_attn_size)
self._set_all_modules_max_attention_size(int(self.local_attn_size))
for block_index, current_num_frames in enumerate(all_num_frames):
noisy_input = noise[:, local_start_frame:local_start_frame + current_num_frames]
for step_idx, current_timestep in enumerate(self.denoising_step_list):
if isinstance(self.local_attn_size, (list, tuple)) or (hasattr(self.local_attn_size, '__iter__') and (not isinstance(self.local_attn_size, (str, bytes)))):
self.generator.model.local_attn_size = int(self.local_attn_size[step_idx])
self._set_all_modules_max_attention_size(int(self.local_attn_size[step_idx]))
exit_flag = step_idx == exit_flags[0] if self.same_step_across_blocks else step_idx == exit_flags[block_index]
timestep = torch.ones([batch_size, current_num_frames], device=noise.device, dtype=torch.int64) * current_timestep
if not exit_flag:
with torch.no_grad():
_, denoised_pred = self.generator(noisy_image_or_video=noisy_input, conditional_dict=conditional_dict, timestep=timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=(current_start_frame + local_start_frame) * self.frame_seq_length)
if step_idx < len(self.denoising_step_list) - 1:
next_timestep = self.denoising_step_list[step_idx + 1]
noisy_input = self.scheduler.add_noise(denoised_pred.flatten(0, 1), torch.randn_like(denoised_pred.flatten(0, 1)), next_timestep * torch.ones([batch_size * current_num_frames], device=noise.device, dtype=torch.long)).unflatten(0, denoised_pred.shape[:2])
else:
enable_grad = local_start_frame >= start_gradient_frame_index
context_manager = torch.enable_grad() if enable_grad else torch.no_grad()
with context_manager:
_, denoised_pred = self.generator(noisy_image_or_video=noisy_input, conditional_dict=conditional_dict, timestep=timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=(current_start_frame + local_start_frame) * self.frame_seq_length)
break
output[:, local_start_frame:local_start_frame + current_num_frames] = denoised_pred
context_timestep = torch.ones_like(timestep) * self.context_noise
context_noisy = self.scheduler.add_noise(denoised_pred.flatten(0, 1), torch.randn_like(denoised_pred.flatten(0, 1)), context_timestep.flatten(0, 1)).unflatten(0, denoised_pred.shape[:2])
with torch.no_grad():
self.generator(noisy_image_or_video=context_noisy, conditional_dict=conditional_dict, timestep=context_timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=(current_start_frame + local_start_frame) * self.frame_seq_length)
local_start_frame += current_num_frames
if not self.same_step_across_blocks:
denoised_timestep_from, denoised_timestep_to = (None, None)
elif exit_flags[0] == len(self.denoising_step_list) - 1:
denoised_timestep_to = 0
denoised_timestep_from = 1000 - torch.argmin((self.scheduler.timesteps.cuda() - self.denoising_step_list[exit_flags[0]].cuda()).abs(), dim=0).item()
else:
denoised_timestep_to = 1000 - torch.argmin((self.scheduler.timesteps.cuda() - self.denoising_step_list[exit_flags[0] + 1].cuda()).abs(), dim=0).item()
denoised_timestep_from = 1000 - torch.argmin((self.scheduler.timesteps.cuda() - self.denoising_step_list[exit_flags[0]].cuda()).abs(), dim=0).item()
if return_sim_step:
return (output, denoised_timestep_from, denoised_timestep_to, exit_flags[0] + 1)
return (output, denoised_timestep_from, denoised_timestep_to)
def inference_with_trajectory(self, noise: torch.Tensor, initial_latent: Optional[torch.Tensor]=None, return_sim_step: bool=False, slice_last_frames: int=21, **conditional_dict) -> torch.Tensor:
batch_size, num_frames, num_channels, height, width = noise.shape
if not self.independent_first_frame or (self.independent_first_frame and initial_latent is not None):
assert num_frames % self.num_frame_per_block == 0
num_blocks = num_frames // self.num_frame_per_block
else:
assert (num_frames - 1) % self.num_frame_per_block == 0
num_blocks = (num_frames - 1) // self.num_frame_per_block
num_input_frames = initial_latent.shape[1] if initial_latent is not None else 0
num_output_frames = num_frames + num_input_frames
output = torch.zeros([batch_size, num_output_frames, num_channels, height, width], device=noise.device, dtype=noise.dtype)
self._initialize_kv_cache(batch_size=batch_size, dtype=noise.dtype, device=noise.device)
self._initialize_crossattn_cache(batch_size=batch_size, dtype=noise.dtype, device=noise.device)
current_start_frame = 0
if initial_latent is not None:
timestep = torch.ones([batch_size, 1], device=noise.device, dtype=torch.int64) * 0
output[:, :1] = initial_latent
with torch.no_grad():
self.generator(noisy_image_or_video=initial_latent, conditional_dict=conditional_dict, timestep=timestep * 0, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=current_start_frame * self.frame_seq_length)
current_start_frame += 1
all_num_frames = [self.num_frame_per_block] * num_blocks
if self.independent_first_frame and initial_latent is None:
all_num_frames = [1] + all_num_frames
num_denoising_steps = len(self.denoising_step_list)
exit_flags = self.generate_and_sync_list(len(all_num_frames), num_denoising_steps, device=noise.device)
start_gradient_frame_index = num_output_frames - slice_last_frames
grad_enable_mask = torch.zeros((batch_size, sum(all_num_frames)), dtype=torch.bool)
if not isinstance(self.local_attn_size, (list, tuple)):
self.generator.model.local_attn_size = int(self.local_attn_size)
self._set_all_modules_max_attention_size(int(self.local_attn_size))
for block_index, current_num_frames in enumerate(all_num_frames):
noisy_input = noise[:, current_start_frame - num_input_frames:current_start_frame + current_num_frames - num_input_frames]
for index, current_timestep in enumerate(self.denoising_step_list):
if isinstance(self.local_attn_size, (list, tuple)):
self.generator.model.local_attn_size = int(self.local_attn_size[index])
self._set_all_modules_max_attention_size(int(self.local_attn_size[index]))
if self.same_step_across_blocks:
exit_flag = index == exit_flags[0]
else:
exit_flag = index == exit_flags[block_index]
timestep = torch.ones([batch_size, current_num_frames], device=noise.device, dtype=torch.int64) * current_timestep
if not exit_flag:
with torch.no_grad():
_, denoised_pred = self.generator(noisy_image_or_video=noisy_input, conditional_dict=conditional_dict, timestep=timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=current_start_frame * self.frame_seq_length)
next_timestep = self.denoising_step_list[index + 1]
noisy_input = self.scheduler.add_noise(denoised_pred.flatten(0, 1), torch.randn_like(denoised_pred.flatten(0, 1)), next_timestep * torch.ones([batch_size * current_num_frames], device=noise.device, dtype=torch.long)).unflatten(0, denoised_pred.shape[:2])
else:
if current_start_frame < start_gradient_frame_index:
grad_enable_mask[:, current_start_frame:current_start_frame + current_num_frames] = False
with torch.no_grad():
_, denoised_pred = self.generator(noisy_image_or_video=noisy_input, conditional_dict=conditional_dict, timestep=timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=current_start_frame * self.frame_seq_length)
else:
grad_enable_mask[:, current_start_frame:current_start_frame + current_num_frames] = True
_, denoised_pred = self.generator(noisy_image_or_video=noisy_input, conditional_dict=conditional_dict, timestep=timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=current_start_frame * self.frame_seq_length)
break
output[:, current_start_frame:current_start_frame + current_num_frames] = denoised_pred
context_timestep = torch.ones_like(timestep) * self.context_noise
denoised_pred = self.scheduler.add_noise(denoised_pred.flatten(0, 1), torch.randn_like(denoised_pred.flatten(0, 1)), context_timestep * torch.ones([batch_size * current_num_frames], device=noise.device, dtype=torch.long)).unflatten(0, denoised_pred.shape[:2])
with torch.no_grad():
self.generator(noisy_image_or_video=denoised_pred, conditional_dict=conditional_dict, timestep=context_timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=current_start_frame * self.frame_seq_length)
current_start_frame += current_num_frames
if not self.same_step_across_blocks:
denoised_timestep_from, denoised_timestep_to = (None, None)
elif exit_flags[0] == len(self.denoising_step_list) - 1:
denoised_timestep_to = 0
denoised_timestep_from = 1000 - torch.argmin((self.scheduler.timesteps.cuda() - self.denoising_step_list[exit_flags[0]].cuda()).abs(), dim=0).item()
else:
denoised_timestep_to = 1000 - torch.argmin((self.scheduler.timesteps.cuda() - self.denoising_step_list[exit_flags[0] + 1].cuda()).abs(), dim=0).item()
denoised_timestep_from = 1000 - torch.argmin((self.scheduler.timesteps.cuda() - self.denoising_step_list[exit_flags[0]].cuda()).abs(), dim=0).item()
if return_sim_step:
return (output, denoised_timestep_from, denoised_timestep_to, exit_flags[0] + 1)
return (output, denoised_timestep_from, denoised_timestep_to)
def _initialize_kv_cache(self, batch_size, dtype, device):
kv_cache1 = []
for _ in range(self.num_transformer_blocks):
kv_cache1.append({'k': torch.zeros([batch_size, self.kv_cache_size, 12, 128], dtype=dtype, device=device), 'v': torch.zeros([batch_size, self.kv_cache_size, 12, 128], dtype=dtype, device=device), 'global_end_index': torch.tensor([0], dtype=torch.long, device=device), 'local_end_index': torch.tensor([0], dtype=torch.long, device=device)})
self.kv_cache1 = kv_cache1
def _initialize_crossattn_cache(self, batch_size, dtype, device):
crossattn_cache = []
for _ in range(self.num_transformer_blocks):
crossattn_cache.append({'k': torch.zeros([batch_size, 512, 12, 128], dtype=dtype, device=device), 'v': torch.zeros([batch_size, 512, 12, 128], dtype=dtype, device=device), 'is_init': False})
self.crossattn_cache = crossattn_cache
def clear_kv_cache(self):
if getattr(self, 'kv_cache1', None) is not None:
for blk in self.kv_cache1:
blk['k'].zero_()
blk['v'].zero_()
if 'global_end_index' in blk:
blk['global_end_index'].zero_()
if 'local_end_index' in blk:
blk['local_end_index'].zero_()
if getattr(self, 'crossattn_cache', None) is not None:
for blk in self.crossattn_cache:
blk['k'].zero_()
blk['v'].zero_()
blk['is_init'] = False
def _set_all_modules_max_attention_size(self, local_attn_size_value: int):
if isinstance(local_attn_size_value, (list, tuple)):
raise ValueError('_set_all_modules_max_attention_size expects an int, got list/tuple.')
if int(local_attn_size_value) == -1:
target_size = 32760
policy = 'global'
else:
target_size = int(local_attn_size_value) * self.frame_seq_length
policy = 'local'
if hasattr(self.generator.model, 'max_attention_size'):
try:
_ = getattr(self.generator.model, 'max_attention_size')
except Exception:
pass
setattr(self.generator.model, 'max_attention_size', target_size)
for name, module in self.generator.model.named_modules():
if hasattr(module, 'max_attention_size'):
try:
setattr(module, 'max_attention_size', target_size)
except Exception:
pass
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