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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | from pipeline.streaming_training import StreamingTrainingPipeline
from typing import List, Optional, Tuple
import torch
import torch.distributed as dist
class StreamingSwitchTrainingPipeline(StreamingTrainingPipeline):
def __init__(self, *args, **kwargs):
_apr_enabled = kwargs.pop('apr_enabled', False)
_apr_alpha_max = kwargs.pop('apr_alpha_max', 0.8)
_apr_d_window = kwargs.pop('apr_d_window', None)
_apr_blend_sink = kwargs.pop('apr_blend_sink', False)
_global_sink = kwargs.pop('global_sink', False)
super().__init__(*args, **kwargs)
self.global_sink = _global_sink
self.apr_enabled = _apr_enabled
self.apr_alpha_max = float(max(0.0, min(0.8, _apr_alpha_max)))
self.apr_d_window = _apr_d_window
self.apr_blend_sink = _apr_blend_sink
if not dist.is_initialized() or dist.get_rank() == 0:
print(f'[StreamingSwitchTrainingPipeline] global_sink={self.global_sink} (config-driven, Opt 14 kwargs.pop fix active)')
def generate_chunk_with_cache(self, noise: torch.Tensor, conditional_dict: dict, *, current_start_frame: int=0, requires_grad: bool=True, switch_frame_index: Optional[int]=None, switch_conditional_dict: Optional[dict]=None, switch_recache_frames: Optional[torch.Tensor]=None, return_sim_step: bool=False) -> Tuple[torch.Tensor, Optional[int], Optional[int]]:
if switch_conditional_dict is None or switch_frame_index is None:
return super().generate_chunk_with_cache(noise=noise, conditional_dict=conditional_dict, current_start_frame=current_start_frame, requires_grad=requires_grad, return_sim_step=return_sim_step)
batch_size, chunk_frames, num_channels, height, width = noise.shape
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
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 = switch_frame_index
local_start_frame = 0
self.generator.model.local_attn_size = int(self.local_attn_size)
self._set_all_modules_max_attention_size(int(self.local_attn_size))
using_second = False
cond_in_use = conditional_dict
for block_index, current_num_frames in enumerate(all_num_frames):
if not using_second and local_start_frame >= switch_frame_index:
self._recache_after_switch(output[:, :local_start_frame, ...], current_start_frame + local_start_frame, switch_conditional_dict, local_start_frame, switch_recache_frames)
cond_in_use = switch_conditional_dict
using_second = True
noisy_input = noise[:, local_start_frame:local_start_frame + current_num_frames]
for step_idx, current_timestep in enumerate(self.denoising_step_list):
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=cond_in_use, 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=cond_in_use, 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=cond_in_use, 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 _recache_after_switch(self, output, current_start_frame, new_conditional_dict, local_start_frame=None, switch_recache_frames=None):
sink_tok_size = None
sink_backup = None
sink_norm_before = None
if self.global_sink and current_start_frame > 0:
gen = self.generator
from torch.distributed.fsdp import FullyShardedDataParallel as _FSDP
if hasattr(gen, '_fsdp_wrapped_module'):
w = gen._fsdp_wrapped_module
m = w.model
_inner_tmp = m._fsdp_wrapped_module if isinstance(m, _FSDP) else m
if hasattr(_inner_tmp, 'base_model') and hasattr(_inner_tmp.base_model, 'model'):
_inner_tmp = _inner_tmp.base_model.model
elif hasattr(gen, 'model'):
_inner_tmp = gen.model
else:
_inner_tmp = None
if _inner_tmp is not None:
sink_tok_size = _inner_tmp.blocks[0].self_attn.sink_size * self.frame_seq_length
sink_backup = [{'k': self.kv_cache1[i]['k'][:, :sink_tok_size].clone(), 'v': self.kv_cache1[i]['v'][:, :sink_tok_size].clone()} for i in range(self.num_transformer_blocks)]
sink_norm_before = torch.norm(self.kv_cache1[0]['k'][:, :sink_tok_size]).float().item()
if not dist.is_initialized() or dist.get_rank() == 0:
print(f'[Recache-Train] switch@frame={current_start_frame}, global_sink=True, sink_tok={sink_tok_size}, sink_norm(block0) BEFORE = {sink_norm_before:.4f}')
elif not self.global_sink:
if not dist.is_initialized() or dist.get_rank() == 0:
print(f'[Recache-Train] switch@frame={current_start_frame}, global_sink=False (sink will be recached with new prompt)')
apr_old_cache = None
if self.apr_enabled and current_start_frame > 0:
apr_old_cache = [{'k': self.kv_cache1[i]['k'].clone(), 'v': self.kv_cache1[i]['v'].clone()} for i in range(self.num_transformer_blocks)]
if not self.global_sink:
for block_idx in range(self.num_transformer_blocks):
cache = self.kv_cache1[block_idx]
cache['k'].zero_()
cache['v'].zero_()
for blk in self.crossattn_cache:
blk['k'].zero_()
blk['v'].zero_()
blk['is_init'] = False
gen = self.generator
from torch.distributed.fsdp import FullyShardedDataParallel as _FSDP
if hasattr(gen, '_fsdp_wrapped_module'):
w = gen._fsdp_wrapped_module
m = w.model
inner = m._fsdp_wrapped_module if isinstance(m, _FSDP) else m
if hasattr(inner, 'base_model') and hasattr(inner.base_model, 'model'):
inner = inner.base_model.model
elif hasattr(gen, 'model'):
inner = gen.model
else:
inner = None
if current_start_frame == 0:
return
if switch_recache_frames is not None:
frames_to_recache = torch.cat([switch_recache_frames, output], dim=1)[:, -21:, ...]
num_recache_frames = frames_to_recache.shape[1]
elif local_start_frame is not None:
num_recache_frames = min(local_start_frame, 21)
frames_to_recache = output[:, -num_recache_frames:]
else:
num_recache_frames = min(current_start_frame, 21)
frames_to_recache = output[:, -num_recache_frames:]
batch_size, num_recache_frames, c, h, w = frames_to_recache.shape
device = frames_to_recache.device
block_mask = self.generator.model._prepare_blockwise_causal_attn_mask(device=device, num_frames=num_recache_frames, frame_seqlen=self.frame_seq_length, num_frame_per_block=self.num_frame_per_block, local_attn_size=21)
context_timestep = torch.ones([batch_size, num_recache_frames], device=device, dtype=torch.int64) * self.context_noise
self.generator.model.block_mask = block_mask
_has_memory = inner is not None and (getattr(inner, 'query_memory_encoder', None) is not None or getattr(inner, 'sink_memory', None) is not None)
if _has_memory:
frame_seqlen = self.frame_seq_length
sink_frames = inner.blocks[0].self_attn.sink_size
max_attn = inner.blocks[0].self_attn.max_attention_size
recent_window_frames = (max_attn - sink_frames * frame_seqlen) // frame_seqlen
inner._ei_prev_window_start = max(sink_frames, current_start_frame - recent_window_frames)
with torch.no_grad():
self.generator(noisy_image_or_video=frames_to_recache, conditional_dict=new_conditional_dict, timestep=context_timestep, kv_cache=self.kv_cache1, crossattn_cache=self.crossattn_cache, current_start=(current_start_frame - num_recache_frames) * self.frame_seq_length)
if sink_backup is not None:
for i in range(self.num_transformer_blocks):
self.kv_cache1[i]['k'][:, :sink_tok_size] = sink_backup[i]['k']
self.kv_cache1[i]['v'][:, :sink_tok_size] = sink_backup[i]['v']
sink_norm_after = torch.norm(self.kv_cache1[0]['k'][:, :sink_tok_size]).float().item()
if not dist.is_initialized() or dist.get_rank() == 0:
delta = abs(sink_norm_after - sink_norm_before)
status = 'PRESERVED ✓' if delta < 0.001 else f'MISMATCH ✗ (delta={delta:.4e})'
print(f'[Recache-Train] global_sink=True: sink_norm AFTER = {sink_norm_after:.4f} [{status}]')
try:
import model.streaming_training as _st_mod
setattr(_st_mod, '_last_recache_sink_delta', delta)
except Exception:
pass
del sink_backup
for blk in self.crossattn_cache:
blk['k'].zero_()
blk['v'].zero_()
blk['is_init'] = False
if self.apr_enabled and apr_old_cache is not None:
self._apply_apr_blend(old_cache=apr_old_cache, recache_start_frame=current_start_frame - num_recache_frames, current_start_frame=current_start_frame, num_recache_frames=num_recache_frames, inner=inner)
del apr_old_cache
if inner is not None and getattr(inner, 'query_memory_encoder', None) is not None:
enc = inner.query_memory_encoder
if getattr(enc, 'memory_recache', False):
enc.reset(batch_size=frames_to_recache.shape[0], device=frames_to_recache.device, dtype=torch.bfloat16)
last_blk = self.num_transformer_blocks - 1
cache = self.kv_cache1[last_blk]
frame_seqlen = self.frame_seq_length
sink_tok = inner.blocks[0].self_attn.sink_size * frame_seqlen
local_end = cache['local_end_index'].item()
if local_end > sink_tok:
recache_k = cache['k'][:, sink_tok:local_end].clone()
recache_v = cache['v'][:, sink_tok:local_end].clone()
sink_k = cache['k'][:, :sink_tok].clone() if sink_tok > 0 else None
sink_v = cache['v'][:, :sink_tok].clone() if sink_tok > 0 else None
enc.update(recache_k, recache_v, sink_k, sink_v)
if inner is not None and getattr(inner, 'sink_memory', None) is not None:
sm = inner.sink_memory
sm.reset()
sink_frames_count = inner.blocks[0].self_attn.sink_size
sink_output = output[:, :sink_frames_count] if output.shape[1] >= sink_frames_count else None
if sink_output is None:
if switch_recache_frames is not None and switch_recache_frames.shape[1] >= sink_frames_count:
sink_output = switch_recache_frames[:, :sink_frames_count]
if sink_output is not None:
device = sink_output.device
batch_size_local = sink_output.shape[0]
sink_ts = torch.ones([batch_size_local, sink_frames_count], device=device, dtype=torch.int64) * self.context_noise
n_heads = inner.blocks[0].self_attn.num_heads
head_dim = inner.blocks[0].self_attn.head_dim
sink_cache_size = sink_frames_count * self.frame_seq_length
temp_kv = [{'k': torch.zeros([batch_size_local, sink_cache_size, n_heads, head_dim], device=device, dtype=torch.bfloat16), 'v': torch.zeros([batch_size_local, sink_cache_size, n_heads, head_dim], device=device, dtype=torch.bfloat16), 'global_end_index': torch.zeros(1, dtype=torch.long, device=device), 'local_end_index': torch.zeros(1, dtype=torch.long, device=device)} for _ in range(self.num_transformer_blocks)]
temp_crossattn = [{'k': torch.zeros_like(self.crossattn_cache[0]['k']), 'v': torch.zeros_like(self.crossattn_cache[0]['v']), 'is_init': False} for _ in range(self.num_transformer_blocks)]
with torch.no_grad():
self.generator(noisy_image_or_video=sink_output, conditional_dict=new_conditional_dict, timestep=sink_ts, kv_cache=temp_kv, crossattn_cache=temp_crossattn, current_start=0)
frame_seqlen_local = self.frame_seq_length
sink_frames_local = inner.blocks[0].self_attn.sink_size
max_attn_local = inner.blocks[0].self_attn.max_attention_size
recent_win = (max_attn_local - sink_frames_local * frame_seqlen_local) // frame_seqlen_local
inner._ei_prev_window_start = max(sink_frames_local, current_start_frame - recent_win)
if not dist.is_initialized() or dist.get_rank() == 0:
print(f'[SinkMem] reset + re-captured on prompt switch at frame {current_start_frame}')
def _apply_apr_blend(self, *, old_cache, recache_start_frame, current_start_frame, num_recache_frames, inner):
frame_seqlen = self.frame_seq_length
sink_size = inner.blocks[0].self_attn.sink_size
sink_tok = sink_size * frame_seqlen
D = self.apr_d_window if self.apr_d_window is not None else num_recache_frames
D = max(1, int(D))
alpha_max = self.apr_alpha_max
n_frames = num_recache_frames
with torch.no_grad():
for blk_idx in range(self.num_transformer_blocks):
new_k = self.kv_cache1[blk_idx]['k']
new_v = self.kv_cache1[blk_idx]['v']
old_k = old_cache[blk_idx]['k']
old_v = old_cache[blk_idx]['v']
if self.apr_blend_sink and (not self.global_sink):
for f in range(sink_size):
d_t = current_start_frame - f
alpha = min(alpha_max, max(0.0, 1.0 - d_t / D))
s0, s1 = (f * frame_seqlen, (f + 1) * frame_seqlen)
new_k[:, s0:s1] = (1 - alpha) * old_k[:, s0:s1] + alpha * new_k[:, s0:s1]
new_v[:, s0:s1] = (1 - alpha) * old_v[:, s0:s1] + alpha * new_v[:, s0:s1]
base = sink_tok
for f in range(n_frames):
absolute_frame = recache_start_frame + f
d_t = current_start_frame - absolute_frame
alpha = min(alpha_max, max(0.0, 1.0 - d_t / D))
s0, s1 = (base + f * frame_seqlen, base + (f + 1) * frame_seqlen)
new_k[:, s0:s1] = (1 - alpha) * old_k[:, s0:s1] + alpha * new_k[:, s0:s1]
new_v[:, s0:s1] = (1 - alpha) * old_v[:, s0:s1] + alpha * new_v[:, s0:s1]
if not dist.is_initialized() or dist.get_rank() == 0:
print(f'[APR] switch@{current_start_frame}: blended {n_frames} frames (α_max={alpha_max}, D_window={D})')
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