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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
MotionCache: motion-aware token-wise cache reuse for autoregressive video generation.
Implements the coarse-to-fine schedule from Xu et al. (2026):
- Phase 1 (first K steps after warmup): chunk-wise binary reuse (FlowCache-style)
- Phase 2: motion-weighted per-token accumulation and selective reuse
"""
import json
import os
from typing import Dict, Optional, Tuple
import torch
from einops import rearrange
from .cachereuse import ChunkWiseCache
class MotionWiseCache(ChunkWiseCache):
"""
Motion-aware cache extending chunk-wise FlowCache with token-level reuse.
Hyperparameters (paper Appendix C for MAGI-1):
alpha: soft-mapping floor for static tokens (default 0.5)
phase1_steps (K): chunk-wise phase duration before token-wise mode (default 9)
rel_l1_thresh (tau): accumulator threshold for token activation
warmup_steps (m): global steps with reuse disabled (default 5)
"""
def __init__(
self,
rel_l1_thresh: float = 0.015,
warmup_steps: int = 5,
phase1_steps: int = 9,
alpha: float = 0.5,
discard_nearly_clean_chunk: bool = False,
log: bool = False,
metric_stats_path: Optional[str] = None,
eps: float = 1e-8,
):
super().__init__(
rel_l1_thresh=rel_l1_thresh,
warmup_steps=warmup_steps,
discard_nearly_clean_chunk=discard_nearly_clean_chunk,
log=log,
metric_stats_path=metric_stats_path,
)
self.phase1_steps = phase1_steps
self.alpha = alpha
self.eps = eps
self.token_accumulator: Dict[int, torch.Tensor] = {}
self.token_active_mask: Dict[int, torch.Tensor] = {}
self.token_motion_weights: Dict[int, torch.Tensor] = {}
self.prev_latent_chunks: Dict[int, torch.Tensor] = {}
self.prev_chunk_last_frame: Dict[int, torch.Tensor] = {}
self.previous_velocity: Dict[int, torch.Tensor] = {}
self.chunk_sparse_flags: Dict[int, bool] = {}
def reset(self):
super().reset()
self.token_accumulator.clear()
self.token_active_mask.clear()
self.token_motion_weights.clear()
self.prev_latent_chunks.clear()
self.prev_chunk_last_frame.clear()
self.previous_velocity.clear()
self.chunk_sparse_flags.clear()
@staticmethod
def expand_token_mask_to_output(
token_mask: torch.Tensor,
output: torch.Tensor,
) -> torch.Tensor:
"""Expand [N, T, H, W] latent mask to match velocity/output [N, C, T, H, W]."""
return token_mask.unsqueeze(1).expand_as(output).to(dtype=output.dtype)
def in_phase1(self, chunk_id: int, chunk_denoise_count: Dict[int, int]) -> bool:
"""Return True while chunk i is still in coarse chunk-wise phase (denoise step < K)."""
return chunk_denoise_count.get(chunk_id, 0) < self.phase1_steps
def compute_motion_weights(
self,
x_chunk: torch.Tensor,
chunk_id: int,
chunk_offset: int,
) -> torch.Tensor:
"""
Compute motion-aware importance weights W in [alpha, 1] per latent frame.
Args:
x_chunk: Latent tensor [N, C, T, H, W] at current denoising step
chunk_id: Global chunk index
chunk_offset: Index of first generated chunk
Returns:
Weights tensor [N, T, H, W]
"""
_, _, num_frames, _, _ = x_chunk.shape
device = x_chunk.device
dtype = x_chunk.dtype
importance = torch.zeros(
x_chunk.size(0), num_frames, x_chunk.size(3), x_chunk.size(4),
device=device, dtype=dtype,
)
for frame_idx in range(num_frames):
if frame_idx > 0:
diff = (x_chunk[:, :, frame_idx] - x_chunk[:, :, frame_idx - 1]).abs().sum(dim=1)
elif chunk_id > chunk_offset:
prev_frame = self.prev_chunk_last_frame.get(chunk_id - 1)
if prev_frame is not None:
diff = (x_chunk[:, :, 0] - prev_frame).abs().sum(dim=1)
else:
diff = torch.zeros(
x_chunk.size(0), x_chunk.size(3), x_chunk.size(4),
device=device, dtype=dtype,
)
else:
continue
importance[:, frame_idx] = diff
if chunk_id == chunk_offset and num_frames > 1:
importance[:, 0] = importance[:, 1]
weights = torch.zeros_like(importance)
for frame_idx in range(num_frames):
frame_importance = importance[:, frame_idx]
min_val = frame_importance.amin(dim=(1, 2), keepdim=True)
max_val = frame_importance.amax(dim=(1, 2), keepdim=True)
normalized = (frame_importance - min_val) / (max_val - min_val + self.eps)
weights[:, frame_idx] = self.alpha + (1.0 - self.alpha) * normalized
return weights
def compute_chunk_delta_l1(
self,
current_features: torch.Tensor,
prev_features: torch.Tensor,
) -> float:
"""Relative L1 distance between consecutive embedded features (Eq. 11)."""
diff = (current_features - prev_features).abs().mean()
denom = prev_features.abs().mean() + self.eps
return (diff / denom).item()
def update_token_policy(
self,
chunk_id: int,
x_chunk: torch.Tensor,
current_features: torch.Tensor,
chunk_offset: int,
chunk_denoise_count: Optional[Dict[int, int]] = None,
) -> torch.Tensor:
"""
Phase-2 token policy: update accumulators and return active mask.
Returns:
Boolean mask [N, T, H, W], True = compute, False = reuse cache
"""
if (
chunk_denoise_count is not None
and chunk_denoise_count.get(chunk_id, 0) == self.phase1_steps
):
mask = torch.ones(
x_chunk.size(0), x_chunk.size(2), x_chunk.size(3), x_chunk.size(4),
device=x_chunk.device, dtype=torch.bool,
)
self.token_active_mask[chunk_id] = mask
self.token_accumulator[chunk_id] = torch.zeros(
x_chunk.size(0), x_chunk.size(2), x_chunk.size(3), x_chunk.size(4),
device=x_chunk.device,
dtype=x_chunk.dtype,
)
return mask
prev_features = self.prev_metric_chunks.get(chunk_id)
if prev_features is None:
mask = torch.ones(
x_chunk.size(0), x_chunk.size(2), x_chunk.size(3), x_chunk.size(4),
device=x_chunk.device, dtype=torch.bool,
)
self.token_active_mask[chunk_id] = mask
return mask
delta_chunk = self.compute_chunk_delta_l1(current_features, prev_features)
weights = self.compute_motion_weights(x_chunk, chunk_id, chunk_offset)
self.token_motion_weights[chunk_id] = weights
if chunk_id not in self.token_accumulator:
self.token_accumulator[chunk_id] = torch.zeros_like(weights)
self.token_accumulator[chunk_id] = (
self.token_accumulator[chunk_id] + weights * delta_chunk
)
mask = self.token_accumulator[chunk_id] > self.rel_l1_thresh
self.token_active_mask[chunk_id] = mask
return mask
def reset_token_accumulator(self, chunk_id: int, mask: torch.Tensor):
"""Reset accumulator for tokens selected for computation."""
if chunk_id in self.token_accumulator:
self.token_accumulator[chunk_id] = torch.where(
mask, torch.zeros_like(self.token_accumulator[chunk_id]),
self.token_accumulator[chunk_id],
)
def should_skip_chunk_forward(
self,
chunk_id: int,
chunk_denoise_count: Dict[int, int],
) -> bool:
"""Return True if the entire chunk can skip the DiT forward pass."""
if self.in_phase1(chunk_id, chunk_denoise_count):
return self.chunk_reuse_flags.get(chunk_id, False)
mask = self.token_active_mask.get(chunk_id)
if mask is None:
return False
return not mask.any()
def store_latent_chunk(self, chunk_id: int, x_chunk: torch.Tensor):
"""Store latent for cross-chunk motion reference."""
self.prev_latent_chunks[chunk_id] = x_chunk.detach().clone()
self.prev_chunk_last_frame[chunk_id] = x_chunk[:, :, -1].detach().clone()
def get_token_mask(
self,
chunk_id: int,
chunk_denoise_count: Dict[int, int],
) -> Optional[torch.Tensor]:
if self.in_phase1(chunk_id, chunk_denoise_count):
return None
return self.token_active_mask.get(chunk_id)
def record_motion_decision(
self,
chunk_id: int,
reused: bool,
active_ratio: Optional[float] = None,
**kwargs,
):
if not self.metric_stats_path:
return
record = {
"infer_idx": kwargs.get("infer_idx"),
"cur_denoise_step": kwargs.get("cur_denoise_step"),
"denoise_stage": kwargs.get("denoise_stage"),
"denoise_idx": kwargs.get("denoise_idx"),
"chunk_idx": chunk_id,
"generated_chunk_idx": chunk_id - kwargs.get("chunk_offset", 0),
"chunk_denoise_count": kwargs.get("chunk_denoise_count_value"),
"phase": (
"phase1_chunk"
if self.in_phase1(chunk_id, kwargs.get("chunk_denoise_count", {}))
else "phase2_token"
),
"reused": bool(reused),
"execution": "reuse" if reused else "compute",
"active_token_ratio": active_ratio,
"phase1_steps": self.phase1_steps,
"alpha": self.alpha,
"rel_l1_thresh": self.rel_l1_thresh,
}
self.execution_records.append(record)
def save_metric_stats(self):
if not self.metric_stats_path:
return
save_dir = os.path.dirname(self.metric_stats_path)
if save_dir:
os.makedirs(save_dir, exist_ok=True)
payload = {
"description": (
"MotionCache metric stats. Phase 1 uses chunk-wise FlowCache policy for "
f"the first {self.phase1_steps} denoise steps per chunk; Phase 2 uses motion-weighted "
"token accumulation with alpha floor and rel_l1_thresh."
),
"hyperparameters": {
"alpha": self.alpha,
"phase1_steps": self.phase1_steps,
"warmup_steps": self.warmup_steps,
"rel_l1_thresh": self.rel_l1_thresh,
},
"chunk_execution_summary": self.get_execution_summary(),
"execution_records": self.execution_records,
"records": self.metric_records,
}
if self.metric_stats_path.endswith((".pt", ".pth")):
torch.save(payload, self.metric_stats_path)
else:
with open(self.metric_stats_path, "w") as f:
json.dump(payload, f, indent=2)
print(f"Saved MotionCache metric stats to {self.metric_stats_path}")
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