FlowCache/FlowCache4MAGI-1-dev3-motion/inference/pipeline/cache/motioncache.py

# Copyright (c) 2025 SandAI. All Rights Reserved.
#
# 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}")