m_diffae_v2/decoder.py

"""mDiffAE v2 decoder: skip-concat topology with dual PDG (token masking + path drop).

No outer RMSNorms (use_other_outer_rms_norms=False during training):
norm_in, latent_norm, and norm_out are all absent.
"""

from __future__ import annotations

import math

import torch
from torch import Tensor, nn

from .adaln import AdaLNZeroLowRankDelta, AdaLNZeroProjector
from .dico_block import DiCoBlock
from .straight_through_encoder import Patchify
from .time_embed import SinusoidalTimeEmbeddingMLP


class Decoder(nn.Module):
    """VP diffusion decoder conditioned on encoder latents and timestep.

    Architecture (skip-concat, 2+4+2 default):
        Patchify x_t -> Fuse with upsampled z
        -> Start blocks (2) -> Middle blocks (4) -> Skip fuse -> End blocks (2)
        -> Conv1x1 -> PixelShuffle

    Dual PDG at inference:
    - Path drop: replace middle block output with ``path_drop_mask_feature``.
    - Token mask: replace a fraction of upsampled latent tokens with
      ``latent_mask_feature`` before fusion.
    """

    def __init__(
        self,
        in_channels: int,
        patch_size: int,
        model_dim: int,
        depth: int,
        start_block_count: int,
        end_block_count: int,
        bottleneck_dim: int,
        mlp_ratio: float,
        depthwise_kernel_size: int,
        adaln_low_rank_rank: int,
        pdg_mask_ratio: float = 0.75,
    ) -> None:
        super().__init__()
        self.patch_size = int(patch_size)
        self.model_dim = int(model_dim)
        self.pdg_mask_ratio = float(pdg_mask_ratio)

        # Input processing (no norm_in — use_other_outer_rms_norms=False)
        self.patchify = Patchify(in_channels, patch_size, model_dim)

        # Latent conditioning path (no latent_norm)
        self.latent_up = nn.Conv2d(bottleneck_dim, model_dim, kernel_size=1, bias=True)
        self.fuse_in = nn.Conv2d(2 * model_dim, model_dim, kernel_size=1, bias=True)

        # Time embedding
        self.time_embed = SinusoidalTimeEmbeddingMLP(model_dim)

        # AdaLN: shared base projector + per-block low-rank deltas
        self.adaln_base = AdaLNZeroProjector(d_model=model_dim, d_cond=model_dim)
        self.adaln_deltas = nn.ModuleList(
            [
                AdaLNZeroLowRankDelta(
                    d_model=model_dim, d_cond=model_dim, rank=adaln_low_rank_rank
                )
                for _ in range(depth)
            ]
        )

        # Block layout: start + middle + end
        middle_count = depth - start_block_count - end_block_count
        self._middle_start_idx = start_block_count
        self._end_start_idx = start_block_count + middle_count

        def _make_blocks(count: int) -> nn.ModuleList:
            return nn.ModuleList(
                [
                    DiCoBlock(
                        model_dim,
                        mlp_ratio,
                        depthwise_kernel_size=depthwise_kernel_size,
                        use_external_adaln=True,
                    )
                    for _ in range(count)
                ]
            )

        self.start_blocks = _make_blocks(start_block_count)
        self.middle_blocks = _make_blocks(middle_count)
        self.fuse_skip = nn.Conv2d(2 * model_dim, model_dim, kernel_size=1, bias=True)
        self.end_blocks = _make_blocks(end_block_count)

        # Learned mask features for dual PDG
        self.latent_mask_feature = nn.Parameter(torch.zeros((1, model_dim, 1, 1)))
        self.path_drop_mask_feature = nn.Parameter(torch.zeros((1, model_dim, 1, 1)))

        # Output head (no norm_out — use_other_outer_rms_norms=False)
        self.out_proj = nn.Conv2d(
            model_dim, in_channels * (patch_size**2), kernel_size=1, bias=True
        )
        self.unpatchify = nn.PixelShuffle(patch_size)

    def _adaln_m_for_layer(self, cond: Tensor, layer_idx: int) -> Tensor:
        """Compute packed AdaLN modulation = shared_base + per-layer delta."""
        act = self.adaln_base.act(cond)
        base_m = self.adaln_base.forward_activated(act)
        delta_m = self.adaln_deltas[layer_idx](act)
        return base_m + delta_m

    def _run_blocks(
        self, blocks: nn.ModuleList, x: Tensor, cond: Tensor, start_index: int
    ) -> Tensor:
        """Run a group of decoder blocks with per-block AdaLN modulation."""
        for local_idx, block in enumerate(blocks):
            adaln_m = self._adaln_m_for_layer(cond, layer_idx=start_index + local_idx)
            x = block(x, adaln_m=adaln_m)
        return x

    def _apply_latent_token_mask(self, z_up: Tensor) -> Tensor:
        """Replace a fraction of upsampled latent tokens with latent_mask_feature.

        Uses 2x2 groupwise masking: divides the spatial grid into 2x2 groups
        and masks floor(ratio * 4) tokens per group (lowest random scores).

        Args:
            z_up: [B, C, H, W] upsampled latent conditioning.

        Returns:
            Masked tensor with same shape.
        """
        b, c, h, w = z_up.shape
        # Pad to even dims if needed
        h_pad = (2 - h % 2) % 2
        w_pad = (2 - w % 2) % 2
        if h_pad > 0 or w_pad > 0:
            z_up = torch.nn.functional.pad(z_up, (0, w_pad, 0, h_pad))
            _, _, h, w = z_up.shape

        # Reshape into 2x2 groups: [B, C, H/2, 2, W/2, 2] -> [B, C, H/2, W/2, 4]
        x = z_up.reshape(b, c, h // 2, 2, w // 2, 2)
        x = x.permute(0, 1, 2, 4, 3, 5).reshape(b, c, h // 2, w // 2, 4)

        # Random scores for each token in each group
        scores = torch.rand(b, 1, h // 2, w // 2, 4, device=z_up.device)

        # Mask the floor(ratio * 4) lowest-scoring tokens per group
        num_mask = math.floor(self.pdg_mask_ratio * 4)
        if num_mask > 0:
            _, indices = scores.sort(dim=-1)
            mask = torch.zeros_like(scores, dtype=torch.bool)
            mask.scatter_(-1, indices[..., :num_mask], True)
        else:
            mask = torch.zeros_like(scores, dtype=torch.bool)

        # Apply mask: replace masked tokens with latent_mask_feature
        mask_feat = self.latent_mask_feature.to(device=z_up.device, dtype=z_up.dtype)
        mask_feat = mask_feat.squeeze(-1).squeeze(-1)  # [1, C]
        mask_feat = mask_feat.view(1, c, 1, 1, 1).expand_as(x)
        mask_expanded = mask.expand_as(x)
        x = torch.where(mask_expanded, mask_feat, x)

        # Reshape back to [B, C, H, W]
        x = x.reshape(b, c, h // 2, w // 2, 2, 2)
        x = x.permute(0, 1, 2, 4, 3, 5).reshape(b, c, h, w)

        # Remove padding
        if h_pad > 0 or w_pad > 0:
            x = x[:, :, : h - h_pad, : w - w_pad]

        return x

    def forward(
        self,
        x_t: Tensor,
        t: Tensor,
        latents: Tensor,
        *,
        drop_middle_blocks: bool = False,
        mask_latent_tokens: bool = False,
    ) -> Tensor:
        """Single decoder forward pass.

        Args:
            x_t: Noised image [B, C, H, W].
            t: Timestep [B] in [0, 1].
            latents: Encoder latents [B, bottleneck_dim, h, w].
            drop_middle_blocks: If True, replace middle block output with
                path_drop_mask_feature (for path-drop PDG).
            mask_latent_tokens: If True, mask a fraction of upsampled latent
                tokens with latent_mask_feature (for token-mask PDG).

        Returns:
            x0 prediction [B, C, H, W].
        """
        # Patchify x_t (no norm_in)
        x_feat = self.patchify(x_t)

        # Upsample latents (no latent_norm)
        z_up = self.latent_up(latents)

        # Token masking for PDG (replaces latent tokens with latent_mask_feature)
        if mask_latent_tokens:
            z_up = self._apply_latent_token_mask(z_up)

        # Fuse x_feat and z_up
        fused = torch.cat([x_feat, z_up], dim=1)
        fused = self.fuse_in(fused)

        # Time conditioning
        cond = self.time_embed(t.to(torch.float32).to(device=x_t.device))

        # Start blocks
        start_out = self._run_blocks(self.start_blocks, fused, cond, start_index=0)

        # Middle blocks (or path_drop_mask_feature for PDG)
        if drop_middle_blocks:
            middle_out = self.path_drop_mask_feature.to(
                device=x_t.device, dtype=x_t.dtype
            ).expand_as(start_out)
        else:
            middle_out = self._run_blocks(
                self.middle_blocks,
                start_out,
                cond,
                start_index=self._middle_start_idx,
            )

        # Skip fusion
        skip_fused = torch.cat([start_out, middle_out], dim=1)
        skip_fused = self.fuse_skip(skip_fused)

        # End blocks
        end_out = self._run_blocks(
            self.end_blocks, skip_fused, cond, start_index=self._end_start_idx
        )

        # Output head (no norm_out)
        patches = self.out_proj(end_out)
        return self.unpatchify(patches)