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ba798d3 | 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 | """Frozen Teacher projectors that rebuild regular-RoPE and ProPE Context K/V."""
from __future__ import annotations
import copy
import torch
from einops import rearrange
from torch import nn
from hyvideo.models.transformers.modules.posemb_layers import (
apply_rotary_emb,
get_nd_rotary_pos_embed,
)
from hyvideo.prope.camera_rope import _prepare_apply_fns_all_dim
class FrozenTeacherContextProjector(nn.Module):
"""Rebuild the exact cache layout ``[regular batches, ProPE batches]``."""
def __init__(self, teacher_transformer: nn.Module, block_ids: tuple[int, ...]) -> None:
super().__init__()
self.block_ids = tuple(int(value) for value in block_ids)
self.heads = int(teacher_transformer.heads_num)
self.head_dim = int(teacher_transformer.hidden_size // self.heads)
self.rope_dim_list = tuple(teacher_transformer.rope_dim_list)
self.rope_theta = float(teacher_transformer.rope_theta)
self.patch_size = tuple(teacher_transformer.patch_size)
self.latent_height = int(getattr(teacher_transformer, "latent_height", 30))
self.latent_width = int(getattr(teacher_transformer, "latent_width", 52))
self.k_proj = nn.ModuleDict()
self.v_proj = nn.ModuleDict()
self.k_norm = nn.ModuleDict()
for block_id in self.block_ids:
block = teacher_transformer.double_blocks[block_id]
key = str(block_id)
self.k_proj[key] = copy.deepcopy(block.img_attn_k)
self.v_proj[key] = copy.deepcopy(block.img_attn_v)
self.k_norm[key] = copy.deepcopy(block.img_attn_k_norm)
self.eval().requires_grad_(False)
def _rope(self, frames: int, height: int, width: int, device: torch.device):
cos, sin = get_nd_rotary_pos_embed(
list(self.rope_dim_list),
(frames // self.patch_size[0], height, width),
theta=self.rope_theta,
use_real=True,
theta_rescale_factor=1,
)
return cos.to(device), sin.to(device)
def forward_block(
self,
block_id: int,
img_modulated: torch.Tensor,
context_viewmats: torch.Tensor,
context_Ks: torch.Tensor,
context_valid_mask: torch.Tensor,
*,
latent_height: int | None = None,
latent_width: int | None = None,
) -> dict[str, torch.Tensor]:
key = str(int(block_id))
if key not in self.k_proj:
raise ValueError(f"Projector has no block {block_id}")
latent_height = self.latent_height if latent_height is None else int(latent_height)
latent_width = self.latent_width if latent_width is None else int(latent_width)
batch, tokens, _ = img_modulated.shape
tokens_per_frame = latent_height * latent_width
if tokens % tokens_per_frame:
raise ValueError("Context features are not frame-aligned")
frames = tokens // tokens_per_frame
if tuple(context_valid_mask.shape) != (batch, tokens):
raise ValueError("Context valid mask shape mismatch")
if tuple(context_viewmats.shape) != (batch, frames, 4, 4):
raise ValueError("Context viewmats shape mismatch")
if tuple(context_Ks.shape) != (batch, frames, 3, 3):
raise ValueError("Context Ks shape mismatch")
if tokens == 0:
empty = img_modulated.new_empty((2 * batch, self.heads, 0, self.head_dim))
return {"k_vision": empty, "v_vision": empty.clone()}
k = rearrange(
self.k_proj[key](img_modulated),
"b s (h d) -> b s h d",
h=self.heads,
)
v = rearrange(
self.v_proj[key](img_modulated),
"b s (h d) -> b s h d",
h=self.heads,
)
k = self.k_norm[key](k).to(v)
cos, sin = self._rope(frames, latent_height, latent_width, img_modulated.device)
_, regular_k = apply_rotary_emb(k, k, (cos, sin), head_first=False)
regular_k = regular_k.permute(0, 2, 1, 3)
regular_v = v.permute(0, 2, 1, 3)
token_viewmats = context_viewmats.repeat_interleave(tokens_per_frame, dim=1)
token_Ks = context_Ks.repeat_interleave(tokens_per_frame, dim=1)
_, apply_kv, _ = _prepare_apply_fns_all_dim(
head_dim=self.head_dim,
viewmats=token_viewmats,
Ks=token_Ks,
patches_x=None,
patches_y=None,
image_width=None,
image_height=None,
)
prope_k = apply_kv(k.permute(0, 2, 1, 3))
prope_v = apply_kv(v.permute(0, 2, 1, 3))
k_cache = torch.cat([regular_k, prope_k], dim=0)
v_cache = torch.cat([regular_v, prope_v], dim=0)
branch_mask = torch.cat([context_valid_mask, context_valid_mask], dim=0)
branch_mask = branch_mask[:, None, :, None]
k_cache = k_cache.masked_fill(~branch_mask, 0)
v_cache = v_cache.masked_fill(~branch_mask, 0)
return {"k_vision": k_cache, "v_vision": v_cache}
def forward(self, context_by_block: dict[int, dict[str, torch.Tensor]]):
return {
int(block_id): self.forward_block(
int(block_id),
values["img_modulated"],
values["context_viewmats"],
values["context_Ks"],
values["context_valid_mask"],
)
for block_id, values in context_by_block.items()
}
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