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# // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // 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.
from functools import lru_cache
from typing import Optional, Tuple
import torch
from einops import rearrange
from rotary_embedding_torch import RotaryEmbedding, apply_rotary_emb
from torch import nn
from common.cache import Cache
class RotaryEmbeddingBase(nn.Module):
def __init__(self, dim: int, rope_dim: int):
super().__init__()
self.rope = RotaryEmbedding(
dim=dim // rope_dim,
freqs_for="pixel",
max_freq=256,
)
# 1. Set model.requires_grad_(True) after model creation will make
# the `requires_grad=False` for rope freqs no longer hold.
# 2. Even if we don't set requires_grad_(True) explicitly,
# FSDP is not memory efficient when handling fsdp_wrap
# with mixed requires_grad=True/False.
# With above consideration, it is easier just remove the freqs
# out of nn.Parameters when `learned_freq=False`
freqs = self.rope.freqs
del self.rope.freqs
self.rope.register_buffer("freqs", freqs.data)
@lru_cache(maxsize=128)
def get_axial_freqs(self, *dims):
return self.rope.get_axial_freqs(*dims)
class RotaryEmbedding3d(RotaryEmbeddingBase):
def __init__(self, dim: int):
super().__init__(dim, rope_dim=3)
self.mm = False
def forward(
self,
q: torch.FloatTensor, # b h l d
k: torch.FloatTensor, # b h l d
size: Tuple[int, int, int],
) -> Tuple[
torch.FloatTensor,
torch.FloatTensor,
]:
T, H, W = size
freqs = self.get_axial_freqs(T, H, W)
q = rearrange(q, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
k = rearrange(k, "b h (T H W) d -> b h T H W d", T=T, H=H, W=W)
q = apply_rotary_emb(freqs, q.float()).to(q.dtype)
k = apply_rotary_emb(freqs, k.float()).to(k.dtype)
q = rearrange(q, "b h T H W d -> b h (T H W) d")
k = rearrange(k, "b h T H W d -> b h (T H W) d")
return q, k
class MMRotaryEmbeddingBase(RotaryEmbeddingBase):
def __init__(self, dim: int, rope_dim: int):
super().__init__(dim, rope_dim)
self.rope = RotaryEmbedding(
dim=dim // rope_dim,
freqs_for="lang",
theta=10000,
)
freqs = self.rope.freqs
del self.rope.freqs
self.rope.register_buffer("freqs", freqs.data)
self.mm = True
class NaMMRotaryEmbedding3d(MMRotaryEmbeddingBase):
def __init__(self, dim: int):
super().__init__(dim, rope_dim=3)
def forward(
self,
vid_q: torch.FloatTensor, # L h d
vid_k: torch.FloatTensor, # L h d
vid_shape: torch.LongTensor, # B 3
txt_q: torch.FloatTensor, # L h d
txt_k: torch.FloatTensor, # L h d
txt_shape: torch.LongTensor, # B 1
cache: Cache,
) -> Tuple[
torch.FloatTensor,
torch.FloatTensor,
torch.FloatTensor,
torch.FloatTensor,
]:
vid_freqs, txt_freqs = cache(
"mmrope_freqs_3d",
lambda: self.get_freqs(vid_shape, txt_shape),
)
vid_q = rearrange(vid_q, "L h d -> h L d")
vid_k = rearrange(vid_k, "L h d -> h L d")
vid_q = apply_rotary_emb(vid_freqs, vid_q.float()).to(vid_q.dtype)
vid_k = apply_rotary_emb(vid_freqs, vid_k.float()).to(vid_k.dtype)
vid_q = rearrange(vid_q, "h L d -> L h d")
vid_k = rearrange(vid_k, "h L d -> L h d")
txt_q = rearrange(txt_q, "L h d -> h L d")
txt_k = rearrange(txt_k, "L h d -> h L d")
txt_q = apply_rotary_emb(txt_freqs, txt_q.float()).to(txt_q.dtype)
txt_k = apply_rotary_emb(txt_freqs, txt_k.float()).to(txt_k.dtype)
txt_q = rearrange(txt_q, "h L d -> L h d")
txt_k = rearrange(txt_k, "h L d -> L h d")
return vid_q, vid_k, txt_q, txt_k
def get_freqs(
self,
vid_shape: torch.LongTensor,
txt_shape: torch.LongTensor,
) -> Tuple[
torch.Tensor,
torch.Tensor,
]:
vid_freqs = self.get_axial_freqs(1024, 128, 128)
txt_freqs = self.get_axial_freqs(1024)
vid_freq_list, txt_freq_list = [], []
for (f, h, w), l in zip(vid_shape.tolist(), txt_shape[:, 0].tolist()):
vid_freq = vid_freqs[l : l + f, :h, :w].reshape(-1, vid_freqs.size(-1))
txt_freq = txt_freqs[:l].repeat(1, 3).reshape(-1, vid_freqs.size(-1))
vid_freq_list.append(vid_freq)
txt_freq_list.append(txt_freq)
return torch.cat(vid_freq_list, dim=0), torch.cat(txt_freq_list, dim=0)
def get_na_rope(rope_type: Optional[str], dim: int):
if rope_type is None:
return None
if rope_type == "mmrope3d":
return NaMMRotaryEmbedding3d(dim=dim)
raise NotImplementedError(f"{rope_type} is not supported.")
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