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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.
# coding: utf-8
import math
import numpy as np
import torch
from torch import nn
from transformers.activations import ACT2FN
# --------------------------------------------------------
# 2D sine-cosine position embedding
# References:
# DiT: https://github.com/facebookresearch/DiT/blob/main/models.py
# --------------------------------------------------------
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
def get_3d_sincos_pos_embed_from_grid(embed_dim, grid):
"""
Get 3D sine-cosine positional embeddings from a grid.
"""
assert embed_dim % 2 == 0, "Embedding dimension must be even for 3D embeddings"
# 维度分配策略保持不变（确保每轴维度为偶数）
d = embed_dim // 3
d = d if d % 2 == 0 else d - 1
dim_t, dim_h = d, d
dim_w = embed_dim - 2 * d
assert dim_w % 2 == 0
emb_t = get_1d_sincos_pos_embed_from_grid(dim_t, grid[0]) # (T*H*W, Dt)
emb_h = get_1d_sincos_pos_embed_from_grid(dim_h, grid[1]) # (T*H*W, Dh)
emb_w = get_1d_sincos_pos_embed_from_grid(dim_w, grid[2]) # (T*H*W, Dw)
return np.concatenate([emb_t, emb_h, emb_w], axis=1)
def get_3d_sincos_pos_embed(embed_dim, t, h, w):
"""
Get 3D sine-cosine positional embeddings (v2 version, using thw indexing).
"""
grid_t = np.arange(t, dtype=np.float32)
grid_h = np.arange(h, dtype=np.float32)
grid_w = np.arange(w, dtype=np.float32)
tt, hh, ww = np.meshgrid(grid_t, grid_h, grid_w, indexing="ij") # (t,h,w)
grid = np.stack([tt, hh, ww], axis=0) # [3, t, h, w]
return get_3d_sincos_pos_embed_from_grid(embed_dim, grid)
# --------------------------------------------------------
# TimestepEmbedder
# Reference:
# DiT: https://github.com/facebookresearch/DiT/blob/main/models.py
# --------------------------------------------------------
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq) # 跟llm的hidden size对齐
return t_emb
class MLPconnector(nn.Module):
def __init__(self, in_dim: int, out_dim: int, hidden_act: str):
super().__init__()
self.activation_fn = ACT2FN[hidden_act]
self.fc1 = nn.Linear(in_dim, out_dim)
self.fc2 = nn.Linear(out_dim, out_dim)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class PositionEmbedding(nn.Module):
def __init__(self, max_num_patch_per_side, hidden_size):
super().__init__()
self.max_num_patch_per_side = max_num_patch_per_side
self.hidden_size = hidden_size
self.pos_embed = nn.Parameter(
torch.zeros(max_num_patch_per_side ** 2, hidden_size),
requires_grad=False
)
self._init_weights()
def _init_weights(self):
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(self.hidden_size, self.max_num_patch_per_side)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float())
def forward(self, position_ids):
return self.pos_embed[position_ids]
class PositionEmbedding3D(nn.Module):
def __init__(self, max_latent_num_frames, max_latent_size, hidden_size):
super().__init__()
self.max_num_latent_frames = max_latent_num_frames # t
self.max_latent_size = max_latent_size # h, w
self.hidden_size = hidden_size
self.pos_embed = nn.Parameter(torch.zeros(max_latent_num_frames * (max_latent_size**2), hidden_size), requires_grad=False)
self._init_weights()
def _init_weights(self):
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_3d_sincos_pos_embed(self.hidden_size, self.max_num_latent_frames, self.max_latent_size, self.max_latent_size)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float())
def forward(self, position_ids):
return self.pos_embed[position_ids]
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