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# Adapted from JiT: https://github.com/LTH14/JiT/blob/main/util/model_util.py
# Lightning-DiT: https://github.com/hustvl/LightningDiT
# Changes: device-agnostic buffers (no hard-coded .cuda())
# --------------------------------------------------------
from __future__ import annotations
from math import pi
import numpy as np
import torch
from einops import rearrange, repeat
from torch import nn
def broadcat(tensors, dim=-1):
num_tensors = len(tensors)
shape_lens = set(list(map(lambda t: len(t.shape), tensors)))
assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
shape_len = list(shape_lens)[0]
dim = (dim + shape_len) if dim < 0 else dim
dims = list(zip(*map(lambda t: list(t.shape), tensors)))
expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
assert all(
[*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]
), "invalid dimensions for broadcastable concatentation"
max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims))
expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims))
expanded_dims.insert(dim, (dim, dims[dim]))
expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims)))
tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes)))
return torch.cat(tensors, dim=dim)
def rotate_half(x):
x = rearrange(x, "... (d r) -> ... d r", r=2)
x1, x2 = x.unbind(dim=-1)
x = torch.stack((-x2, x1), dim=-1)
return rearrange(x, "... d r -> ... (d r)")
class VisionRotaryEmbeddingFast(nn.Module):
def __init__(
self,
dim,
pt_seq_len=16,
ft_seq_len=None,
custom_freqs=None,
freqs_for="lang",
theta=10000,
max_freq=10,
num_freqs=1,
num_cls_token=0,
):
super().__init__()
if custom_freqs:
freqs = custom_freqs
elif freqs_for == "lang":
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
elif freqs_for == "pixel":
freqs = torch.linspace(1.0, max_freq / 2, dim // 2) * pi
elif freqs_for == "constant":
freqs = torch.ones(num_freqs).float()
else:
raise ValueError(f"unknown modality {freqs_for}")
if ft_seq_len is None:
ft_seq_len = pt_seq_len
t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len
freqs = torch.einsum("..., f -> ... f", t, freqs)
freqs = repeat(freqs, "... n -> ... (n r)", r=2)
freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim=-1)
if num_cls_token > 0:
freqs_flat = freqs.view(-1, freqs.shape[-1])
cos_img = freqs_flat.cos()
sin_img = freqs_flat.sin()
n_img, d = cos_img.shape
cos_pad = torch.ones(num_cls_token, d, dtype=cos_img.dtype)
sin_pad = torch.zeros(num_cls_token, d, dtype=sin_img.dtype)
self.register_buffer("freqs_cos", torch.cat([cos_pad, cos_img], dim=0), persistent=False)
self.register_buffer("freqs_sin", torch.cat([sin_pad, sin_img], dim=0), persistent=False)
else:
self.register_buffer("freqs_cos", freqs.cos().view(-1, freqs.shape[-1]), persistent=False)
self.register_buffer("freqs_sin", freqs.sin().view(-1, freqs.shape[-1]), persistent=False)
def forward(self, t):
return t * self.freqs_cos + rotate_half(t) * self.freqs_sin
class RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return (self.weight * hidden_states).to(input_dtype)
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)
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
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])
emb = np.concatenate([emb_h, emb_w], axis=1)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega
pos = pos.reshape(-1)
out = np.einsum("m,d->md", pos, omega)
emb_sin = np.sin(out)
emb_cos = np.cos(out)
emb = np.concatenate([emb_sin, emb_cos], axis=1)
return emb
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