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# Adapted from Diffusers and Open-Sora-Plan
import torch
from diffusers.utils import logging
logger = logging.get_logger(__name__)
class PositionGetter3D(object):
""" return positions of patches """
def __init__(self, ):
self.cache_positions = {}
def __call__(self, b, t, h, w, device):
if not (b, t,h,w) in self.cache_positions:
x = torch.arange(w, device=device)
y = torch.arange(h, device=device)
z = torch.arange(t, device=device)
pos = torch.cartesian_prod(z, y, x)
pos = pos.reshape(t * h * w, 3).transpose(0, 1).reshape(3, 1, -1).contiguous().expand(3, b, -1).clone()
poses = (pos[0].contiguous(), pos[1].contiguous(), pos[2].contiguous())
max_poses = (int(poses[0].max()), int(poses[1].max()), int(poses[2].max()))
self.cache_positions[b, t, h, w] = (poses, max_poses)
pos = self.cache_positions[b, t, h, w]
return pos
class RoPE3D(torch.nn.Module):
def __init__(self, freq=10000.0, F0=1.0, interpolation_scale_thw=(1, 1, 1)):
super().__init__()
self.base = freq
self.F0 = F0
self.interpolation_scale_t = interpolation_scale_thw[0]
self.interpolation_scale_h = interpolation_scale_thw[1]
self.interpolation_scale_w = interpolation_scale_thw[2]
self.cache = {}
def get_cos_sin(self, D, seq_len, device, dtype, interpolation_scale=1):
if (D, seq_len, device, dtype) not in self.cache:
inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D))
t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) / interpolation_scale
freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype)
freqs = torch.cat((freqs, freqs), dim=-1)
cos = freqs.cos() # (Seq, Dim)
sin = freqs.sin()
self.cache[D, seq_len, device, dtype] = (cos, sin)
return self.cache[D, seq_len, device, dtype]
@staticmethod
def rotate_half(x):
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=-1)
def apply_rope1d(self, tokens, pos1d, cos, sin):
assert pos1d.ndim == 2
# for (batch_size x ntokens x nheads x dim)
cos = torch.nn.functional.embedding(pos1d, cos)[:, None, :, :]
sin = torch.nn.functional.embedding(pos1d, sin)[:, None, :, :]
return (tokens * cos) + (self.rotate_half(tokens) * sin)
def forward(self, tokens, positions):
"""
input:
* tokens: batch_size x nheads x ntokens x dim
* positions: batch_size x ntokens x 3 (t, y and x position of each token)
output:
* tokens after appplying RoPE3D (batch_size x nheads x ntokens x x dim)
"""
assert tokens.size(3) % 3 == 0, "number of dimensions should be a multiple of three"
D = tokens.size(3) // 3
poses, max_poses = positions
assert len(poses) == 3 and poses[0].ndim == 2# Batch, Seq, 3
cos_t, sin_t = self.get_cos_sin(D, max_poses[0] + 1, tokens.device, tokens.dtype, self.interpolation_scale_t)
cos_y, sin_y = self.get_cos_sin(D, max_poses[1] + 1, tokens.device, tokens.dtype, self.interpolation_scale_h)
cos_x, sin_x = self.get_cos_sin(D, max_poses[2] + 1, tokens.device, tokens.dtype, self.interpolation_scale_w)
# split features into three along the feature dimension, and apply rope1d on each half
t, y, x = tokens.chunk(3, dim=-1)
t = self.apply_rope1d(t, poses[0], cos_t, sin_t)
y = self.apply_rope1d(y, poses[1], cos_y, sin_y)
x = self.apply_rope1d(x, poses[2], cos_x, sin_x)
tokens = torch.cat((t, y, x), dim=-1)
return tokens
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