Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,368 Bytes
9eb3654 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
from math import pi
import torch
from torch import nn
from einops import rearrange, repeat
import logging
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 VisionRotaryEmbedding(nn.Module):
def __init__(
self,
dim,
pt_seq_len,
ft_seq_len=None,
custom_freqs = None,
freqs_for = 'lang',
theta = 10000,
max_freq = 10,
num_freqs = 1,
):
super().__init__()
if custom_freqs:
freqs = custom_freqs
elif freqs_for == 'lang':
freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
elif freqs_for == 'pixel':
freqs = torch.linspace(1., 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_h = torch.einsum('..., f -> ... f', t, freqs)
freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2)
freqs_w = torch.einsum('..., f -> ... f', t, freqs)
freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2)
freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1)
self.register_buffer("freqs_cos", freqs.cos())
self.register_buffer("freqs_sin", freqs.sin())
logging.info(f'Shape of rope freq: {self.freqs_cos.shape}')
def forward(self, t, start_index = 0):
rot_dim = self.freqs_cos.shape[-1]
end_index = start_index + rot_dim
assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}'
t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:]
t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin)
return torch.cat((t_left, t, t_right), dim = -1)
class VisionRotaryEmbeddingFast(nn.Module):
def __init__(
self,
dim,
pt_seq_len,
ft_seq_len=None,
custom_freqs = None,
freqs_for = 'lang',
theta = 10000,
max_freq = 10,
num_freqs = 1,
patch_dropout = 0.
):
super().__init__()
if custom_freqs:
freqs = custom_freqs
elif freqs_for == 'lang':
freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
elif freqs_for == 'pixel':
freqs = torch.linspace(1., 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)
freqs_cos = freqs.cos().view(-1, freqs.shape[-1])
freqs_sin = freqs.sin().view(-1, freqs.shape[-1])
self.patch_dropout = patch_dropout
self.register_buffer("freqs_cos", freqs_cos)
self.register_buffer("freqs_sin", freqs_sin)
logging.info(f'Shape of rope freq: {self.freqs_cos.shape}')
def forward(self, t, patch_indices_keep=None):
if patch_indices_keep is not None:
batch = t.size()[0]
batch_indices = torch.arange(batch)
batch_indices = batch_indices[..., None]
freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
freqs_cos = freqs_cos[batch_indices, patch_indices_keep]
freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j')
freqs_sin = freqs_sin[batch_indices, patch_indices_keep]
freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j')
return t * freqs_cos + rotate_half(t) * freqs_sin
return t * self.freqs_cos + rotate_half(t) * self.freqs_sin |