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Running
on
Zero
File size: 5,368 Bytes
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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 |