| 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 |