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| # Copyright (C) 2022-present Naver Corporation. All rights reserved. | |
| # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). | |
| # -------------------------------------------------------- | |
| # Position embedding utils | |
| # -------------------------------------------------------- | |
| import numpy as np | |
| import torch | |
| # -------------------------------------------------------- | |
| # 2D sine-cosine position embedding | |
| # References: | |
| # MAE: https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py | |
| # Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py | |
| # MoCo v3: https://github.com/facebookresearch/moco-v3 | |
| # -------------------------------------------------------- | |
| def get_2d_sincos_pos_embed(embed_dim, grid_size, n_cls_token=0): | |
| """ | |
| grid_size: int of the grid height and width | |
| return: | |
| pos_embed: [grid_size*grid_size, embed_dim] or [n_cls_token+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) | |
| """ | |
| 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) # here w goes first | |
| 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 n_cls_token>0: | |
| pos_embed = np.concatenate([np.zeros([n_cls_token, 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 | |
| # use half of dimensions to encode grid_h | |
| emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) | |
| emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) | |
| emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) | |
| return emb | |
| def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): | |
| """ | |
| embed_dim: output dimension for each position | |
| pos: a list of positions to be encoded: size (M,) | |
| out: (M, D) | |
| """ | |
| assert embed_dim % 2 == 0 | |
| omega = np.arange(embed_dim // 2, dtype=float) | |
| omega /= embed_dim / 2. | |
| omega = 1. / 10000**omega # (D/2,) | |
| pos = pos.reshape(-1) # (M,) | |
| out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product | |
| emb_sin = np.sin(out) # (M, D/2) | |
| emb_cos = np.cos(out) # (M, D/2) | |
| emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) | |
| return emb | |
| # -------------------------------------------------------- | |
| # Interpolate position embeddings for high-resolution | |
| # References: | |
| # MAE: https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py | |
| # DeiT: https://github.com/facebookresearch/deit | |
| # -------------------------------------------------------- | |
| def interpolate_pos_embed(model, checkpoint_model): | |
| if 'pos_embed' in checkpoint_model: | |
| pos_embed_checkpoint = checkpoint_model['pos_embed'] | |
| embedding_size = pos_embed_checkpoint.shape[-1] | |
| num_patches = model.patch_embed.num_patches | |
| num_extra_tokens = model.pos_embed.shape[-2] - num_patches | |
| # height (== width) for the checkpoint position embedding | |
| orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) | |
| # height (== width) for the new position embedding | |
| new_size = int(num_patches ** 0.5) | |
| # class_token and dist_token are kept unchanged | |
| if orig_size != new_size: | |
| print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) | |
| extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] | |
| # only the position tokens are interpolated | |
| pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] | |
| pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) | |
| pos_tokens = torch.nn.functional.interpolate( | |
| pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) | |
| pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) | |
| new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) | |
| checkpoint_model['pos_embed'] = new_pos_embed | |
| #---------------------------------------------------------- | |
| # RoPE2D: RoPE implementation in 2D | |
| #---------------------------------------------------------- | |
| try: | |
| from models.curope import cuRoPE2D | |
| RoPE2D = cuRoPE2D | |
| except ImportError: | |
| print('Warning, cannot find cuda-compiled version of RoPE2D, using a slow pytorch version instead') | |
| class RoPE2D(torch.nn.Module): | |
| def __init__(self, freq=100.0, F0=1.0): | |
| super().__init__() | |
| self.base = freq | |
| self.F0 = F0 | |
| self.cache = {} | |
| def get_cos_sin(self, D, seq_len, device, dtype): | |
| 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) | |
| 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] | |
| 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 | |
| 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 2 (y and x position of each token) | |
| output: | |
| * tokens after appplying RoPE2D (batch_size x nheads x ntokens x dim) | |
| """ | |
| assert tokens.size(3)%2==0, "number of dimensions should be a multiple of two" | |
| D = tokens.size(3) // 2 | |
| assert positions.ndim==3 and positions.shape[-1] == 2 # Batch, Seq, 2 | |
| cos, sin = self.get_cos_sin(D, int(positions.max())+1, tokens.device, tokens.dtype) | |
| # split features into two along the feature dimension, and apply rope1d on each half | |
| y, x = tokens.chunk(2, dim=-1) | |
| y = self.apply_rope1d(y, positions[:,:,0], cos, sin) | |
| x = self.apply_rope1d(x, positions[:,:,1], cos, sin) | |
| tokens = torch.cat((y, x), dim=-1) | |
| return tokens |