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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]
@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
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 |