Spaces:
Runtime error
Runtime error
File size: 9,610 Bytes
bab971b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 |
# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
# croco: https://github.com/naver/croco
# diffusers: https://github.com/huggingface/diffusers
# --------------------------------------------------------
# Position embedding utils
# --------------------------------------------------------
import numpy as np
import torch
def get_2d_sincos_pos_embed(
embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=16
):
"""
grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
if isinstance(grid_size, int):
grid_size = (grid_size, grid_size)
grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
# 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(
embed_dim, length, interpolation_scale=1.0, base_size=16
):
pos = torch.arange(0, length).unsqueeze(1) / interpolation_scale
pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, pos)
return pos_embed
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)
"""
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 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
# ----------------------------------------------------------
# RoPE2D: RoPE implementation in 2D
# ----------------------------------------------------------
try:
from .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=10000.0, F0=1.0, scaling_factor=1.0):
super().__init__()
self.base = freq
self.F0 = F0
self.scaling_factor = scaling_factor
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
class LinearScalingRoPE2D(RoPE2D):
"""Code from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L148"""
def forward(self, tokens, positions):
# difference to the original RoPE: a scaling factor is aplied to the position ids
dtype = positions.dtype
positions = positions.float() / self.scaling_factor
positions = positions.to(dtype)
tokens = super().forward(tokens, positions)
return tokens
try:
from .curope import cuRoPE1D
RoPE1D = cuRoPE1D
except ImportError:
print('Warning, cannot find cuda-compiled version of RoPE2D, using a slow pytorch version instead')
class RoPE1D(torch.nn.Module):
def __init__(self, freq=10000.0, F0=1.0, scaling_factor=1.0):
super().__init__()
self.base = freq
self.F0 = F0
self.scaling_factor = scaling_factor
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 (t position of each token)
output:
* tokens after appplying RoPE2D (batch_size x nheads x ntokens x dim)
"""
D = tokens.size(3)
assert positions.ndim == 2 # Batch, Seq
cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype)
tokens = self.apply_rope1d(tokens, positions, cos, sin)
return tokens
class LinearScalingRoPE1D(RoPE1D):
"""Code from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L148"""
def forward(self, tokens, positions):
# difference to the original RoPE: a scaling factor is aplied to the position ids
dtype = positions.dtype
positions = positions.float() / self.scaling_factor
positions = positions.to(dtype)
tokens = super().forward(tokens, positions)
return tokens
class PositionGetter2D(object):
""" return positions of patches """
def __init__(self):
self.cache_positions = {}
def __call__(self, b, h, w, device):
if not (h,w) in self.cache_positions:
x = torch.arange(w, device=device)
y = torch.arange(h, device=device)
self.cache_positions[h,w] = torch.cartesian_prod(y, x) # (h, w, 2)
pos = self.cache_positions[h,w].view(1, h*w, 2).expand(b, -1, 2).clone()
return pos
class PositionGetter1D(object):
""" return positions of patches """
def __init__(self):
self.cache_positions = {}
def __call__(self, b, l, device):
if not (l) in self.cache_positions:
x = torch.arange(l, device=device)
self.cache_positions[l] = x # (l, )
pos = self.cache_positions[l].view(1, l).expand(b, -1).clone()
return pos |