130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
# -*- coding: utf-8 -*-
|
|
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
# All rights reserved.
|
|
# This file contains code that is adapted from
|
|
# timm: https://github.com/huggingface/pytorch-image-models
|
|
# pixart: https://github.com/PixArt-alpha/PixArt-alpha
|
|
from itertools import repeat as iter_repeat
|
|
from typing import Iterable
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
|
|
def _ntuple(n):
|
|
def parse(x):
|
|
if isinstance(x, Iterable) and not isinstance(x, str):
|
|
return x
|
|
return tuple(iter_repeat(x, n))
|
|
|
|
return parse
|
|
|
|
|
|
to_1tuple = _ntuple(1)
|
|
to_2tuple = _ntuple(2)
|
|
|
|
|
|
def get_2d_sincos_pos_embed(embed_dim,
|
|
grid_size,
|
|
cls_token=False,
|
|
extra_tokens=0,
|
|
lewei_scale=1.0,
|
|
base_h_size=16.,
|
|
base_w_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 = to_2tuple(grid_size)
|
|
grid_h = np.arange(grid_size[0], dtype=np.float32) / (
|
|
grid_size[0] / base_h_size) / lewei_scale
|
|
grid_w = np.arange(grid_size[1], dtype=np.float32) / (
|
|
grid_size[1] / base_w_size) / lewei_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):
|
|
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)
|
|
|
|
return np.concatenate([emb_h, emb_w], axis=1)
|
|
|
|
|
|
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=np.float64)
|
|
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)
|
|
return np.concatenate([emb_sin, emb_cos], axis=1)
|
|
|
|
|
|
def apply_2d_rope(xq,
|
|
xk,
|
|
padded_pos_index,
|
|
num_head,
|
|
head_dim,
|
|
rotary_base=10000):
|
|
'''
|
|
x query/key: [b, seq, num_head*head_dim]
|
|
padded_pos_index: [b, seq, 2]
|
|
'''
|
|
b = xq.shape[0]
|
|
assert head_dim % 4 == 0, 'the 2d_rope dims should be divided by 4'
|
|
rope_dim = head_dim // 2 # 2d_rope_dim, 1d_rope_dim = head_dim
|
|
# 1. theta_d = b ** (-2d/D)
|
|
theta = 1.0 / (rotary_base**(
|
|
torch.arange(0, rope_dim, 2)[:(rope_dim // 2)].float() / rope_dim))
|
|
# 2. [h * Theta || w * Theta]
|
|
theta = theta.to(xq.device).expand(b, 1, rope_dim // 2)
|
|
freqs_h = torch.bmm(padded_pos_index[:, :, :1],
|
|
theta).float() # h * \theta
|
|
freqs_w = torch.bmm(padded_pos_index[:, :, 1:],
|
|
theta).float() # w * \theta
|
|
freqs = torch.cat([freqs_h, freqs_w], dim=2).repeat(1, 1,
|
|
num_head) # multi-head
|
|
# 3. as_complex for complex multiply
|
|
# if freqs = [x, y] then freqs_cis = [cos(x) + sin(x)i, cos(y) + sin(y)i]
|
|
freqs_cis = torch.polar(
|
|
torch.ones_like(freqs),
|
|
freqs) # torch.polar(abs, angle)=> abs⋅cos(angle)+abs⋅sin(angle)⋅j
|
|
# xq.shape = [b, seq_len, dim]
|
|
# xq_.shape = [b, seq_len, dim // 2, 2]
|
|
xq_ = xq.float().reshape(*xq.shape[:-1], -1, 2)
|
|
xk_ = xk.float().reshape(*xk.shape[:-1], -1, 2)
|
|
# 转为复数域
|
|
xq_ = torch.view_as_complex(
|
|
xq_) # [b, seq_len, dim // 2, 2]=>xq.shape = [b, seq_len, dim]
|
|
xk_ = torch.view_as_complex(xk_)
|
|
# 4. complex multiply and as real
|
|
# xq_out.shape = [b, seq_len, dim]
|
|
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(
|
|
2) # point_wise mul, then flatten eg[[1,2],[3,4],[5,6]]->[1,2,3,4,5,6]
|
|
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(2)
|
|
return xq_out.type_as(xq), xk_out.type_as(xk)
|