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import torch
import torch.nn as nn
import re
import math
from transformers.modeling_utils import (
PreTrainedModel,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
from typing import Any, Optional, Tuple, Union
from transformers.modeling_outputs import BaseModelOutput
from functools import partial
from einops import rearrange
import numpy as np
import random
# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List, Union
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trunc_normal_
from torchvision import transforms
from torchvision.transforms import InterpolationMode
def build_matryoshka_query_transformer(config, delay_load=False, **kwargs):
target_sequence_length = 256
import math
grid_size = int(math.sqrt(target_sequence_length))
resampler = Resampler(
grid_size=grid_size,
embed_dim =config.hidden_size,
num_heads = 16,
kv_dim=1024,
)
return resampler
def get_matry_n(num_visual_tokens):
if num_visual_tokens == 'first_stage':
return 256
elif num_visual_tokens == 'second_stage':
matry_list = range(2, 258, 2)
return random.choice(matry_list)
try:
num_visual_tokens = int(num_visual_tokens)
if 1 <= num_visual_tokens <= 256:
return num_visual_tokens
except (ValueError, TypeError):
print('The num_visual_tokens is should be an integer between 1 and 256')
raise ValueError(f"Invalid input: {num_visual_tokens}")
def get_abs_pos(abs_pos, tgt_size):
# abs_pos: L, C
# tgt_size: (H, W)
# return: M, C
src_size = int(math.sqrt(abs_pos.size(0)))
# tgt_size = int(math.sqrt(tgt_size))
dtype = abs_pos.dtype
return F.interpolate(
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
size=(tgt_size[0], tgt_size[1]),
mode="bicubic",
align_corners=False,
).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
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)
"""
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 cls_token:
pos_embed = np.concatenate([np.zeros([1, 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=np.float32)
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
class Resampler(nn.Module):
"""
A 2D perceiver-resampler network with one cross attention layers by
(grid_size**2) learnable queries and 2d sincos pos_emb
Outputs:
A tensor with the shape of (grid_size**2, embed_dim)
"""
def __init__(
self,
grid_size,
embed_dim,
num_heads,
kv_dim=None,
norm_layer=partial(nn.LayerNorm, eps=1e-6)
):
super().__init__()
self.num_queries = grid_size ** 2
self.embed_dim = embed_dim
self.num_heads = num_heads
self.pos_embed = nn.Parameter(
torch.from_numpy(get_2d_sincos_pos_embed(kv_dim, grid_size)).half()
).requires_grad_(False)
self.query = nn.Parameter(torch.zeros(self.num_queries, kv_dim))
trunc_normal_(self.query, std=.02)
self.attn = nn.MultiheadAttention(kv_dim, num_heads)
self.ln_q = norm_layer(kv_dim)
self.ln_k = norm_layer(kv_dim)
self.ln_v = norm_layer(kv_dim)
# self.ln_post = norm_layer(kv_dim)
self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(kv_dim, embed_dim))
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x, num_visual_tokens=256, tgt_size=(24,24), attn_mask=None):
pos_embed = get_abs_pos(self.pos_embed, tgt_size)
x = (x).permute(1, 0, 2)
N = x.shape[1]
matry_n = get_matry_n(num_visual_tokens)
#print("number of visual tokens is:",matry_n)
q = (self.query[:matry_n])
q = self._repeat(q, N)
out = self.attn(
self.ln_q(q + self.pos_embed[:matry_n].unsqueeze(1)),
self.ln_k(self._repeat(pos_embed, N)),
self.ln_v(x),
attn_mask=attn_mask)[0]
x = out.permute(1, 0, 2)
x = x @ self.proj
return x
def _repeat(self, query, N: int):
return query.unsqueeze(1).repeat(1, N, 1)
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