File size: 9,588 Bytes
5a5bd88 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 |
# 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
import pickle
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import os
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
class GLU(nn.Module):
def __init__(self,hidden_size):
super().__init__()
self.linear_proj = nn.Linear(hidden_size,hidden_size,bias=False)
self.norm1 = nn.LayerNorm(hidden_size)
self.act1 = nn.GELU()
self.act2 = nn.functional.silu
self.dense_h_to_4h = nn.Linear(hidden_size,hidden_size*4,bias=False)
self.gate_proj = nn.Linear(hidden_size,hidden_size*4,bias=False)
self.dense_4h_to_h = nn.Linear(hidden_size*4,hidden_size,bias=False)
def forward(self,x):
x = self.linear_proj(x)
x = self.act1(self.norm1(x))
x = self.act2(self.gate_proj(x))*self.dense_h_to_4h(x)
x = self.dense_4h_to_h(x)
return x
def swiglu(x):
x = torch.chunk(x, 2, dim=-1)
return nn.functional.silu(x[0]) * x[1]
class GLU_new(nn.Module):
def __init__(self,hidden_size, dropout=0.1):
super().__init__()
intermediate_size = int((4 * hidden_size * 2 / 3) / 64) * 64
intermediate_size = 1280
self.act = swiglu
self.dense_h_to_4h = nn.Linear(hidden_size, intermediate_size * 2, bias=False)
self.dense_4h_to_h = nn.Linear(intermediate_size, hidden_size, bias=False)
self.dropout = nn.Dropout(p=dropout)
def forward(self,x):
x = self.dense_h_to_4h(x)
x = self.act(x)
x = self.dense_4h_to_h(x)
x = self.dropout(x)
return x
n_queries = 32
def get_abs_pos(abs_pos, tgt_size):
# abs_pos: L, C
# tgt_size: M
# return: M, C
src_size = int(math.sqrt(abs_pos.size(0)))
tgt_size = int(math.sqrt(tgt_size))
dtype = abs_pos.dtype
if src_size != tgt_size:
return F.interpolate(
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
size=(tgt_size, tgt_size),
mode="bicubic",
align_corners=False,
).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
else:
return abs_pos
from einops import rearrange, repeat
def get_1d_sincos_pos_embed(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):
def __init__(
self,
kv_dim,
embed_dim,
num_heads=8,
n_queries=64,
max_seqlen=1024,
perceiver_resampler_positional_emb=True,
use_GLU=False,
bos_init=False,
dropout=0.0
):
super().__init__()
self.perceiver_resampler_positional_emb = perceiver_resampler_positional_emb
if self.perceiver_resampler_positional_emb:
assert n_queries <= max_seqlen
self.stride = max_seqlen // n_queries
# self.nan_emb = nn.Parameter(torch.randn(1, kv_dim))
# nn.init.trunc_normal_(self.nan_emb, std=.02)
pos = np.arange(max_seqlen, dtype=np.float32)
self.register_buffer(
"pos_embed",
torch.from_numpy(get_1d_sincos_pos_embed(embed_dim, pos)).float()
)
self.latents = nn.Parameter(torch.randn(n_queries, embed_dim))
if bos_init:
self.latents.load('')
else:
nn.init.trunc_normal_(self.latents, std=1e-3)
self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
self.attn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True, dropout=dropout)
self.ln_q = nn.LayerNorm(embed_dim)
self.ln_kv = nn.LayerNorm(embed_dim)
self.ln_post = nn.LayerNorm(embed_dim)
if use_GLU:
print('GLU *********************************')
self.proj = GLU_new(embed_dim, dropout=dropout)
else:
self.proj = nn.Linear(embed_dim, embed_dim, bias=False)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.trunc_normal_(m.weight, std=1e-3)
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, struc_x):
"""
Args:
x (torch.Tensor): protein structure features
shape (B, L, C)
Returns:
shape (B, n, C) where n is self.num_latents
"""
x = struc_x["encoder_out"]
mask = struc_x["encoder_padding_mask"]
nan_mask = torch.isnan(x)
if nan_mask.any():
x = x.masked_fill(nan_mask, 0.0)
# nan_mask = nan_mask.sum(dim=-1).bool()
# x[nan_mask] += self.nan_emb
x = self.kv_proj(x)
x = self.ln_kv(x)
b, seqlen = x.shape[:2]
latents = self.ln_q(self.latents)
if self.perceiver_resampler_positional_emb:
# TODO: interpolate
latents = latents + self.pos_embed[::self.stride].contiguous()
pos_emb = self.pos_embed[:seqlen].unsqueeze(0)
x = x + pos_emb.contiguous()
# blocks
latents = repeat(latents, "n d -> b n d", b=b)
out = self.attn(latents, x, x, key_padding_mask=~mask)[0]
out = self.ln_post(out)
out = self.proj(out)
return out
class StructureTransformer(nn.Module):
def __init__(
self,
width: int = 640,
n_queries: int = 32,
output_dim: int = 4096,
embedding_keys=set(["mpnn_emb"]),
max_seqlen: int=1024,
num_heads: int=8,
structure_emb_path_prefix='structure_emb',
**kwargs
):
super().__init__()
self.structure_emb_path_prefix = structure_emb_path_prefix
# self.transformer = None # replace None with a pretrained strucure encoder
self.embedding_keys = embedding_keys
self.max_seqlen = max_seqlen
self.width = width
self.n_queries = n_queries
self.attn_pool = Resampler(
embed_dim=output_dim,
kv_dim=width,
n_queries=n_queries,
max_seqlen=max_seqlen,
num_heads=num_heads,
**kwargs
)
def prepare_structure(self, sample):
emb_pad = torch.zeros((self.max_seqlen, self.width))
emb_mask = torch.zeros((self.max_seqlen), dtype=bool)
if "pifold_emb" in self.embedding_keys and "pifold_mask" in sample:
mask = sample["pifold_mask"]
pifold_emb = sample["pifold_emb"]
new_pifold_emb = pifold_emb.new_zeros(mask.shape[0], pifold_emb.shape[1]).fill_(float("nan"))
new_pifold_emb[mask > 0] = pifold_emb
sample["pifold_emb"] = new_pifold_emb
### domians ###
emb = []
for ek in self.embedding_keys:
if ek in sample:
if isinstance( sample[ek], List):
emb.append(torch.cat(sample[ek]))
else:
emb.append(sample[ek])
# emb = [sample[ek] for ek in self.embedding_keys if ek in sample]
emb = torch.cat(emb, dim=-1)
emb_pad[:len(emb)] = emb
emb_mask[:len(emb)] = 1
return emb_pad, emb_mask
def forward(self, x):
# x = self.transformer(x)
x = self.attn_pool(x)
return x
def encode(self, structure_paths: List[str]):
structure_embs = []
structure_mask = []
for structure_path in structure_paths:
structure_path = [chr(s) for s in structure_path[:self.n_queries].tolist() if s > 0]
structure_path = os.path.join(self.structure_emb_path_prefix, ''.join(structure_path))
if not os.path.exists(structure_path):
print('no structure found')
return None
with open(structure_path, 'rb') as f:
structure, struc_mask = self.prepare_structure(pickle.load(f))
structure_embs.append(structure)
structure_mask.append(struc_mask)
structure_embs = torch.stack(structure_embs, dim=0).to(
device=next(self.attn_pool.parameters()).device,
dtype=next(self.attn_pool.parameters()).dtype)
structure_mask = torch.stack(structure_mask, dim=0).to(
device=next(self.attn_pool.parameters()).device)
return self({
'encoder_out': structure_embs,
'encoder_padding_mask': structure_mask
})
|