File size: 16,513 Bytes
e2b7617 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 |
import math
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
import partialsmiles as ps
# logger = logging.getLogger(__name__)
from SCMG.config import varables as VBS
from torch.autograd import Variable
import partialsmiles as ps
from SCMG.utils.utils_rsd import *
from rdkit import Chem
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
class PositionalEncoder(nn.Module):
def __init__(self, config):
super(PositionalEncoder, self).__init__()
self.Dropout = nn.Dropout(p=config[VBS.RATE_DROPOUT])
max_len = config[VBS.SIZE_BLOCK]
pe = torch.zeros(max_len, config[VBS.DIM_EMBEDDING])
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, config[VBS.DIM_EMBEDDING], 2).float() * (-math.log(10000.0) / config[VBS.DIM_EMBEDDING]))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self, T):
x = self.Dropout(self.pe[:,:T, :])
return x
class Attention(nn.Module):
def __init__(self, config):
super().__init__()
assert config[VBS.DIM_ATTENTION] % config[VBS.NUM_HEADS] == 0
self.Key = nn.Linear(config[VBS.DIM_EMBEDDING], config[VBS.DIM_ATTENTION])
self.Query = nn.Linear(config[VBS.DIM_EMBEDDING], config[VBS.DIM_ATTENTION])
self.Value = nn.Linear(config[VBS.DIM_EMBEDDING], config[VBS.DIM_ATTENTION])
self.Dropout_Attention = nn.Dropout(config[VBS.RATE_DROPOUT])
self.Dropout_Residue = nn.Dropout(config[VBS.RATE_DROPOUT])
self.Projection = nn.Linear(config[VBS.DIM_ATTENTION], config[VBS.DIM_EMBEDDING])
self.NumberOfHeads = config[VBS.NUM_HEADS]
self.DimHead = config[VBS.DIM_ATTENTION] // self.NumberOfHeads
self.DimAttention = config[VBS.DIM_ATTENTION]
def forward(self, X_1,X_2, mask=None):
if X_2 is None:
X_2 = X_1
BatchSize, T_Encoder, _ = X_1.size()
BatchSize, T_Decoder, _ = X_2.size()
K = self.Key( X_1).view(BatchSize, T_Encoder, self.NumberOfHeads,self.DimHead).transpose(1, 2)
Q = self.Query(X_2).view(BatchSize, T_Decoder, self.NumberOfHeads,self.DimHead).transpose(1, 2)
V = self.Value(X_1).view(BatchSize, T_Encoder, self.NumberOfHeads,self.DimHead).transpose(1, 2)
# k,q,v dimension: (BatchSize, SequenceSize, NumberOfHeads, HeadDimension) 3,4,5,16
ScoreAttention = (Q @ K.transpose(-2, -1)) / math.sqrt(self.DimHead)
ScoreAttention = ScoreAttention.masked_fill(mask==0, -1e9)
ScoreAttention = F.softmax(ScoreAttention, dim=-1)
ScoreAttention = self.Dropout_Attention(ScoreAttention)
# k.transpose(-2,-1): 3,4,16,5
# (q@(k.transpose(-2,-1))): 3,4,5,5
Z = ScoreAttention @ V
# y dimension: 3,4,5,16
Z = Z.transpose(1, 2).contiguous().view(BatchSize, T_Decoder, self.DimAttention)
# y dimension: 3,5,64
Z = self.Dropout_Residue(self.Projection(Z))
return Z
class FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
if config[VBS.DIM_FEEDFORWARD] == 0:
Dim_FeedForward = config[VBS.DIM_ATTENTION] *4
else:
Dim_FeedForward = config[VBS.DIM_FEEDFORWARD]
self.Linear1 = nn.Linear(config[VBS.DIM_EMBEDDING], Dim_FeedForward)
self.GELU = nn.GELU()
self.Linear2 = nn.Linear(Dim_FeedForward, config[VBS.DIM_EMBEDDING])
self.Dropout = nn.Dropout(config[VBS.RATE_DROPOUT])
def forward(self,x):
x = self.Linear1(x)
x = self.GELU (x)
x = self.Dropout(x)
x = self.Linear2(x)
return x
class EncoderBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.LayerNorm1 = nn.LayerNorm(config[VBS.DIM_EMBEDDING])
self.LayerNorm2 = nn.LayerNorm(config[VBS.DIM_EMBEDDING])
self.Dropout1 = nn.Dropout(config[VBS.RATE_DROPOUT])
self.Dropout2 = nn.Dropout(config[VBS.RATE_DROPOUT])
self.Attention = Attention( config)
self.FeedForward = FeedForward(config)
def forward(self, X_Encoder,Mask_Encoder):
X_Encoder = self.LayerNorm1(X_Encoder + self.Attention (self.Dropout1(X_Encoder), None, Mask_Encoder))
X_Encoder = self.LayerNorm2(X_Encoder + self.FeedForward(self.Dropout2(X_Encoder)))
return X_Encoder
class DecoderBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.LayerNorm1 = nn.LayerNorm(config[VBS.DIM_EMBEDDING])
self.LayerNorm2 = nn.LayerNorm(config[VBS.DIM_EMBEDDING])
self.LayerNorm3 = nn.LayerNorm(config[VBS.DIM_EMBEDDING])
self.Dropout1 = nn.Dropout(config[VBS.RATE_DROPOUT])
self.Dropout2 = nn.Dropout(config[VBS.RATE_DROPOUT])
self.Dropout3 = nn.Dropout(config[VBS.RATE_DROPOUT])
self.AttentionMasked = Attention( config)
self.AttentionCross = Attention( config)
self.FeedForward = FeedForward(config)
def forward(self, X_Encoder,X_Decoder,Mask_Cross,Mask_Decoder):
X_Decoder = self.LayerNorm1(X_Decoder + self.AttentionMasked(self.Dropout1(X_Decoder), None, Mask_Decoder))
X_Decoder = self.LayerNorm2(X_Decoder + self.AttentionCross ( X_Encoder, self.Dropout2(X_Decoder), Mask_Cross ))
X_Decoder = self.LayerNorm3(X_Decoder + self.FeedForward (self.Dropout3(X_Decoder) ))
return X_Decoder
class Model(nn.Module):
def __init__(self, config):
super().__init__()
# VBS
self.Dim_Embedding = config[VBS.DIM_EMBEDDING]
self.Token_Padding_Encoder = config["Token_Padding_Encoder"]
self.Token_Padding_Decoder = config["Token_Padding_Decoder"]
# Embedding and positional encoding layers
self.Embedding_Encoder = nn.Embedding(len(config["vocab_encoder"]), config[VBS.DIM_EMBEDDING])
self.Embedding_Decoder = nn.Embedding(len(config["vocab_decoder"]), config[VBS.DIM_EMBEDDING])
self.pos_emb = PositionalEncoder(config)
# Dropout and normalization layers
self.Dropout1 = nn.Dropout(config[VBS.RATE_DROPOUT])
self.Dropout2 = nn.Dropout(config[VBS.RATE_DROPOUT])
self.LayerNorm1 = nn.LayerNorm(config[VBS.DIM_EMBEDDING])
self.LayerNorm2 = nn.LayerNorm(config[VBS.DIM_EMBEDDING])
# Transformer layers
self.encoder_blocks = nn.ModuleList([EncoderBlock(config) for _ in range(config[VBS.NUM_LAYERS])])
self.decoder_blocks = nn.ModuleList([DecoderBlock(config) for _ in range(config[VBS.NUM_LAYERS])])
# Output layer
self.head = nn.Linear(config[VBS.DIM_EMBEDDING], len(config["vocab_decoder"]), bias=False)
# Init
self.apply(self._init_weights)
self.optimizer = None
self.Alpha_LabelSmoothing = None
self.TokenWeight = None
# logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
def _set_train_params(self,Config):
self.Alpha_LabelSmoothing = Config["Alpha_LabelSmoothing"]
self.TokenWeight = Config["TokenWeight"]
def _init_weights(self, module):
for p in module.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
# if isinstance(module, (nn.Linear, nn.Embedding)):
# module.weight.data.normal_(mean=0.0, std=0.02)
# if isinstance(module, nn.Linear) and module.bias is not None:
# module.bias.data.zero_()
# elif isinstance(module, nn.LayerNorm):
# module.bias.data.zero_()
# module.weight.data.fill_(1.0)
def init_optimizers(self,train_config):
optimizer = torch.optim.Adam(self.parameters(), lr=train_config[VBS.RATE_LEARNING])
return optimizer
def init_scheduler(self,train_config):
scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=train_config[VBS.SIZE_STEP], gamma=train_config[VBS.GAMMA])
return scheduler
def get_collate_fn(self, vocab_encoder,vocab_decoder):
def collate(results):
X_Encoder = [a[0] for a in results]
X_Decoder = [a[1] for a in results]
Auxiliary = [a[2] for a in results]
#
max_len_x = max([len(a) for a in X_Encoder])
max_len_y = max([len(a) for a in X_Decoder])
#
x = torch.tensor([(a+[vocab_encoder[VBS.TOKEN_PAD] for _ in range(max_len_x-len(a))]) for a in X_Encoder],dtype=torch.long)
y = torch.tensor([(a+[vocab_decoder[VBS.TOKEN_PAD] for _ in range(max_len_y-len(a))]) for a in X_Decoder],dtype=torch.long)
if isinstance(Auxiliary[0],list):
MaxLen_Auxiliary = max([len(TruthTable) for TruthTable in Auxiliary])
Len_Vocab = len(self.List_Vocab_Decoder)
Auxiliary = torch.tensor([TruthTable+[[0 for _ in range(Len_Vocab)] for _ in range(MaxLen_Auxiliary-len(TruthTable))] for TruthTable in Auxiliary])
##
#
return x,y,Auxiliary
return collate
def customize_model_fn(self,diex):
def fn(diex):
bos_token = diex[VBS.COLUMN_TASK_TYPE]
# Encoder
x_in = self.tokenizer(diex[VBS.COLUMN_ENCODER])
if len(x_in)>0:
x_in = [bos_token] + x_in + [VBS.TOKEN_END]
x_in = [self.vocab_encoder[a] if a in self.vocab_encoder.keys() else self.vocab_encoder["<unk>"] for a in x_in ]
# Decoder
y_in = self.tokenizer(diex[VBS.COLUMN_DECODER])
y_in = [bos_token] + y_in + [VBS.TOKEN_END]
# Auxiliary
## 1. partial
## Is Valid
TruthTable = []
for CurrentIndex in range(1,len(y_in)):
if (y_in[CurrentIndex] == "|" or "<" in y_in[CurrentIndex]) and y_in[CurrentIndex] != VBS.TOKEN_END:
TruthTable.append([0 for _ in range(len(self.List_Vocab_Decoder))])
continue
CurrentTruthTable = []
for CurrentToken in self.List_Vocab_Decoder:
try:
_ = ps.ParseSmiles("".join(y_in[1:CurrentIndex])+CurrentToken, partial=True)
IsValid = 1
except:
IsValid = 0
if CurrentToken == VBS.TOKEN_END:
CurrentSMI = join_scaf_deco(diex[VBS.COLUMN_ENCODER],"".join(y_in[1:CurrentIndex]))
if len(CurrentSMI) > 0:
IsValid = 1
CurrentTruthTable.append(IsValid)
TruthTable.append(CurrentTruthTable)
# StrPrint = "".join([f"{a:3}" for a in TruthTable])
# print(f'''{y_in[i][:5]:5} {StrPrint}''')
y_in = [self.vocab_decoder[a] if a in self.vocab_decoder.keys() else self.vocab_decoder["<unk>"] for a in y_in ]
Auxiliary = TruthTable
return x_in,y_in,Auxiliary
return fn
def generate_masks(self,X_Encoder, X_Decoder):
with torch.no_grad():
# Generate encoder, decoder, cross masks
T = X_Decoder.shape[1]
Mask_Encoder = (X_Encoder != self.Token_Padding_Encoder).unsqueeze(-2).unsqueeze(-2)
Mask_Decoder = (X_Decoder != self.Token_Padding_Decoder).unsqueeze(-2).unsqueeze(-2).repeat(1,1,T,1)
Mask_Cross = (X_Encoder != self.Token_Padding_Encoder).unsqueeze(-2).unsqueeze(-2)
mask_tril = torch.tril(torch.ones(T, T)).view(1, 1, T, T).to(Mask_Decoder.device)
Mask_Decoder = Mask_Decoder.masked_fill(mask_tril==0,0)
return Mask_Encoder,Mask_Decoder,Mask_Cross
def forward(self, X_Encoder, X_Decoder, Y_Decoder_Ref=None,Auxiliary=None):
Mask_Encoder, Mask_Decoder,Mask_Cross = self.generate_masks(X_Encoder, X_Decoder)
# preprocess
X_Encoder = self.Dropout1(self.Embedding_Encoder(X_Encoder) * math.sqrt(self.Dim_Embedding) + self.pos_emb(X_Encoder.size(1)))
X_Decoder = self.Dropout2(self.Embedding_Decoder(X_Decoder) * math.sqrt(self.Dim_Embedding) + self.pos_emb(X_Decoder.size(1)))
#### Now X_Encoder: BatchSize, SequenceLength, DimAttention
# Encoder blocks
for encoder_block in self.encoder_blocks:
X_Encoder = encoder_block(X_Encoder,Mask_Encoder)
# X_Encoder = self.LayerNorm1(X_Encoder)
# Decoder blocks
for decoder_block in self.decoder_blocks:
X_Decoder = decoder_block(X_Encoder,X_Decoder,Mask_Cross,Mask_Decoder)
# X_Decoder = self.LayerNorm2(X_Decoder)
Y_Decoder_Logits = self.head(X_Decoder)
loss = None
if Y_Decoder_Ref is not None:
with torch.no_grad():
Y_OneHot = F.one_hot(Y_Decoder_Ref, num_classes=len(self.vocab_decoder)) * (1-self.Alpha_LabelSmoothing)
# LabelSmooth
LabelSmooth = torch.ones(len(self.List_Vocab_Decoder),device = Y_Decoder_Ref.device) * self.Alpha_LabelSmoothing / (len(self.List_Vocab_Decoder)-1)
Y_OneHot = Y_OneHot + LabelSmooth
# PartialSMILES
TruthTables = Auxiliary
Y_OneHot = Y_OneHot * TruthTables
# TokenWeight
if self.TokenWeight is not None:
Weight = torch.tensor(
self.TokenWeight,
device = Y_Decoder_Ref.device).unsqueeze(0).unsqueeze(0)
Y_OneHot = Y_OneHot * Weight
# IgnoreIndex
Y_OneHot[Y_Decoder_Ref==self.Token_Padding_Decoder] = 0.
Y_Decoder_Logits_LogSoftmax = F.log_softmax(Y_Decoder_Logits,dim=-1)
loss = -(Y_OneHot * Y_Decoder_Logits_LogSoftmax).sum(dim=-1)
loss = loss.mean()
# loss2 = F.kl_div(F.log_softmax(Y_Decoder_Logits,dim=-1),F.one_hot(Y_Decoder_Ref,num_classes=Y_Decoder_Logits.shape[-1]).type_as(Y_Decoder_Logits))
return Y_Decoder_Logits, loss
# self = trainer.model_module
# X_Encoder = trainer.X_Encoder
# X_Decoder = trainer.X_Decoder
# Y_Decoder_Ref = trainer.Y_Decoder_Ref
# Auxiliary = trainer.Auxiliary
# from torch.nn import functional as F
# Y_OneHot = F.one_hot(trainer.Y_Decoder_Ref,num_classes=len(trainer.model.vocab_decoder))
# import math
# import logging
# import torch
# import torch.nn as nn
# from torch.nn import functional as F
# # logger = logging.getLogger(__name__)
# from SCMG.config import varables as VBS
# from torch.autograd import Variable
# from SmilesPE.pretokenizer import atomwise_tokenizer
# class debug1():
# def __init__(self):
# self.tokenizer = atomwise_tokenizer
# self.vocab_encoder = torch.load("vocab_atom.pt")
# self.vocab_decoder = torch.load("vocab_atom.pt")
# self = debug1()
# bos_token = "bos_token"
# diex={
# VBS.COLUMN_ENCODER:"[*]c1cc(NC(=O)c2ccccc2)ccc1F",
# VBS.COLUMN_DECODER:"[*]c1cc(NC(=O)c2cc3c(cn2)OCCO3)ccc1F",
# VBS.COLUMN_TASK_TYPE:"<scmg_char_rand>",
# VBS.TOKEN_END:"<pad>",
# }
# customize_model_fn(self,diex)
# rm -r checkpoints/TFdebug9_512_512_6_20220401_0
# python -i scripts/create_model_SCMG.py \
# --model_type=Transformer_debug9 \
# --model_name=TF_512_512_6_debug9 \
# --num_decoder_layers=6 \
# --num_heads=8 \
# --dim_attention=512 \
# --dim_feedforward=2048 \
# --dim_embedding=512 \
# --rate_dropout=0.2 \
# --tokenizer=atom \
# --size_block=300 \
# --filepath_vocab_encoder=vocab_atom.pt \
# --filepath_vocab_decoder=vocab_atom.pt \
# --dirpath_checkpoint=checkpoints/TFdebug9_512_512_6_20220401_0
# python \
# -i \
# scripts/train/train_SCMG.py \
# --dirpath_data=PreProcess_DecoderOnly/TrainingSets_EncoderDecoder_OneDecoder/ \
# --size_batch=192 \
# --size_step=1500 \
# --rate_learning=0.0001 \
# --gamma=0.1 \
# --num_workers=32 \
# --epochs=49 \
# --dirpath_checkpoint=checkpoints/TFdebug9_512_512_6_20220401_0/ \
# --log_level=INFO \
# --run_one_epoch=0 \
# --dry_run=0 \
# --dump=1 \
# --Alpha_LabelSmoothing=0.1 |