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import random
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
import os
import math
import argparse
import torch
from torch import nn
from torch import optim
from torch.autograd import Variable
from torch.nn.utils import clip_grad_norm_
from torch.nn import functional as F
from .build_vocab import WordVocab
from .dataset import Seq2seqDataset
from project_paths import VOCAB_PATH
PAD = 0
UNK = 1
EOS = 2
SOS = 3
MASK = 4
class Encoder(nn.Module):
def __init__(self, input_size, embed_size, hidden_size,
n_layers=1, dropout=0.5):
super(Encoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.embed_size = embed_size
self.embed = nn.Embedding(input_size, embed_size)
self.gru = nn.GRU(embed_size, hidden_size, n_layers,
dropout=dropout, bidirectional=True)
def forward(self, src, hidden=None):
# src: (T,B)
embedded = self.embed(src)# (T,B,H)
outputs, hidden = self.gru(embedded, hidden) # (T,B,2H), (2L,B,H)
# sum bidirectional outputs
outputs = (outputs[:, :, :self.hidden_size] +
outputs[:, :, self.hidden_size:])
return outputs, hidden # (T,B,H), (2L,B,H)
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Parameter(torch.rand(hidden_size))
stdv = 1. / math.sqrt(self.v.size(0))
self.v.data.uniform_(-stdv, stdv)
def forward(self, hidden, encoder_outputs):
timestep = encoder_outputs.size(0)
h = hidden.repeat(timestep, 1, 1).transpose(0, 1)
encoder_outputs = encoder_outputs.transpose(0, 1) # [B*T*H]
attn_energies = self.score(h, encoder_outputs)
return F.relu(attn_energies).unsqueeze(1)
def score(self, hidden, encoder_outputs):
# [B*T*2H]->[B*T*H]
energy = F.softmax(self.attn(torch.cat([hidden, encoder_outputs], 2)))
energy = energy.transpose(1, 2) # [B*H*T]
v = self.v.repeat(encoder_outputs.size(0), 1).unsqueeze(1) # [B*1*H]
energy = torch.bmm(v, energy) # [B*1*T]
return energy.squeeze(1) # [B*T]
class Decoder(nn.Module):
def __init__(self, embed_size, hidden_size, output_size,
n_layers=1, dropout=0.2):
super(Decoder, self).__init__()
self.embed_size = embed_size
self.hidden_size = hidden_size
self.output_size = output_size
self.n_layers = n_layers
self.embed = nn.Embedding(output_size, embed_size)
self.dropout = nn.Dropout(dropout, inplace=True)
self.attention = Attention(hidden_size)
self.gru = nn.GRU(hidden_size + embed_size, hidden_size,
n_layers, dropout=dropout)
self.out = nn.Linear(hidden_size * 2, output_size)
def forward(self, input, last_hidden, encoder_outputs):
# Get the embedding of the current input word (last output word)
embedded = self.embed(input).unsqueeze(0) # (1,B,N)
embedded = self.dropout(embedded)
# Calculate attention weights and apply to encoder outputs
attn_weights = self.attention(last_hidden[-1], encoder_outputs)
context = attn_weights.bmm(encoder_outputs.transpose(0, 1)) # (B,1,N)
context = context.transpose(0, 1) # (1,B,N)
# Combine embedded input word and attended context, run through RNN
rnn_input = torch.cat([embedded, context], 2)
output, hidden = self.gru(rnn_input, last_hidden)
output = output.squeeze(0) # (1,B,N) -> (B,N)
context = context.squeeze(0)
output = self.out(torch.cat([output, context], 1))
output = F.log_softmax(output, dim=1) # log???
return output, hidden, attn_weights
class RNNSeq2Seq(nn.Module):
def __init__(self, in_size, hidden_size, out_size, n_layers):
super(RNNSeq2Seq, self).__init__()
self.encoder = Encoder(in_size, hidden_size, hidden_size, n_layers)
self.decoder = Decoder(hidden_size, hidden_size, out_size, n_layers)
def forward(self, src, trg, teacher_forcing_ratio=0.5): # (T,B)
batch_size = src.size(1)
max_len = trg.size(0)
vocab_size = self.decoder.output_size
outputs = Variable(torch.zeros(max_len, batch_size, vocab_size)).cuda() # (T,B,V)
encoder_output, hidden = self.encoder(src) # (T,B,H), (2L,B,H)
hidden = hidden[:self.decoder.n_layers] # (L,B,H)
output = Variable(trg.data[0, :]) # sos
for t in range(1, max_len):
output, hidden, attn_weights = self.decoder(output, hidden, encoder_output) # (B,V), (L,B,H)
outputs[t] = output
is_teacher = random.random() < teacher_forcing_ratio
top1 = output.data.max(dim=1)[1] # (B)
output = Variable(trg.data[t] if is_teacher else top1).cuda()
return outputs # (T,B,V)
def _encode(self, src):
# src: (T,B)
embedded = self.encoder.embed(src)# (T,B,H)
_, hidden = self.encoder.gru(embedded, None) # (T,B,2H), (2L,B,H)
hidden = hidden.detach().numpy()
return np.hstack(hidden[2:]) #(B,4H)
def encode(self, src):
# src: (T,B)
batch_size = src.shape[1]
if batch_size<=100:
return self._encode(src)
else: # Batch is too large to load
print('There are {:d} molecules. It will take a little time.'.format(batch_size))
st,ed = 0,100
out = self._encode(src[:,st:ed]) # (B,4H)
while ed<batch_size:
st += 100
ed += 100
out = np.concatenate([out, self._encode(src[:,st:ed])], axis=0)
return out
def parse_arguments():
parser = argparse.ArgumentParser(description='Hyperparams')
parser.add_argument('--n_epoch', '-e', type=int, default=20, help='number of epochs')
parser.add_argument('--vocab', '-v', type=str, default=str(VOCAB_PATH), help='vocabulary (.pkl)')
parser.add_argument('--train_data', type=str, default='data/chembl24_bert_train.csv', help='train corpus (.csv)')
parser.add_argument('--test_data', type=str, default='data/chembl24_bert_test.csv', help='test corpus (.csv)')
parser.add_argument('--out-dir', '-o', type=str, default='../result', help='output directory')
parser.add_argument('--name', '-n', type=str, default='ST', help='model name')
parser.add_argument('--seq_len', type=int, default=220, help='maximum length of the paired seqence')
parser.add_argument('--batch_size', '-b', type=int, default=16, help='batch size')
parser.add_argument('--n_worker', '-w', type=int, default=16, help='number of workers')
parser.add_argument('--hidden', type=int, default=256, help='length of hidden vector')
parser.add_argument('--n_layer', '-l', type=int, default=8, help='number of layers')
parser.add_argument('--n_head', type=int, default=8, help='number of attention heads')
parser.add_argument('--dropout', '-d', type=float, default=0.1, help='dropout rate')
parser.add_argument('--lr', type=float, default=1e-4, help='Adam learning rate')
parser.add_argument('--lr-decay', type=int, default=50000, help='lr decay step size')
parser.add_argument('--log-freq', type=int, default=100, help='log frequency')
parser.add_argument('--gpu', metavar='N', type=int, nargs='+', help='list of GPU IDs to use')
parser.add_argument('--checkpoint', '-c', type=str, default=None, help='Parameter to load')
parser.add_argument('-grad_clip', type=float, default=10.0, help='in case of gradient explosion')
return parser.parse_args()
def evaluate(model, val_loader, vocab):
model.eval()
total_loss = 0
for b, data in enumerate(val_loader):
sm1, sm2 = torch.t(data[0].cuda()), torch.t(data[1].cuda()) # (T,B)
with torch.no_grad():
output = model(sm1, sm2, teacher_forcing_ratio=0.0) # (T,B,V)
loss = F.nll_loss(output[1:].view(-1, len(vocab)),
sm2[1:].contiguous().view(-1),
ignore_index=PAD)
total_loss += loss.item()
return total_loss / len(val_loader)
def main():
args = parse_arguments()
hidden_size = 256
embed_size = 256
assert torch.cuda.is_available()
vocab = WordVocab.load_vocab(args.vocab)
print("[!] Instantiating models...")
encoder = Encoder(len(vocab), embed_size, hidden_size, n_layers=3, dropout=0.5)
decoder = Decoder(embed_size, hidden_size, len(vocab), n_layers=3, dropout=0.5)
model = RNNSeq2Seq(encoder, decoder).cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
train_dataset = Seq2seqDataset(args.train_data, vocab)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.n_worker)
val_dataset = Seq2seqDataset(args.test_data, vocab, is_train=False)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.n_worker)
print(model)
print('Total parameters:', sum(p.numel() for p in model.parameters()))
best_loss = None
for e in range(1, args.n_epoch):
for b,data in tqdm(enumerate(train_loader)):
model.train()
sm1, sm2 = torch.t(data[0].cuda()), torch.t(data[1].cuda()) # (T,B)
optimizer.zero_grad()
output = model(sm1, sm2, teacher_forcing_ratio=1.0) # (T,B,V)
loss = F.nll_loss(output[1:].view(-1, len(vocab)),
sm2[1:].contiguous().view(-1), ignore_index=PAD)
loss.backward()
clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
if b%100==0:
print('Train {:3d}: iter {:5d} | loss {:.3f} | ppl {:.3f}'.format(e, b, loss.item(), math.exp(loss.item())))
if b%1000==0:
loss = evaluate(model, val_loader, vocab)
print('Val {:3d}: iter {:5d} | loss {:.3f} | ppl {:.3f}'.format(e, b, loss, math.exp(loss)))
# Save the model if the validation loss is the best we've seen so far.
print("[!] saving model...")
if not os.path.isdir(".save"):
os.makedirs(".save")
torch.save(model.state_dict(), './.save/rnnenum_%d_%d.pkl' % (e,b))
best_loss = loss
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt as e:
print("[STOP]", e)
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