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train_iwslt14_small.py
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train_iwslt14_small.py
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from data.iwslt14_small.dataset_utils import create_dataset, BOS_TOKEN, EOS_TOKEN, PAD_TOKEN
from model.lstm2d import LSTM2d
from util.checkpoint_utils import save_checkpoint
from data.data_utils import get_bucket_iterator
import argparse
import torch
import numpy as np
import os
from tensorboardX import SummaryWriter
from datetime import datetime
ROOT_DIR = os.path.dirname(os.path.abspath(os.path.join(__file__, os.pardir)))
CHECKPOINT_DIR = ROOT_DIR + '/checkpoints'
# define options
parser = argparse.ArgumentParser(description='train_iwslt14_small.py')
parser.add_argument('--batch_size', type=int, default=32,
help='The batch size to use for training and inference.')
parser.add_argument('--epochs', type=int, default=20,
help='The number of epochs to train.')
parser.add_argument('--shuffle', type=bool, default=True,
help='Whether or not to shuffle the training examples.')
parser.add_argument('--lr', type=float, default=0.0005,
help='The learning rate to use.')
parser.add_argument('--embed_dim', type=int, default=128,
help='The dimension of the embedding vectors for both the source and target language.')
parser.add_argument('--encoder_state_dim', type=int, default=64,
help='The dimension of the bidirectional encoder LSTM states.')
parser.add_argument('--state_2d_dim', type=int, default=128,
help='The dimension of the 2D-LSTM hidden & cell states.')
parser.add_argument('--disable_cuda', default=False, action='store_true',
help='Disable CUDA (i.e. use the CPU for all computations)')
parser.add_argument('--dropout_p', type=float, default=0.2,
help='The dropout probability.')
options = parser.parse_args()
# determine the device (CPU or GPU)
options.device = None
if not options.disable_cuda and torch.cuda.is_available():
options.device = torch.device('cuda')
else:
options.device = torch.device('cpu')
print('Using device: {}'.format(options.device))
# create a summary writer
experiment_name = 'b{}_lr{}_emb{}_encstate{}_2dstate{}_drop-p{}_@{}'\
.format(options.batch_size, options.lr, options.embed_dim, options.encoder_state_dim,
options.state_2d_dim, options.dropout_p, datetime.now())
writer = SummaryWriter(log_dir='runs/{}'.format(experiment_name))
def main():
dataset = create_dataset()
src_vocab_size = len(dataset.src.vocab)
tgt_vocab_size = len(dataset.tgt.vocab)
bos_token = dataset.tgt.vocab.stoi[BOS_TOKEN]
eos_token = dataset.tgt.vocab.stoi[EOS_TOKEN]
pad_token = dataset.tgt.vocab.stoi[PAD_TOKEN]
model = LSTM2d(
embed_dim=options.embed_dim,
state_dim_2d=options.state_2d_dim,
encoder_state_dim=options.encoder_state_dim,
input_vocab_size=src_vocab_size,
output_vocab_size=tgt_vocab_size,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
device=options.device,
dropout_p=options.dropout_p
)
train_iterator = get_bucket_iterator(dataset.train, batch_size=options.batch_size, shuffle=options.shuffle)
optimizer = torch.optim.Adam(model.parameters(), lr=options.lr)
batches_per_epoch = len(dataset.train.examples) // options.batch_size
val_loss_history = []
global_step = 0
for epoch in range(options.epochs):
print('Starting epoch #{}'.format(epoch + 1))
writer.add_histogram('input_embeddings', model.input_embedding.weight.data, global_step)
writer.add_histogram('output_embeddings', model.output_embedding.weight.data, global_step)
if model.input_embedding.weight.grad is not None and model.output_embedding.weight.grad is not None:
writer.add_histogram('input_embeddings/gradient', model.input_embedding.weight.grad, global_step)
writer.add_histogram('output_embeddings/gradient', model.output_embedding.weight.grad, global_step)
if epoch > 0 and not epoch % 5:
save_checkpoint(model, optimizer, epoch, options)
test_model(model, dataset)
model.train()
loss_history = []
train_iterator.init_epoch()
for i, batch in enumerate(train_iterator):
optimizer.zero_grad()
x, x_lengths = batch.src
x_lengths[x_lengths <= 0] = 1 # crashes for values <= 0 (seems to be a bug)
y = batch.tgt
y_pred = model.forward(x=x, x_lengths=x_lengths, y=y)
loss_value = model.loss(y_pred, y)
loss_history.append(loss_value.item())
writer.add_scalar('train_loss', loss_value, global_step=epoch*batches_per_epoch + i)
loss_value.backward()
optimizer.step()
if i > 0 and not i % 100:
avg_loss = np.mean(loss_history)
print('Average loss after {} batches (in epoch #{}): {}'.format(i, epoch + 1, avg_loss))
# calculate loss metrics
train_loss = np.mean(loss_history)
val_loss = validate_model(model, dataset)
val_loss_history.append(val_loss)
global_step = (epoch+1)*batches_per_epoch
writer.add_scalar('train_loss', train_loss, global_step)
writer.add_scalar('val_loss', val_loss, global_step)
finalize()
def finalize():
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
def test_model(model, dataset):
model.eval()
example_sentence = 'Good morning , how are you ? <eos>'
tokens = example_sentence.split(' ')
x = torch.tensor([[dataset.src.vocab.stoi[w] for w in tokens]], dtype=torch.long).t()
x_lengths = torch.tensor([len(tokens)], dtype=torch.long)
pred = model.forward(x=x, x_lengths=x_lengths)
predicted_tokens = list(torch.argmax(pred, dim=-1).view(-1))
output_sentence = ' '.join([dataset.tgt.vocab.itos[i] for i in predicted_tokens])
print('translate(\"{}\") ==> \"{}\"'.format(example_sentence, output_sentence))
def validate_model(model, dataset):
print("Running validation...")
val_iterator = get_bucket_iterator(dataset.val, batch_size=options.batch_size, shuffle=False)
loss_history = []
model.eval()
for i, batch in enumerate(val_iterator):
x, x_lengths = batch.src
y = batch.tgt
x_lengths[x_lengths <= 0] = 1
y_pred = model.forward(x=x, x_lengths=x_lengths, y=y)
loss_value = model.padded_loss(y_pred, y)
loss_history.append(loss_value.item())
avg_loss = np.mean(loss_history)
print("Average loss on validation dataset: {}".format(avg_loss))
return avg_loss
if __name__ == '__main__':
main()