import argparse
import os, sys
import time
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import gc
import data
import model
from utils import batchify, get_batch, repackage_hidden, create_exp_dir, save_checkpoint
parser = argparse.ArgumentParser(description='PyTorch PennTreeBank/WikiText2 RNN/LSTM Language Model')
parser.add_argument('--data', type=str, default='./penn/',
help='location of the data corpus')
parser.add_argument('--model', type=str, default='LSTM',
help='type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU, SRU)')
parser.add_argument('--emsize', type=int, default=400,
help='size of word embeddings')
parser.add_argument('--nhid', type=int, default=1150,
help='number of hidden units per layer')
parser.add_argument('--nhidlast', type=int, default=-1,
help='number of hidden units for the last rnn layer')
parser.add_argument('--nlayers', type=int, default=3,
help='number of layers')
parser.add_argument('--lr', type=float, default=30,
help='initial learning rate')
parser.add_argument('--clip', type=float, default=0.25,
help='gradient clipping')
parser.add_argument('--epochs', type=int, default=8000,
help='upper epoch limit')
parser.add_argument('--batch_size', type=int, default=20, metavar='N',
help='batch size')
parser.add_argument('--bptt', type=int, default=70,
help='sequence length')
parser.add_argument('--dropout', type=float, default=0.4,
help='dropout applied to layers (0 = no dropout)')
parser.add_argument('--dropouth', type=float, default=0.3,
help='dropout for rnn layers (0 = no dropout)')
parser.add_argument('--dropouti', type=float, default=0.65,
help='dropout for input embedding layers (0 = no dropout)')
parser.add_argument('--dropoute', type=float, default=0.1,
help='dropout to remove words from embedding layer (0 = no dropout)')
parser.add_argument('--dropoutl', type=float, default=-0.2,
help='dropout applied to layers (0 = no dropout)')
parser.add_argument('--wdrop', type=float, default=0.5,
help='amount of weight dropout to apply to the RNN hidden to hidden matrix')
parser.add_argument('--tied', action='store_false',
help='tie the word embedding and softmax weights')
parser.add_argument('--seed', type=int, default=1111,
help='random seed')
parser.add_argument('--nonmono', type=int, default=5,
help='random seed')
parser.add_argument('--cuda', action='store_false',
help='use CUDA')
parser.add_argument('--log-interval', type=int, default=200, metavar='N',
help='report interval')
parser.add_argument('--save', type=str, default='EXP',
help='path to save the final model')
parser.add_argument('--alpha', type=float, default=2,
help='alpha L2 regularization on RNN activation (alpha = 0 means no regularization)')
parser.add_argument('--beta', type=float, default=1,
help='beta slowness regularization applied on RNN activiation (beta = 0 means no regularization)')
parser.add_argument('--wdecay', type=float, default=1.2e-6,
help='weight decay applied to all weights')
parser.add_argument('--continue_train', action='store_true',
help='continue train from a checkpoint')
parser.add_argument('--n_experts', type=int, default=10,
help='number of experts')
parser.add_argument('--small_batch_size', type=int, default=-1,
help='the batch size for computation. batch_size should be divisible by small_batch_size.\
In our implementation, we compute gradients with small_batch_size multiple times, and accumulate the gradients\
until batch_size is reached. An update step is then performed.')
parser.add_argument('--max_seq_len_delta', type=int, default=40,
help='max sequence length')
parser.add_argument('--single_gpu', default=False, action='store_true',
help='use single GPU')
args = parser.parse_args()
if args.nhidlast < 0:
args.nhidlast = args.emsize
if args.dropoutl < 0:
args.dropoutl = args.dropouth
if args.small_batch_size < 0:
args.small_batch_size = args.batch_size
if not args.continue_train:
args.save = '{}-{}'.format(args.save, time.strftime("%Y%m%d-%H%M%S"))
create_exp_dir(args.save, scripts_to_save=['main.py', 'model.py'])
def logging(s, print_=True, log_=True):
if print_:
print(s)
if log_:
with open(os.path.join(args.save, 'log.txt'), 'a+') as f_log:
f_log.write(s + '\n')
# Set the random seed manually for reproducibility.
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
else:
torch.cuda.manual_seed_all(args.seed)
###############################################################################
# Load data
###############################################################################
corpus = data.Corpus(args.data)
eval_batch_size = 10
test_batch_size = 1
train_data = batchify(corpus.train, args.batch_size, args)
val_data = batchify(corpus.valid, eval_batch_size, args)
test_data = batchify(corpus.test, test_batch_size, args)
###############################################################################
# Build the model
###############################################################################
ntokens = len(corpus.dictionary)
if args.continue_train:
model = torch.load(os.path.join(args.save, 'model.pt'))
else:
model = model.RNNModel(args.model, ntokens, args.emsize, args.nhid, args.nhidlast, args.nlayers,
args.dropout, args.dropouth, args.dropouti, args.dropoute, args.wdrop,
args.tied, args.dropoutl, args.n_experts)
if args.cuda:
if args.single_gpu:
parallel_model = model.cuda()
else:
parallel_model = nn.DataParallel(model, dim=1).cuda()
else:
parallel_model = model
total_params = sum(x.data.nelement() for x in model.parameters())
logging('Args: {}'.format(args))
logging('Model total parameters: {}'.format(total_params))
criterion = nn.CrossEntropyLoss()
###############################################################################
# Training code
###############################################################################
def evaluate(data_source, batch_size=10):
# Turn on evaluation mode which disables dropout.
model.eval()
total_loss = 0
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(batch_size)
with torch.no_grad():
for i in range(0, data_source.size(0) - 1, args.bptt):
data, targets = get_batch(data_source, i, args)
targets = targets.view(-1)
log_prob, hidden = parallel_model(data, hidden)
loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets).data
total_loss += loss * len(data)
hidden = repackage_hidden(hidden)
return total_loss.item() / len(data_source)
def train():
assert args.batch_size % args.small_batch_size == 0, 'batch_size must be divisible by small_batch_size'
# Turn on training mode which enables dropout.
total_loss = 0
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden = [model.init_hidden(args.small_batch_size) for _ in range(args.batch_size // args.small_batch_size)]
batch, i = 0, 0
while i < train_data.size(0) - 1 - 1:
bptt = args.bptt if np.random.random() < 0.95 else args.bptt / 2.
# Prevent excessively small or negative sequence lengths
seq_len = max(5, int(np.random.normal(bptt, 5)))
# There's a very small chance that it could select a very long sequence length resulting in OOM
seq_len = min(seq_len, args.bptt + args.max_seq_len_delta)
lr2 = optimizer.param_groups[0]['lr']
optimizer.param_groups[0]['lr'] = lr2 * seq_len / args.bptt
model.train()
data, targets = get_batch(train_data, i, args, seq_len=seq_len)
optimizer.zero_grad()
start, end, s_id = 0, args.small_batch_size, 0
while start < args.batch_size:
cur_data, cur_targets = data[:, start: end], targets[:, start: end].contiguous().view(-1)
# Starting each batch, we detach the hidden state from how it was previously produced.
# If we didn't, the model would try backpropagating all the way to start of the dataset.
hidden[s_id] = repackage_hidden(hidden[s_id])
log_prob, hidden[s_id], rnn_hs, dropped_rnn_hs = parallel_model(cur_data, hidden[s_id], return_h=True)
raw_loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), cur_targets)
loss = raw_loss
# Activiation Regularization
loss = loss + sum(args.alpha * dropped_rnn_h.pow(2).mean() for dropped_rnn_h in dropped_rnn_hs[-1:])
# Temporal Activation Regularization (slowness)
loss = loss + sum(args.beta * (rnn_h[1:] - rnn_h[:-1]).pow(2).mean() for rnn_h in rnn_hs[-1:])
loss *= args.small_batch_size / args.batch_size
total_loss += raw_loss.data * args.small_batch_size / args.batch_size
loss.backward()
s_id += 1
start = end
end = start + args.small_batch_size
gc.collect()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
# total_loss += raw_loss.data
optimizer.param_groups[0]['lr'] = lr2
if batch % args.log_interval == 0 and batch > 0:
cur_loss = total_loss.item() / args.log_interval
elapsed = time.time() - start_time
logging('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
'loss {:5.2f} | ppl {:8.2f}'.format(
epoch, batch, len(train_data) // args.bptt, optimizer.param_groups[0]['lr'],
elapsed * 1000 / args.log_interval, cur_loss, math.exp(cur_loss)))
total_loss = 0
start_time = time.time()
###
batch += 1
i += seq_len
# Loop over epochs.
lr = args.lr
best_val_loss = []
stored_loss = 100000000
# At any point you can hit Ctrl + C to break out of training early.
try:
if args.continue_train:
optimizer_state = torch.load(os.path.join(args.save, 'optimizer.pt'))
if 't0' in optimizer_state['param_groups'][0]:
optimizer = torch.optim.ASGD(model.parameters(), lr=args.lr, t0=0, lambd=0., weight_decay=args.wdecay)
else:
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.wdecay)
optimizer.load_state_dict(optimizer_state)
else:
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.wdecay)
for epoch in range(1, args.epochs+1):
epoch_start_time = time.time()
train()
if 't0' in optimizer.param_groups[0]:
tmp = {}
for prm in model.parameters():
tmp[prm] = prm.data.clone()
prm.data = optimizer.state[prm]['ax'].clone()
val_loss2 = evaluate(val_data)
logging('-' * 89)
logging('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss2, math.exp(val_loss2)))
logging('-' * 89)
if val_loss2 < stored_loss:
save_checkpoint(model, optimizer, args.save)
logging('Saving Averaged!')
stored_loss = val_loss2
for prm in model.parameters():
prm.data = tmp[prm].clone()
else:
val_loss = evaluate(val_data, eval_batch_size)
logging('-' * 89)
logging('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
logging('-' * 89)
if val_loss < stored_loss:
save_checkpoint(model, optimizer, args.save)
logging('Saving Normal!')
stored_loss = val_loss
if 't0' not in optimizer.param_groups[0] and (len(best_val_loss)>args.nonmono and val_loss > min(best_val_loss[:-args.nonmono])):
logging('Switching!')
optimizer = torch.optim.ASGD(model.parameters(), lr=args.lr, t0=0, lambd=0., weight_decay=args.wdecay)
#optimizer.param_groups[0]['lr'] /= 2.
best_val_loss.append(val_loss)
except KeyboardInterrupt:
logging('-' * 89)
logging('Exiting from training early')
# Load the best saved model.
model = torch.load(os.path.join(args.save, 'model.pt'))
parallel_model = nn.DataParallel(model, dim=1).cuda()
# Run on test data.
test_loss = evaluate(test_data, test_batch_size)
logging('=' * 89)
logging('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
test_loss, math.exp(test_loss)))
logging('=' * 89)
