# Modified from https://github.com/quark0/darts
import os, gc, sys, time, math
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
from utils import print_log, obtain_accuracy, AverageMeter
from utils import time_string, convert_secs2time
from utils import count_parameters_in_MB
from datasets import Corpus
from nas_rnn import batchify, get_batch, repackage_hidden
from nas_rnn import DARTSCell, RNNModel
def obtain_best(accuracies):
if len(accuracies) == 0: return (0, 0)
tops = [value for key, value in accuracies.items()]
s2b = sorted( tops )
return s2b[-1]
def main_procedure(config, genotype, save_dir, print_freq, log):
print_log('-'*90, log)
print_log('save-dir : {:}'.format(save_dir), log)
print_log('genotype : {:}'.format(genotype), log)
print_log('config : {:}'.format(config), log)
corpus = Corpus(config.data_path)
train_data = batchify(corpus.train, config.train_batch, True)
valid_data = batchify(corpus.valid, config.eval_batch , True)
test_data = batchify(corpus.test, config.test_batch , True)
ntokens = len(corpus.dictionary)
print_log("Train--Data Size : {:}".format(train_data.size()), log)
print_log("Valid--Data Size : {:}".format(valid_data.size()), log)
print_log("Test---Data Size : {:}".format( test_data.size()), log)
print_log("ntokens = {:}".format(ntokens), log)
model = RNNModel(ntokens, config.emsize, config.nhid, config.nhidlast,
config.dropout, config.dropouth, config.dropoutx, config.dropouti, config.dropoute,
cell_cls=DARTSCell, genotype=genotype)
model = model.cuda()
print_log('Network =>\n{:}'.format(model), log)
print_log('Genotype : {:}'.format(genotype), log)
print_log('Parameters : {:.3f} MB'.format(count_parameters_in_MB(model)), log)
checkpoint_path = os.path.join(save_dir, 'checkpoint-{:}.pth'.format(config.data_name))
Soptimizer = torch.optim.SGD (model.parameters(), lr=config.LR, weight_decay=config.wdecay)
Aoptimizer = torch.optim.ASGD(model.parameters(), lr=config.LR, t0=0, lambd=0., weight_decay=config.wdecay)
if os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
model.load_state_dict( checkpoint['state_dict'] )
Soptimizer.load_state_dict( checkpoint['SGD_optimizer'] )
Aoptimizer.load_state_dict( checkpoint['ASGD_optimizer'] )
epoch = checkpoint['epoch']
use_asgd = checkpoint['use_asgd']
print_log('load checkpoint from {:} and start train from {:}'.format(checkpoint_path, epoch), log)
else:
epoch, use_asgd = 0, False
start_time, epoch_time = time.time(), AverageMeter()
valid_loss_from_sgd, losses = [], {-1 : 1e9}
while epoch < config.epochs:
need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
print_log("\n==>>{:s} [Epoch={:04d}/{:04d}] {:}".format(time_string(), epoch, config.epochs, need_time), log)
if use_asgd : optimizer = Aoptimizer
else : optimizer = Soptimizer
try:
Dtime, Btime = train(model, optimizer, corpus, train_data, config, epoch, print_freq, log)
except:
torch.cuda.empty_cache()
checkpoint = torch.load(checkpoint_path)
model.load_state_dict( checkpoint['state_dict'] )
Soptimizer.load_state_dict( checkpoint['SGD_optimizer'] )
Aoptimizer.load_state_dict( checkpoint['ASGD_optimizer'] )
epoch = checkpoint['epoch']
use_asgd = checkpoint['use_asgd']
valid_loss_from_sgd = checkpoint['valid_loss_from_sgd']
continue
if use_asgd:
tmp = {}
for prm in model.parameters():
tmp[prm] = prm.data.clone()
prm.data = Aoptimizer.state[prm]['ax'].clone()
val_loss = evaluate(model, corpus, valid_data, config.eval_batch, config.bptt)
for prm in model.parameters():
prm.data = tmp[prm].clone()
else:
val_loss = evaluate(model, corpus, valid_data, config.eval_batch, config.bptt)
if len(valid_loss_from_sgd) > config.nonmono and val_loss > min(valid_loss_from_sgd):
use_asgd = True
valid_loss_from_sgd.append( val_loss )
print_log('{:} end of epoch {:3d} with {:} | valid loss {:5.2f} | valid ppl {:8.2f}'.format(time_string(), epoch, 'ASGD' if use_asgd else 'SGD', val_loss, math.exp(val_loss)), log)
if val_loss < min(losses.values()):
if use_asgd:
tmp = {}
for prm in model.parameters():
tmp[prm] = prm.data.clone()
prm.data = Aoptimizer.state[prm]['ax'].clone()
torch.save({'epoch' : epoch,
'use_asgd' : use_asgd,
'valid_loss_from_sgd': valid_loss_from_sgd,
'state_dict': model.state_dict(),
'SGD_optimizer' : Soptimizer.state_dict(),
'ASGD_optimizer': Aoptimizer.state_dict()},
checkpoint_path)
if use_asgd:
for prm in model.parameters():
prm.data = tmp[prm].clone()
print_log('save into {:}'.format(checkpoint_path), log)
if use_asgd:
tmp = {}
for prm in model.parameters():
tmp[prm] = prm.data.clone()
prm.data = Aoptimizer.state[prm]['ax'].clone()
test_loss = evaluate(model, corpus, test_data, config.test_batch, config.bptt)
if use_asgd:
for prm in model.parameters():
prm.data = tmp[prm].clone()
print_log('| epoch={:03d} | test loss {:5.2f} | test ppl {:8.2f}'.format(epoch, test_loss, math.exp(test_loss)), log)
losses[epoch] = val_loss
epoch = epoch + 1
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
print_log('--------------------- Finish Training ----------------', log)
checkpoint = torch.load(checkpoint_path)
model.load_state_dict( checkpoint['state_dict'] )
test_loss = evaluate(model, corpus, test_data , config.test_batch, config.bptt)
print_log('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(test_loss, math.exp(test_loss)), log)
vali_loss = evaluate(model, corpus, valid_data, config.eval_batch, config.bptt)
print_log('| End of training | valid loss {:5.2f} | valid ppl {:8.2f}'.format(vali_loss, math.exp(vali_loss)), log)
def evaluate(model, corpus, data_source, batch_size, bptt):
# Turn on evaluation mode which disables dropout.
model.eval()
total_loss, total_length = 0.0, 0.0
with torch.no_grad():
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(batch_size)
for i in range(0, data_source.size(0) - 1, bptt):
data, targets = get_batch(data_source, i, bptt)
targets = targets.view(-1)
log_prob, hidden = model(data, hidden)
loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets)
total_loss += loss.item() * len(data)
total_length += len(data)
hidden = repackage_hidden(hidden)
return total_loss / total_length
def train(model, optimizer, corpus, train_data, config, epoch, print_freq, log):
# Turn on training mode which enables dropout.
total_loss, data_time, batch_time = 0, AverageMeter(), AverageMeter()
start_time = time.time()
ntokens = len(corpus.dictionary)
hidden_train = model.init_hidden(config.train_batch)
batch, i = 0, 0
while i < train_data.size(0) - 1 - 1:
bptt = config.bptt if np.random.random() < 0.95 else config.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, config.bptt + config.max_seq_len_delta)
lr2 = optimizer.param_groups[0]['lr']
optimizer.param_groups[0]['lr'] = lr2 * seq_len / config.bptt
model.train()
data, targets = get_batch(train_data, i, seq_len)
targets = targets.contiguous().view(-1)
# count data preparation time
data_time.update(time.time() - start_time)
optimizer.zero_grad()
hidden_train = repackage_hidden(hidden_train)
log_prob, hidden_train, rnn_hs, dropped_rnn_hs = model(data, hidden_train, return_h=True)
raw_loss = nn.functional.nll_loss(log_prob.view(-1, log_prob.size(2)), targets)
loss = raw_loss
# Activiation Regularization
if config.alpha > 0:
loss = loss + sum(config.alpha * dropped_rnn_h.pow(2).mean() for dropped_rnn_h in dropped_rnn_hs[-1:])
# Temporal Activation Regularization (slowness)
loss = loss + sum(config.beta * (rnn_h[1:] - rnn_h[:-1]).pow(2).mean() for rnn_h in rnn_hs[-1:])
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config.clip)
optimizer.step()
gc.collect()
optimizer.param_groups[0]['lr'] = lr2
total_loss += raw_loss.item()
assert torch.isnan(loss) == False, '--- Epoch={:04d} :: {:03d}/{:03d} Get Loss = Nan'.format(epoch, batch, len(train_data)//config.bptt)
batch_time.update(time.time() - start_time)
start_time = time.time()
batch, i = batch + 1, i + seq_len
if batch % print_freq == 0:
cur_loss = total_loss / print_freq
print_log(' >> Epoch: {:04d} :: {:03d}/{:03d} || loss = {:5.2f}, ppl = {:8.2f}'.format(epoch, batch, len(train_data) // config.bptt, cur_loss, math.exp(cur_loss)), log)
total_loss = 0
return data_time.sum, batch_time.sum