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ez_train.py
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ez_train.py
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import torch
import numpy as np
import math
from torch.autograd import Variable
from tqdm import tqdm, trange
from model import Transformer, FastTransformer
from utils import Metrics, Best, computeGLEU, computeBLEU
# helper functions
def register_nan_checks(m):
def check_grad(module, grad_input, grad_output):
if any(np.any(np.isnan(gi.data.cpu().numpy())) for gi in grad_input if gi is not None):
print('NaN gradient in ' + type(module).__name__)
1/0
m.apply(lambda module: module.register_backward_hook(check_grad))
def export(x):
try:
with torch.cuda.device_of(x):
return x.data.cpu().float().mean()
except Exception:
return 0
def devol(batch):
new_batch = copy.copy(batch)
new_batch.src = Variable(batch.src.data, volatile=True)
return new_batch
tokenizer = lambda x: x.replace('@@ ', '').split()
def valid_model(args, model, dev, dev_metrics=None, distillation=False, print_out=False):
print_seqs = ['[sources]', '[targets]', '[decoded]', '[fertili]', '[origind]']
trg_outputs, dec_outputs = [], []
outputs = {}
model.eval()
for j, dev_batch in enumerate(dev):
inputs, input_masks, \
targets, target_masks, \
sources, source_masks, \
encoding, batch_size = model.quick_prepare(dev_batch, distillation)
decoder_inputs, input_reorder, fertility_cost = inputs, None, None
if type(model) is FastTransformer:
decoder_inputs, input_reorder, decoder_masks, fertility_cost, pred_fertility = \
model.prepare_initial(encoding, sources, source_masks, input_masks, None, mode='argmax')
else:
decoder_masks = input_masks
decoding, out, probs = model(encoding, source_masks, decoder_inputs, decoder_masks, decoding=True, return_probs=True)
dev_outputs = [('src', sources), ('trg', targets), ('trg', decoding)]
if type(model) is FastTransformer:
dev_outputs += [('src', input_reorder)]
dev_outputs = [model.output_decoding(d) for d in dev_outputs]
gleu = computeGLEU(dev_outputs[2], dev_outputs[1], corpus=False, tokenizer=tokenizer)
if print_out:
for k, d in enumerate(dev_outputs):
args.logger.info("{}: {}".format(print_seqs[k], d[0]))
args.logger.info('------------------------------------------------------------------')
trg_outputs += dev_outputs[1]
dec_outputs += dev_outputs[2]
if dev_metrics is not None:
values = [0, gleu]
if fertility_cost is not None:
values += [fertility_cost]
dev_metrics.accumulate(batch_size, *values)
corpus_gleu = computeGLEU(dec_outputs, trg_outputs, corpus=True, tokenizer=tokenizer)
corpus_bleu = computeBLEU(dec_outputs, trg_outputs, corpus=True, tokenizer=tokenizer)
outputs['corpus_gleu'] = corpus_gleu
outputs['corpus_bleu'] = corpus_bleu
if dev_metrics is not None:
args.logger.info(dev_metrics)
args.logger.info("The dev-set corpus GLEU = {}".format(corpus_gleu))
args.logger.info("The dev-set corpus BLEU = {}".format(corpus_bleu))
return outputs
def train_model(args, model, train, dev, save_path=None, maxsteps=None):
if args.tensorboard and (not args.debug):
from tensorboardX import SummaryWriter
writer = SummaryWriter('./runs/{}'.format(args.prefix+args.hp_str))
# optimizer
if args.optimizer == 'Adam':
opt = torch.optim.Adam([p for p in model.parameters() if p.requires_grad], betas=(0.9, 0.98), eps=1e-9)
else:
raise NotImplementedError
# if resume training
if (args.load_from is not None) and (args.resume):
with torch.cuda.device(args.gpu): # very important.
offset, opt_states = torch.load('./models/' + args.load_from + '.pt.states',
map_location=lambda storage, loc: storage.cuda())
opt.load_state_dict(opt_states)
else:
offset = 0
# metrics
if save_path is None:
save_path = args.model_name
best = Best(max, 'corpus_bleu', 'corpus_gleu', 'gleu', 'loss', 'i', model=model, opt=opt, path=save_path, gpu=args.gpu)
train_metrics = Metrics('train', 'loss', 'real', 'fake')
dev_metrics = Metrics('dev', 'loss', 'gleu', 'real_loss', 'fake_loss', 'distance', 'alter_loss', 'distance2', 'fertility_loss', 'corpus_gleu')
progressbar = tqdm(total=args.eval_every, desc='start training.')
for iters, batch in enumerate(train):
iters += offset
# --- saving --- #
if iters % args.save_every == 0:
args.logger.info('save (back-up) checkpoints at iter={}'.format(iters))
with torch.cuda.device(args.gpu):
torch.save(best.model.state_dict(), '{}_iter={}.pt'.format(args.model_name, iters))
torch.save([iters, best.opt.state_dict()], '{}_iter={}.pt.states'.format(args.model_name, iters))
# --- validation --- #
if iters % args.eval_every == 0:
progressbar.close()
dev_metrics.reset()
if args.distillation:
outputs_course = valid_model(args, model, dev, dev_metrics, distillation=True)
outputs_data = valid_model(args, model, dev, None if args.distillation else dev_metrics, print_out=True)
if args.tensorboard and (not args.debug):
writer.add_scalar('dev/GLEU_sentence_', dev_metrics.gleu, iters)
writer.add_scalar('dev/Loss', dev_metrics.loss, iters)
writer.add_scalar('dev/GLEU_corpus_', outputs_data['corpus_gleu'], iters)
writer.add_scalar('dev/BLEU_corpus_', outputs_data['corpus_bleu'], iters)
if args.distillation:
writer.add_scalar('dev/GLEU_corpus_dis', outputs_course['corpus_gleu'], iters)
writer.add_scalar('dev/BLEU_corpus_dis', outputs_course['corpus_bleu'], iters)
if not args.debug:
best.accumulate(outputs_data['corpus_bleu'], outputs_data['corpus_gleu'], dev_metrics.gleu, dev_metrics.loss, iters)
args.logger.info('the best model is achieved at {}, average greedy GLEU={}, corpus GLEU={}, corpus BLEU={}'.format(
best.i, best.gleu, best.corpus_gleu, best.corpus_bleu))
args.logger.info('model:' + args.prefix + args.hp_str)
# ---set-up a new progressor---
progressbar = tqdm(total=args.eval_every, desc='start training.')
if maxsteps is None:
maxsteps = args.maximum_steps
if iters > maxsteps:
args.logger.info('reach the maximum updating steps.')
break
# --- training --- #
model.train()
def get_learning_rate(i, lr0=0.1, disable=False):
if not disable:
return lr0 * 10 / math.sqrt(args.d_model) * min(1 / math.sqrt(i), i / (args.warmup * math.sqrt(args.warmup)))
return 0.00002
opt.param_groups[0]['lr'] = get_learning_rate(iters + 1, disable=args.disable_lr_schedule)
opt.zero_grad()
# prepare the data
inputs, input_masks, \
targets, target_masks, \
sources, source_masks,\
encoding, batch_size = model.quick_prepare(batch, args.distillation)
input_reorder, fertility_cost, decoder_inputs = None, None, inputs
#print(input_masks.size(), target_masks.size(), input_masks.sum())
if type(model) is FastTransformer:
batch_fer = batch.fer_dec if args.distillation else batch.fer
inputs, input_reorder, input_masks, fertility_cost = model.prepare_initial(encoding, sources, source_masks, input_masks, batch_fer)
# Maximum Likelihood Training
loss = model.cost(targets, target_masks, out=model(encoding, source_masks, inputs, input_masks))
if hasattr(args, 'fertility') and args.fertility:
loss += fertility_cost
# accmulate the training metrics
train_metrics.accumulate(batch_size, loss, print_iter=None)
train_metrics.reset()
loss.backward()
opt.step()
info = 'training step={}, loss={:.3f}, lr={:.5f}'.format(iters, export(loss), opt.param_groups[0]['lr'])
if hasattr(args, 'fertility') and args.fertility:
info += '| RE:{:.3f}'.format(export(fertility_cost))
if args.tensorboard and (not args.debug):
writer.add_scalar('train/Loss', export(loss), iters)
progressbar.update(1)
progressbar.set_description(info)