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trainer.py
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trainer.py
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import os
import sys
import json
import argparse
import numpy as np
import random
import torch
from data import get_data, pad_batch, batchify, Batch
from transformer import NoamOpt, make_transformer_model, make_lstm_model, make_caml_model, make_cnn_model
import logging
from sklearn import metrics
parser = argparse.ArgumentParser(description='Clinical Dataset')
# paths
parser.add_argument("--corpus", type=str, default='psvg', help="psvg|csu|pp|cnn")
parser.add_argument("--hypes", type=str, default='hypes/default.json', help="load in a hyperparameter file")
parser.add_argument("--outputdir", type=str, default='exp/', help="Output directory")
parser.add_argument("--inputdir", type=str, default='', help="Input model dir")
parser.add_argument("--cut_down_len", type=int, default="600", help="sentence will be cut down if tokens num greater than this")
# training
parser.add_argument("--n_epochs", type=int, default=10)
parser.add_argument("--bptt_size", type=int, default=50)
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--dpout", type=float, default=0.1, help="residual, embedding, attention dropout") # 3 dropouts
parser.add_argument("--warmup_steps", type=int, default=8000, help="OpenNMT uses steps") # TransformerLM uses 0.2% of training data as warmup step, that's 5785 for DisSent5/8, and 8471 for DisSent-All
parser.add_argument("--factor", type=float, default=1.0, help="learning rate scaling factor")
parser.add_argument("--l2", type=float, default=0.01, help="on non-bias non-gain weights")
parser.add_argument("--max_norm", type=float, default=2., help="max norm (grad clipping). Original paper uses 1.")
parser.add_argument("--log_interval", type=int, default=100, help="how many batches to log once")
parser.add_argument('--lm_coef', type=float, default=0.5)
parser.add_argument("--train_emb", default=False, action='store_true', help="Allow to learn embedding, default to False")
parser.add_argument("--init_emb", default=False, action='store_true', help="Initialize embedding randomly, default to False")
parser.add_argument("--pick_hid", default=True, action='store_true', help="Pick correct hidden states")
parser.add_argument("--tied", default=True, action='store_true', help="Tie weights to embedding, should be always flagged True")
parser.add_argument("--model_type", type=str, default="transformer", help="transformer|lstm|caml")
parser.add_argument("--hierachical", default=False, action='store_true', help="hierachical training")
parser.add_argument("--char", default=False, action='store_true', help="Use character model")
# model
parser.add_argument("--d_ff", type=int, default=2048, help="decoder nhid dimension")
parser.add_argument("--d_model", type=int, default=768, help="decoder nhid dimension")
parser.add_argument("--n_heads", type=int, default=8, help="number of attention heads")
parser.add_argument("--n_layers", type=int, default=6, help="decoder num layers")
parser.add_argument("--n_lstm_layers", type=int, default=1, help="decoder num lstm layers")
parser.add_argument("--n_kernels", type=int, default=50, help="caml kernel number")
parser.add_argument("--kernel_size", type=int, default=4, help="caml kernel size")
# gpu
parser.add_argument("--seed", type=int, default=1234, help="seed")
params, _ = parser.parse_known_args()
"""
SEED
"""
random.seed(params.seed)
np.random.seed(params.seed)
torch.manual_seed(params.seed)
torch.cuda.manual_seed(params.seed)
"""
Logging
"""
logging.basicConfig(format='[%(asctime)s] %(levelname)s: %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p', level=logging.INFO)
logger = logging.getLogger(__name__)
if not os.path.exists(params.outputdir): os.makedirs(params.outputdir)
file_handler = logging.FileHandler("{0}/log.txt".format(params.outputdir))
formatter = logging.Formatter('[%(asctime)s] %(levelname)s: %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')
file_handler.setFormatter(formatter)
logging.getLogger().addHandler(file_handler)
logger.info('\nTogrep : {0}\n'.format(sys.argv[1:]))
logger.info(params)
"""
Default json file loading
"""
json_config = json.load(open(params.hypes))
data_dir = json_config['data_dir']
prefix = json_config['prefix']
encoder_path = json_config['encoder_path']
label_size = json_config['label_size']
if params.init_emb: wordvec_path = json_config['wordvec_path']
"""
BPE encoder
"""
encoder = json.load(open(encoder_path))
encoder['_pad_'] = len(encoder)
encoder['_start_'] = len(encoder)
encoder['_end_'] = len(encoder)
encoder['_unk_'] = len(encoder)
n_special = 4
"""
DATA
"""
train, valid, test = get_data(encoder, data_dir, prefix, params.cut_down_len, label_size, char=params.char)
max_len = 0.
if params.corpus == 'psvg':
train['text'] = batchify(np.array(train['text'][0]), params.batch_size)
valid['text'] = batchify(np.array(valid['text'][0]), params.batch_size)
test['text'] = batchify(np.array(test['text'][0]), params.batch_size)
"""
Params
"""
if params.init_emb:
word_embeddings = np.concatenate([np.load(wordvec_path).astype(np.float32),
np.zeros((1, params.d_model), np.float32), # pad, zero-value!
(np.random.randn(n_special - 1, params.d_model) * 0.02).astype(np.float32)], 0)
else:
word_embeddings = (np.random.randn(len(encoder), params.d_model) * 0.02).astype(np.float32)
"""
MODEL
"""
# model config
config_model = {
'n_words': len(encoder),
'd_model': params.d_model, # same as word embedding size
'd_ff': params.d_ff, # this is the bottleneck blowup dimension
'n_layers': params.n_layers,
'dpout': params.dpout,
'bsize': params.batch_size,
'n_classes': label_size,
'n_heads': params.n_heads,
'train_emb': params.train_emb,
'init_emb': params.init_emb,
'pick_hid': params.pick_hid,
'tied': params.tied,
'n_lstm_layers': params.n_lstm_layers,
'n_kernels': params.n_kernels,
'kernel_size': params.kernel_size
}
if params.model_type == "lstm":
logger.info('model lstm')
model = make_lstm_model(encoder, config_model, word_embeddings)
elif params.model_type == 'caml':
logger.info('model caml')
model = make_caml_model(encoder, config_model, word_embeddings)
elif params.model_type == 'cnn':
logger.info('model cnn')
model = make_cnn_model(encoder, config_model, word_embeddings)
else:
logger.info('model transformer')
model = make_transformer_model(encoder, config_model, word_embeddings)
logger.info(model)
need_grad = lambda x: x.requires_grad
model_opt = NoamOpt(params.d_model, params.factor, params.warmup_steps, torch.optim.Adam(filter(need_grad, model.parameters()), lr=0, betas=(0.9, 0.98), eps=1e-9))
model.cuda()
"""
TRAIN
"""
def train_epoch_csu(epoch):
# initialize
logger.info('\nTRAINING : Epoch {}'.format(epoch))
model.train()
all_costs, all_em, all_p, all_r, all_f1 = [], [], [], [], []
# shuffle the data
permutation = np.random.permutation(len(train['text']))
text = train['text'][permutation]
label = train['label'][permutation]
for stidx in range(0, len(text), params.batch_size):
# prepare batch
text_batch = pad_batch(text[stidx: stidx + params.batch_size].tolist(), encoder, pad_start_end=True)
label_batch = label[stidx: stidx + params.batch_size]
b = Batch(text_batch, label_batch, encoder['_pad_'])
# model forward
if params.lm_coef == 0.: clf_output = model(b, clf=True, lm=False)
else: clf_output, text_y_hat = model(b, clf=True, lm=True)
# evaluation
pred = (torch.sigmoid(clf_output) > 0.5).data.cpu().numpy().astype(float)
em = metrics.accuracy_score(label_batch, pred)
p, r, f1, s = metrics.precision_recall_fscore_support(label_batch, pred, average='weighted')
all_em.append(em)
all_p.append(p)
all_r.append(r)
all_f1.append(f1)
if params.hierachical: loss = model.compute_hierachical_loss(clf_output, b.label)
else: loss = model.compute_clf_loss(clf_output, b.label)
if params.lm_coef != 0.0:
lm_loss = model.compute_lm_loss(text_y_hat, b.text_y, b.text_loss_mask)
loss += params.lm_coef * lm_loss
all_costs.append(loss.data.item())
# backward
model_opt.optimizer.zero_grad()
loss.backward()
# optimizer step
model_opt.step()
# log and reset
if len(all_costs) == params.log_interval:
logger.info('{}; loss {}; em {}; p {}; r {}; f1 {}; lr {}; embed_norm {}'.format(
stidx,
round(np.mean(all_costs), 2),
round(np.mean(all_em), 3),
round(np.mean(all_p), 3),
round(np.mean(all_r), 3),
round(np.mean(all_f1), 3),
model_opt.rate(),
model.tgt_embed[0].lut.weight.data.norm()
))
all_costs, all_em, all_p, all_r, all_f1 = [], [], [], [], []
# save
torch.save(model, os.path.join(params.outputdir, "model-{}.pickle".format(epoch)))
def evaluate_epoch_csu(epoch, eval_type='valid'):
# initialize
logger.info('\n{} : Epoch {}'.format(eval_type.upper(), epoch))
model.eval()
# data without shuffle
if eval_type == 'train': text, label = train['text'], train['label']
elif eval_type == 'valid': text, label = valid['text'], valid['label']
else: text, label = test['text'], test['label']
valid_scores, valid_preds, valid_labels = [], [], []
for stidx in range(0, len(text), params.batch_size):
# prepare batch
text_batch = pad_batch(text[stidx: stidx + params.batch_size].tolist(), encoder, pad_start_end=True)
label_batch = label[stidx: stidx + params.batch_size]
b = Batch(text_batch, label_batch, encoder['_pad_'])
# model forward
clf_output = model(b, clf=True, lm=False)
# evaluation
score = torch.sigmoid(clf_output).data.cpu().numpy()
pred = (score > 0.5).astype(float)
valid_scores.extend(score.tolist())
valid_preds.extend(pred.tolist())
valid_labels.extend(label_batch.tolist())
valid_scores, valid_preds, valid_labels = np.array(valid_scores), np.array(valid_preds), np.array(valid_labels)
np.save('{}/scores-{}.npy'.format(params.outputdir, epoch), valid_scores)
if params.hierachical:
parents = json.load(open('data/parents.json'))
id2label = json.load(open('data/labels.json'))
label2id = dict([(j, i) for i, j in enumerate(id2label)])
for i in range(valid_preds.shape[0]):
last_pred_i = valid_preds[i].copy()
while True:
for j in range(valid_preds.shape[1]):
did = id2label[j]
flag = True
now = did
while now in parents:
now = parents[now]
if now not in label2id: break
if valid_preds[i, label2id[now]] == 0:
flag = False
break
if not flag:
valid_preds[i, j] = 0.
if (valid_preds[i] == last_pred_i).all(): break
last_pred_i = valid_preds[i].copy()
em = metrics.accuracy_score(valid_labels, valid_preds)
p, r, f1, s = metrics.precision_recall_fscore_support(valid_labels, valid_preds, average='weighted')
logger.info('{}; em {}; p {}; r {}; f1 {}'.format(
epoch,
round(em, 3),
round(p, 3),
round(r, 3),
round(f1, 3)
))
def train_epoch_psvg(epoch):
# initialize
logger.info('\nTRAINING : Epoch {}'.format(epoch))
model.train()
all_costs = []
text = train['text']
for stidx in range(0, len(text[0]), params.bptt_size):
# prepare batch
text_batch = text[:, stidx: stidx + params.bptt_size + 1]
b = Batch(text_batch, [], encoder['_pad_'])
# model forward
text_y_hat = model(b, clf=False, lm=True)
# loss
loss = model.compute_lm_loss(text_y_hat, b.text_y, b.text_loss_mask)
all_costs.append(loss.data.item())
# backward
model_opt.optimizer.zero_grad()
loss.backward()
# optimizer step
model_opt.step()
# log and reset
if len(all_costs) == params.log_interval:
logger.info('{}; loss {}; perplexity: {}; lr {}; embed_norm: {}'.format(
stidx,
round(np.mean(all_costs), 2),
round(np.exp(np.mean(all_costs)), 2),
model_opt.rate(),
model.tgt_embed[0].lut.weight.data.norm()
))
all_costs = []
# save
torch.save(model, os.path.join(params.outputdir, "model-{}.pickle".format(epoch)))
def evaluate_epoch_psvg(epoch, eval_type='valid'):
# initialize
logger.info('\n{} : Epoch {}'.format(eval_type.upper(), epoch))
model.eval()
text = valid['text'] if eval_type == 'valid' else test['text']
all_costs = []
for stidx in range(0, len(text[0]), params.bptt_size):
# prepare batch
text_batch = text[stidx: stidx + params.batch_size + 1]
b = Batch(text_batch, [], encoder['_pad_'])
# model forward
text_y_hat = model(b, clf=False, lm=True)
# loss
loss = model.compute_lm_loss(text_y_hat, b.text_y, b.text_loss_mask)
all_costs.append(loss.data.item())
logger.info('loss {}; perplexity: {}'.format(
round(np.mean(all_costs), 2),
round(np.exp(np.mean(all_costs)), 2),
))
if __name__ == '__main__':
epoch = 1
if params.corpus == 'pp':
del model
model = torch.load(params.inputdir)
model.config = config_model
evaluate_epoch_csu(epoch, eval_type='test')
elif params.corpus == 'csu':
# del model
# model = torch.load(params.inputdir)
# evaluate_epoch_csu(epoch, eval_type='test')
if len(params.inputdir) != 0:
logger.info('Load Model from %s' % (params.inputdir))
model.load_state_dict(torch.load(params.inputdir), strict=False)
while epoch <= params.n_epochs:
train_epoch_csu(epoch)
evaluate_epoch_csu(epoch, eval_type='valid')
evaluate_epoch_csu(epoch, eval_type='test')
epoch += 1
elif params.corpus == 'psvg':
while epoch <= params.n_epochs:
train_epoch_psvg(epoch)
evaluate_epoch_psvg(epoch)
evaluate_epoch_psvg(epoch, eval_type='test')
epoch += 1