def train_skipgram (corpus_dir, extn, learning_rate, embedding_size, num_negsample, epochs, batch_size, output_dir,valid_size):
'''
:param corpus_dir: folder containing WL kernel relabeled files. All the files in this folder will be relabled
according to WL relabeling strategy and the format of each line in these folders shall be: <target> <context 1> <context 2>....
:param extn: Extension of the WL relabled file
:param learning_rate: learning rate for the skipgram model (will involve a linear decay)
:param embedding_size: number of dimensions to be used for learning subgraph representations
:param num_negsample: number of negative samples to be used by the skipgram model
:param epochs: number of iterations the dataset is traversed by the skipgram model
:param batch_size: size of each batch for the skipgram model
:param output_dir: the folder where embedding file will be stored
:param valid_size: number of subgraphs to be chosen at random to validate the goodness of subgraph representation
learning process in every epoc
:return: name of the file that contains the subgraph embeddings (in word2vec format proposed by Mikolov et al (2013))
'''
op_fname = '_'.join([os.path.basename(corpus_dir), 'dims', str(embedding_size), 'epochs', str(epochs),'embeddings.txt'])
op_fname = os.path.join(output_dir, op_fname)
if os.path.isfile(op_fname):
logging.info('The embedding file: {} is already present, hence NOT training skipgram model '
'for subgraph vectors'.format(op_fname))
return op_fname
logging.info("Initializing SKIPGRAM...")
corpus = Corpus(corpus_dir, extn = extn, max_files=0) # just load 'max_files' files from this folder
corpus.scan_and_load_corpus()
valid_examples = np.concatenate((np.random.choice(corpus.high_freq_word_ids, valid_size, replace=False),
np.random.choice(corpus.low_freq_word_ids, valid_size, replace=False)))
model_skipgram = skipgram(
doc_size=corpus._vocabsize, # for doc2vec skipgram model, the doc size should be same as word size
vocabulary_size=corpus._vocabsize, # size of i/p and o/p layers
learning_rate=learning_rate, # will decay over time?
embedding_size=embedding_size, # hidden layer neurons
num_negsample=num_negsample,
num_steps=epochs, # no. of time the training set will be iterated through
corpus=corpus, # data set of (target,context) tuples
valid_dataset=valid_examples, # validation set (a small subset) of (target, context) tuples?
)
final_embeddings, final_weights = model_skipgram.train(
corpus=corpus,
batch_size=batch_size,
valid_dataset=valid_examples,
)
logging.info('Write the matrix to a word2vec format file')
save_embeddings(corpus, final_embeddings, embedding_size, op_fname)
logging.info('Completed writing the final embeddings, pls check file: {} for the same'.format(op_fname))
return op_fname
def get_corpus(datadir, dataset):
fn = os.path.join(datadir, 'cache.pt')
if os.path.exists(fn):
print('Load cached dataset...')
corpus = torch.load(fn)
else:
corpus = Corpus(datadir, dataset)
def setup(seq_len, corpus_name, model_name):
global seq_length, corpus_path, sample_path, corpus, vocab_size, model
seq_length = seq_len
corpus_path = './data/' + corpus_name
sample_path = './sample/sample.txt'
corpus = Corpus()
corpus.get_data(corpus_path, batch_size)
vocab_size = len(corpus.dictionary)
model = RNNLM(vocab_size, embed_size, hidden_size, num_layers)
model = model.cuda()
model.load_state_dict(
torch.load('./model/' + model_name,
map_location=lambda storage, loc: storage))
def build_dataset(train_bs, test_bs):
path = '../../data/ptb'
train_path = os.path.join(path, 'train.txt')
val_path = os.path.join(path, 'valid.txt')
test_path = os.path.join(path, 'test.txt')
corpus = Corpus([train_path, val_path, test_path], train_bs, train_bs,
test_bs)
print('Data is loaded.')
return corpus
def predict(args, is_eval=False):
args.to_resume_model = True
if is_eval:
input_file_name = "dev.csv"
else:
input_file_name = "test.csv"
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
if args.model_name in MODEL_MAP:
Config, Model, Tokenizer, Tansform = MODEL_MAP[args.model_name]
config = Config.from_pretrained(args.pretrained_model_path,
num_labels=args.num_labels)
config = add_args_to_config(args, config)
tokenizer = Tokenizer.from_pretrained(args.pretrained_model_path,
do_lower_case=args.do_lower_case)
transform = Tansform(tokenizer, args)
model = load_model(Model, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
pub_data = Corpus(args, input_file_name, transform)
pub_sampler = SequentialSampler(pub_data)
pub_loader = DataLoader(pub_data,
batch_size=args.eval_batch_size,
sampler=pub_sampler)
logits, _, _ = do_inference(model, pub_loader, device)
df = pd.read_csv(os.path.join(args.data_dir, input_file_name))
inference_label = logits.argmax(axis=1)
df['label_pre'] = inference_label
if is_eval:
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
df[['id', 'label', 'label_pre', 'label_0',
'label_1']].to_csv(os.path.join(args.out_dir, "dev_sub.csv"),
index=False)
else:
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
filename = time.ctime().replace(' ', '-')
label_filename = "label-" + filename
filename = filename.replace(':', '-') + ".csv"
label_filename = label_filename.replace(':', '-') + ".csv"
df[['id', 'label_0',
'label_1']].to_csv(os.path.join(args.out_dir, filename),
index=False)
# df[['id', 'label_pre']].to_csv(os.path.join(args.out_dir, label_filename), index=False)
with open(os.path.join(args.out_dir, label_filename),
'w',
encoding='utf-8') as out:
for i in range(df.shape[0]):
out.write("{}\t{}\n".format(df['id'][i],
df['label_pre'][i]))
def train(args):
start = time.time()
print(
'Train with hid=%d layers=%d drop=%.3lf seq_len=%d lr=%.5lf, seed=%s' %
(args.hidden_size, args.num_layers, args.dropout, args.seq_length,
args.lr, args.seed))
continuous_no_update_epochs = 0
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
_ = torch.empty(size=[0], device=device)
train_data, eval_data, test_data, vocab_size = Corpus().get_data(
args.data_dir, args.batch_size)
model = RNNLM(vocab_size=vocab_size,
embed_size=args.hidden_size,
hidden_size=args.hidden_size,
num_layers=args.num_layers,
device=device,
dropout=args.dropout,
batch_size=args.batch_size).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = get_optimizer(args.optimizer, model)
best_val_loss = None
for nth_epoch in range(1, args.epoch + 1):
train_epoch(nth_epoch, model, train_data, criterion, optimizer, args)
eval_loss = evaluate(model,
data=eval_data,
criterion=criterion,
seq_len=args.seq_length,
epoch=nth_epoch)
if not best_val_loss or eval_loss < best_val_loss:
print(' >>> Save model %.3lf -> %.3lf' %
((np.exp(best_val_loss) if best_val_loss else 0.0),
np.exp(eval_loss)),
flush=True)
best_val_loss = eval_loss
continuous_no_update_epochs = 0
model.save(get_model_path(args))
else:
continuous_no_update_epochs += 1
print('', flush=True)
if continuous_no_update_epochs == args.continuous_no_update_epochs_threshold:
break
print('Test result is %s' % (np.exp(
evaluate(RNNLM.load(get_model_path(args)),
data=test_data,
criterion=criterion,
seq_len=args.seq_length,
test=True))))
print('Finished in %.3lfms\n' % ((time.time() - start) / 60))
def predict(args, Model, tokenizer, config, transform, is_eval=False):
args.to_resume_model = True
if is_eval:
input_file_name = "dev.csv"
else:
input_file_name = "test.csv"
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
# if args.model_name in MODEL_MAP:
# Config, Model, Tokenizer,Tansform = MODEL_MAP[args.model_name]
# config = Config.from_pretrained(args.pretrained_model_path, num_labels=args.num_labels)
# config = add_args_to_config(args,config)
# tokenizer = Tokenizer.from_pretrained(args.pretrained_model_path, do_lower_case=args.do_lower_case)
# transform = Tansform(tokenconfig_Tue-Dec-10-01-05-51-2019izer,args)
model = load_model(Model, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
pub_data = Corpus(args, input_file_name, transform)
pub_sampler = SequentialSampler(pub_data)
pub_loader = DataLoader(pub_data,
batch_size=args.eval_batch_size,
sampler=pub_sampler)
logits, _, _ = do_inference(model, pub_loader, device)
df = pd.read_csv(os.path.join(args.data_dir, input_file_name))
inference_label = logits.argmax(axis=1)
df['label_pre'] = inference_label
if is_eval:
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
df[[
'id', 'label', 'label_pre', 'label_0', 'label_1', 'question1',
'question2'
]].to_csv(os.path.join(args.out_dir, "dev_sub.csv"), index=False)
else:
df['label_0'] = logits[:, 0]
df['label_1'] = logits[:, 1]
filename = time.ctime().replace(' ', '-')
label_filename = "label-" + filename
filename = filename.replace(':', '-') + ".csv"
label_filename = label_filename.replace(':', '-') + ".csv"
df[['id', 'label_0',
'label_1']].to_csv(os.path.join(args.out_dir, filename),
index=False)
df[['id', 'label_pre']].to_csv(os.path.join(args.out_dir,
label_filename),
index=False,
header=False,
sep='\t')
import torch
from torch.autograd import Variable
from torch import nn, optim
from data_utils import Corpus
seq_length = 30
train_file = 'train.txt'
valid_file = 'valid.txt'
test_file = 'test.txt'
train_corpus = Corpus()
valid_corpus = Corpus()
test_corpus = Corpus()
train_id = train_corpus.get_data(train_file)
valid_id = valid_corpus.get_data(valid_file)
test_id = test_corpus.get_data(test_file)
vocab_size = len(train_corpus.dic)
num_batches = train_id.size(1) // seq_length
class languagemodel(nn.Module):
def __init__(self, vocab_size, embed_dim, hidden_size, num_layers):
super(languagemodel, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_dim)
self.lstm = nn.LSTM(embed_dim,
hidden_size,
num_layers,
batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
def train(args, Model, tokenizer, config, transform): ##
"""
:param args: training arguments
:param Model: Class of Model
:param tokenizer: word tokenizer
:param config: bert config instance
:param transform: data transform instance
:return:
"""
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
best_f1 = 0
logger.info("the current config is :\n {}".format(str(vars(args))))
set_seed(args)
# if args.model_name in MODEL_MAP:
# Config, Model, Tokenizer, Transform = MODEL_MAP[args.model_name]
# config = BertConfig.from_pretrained(args.pretrained_model_path, num_labels=args.num_labels)
# config = add_args_to_config(args,config) ##add customized args
# tokenizer = BertTokenizer.from_pretrained(args.pretrained_model_path, do_lower_case=args.do_lower_case)
model = load_model(Model, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
train_data = Corpus(args, "train.csv", transform)
dev_data = Corpus(args, 'dev.csv', transform)
dev_sampler = SequentialSampler(dev_data)
dev_loader = DataLoader(dev_data,
batch_size=args.eval_batch_size,
sampler=dev_sampler)
# Run prediction for full data
eval_sampler = SequentialSampler(dev_data)
dev_loader = DataLoader(dev_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
train_sampler = RandomSampler(train_data)
test_sampler = SubsetRandomSampler(
np.random.randint(low=0, high=(len(train_data)), size=len(dev_data)))
train_loader = DataLoader(train_data,
batch_size=args.batch_size,
sampler=train_sampler,
drop_last=True)
test_loader = DataLoader(train_data,
batch_size=args.eval_batch_size,
sampler=test_sampler)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", args.batch_size)
logger.info(" Num steps = %d", args.epochs)
logger.info(" Early Stoppi ng dev_loss = %f", args.dev_loss)
bar = tqdm(total=len(train_loader) * args.epochs)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=0,
t_total=len(bar))
steps = 0
total_train_loss = 0
set_seed(args)
for _ in range(args.epochs):
for step, data_batch in enumerate(train_loader):
bar.update(1)
model.train()
for k, v in data_batch.items():
data_batch[k] = v.to(device)
loss = model(batch=data_batch, feed_labels=True)
if args.n_gpus > 1:
loss = loss.mean()
optimizer.zero_grad() ## clear previous grad
loss.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(), args.max_grad_norm
) ##grad norm according to the glue it is useful.
optimizer.step()
scheduler.step()
# while len(model.points)>100:
# model.points.pop(0)
##setting bar
steps += 1
if steps > args.optimize_steps:
print("early stopping in {} steps".format(args.optimize_steps))
break
total_train_loss += loss.item()
bar.set_description("training loss {}".format(loss.item()))
if (steps) % args.eval_steps == 0:
logits, loss, dev_labels = do_inference(
model, dev_loader, device)
test_logits, test_loss, test_labels = do_inference(
model, test_loader, device)
inference_labels = logits.argmax(axis=1)
test_inference_labels = test_logits.argmax(axis=1)
f1 = f1_score(y_true=dev_labels,
y_pred=inference_labels,
average='macro')
c1_f1, c2_f1 = f1_score(y_true=dev_labels,
y_pred=inference_labels,
average=None)
test_f1 = f1_score(y_true=test_labels,
y_pred=test_inference_labels,
average='macro')
acc = accuracy_score(dev_labels, inference_labels)
logger.info("=========eval report =========")
logger.info("step : %s ", str(steps))
logger.info("average_train loss: %s" %
(str(total_train_loss / steps)))
logger.info("subset train loss: %s" % (str(test_loss)))
logger.info("subset train f1 score: %s", str(test_f1))
logger.info("eval loss: %s", str(loss))
logger.info("eval acc: %s", str(acc))
logger.info("eval f1 score: %s", str(f1))
logger.info("eval label 0 f1 score: %s", str(c1_f1))
logger.info("eval label 1 f1 score: %s", str(c2_f1))
output_eval_file = os.path.join(args.out_dir,
"eval_records.txt")
with open(output_eval_file, "a") as writer:
if steps == args.eval_steps:
writer.write("\n%s\n" % (args.memo))
writer.write("=========eval report =========\n")
writer.write("step : %s \n" % (str(steps)))
writer.write("average_train loss: %s\n" %
(str(total_train_loss / steps)))
writer.write("subset train loss: %s\n" % (str(test_loss)))
writer.write("subset f 1 score: %s\n" % (str(test_f1)))
writer.write("eval loss: %s\n" % (str(loss)))
writer.write("eval f1 score: %s\n" % (str(f1)))
writer.write("eval label 0 f1 score: %s\n" % str(c1_f1))
writer.write("eval label 1 f1 score: %s\n" % str(c2_f1))
writer.write('\n')
if f1 > best_f1:
logger.info("we get a best dev f1 %s saving model....",
str(f1))
output_path = os.path.join(args.out_dir,
"pytorch_model.bin")
if hasattr(model, 'module'):
logger.info("model has module")
model_to_save = model.module if hasattr(
model, 'module') else model
torch.save(model_to_save.state_dict(), output_path)
logger.info("model saved")
best_f1 = f1
save_config(args)
logger.info("args saved")
##load the final model
args.to_resume_model = True
model = load_model(Model, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
dev_logits, loss, dev_labels = do_inference(
model, dev_loader, device) ##do the inference for dev set
pub_data = Corpus(args, 'test.csv', transform)
pub_sampler = SequentialSampler(pub_data)
pub_loader = DataLoader(pub_data,
batch_size=args.eval_batch_size,
sampler=pub_sampler)
logits, loss, dev_labels = do_inference(model, dev_loader, device)
test_logits, _, _ = do_inference(model, pub_loader, device)
return dev_logits, dev_labels, test_logits
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 5
num_samples = 1000 # number of words to be sampled
batch_size = 20
seq_length = 30
learning_rate = 0.002
# Load "Penn Treebank" dataset
corpus = Corpus()
ids = corpus.get_data('data/short.txt',
batch_size) # label each word with word ids
# print(ids.shape)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
# RNN based language model
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size,
hidden_size,
num_layers,
def forward(self, x, h):
x = self.embedding(x)
x, hi = self.lstm(x, h)
b, s, h = x.size()
x = x.contiguous().view(b * s, h)
x = self.linear(x)
return x, hi
seq_length = 30
train_file = 'train.txt'
val_file = 'val.txt'
test_file = 'test.txt'
train_corpus = Corpus()
val_corpus = Corpus()
test_corpus = Corpus()
train_id = train_corpus.get_data(train_file)
val_id = train_corpus.get_data(val_file)
test_id = train_corpus.get_data(test_file)
vocab_size = len(train_corpus.dic)
num_batches = train_id.size(1) // seq_length
model = language_model(vocab_size, 128, 1024, 1)
# if torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
if torch.cuda.is_available():
from data_utils import Corpus
from config import args
import os
import pickle
from model import MOS
from sklearn.utils import shuffle
if __name__ == '__main__':
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
data = Corpus(args.data)
vocab_size = len(data.dictionary)
train_data = data.train
val_data = data.valid
test_data = data.test
model = MOS(vocab_size)
model.train(train_data, val_data)
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 5
num_samples = 1000 # number of words to be sampled
batch_size = 20
seq_length = 30
learning_rate = 0.002
# Load "Penn Treebank" dataset
corpus = Corpus()
ids = corpus.get_data('data/train.txt', batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
# RNN based language model
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
def forward(self, x, h):
# Embed word ids to vectors
from data_utils import Dictionary, Corpus
# Hyper Parameters
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 5
num_samples = 1000 # number of words to be sampled
batch_size = 20
seq_length = 30
learning_rate = 0.002
# Load Penn Treebank Dataset
train_path = './data/train.txt'
sample_path = './sample.txt'
corpus = Corpus()
ids = corpus.get_data(train_path, batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
# RNN Based Language Model
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size,
hidden_size,
num_layers,
batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
print(device, '\n')
# Hyper-parameters
embed_size = 220
hidden_size = 220
num_layers = 2
num_epochs = 40
num_samples = 5 # number of words to be sampled
batch_size = 20
seq_length = 30
dropout = 0.3
learning_rate = 0.005
dropout = 0.5
learning_rate = 0.01
# Load "Penn Treebank" dataset
corpus = Corpus()
ids = corpus.get_data('data/train.txt', batch_size) # divide to batch size
valid_d = corpus.get_data('data/valid.txt', batch_size)
test_d = corpus.get_data('data/test.txt', batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
best_val_loss = None
model = RNNLM(vocab_size, embed_size, hidden_size, num_layers, dropout).to(device)
# Calculate the number of trainable parameters in the model
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print('Number of trainable parameters: ', params)
# Loss and optimizer
from data_utils import Dictionary, Corpus
# Hyper Parameters
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 5
num_samples = 1000 # number of words to be sampled
batch_size = 20
seq_length = 30
learning_rate = 0.002
# Load Penn Treebank Dataset
train_path = './data/train.txt'
sample_path = './sample.txt'
corpus = Corpus()
ids = corpus.get_data(train_path, batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
# RNN Based Language Model
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
self.init_weights()
def init_weights(self):
self.embed.weight.data.uniform_(-0.1, 0.1)
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 5
num_samples = 10000 # number of words to be sampled
batch_size = 20
seq_length = 30
learning_rate = 0.002
# Load "Penn Treebank" dataset
corpus = Corpus()
ids = corpus.get_data('data/shakespeare.txt', batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
infer_mode = False
model_path = "model.ckpt"
import sys
if len(sys.argv) > 1 and sys.argv[1] == 'infer':
infer_mode = True
if infer_mode:
print("Inference mode..")
hidden_size = 200
num_layers = 2
num_epochs = 20
num_samples = 1000 # number of words to be sampled
batch_size = 20
seq_length = 20
learning_rate = 0.002
num_steps = 20
unrolling_size = 5
dtype = torch.cuda.LongTensor
ftype = torch.cuda.FloatTensor
# Load Penn Treebank Dataset
train_path = './data/ptb.train.txt'
test_path = '/data/ptb.test.txt'
corpus = Corpus('./data/ptb.train.txt')
raw_data = corpus.get_data(train_path, batch_size)
vocab_size = len(corpus.dictionary)
data_len = len(raw_data)
n_seq = (data_len - 1) // num_steps
raw_data_x = raw_data[0:n_seq * num_steps].view(n_seq, num_steps)
raw_data_y = raw_data[1:n_seq * num_steps + 1].view(n_seq, num_steps)
logger = Logger('./logs')
def convert(data, unroll, num_steps):
datalist = torch.split(data, 1, dim=1)
x0 = torch.cat(datalist[:unroll], dim=1)
x1 = torch.cat(datalist[unroll:], dim=1)
dataconvert = torch.cat((x1, x0), dim=1)
return dataconvert
def train(args):
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
best_f1 = 0
logger.info("the current config is :\n {}".format(str(vars(args))))
set_seed(args)
if args.model_name in MODEL_MAP:
Config, Model, Tokenizer, Transform = MODEL_MAP[args.model_name]
config = BertConfig.from_pretrained(args.pretrained_model_path,
num_labels=args.num_labels)
config = add_args_to_config(args, config) ##add customized args
tokenizer = BertTokenizer.from_pretrained(
args.pretrained_model_path, do_lower_case=args.do_lower_case)
model = load_model(LinearBertModel, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
###adv training
pgd = PGD(model)
###adv training
transform = base_transform(tokenizer, args)
train_data = Corpus(args, "train.csv", transform)
dev_data = Corpus(args, 'dev.csv', transform)
dev_sampler = SequentialSampler(dev_data)
dev_loader = DataLoader(dev_data,
batch_size=args.eval_batch_size,
sampler=dev_sampler)
# Run prediction for full data
eval_sampler = SequentialSampler(dev_data)
dev_loader = DataLoader(dev_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
train_sampler = RandomSampler(train_data)
test_sampler = SubsetRandomSampler(
np.random.randint(low=0,
high=(len(train_data)),
size=len(dev_data)))
train_loader = DataLoader(train_data,
batch_size=args.batch_size,
sampler=train_sampler,
drop_last=True)
test_loader = DataLoader(train_data,
batch_size=args.eval_batch_size,
sampler=test_sampler)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", args.batch_size)
logger.info(" Num steps = %d", args.epochs)
logger.info(" Early Stopping dev_loss = %f", args.dev_loss)
bar = tqdm(total=len(train_loader) * args.epochs)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=0,
t_total=len(bar))
steps = 0
total_train_loss = 0
set_seed(args)
model.zero_grad()
for _ in range(args.epochs):
for step, data_batch in enumerate(train_loader):
bar.update(1)
model.train()
for k, v in data_batch.items():
data_batch[k] = v.to(device)
loss = model(batch=data_batch, feed_labels=True)
if args.n_gpus > 1:
loss = loss.mean()
loss.backward()
###adv training
pgd.backup_grad()
for t in range(2):
pgd.attack(is_first_attack=(t == 0))
if t != 2 - 1:
model.zero_grad()
else:
pgd.restore_grad()
loss_adv = model(batch=data_batch, feed_labels=True)
if args.n_gpus > 1:
loss_adv = loss_adv.mean()
loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度
pgd.restore() # 恢复embedding参数
###adv training
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step()
model.zero_grad() ##delete the grad in the graph
##setting bar
steps += 1
total_train_loss += loss.item()
bar.set_description("training loss {}".format(loss.item()))
if (steps) % args.eval_steps == 0:
logits, loss, dev_labels = do_inference(
model, dev_loader, device)
test_logits, test_loss, test_labels = do_inference(
model, test_loader, device)
inference_labels = logits.argmax(axis=1)
test_inference_labels = test_logits.argmax(axis=1)
f1 = f1_score(y_true=dev_labels, y_pred=inference_labels)
test_f1 = f1_score(y_true=test_labels,
y_pred=test_inference_labels)
acc = accuracy_score(dev_labels, inference_labels)
logger.info("=========eval report =========")
logger.info("step : %s ", str(steps))
logger.info("average_train loss: %s" %
(str(total_train_loss / steps)))
logger.info("subset train loss: %s" % (str(test_loss)))
logger.info("subset train f1 score: %s", str(test_f1))
logger.info("eval loss: %s", str(loss))
logger.info("eval f1 score: %s", str(f1))
logger.info("eval acc: %s", str(acc))
output_eval_file = os.path.join(args.out_dir,
"eval_records.txt")
with open(output_eval_file, "a") as writer:
if steps == args.eval_steps:
writer.write("\n%s\n" % (args.memo))
writer.write("=========eval report =========\n")
writer.write("step : %s \n" % (str(steps)))
writer.write("average_train loss: %s\n" %
(str(total_train_loss / steps)))
writer.write("subset train loss: %s\n" %
(str(test_loss)))
writer.write("subset f1 score: %s\n" % (str(test_f1)))
writer.write("eval loss: %s\n" % (str(loss)))
writer.write("eval f1 score: %s\n" % (str(f1)))
writer.write('\n')
if f1 > best_f1:
logger.info("we get a best dev f1 %s saving model....",
str(f1))
output_path = os.path.join(args.out_dir,
"pytorch_model.bin")
if hasattr(model, 'module'):
logger.info("model has module")
model_to_save = model.module if hasattr(
model, 'module') else model
torch.save(model_to_save.state_dict(), output_path)
logger.info("model saved")
best_f1 = f1
save_config(args)
logger.info("args saved")
##load the final model
args.to_resume_model = True
model = load_model(Model, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
dev_logits, loss, dev_labels = do_inference(
model, dev_loader, device) ##do the inference for dev set
pub_data = Corpus(args, 'test.csv', transform)
pub_sampler = SequentialSampler(pub_data)
pub_loader = DataLoader(pub_data,
batch_size=args.eval_batch_size,
sampler=pub_sampler)
# logits, loss, dev_labels = do_inference(model, dev_loader, device)
test_logits, _, _ = do_inference(model, pub_loader, device)
return dev_logits, dev_labels, test_logits
else:
logger.info("the model %s is not registered", args.model_name)
return
def train_skipgram(corpus_dir, extn, learning_rate, embedding_size,
num_negsample, epochs, batch_size, output_dir, valid_size):
'''
:param corpus_dir: folder containing WL kernel relabeled files. All the files in this folder will be relabled
according to WL relabeling strategy and the format of each line in these folders shall be: <target> <context 1> <context 2>....
:param extn: Extension of the WL relabled file
:param learning_rate: learning rate for the skipgram model (will involve a linear decay)
:param embedding_size: number of dimensions to be used for learning subgraph representations
:param num_negsample: number of negative samples to be used by the skipgram model
:param epochs: number of iterations the dataset is traversed by the skipgram model
:param batch_size: size of each batch for the skipgram model
:param output_dir: the folder where embedding file will be stored
:param valid_size: number of subgraphs to be chosen at random to validate the goodness of subgraph representation
learning process in every epoc
:return: name of the file that contains the subgraph embeddings (in word2vec format proposed by Mikolov et al (2013))
'''
op_fname = '_'.join([
os.path.basename(corpus_dir), 'dims',
str(embedding_size), 'epochs',
str(epochs), 'embeddings.txt'
])
op_fname = os.path.join(output_dir, op_fname)
if os.path.isfile(op_fname):
logging.info(
'The embedding file: {} is already present, hence NOT training skipgram model '
'for subgraph vectors'.format(op_fname))
return op_fname
logging.info("Initializing SKIPGRAM...")
corpus = Corpus(
corpus_dir, extn=extn,
max_files=0) # just load 'max_files' files from this folder
corpus.scan_and_load_corpus()
valid_examples = np.concatenate(
(np.random.choice(corpus.high_freq_word_ids, valid_size,
replace=False),
np.random.choice(corpus.low_freq_word_ids, valid_size,
replace=False)))
model_skipgram = skipgram(
doc_size=corpus.
_vocabsize, # for doc2vec skipgram model, the doc size should be same as word size
vocabulary_size=corpus._vocabsize, # size of i/p and o/p layers
learning_rate=learning_rate, # will decay over time?
embedding_size=embedding_size, # hidden layer neurons
num_negsample=num_negsample,
num_steps=
epochs, # no. of time the training set will be iterated through
corpus=corpus, # data set of (target,context) tuples
valid_dataset=
valid_examples, # validation set (a small subset) of (target, context) tuples?
)
final_embeddings, final_weights = model_skipgram.train(
corpus=corpus,
batch_size=batch_size,
valid_dataset=valid_examples,
)
logging.info('Write the matrix to a word2vec format file')
save_embeddings(corpus, final_embeddings, embedding_size, op_fname)
logging.info(
'Completed writing the final embeddings, pls check file: {} for the same'
.format(op_fname))
return op_fname
def train(args):
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
set_seed(args)
best_f1 = 0
logger.info("the current config is :\n {}".format(str(vars(args))))
if args.model_name in MODEL_MAP:
Config, Model, Tokenizer, Transform = MODEL_MAP[args.model_name]
config = Config.from_pretrained(args.pretrained_model_path,
num_labels=args.num_labels)
config = add_args_to_config(args, config) ##add customized args
tokenizer = Tokenizer.from_pretrained(args.pretrained_model_path,
do_lower_case=args.do_lower_case)
model = load_model(Model, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
###adv training
pgd = PGD(model)
transform = Transform(tokenizer, args)
train_data = Corpus(args, "train.csv", transform)
###get the weighted sample with the weight [0.9,0.2,0.5]
# weight = [0.9,0.2,0.5]
# weight_sequence = []
# for i in range(len(train_data)):
# data = train_data[i]
# label =data.get('label').item()
# weight_sequence.append(weight[label]) ###add the weight of this label
dev_data = Corpus(args, 'dev.csv', transform)
dev_sampler = SequentialSampler(dev_data)
dev_loader = DataLoader(dev_data,
batch_size=args.eval_batch_size,
sampler=dev_sampler)
# Run prediction for full data
eval_sampler = SequentialSampler(dev_data)
dev_loader = DataLoader(dev_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
train_sampler = RandomSampler(train_data)
# weight_sampler = WeightedRandomSampler(weights=weight_sequence,num_samples=args.epochs*len(train_data), replacement=True)
test_sampler = SubsetRandomSampler(
np.random.randint(low=0,
high=(len(train_data)),
size=len(dev_data)))
train_loader = DataLoader(train_data,
batch_size=args.batch_size,
sampler=train_sampler,
drop_last=True)
test_loader = DataLoader(train_data,
batch_size=args.eval_batch_size,
sampler=test_sampler)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", args.batch_size)
logger.info(" Num steps = %d", args.epochs)
logger.info(" Early Stopping dev_loss = %f", args.dev_loss)
bar = tqdm(range(len(train_loader) * args.epochs),
total=len(train_loader) * args.epochs)
train_loader = cycle(train_loader)
##get optimizer
param_optimizer = list(model.named_parameters())
# hack to remove pooler, which is not used
# thus it produce None grad that break apex
param_optimizer = [n for n in param_optimizer]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
args.weight_decay
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=0,
t_total=len(bar))
steps = 0
# dev_labels = dev_data.get_feature("label")
# dev_labels = [i.item() for i in dev_labels]# get gold label
total_train_loss = 0
for step in bar:
model.train()
data_batch = next(train_loader)
for k, v in data_batch.items():
data_batch[k] = v.to(device)
loss = model(batch=data_batch, feed_labels=True)
if args.n_gpus > 1:
loss = loss.mean()
loss.backward()
###adv training
pgd.backup_grad()
for t in range(1):
pgd.attack(is_first_attack=(t == 0))
if t != 1 - 1:
model.zero_grad()
else:
pgd.restore_grad()
loss_adv = model(batch=data_batch, feed_labels=True)
if args.n_gpus > 1:
loss_adv = loss_adv.mean()
loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度
pgd.restore() # 恢复embedding参数
###adv training
optimizer.step()
optimizer.zero_grad()
scheduler.step()
##setting bar
steps += 1
total_train_loss += loss.item()
bar.set_description("training loss {}".format(loss.item()))
if (steps) % args.eval_steps == 0:
logits, loss, dev_labels = do_inference(
model, dev_loader, device)
test_logits, test_loss, test_labels = do_inference(
model, test_loader, device)
inference_labels = logits.argmax(axis=1)
test_inference_labels = test_logits.argmax(axis=1)
f1 = f1_score(dev_labels,
inference_labels,
labels=[0, 1, 2],
average="macro")
test_f1 = f1_score(test_labels,
test_inference_labels,
labels=[0, 1, 2],
average="macro")
# acc = accuracy_score(dev_labels, inference_labels)
logger.info("=========eval report =========")
logger.info("step : %s ", str(steps))
logger.info("average_train loss: %s" %
(str(total_train_loss / steps)))
logger.info("subset train loss: %s" % (str(test_loss)))
logger.info("subset train f1 score: %s", str(test_f1))
logger.info("eval loss: %s", str(loss))
logger.info("eval f1 score: %s", str(f1))
output_eval_file = os.path.join(args.out_dir,
"eval_records.txt")
with open(output_eval_file, "a") as writer:
if steps == args.eval_steps:
writer.write("\n%s\n" % (args.memo))
writer.write("=========eval report =========\n")
writer.write("step : %s \n" % (str(steps)))
writer.write("average_train loss: %s\n" %
(str(total_train_loss / steps)))
writer.write("subset train loss: %s\n" % (str(test_loss)))
writer.write("subset f1 score: %s\n" % (str(test_f1)))
writer.write("eval loss: %s\n" % (str(loss)))
writer.write("eval f1 score: %s\n" % (str(f1)))
writer.write('\n')
if f1 > best_f1:
logger.info("we get a best dev f1 %s saving model....",
str(f1))
output_path = os.path.join(args.out_dir,
"pytorch_model.bin")
if hasattr(model, 'module'):
logger.info("model has module")
model_to_save = model.module if hasattr(
model, 'module') else model
torch.save(model_to_save.state_dict(), output_path)
logger.info("model saved")
best_f1 = f1
save_config(args)
logger.info("args saved")
##load the final model
args.to_resume_model = True
model = load_model(Model, args, config)
model = model.to(device)
if args.n_gpus > 1:
model = nn.DataParallel(model)
dev_logits, loss, dev_labels = do_inference(
model, dev_loader, device) ##do the inference for dev set
pub_data = Corpus(args, 'test.csv', transform)
pub_sampler = SequentialSampler(pub_data)
pub_loader = DataLoader(pub_data,
batch_size=args.eval_batch_size,
sampler=pub_sampler)
# logits, loss, dev_labels = do_inference(model, dev_loader, device)
test_logits, _, _ = do_inference(model, pub_loader, device)
return dev_logits, dev_labels, test_logits
else:
logger.info("the model %s is not registered", args.model_name)
return
num_layers = 1
#num_layers = 2
num_epochs = 10
num_samples = 10000 # number of words to be sampled
batch_size = 20
seq_length = 30
learning_rate = 0.002
# Load "Penn Treebank" dataset
corpus = Corpus()
ids = corpus.get_data('data/wikitext-2-v1.train.tokens', batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
# RNN based language model
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
path_data = os.path.join(path_this, 'data', 'meditations.mb.txt')
from data_utils import Corpus
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epoch = 20
batch_size = 20
max_seq = 30
learning_rate = 0.002
max_vocab = None
corpus = Corpus()
tensor = corpus.fit(path_data, limit=max_vocab)
vocab_size = len(corpus.vocab.stoi)
tot_batch = tensor.shape[1] // max_seq
# not yet?
# tensor = tensor[:, :max_seq * tot_batch] # remove spilled
# RNN based language model
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size,
hidden_size,
import numpy as np
from torch.nn.utils import clip_grad_norm_
from data_utils import Dictionary, Corpus
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 5
num_samples = 1000
batch_size = 20
seq_length = 30
learning_rate = 0.002
corpus = Corpus()
ids = corpus.get_data('data/train.txt', batch_size)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size,
hidden_size,
num_layers,
batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
from torch.nn.utils import clip_grad_norm_
from data_utils import Dictionary, Corpus
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
embed_size = 128
hidden_size = 1024
num_layers = 1
num_epochs = 0
num_samples = 1000
batch_size = 20
seq_length = 30
learning_rate = 0.002
corpus = Corpus()
ids = corpus.get_data('data/train.txt',
batch_size) # (batch_size, word_ids from many sentence)
vocab_size = len(corpus.dictionary)
num_batches = ids.size(1) // seq_length
class RNNLM(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
super(RNNLM, self).__init__()
self.embed = nn.Embedding(vocab_size, embed_size)
self.lstm = nn.LSTM(embed_size,
hidden_size,
num_layers,
batch_first=True)
self.linear = nn.Linear(hidden_size, vocab_size)
