def __init__(self,
hparams=DotDict({
'model_type': 'transformer',
'ninp': 128,
'nhead': 2,
'nhid': 512,
'nlayers': 2,
'tie_layers': True,
'tie_encoder_decoder': True,
'dropout': 0.1,
})):
super(LanguageModelTrainer, self).__init__()
self.hparams = hparams if isinstance(hparams, DotDict) \
else DotDict(hparams)
from utils import get_default_tokenizer
self.vocab_size = get_default_tokenizer()._tokenizer.get_vocab_size()
self.model_type = hparams.get('model_type', 'transformer')
assert self.model_type in ['transformer', 'lstm']
if self.model_type == 'transformer':
self.model = TransformerModel(ntoken=self.vocab_size, **hparams)
else:
self.model = LSTMModel(ntoken=self.vocab_size, **hparams)
self.batch_size = hparams.get('batch_size', 64)
self.bptt = hparams.get('bptt', 128)
def run(stock: str, model_type: str, stationary=True):
df = Analysis.get_data(stock)
df["Company stock name"] = stock.split('/')[-1].split('.')[0]
dataset = GetDataset(df)
dataset.get_dataset(scale=False, stationary=stationary)
train_data, test_data, train_data_len = dataset.split(train_split_ratio=0.8, time_period=30)
train_data, test_data = dataset.get_torchdata()
x_train, y_train = train_data
x_test, y_test = test_data
if model_type == 'lstm':
params = rnn_params
model = TorchRNN(rnn_type=params.rnn_type, input_dim=params.input_dim,
hidden_dim=params.hidden_dim, output_dim=params.output_dim,
num_layers=params.num_layers)
elif model_type == 'transformer':
params = transf_params
model = TransformerModel(params)
else:
raise ValueError('Wrong model type selection, select either "rnn" or "transformer"!')
clf = Classifier(model)
clf.train([x_train, y_train], params=params)
y_scaler = dataset.y_scaler
predictions = clf.predict([x_test, y_test], y_scaler, data_scaled=False)
predictions = pd.DataFrame(predictions)
predictions.reset_index(drop=True, inplace=True)
predictions.index = df.index[-len(x_test):]
predictions['Actual'] = y_test[:-1]
predictions.rename(columns={0: 'Predictions'}, inplace=True)
if stationary:
predictions = Analysis.inverse_stationary_data(old_df=df, new_df=predictions,
orig_feature='Actual', new_feature='Predictions',
diff=12, do_orig=False)
plot_predictions(df, train_data_len, predictions["Predictions"].values, model_type)
def __init__(self, hparams: dict(), **kwargs) -> 'LightningTemplateModel':
# init superclass
super().__init__(**kwargs)
self.save_hyperparameters()
self.hparams = hparams
if self.hparams.model == 'awd':
self.model = WDLSTM(
self.hparams.num_tokens,
num_layers=self.hparams.num_layers,
num_hidden=self.hparams.num_hidden,
num_embedding=self.hparams.num_embedding,
tie_weights=self.hparams.tie_weights,
embedding_dropout=self.hparams.embedding_dropout,
input_dropout=self.hparams.input_dropout,
hidden_dropout=self.hparams.hidden_dropout,
output_dropout=self.hparams.output_dropout,
weight_dropout=self.hparams.weight_dropout)
self.model(
torch.zeros(self.hparams.bptt, self.hparams.batch_size).long(),
self.model.init_hidden(self.hparams.batch_size))
elif self.hparams.model == 'rnn':
self.model = RNNModel(self.hparams.rnn_type,
self.hparams.num_tokens,
num_embedding=self.hparams.num_embedding,
num_hidden=self.hparams.num_hidden,
num_layers=self.hparams.num_layers,
dropout=self.hparams.dropout,
tie_weights=self.hparams.tie_weights)
elif self.hparams.model == 'transformer':
self.model = TransformerModel(
self.hparams.num_tokens,
num_embedding=self.hparams.num_embedding,
num_hidden=self.hparams.num_hidden,
num_layers=self.hparams.num_layers,
dropout=self.hparams.dropout,
num_heads=self.hparams.num_heads)
else:
raise ValueError(f'Model {self.hparams.model} not recognized.')
self.hiddens = None
self.criterion = torch.nn.NLLLoss()
self.avg_loss = 0
def create_model(self):
"""
根据config文件选择对应的模型，并初始化
:return:
"""
if self.config["model_name"] == "textcnn":
self.model = TextCnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm":
self.model = BiLstmModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm_atten":
self.model = BiLstmAttenModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "rcnn":
self.model = RcnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "transformer":
self.model = TransformerModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
def initialize_model(type_model, args):
if type_model.lower() == 'lstm':
model = Stacked_LSTM(args)
elif type_model.lower() == 'attention_lstm':
model = LSTM_and_Attention(args)
elif type_model.lower() == 'transformer':
model = TransformerModel(d_input=3)
elif type_model.lower() == 'cnn':
model = CNN(n_out=36, dropout=0.01)
else:
raise ValueError("Invalid name!")
loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
return model, optimizer, loss_fn
def create_new_model():
if args.model_type == "seq2seq":
return Seq2Seq(word_vectors=word_vectors,
hidden_size=args.hidden_size,
output_size=vocab_size,
device=device)
elif args.model_type == "seq2seq_attn":
return Seq2SeqAttn(word_vectors=word_vectors,
hidden_size=args.hidden_size,
output_size=vocab_size,
device=device)
elif args.model_type == "transformer":
return TransformerModel(vocab_size,
device,
num_encoder_layers=2,
num_decoder_layers=2,
dropout=0.1)
def __init__(self,
train_dataset,
test_dataset,
*,
val_dataset,
n_layers=6,
n_head=8,
d_model=512,
d_inner_hid=1024,
d_k=64,
d_v=64,
edrop=0.25,
odrop=0.25,
hdrop=0.1,
propagate=False,
steps=15,
avg_window=AVERAGING_WINDOW,
clip_grad=5,
min_length=TRAIN_PERIODS,
tf_decay=0.7**(1 / 6),
tf_min=0.02,
tf_warmup=12000,
tf_steps=2000):
self.name = "transformer"
if propagate:
self.name += "_tf"
super(TransformerBot, self).__init__(train_dataset,
test_dataset,
clip_grad=clip_grad,
val_dataset=val_dataset,
avg_window=avg_window)
self.model = TransformerModel(n_max_seq=TRAIN_PERIODS,
n_layers=n_layers,
n_head=n_head,
d_word_vec=d_model,
d_model=d_model,
d_inner_hid=d_inner_hid,
d_k=d_k,
d_v=d_v,
propagate=propagate,
hdrop=hdrop,
edrop=edrop,
odrop=odrop,
min_length=min_length,
y_scale_by=1 / self.global_stds[0],
steps=steps)
self.model.cuda()
self.current_tf_ratio = 1
self.best_tf_ratio = 1
self.tf_min = tf_min
self.tf_decay = tf_decay
self.tf_steps = tf_steps
self.tf_warmup = tf_warmup
self.logger.info(str(self.model))
if propagate:
self.logger.info(
"TF min: {:.2f} TF decay: {:.4f} TF steps: {:d} TF warmup: {:d}"
.format(tf_min, tf_decay, tf_steps, tf_warmup))
self.tbwriter.add_text("model_structure", str(self.model))
self.tbwriter.add_text(
"TF_setting",
"TF min: {:.2f} TF decay: {:.4f} TF steps: {:d} TF warmup: {:d}".
format(tf_min, tf_decay, tf_steps, tf_warmup))
cuda = config['train']['cuda']
#Main Loop
while True:
min_test_loss = 1.e6
loss = 0.0
train_loss_seq = []
test_loss_seq = []
if model_type == 'Transformer':
model = TransformerModel(config)
elif model_type == 'LSTM':
model = LSTMModel(config)
if cuda:
model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(),
lr=config['train']['learning_rate'],
weight_decay=config['train']['weight_decay'])
criterion = torch.nn.MSELoss()
optimizer.zero_grad()
for it in range(n_iter):
model.train()
country = random.choice(train_countries)
def launch(model_params, checkpoint_path, device='cuda'):
print('model_params:\t', model_params)
max_length = model_params['bptt']
tokenizer = get_default_tokenizer()
eos_token = tokenizer.token_to_id('[SEP]')
eod_token = tokenizer.token_to_id('[DOC_SEP]')
vocab_size = tokenizer._tokenizer.get_vocab_size()
assert eos_token is not None, 'Invalid tokenizer files - EOS token cannot be null'
# Model
from models import TransformerModel, LSTMModel
model_type = model_params.get('model_type', 'transformer')
assert model_type in ['transformer', 'lstm']
if model_type == 'transformer':
model = TransformerModel(ntoken=vocab_size, **model_params)
else:
model = LSTMModel(ntoken=vocab_size, **model_params)
model = model.to(device)
if checkpoint_path and path.exists(checkpoint_path):
print(f'Loading checkpoint from {checkpoint_path}')
checkpoint_state = torch.load(checkpoint_path)
model.load_state_dict(checkpoint_state)
@torch.no_grad()
def _generate(input_ids=None,
max_length=max_length,
do_sample=True,
num_beams=5,
temperature=1.3,
top_k=50,
top_p=1.0,
repetition_penalty=1.2,
eos_token_ids=[eos_token, eod_token],
length_penalty=1.0,
num_return_sequences=1,
vocab_size=vocab_size):
pad_token_id = 0
model.eval()
batch_size = 1
cur_len = input_ids.shape[1]
# Expand input to num beams
input_ids = input_ids.unsqueeze(1).expand(batch_size, num_beams,
cur_len)
input_ids = input_ids.contiguous().view(batch_size * num_beams,
cur_len)
# generated hypotheses
generated_hyps = [
BeamHypotheses(num_beams,
max_length,
length_penalty,
early_stopping=False) for _ in range(batch_size)
]
# scores for each sentence in the beam
beam_scores = torch.zeros((batch_size, num_beams),
dtype=torch.float,
device=input_ids.device)
beam_scores[:, 1:] = -1e9
beam_scores = beam_scores.view(-1) # shape (batch_size * num_beams,)
# cache compute states
past = None
# done sentences
done = [False for _ in range(batch_size)]
while cur_len < max_length:
outputs = model(input_ids.t())
outputs = outputs.permute(1, 0, 2)
# print(input_ids)
# print(torch.argmax(outputs))
scores = outputs[:, -1, :]
# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
if repetition_penalty != 1.0:
for i in range(batch_size * num_beams):
for previous_token in set(input_ids[i].tolist()):
# if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if scores[i, previous_token] < 0:
scores[i, previous_token] *= repetition_penalty
else:
scores[i, previous_token] /= repetition_penalty
if do_sample:
# Temperature (higher temperature => more likely to sample low probability tokens)
if temperature != 1.0:
scores = scores / temperature
# Top-p/top-k filtering
# min_value = torch.min(scores, dim=-1)[]
scores = top_k_top_p_filtering(
scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
) # (batch_size * num_beams, vocab_size)
# Sample 2 next words for each beam (so we have some spare tokens and match output of greedy beam search)
try:
next_words = torch.multinomial(
torch.softmax(scores, dim=-1),
num_samples=2,
replacement=True) # (batch_size * num_beams, 2)
except:
print((torch.softmax(scores, dim=-1) > 0).sum())
raise ValueError()
# Compute next scores
_scores = F.log_softmax(
scores, dim=-1) # (batch_size * num_beams, vocab_size)
_scores = torch.gather(
_scores, -1, next_words) # (batch_size * num_beams, 2)
next_scores = _scores + beam_scores[:, None].expand_as(
_scores) # (batch_size * num_beams, 2)
# Match shape of greedy beam search
next_words = next_words.view(
batch_size, 2 * num_beams) # (batch_size, 2 * num_beams)
next_scores = next_scores.view(
batch_size, 2 * num_beams) # (batch_size, 2 * num_beams)
else:
# do greedy beam search
scores = F.log_softmax(
scores, dim=-1) # (batch_size * num_beams, vocab_size)
assert scores.size() == (batch_size * num_beams, vocab_size)
# Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product)
_scores = scores + beam_scores[:, None].expand_as(
scores) # (batch_size * num_beams, vocab_size)
# re-organize to group the beam together (we are keeping top hypothesis accross beams)
_scores = _scores.view(
batch_size, num_beams *
vocab_size) # (batch_size, num_beams * vocab_size)
next_scores, next_words = torch.topk(_scores,
2 * num_beams,
dim=1,
largest=True,
sorted=True)
assert next_scores.size() == next_words.size() == (batch_size,
2 * num_beams)
# next batch beam content
# list of (batch_size * num_beams) tuple(next hypothesis score, next word, current position in the batch)
next_batch_beam = []
# for each sentence
for batch_ex in range(batch_size):
# if we are done with this sentence
done[batch_ex] = done[batch_ex] or generated_hyps[
batch_ex].is_done(next_scores[batch_ex].max().item())
if done[batch_ex]:
next_batch_beam.extend([(0, pad_token_id, 0)] *
num_beams) # pad the batch
continue
# next sentence beam content
next_sent_beam = []
# next words for this sentence
for idx, score in zip(next_words[batch_ex],
next_scores[batch_ex]):
# get beam and word IDs
beam_id = idx // vocab_size
word_id = idx % vocab_size
# end of sentence, or next word
if word_id.item(
) in eos_token_ids or cur_len + 1 == max_length:
generated_hyps[batch_ex].add(
input_ids[batch_ex * num_beams +
beam_id, :cur_len].clone(), score.item())
else:
next_sent_beam.append(
(score, word_id, batch_ex * num_beams + beam_id))
# the beam for next step is full
if len(next_sent_beam) == num_beams:
break
# update next beam content
assert len(next_sent_beam
) == 0 if cur_len + 1 == max_length else num_beams
if len(next_sent_beam) == 0:
next_sent_beam = [(0, pad_token_id, 0)
] * num_beams # pad the batch
next_batch_beam.extend(next_sent_beam)
assert len(next_batch_beam) == num_beams * (batch_ex + 1)
# sanity check / prepare next batch
assert len(next_batch_beam) == batch_size * num_beams
beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
beam_words = input_ids.new([x[1] for x in next_batch_beam])
beam_idx = input_ids.new([x[2] for x in next_batch_beam])
# re-order batch
input_ids = input_ids[beam_idx, :]
input_ids = torch.cat([input_ids, beam_words.unsqueeze(1)], dim=-1)
# re-order internal states
if past:
reordered_past = []
for layer_past in past:
# get the correct batch idx from layer past batch dim
# batch dim of `past` and `mems` is at 2nd position
reordered_layer_past = [
layer_past[:, i].unsqueeze(1).clone().detach()
for i in beam_idx
]
reordered_layer_past = torch.cat(reordered_layer_past,
dim=1)
# check that shape matches
assert reordered_layer_past.shape == layer_past.shape
reordered_past.append(reordered_layer_past)
past = tuple(reordered_past)
# update current length
cur_len = cur_len + 1
# stop when we are done with each sentence
if all(done):
break
# visualize hypotheses
# print([len(x) for x in generated_hyps], cur_len)
# globals().update( locals() );
# !import code; code.interact(local=vars())
# for ii in range(batch_size):
# for ss, ww in sorted(generated_hyps[ii].hyp, key=lambda x: x[0], reverse=True):
# print("%.3f " % ss + " ".join(self.dico[x] for x in ww.tolist()))
# print("")
# select the best hypotheses
tgt_len = input_ids.new(batch_size)
best = []
for i, hypotheses in enumerate(generated_hyps):
if len(hypotheses.hyp) == 0:
continue
best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]
tgt_len[i] = len(best_hyp) + 1 # +1 for the <EOS> symbol
best.append(best_hyp)
# generate target batch
decoded = input_ids.new(batch_size,
tgt_len.max().item()).fill_(pad_token_id)
for i, hypo in enumerate(best):
decoded[i, :tgt_len[i] - 1] = hypo
decoded[i, tgt_len[i] - 1] = eos_token_ids[0]
return decoded
model_input = LEADING_TEXT
while True:
user_prompt = input(' >>> ')
if user_prompt == 'exit':
exit()
else:
num_return_sequences = 1
model_input += ' [P0] ' + user_prompt + ' [SEP] [P1] '
input_ids = tokenizer.encode(model_input).ids
input_ids = torch.LongTensor(input_ids).unsqueeze(0)
input_ids = input_ids.to(device)
output = _generate(input_ids=input_ids,
max_length=min(max_length,
input_ids.size(1) + 40))
if num_return_sequences != 1:
output = output.view(batch_size, num_return_sequences, -1)
response = tokenizer.decode(output[0].cpu().tolist(),
skip_special_tokens=False)
eod_token = '[DOC_SEP]'
if eod_token in response:
response = response[response.index(eod_token):]
start_token = '[P1]'
sep_token = '[SEP]'
if start_token in response:
start_idx = response.index(start_token) + len(start_token) + 1
response = response[start_idx:]
if sep_token in response:
sep_idx = response.index(sep_token)
response = response[:sep_idx]
model_input += response + f' {sep_token} '
print('Bot: ' + response)
class TransformerBot(BaseBot):
def __init__(self,
train_dataset,
test_dataset,
*,
val_dataset,
n_layers=6,
n_head=8,
d_model=512,
d_inner_hid=1024,
d_k=64,
d_v=64,
edrop=0.25,
odrop=0.25,
hdrop=0.1,
propagate=False,
steps=15,
avg_window=AVERAGING_WINDOW,
clip_grad=5,
min_length=TRAIN_PERIODS,
tf_decay=0.7**(1 / 6),
tf_min=0.02,
tf_warmup=12000,
tf_steps=2000):
self.name = "transformer"
if propagate:
self.name += "_tf"
super(TransformerBot, self).__init__(train_dataset,
test_dataset,
clip_grad=clip_grad,
val_dataset=val_dataset,
avg_window=avg_window)
self.model = TransformerModel(n_max_seq=TRAIN_PERIODS,
n_layers=n_layers,
n_head=n_head,
d_word_vec=d_model,
d_model=d_model,
d_inner_hid=d_inner_hid,
d_k=d_k,
d_v=d_v,
propagate=propagate,
hdrop=hdrop,
edrop=edrop,
odrop=odrop,
min_length=min_length,
y_scale_by=1 / self.global_stds[0],
steps=steps)
self.model.cuda()
self.current_tf_ratio = 1
self.best_tf_ratio = 1
self.tf_min = tf_min
self.tf_decay = tf_decay
self.tf_steps = tf_steps
self.tf_warmup = tf_warmup
self.logger.info(str(self.model))
if propagate:
self.logger.info(
"TF min: {:.2f} TF decay: {:.4f} TF steps: {:d} TF warmup: {:d}"
.format(tf_min, tf_decay, tf_steps, tf_warmup))
self.tbwriter.add_text("model_structure", str(self.model))
self.tbwriter.add_text(
"TF_setting",
"TF min: {:.2f} TF decay: {:.4f} TF steps: {:d} TF warmup: {:d}".
format(tf_min, tf_decay, tf_steps, tf_warmup))
def get_model_params(self, steps=0, is_train=True):
if is_train:
if steps < self.tf_warmup:
return {"tf_ratio": 1}
if (steps - self.tf_warmup) % self.tf_steps == 0:
self.current_tf_ratio = max(
self.current_tf_ratio * self.tf_decay, self.tf_min)
return {"tf_ratio": self.current_tf_ratio}
return {"tf_ratio": 0}
def reset_params(self):
self.current_tf_ratio = 1
self.best_tf_ratio = 1
def additional_logging(self, step):
if self.model.propagate:
self.logger.info("Current tf_ratio: {:.4f}".format(
self.current_tf_ratio))
self.tbwriter.add_scalar("tf_ratio", self.current_tf_ratio, step)
def save_state(self):
self.best_tf_ratio = self.current_tf_ratio
sort_within_batch=False)
valid_iterator = BucketIterator(valid,
batch_size=batch_size,
device=device,
sort=False,
sort_within_batch=False)
# sumeval evaluator
evaluator = SumEvaluator(metrics=metrics, stopwords=False, lang="en")
# Transformer model
model = TransformerModel(
len(IN_TEXT.vocab),
t_conf["model"]["params"]["d_model"], # emb_size
len(OUT_TEXT.vocab),
pretrained_vectors=None,
nhead=t_conf["model"]["params"]["nhead"],
num_encoder_layers=t_conf["model"]["params"]["num_encoder_layer"],
num_decoder_layers=t_conf["model"]["params"]["num_decoder_layer"],
dim_feedforward=t_conf["model"]["params"]["dim_feedforward"],
dropout=t_conf["model"]["params"]["dropout"]).to(device)
# Optimizer
# General template to make an optimzier instance
# e.g.,)
# optimizer = optim.SGD(model.parameters(),
# lr=0.1,
# momentum=0.9,
# nesterov=True)
optimizer = eval("{}(model.parameters(), **{})".format(
t_conf["training"]["optimizer"]["cls"],
str(t_conf["training"]["optimizer"]["params"])))
def get_batch(source, i):
seq_len = min(bptt, len(source) - 1 - i)
data = source[i:i + seq_len]
target = source[i + 1:i + 1 + seq_len].view(-1)
return data, target
ntokens = len(TEXT.vocab.stoi) # the size of vocabulary
emsize = 200 # embedding dimension
nhid = 200 # the dimension of the feedforward network model in nn.TransformerEncoder
nlayers = 2 # the number of nn.TransformerEncoderLayer in nn.TransformerEncoder
nhead = 2 # the number of heads in the multiheadattention models
dropout = 0.2 # the dropout value
model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers,
dropout).to(device)
criterion = nn.CrossEntropyLoss()
lr = 5.0 # learning rate
optimizer = torch.optim.SGD(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
def train():
model.train() # Turn on the train mode
total_loss = 0.
start_time = time.time()
ntokens = len(TEXT.vocab.stoi)
for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):
data, targets = get_batch(train_data, i)
optimizer.zero_grad()
def main(args):
# Set up logging and devices
startime = datetime.now()
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=True)
log = util.get_logger(args.save_dir, args.name)
time_log = args.log_time
if time_log > 0:
log.info(f'Start training at: {startime.strftime("%H:%M:%S")}')
tbx = SummaryWriter(args.save_dir)
device, args.gpu_ids = util.get_available_devices()
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
args.batch_size *= max(1, len(args.gpu_ids))
model_type = args.model
# Set random seed
log.info(f'Using random seed {args.seed}...')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# check this
#useCharEmbeddings = args.model == 'BiDAFplus'
# Get embeddings
log.info('Loading embeddings...')
print(f'{args.word_emb_file}')
word_vectors = util.torch_from_json(args.word_emb_file)
char_vectors = util.torch_from_json(args.char_emb_file)
if time_log > 0:
log.info(f'Loaded embeddings: {(datetime.now()-startime).seconds}')
# load_char_vectors
# Get model
log.info('Building model...')
if model_type == 'BiDAFplus': #
model = BiDAFplus(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
params=get_params(model_type, args.params))
elif model_type == 'BiDAFbase':
model = BiDAFbase(word_vectors=word_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
elif model_type == "Transformer":
model = TransformerModel(word_vectors=word_vectors,
char_vectors=char_vectors,
params=get_params(model_type, args.params))
elif model_type == 'BiDAF':
model = BiDAF(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
params=get_params(model_type, args.params))
model = nn.DataParallel(model, args.gpu_ids)
if time_log > 0:
log.info(f'Built model: {(datetime.now()-startime).seconds}')
if args.load_path:
log.info(f'Loading checkpoint from {args.load_path}...')
model, step = util.load_model(model, args.load_path, args.gpu_ids)
else:
step = 0
model = model.to(device)
model.train()
ema = util.EMA(model, args.ema_decay)
# Get saver
saver = util.CheckpointSaver(args.save_dir,
max_checkpoints=args.max_checkpoints,
metric_name=args.metric_name,
maximize_metric=args.maximize_metric,
log=log)
# Get optimizer and scheduler
optimizer = optim.Adadelta(model.parameters(),
args.lr,
weight_decay=args.l2_wd)
scheduler = sched.LambdaLR(optimizer, lambda s: 1.) # Constant LR
# Get data loader
log.info('Building dataset...')
if args.mode != 'quick_eval':
train_dataset = SQuAD(args.train_record_file, args.use_squad_v2)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collate_fn)
dev_dataset = SQuAD(args.dev_record_file, args.use_squad_v2)
dev_loader = data.DataLoader(dev_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
else:
loaded_data = quick_eval_data_loader()
train_loader = [loaded_data for _ in range(5)]
dev_loader = [quick_eval_data_loader(dev=True)]
train_dataset = train_loader
dev_dataset = dev_loader
log.info('Built dataset: {}:{}'.format(*divmod((datetime.now() -
startime).seconds, 60)))
# Train
log.info('Training...')
steps_till_eval = args.eval_steps
epoch = step // len(train_dataset)
if time_log > 0:
traintime = datetime.now()
total_iterations = 0
while epoch != args.num_epochs:
epoch += 1
log.info(f'Starting epoch {epoch}...')
if time_log > 0:
epochtime = datetime.now()
if args.mode != 'quick_eval':
progress_len = len(train_loader.dataset)
else:
progress_len = len(train_loader)
with torch.enable_grad(), \
tqdm(total=progress_len) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in train_loader:
#quick_eval_data_saver(cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids)
#########
if time_log > 0:
itertime = datetime.now()
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
optimizer.zero_grad()
if model_type == 'BiDAF' or model_type == "Transformer":
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
log_p1, log_p2 = model(cc_idxs, qc_idxs, cw_idxs, qw_idxs)
# Forward
elif model_type == 'BiDAFbase':
log_p1, log_p2 = model(cw_idxs, qw_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
loss_val = loss.item()
if time_log > 2:
forwardtime = datetime.now()
log.info('Forward time {}:{}'.format(
*divmod((forwardtime - itertime).seconds, 60)))
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step(step // batch_size)
ema(model, step // batch_size)
if time_log > 2:
backwardtime = datetime.now()
log.info('Backward time {}:{}'.format(
*divmod((backwardtime - forwardtime).seconds, 60)))
# Log info
step += batch_size
progress_bar.update(batch_size)
progress_bar.set_postfix(epoch=epoch, NLL=loss_val)
tbx.add_scalar('train/NLL', loss_val, step)
tbx.add_scalar('train/LR', optimizer.param_groups[0]['lr'],
step)
if time_log > 0:
enditertime = datetime.now()
#log.info('Iteration {} {}:{}'.format(total_iterations,
# *divmod((enditertime-itertime).seconds, 60)))
steps_till_eval -= batch_size
if steps_till_eval <= 0 or args.mode == 'quick_eval':
steps_till_eval = args.eval_steps
# Evaluate and save checkpoint
log.info(f'Evaluating at step {step}...')
ema.assign(model)
results, pred_dict = evaluate(
model,
dev_loader,
device,
args.dev_eval_file,
args.max_ans_len,
args.use_squad_v2,
model_type,
quick_eval=args.mode == 'quick_eval')
saver.save(step, model, results[args.metric_name], device)
ema.resume(model)
# Log to console
if time_log > 1:
log.info('Eval time {}:{}'.format(
*divmod((datetime.now() -
enditertime).seconds, 60)))
results_str = ', '.join(f'{k}: {v:05.2f}'
for k, v in results.items())
log.info(f'Dev {results_str}')
# Log to TensorBoard
log.info('Visualizing in TensorBoard...')
for k, v in results.items():
tbx.add_scalar(f'dev/{k}', v, step)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=args.dev_eval_file,
step=step,
split='dev',
num_visuals=args.num_visuals)
total_iterations += 1
if ((time_log == 2) and (total_iterations % 10 == 0)) or (
(time_log == 1) and (total_iterations % 100 == 0)):
log.info('Mean iteration time {}:{}'.format(
*divmod((enditertime - traintime).seconds /
total_iterations, 60)))
if time_log > 0:
endepochtime = datetime.now()
log.info('Epoch time {}:{}'.format(
*divmod((endepochtime - epochtime).seconds, 60)))
torch.manual_seed(args.seed)
scan_all = ge.load_scan_file('all', 'train')
scan_all_var = ge.load_scan_var('all', 'train')
input_symbols_scan = get_unique_words([c[0] for c in scan_all])
output_symbols_scan = get_unique_words([c[1] for c in scan_all])
all_symbols_scan = input_symbols_scan + output_symbols_scan
all_lang = Lang(all_symbols_scan)
ntoken = all_lang.n_symbols
# set up transformer encoder-decoder model, loss, optimizer
model = TransformerModel(ntoken=ntoken,
emsize=args.emsize,
nhead=args.nhead,
nhid=args.nhid,
nlayers=args.nlayers,
dropout=args.dropout)
model = nn.DataParallel(model).cuda()
criterion = nn.NLLLoss().cuda()
optimizer = torch.optim.Adam(model.parameters(), args.lr)
if args.model_path:
if os.path.isfile(args.model_path):
print('Loading model at:', args.model_path)
checkpoint = torch.load(args.model_path)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
print("=> no checkpoint found at '{}'".format(args.model_path))
def main():
args = parse_args()
if args.deterministic:
random.seed(0)
torch.manual_seed(0)
np.random.seed(0)
torch.backends.cudnn.deterministic = True
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.gpu = 0
TEXT = torchtext.data.Field(tokenize=get_tokenizer("basic_english"),
init_token='<sos>',
eos_token='<eos>',
lower=False)
train_txt, val_txt, test_txt = torchtext.datasets.WikiText2.splits(
TEXT, root=args.data_dir)
TEXT.build_vocab(train_txt)
model = TransformerModel(len(TEXT.vocab.stoi), args.em_size,
args.num_heads, args.hid_size,
args.num_layers).to(device)
# model = torch.nn.DataParallel(model, dim=1)
# optimiser = optim.Adam(model.parameters())
# optimiser = Ranger(model.parameters())
optimiser = RAdam(model.parameters())
if args.eval:
dataloaders = {
"test":
DataLoader(TextEvalDataset(test_txt, args.ngram, TEXT),
batch_size=args.eval_batch_size,
shuffle=False)
}
if args.resume:
resume(model, args)
test_loss, test_acc = eval_pll(device, model, dataloaders["test"],
args)
logger.info(f"Eval: Test Loss = {test_loss}, Test Acc = {test_acc}")
else:
dataloaders = {
"train":
DataLoader(TextTrainDataset(train_txt, args.ngram, TEXT,
args.poisson_rate),
batch_size=args.train_batch_size,
shuffle=True),
"val":
DataLoader(TextEvalDataset(val_txt, args.ngram, TEXT),
batch_size=args.eval_batch_size,
shuffle=False),
"test":
DataLoader(TextEvalDataset(test_txt, args.ngram, TEXT),
batch_size=args.eval_batch_size,
shuffle=False)
}
args.start_epoch = 0
args.best_acc = 1 / args.ngram
if args.resume:
resume(model, args, optimiser)
# Create folder for the current model and save args
model_dir = time.ctime().replace(" ", "_").replace(":", "_")
args.model_dir = os.path.join("models", model_dir)
os.makedirs(args.model_dir, exist_ok=True)
with open(os.path.join(args.model_dir, "args.json"), "w") as f:
json.dump(args.__dict__, f, indent=2)
args.logger = logger
train_pll(device, model, optimiser, dataloaders, args)
device=device,
sort=False,
sort_within_batch=False)
agg_test_iterator = BucketIterator(agg_test,
batch_size=batch_size,
device=device,
sort=False,
sort_within_batch=False)
# ================================================================
# Load model
model = TransformerModel(
len(IN_TEXT.vocab),
t_conf["model"]["params"]["d_model"], # emb_size
len(OUT_TEXT.vocab),
pretrained_vectors=None,
nhead=t_conf["model"]["params"]["nhead"],
num_encoder_layers=t_conf["model"]["params"]["num_encoder_layer"],
num_decoder_layers=t_conf["model"]["params"]["num_decoder_layer"],
dim_feedforward=t_conf["model"]["params"]["dim_feedforward"],
dropout=t_conf["model"]["params"]["dropout"]).to(device)
model.load_state_dict(torch.load(model_filepath, map_location=device))
# sumeval evaluator
evaluator = SumEvaluator(metrics=t_conf["metrics"],
stopwords=False,
lang="en")
# Old script used t_conf["training"]["gen_maxlen"]
gen_maxlen = g_conf["gen_maxtoken"]
## 1. Generation for each entity in "aggregated" test_{}.csv
train_data, val_data, test_data, vocab = dh.get_data()
#Hyper params
n_tokens = len(vocab.stoi) # the size of vocabulary
emb_size = 512 # embedding size
n_hidden = 200 # dimension of the FF network inside the transformer
n_layers = 2 # number of transformer layers
n_heads = 2 # the number of heads in the multiheadattention models
dropout = 0.2 # dropout percentage
criterion = nn.CrossEntropyLoss() #loss function
lr = 5.0 # learning rate
# Creating the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
model = TransformerModel(n_tokens, emb_size, n_heads, n_hidden, n_layers,
dropout).to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=lr) #Optimizer
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, 1, gamma=0.95) #Scheduler for the optimizer
def train(model):
model.train() # Turn on the train mode
total_loss = 0.
start_time = time.time()
src_mask = model.generate_square_subsequent_mask(dh.bptt).to(device)
for batch, i in enumerate(range(0, train_data.size(0) - 1, dh.bptt)):
data, targets = dh.get_batch(train_data, i)
optimizer.zero_grad()
if data.size(0) != dh.bptt:
eos_token='<eos>',
lower=True)
train_data, val_data, test_data = Multi30k.splits(exts=('.de', '.en'),
fields=(SRC, TRG))
train_iter, val_iter, test_iter = BucketIterator.splits(
(train_data, val_data, test_data), batch_size=args.batch_size)
print(len(train_iter))
SRC.build_vocab(train_data, min_freq=2)
TRG.build_vocab(train_data, min_freq=2)
# Create model
model = TransformerModel(len(SRC.vocab), len(TRG.vocab), args.d_model,
args.n_head, args.num_enc_layers,
args.num_dec_layers, args.dim_feedforword,
args.dropout, args.activation).to(device)
if args.resume_model is not None:
start_epoch, best_wer = resume_model(model, args.resume_model)
# Run the model parallelly
if torch.cuda.device_count() > 1:
logger.info("Using {} GPUs".format(torch.cuda.device_count()))
model = nn.DataParallel(model)
# Create loss criterion & optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
# Start training
logger.info("Training Started".center(60, '#'))
for epoch in range(start_epoch, args.epochs):
# Train the model
.with_ascii_quotes_replacement()\
.with_possessive_elimination()\
.with_punct_removal()\
.with_stopwords_removal()\
.with_digit_removal()\
.build()
clean_train_df, edited_col_name_train = preprocessor.preprocess(train_df)
clean_test_df, edited_col_name_test = preprocessor.preprocess(test_df)
# We set our training data and test data
training_data = clean_train_df[edited_col_name_train]
test_data = clean_test_df[edited_col_name_test]
# Create tokenizer, model
model = TransformerModel.TransformerModelBuilder().build()
print(model)
print("Model initialised.")
# Prepare the dataset
train_X = model.tokenize(training_data.to_list())
train_dataset = Task1Dataset(train_X, train_df['meanGrade'])
model.to(device)
train_proportion = 0.8
train_examples = round(len(train_dataset) * train_proportion)
dev_examples = len(train_dataset) - train_examples
train_dataset, dev_dataset = random_split(train_dataset,
class Predictor(PredictorBase):
def __init__(self, config):
super(Predictor, self).__init__(config)
self.model = None
self.config = config
self.word_to_index, self.label_to_index = self.load_vocab()
self.index_to_label = {
value: key
for key, value in self.label_to_index.items()
}
self.vocab_size = len(self.word_to_index)
self.word_vectors = None
self.sequence_length = self.config["sequence_length"]
# 创建模型
self.create_model()
# 加载计算图
self.load_graph()
def load_vocab(self):
# 将词汇-索引映射表加载出来
with open(os.path.join(self.output_path, "word_to_index.pkl"),
"rb") as f:
word_to_index = pickle.load(f)
with open(os.path.join(self.output_path, "label_to_index.pkl"),
"rb") as f:
label_to_index = pickle.load(f)
return word_to_index, label_to_index
def sentence_to_idx(self, sentence):
"""
将分词后的句子转换成idx表示
:param sentence:
:return:
"""
sentence_ids = [
self.word_to_index.get(token, self.word_to_index["<UNK>"])
for token in sentence
]
sentence_pad = sentence_ids[: self.sequence_length] if len(sentence_ids) > self.sequence_length \
else sentence_ids + [0] * (self.sequence_length - len(sentence_ids))
return sentence_pad
def load_graph(self):
"""
加载计算图
:return:
"""
self.sess = tf.Session()
ckpt = tf.train.get_checkpoint_state(
os.path.join(os.path.abspath(os.path.dirname(os.getcwd())),
self.config["ckpt_model_path"]))
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print('Reloading model parameters..')
self.model.saver.restore(self.sess, ckpt.model_checkpoint_path)
else:
raise ValueError('No such file:[{}]'.format(
self.config["ckpt_model_path"]))
def create_model(self):
"""
根据config文件选择对应的模型，并初始化
:return:
"""
if self.config["model_name"] == "textcnn":
self.model = TextCnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm":
self.model = BiLstmModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm_atten":
self.model = BiLstmAttenModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "rcnn":
self.model = RcnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "transformer":
self.model = TransformerModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
def predict(self, sentence):
"""
给定分词后的句子，预测其分类结果
:param sentence:
:return:
"""
sentence_ids = self.sentence_to_idx(sentence)
prediction = self.model.infer(self.sess, [sentence_ids]).tolist()[0]
label = self.index_to_label[prediction[0]]
return label
class Trainer(TrainerBase):
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
with open(
os.path.join(os.path.abspath(os.path.dirname(os.getcwd())),
args.config_path), "r") as fr:
self.config = json.load(fr)
self.train_data_obj = None
self.eval_data_obj = None
self.model = None
# save_path模型保存目录
self.save_path = os.path.join(
os.path.abspath(os.path.dirname(os.getcwd())),
self.config["ckpt_model_path"])
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
# self.builder = tf.saved_model.builder.SavedModelBuilder("../pb_model/weibo/bilstm/savedModel")
# 加载数据集
self.load_data()
self.train_inputs, self.train_labels, label_to_idx = self.train_data_obj.gen_data(
)
print("train data size: {}".format(len(self.train_labels)))
self.vocab_size = self.train_data_obj.vocab_size
print("vocab size: {}".format(self.vocab_size))
self.word_vectors = self.train_data_obj.word_vectors
self.label_list = [value for key, value in label_to_idx.items()]
self.eval_inputs, self.eval_labels = self.eval_data_obj.gen_data()
print("eval data size: {}".format(len(self.eval_labels)))
print("label numbers: ", len(self.label_list))
# 初始化模型对象
self.create_model()
def load_data(self):
"""
创建数据对象
:return:
"""
# 生成训练集对象并生成训练数据
self.train_data_obj = TrainData(self.config)
# 生成验证集对象和验证集数据
self.eval_data_obj = EvalData(self.config)
def create_model(self):
"""
根据config文件选择对应的模型，并初始化
:return:
"""
if self.config["model_name"] == "textcnn":
self.model = TextCnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm":
self.model = BiLstmModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm_atten":
self.model = BiLstmAttenModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "rcnn":
self.model = RcnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "transformer":
self.model = TransformerModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
def train(self):
"""
训练模型
:return:
"""
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9,
allow_growth=True)
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True,
gpu_options=gpu_options)
with tf.Session(config=sess_config) as sess:
# 初始化变量值
sess.run(tf.global_variables_initializer())
current_step = 0
eval_loss_lis = [0]
# 创建train和eval的summary路径和写入对象
train_summary_path = os.path.join(
os.path.abspath(os.path.dirname(os.getcwd())),
self.config["output_path"] + "/summary/train")
if not os.path.exists(train_summary_path):
os.makedirs(train_summary_path)
train_summary_writer = tf.summary.FileWriter(
train_summary_path, sess.graph)
eval_summary_path = os.path.join(
os.path.abspath(os.path.dirname(os.getcwd())),
self.config["output_path"] + "/summary/eval")
if not os.path.exists(eval_summary_path):
os.makedirs(eval_summary_path)
eval_summary_writer = tf.summary.FileWriter(
eval_summary_path, sess.graph)
for epoch in range(self.config["epochs"]):
print("----- Epoch {}/{} -----".format(epoch + 1,
self.config["epochs"]))
for batch in self.train_data_obj.next_batch(
self.train_inputs, self.train_labels,
self.config["batch_size"]):
summary, loss, predictions = self.model.train(
sess, batch, self.config["keep_prob"],
self.config['learning_rate'])
train_summary_writer.add_summary(summary)
current_step += 1
if self.config[
"num_classes"] == 1 and current_step % self.config[
"print_every"] == 0:
acc, auc, recall, prec, f_beta = get_binary_metrics(
pred_y=predictions, true_y=batch["y"])
print(
"train: step: {}, loss: {}, acc: {}, auc: {}, recall: {}, precision: {}, f_beta: {}"
.format(current_step, loss, acc, auc, recall, prec,
f_beta))
elif self.config[
"num_classes"] > 1 and current_step % self.config[
"print_every"] == 0:
acc, recall, prec, f_beta = get_multi_metrics(
pred_y=predictions,
true_y=batch["y"],
labels=self.label_list)
print(
"train: step: {}, loss: {}, acc: {}, recall: {}, precision: {}, f_beta: {}"
.format(current_step, loss, acc, recall, prec,
f_beta))
#每训练一个epoch输出验证集的评测结果
if self.eval_data_obj:
eval_losses = []
eval_accs = []
eval_aucs = []
eval_recalls = []
eval_precs = []
eval_f_betas = []
for eval_batch in self.eval_data_obj.next_batch(
self.eval_inputs, self.eval_labels,
self.config["batch_size"]):
eval_summary, eval_loss, eval_predictions = self.model.eval(
sess, eval_batch)
eval_summary_writer.add_summary(eval_summary)
eval_losses.append(eval_loss)
if self.config["num_classes"] == 1:
acc, auc, recall, prec, f_beta = get_binary_metrics(
pred_y=eval_predictions,
true_y=eval_batch["y"])
eval_accs.append(acc)
eval_aucs.append(auc)
eval_recalls.append(recall)
eval_precs.append(prec)
eval_f_betas.append(f_beta)
elif self.config["num_classes"] > 1:
acc, recall, prec, f_beta = get_multi_metrics(
pred_y=eval_predictions,
true_y=eval_batch["y"],
labels=self.label_list)
eval_accs.append(acc)
eval_recalls.append(recall)
eval_precs.append(prec)
eval_f_betas.append(f_beta)
eval_loss_lis.append(mean(eval_losses))
print("\n")
print(
"eval: loss: {}, acc: {}, auc: {}, recall: {}, precision: {}, f_beta: {}"
.format(mean(eval_losses), mean(eval_accs),
mean(eval_aucs), mean(eval_recalls),
mean(eval_precs), mean(eval_f_betas)))
print("\n")
if self.config["ckpt_model_path"] and eval_loss_lis[
-1] >= max(eval_loss_lis):
#self.model_save_path是模型保存具体的名字
self.model_save_path = os.path.join(
self.save_path, self.config["model_name"])
self.model.saver.save(sess,
self.model_save_path,
global_step=epoch + 1)
elif self.config["ckpt_model_path"] and eval_loss_lis[
-1] < max(eval_loss_lis):
if self.config['batch_size'] <= 256:
self.config['batch_size'] *= 2
if self.config['learning_rate'] <= 0.00001:
self.config['learning_rate'] *= 0.95
print(
"epoch: {} lr: {} self.batch_size: {}".format(
epoch, self.lr, self.batch_size))
self.save_path = tf.train.latest_checkpoint(
self.save_path)
print('最新加载的模型路径{}'.format(self.save_path))
else:
print('learn_rate 小于0.00001，训练结束')
class LanguageModel(LightningModule):
def __init__(self, hparams: dict(), **kwargs) -> 'LightningTemplateModel':
# init superclass
super().__init__(**kwargs)
self.save_hyperparameters()
self.hparams = hparams
if self.hparams.model == 'awd':
self.model = WDLSTM(
self.hparams.num_tokens,
num_layers=self.hparams.num_layers,
num_hidden=self.hparams.num_hidden,
num_embedding=self.hparams.num_embedding,
tie_weights=self.hparams.tie_weights,
embedding_dropout=self.hparams.embedding_dropout,
input_dropout=self.hparams.input_dropout,
hidden_dropout=self.hparams.hidden_dropout,
output_dropout=self.hparams.output_dropout,
weight_dropout=self.hparams.weight_dropout)
self.model(
torch.zeros(self.hparams.bptt, self.hparams.batch_size).long(),
self.model.init_hidden(self.hparams.batch_size))
elif self.hparams.model == 'rnn':
self.model = RNNModel(self.hparams.rnn_type,
self.hparams.num_tokens,
num_embedding=self.hparams.num_embedding,
num_hidden=self.hparams.num_hidden,
num_layers=self.hparams.num_layers,
dropout=self.hparams.dropout,
tie_weights=self.hparams.tie_weights)
elif self.hparams.model == 'transformer':
self.model = TransformerModel(
self.hparams.num_tokens,
num_embedding=self.hparams.num_embedding,
num_hidden=self.hparams.num_hidden,
num_layers=self.hparams.num_layers,
dropout=self.hparams.dropout,
num_heads=self.hparams.num_heads)
else:
raise ValueError(f'Model {self.hparams.model} not recognized.')
self.hiddens = None
self.criterion = torch.nn.NLLLoss()
self.avg_loss = 0
def forward(self, x, hiddens=None):
if self.hparams.model != 'transformer':
return self.model(x, hiddens)
return self.model(x)
def on_train_epoch_start(self):
self.train_len = len(
self.train_dataloader().batch_sampler) * self.hparams.bptt
if self.hparams.model != 'transformer':
self.hiddens = self.model.init_hidden(self.hparams.batch_size)
def training_step(self, batch, batch_idx):
x, y = batch
if self.hparams.model == 'awd':
self.hiddens = repackage_hidden(self.hiddens)
out, self.hiddens, (hs, dropped_hs) = self(x, self.hiddens)
elif self.hparams.model == 'rnn':
self.hiddens = repackage_hidden(
self.hiddens) if self.hiddens else self.hiddens
out, self.hiddens = self(x, self.hiddens)
elif self.hparams.model == 'transformer':
out = self(x)
raw_loss = self.criterion(out, y)
loss = raw_loss
# The AR and TAR loss are only applied to the output of the final
# RNN layer, not to all layers
if self.hparams.model == 'awd':
# WARNING: It is implementing here \ell_2^2 instead of \ell_2
# Activation Regularization
if self.hparams.alpha > 0:
loss += self.hparams.alpha * dropped_hs[-1].pow(2).mean()
# Temporal Activation Regularization (slowness)
if self.hparams.beta > 0:
loss += self.hparams.beta * \
(hs[-1][1:] - hs[-1][:-1]).pow(2).mean()
ppl = torch.exp(raw_loss)
bpc = raw_loss / math.log(2)
self.log('train_loss', loss)
self.log('train_ppl', ppl, prog_bar=True)
self.log('train_bpc', bpc, prog_bar=True)
return loss
def on_validation_epoch_start(self):
self.val_len = len(
self.val_dataloader().batch_sampler) * self.hparams.bptt
if self.hparams.model != 'transformer':
self.hiddens = self.model.init_hidden(self.hparams.batch_size)
def validation_step(self, batch, batch_idx):
x, y = batch
if self.hparams.model == 'awd':
self.hiddens = repackage_hidden(self.hiddens)
out, self.hiddens, (hs, dropped_hs) = self(x, self.hiddens)
elif self.hparams.model == 'rnn':
self.hiddens = repackage_hidden(
self.hiddens) if self.hiddens else self.hiddens
out, self.hiddens = self(x, self.hiddens)
elif self.hparams.model == 'transformer':
out = self(x)
loss = self.criterion(out, y)
self.log('val_loss',
len(x) * loss,
prog_bar=True,
reduce_fx=lambda x: torch.sum(x) / self.val_len)
self.log('val_bpc',
len(x) * loss,
prog_bar=True,
reduce_fx=lambda x:
(torch.sum(x) / self.val_len) / math.log(2))
self.log('val_ppl',
len(x) * loss,
prog_bar=True,
reduce_fx=lambda x: torch.exp(torch.sum(x) / self.val_len))
return loss
def on_test_epoch_start(self):
self.test_len = len(
self.test_dataloader().batch_sampler) * self.hparams.bptt
if self.hparams.model != 'transformer':
self.hiddens = self.model.init_hidden(self.hparams.batch_size)
def test_step(self, batch, batch_idx):
x, y = batch
if self.hparams.model == 'awd':
self.hiddens = repackage_hidden(self.hiddens)
out, self.hiddens, (hs, dropped_hs) = self(x, self.hiddens)
elif self.hparams.model == 'rnn':
self.hiddens = repackage_hidden(
self.hiddens) if self.hiddens else self.hiddens
out, self.hiddens = self(x, self.hiddens)
elif self.hparams.model == 'transformer':
out = self(x)
loss = self.criterion(out, y)
self.log('test_loss',
len(x) * loss,
prog_bar=True,
reduce_fx=lambda x: torch.sum(x) / self.test_len)
self.log('test_bpc',
len(x) * loss,
prog_bar=True,
reduce_fx=lambda x:
(torch.sum(x) / self.test_len) / math.log(2))
self.log('test_ppl',
len(x) * loss,
prog_bar=True,
reduce_fx=lambda x: torch.exp(torch.sum(x) / self.test_len))
return loss
def configure_optimizers(self):
"""
Return whatever optimizers and learning rate schedulers you want here.
At least one optimizer is required.
WARNING: The paper use a variation of ASGD, called non-monotonically
triggered ASGD (Algorithm 1), which is not implemented yet, They used L
to be the number of iterations in an epoch (i.e., after training epoch
ends) and n=5.
"""
if self.hparams.optimizer == 'sgd':
optimizer = torch.optim.SGD(self.parameters(),
lr=self.hparams.learning_rate,
weight_decay=self.hparams.weight_decay)
if self.hparams.optimizer == 'adam':
optimizer = torch.optim.Adam(
self.parameters(),
lr=self.hparams.learning_rate,
weight_decay=self.hparams.weight_decay)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(
# optimizer, self.hparams.multi_step_lr_milestones, gamma=0.1)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=self.hparams.learning_rate,
epochs=self.hparams.max_epochs,
steps_per_epoch=len(self.train_dataloader()))
return [optimizer], [{'scheduler': scheduler, 'interval': 'step'}]
@staticmethod
def add_model_specific_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument('--num-embedding',
type=int,
default=400,
help='size of word embeddings')
parser.add_argument('--num-hidden',
type=int,
default=1150,
help='number of hidden units per layer')
parser.add_argument('--num-layers',
type=int,
default=3,
help='number of layers')
parser.add_argument('--learning_rate',
'--learning-rate',
type=float,
default=30.0,
help='initial learning rate')
parser.add_argument('--batch-size',
type=int,
default=80,
metavar='N',
help='batch size')
parser.add_argument('--bptt',
type=int,
default=70,
help='sequence length')
parser.add_argument('--output-dropout',
type=float,
default=0.4,
help='dropout applied to layers (0 = no dropout)')
parser.add_argument('--hidden-dropout',
type=float,
default=0.3,
help='dropout for rnn layers (0 = no dropout)')
parser.add_argument(
'--input-dropout',
type=float,
default=0.65,
help='dropout for input embedding layers (0 = no dropout)')
parser.add_argument(
'--embedding-dropout',
type=float,
default=0.1,
help='dropout to remove words from embedding layer '
'(0 = no dropout)')
parser.add_argument(
'--weight-dropout',
type=float,
default=0.5,
help='amount of weight dropout to apply to the RNN hidden to '
'hidden matrix')
parser.add_argument(
'--alpha',
type=float,
default=0,
help='alpha L2 regularization on RNN activation (alpha = 0 means'
' no regularization)')
parser.add_argument(
'--beta',
type=float,
default=0,
help='beta slowness regularization applied on RNN activiation '
'(beta = 0 means no regularization)')
parser.add_argument('--weight-decay',
type=float,
default=1.2e-6,
help='weight decay applied to all weights')
parser.add_argument('--optimizer',
type=str,
default='sgd',
help='optimizer to use (sgd, adam)')
parser.add_argument(
'--no-tie-weights',
dest='tie_weights',
default=True,
action='store_false',
help='if set, does not tie the input/output embedding weights')
parser.add_argument('--rnn-type',
choices=['LSTM', 'GRU', 'RNN_TANH', 'RNN_RELU'],
default='LSTM')
parser.add_argument('--dropout', type=float, default=0.2)
parser.add_argument(
'--num-heads',
type=int,
default=2,
help='the number of heads in the encoder/decoder of the '
' transformer model')
return parser
def main(args):
since = time.time()
output_dir = os.path.join(os.getcwd(), 'outputs')
os.makedirs(output_dir, exist_ok=True)
data_loaders = get_dataloader(
input_dir=args.input_dir,
which_challenge='3rd_challenge',
phases=['test'],
max_frame_length=args.max_frame_length,
max_vid_label_length=args.max_vid_label_length,
max_seg_label_length=args.max_seg_label_length,
rgb_feature_size=args.rgb_feature_size,
audio_feature_size=args.audio_feature_size,
batch_size=args.batch_size,
num_workers=args.num_workers)
model = TransformerModel(
n_layers=args.n_layers,
n_heads=args.n_heads,
rgb_feature_size=args.rgb_feature_size,
audio_feature_size=args.audio_feature_size,
d_rgb=args.d_rgb,
d_audio=args.d_audio,
d_model=args.d_model,
d_ff=args.d_ff,
d_proj=args.d_proj,
n_attns = args.n_attns,
num_classes=args.num_classes,
dropout=args.dropout)
model = model.to(device)
checkpoint = torch.load(os.path.join(os.getcwd(), 'models/model-epoch-04.ckpt'))
model.load_state_dict(checkpoint['state_dict'])
model.eval()
df_outputs = {i: pd.DataFrame(columns=['vid_id', 'vid_label_pred', 'vid_prob', 'seg_label_pred', 'seg_prob']) \
for i in range(1, args.num_classes+1)}
for idx, (vid_ids, frame_lengths, frame_rgbs, frame_audios, vid_labels, seg_labels, seg_times) \
in enumerate(data_loaders['test']):
if idx%10 == 0:
print('idx:', idx)
# frame_rgbs: [batch_size, frame_length, rgb_feature_size]
# frame_audios: [batch_size, frame_length, audio_feature_size]
frame_rgbs = frame_rgbs.to(device)
frame_audios = frame_audios.to(device)
batch_size = frame_audios.size(0)
# vid_probs: [batch_size, num_classes]
# attn_idc: [batch_size, num_classes]
# scores: [batch_size, max_seg_length, n_attns]
# attn_weights: [batch_size, max_seg_length, n_attns]
vid_probs, attn_idc, scores, attn_weights, conv_loss = model(frame_rgbs, frame_audios, device)
# vid_probs: [batch_size, vid_pred_length]
# vid_label_preds: [batch_size, vid_pred_length]
vid_probs, vid_label_preds = torch.topk(vid_probs, args.vid_pred_length)
vid_label_preds = vid_label_preds + 1
# attn_idc: [batch_size, num_classes+1]
zeros = torch.zeros(batch_size, 1).long().to(device)
attn_idc = torch.cat((zeros, attn_idc), dim=1)
# selected_attn_idc: [batch_size, vid_pred_length]
selected_attn_idc = torch.gather(attn_idc, 1, vid_label_preds)
# attn_weights: [batch_size, n_attns, max_seg_length]
attn_weights = attn_weights.transpose(1, 2)
# selected_attn_weights: [batch_size, vid_pred_length, max_seg_length]
selected_attn_weights = batched_index_select(attn_weights, 1, selected_attn_idc)
# seg_probs: [batch_size, vid_pred_length, seg_pred_length]
# seg_label_preds: [batch_size, vid_pred_length, seg_pred_length]
seg_probs, seg_label_preds = torch.topk(selected_attn_weights, args.seg_pred_length)
seg_label_preds = seg_label_preds + 1
# seg_prob_min, seg_prob_max: [batch_size, vid_pred_length]
seg_prob_min, _ = seg_probs.min(dim=2)
seg_prob_max, _ = seg_probs.max(dim=2)
# seg_prob_min, seg_prob_max: [batch_size, vid_pred_length, seg_pred_length]
seg_prob_min = seg_prob_min.unsqueeze(2).expand(batch_size, args.vid_pred_length, args.seg_pred_length)
seg_prob_max = seg_prob_max.unsqueeze(2).expand(batch_size, args.vid_pred_length, args.seg_pred_length)
# seg_probs: [batch_size, vid_pred_length, seg_pred_length]
seg_probs = (seg_probs - seg_prob_min) / (seg_prob_max - seg_prob_min + 1e-6)
# To save predictions, converted to numpy data.
vid_probs = vid_probs.cpu().detach().numpy()
vid_label_preds = vid_label_preds.cpu().numpy()
seg_probs = seg_probs.cpu().detach().numpy()
seg_label_preds = seg_label_preds.cpu().numpy()
for i in range(batch_size):
for j in range(args.vid_pred_length):
vid_label_pred = vid_label_preds[i][j]
df_outputs[vid_label_pred] = df_outputs[vid_label_pred].append(
{'vid_id': vid_ids[i],
'vid_label_pred': vid_label_pred,
'vid_prob': vid_probs[i][j],
'seg_label_pred': list(seg_label_preds[i][j]),
'seg_prob': list(seg_probs[i][j])}, ignore_index=True)
for i in range(1, args.num_classes+1):
df_outputs[i].to_csv(os.path.join(output_dir, '%04d.csv'%i), index=False)
time_elapsed = time.time() - since
print('=> Running time in a epoch: {:.0f}h {:.0f}m {:.0f}s'
.format(time_elapsed // 3600, (time_elapsed % 3600) // 60, time_elapsed % 60))
def generate_summary(model, tokenizer, document, decoder):
""" Generates a summary for a single document
Parameters
----------
model: ``BartForConditionalGeneration`` A BART model that has been
fine-tuned for summarization
tokenizer: ``BartForConditionalGeneration``: A corresponding BART tokenizer
document: ``str`` A single document to be summarized
decoder: ``str`` The decoder to use for decoding
Returns:
----------
summary: ``str`` A generated summary of the input document
summary_score: ``float`` The log-probability score of the summary
"""
input_ids = tokenizer(document, truncation=True,
return_tensors='pt')['input_ids']
metadata = {'input_ids': input_ids}
model_wrapper = TransformerModel(model)
if decoder == 'greedy':
top_candidate = decoders.greedy_decoding(
model=model_wrapper,
max_length=50,
eos_id=tokenizer.eos_token_id,
decoded_ids=[tokenizer.bos_token_id],
metadata=metadata)
elif decoder == 'beam_search':
top_candidate = decoders.beam_search_decoding(
model=model_wrapper,
beam_size=3,
max_length=50,
eos_id=tokenizer.eos_token_id,
decoded_ids=[tokenizer.bos_token_id],
metadata=metadata)[0]
elif decoder == 'random':
# Random sampling
top_candidate = decoders.top_k_sampling(
model=model_wrapper,
top_k=int(1e9), # random sampling is top-K with large K
temperature=1,
max_length=50,
eos_id=tokenizer.eos_token_id,
decoded_ids=[tokenizer.bos_token_id],
metadata=metadata)
elif decoder == 'top_k':
top_candidate = decoders.top_k_sampling(
model=model_wrapper,
top_k=3,
temperature=0.5,
max_length=50,
eos_id=tokenizer.eos_token_id,
decoded_ids=[tokenizer.bos_token_id],
metadata=metadata)
elif decoder == 'nucleus':
top_candidate = decoders.nucleus_sampling(
model=model_wrapper,
top_p=0.2,
max_length=50,
eos_id=tokenizer.eos_token_id,
decoded_ids=[tokenizer.bos_token_id],
metadata=metadata)
summary_ids = top_candidate.decoded_ids
summary = tokenizer.decode(summary_ids, skip_special_tokens=True)
summary_score = top_candidate.score
return summary, summary_score
def main(args):
# Set up logging
args.save_dir = util.get_save_dir(args.save_dir, args.name, training=False)
log = util.get_logger(args.save_dir, args.name)
log.info(f'Args: {dumps(vars(args), indent=4, sort_keys=True)}')
device, gpu_ids = util.get_available_devices()
args.batch_size *= max(1, len(gpu_ids))
model_type = args.model
# Get embeddings
log.info('Loading embeddings...')
word_vectors = util.torch_from_json(args.word_emb_file)
char_vectors = util.torch_from_json(args.char_emb_file)
# char_vectors = load_char_vectors
# Get model
log.info('Building model...')
if model_type == 'BiDAFplus': #
model = BiDAFplus(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
params=get_params(model_type, args.params))
elif model_type == 'BiDAFbase':
model = BiDAFbase(word_vectors=word_vectors,
hidden_size=args.hidden_size,
drop_prob=args.drop_prob)
elif model_type == "Transformer":
model = TransformerModel(word_vectors=word_vectors,
char_vectors=char_vectors,
input_size=len(word_vectors),
hidden_size=args.hidden_size)
elif model_type == 'BiDAF':
model = BiDAF(word_vectors=word_vectors,
char_vectors=char_vectors,
hidden_size=args.hidden_size,
params=get_params(model_type, args.params))
model = nn.DataParallel(model, gpu_ids)
log.info(f'Loading checkpoint from {args.load_path}...')
model = util.load_model(model, args.load_path, gpu_ids, return_step=False)
model = model.to(device)
model.eval()
# Get data loader
log.info('Building dataset...')
record_file = vars(args)[f'{args.split}_record_file']
dataset = SQuAD(record_file, args.use_squad_v2)
data_loader = data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn)
# Evaluate
log.info(f'Evaluating on {args.split} split...')
nll_meter = util.AverageMeter()
pred_dict = {} # Predictions for TensorBoard
sub_dict = {} # Predictions for submission
eval_file = vars(args)[f'{args.split}_eval_file']
with open(eval_file, 'r') as fh:
gold_dict = json_load(fh)
with torch.no_grad(), \
tqdm(total=len(dataset)) as progress_bar:
for cw_idxs, cc_idxs, qw_idxs, qc_idxs, y1, y2, ids in data_loader:
# Setup for forward
cw_idxs = cw_idxs.to(device)
qw_idxs = qw_idxs.to(device)
batch_size = cw_idxs.size(0)
if model_type == 'BiDAF' or model_type == 'BiDAFplus':
cc_idxs = cc_idxs.to(device)
qc_idxs = qc_idxs.to(device)
log_p1, log_p2 = model(cc_idxs, qc_idxs, cw_idxs, qw_idxs)
# Forward
else:
log_p1, log_p2 = model(cw_idxs, qw_idxs)
y1, y2 = y1.to(device), y2.to(device)
loss = F.nll_loss(log_p1, y1) + F.nll_loss(log_p2, y2)
nll_meter.update(loss.item(), batch_size)
# Get F1 and EM scores
p1, p2 = log_p1.exp(), log_p2.exp()
starts, ends = util.discretize(p1, p2, args.max_ans_len,
args.use_squad_v2)
# Log info
progress_bar.update(batch_size)
if args.split != 'test':
# No labels for the test set, so NLL would be invalid
progress_bar.set_postfix(NLL=nll_meter.avg)
idx2pred, uuid2pred = util.convert_tokens(gold_dict, ids.tolist(),
starts.tolist(),
ends.tolist(),
args.use_squad_v2)
pred_dict.update(idx2pred)
sub_dict.update(uuid2pred)
# Log results (except for test set, since it does not come with labels)
if args.split != 'test':
results = util.eval_dicts(gold_dict, pred_dict, args.use_squad_v2)
results_list = [('NLL', nll_meter.avg), ('F1', results['F1']),
('EM', results['EM'])]
if args.use_squad_v2:
results_list.append(('AvNA', results['AvNA']))
results = OrderedDict(results_list)
# Log to console
results_str = ', '.join(f'{k}: {v:05.2f}' for k, v in results.items())
log.info(f'{args.split.title()} {results_str}')
# Log to TensorBoard
tbx = SummaryWriter(args.save_dir)
util.visualize(tbx,
pred_dict=pred_dict,
eval_path=eval_file,
step=0,
split=args.split,
num_visuals=args.num_visuals)
# Write submission file
sub_path = join(args.save_dir, args.split + '_' + args.sub_file)
log.info(f'Writing submission file to {sub_path}...')
with open(sub_path, 'w', newline='', encoding='utf-8') as csv_fh:
csv_writer = csv.writer(csv_fh, delimiter=',')
csv_writer.writerow(['Id', 'Predicted'])
for uuid in sorted(sub_dict):
csv_writer.writerow([uuid, sub_dict[uuid]])
