import torch
import random
import numpy as np
from config import global_config as cfg
from reader import CamRest676Reader, get_glove_matrix
from reader import KvretReader
from reader import UbuntuDialogueReader
from reader import JDCorpusReader
from unsup_net import UnsupervisedSEDST, cuda_
from torch.optim import Adam, RMSprop
from torch.autograd import Variable
from reader import pad_sequences
import argparse, time
from metric import CamRestEvaluator, KvretEvaluator, GenericEvaluator
import logging
class Model:
def __init__(self, dataset, inference_only=False):
reader_dict = {
'camrest': CamRest676Reader,
'kvret': KvretReader,
'ubuntu': UbuntuDialogueReader,
'jd': JDCorpusReader
}
model_dict = {
'SEDST': UnsupervisedSEDST,
}
evaluator_dict = {
'camrest': CamRestEvaluator,
'kvret': KvretEvaluator,
'ubuntu': GenericEvaluator,
'jd': GenericEvaluator
}
self.reader = reader_dict[dataset]()
self.m = model_dict[cfg.m](embed_size=cfg.embedding_size,
hidden_size=cfg.hidden_size,
q_hidden_size=cfg.q_hidden_size,
vocab_size=cfg.vocab_size,
layer_num=cfg.layer_num,
dropout_rate=cfg.dropout_rate,
z_length=cfg.z_length,
alpha=cfg.alpha,
max_ts=cfg.max_ts,
beam_search=cfg.beam_search,
beam_size=cfg.beam_size,
eos_token_idx=self.reader.vocab.encode('EOS_M'),
vocab=self.reader.vocab,
teacher_force=cfg.teacher_force,
degree_size=cfg.degree_size)
self.EV = evaluator_dict[dataset] # evaluator class
if cfg.cuda: self.m = self.m.cuda()
self.base_epoch = -1
def _convert_batch(self, py_batch, prev_z_py=None):
u_input_py = py_batch['user']
u_len_py = py_batch['u_len']
kw_ret = {}
if cfg.prev_z_method == 'concat' and prev_z_py is not None:
for i in range(len(u_input_py)):
eob = self.reader.vocab.encode('EOS_Z2')
if eob in prev_z_py[i] and prev_z_py[i].index(eob) != len(prev_z_py[i]) - 1:
idx = prev_z_py[i].index(eob)
u_input_py[i] = prev_z_py[i][:idx + 1] + u_input_py[i]
else:
u_input_py[i] = prev_z_py[i] + u_input_py[i]
u_len_py[i] = len(u_input_py[i])
for j, word in enumerate(prev_z_py[i]):
if word >= cfg.vocab_size:
prev_z_py[i][j] = 2 # unk
elif cfg.prev_z_method == 'separate' and prev_z_py is not None:
for i in range(len(prev_z_py)):
eob = self.reader.vocab.encode('EOS_Z2')
if eob in prev_z_py[i] and prev_z_py[i].index(eob) != len(prev_z_py[i]) - 1:
idx = prev_z_py[i].index(eob)
prev_z_py[i] = prev_z_py[i][:idx + 1]
for j, word in enumerate(prev_z_py[i]):
if word >= cfg.vocab_size:
prev_z_py[i][j] = 2 # unk
prev_z_input_np = pad_sequences(prev_z_py, cfg.max_ts, padding='post', truncating='pre').transpose((1, 0))
prev_z_len = np.array([len(_) for _ in prev_z_py])
prev_z_input = cuda_(Variable(torch.from_numpy(prev_z_input_np).long()))
kw_ret['prev_z_len'] = prev_z_len
kw_ret['prev_z_input'] = prev_z_input
kw_ret['prev_z_input_np'] = prev_z_input_np
degree_input_np = np.array(py_batch['degree'])
u_input_np = pad_sequences(u_input_py, cfg.max_ts, padding='post', truncating='pre').transpose((1, 0))
z_input_np = pad_sequences(py_batch['latent'], padding='post').transpose((1, 0))
if cfg.pretrain:
m_input_np = pad_sequences(py_batch['response'], cfg.max_ts, padding='post', truncating='post').transpose(
(1, 0))
else:
m_input_np = pad_sequences(py_batch['response'], cfg.max_ts, padding='post', truncating='pre').transpose(
(1, 0))
p_input_np = pad_sequences(py_batch['post'], cfg.max_ts, padding='post', truncating='pre').transpose((1, 0))
u_len = np.array(u_len_py)
m_len = np.array(py_batch['m_len'])
p_len = np.array(py_batch['p_len'])
degree_input = cuda_(Variable(torch.from_numpy(degree_input_np).float()))
u_input = cuda_(Variable(torch.from_numpy(u_input_np).long()))
z_input = cuda_(Variable(torch.from_numpy(z_input_np).long()))
m_input = cuda_(Variable(torch.from_numpy(m_input_np).long()))
p_input = cuda_(Variable(torch.from_numpy(p_input_np).long()))
supervised = py_batch['supervised'][0]
kw_ret['z_input_np'] = z_input_np
return u_input, u_input_np, z_input, m_input, m_input_np, p_input, p_input_np, u_len, m_len, p_len, \
degree_input, supervised, kw_ret
def train(self):
lr = cfg.lr
prev_min_loss, early_stop_count = 1 << 30, cfg.early_stop_count
train_time = 0
for epoch in range(cfg.epoch_num):
sw = time.time()
if epoch < cfg.base_epoch:
continue
sup_loss, unsup_loss = 0, 0
sup_cnt, unsup_cnt = 0, 0
data_iterator = self.reader.mini_batch_iterator('train')
optim = Adam(lr=lr, params=filter(lambda x: x.requires_grad, self.m.parameters()), weight_decay=1e-6)
for iter_num, dial_batch in enumerate(data_iterator):
if epoch == cfg.base_epoch and iter_num < cfg.base_iter:
continue
turn_states = {}
turn_states_q = {}
prev_z = None
trunc_cnt = 1
for turn_num, turn_batch in enumerate(dial_batch):
if cfg.truncated:
logging.debug('iter %d turn %d' % (iter_num, turn_num))
optim.zero_grad()
u_input, u_input_np, z_input, m_input, m_input_np, p_input, p_input_np, u_len, \
m_len, p_len, degree_input, supervised, kw_ret \
= self._convert_batch(turn_batch, prev_z)
loss, m_loss, p_loss, kl_div_loss, turn_states, turn_states_q = self.m(u_input=u_input,
z_input=None,
m_input=m_input,
p_len=p_len,
degree_input=degree_input,
u_input_np=u_input_np,
m_input_np=m_input_np,
z_supervised=False,
turn_states=turn_states,
p_input=p_input,
p_input_np=p_input_np,
u_len=u_len, m_len=m_len,
mode='train',
turn_states_q=turn_states_q,
**kw_ret)
if turn_num == len(dial_batch) - 1 or (trunc_cnt and trunc_cnt % cfg.trunc_turn == 0):
for k in turn_states:
turn_states[k] = cuda_(Variable(turn_states[k].data))
loss.backward(retain_graph=False)
else:
loss.backward(retain_graph=True)
trunc_cnt += 1
grad = torch.nn.utils.clip_grad_norm(self.m.parameters(), 4.0)
optim.step()
unsup_loss += loss.item()
if cfg.truncated and not np.isnan(loss.data.cpu().numpy()) and not np.isnan(
grad) and iter_num % 10 == 0 and iter_num != 0:
self.save_model(epoch)
unsup_cnt += 1
logging.debug(
'unsupervised loss:{} m_loss:{} p_loss:{} kl_div_loss:{} grad:{}'.format(loss.item(),
m_loss.item(),
p_loss.item(),
kl_div_loss.data[
0], grad))
prev_z = turn_batch['latent']
epoch_sup_loss, epoch_unsup_loss = sup_loss / (sup_cnt + 1e-8), unsup_loss / (unsup_cnt + 1e-8)
train_time += time.time() - sw
logging.info('Traning time: {}'.format(train_time))
logging.info('avg training loss in epoch %d sup:%6f unsup:%6f' % (epoch, epoch_sup_loss, epoch_unsup_loss))
# do validation
valid_sup_loss, valid_unsup_loss = self.validate()
logging.info('validation loss in epoch %d sup:%6f unsup:%6f' % (epoch, valid_sup_loss, valid_unsup_loss))
logging.info('time for epoch %d: %6f' % (epoch, time.time() - sw))
valid_loss = valid_sup_loss + valid_unsup_loss
self.save_model(epoch)
if valid_loss <= prev_min_loss:
prev_min_loss = valid_loss
else:
early_stop_count -= 1
lr *= cfg.lr_decay
if not early_stop_count:
break
logging.info('early stop countdown %d, learning rate %6f' % (early_stop_count, lr))
def eval(self, data='test'):
self.m.eval()
self.reader.result_file = None
with torch.no_grad():
data_iterator = self.reader.mini_batch_iterator(data)
mode = 'test' # if not cfg.pretrain else 'pretrain_test'
for batch_num, dial_batch in enumerate(data_iterator):
turn_states = {}
turn_states_q = {}
prev_z = None
for turn_num, turn_batch in enumerate(dial_batch):
u_input, u_input_np, z_input, m_input, m_input_np, p_input, p_input_np, u_len, \
m_len, p_len, degree_input, supervised, kw_ret \
= self._convert_batch(turn_batch, prev_z)
m_idx, z_idx, turn_states = self.m(mode=mode, u_input=u_input, u_len=u_len, z_input=z_input,
m_input=m_input,
degree_input=degree_input, u_input_np=u_input_np,
m_input_np=m_input_np,
p_input=p_input, p_input_np=p_input_np, p_len=p_len,
m_len=m_len, z_supervised=None, turn_states=turn_states,
**kw_ret)
if not cfg.last_turn_only or turn_num == len(dial_batch) - 1:
self.reader.wrap_result(turn_batch, m_idx, z_idx)
prev_z = z_idx
# print('{}\r'.format(batch_num))
ev = self.EV(result_path=cfg.result_path)
res = ev.run_metrics()
self.m.train()
return res
def validate(self, data='dev'):
self.m.eval()
with torch.no_grad():
data_iterator = self.reader.mini_batch_iterator(data)
sup_loss, unsup_loss = 0, 0
sup_cnt, unsup_cnt = 0, 0
for d, dial_batch in enumerate(data_iterator):
turn_states = {}
for turn_num, turn_batch in enumerate(dial_batch):
if turn_num <= 0 or turn_num < len(dial_batch) - cfg.max_turn:
continue
u_input, u_input_np, z_input, m_input, m_input_np, p_input, p_input_np, u_len, \
m_len, p_len, degree_input, supervised, kw_ret \
= self._convert_batch(turn_batch)
loss, m_loss, p_loss, kl_div_loss, turn_states, _ = self.m(u_input=u_input, z_input=None,
m_input=m_input,
z_supervised=False,
turn_states=turn_states,
u_input_np=u_input_np,
m_input_np=m_input_np,
p_input=p_input, p_input_np=p_input_np,
p_len=p_len,
u_len=u_len, m_len=m_len, mode='train',
degree_input=degree_input,
turn_states_q={}, **kw_ret)
if not cfg.last_turn_only or turn_num == len(dial_batch) - 1:
unsup_loss += m_loss.item()
unsup_cnt += 1
logging.debug(
'unsupervised loss:{} m_loss:{} p_loss:{} kl_div_loss:{}'.format(loss.item(), m_loss.item(),
p_loss.item(),
kl_div_loss.item()))
for k in turn_states:
turn_states[k] = turn_states[k].detach()
sup_loss /= (sup_cnt + 1e-8)
unsup_loss /= (unsup_cnt + 1e-8)
self.m.train()
res = self.eval()
return sup_loss, unsup_loss
def save_model(self, epoch, path=None):
if not path:
path = cfg.model_path
all_state = {'sedst': self.m.state_dict(),
'config': cfg.__dict__,
'epoch': epoch}
with open(path, 'wb') as f:
torch.save(all_state, path)
def load_model(self, path=None):
if not path:
path = cfg.model_path
with open(path, 'rb') as f:
all_state = torch.load(path)
self.m.load_state_dict(all_state['sedst'], strict=False)
self.base_epoch = all_state.get('epoch', 0)
def freeze_module(self, module):
for param in module.parameters():
param.requires_grad = False
def unfreeze_module(self, module):
for param in module.parameters():
param.requires_grad = True
def load_glove_embedding(self, freeze=False):
initial_arr = self.m.u_encoder.embedding.weight.data.cpu().numpy()
mat = get_glove_matrix(self.reader.vocab, initial_arr)
# np.save('./data/embedding.npy',mat)
# mat = np.load('./data/embedding.npy')
embedding_arr = torch.from_numpy(mat)
self.m.u_encoder.embedding.weight.data.copy_(embedding_arr)
self.m.p_encoder.embedding.weight.data.copy_(embedding_arr)
self.m.m_decoder.emb.weight.data.copy_(embedding_arr)
self.m.p_decoder.emb.weight.data.copy_(embedding_arr)
self.m.qz_decoder.mu.weight.data.copy_(embedding_arr.transpose(1, 0))
self.m.pz_decoder.mu.weight.data.copy_(embedding_arr.transpose(1, 0))
if freeze:
self.freeze_module(self.m.u_encoder.embedding)
self.freeze_module(self.m.m_e.embedding)
self.freeze_module(self.m.m_decoder.emb)
def count_params(self):
module_parameters = filter(lambda p: p.requires_grad, self.m.parameters())
param_cnt = sum([np.prod(p.size()) for p in module_parameters])
print('total trainable params: %d' % param_cnt)
def main():
torch.manual_seed(1)
torch.cuda.manual_seed(1)
random.seed(1)
np.random.seed(1)
parser = argparse.ArgumentParser()
parser.add_argument('-mode')
parser.add_argument('-dataset')
parser.add_argument('-cfg', nargs='*')
args = parser.parse_args()
cfg.init_handler(args.dataset)
if args.cfg:
for pair in args.cfg:
k, v = tuple(pair.split('='))
dtype = type(getattr(cfg, k))
if dtype == type(None):
raise ValueError()
if dtype is bool:
v = False if v == 'False' else True
else:
v = dtype(v)
setattr(cfg, k, v)
logging.debug(str(cfg))
if cfg.cuda:
torch.cuda.set_device(cfg.cuda_device)
logging.debug('Device: {}'.format(torch.cuda.current_device()))
cfg.mode = args.mode
m = Model(args.dataset.split('-')[-1])
m.count_params()
if args.mode == 'train':
m.load_glove_embedding()
m.train()
elif args.mode == 'adjust':
m.load_model()
m.train()
elif args.mode == 'test':
m.load_model()
m.eval()
if __name__ == '__main__':
main()
