def train():
# Initialize torch.distributed
init_distributed()
print_rank_0('AutoMP: training GPT2...')
# Use fake train data
batch_size = args.batch_size
sequence_length = args.sequence_length
hidden_size = args.hidden_size
vocab_size = args.vocab_size
dropout_prob = args.hidden_dropout
input_indices = torch.randint(low=0,
high=vocab_size,
size=(batch_size, sequence_length))
input_indices = input_indices.to(torch.cuda.current_device())
position_indices = torch.tile(torch.arange(start=0, end=sequence_length),
(batch_size, 1))
position_indices = position_indices.to(torch.cuda.current_device())
print_rank_0(f'AutoMP: input_indices shape = {input_indices.size()}')
print_rank_0(f'AutoMP: position_indices shape = {position_indices.size()}')
def init_method_normal(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=1.0)
embedding = Embedding(hidden_size=hidden_size,
vocab_size=vocab_size,
max_sequence_length=sequence_length,
embedding_dropout_prob=dropout_prob,
init_method=init_method_normal)
optimizer = torch.optim.SGD(embedding.parameters(), lr=0.01)
profiler = Profiler(os.path.join('benchmark', args.exp_name))
num_epochs = 5
tot_time = 0
nproc = torch.distributed.get_world_size()
for epoch in range(num_epochs):
overall_name = f'emb_np-{nproc}_vs-{vocab_size}'
profiler.start(overall_name)
# Forward pass
profiler.start(f'emb_forward_np-{nproc}_vs-{vocab_size}')
embedding_output = embedding.forward(input_indices, position_indices)
train_loss = torch.mean(embedding_output)
torch.cuda.synchronize()
profiler.stop(f'emb_forward_np-{nproc}_vs-{vocab_size}')
# Backward pass
profiler.start(f'emb_backward_np-{nproc}_vs-{vocab_size}')
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
torch.cuda.synchronize()
profiler.stop(f'emb_backward_np-{nproc}_vs-{vocab_size}')
profiler.stop(overall_name)
def __init__(self, dataset, config):
super(Classifier, self).__init__()
self.config = config
self.token_embedding = \
Embedding(dataset.token_map, config.embedding.dimension,
cDataset.DOC_TOKEN, config, dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.token_pretrained_file,
mode=EmbeddingProcessType.FLAT,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
activation_type=ActivationType.NONE,
model_mode=dataset.model_mode)
self.char_embedding = \
Embedding(dataset.char_map, config.embedding.dimension,
cDataset.DOC_CHAR, config, dataset.VOCAB_PADDING,
mode=EmbeddingProcessType.FLAT,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
activation_type=ActivationType.NONE,
model_mode=dataset.model_mode)
self.dropout = torch.nn.Dropout(p=config.train.hidden_layer_dropout)
def train():
# Initialize torch.distributed
init_distributed()
print_rank_0('AutoMP: training GPT2...')
# Use fake train data
args = get_args()
sequence_length = 1024
vocab_size = 4096
dropout_prob = 0.1
input_indices = torch.randint(low=0,
high=vocab_size,
size=(args.batch_size, sequence_length))
input_indices = input_indices.to(torch.cuda.current_device())
position_indices = torch.tile(torch.arange(start=0, end=sequence_length),
(args.batch_size, 1))
position_indices = position_indices.to(torch.cuda.current_device())
print_rank_0(f'AutoMP: input_indices shape = {input_indices.size()}')
print_rank_0(f'AutoMP: position_indices shape = {position_indices.size()}')
def init_method_normal(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=1.0)
embedding = Embedding(hidden_size=args.hidden_size,
vocab_size=vocab_size,
max_sequence_length=sequence_length,
embedding_dropout_prob=dropout_prob,
init_method=init_method_normal)
embedding_output = embedding.forward(input_indices, position_indices)
# print_rank_0(f'AutoMP: embedding_output = {embedding_output}')
def gpt2_attention_mask_func(attention_scores, ltor_mask):
attention_scores.masked_fill_(ltor_mask, -10000.0)
return attention_scores
transformer = ParallelTransformer(
attention_mask_func=gpt2_attention_mask_func,
num_layers=args.num_layers,
hidden_size=args.hidden_size,
layernorm_epsilon=args.layernorm_epsilon,
num_attention_heads=args.num_attention_heads,
attention_dropout=0.1,
hidden_dropout=0.1)
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
input_indices, vocab_size - 1)
transformer_output = transformer.forward(hidden_states=embedding_output,
attention_mask=attention_mask)
print_rank_0(f'AutoMP: transformer_output = {transformer_output}')
def train():
# Initialize torch.distributed
init_distributed()
print_rank_0('AutoMP: training self attention layer...')
# Use fake train data
args = get_args()
batch_size = 32
sequence_length = 1024
hidden_size = args.hidden_size
vocab_size = 4096
dropout_prob = 0.1
input_indices = torch.randint(low=0, high=vocab_size, size=(batch_size, sequence_length))
input_indices = input_indices.to(torch.cuda.current_device())
position_indices = torch.tile(torch.arange(start=0, end=sequence_length), (batch_size, 1))
position_indices = position_indices.to(torch.cuda.current_device())
print_rank_0(f'AutoMP: input_indices shape = {input_indices.size()}')
print_rank_0(f'AutoMP: position_indices shape = {position_indices.size()}')
def init_method_normal(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=1.0)
embedding = Embedding(hidden_size=hidden_size,
vocab_size=vocab_size,
max_sequence_length=sequence_length,
embedding_dropout_prob=dropout_prob,
init_method=init_method_normal)
embedding_output = embedding.forward(input_indices, position_indices)
# print_rank_0(f'AutoMP: embedding_output = {embedding_output}')
def gpt2_attention_mask_func(attention_scores, ltor_mask):
print(f'ALBERT_DEBUG: attention_scores.size() = {attention_scores.size()}')
print(f'ALBERT_DEBUG: ltor_mask.size() = {ltor_mask.size()}')
attention_scores.masked_fill_(ltor_mask, -10000.0)
return attention_scores
self_attention = ParallelSelfAttention(
attention_mask_func=gpt2_attention_mask_func,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
attention_dropout=0.1
)
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(input_indices, vocab_size - 1)
print(f'ALBERT_DEBUG: embedding_output.size() = {embedding_output.size()}')
self_att_output = self_attention.forward(hidden_states=embedding_output, attention_mask=attention_mask)
print_rank_0(f'AutoMP: self_att_output = {self_att_output}')
def __init__(self, dataset, config):
super(FastText, self).__init__()
self.config = config
assert "token" in self.config.feature.feature_names
self.token_embedding = \
Embedding(dataset.token_map,
config.embedding.dimension,
cDataset.DOC_TOKEN, config,
padding_idx=dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.token_pretrained_file,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
if self.config.feature.token_ngram > 1:
self.token_ngram_embedding = \
Embedding(dataset.token_ngram_map,
config.embedding.dimension,
cDataset.DOC_TOKEN_NGRAM, config,
padding_idx=dataset.VOCAB_PADDING,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
if "keyword" in self.config.feature.feature_names:
self.keyword_embedding = \
Embedding(dataset.keyword_map,
config.embedding.dimension,
cDataset.DOC_KEYWORD, config,
padding_idx=dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.keyword_pretrained_file,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
if "topic" in self.config.feature.feature_names:
self.topic_embedding = \
Embedding(dataset.topic_map,
config.embedding.dimension,
cDataset.DOC_TOPIC, config,
padding_idx=dataset.VOCAB_PADDING,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
self.linear = torch.nn.Linear(config.embedding.dimension,
len(dataset.label_map))
self.dropout = torch.nn.Dropout(p=config.train.hidden_layer_dropout)
def __init__(self, embedding_info: Dict, encoder_info: Dict,
decoder_info: Dict, hidden_states: Dict, token_to_id: Dict,
type_to_id: Dict, label_to_id: Dict):
super().__init__()
self.embedding_info = embedding_info
self.encoder_info = encoder_info
self.decoder_info = decoder_info
self.hidden_states = hidden_states
self.token_to_id = token_to_id
self.type_to_id = type_to_id
self.label_to_id = label_to_id
self.embedding = Embedding(h_emb=self.hidden_states['embedding'],
token_to_id=self.token_to_id,
type_to_id=self.type_to_id,
**self.embedding_info)
self.encoder = Encoder(h_emb=self.hidden_states['embedding'],
h_enc=self.hidden_states['encoder'],
**self.encoder_info)
self.decoder = Decoder(h_enc=self.hidden_states['encoder'],
h_dec=self.hidden_states['decoder'],
label_to_id=self.label_to_id,
**self.decoder_info)
def __init__(self, hidden_size, vocab_size, sequence_length,
hidden_dropout, attention_mask_func, num_layers,
layernorm_epsilon, num_attention_heads, attention_dropout,
init_method):
super(TransformerLanguageModel, self).__init__()
# self.hidden_size = hidden_size
# self.vocab_size = vocab_size
# self.sequence_length = sequence_length
# self.hidden_dropout = hidden_dropout
# self.init_method = init_method
# self.num_layers = num_layers
# self.layernorm_epsilon = layernorm_epsilon
# self.num_attention_heads = num_attention_heads
# self.attention_dropout = attention_dropout
# Embeddings
self.embedding = Embedding(hidden_size, vocab_size, sequence_length,
hidden_dropout, init_method)
self._embedding_key = 'embedding'
# Transformer
self.transformer = ParallelTransformer(attention_mask_func, num_layers,
hidden_size, layernorm_epsilon,
num_attention_heads,
attention_dropout,
hidden_dropout)
self._transformer_key = 'transformer'
def get_embedding(self, embed_id):
""" Return the actual word embedding associated with a given ID """
if not embed_id in self.embedding_meta:
return None
if embed_id in self.embedding_cache:
log.info("Using cached embedding for %s" % embed_id)
return self.embedding_cache[embed_id]
# load the associated word embedding
em = self.embedding_meta[embed_id]
in_path = em.dir_base / em["file"]
log.info("Loading word embedding from %s" % in_path)
try:
self.embedding_cache[embed_id] = Embedding(in_path)
except Exception as e:
log.warning("Failed to load word embedding: %s" % in_path)
log.warning(e)
return None
return self.embedding_cache[embed_id]
def create_model(self, fetch_data=None):
with tf.variable_scope('variables'):
sentence_simple_input_placeholder = []
sentence_complex_input_placeholder = []
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
sentence_simple_segment_input_placeholder = []
sentence_complex_segment_input_placeholder = []
obj = {}
if fetch_data is not None and self.model_config.fetch_mode == 'tf_example_dataset':
for t in tf.unstack(fetch_data['line_comp_ids'], axis=1):
sentence_complex_input_placeholder.append(t)
for t in tf.unstack(fetch_data['line_simp_ids'], axis=1):
sentence_simple_input_placeholder.append(t)
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
for t in tf.unstack(fetch_data['line_comp_segids'],
axis=1):
sentence_complex_segment_input_placeholder.append(t)
for t in tf.unstack(fetch_data['line_simp_segids'],
axis=1):
sentence_simple_segment_input_placeholder.append(t)
obj['line_comp_segids'] = tf.stack(
sentence_complex_segment_input_placeholder, axis=1)
obj['line_simp_segids'] = tf.stack(
sentence_simple_segment_input_placeholder, axis=1)
score = None
if self.model_config.tune_style:
if self.is_train:
# In training, score are from fetch data
scores = []
if self.model_config.tune_style[0]:
ppdb_score = fetch_data['ppdb_score']
scores.append(ppdb_score)
print('Tune ppdb score!')
if 'plus' in self.model_config.tune_mode:
# to avoid most ppdb scores are 0
ppdb_score += 0.1
if self.model_config.tune_style[1]:
add_score = fetch_data['dsim_score']
scores.append(add_score)
print('Tune dsim_score score!')
if self.model_config.tune_style[2]:
add_score = fetch_data['add_score']
scores.append(add_score)
print('Tune add score!')
if self.model_config.tune_style[3]:
len_score = fetch_data['len_score']
scores.append(len_score)
print('Tune length score!')
else:
# In evaluating/predict, scores may be a factor to multiply if in pred mode
# or actual user provided score
# TODO(sanqiang): not used for now because not fech_data in eval
raise NotImplementedError('No tune style for training')
# ppdb_score = tf.constant(
# self.model_config.tune_style[0], shape=[self.model_config.batch_size], dtype=tf.float32)
# add_score = tf.constant(
# self.model_config.tune_style[1], shape=[self.model_config.batch_size], dtype=tf.float32)
# len_score = tf.constant(
# self.model_config.tune_style[2], shape=[self.model_config.batch_size], dtype=tf.float32)
# Assemble scores
dimension_unit = int(self.model_config.dimension /
len(scores))
dimension_runit = self.model_config.dimension - (
len(scores) - 1) * dimension_unit
for s_i, score in enumerate(scores):
if s_i < len(scores) - 1:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_unit]),
axis=1)
else:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_runit]),
axis=1)
score = tf.concat(scores, axis=-1)
else:
for step in range(self.model_config.max_simple_sentence):
sentence_simple_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='simple_input'))
for step in range(self.model_config.max_complex_sentence):
sentence_complex_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='complex_input'))
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
for step in range(self.model_config.max_simple_sentence):
sentence_simple_segment_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='simple_seg_input'))
for step in range(self.model_config.max_complex_sentence):
sentence_complex_segment_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='complex_seg_input'))
obj['line_comp_segids'] = tf.stack(
sentence_complex_segment_input_placeholder, axis=1)
obj['line_simp_segids'] = tf.stack(
sentence_simple_segment_input_placeholder, axis=1)
score = None
if self.model_config.tune_style:
if self.is_train:
raise NotImplementedError('No tune style for training')
#
# ppdb_score = tf.constant(
# self.model_config.tune_style, shape=[self.model_config.batch_size], dtype=tf.float32)
# ppdb_score = tf.expand_dims(tf.tile(
# tf.expand_dims(ppdb_score, axis=-1),
# [1, self.model_config.dimension]), axis=1)
else:
scores = []
if self.model_config.tune_style:
if self.model_config.tune_style[0]:
ppdb_score = tf.constant(
self.model_config.tune_style[0],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(ppdb_score)
print('tune ppdb score')
if self.model_config.tune_style[1]:
dsim_score = tf.constant(
self.model_config.tune_style[1],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(dsim_score)
print('tune dsim score')
if self.model_config.tune_style[2]:
add_score = tf.constant(
self.model_config.tune_style[2],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(add_score)
print('tune add score')
if self.model_config.tune_style[3]:
len_score = tf.constant(
self.model_config.tune_style[3],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(len_score)
print('tune len score')
# Assemble scores
dimension_unit = int(self.model_config.dimension /
len(scores))
dimension_runit = self.model_config.dimension - (
len(scores) - 1) * dimension_unit
for s_i, score in enumerate(scores):
if s_i < len(scores) - 1:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_unit]),
axis=1)
else:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_runit]),
axis=1)
score = tf.concat(scores, axis=-1)
# For self.model_config.tune_style:
comp_features = {}
comp_add_score = tf.zeros(self.model_config.batch_size,
tf.float32,
name='comp_add_score_input')
comp_length = tf.zeros(self.model_config.batch_size,
tf.float32,
name='comp_length_input')
comp_features['comp_add_score'] = comp_add_score
comp_features['comp_length'] = comp_length
sentence_idxs = tf.zeros(self.model_config.batch_size,
tf.int32,
name='sent_idx')
self.embedding = Embedding(self.data.vocab_complex,
self.data.vocab_simple,
self.model_config)
if self.model_config.bert_mode:
emb_complex = None
else:
emb_complex = self.embedding.get_complex_embedding()
if self.model_config.bert_mode and (
self.model_config.tie_embedding == 'all'
or self.model_config.tie_embedding == 'enc_dec'):
emb_simple = None
else:
emb_simple = self.embedding.get_simple_embedding()
if (self.is_train and self.model_config.pretrained_embedding):
self.embed_complex_placeholder = tf.placeholder(
tf.float32, (self.data.vocab_complex.vocab_size(),
self.model_config.dimension), 'complex_emb')
self.replace_emb_complex = emb_complex.assign(
self.embed_complex_placeholder)
self.embed_simple_placeholder = tf.placeholder(
tf.float32, (self.data.vocab_simple.vocab_size(),
self.model_config.dimension), 'simple_emb')
self.replace_emb_simple = emb_simple.assign(
self.embed_simple_placeholder)
if self.model_config.bert_mode and (
self.model_config.tie_embedding == 'all'
or self.model_config.tie_embedding == 'dec_out'):
w = None
else:
w = self.embedding.get_w()
b = self.embedding.get_b()
mem_contexts, mem_outputs, mem_counter = None, None, None
rule_id_input_placeholder, rule_target_input_placeholder = [], []
if 'rule' in self.model_config.memory:
with tf.device('/cpu:0'):
context_size = 0
if self.model_config.framework == 'transformer':
context_size = 1
elif self.model_config.framework == 'seq2seq':
context_size = 2
mem_contexts = tf.get_variable(
'mem_contexts',
initializer=tf.constant(
0,
dtype=tf.float32,
shape=(self.data.vocab_rule.get_rule_size(),
self.model_config.max_target_rule_sublen,
self.model_config.dimension *
context_size)),
trainable=False,
dtype=tf.float32)
mem_outputs = tf.get_variable(
'mem_outputs',
initializer=tf.constant(
0,
dtype=tf.float32,
shape=(self.data.vocab_rule.get_rule_size(),
self.model_config.max_target_rule_sublen,
self.model_config.dimension)),
trainable=False,
dtype=tf.float32)
mem_counter = tf.get_variable(
'mem_counter',
initializer=tf.constant(
0,
dtype=tf.int32,
shape=(self.data.vocab_rule.get_rule_size(), 1)),
trainable=False,
dtype=tf.int32)
if 'direct' in self.model_config.memory or 'rule' in self.model_config.memory:
if fetch_data is not None and self.model_config.fetch_mode == 'tf_example_dataset':
for t in tf.unstack(fetch_data['rule_id'], axis=1):
rule_id_input_placeholder.append(t)
for t in tf.unstack(fetch_data['rule_target'], axis=1):
rule_target_input_placeholder.append(t)
else:
for step in range(self.model_config.max_cand_rules):
rule_id_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='rule_id_input'))
for step in range(self.model_config.max_cand_rules):
if 'direct' in self.model_config.memory:
rule_target_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='rule_target_input'))
elif 'rule' in self.model_config.memory:
rule_target_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.string,
name='rule_target_input'))
with tf.variable_scope('model'):
output = self.model_fn(sentence_complex_input_placeholder,
emb_complex,
sentence_simple_input_placeholder,
emb_simple, w, b, rule_id_input_placeholder,
rule_target_input_placeholder, mem_contexts,
mem_outputs, self.global_step, score,
comp_features, obj)
encoder_embs, final_outputs = None, None
if self.model_config.replace_unk_by_emb:
encoder_embs = tf.stack(output.encoder_embed_inputs_list,
axis=1)
if output.decoder_outputs_list is not None:
if type(output.decoder_outputs_list) == list:
decoder_outputs_list = output.decoder_outputs_list
decoder_outputs = tf.stack(decoder_outputs_list, axis=1)
else:
decoder_outputs = output.decoder_outputs_list
if output.final_outputs_list is not None:
if type(output.final_outputs_list) == list:
final_outputs_list = output.final_outputs_list
final_outputs = tf.stack(final_outputs_list, axis=1)
else:
final_outputs = output.final_outputs_list
attn_distr = None
if self.model_config.replace_unk_by_attn:
attn_distr = output.attn_distr_list
if not self.is_train:
# in beam search, it directly provide decoder target list
decoder_target = tf.stack(output.decoder_target_list, axis=1)
loss = tf.reduce_mean(output.decoder_score)
obj = {
'sentence_idxs': sentence_idxs,
'sentence_simple_input_placeholder':
sentence_simple_input_placeholder,
'sentence_complex_input_placeholder':
sentence_complex_input_placeholder,
'decoder_target_list': decoder_target,
'final_outputs': final_outputs,
'encoder_embs': encoder_embs,
'attn_distr': attn_distr
}
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
obj['sentence_complex_segment_input_placeholder'] = sentence_complex_segment_input_placeholder
obj['sentence_simple_segment_input_placeholder'] = sentence_simple_segment_input_placeholder
if 'rule' in self.model_config.memory or 'direct' in self.model_config.memory:
obj['rule_id_input_placeholder'] = rule_id_input_placeholder
obj['rule_target_input_placeholder'] = rule_target_input_placeholder
if self.model_config.tune_style:
obj['comp_features'] = comp_features
return loss, obj
else:
# Memory Populate
if 'rule' in self.model_config.memory:
# Update Memory through python injection
def update_memory(mem_contexts_tmp, mem_outputs_tmp,
mem_counter_tmp, decoder_targets,
decoder_outputs, contexts,
rule_target_input_placeholder,
rule_id_input_placeholder, global_step,
encoder_outputs):
def _seq_contain(arr, tar):
j = 0
for i in range(len(arr)):
if arr[i] == tar[j]:
j += 1
if j == len(tar):
return i - len(tar) + 1
else:
j = 0
return -1
# if 'stopgrad' in self.model_config.rl_configs and global_step % 2 != 0:
# return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
# if global_step <= self.model_config.memory_prepare_step:
# return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
batch_size = np.shape(rule_target_input_placeholder)[0]
max_rules = np.shape(rule_target_input_placeholder)[1]
decoder_targets_str = [
' '.join(sent) for sent in truncate_sents(
decode(
decoder_targets, self.data.vocab_simple,
self.model_config.subword_vocab_size > 0
or 'bert_token' in
self.model_config.bert_mode))
]
for batch_id in range(batch_size):
cur_decoder_targets = decoder_targets[batch_id, :]
cur_decoder_targets_str = decoder_targets_str[
batch_id]
cur_decoder_outputs = decoder_outputs[batch_id, :]
cur_contexts = contexts[batch_id, :]
cur_rule_target_input_placeholder = rule_target_input_placeholder[
batch_id, :]
cur_rule_target_input_placeholder = [
tmp.decode("utf-8").strip('\x00')
for tmp in cur_rule_target_input_placeholder
if not tmp.decode("utf-8").strip().startswith(
constant.SYMBOL_PAD)
]
cur_rule_id_input_placeholder = rule_id_input_placeholder[
batch_id, :]
# Build the valid mapper from rule id => target words ids
rule_mapper = {}
for step in range(
len(cur_rule_target_input_placeholder)):
rule_target_str = cur_rule_target_input_placeholder[
step]
if rule_target_str == constant.SYMBOL_PAD:
continue
rule_id = cur_rule_id_input_placeholder[step]
if rule_id != 0 and re.search(
r'\b%s\b' % rule_target_str,
cur_decoder_targets_str
): # decoder_target_str in cur_decoder_targets_str:
decoder_target_wids = self.data.vocab_simple.encode(
rule_target_str)
dec_s_idx = _seq_contain(
cur_decoder_targets,
decoder_target_wids)
if dec_s_idx != -1:
print('rule_target_str:%s' %
rule_target_str)
print('cur_decoder_targets_str:%s' %
cur_decoder_targets_str)
print('cur_decoder_targets:%s' %
cur_decoder_targets)
print('decoder_target_wids:%s' %
decoder_target_wids)
rule_mapper[rule_id] = list(
range(
dec_s_idx, dec_s_idx +
len(decoder_target_wids)))
for rule_id in rule_mapper:
dec_idxs = rule_mapper[rule_id]
for idx, dec_idx in enumerate(dec_idxs):
if mem_counter_tmp[rule_id, 0] == 0:
mem_contexts_tmp[
rule_id,
idx, :] = cur_contexts[dec_idx, :]
mem_outputs_tmp[
rule_id,
idx, :] = cur_decoder_outputs[
dec_idx, :]
else:
mem_contexts_tmp[rule_id, idx, :] = (
cur_contexts[dec_idx, :] +
mem_contexts_tmp[rule_id,
idx, :]) / 2
mem_outputs_tmp[rule_id, idx, :] = (
cur_decoder_outputs[dec_idx, :] +
mem_outputs_tmp[rule_id,
idx, :]) / 2
mem_counter_tmp[rule_id, 0] += 1
return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
mem_output_input = None
if 'mofinal' in self.model_config.memory_config:
mem_output_input = final_outputs
# elif 'modecode' in self.model_config.memory_config:
# mem_output_input = decoder_outputs
# elif 'moemb' in self.model_config.memory_config:
# mem_output_input = tf.stack(
# self.embedding_fn(sentence_simple_input_placeholder, emb_simple),
# axis=1)
mem_contexts, mem_outputs, mem_counter = tf.py_func(
update_memory, [
mem_contexts, mem_outputs, mem_counter,
tf.stack(output.decoder_target_list, axis=1),
mem_output_input, output.contexts,
tf.stack(rule_target_input_placeholder, axis=1),
tf.stack(rule_id_input_placeholder, axis=1),
self.global_step, output.encoder_outputs
], [tf.float32, tf.float32, tf.int32],
stateful=False,
name='update_memory')
#Loss and corresponding prior/mask
decode_word_weight_list = [
tf.to_float(
tf.not_equal(
d,
self.data.vocab_simple.encode(
constant.SYMBOL_PAD)))
for d in output.gt_target_list
]
decode_word_weight = tf.stack(decode_word_weight_list, axis=1)
gt_target = tf.stack(output.gt_target_list, axis=1)
def self_critical_loss():
# For minimize the negative log of probabilities
rewards = tf.py_func(
self.metric.self_crititcal_reward,
[
sentence_idxs,
tf.stack(output.sample_target_list, axis=-1),
tf.stack(output.decoder_target_list, axis=-1),
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(sentence_complex_input_placeholder,
axis=-1),
tf.ones((1, 1)),
# tf.stack(rule_target_input_placeholder, axis=1)
],
tf.float32,
stateful=False,
name='reward')
rewards.set_shape((self.model_config.batch_size,
self.model_config.max_simple_sentence))
rewards = tf.unstack(rewards, axis=1)
weighted_probs_list = [
rewards[i] * decode_word_weight_list[i] *
-output.sample_logit_list[i]
for i in range(len(decode_word_weight_list))
]
total_size = tf.reduce_sum(decode_word_weight_list)
total_size += 1e-12
weighted_probs = tf.reduce_sum(
weighted_probs_list) / total_size
loss = weighted_probs
return loss
def teacherforce_critical_loss():
losses = []
for step in range(self.model_config.max_simple_sentence):
logit = output.decoder_logit_list[step]
greedy_target_unit = tf.stop_gradient(
tf.argmax(logit, axis=1))
if self.model_config.train_mode == 'teachercriticalv2':
sampled_target_unit, reward = tf.py_func(
self.metric.self_crititcal_reward_unitv2, [
sentence_idxs, step, greedy_target_unit,
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(
sentence_complex_input_placeholder,
axis=-1), self.global_step
], [tf.int32, tf.float32],
stateful=False,
name='reward')
reward.set_shape((self.model_config.batch_size, ))
sampled_target_unit.set_shape(
(self.model_config.batch_size, ))
elif self.model_config.train_mode == 'teachercritical':
sampled_target_unit = tf.cast(
tf.squeeze(tf.multinomial(logit, 1), axis=1),
tf.int32)
sampled_target_unit, reward = tf.py_func(
self.metric.self_crititcal_reward_unit, [
sentence_idxs,
step,
sampled_target_unit,
greedy_target_unit,
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(
sentence_complex_input_placeholder,
axis=-1),
self.global_step,
], [tf.int32, tf.float32],
stateful=False,
name='reward')
reward.set_shape((self.model_config.batch_size, ))
sampled_target_unit.set_shape(
(self.model_config.batch_size, ))
indices = tf.stack([
tf.range(0,
self.model_config.batch_size,
dtype=tf.int32),
tf.squeeze(sampled_target_unit)
],
axis=-1)
logit_unit = tf.gather_nd(tf.nn.softmax(logit, axis=1),
indices)
decode_word_weight = decode_word_weight_list[step]
losses.append(-logit_unit * reward *
decode_word_weight)
loss = tf.add_n(losses)
return loss
def teacherforce_loss():
if self.model_config.number_samples > 0:
loss_fn = tf.nn.sampled_softmax_loss
else:
loss_fn = None
loss = sequence_loss(
logits=tf.stack(output.decoder_logit_list, axis=1),
targets=gt_target,
weights=decode_word_weight,
# softmax_loss_function=loss_fn,
# w=w,
# b=b,
# decoder_outputs=decoder_outputs,
# number_samples=self.model_config.number_samples
)
return loss
if self.model_config.train_mode == 'dynamic_self-critical':
loss = self_critical_loss()
# loss = tf.cond(
# tf.greater(self.global_step, 50000),
# # tf.logical_and(tf.greater(self.global_step, 100000), tf.equal(tf.mod(self.global_step, 2), 0)),
# lambda : self_critical_loss(),
# lambda : teacherforce_loss())
elif self.model_config.train_mode == 'teachercritical' or self.model_config.train_mode == 'teachercriticalv2':
loss = tf.cond(tf.equal(tf.mod(self.global_step, 2),
0), lambda: teacherforce_loss(),
lambda: teacherforce_critical_loss())
# loss = teacherforce_critical_loss()
else:
loss = teacherforce_loss()
if self.model_config.architecture == 'ut2t':
assert 'extra_encoder_loss' in output.obj_tensors and 'extra_decoder_loss' in output.obj_tensors
loss += output.obj_tensors['extra_encoder_loss']
loss += output.obj_tensors['extra_decoder_loss']
print('Use U T2T with ACT')
self.loss_style = tf.constant(0.0, dtype=tf.float32)
if output.pred_score_tuple is not None and 'pred' in self.model_config.tune_mode:
print('Create loss for predicting style')
ppdb_pred_score, add_pred_score, len_pred_score = output.pred_score_tuple
# ppdb_pred_score = tf.Print(ppdb_pred_score, [ppdb_pred_score, fetch_data['ppdb_score']],
# message='ppdb_pred_score:', first_n=-1, summarize=100)
# add_pred_score = tf.Print(add_pred_score, [add_pred_score, fetch_data['add_score']],
# message='add_pred_score:', first_n=-1, summarize=100)
# len_pred_score = tf.Print(len_pred_score, [len_pred_score, fetch_data['len_score']],
# message='len_pred_score:', first_n=-1, summarize=100)
# loss = tf.Print(loss, [loss], message='loss before:', summarize=100)
self.loss_style += tf.losses.absolute_difference(
ppdb_pred_score, fetch_data['ppdb_score'])
self.loss_style += tf.losses.absolute_difference(
add_pred_score, fetch_data['add_score'])
self.loss_style += tf.losses.absolute_difference(
len_pred_score, fetch_data['len_score'])
loss += self.loss_style
# loss = tf.Print(loss, [loss], message='loss after:', summarize=100)
obj = {
'decoder_target_list':
output.decoder_target_list,
'sentence_idxs':
sentence_idxs,
'sentence_simple_input_placeholder':
sentence_simple_input_placeholder,
'sentence_complex_input_placeholder':
sentence_complex_input_placeholder,
}
self.logits = output.decoder_logit_list
if 'rule' in self.model_config.memory:
obj['rule_id_input_placeholder'] = rule_id_input_placeholder
obj['rule_target_input_placeholder'] = rule_target_input_placeholder
# obj['rule_pair_input_placeholder'] = rule_pair_input_placeholder
obj['mem_contexts'] = mem_contexts
obj['mem_outputs'] = mem_outputs
obj['mem_counter'] = mem_counter
return loss, obj
import numpy as np
import jieba
import os
import sys
# import thulac
# thulac_seg = thulac.thulac(seg_only=True)
base_path = os.path.dirname(os.path.realpath(__file__))
model = Network(None, os.path.join(base_path, 'log.nosync/network_demo/run1'))
model.load_model(
os.path.join(base_path,
'model_checkpoint/lstm_early_stopping_without_conc'))
path = os.path.join(base_path, 'word2vec_model/wiki.zh.nosync/wiki.zh.vec')
embedding = Embedding()
embedding.load_w2v_model(path, False)
max_steps = model.n_input_steps
n_embedding = model.n_embedding
categories = [
'开关语音播报', '打电话', '发短信', '发邮件', '导航', '离职倾向', 'KPI', '访问网站', '会议室预定',
'设置提醒', '查日程安排', '查会议安排', '查会议室安排情况', '查月度工作任务', '查工作任务完成情况', '查月度预算执行情况',
'查当月费用报销情况', '查借款情况', '查应收款', '查应付款', '查考勤', '查出差情况', '查天气', '查股票', '讲笑话',
'讲故事', '讲新闻', '订机票', '订火车票'
]
def run(s, verbose=False):
s = s.lower()
class FastText(torch.nn.Module):
"""Implement fasttext classification method
Reference: "Bag of Tricks for Efficient Text Classification"
"""
def __init__(self, dataset, config):
super(FastText, self).__init__()
self.config = config
assert "token" in self.config.feature.feature_names
self.token_embedding = \
Embedding(dataset.token_map,
config.embedding.dimension,
cDataset.DOC_TOKEN, config,
padding_idx=dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.token_pretrained_file,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
if self.config.feature.token_ngram > 1:
self.token_ngram_embedding = \
Embedding(dataset.token_ngram_map,
config.embedding.dimension,
cDataset.DOC_TOKEN_NGRAM, config,
padding_idx=dataset.VOCAB_PADDING,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
if "keyword" in self.config.feature.feature_names:
self.keyword_embedding = \
Embedding(dataset.keyword_map,
config.embedding.dimension,
cDataset.DOC_KEYWORD, config,
padding_idx=dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.keyword_pretrained_file,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
if "topic" in self.config.feature.feature_names:
self.topic_embedding = \
Embedding(dataset.topic_map,
config.embedding.dimension,
cDataset.DOC_TOPIC, config,
padding_idx=dataset.VOCAB_PADDING,
mode=EmbeddingProcessType.SUM, dropout=0,
init_type=config.embedding.initializer,
low=-config.embedding.uniform_bound,
high=config.embedding.uniform_bound,
std=config.embedding.random_stddev,
activation_type=ActivationType.NONE)
self.linear = torch.nn.Linear(config.embedding.dimension,
len(dataset.label_map))
self.dropout = torch.nn.Dropout(p=config.train.hidden_layer_dropout)
def get_parameter_optimizer_dict(self):
params = list()
params.append({'params': self.token_embedding.parameters()})
if self.config.feature.token_ngram > 1:
params.append({'params': self.token_ngram_embedding.parameters()})
if "keyword" in self.config.feature.feature_names:
params.append({'params': self.keyword_embedding.parameters()})
if "topic" in self.config.feature.feature_names:
params.append({'params': self.topic_embedding.parameters()})
params.append({'params': self.linear.parameters()})
return params
def update_lr(self, optimizer, epoch):
"""Update lr
"""
if epoch > self.config.train.num_epochs_static_embedding:
for param_group in optimizer.param_groups:
param_group["lr"] = self.config.optimizer.learning_rate
else:
for param_group in optimizer.param_groups:
param_group["lr"] = 0
def forward(self, batch):
doc_embedding = self.token_embedding(
batch[cDataset.DOC_TOKEN].to(self.config.device),
batch[cDataset.DOC_TOKEN_OFFSET].to(self.config.device))
length = batch[cDataset.DOC_TOKEN_LEN].to(self.config.device)
if self.config.feature.token_ngram > 1:
doc_embedding += self.token_ngram_embedding(
batch[cDataset.DOC_TOKEN_NGRAM].to(self.config.device),
batch[cDataset.DOC_TOKEN_NGRAM_OFFSET].to(self.config.device))
length += batch[cDataset.DOC_TOKEN_NGRAM_LEN].to(
self.config.device)
if "keyword" in self.config.feature.feature_names:
doc_embedding += self.keyword_embedding(
batch[cDataset.DOC_KEYWORD].to(self.config.device),
batch[cDataset.DOC_KEYWORD_OFFSET].to(self.config.device))
length += batch[cDataset.DOC_KEYWORD_LEN].to(self.config.device)
if "topic" in self.config.feature.feature_names:
doc_embedding += self.topic_embedding(
batch[cDataset.DOC_TOPIC].to(self.config.device),
batch[cDataset.DOC_TOPIC_OFFSET].to(self.config.device))
length += batch[cDataset.DOC_TOPIC_LEN].to(self.config.device)
doc_embedding /= length.resize_(doc_embedding.size()[0], 1)
doc_embedding = self.dropout(doc_embedding)
return self.linear(doc_embedding)
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import time
if __name__ == "__main__":
dataset = SkipGram('data/rawdata.txt', 3)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1024,
shuffle=True)
net = Embedding(len(dataset.idx_to_token), 2)
optimizer = torch.optim.SGD(net.parameters(), lr=1e-1, momentum=0.9)
MAX_EPOCH = 2500
print('MAX_EPOCH', MAX_EPOCH)
for epoch in range(MAX_EPOCH):
if (epoch + 1) % 100 == 1:
start, l_sum, n = time.time(), 0.0, 0
for center_word, context_word, negative_word in dataloader:
optimizer.zero_grad()
l = net(center_word.view(-1, 1), context_word.view(-1, 1),
negative_word.view(-1, 1))
l.backward()
optimizer.step()
l_sum += l.cpu().item()
def train():
# Initialize torch.distributed
init_distributed()
print_rank_0('AutoMP: training ParallelTransformerLayer...')
batch_size = args.batch_size
sequence_length = args.sequence_length
hidden_size = args.hidden_size
vocab_size = args.vocab_size
hidden_dropout = args.hidden_dropout
attention_dropout = args.attention_dropout
num_layers = args.num_layers
layernorm_epsilon = args.layernorm_epsilon
num_attention_heads = args.num_attention_heads
input_indices = torch.randint(low=0,
high=vocab_size,
size=(batch_size, sequence_length))
input_indices = input_indices.to(torch.cuda.current_device())
labels = torch.randint(low=0,
high=vocab_size,
size=(batch_size, sequence_length))
labels = labels.to(torch.cuda.current_device())
position_indices = torch.tile(torch.arange(start=0, end=sequence_length),
(batch_size, 1))
position_indices = position_indices.to(torch.cuda.current_device())
def init_method_normal(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=1.0)
def gpt2_attention_mask_func(attention_scores, ltor_mask):
attention_scores.masked_fill_(ltor_mask, -10000.0)
return attention_scores
def init_method_normal(tensor):
return torch.nn.init.normal_(tensor, mean=0.0, std=1.0)
embedding = Embedding(hidden_size=hidden_size,
vocab_size=vocab_size,
max_sequence_length=sequence_length,
embedding_dropout_prob=hidden_dropout,
init_method=init_method_normal)
embedding_output = embedding.forward(input_indices, position_indices)
transformer_layer = ParallelTransformerLayer(
attention_mask_func=gpt2_attention_mask_func,
layer_number=0,
hidden_size=hidden_size,
layernorm_epsilon=layernorm_epsilon,
num_attention_heads=num_attention_heads,
attention_dropout=attention_dropout,
hidden_dropout=hidden_dropout)
# attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(input_indices, vocab_size - 1)
attention_mask = (torch.randint(
low=0,
high=2,
size=(sequence_length,
divide(num_attention_heads, torch.distributed.get_world_size()),
batch_size, batch_size)) < 0).cuda()
optimizer = torch.optim.SGD(transformer_layer.parameters(), lr=0.01)
profiler = Profiler(os.path.join('benchmark', args.exp_name))
num_epochs = 5
tot_time = 0
nproc = torch.distributed.get_world_size()
for epoch in range(num_epochs):
input_ = torch.rand(size=embedding_output.size()).cuda()
overall_name = f'transformer_layer_np-{nproc}_hs-{hidden_size}_nah-{num_attention_heads}_bsz-{batch_size}'
profiler.start(overall_name)
fname = f'transformer_layer_forward_np-{nproc}_hs-{hidden_size}_nah-{num_attention_heads}_bsz-{batch_size}'
# Forward pass
profiler.start(fname)
loss = transformer_layer.forward(input_, attention_mask)
train_loss = torch.mean(loss)
# print(train_loss)
torch.cuda.synchronize()
profiler.stop(fname)
# Backward pass
bname = f'transformer_layer_backward_np-{nproc}_hs-{hidden_size}_nah-{num_attention_heads}_bsz-{batch_size}'
profiler.start(bname)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
torch.cuda.synchronize()
profiler.stop(bname)
profiler.stop(overall_name)
def __init__(self, dataset, config):
super(Classifier, self).__init__()
self.config = config
assert len(self.config.feature.feature_names) == 1
assert self.config.feature.feature_names[0] == "token" or \
self.config.feature.feature_names[0] == "char"
if config.embedding.type == EmbeddingType.EMBEDDING:
self.token_embedding = \
Embedding(dataset.token_map, config.embedding.dimension,
cDataset.DOC_TOKEN, config, dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.token_pretrained_file,
mode=EmbeddingProcessType.FLAT,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
activation_type=ActivationType.NONE)
self.char_embedding = \
Embedding(dataset.char_map, config.embedding.dimension,
cDataset.DOC_CHAR, config, dataset.VOCAB_PADDING,
mode=EmbeddingProcessType.FLAT,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
activation_type=ActivationType.NONE)
elif config.embedding.type == EmbeddingType.REGION_EMBEDDING:
self.token_embedding = RegionEmbeddingLayer(
dataset.token_map, config.embedding.dimension,
config.embedding.region_size, cDataset.DOC_TOKEN, config,
padding=dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.token_pretrained_file,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
region_embedding_type=config.embedding.region_embedding_type)
self.char_embedding = RegionEmbeddingLayer(
dataset.char_map, config.embedding.dimension,
config.embedding.region_size, cDataset.DOC_CHAR, config,
padding=dataset.VOCAB_PADDING,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
region_embedding_type=config.embedding.region_embedding_type)
else:
raise TypeError(
"Unsupported embedding type: %s. " % config.embedding.type)
self.dropout = torch.nn.Dropout(p=config.train.hidden_layer_dropout)
class Graph():
def __init__(self, data, is_train, model_config):
self.model_config = model_config
self.data = data
self.is_train = is_train
self.model_fn = None
self.rand_unif_init = tf.random_uniform_initializer(-0, .08, 0.08)
self.metric = Metric(self.model_config, self.data)
def embedding_fn(self, inputs, embedding):
if type(inputs) == list:
if not inputs:
return []
else:
return [
tf.nn.embedding_lookup(embedding, inp) for inp in inputs
]
else:
return tf.nn.embedding_lookup(embedding, inputs)
def output_to_logit(self, prev_out, w, b):
prev_logit = tf.add(tf.matmul(prev_out, tf.transpose(w)), b)
return prev_logit
def create_model_multigpu(self):
losses = []
grads = []
ops = [tf.constant(0)]
self.objs = []
self.global_step = tf.train.get_or_create_global_step()
optim = self.get_optim()
fetch_data = None
if self.model_config.fetch_mode == 'tf_example_dataset':
fetch_data = self.data.get_data_sample()
with tf.variable_scope(tf.get_variable_scope()) as scope:
for gpu_id in range(self.model_config.num_gpus):
with tf.device('/device:GPU:%d' % gpu_id):
with tf.name_scope('%s_%d' % ('gpu_scope', gpu_id)):
loss, obj = self.create_model(fetch_data=fetch_data)
if self.model_config.npad_mode == 'v1':
vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope=
'model/transformer_decoder/decoder/layer_5/npad/'
)
grad = optim.compute_gradients(
loss,
colocate_gradients_with_ops=True,
var_list=vars)
elif self.model_config.npad_mode == 'static_seq':
vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='model/transformer_decoder/npad/')
grad = optim.compute_gradients(
loss,
colocate_gradients_with_ops=True,
var_list=vars)
else:
grad = optim.compute_gradients(
loss, colocate_gradients_with_ops=True)
tf.get_variable_scope().reuse_variables()
losses.append(loss)
grads.append(grad)
if 'rule' in self.model_config.memory and self.is_train:
ops.append(obj['mem_contexts'])
ops.append(obj['mem_outputs'])
ops.append(obj['mem_counter'])
self.objs.append(obj)
with tf.variable_scope('optimization'):
self.loss = tf.divide(tf.add_n(losses), self.model_config.num_gpus)
self.perplexity = tf.exp(tf.reduce_mean(self.loss))
if self.is_train:
avg_grad = self.average_gradients(grads)
grads = [g for (g, v) in avg_grad]
clipped_grads, _ = tf.clip_by_global_norm(
grads, self.model_config.max_grad_norm)
if self.model_config.npad_mode == 'v1':
vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='model/transformer_decoder/decoder/layer_5/npad/'
)
elif self.model_config.npad_mode == 'static_seq':
vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES,
scope='model/transformer_decoder/npad/')
else:
vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
self.train_op = optim.apply_gradients(
zip(clipped_grads, vars), global_step=self.global_step)
self.increment_global_step = tf.assign_add(self.global_step, 1)
self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V2)
self.ops = tf.tuple(ops)
def create_model(self, fetch_data=None):
with tf.variable_scope('variables'):
sentence_simple_input_placeholder = []
sentence_complex_input_placeholder = []
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
sentence_simple_segment_input_placeholder = []
sentence_complex_segment_input_placeholder = []
obj = {}
if fetch_data is not None and self.model_config.fetch_mode == 'tf_example_dataset':
for t in tf.unstack(fetch_data['line_comp_ids'], axis=1):
sentence_complex_input_placeholder.append(t)
for t in tf.unstack(fetch_data['line_simp_ids'], axis=1):
sentence_simple_input_placeholder.append(t)
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
for t in tf.unstack(fetch_data['line_comp_segids'],
axis=1):
sentence_complex_segment_input_placeholder.append(t)
for t in tf.unstack(fetch_data['line_simp_segids'],
axis=1):
sentence_simple_segment_input_placeholder.append(t)
obj['line_comp_segids'] = tf.stack(
sentence_complex_segment_input_placeholder, axis=1)
obj['line_simp_segids'] = tf.stack(
sentence_simple_segment_input_placeholder, axis=1)
score = None
if self.model_config.tune_style:
if self.is_train:
# In training, score are from fetch data
scores = []
if self.model_config.tune_style[0]:
ppdb_score = fetch_data['ppdb_score']
scores.append(ppdb_score)
print('Tune ppdb score!')
if 'plus' in self.model_config.tune_mode:
# to avoid most ppdb scores are 0
ppdb_score += 0.1
if self.model_config.tune_style[1]:
add_score = fetch_data['dsim_score']
scores.append(add_score)
print('Tune dsim_score score!')
if self.model_config.tune_style[2]:
add_score = fetch_data['add_score']
scores.append(add_score)
print('Tune add score!')
if self.model_config.tune_style[3]:
len_score = fetch_data['len_score']
scores.append(len_score)
print('Tune length score!')
else:
# In evaluating/predict, scores may be a factor to multiply if in pred mode
# or actual user provided score
# TODO(sanqiang): not used for now because not fech_data in eval
raise NotImplementedError('No tune style for training')
# ppdb_score = tf.constant(
# self.model_config.tune_style[0], shape=[self.model_config.batch_size], dtype=tf.float32)
# add_score = tf.constant(
# self.model_config.tune_style[1], shape=[self.model_config.batch_size], dtype=tf.float32)
# len_score = tf.constant(
# self.model_config.tune_style[2], shape=[self.model_config.batch_size], dtype=tf.float32)
# Assemble scores
dimension_unit = int(self.model_config.dimension /
len(scores))
dimension_runit = self.model_config.dimension - (
len(scores) - 1) * dimension_unit
for s_i, score in enumerate(scores):
if s_i < len(scores) - 1:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_unit]),
axis=1)
else:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_runit]),
axis=1)
score = tf.concat(scores, axis=-1)
else:
for step in range(self.model_config.max_simple_sentence):
sentence_simple_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='simple_input'))
for step in range(self.model_config.max_complex_sentence):
sentence_complex_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='complex_input'))
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
for step in range(self.model_config.max_simple_sentence):
sentence_simple_segment_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='simple_seg_input'))
for step in range(self.model_config.max_complex_sentence):
sentence_complex_segment_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='complex_seg_input'))
obj['line_comp_segids'] = tf.stack(
sentence_complex_segment_input_placeholder, axis=1)
obj['line_simp_segids'] = tf.stack(
sentence_simple_segment_input_placeholder, axis=1)
score = None
if self.model_config.tune_style:
if self.is_train:
raise NotImplementedError('No tune style for training')
#
# ppdb_score = tf.constant(
# self.model_config.tune_style, shape=[self.model_config.batch_size], dtype=tf.float32)
# ppdb_score = tf.expand_dims(tf.tile(
# tf.expand_dims(ppdb_score, axis=-1),
# [1, self.model_config.dimension]), axis=1)
else:
scores = []
if self.model_config.tune_style:
if self.model_config.tune_style[0]:
ppdb_score = tf.constant(
self.model_config.tune_style[0],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(ppdb_score)
print('tune ppdb score')
if self.model_config.tune_style[1]:
dsim_score = tf.constant(
self.model_config.tune_style[1],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(dsim_score)
print('tune dsim score')
if self.model_config.tune_style[2]:
add_score = tf.constant(
self.model_config.tune_style[2],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(add_score)
print('tune add score')
if self.model_config.tune_style[3]:
len_score = tf.constant(
self.model_config.tune_style[3],
shape=[self.model_config.batch_size],
dtype=tf.float32)
scores.append(len_score)
print('tune len score')
# Assemble scores
dimension_unit = int(self.model_config.dimension /
len(scores))
dimension_runit = self.model_config.dimension - (
len(scores) - 1) * dimension_unit
for s_i, score in enumerate(scores):
if s_i < len(scores) - 1:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_unit]),
axis=1)
else:
scores[s_i] = tf.expand_dims(tf.tile(
tf.expand_dims(scores[s_i], axis=-1),
[1, dimension_runit]),
axis=1)
score = tf.concat(scores, axis=-1)
# For self.model_config.tune_style:
comp_features = {}
comp_add_score = tf.zeros(self.model_config.batch_size,
tf.float32,
name='comp_add_score_input')
comp_length = tf.zeros(self.model_config.batch_size,
tf.float32,
name='comp_length_input')
comp_features['comp_add_score'] = comp_add_score
comp_features['comp_length'] = comp_length
sentence_idxs = tf.zeros(self.model_config.batch_size,
tf.int32,
name='sent_idx')
self.embedding = Embedding(self.data.vocab_complex,
self.data.vocab_simple,
self.model_config)
if self.model_config.bert_mode:
emb_complex = None
else:
emb_complex = self.embedding.get_complex_embedding()
if self.model_config.bert_mode and (
self.model_config.tie_embedding == 'all'
or self.model_config.tie_embedding == 'enc_dec'):
emb_simple = None
else:
emb_simple = self.embedding.get_simple_embedding()
if (self.is_train and self.model_config.pretrained_embedding):
self.embed_complex_placeholder = tf.placeholder(
tf.float32, (self.data.vocab_complex.vocab_size(),
self.model_config.dimension), 'complex_emb')
self.replace_emb_complex = emb_complex.assign(
self.embed_complex_placeholder)
self.embed_simple_placeholder = tf.placeholder(
tf.float32, (self.data.vocab_simple.vocab_size(),
self.model_config.dimension), 'simple_emb')
self.replace_emb_simple = emb_simple.assign(
self.embed_simple_placeholder)
if self.model_config.bert_mode and (
self.model_config.tie_embedding == 'all'
or self.model_config.tie_embedding == 'dec_out'):
w = None
else:
w = self.embedding.get_w()
b = self.embedding.get_b()
mem_contexts, mem_outputs, mem_counter = None, None, None
rule_id_input_placeholder, rule_target_input_placeholder = [], []
if 'rule' in self.model_config.memory:
with tf.device('/cpu:0'):
context_size = 0
if self.model_config.framework == 'transformer':
context_size = 1
elif self.model_config.framework == 'seq2seq':
context_size = 2
mem_contexts = tf.get_variable(
'mem_contexts',
initializer=tf.constant(
0,
dtype=tf.float32,
shape=(self.data.vocab_rule.get_rule_size(),
self.model_config.max_target_rule_sublen,
self.model_config.dimension *
context_size)),
trainable=False,
dtype=tf.float32)
mem_outputs = tf.get_variable(
'mem_outputs',
initializer=tf.constant(
0,
dtype=tf.float32,
shape=(self.data.vocab_rule.get_rule_size(),
self.model_config.max_target_rule_sublen,
self.model_config.dimension)),
trainable=False,
dtype=tf.float32)
mem_counter = tf.get_variable(
'mem_counter',
initializer=tf.constant(
0,
dtype=tf.int32,
shape=(self.data.vocab_rule.get_rule_size(), 1)),
trainable=False,
dtype=tf.int32)
if 'direct' in self.model_config.memory or 'rule' in self.model_config.memory:
if fetch_data is not None and self.model_config.fetch_mode == 'tf_example_dataset':
for t in tf.unstack(fetch_data['rule_id'], axis=1):
rule_id_input_placeholder.append(t)
for t in tf.unstack(fetch_data['rule_target'], axis=1):
rule_target_input_placeholder.append(t)
else:
for step in range(self.model_config.max_cand_rules):
rule_id_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='rule_id_input'))
for step in range(self.model_config.max_cand_rules):
if 'direct' in self.model_config.memory:
rule_target_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='rule_target_input'))
elif 'rule' in self.model_config.memory:
rule_target_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.string,
name='rule_target_input'))
with tf.variable_scope('model'):
output = self.model_fn(sentence_complex_input_placeholder,
emb_complex,
sentence_simple_input_placeholder,
emb_simple, w, b, rule_id_input_placeholder,
rule_target_input_placeholder, mem_contexts,
mem_outputs, self.global_step, score,
comp_features, obj)
encoder_embs, final_outputs = None, None
if self.model_config.replace_unk_by_emb:
encoder_embs = tf.stack(output.encoder_embed_inputs_list,
axis=1)
if output.decoder_outputs_list is not None:
if type(output.decoder_outputs_list) == list:
decoder_outputs_list = output.decoder_outputs_list
decoder_outputs = tf.stack(decoder_outputs_list, axis=1)
else:
decoder_outputs = output.decoder_outputs_list
if output.final_outputs_list is not None:
if type(output.final_outputs_list) == list:
final_outputs_list = output.final_outputs_list
final_outputs = tf.stack(final_outputs_list, axis=1)
else:
final_outputs = output.final_outputs_list
attn_distr = None
if self.model_config.replace_unk_by_attn:
attn_distr = output.attn_distr_list
if not self.is_train:
# in beam search, it directly provide decoder target list
decoder_target = tf.stack(output.decoder_target_list, axis=1)
loss = tf.reduce_mean(output.decoder_score)
obj = {
'sentence_idxs': sentence_idxs,
'sentence_simple_input_placeholder':
sentence_simple_input_placeholder,
'sentence_complex_input_placeholder':
sentence_complex_input_placeholder,
'decoder_target_list': decoder_target,
'final_outputs': final_outputs,
'encoder_embs': encoder_embs,
'attn_distr': attn_distr
}
if self.model_config.subword_vocab_size and self.model_config.seg_mode:
obj['sentence_complex_segment_input_placeholder'] = sentence_complex_segment_input_placeholder
obj['sentence_simple_segment_input_placeholder'] = sentence_simple_segment_input_placeholder
if 'rule' in self.model_config.memory or 'direct' in self.model_config.memory:
obj['rule_id_input_placeholder'] = rule_id_input_placeholder
obj['rule_target_input_placeholder'] = rule_target_input_placeholder
if self.model_config.tune_style:
obj['comp_features'] = comp_features
return loss, obj
else:
# Memory Populate
if 'rule' in self.model_config.memory:
# Update Memory through python injection
def update_memory(mem_contexts_tmp, mem_outputs_tmp,
mem_counter_tmp, decoder_targets,
decoder_outputs, contexts,
rule_target_input_placeholder,
rule_id_input_placeholder, global_step,
encoder_outputs):
def _seq_contain(arr, tar):
j = 0
for i in range(len(arr)):
if arr[i] == tar[j]:
j += 1
if j == len(tar):
return i - len(tar) + 1
else:
j = 0
return -1
# if 'stopgrad' in self.model_config.rl_configs and global_step % 2 != 0:
# return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
# if global_step <= self.model_config.memory_prepare_step:
# return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
batch_size = np.shape(rule_target_input_placeholder)[0]
max_rules = np.shape(rule_target_input_placeholder)[1]
decoder_targets_str = [
' '.join(sent) for sent in truncate_sents(
decode(
decoder_targets, self.data.vocab_simple,
self.model_config.subword_vocab_size > 0
or 'bert_token' in
self.model_config.bert_mode))
]
for batch_id in range(batch_size):
cur_decoder_targets = decoder_targets[batch_id, :]
cur_decoder_targets_str = decoder_targets_str[
batch_id]
cur_decoder_outputs = decoder_outputs[batch_id, :]
cur_contexts = contexts[batch_id, :]
cur_rule_target_input_placeholder = rule_target_input_placeholder[
batch_id, :]
cur_rule_target_input_placeholder = [
tmp.decode("utf-8").strip('\x00')
for tmp in cur_rule_target_input_placeholder
if not tmp.decode("utf-8").strip().startswith(
constant.SYMBOL_PAD)
]
cur_rule_id_input_placeholder = rule_id_input_placeholder[
batch_id, :]
# Build the valid mapper from rule id => target words ids
rule_mapper = {}
for step in range(
len(cur_rule_target_input_placeholder)):
rule_target_str = cur_rule_target_input_placeholder[
step]
if rule_target_str == constant.SYMBOL_PAD:
continue
rule_id = cur_rule_id_input_placeholder[step]
if rule_id != 0 and re.search(
r'\b%s\b' % rule_target_str,
cur_decoder_targets_str
): # decoder_target_str in cur_decoder_targets_str:
decoder_target_wids = self.data.vocab_simple.encode(
rule_target_str)
dec_s_idx = _seq_contain(
cur_decoder_targets,
decoder_target_wids)
if dec_s_idx != -1:
print('rule_target_str:%s' %
rule_target_str)
print('cur_decoder_targets_str:%s' %
cur_decoder_targets_str)
print('cur_decoder_targets:%s' %
cur_decoder_targets)
print('decoder_target_wids:%s' %
decoder_target_wids)
rule_mapper[rule_id] = list(
range(
dec_s_idx, dec_s_idx +
len(decoder_target_wids)))
for rule_id in rule_mapper:
dec_idxs = rule_mapper[rule_id]
for idx, dec_idx in enumerate(dec_idxs):
if mem_counter_tmp[rule_id, 0] == 0:
mem_contexts_tmp[
rule_id,
idx, :] = cur_contexts[dec_idx, :]
mem_outputs_tmp[
rule_id,
idx, :] = cur_decoder_outputs[
dec_idx, :]
else:
mem_contexts_tmp[rule_id, idx, :] = (
cur_contexts[dec_idx, :] +
mem_contexts_tmp[rule_id,
idx, :]) / 2
mem_outputs_tmp[rule_id, idx, :] = (
cur_decoder_outputs[dec_idx, :] +
mem_outputs_tmp[rule_id,
idx, :]) / 2
mem_counter_tmp[rule_id, 0] += 1
return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
mem_output_input = None
if 'mofinal' in self.model_config.memory_config:
mem_output_input = final_outputs
# elif 'modecode' in self.model_config.memory_config:
# mem_output_input = decoder_outputs
# elif 'moemb' in self.model_config.memory_config:
# mem_output_input = tf.stack(
# self.embedding_fn(sentence_simple_input_placeholder, emb_simple),
# axis=1)
mem_contexts, mem_outputs, mem_counter = tf.py_func(
update_memory, [
mem_contexts, mem_outputs, mem_counter,
tf.stack(output.decoder_target_list, axis=1),
mem_output_input, output.contexts,
tf.stack(rule_target_input_placeholder, axis=1),
tf.stack(rule_id_input_placeholder, axis=1),
self.global_step, output.encoder_outputs
], [tf.float32, tf.float32, tf.int32],
stateful=False,
name='update_memory')
#Loss and corresponding prior/mask
decode_word_weight_list = [
tf.to_float(
tf.not_equal(
d,
self.data.vocab_simple.encode(
constant.SYMBOL_PAD)))
for d in output.gt_target_list
]
decode_word_weight = tf.stack(decode_word_weight_list, axis=1)
gt_target = tf.stack(output.gt_target_list, axis=1)
def self_critical_loss():
# For minimize the negative log of probabilities
rewards = tf.py_func(
self.metric.self_crititcal_reward,
[
sentence_idxs,
tf.stack(output.sample_target_list, axis=-1),
tf.stack(output.decoder_target_list, axis=-1),
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(sentence_complex_input_placeholder,
axis=-1),
tf.ones((1, 1)),
# tf.stack(rule_target_input_placeholder, axis=1)
],
tf.float32,
stateful=False,
name='reward')
rewards.set_shape((self.model_config.batch_size,
self.model_config.max_simple_sentence))
rewards = tf.unstack(rewards, axis=1)
weighted_probs_list = [
rewards[i] * decode_word_weight_list[i] *
-output.sample_logit_list[i]
for i in range(len(decode_word_weight_list))
]
total_size = tf.reduce_sum(decode_word_weight_list)
total_size += 1e-12
weighted_probs = tf.reduce_sum(
weighted_probs_list) / total_size
loss = weighted_probs
return loss
def teacherforce_critical_loss():
losses = []
for step in range(self.model_config.max_simple_sentence):
logit = output.decoder_logit_list[step]
greedy_target_unit = tf.stop_gradient(
tf.argmax(logit, axis=1))
if self.model_config.train_mode == 'teachercriticalv2':
sampled_target_unit, reward = tf.py_func(
self.metric.self_crititcal_reward_unitv2, [
sentence_idxs, step, greedy_target_unit,
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(
sentence_complex_input_placeholder,
axis=-1), self.global_step
], [tf.int32, tf.float32],
stateful=False,
name='reward')
reward.set_shape((self.model_config.batch_size, ))
sampled_target_unit.set_shape(
(self.model_config.batch_size, ))
elif self.model_config.train_mode == 'teachercritical':
sampled_target_unit = tf.cast(
tf.squeeze(tf.multinomial(logit, 1), axis=1),
tf.int32)
sampled_target_unit, reward = tf.py_func(
self.metric.self_crititcal_reward_unit, [
sentence_idxs,
step,
sampled_target_unit,
greedy_target_unit,
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(
sentence_complex_input_placeholder,
axis=-1),
self.global_step,
], [tf.int32, tf.float32],
stateful=False,
name='reward')
reward.set_shape((self.model_config.batch_size, ))
sampled_target_unit.set_shape(
(self.model_config.batch_size, ))
indices = tf.stack([
tf.range(0,
self.model_config.batch_size,
dtype=tf.int32),
tf.squeeze(sampled_target_unit)
],
axis=-1)
logit_unit = tf.gather_nd(tf.nn.softmax(logit, axis=1),
indices)
decode_word_weight = decode_word_weight_list[step]
losses.append(-logit_unit * reward *
decode_word_weight)
loss = tf.add_n(losses)
return loss
def teacherforce_loss():
if self.model_config.number_samples > 0:
loss_fn = tf.nn.sampled_softmax_loss
else:
loss_fn = None
loss = sequence_loss(
logits=tf.stack(output.decoder_logit_list, axis=1),
targets=gt_target,
weights=decode_word_weight,
# softmax_loss_function=loss_fn,
# w=w,
# b=b,
# decoder_outputs=decoder_outputs,
# number_samples=self.model_config.number_samples
)
return loss
if self.model_config.train_mode == 'dynamic_self-critical':
loss = self_critical_loss()
# loss = tf.cond(
# tf.greater(self.global_step, 50000),
# # tf.logical_and(tf.greater(self.global_step, 100000), tf.equal(tf.mod(self.global_step, 2), 0)),
# lambda : self_critical_loss(),
# lambda : teacherforce_loss())
elif self.model_config.train_mode == 'teachercritical' or self.model_config.train_mode == 'teachercriticalv2':
loss = tf.cond(tf.equal(tf.mod(self.global_step, 2),
0), lambda: teacherforce_loss(),
lambda: teacherforce_critical_loss())
# loss = teacherforce_critical_loss()
else:
loss = teacherforce_loss()
if self.model_config.architecture == 'ut2t':
assert 'extra_encoder_loss' in output.obj_tensors and 'extra_decoder_loss' in output.obj_tensors
loss += output.obj_tensors['extra_encoder_loss']
loss += output.obj_tensors['extra_decoder_loss']
print('Use U T2T with ACT')
self.loss_style = tf.constant(0.0, dtype=tf.float32)
if output.pred_score_tuple is not None and 'pred' in self.model_config.tune_mode:
print('Create loss for predicting style')
ppdb_pred_score, add_pred_score, len_pred_score = output.pred_score_tuple
# ppdb_pred_score = tf.Print(ppdb_pred_score, [ppdb_pred_score, fetch_data['ppdb_score']],
# message='ppdb_pred_score:', first_n=-1, summarize=100)
# add_pred_score = tf.Print(add_pred_score, [add_pred_score, fetch_data['add_score']],
# message='add_pred_score:', first_n=-1, summarize=100)
# len_pred_score = tf.Print(len_pred_score, [len_pred_score, fetch_data['len_score']],
# message='len_pred_score:', first_n=-1, summarize=100)
# loss = tf.Print(loss, [loss], message='loss before:', summarize=100)
self.loss_style += tf.losses.absolute_difference(
ppdb_pred_score, fetch_data['ppdb_score'])
self.loss_style += tf.losses.absolute_difference(
add_pred_score, fetch_data['add_score'])
self.loss_style += tf.losses.absolute_difference(
len_pred_score, fetch_data['len_score'])
loss += self.loss_style
# loss = tf.Print(loss, [loss], message='loss after:', summarize=100)
obj = {
'decoder_target_list':
output.decoder_target_list,
'sentence_idxs':
sentence_idxs,
'sentence_simple_input_placeholder':
sentence_simple_input_placeholder,
'sentence_complex_input_placeholder':
sentence_complex_input_placeholder,
}
self.logits = output.decoder_logit_list
if 'rule' in self.model_config.memory:
obj['rule_id_input_placeholder'] = rule_id_input_placeholder
obj['rule_target_input_placeholder'] = rule_target_input_placeholder
# obj['rule_pair_input_placeholder'] = rule_pair_input_placeholder
obj['mem_contexts'] = mem_contexts
obj['mem_outputs'] = mem_outputs
obj['mem_counter'] = mem_counter
return loss, obj
def get_optim(self):
learning_rate = tf.constant(self.model_config.learning_rate)
if self.model_config.optimizer == 'adagrad':
opt = tf.train.AdagradOptimizer(learning_rate)
# Adam need lower learning rate
elif self.model_config.optimizer == 'adam':
opt = tf.train.AdamOptimizer(learning_rate)
elif self.model_config.optimizer == 'lazy_adam':
if not hasattr(self, 'hparams'):
# In case not using Transformer model
from tensor2tensor.models import transformer
self.hparams = transformer.transformer_base()
opt = tf.contrib.opt.LazyAdamOptimizer(
self.hparams.learning_rate / 100.0,
beta1=self.hparams.optimizer_adam_beta1,
beta2=self.hparams.optimizer_adam_beta2,
epsilon=self.hparams.optimizer_adam_epsilon)
elif self.model_config.optimizer == 'adadelta':
opt = tf.train.AdadeltaOptimizer(learning_rate)
elif self.model_config.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise Exception('Not Implemented Optimizer!')
# if self.model_config.max_grad_staleness > 0:
# opt = tf.contrib.opt.DropStaleGradientOptimizer(opt, self.model_config.max_grad_staleness)
return opt
# Got from https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10/cifar10_multi_gpu_train.py#L101
def average_gradients(self, tower_grads):
"""Calculate the average gradient for each shared variable across all towers.
Note that this function provides a synchronization point across all towers.
Args:
tower_grads: List of lists of (gradient, variable) tuples. The outer list
is over individual gradients. The inner list is over the gradient
calculation for each tower.
Returns:
List of pairs of (gradient, variable) where the gradient has been averaged
across all towers.
"""
average_grads = []
for grad_and_vars in zip(*tower_grads):
# Note that each grad_and_vars looks like the following:
# ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
grads = []
for g, _ in grad_and_vars:
# Add 0 dimension to the gradients to represent the tower.
if g is None:
print('Useless tensors:%s' % grad_and_vars)
expanded_g = tf.expand_dims(g, 0)
# Append on a 'tower' dimension which we will average over below.
grads.append(expanded_g)
# Average over the 'tower' dimension.
grad = tf.concat(axis=0, values=grads)
grad = tf.reduce_mean(grad, 0)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
return average_grads
class Classifier(torch.nn.Module):
def __init__(self, dataset, config):
super(Classifier, self).__init__()
self.config = config
self.token_embedding = \
Embedding(dataset.token_map, config.embedding.dimension,
cDataset.DOC_TOKEN, config, dataset.VOCAB_PADDING,
pretrained_embedding_file=
config.feature.token_pretrained_file,
mode=EmbeddingProcessType.FLAT,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
activation_type=ActivationType.NONE,
model_mode=dataset.model_mode)
self.char_embedding = \
Embedding(dataset.char_map, config.embedding.dimension,
cDataset.DOC_CHAR, config, dataset.VOCAB_PADDING,
mode=EmbeddingProcessType.FLAT,
dropout=self.config.embedding.dropout,
init_type=self.config.embedding.initializer,
low=-self.config.embedding.uniform_bound,
high=self.config.embedding.uniform_bound,
std=self.config.embedding.random_stddev,
fan_mode=self.config.embedding.fan_mode,
activation_type=ActivationType.NONE,
model_mode=dataset.model_mode)
self.dropout = torch.nn.Dropout(p=config.train.hidden_layer_dropout)
def get_embedding(self, batch, pad_shape=None, pad_value=0):
token_id = batch[cDataset.DOC_TOKEN].to(self.config.device)
if pad_shape is not None:
token_id = torch.nn.functional.pad(token_id,
pad_shape,
mode='constant',
value=pad_value)
embedding = self.token_embedding(token_id)
length = batch[cDataset.DOC_TOKEN_LEN].to(self.config.device)
mask = batch[cDataset.DOC_TOKEN_MASK].to(self.config.device)
return embedding, length, mask
def get_parameter_optimizer_dict(self):
params = list()
params.append({
'params': self.token_embedding.parameters(),
'is_embedding': True
})
return params
def update_lr(self, optimizer, epoch):
"""Update lr
"""
if epoch > self.config.train.num_epochs_static_embedding:
for param_group in optimizer.param_groups[:2]:
param_group["lr"] = self.config.optimizer.learning_rate
else:
for param_group in optimizer.param_groups[:2]:
param_group["lr"] = 0
def forward(self, batch):
raise NotImplementedError
def parse_emb_str(embs_str: str):
emb_strs = embs_str.split()
emb = Embedding()
for emb_str in emb_strs:
emb.add_dim(float(emb_str))
return emb
import numpy as np
import os
import random
import jieba
# import thulac
# thulac_seg = thulac.thulac(seg_only=True)
max_sample = 1000
max_steps = 15
n_embedding = 300
base_path = os.path.dirname(os.path.realpath(__file__))
path = os.path.join(base_path, 'word2vec_model/wiki.zh.nosync/wiki.zh.vec')
model_path = os.path.join(base_path, Config.CURRENT_MODEL_BASE_PATH)
embedding = Embedding()
embedding.load_w2v_model(path, False)
data = dict()
data_dir_path = os.path.join(model_path, 'data.nosync')
for fname in os.listdir(data_dir_path):
if '.txt' not in fname:
continue
train_file_path = os.path.join(data_dir_path, fname)
with open(train_file_path, 'r') as f:
for l in f:
comps = l.strip().split()
label = int(comps[1])
if label not in data:
data[label] = []
data[label].append((comps[0], label))
def create_model(self):
with tf.variable_scope('variables'):
sentence_simple_input_placeholder = []
for step in range(self.model_config.max_simple_sentence):
sentence_simple_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='simple_input'))
sentence_complex_input_placeholder = []
for step in range(self.model_config.max_complex_sentence):
sentence_complex_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='complex_input'))
sentence_idxs = tf.zeros(self.model_config.batch_size,
tf.int32,
name='sent_idx')
embedding = Embedding(self.data.vocab_complex,
self.data.vocab_simple, self.model_config)
emb_complex = embedding.get_complex_embedding()
emb_simple = embedding.get_simple_embedding()
w = embedding.get_w()
b = embedding.get_b()
mem_contexts, mem_outputs, mem_counter = None, None, None
rule_id_input_placeholder, rule_target_input_placeholder = [], []
rule_pair_input_placeholder = []
if 'rule' in self.model_config.memory:
with tf.device('/cpu:0'):
context_size = 0
if self.model_config.framework == 'transformer':
context_size = 1
elif self.model_config.framework == 'seq2seq':
context_size = 2
mem_contexts = tf.get_variable(
'mem_contexts',
initializer=tf.constant(
0,
dtype=tf.float32,
shape=(self.data.vocab_rule.get_rule_size(),
self.model_config.dimension *
context_size)),
trainable=False,
dtype=tf.float32)
mem_outputs = tf.get_variable(
'mem_outputs',
initializer=tf.constant(
0,
dtype=tf.float32,
shape=(self.data.vocab_rule.get_rule_size(),
self.model_config.dimension)),
trainable=False,
dtype=tf.float32)
mem_counter = tf.get_variable(
'mem_counter',
initializer=tf.constant(
0,
dtype=tf.int32,
shape=(self.data.vocab_rule.get_rule_size(), 1)),
trainable=False,
dtype=tf.int32)
for step in range(self.model_config.max_cand_rules):
rule_id_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='rule_id_input'))
for step in range(self.model_config.max_cand_rules):
rule_target_input_placeholder.append(
tf.zeros(self.model_config.batch_size,
tf.int32,
name='rule_target_input'))
for step in range(self.model_config.max_cand_rules):
rule_pair_input_placeholder.append(
tf.zeros([self.model_config.batch_size, 2],
tf.int32,
name='rule_pair_input'))
with tf.variable_scope('model'):
output = self.model_fn(sentence_complex_input_placeholder,
emb_complex,
sentence_simple_input_placeholder,
emb_simple, w, b, rule_id_input_placeholder,
mem_contexts, mem_outputs, self.global_step)
encoder_embs, final_outputs = None, None
if self.model_config.replace_unk_by_emb:
encoder_embs = tf.stack(output.encoder_embed_inputs_list,
axis=1)
if output.decoder_outputs_list is not None:
if type(output.decoder_outputs_list) == list:
decoder_outputs_list = output.decoder_outputs_list
decoder_outputs = tf.stack(decoder_outputs_list, axis=1)
else:
decoder_outputs = output.decoder_outputs_list
if output.final_outputs_list is not None:
if type(output.final_outputs_list) == list:
final_outputs_list = output.final_outputs_list
final_outputs = tf.stack(final_outputs_list, axis=1)
else:
final_outputs = output.final_outputs_list
attn_distr = None
if self.model_config.replace_unk_by_attn:
attn_distr = output.attn_distr_list
if not self.is_train:
# in beam search, it directly provide decoder target list
decoder_target = tf.stack(output.decoder_target_list, axis=1)
loss = tf.reduce_mean(output.decoder_score)
obj = {
'sentence_idxs': sentence_idxs,
'sentence_simple_input_placeholder':
sentence_simple_input_placeholder,
'sentence_complex_input_placeholder':
sentence_complex_input_placeholder,
'decoder_target_list': decoder_target,
'final_outputs': final_outputs,
'encoder_embs': encoder_embs,
'attn_distr': attn_distr
}
if 'rule' in self.model_config.memory:
obj['rule_id_input_placeholder'] = rule_id_input_placeholder
obj['rule_target_input_placeholder'] = rule_target_input_placeholder
return loss, obj
else:
# Memory Populate
if 'rule' in self.model_config.memory:
# Update Memory through python injection
def update_memory(mem_contexts_tmp, mem_outputs_tmp,
mem_counter_tmp, decoder_targets,
decoder_outputs, contexts,
rule_target_input_placeholder,
rule_id_input_placeholder, global_step,
emb_simple, encoder_outputs):
if global_step <= self.model_config.memory_prepare_step:
return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
batch_size = np.shape(rule_target_input_placeholder)[0]
max_rules = np.shape(rule_target_input_placeholder)[1]
for batch_id in range(batch_size):
cur_decoder_targets = decoder_targets[batch_id, :]
cur_decoder_outputs = decoder_outputs[batch_id, :]
cur_contexts = contexts[batch_id, :]
cur_rule_target_input_placeholder = rule_target_input_placeholder[
batch_id, :]
cur_rule_id_input_placeholder = rule_id_input_placeholder[
batch_id, :]
rule_mapper = {}
for step in range(max_rules):
rule_id = cur_rule_id_input_placeholder[step]
if rule_id != 0:
decoder_target = cur_rule_target_input_placeholder[
step]
if rule_id not in rule_mapper:
rule_mapper[rule_id] = []
rule_mapper[rule_id].append(decoder_target)
for rule_id in rule_mapper:
rule_targets = rule_mapper[rule_id]
decoder_target_orders = np.where(
cur_decoder_targets == rule_targets[0])[0]
for decoder_target_order in decoder_target_orders:
if len(rule_targets) > 1:
if decoder_target_order + 1 >= len(
cur_decoder_targets
) or rule_targets[
1] != cur_decoder_targets[
decoder_target_order + 1]:
continue
if len(rule_targets) > 2:
if decoder_target_order + 2 >= len(
cur_decoder_targets
) or rule_targets[
2] != cur_decoder_targets[
decoder_target_order + 2]:
continue
cur_context, cur_outputs = None, None
for step, _ in enumerate(rule_targets):
if step == 0:
cur_context = cur_contexts[
decoder_target_order, :]
cur_outputs = cur_decoder_outputs[
decoder_target_order, :]
else:
cur_context += cur_contexts[
step + decoder_target_order, :]
cur_outputs += cur_decoder_outputs[
step + decoder_target_order, :]
cur_context /= len(rule_targets)
cur_outputs /= len(rule_targets)
if mem_counter_tmp[rule_id, 0] == 0:
mem_contexts_tmp[
rule_id, :] = cur_context
mem_outputs_tmp[
rule_id, :] = cur_outputs
else:
mem_contexts_tmp[rule_id, :] = (
cur_context +
mem_contexts_tmp[rule_id, :]) / 2
mem_outputs_tmp[rule_id, :] = (
cur_outputs +
mem_outputs_tmp[rule_id, :]) / 2
mem_counter_tmp[rule_id, 0] += 1
return mem_contexts_tmp, mem_outputs_tmp, mem_counter_tmp
mem_output_input = None
if 'mofinal' in self.model_config.memory_config:
mem_output_input = final_outputs
# elif 'modecode' in self.model_config.memory_config:
# mem_output_input = decoder_outputs
# elif 'moemb' in self.model_config.memory_config:
# mem_output_input = tf.stack(
# self.embedding_fn(sentence_simple_input_placeholder, emb_simple),
# axis=1)
mem_contexts, mem_outputs, mem_counter = tf.py_func(
update_memory, [
mem_contexts, mem_outputs, mem_counter,
tf.stack(output.decoder_target_list, axis=1),
mem_output_input, output.contexts,
tf.stack(rule_target_input_placeholder, axis=1),
tf.stack(rule_id_input_placeholder,
axis=1), self.global_step, emb_simple,
output.encoder_outputs
], [tf.float32, tf.float32, tf.int32],
stateful=False,
name='update_memory')
#Loss and corresponding prior/mask
decode_word_weight_list = [
tf.to_float(
tf.not_equal(
d,
self.data.vocab_simple.encode(
constant.SYMBOL_PAD)))
for d in output.gt_target_list
]
decode_word_weight = tf.stack(decode_word_weight_list, axis=1)
gt_target = tf.stack(output.gt_target_list, axis=1)
def self_critical_loss():
# For minimize the negative log of probabilities
rewards = tf.py_func(
self.metric.self_crititcal_reward,
[
sentence_idxs,
tf.stack(output.sample_target_list, axis=-1),
tf.stack(output.decoder_target_list, axis=-1),
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(sentence_complex_input_placeholder,
axis=-1),
tf.ones((1, 1)),
# tf.stack(rule_target_input_placeholder, axis=1)
],
tf.float32,
stateful=False,
name='reward')
rewards.set_shape((self.model_config.batch_size,
self.model_config.max_simple_sentence))
rewards = tf.unstack(rewards, axis=1)
weighted_probs_list = [
rewards[i] * decode_word_weight_list[i] *
-output.sample_logit_list[i]
for i in range(len(decode_word_weight_list))
]
total_size = tf.reduce_sum(decode_word_weight_list)
total_size += 1e-12
weighted_probs = tf.reduce_sum(
weighted_probs_list) / total_size
loss = weighted_probs
return loss
def teacherforce_critical_loss():
losses = []
for step in range(self.model_config.max_simple_sentence):
logit = output.decoder_logit_list[step]
greedy_target_unit = tf.stop_gradient(
tf.argmax(logit, axis=1))
if self.model_config.train_mode == 'teachercriticalv2':
sampled_target_unit, reward = tf.py_func(
self.metric.self_crititcal_reward_unitv2, [
sentence_idxs,
step,
greedy_target_unit,
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(
sentence_complex_input_placeholder,
axis=-1),
], [tf.int32, tf.float32],
stateful=False,
name='reward')
reward.set_shape((self.model_config.batch_size, ))
sampled_target_unit.set_shape(
(self.model_config.batch_size, ))
elif self.model_config.train_mode == 'teachercritical':
sampled_target_unit = tf.cast(
tf.squeeze(tf.multinomial(logit, 1), axis=1),
tf.int32)
reward = tf.py_func(
self.metric.self_crititcal_reward_unit, [
sentence_idxs,
step,
sampled_target_unit,
greedy_target_unit,
tf.stack(sentence_simple_input_placeholder,
axis=-1),
tf.stack(
sentence_complex_input_placeholder,
axis=-1),
tf.ones((1, 1)),
],
tf.float32,
stateful=False,
name='reward')
reward.set_shape((self.model_config.batch_size, ))
indices = tf.stack([
tf.range(0,
self.model_config.batch_size,
dtype=tf.int32),
tf.squeeze(sampled_target_unit)
],
axis=-1)
logit_unit = tf.gather_nd(tf.nn.softmax(logit, axis=1),
indices)
decode_word_weight = decode_word_weight_list[step]
losses.append(-logit_unit * reward *
decode_word_weight)
loss = tf.add_n(losses)
return loss
def teacherforce_loss():
if self.model_config.number_samples > 0:
loss_fn = tf.nn.sampled_softmax_loss
else:
loss_fn = None
loss = sequence_loss(
logits=tf.stack(output.decoder_logit_list, axis=1),
targets=gt_target,
weights=decode_word_weight,
# softmax_loss_function=loss_fn,
# w=w,
# b=b,
# decoder_outputs=decoder_outputs,
# number_samples=self.model_config.number_samples
)
return loss
if self.model_config.train_mode == 'dynamic_self-critical':
loss = self_critical_loss()
# loss = tf.cond(
# tf.greater(self.global_step, 50000),
# # tf.logical_and(tf.greater(self.global_step, 100000), tf.equal(tf.mod(self.global_step, 2), 0)),
# lambda : self_critical_loss(),
# lambda : teacherforce_loss())
elif self.model_config.train_mode == 'teachercritical' or self.model_config.train_mode == 'teachercriticalv2':
loss = tf.cond(tf.equal(tf.mod(self.global_step, 3),
0), lambda: teacherforce_loss(),
lambda: teacherforce_critical_loss())
# loss = teacherforce_critical_loss()
else:
loss = teacherforce_loss()
# if 'ruleattn' in self.model_config.external_loss:
# batch_pos = tf.range(
# self.model_config.batch_size * self.model_config.max_cand_rules) // self.model_config.max_cand_rules
# batch_pos = tf.reshape(
# batch_pos, [self.model_config.batch_size, self.model_config.max_cand_rules])
# batch_pos = tf.expand_dims(batch_pos, axis=2)
# ids = tf.stack(rule_pair_input_placeholder, axis=1)
# bias = 1.0 - tf.to_float(
# tf.logical_and(tf.equal(ids[:, :, 0], 0), tf.equal(ids[:, :, 1], 0)))
# ids = tf.concat([batch_pos, ids], axis=2)
# distrs = tf.stack(output.attn_distr_list, axis=1)
# ruleattn_loss = -tf.gather_nd(distrs, ids)*bias
# loss += ruleattn_loss
# self.pairs = tf.stack(rule_pair_input_placeholder, axis=1)
obj = {
'sentence_idxs':
sentence_idxs,
'sentence_simple_input_placeholder':
sentence_simple_input_placeholder,
'sentence_complex_input_placeholder':
sentence_complex_input_placeholder,
}
self.logits = output.decoder_logit_list
if 'rule' in self.model_config.memory:
obj['rule_id_input_placeholder'] = rule_id_input_placeholder
obj['rule_target_input_placeholder'] = rule_target_input_placeholder
obj['rule_pair_input_placeholder'] = rule_pair_input_placeholder
obj['mem_contexts'] = mem_contexts
obj['mem_outputs'] = mem_outputs
obj['mem_counter'] = mem_counter
return loss, obj
random.seed, torch.manual_seed, torch.cuda.manual_seed_all
]:
set_random_seed(opts.seed)
base_model = opts.backbone(pretrained=True)
if opts.l2norm:
model = L2NormEmbedding(
base_model,
feature_size=base_model.output_size,
embedding_size=opts.embedding_size,
).cuda()
else:
model = Embedding(
base_model,
feature_size=base_model.output_size,
embedding_size=opts.embedding_size,
).cuda()
if opts.load is not None:
model.load_state_dict(torch.load(opts.load))
print("Loaded Model from %s" % opts.load)
train_transform, test_transform = build_transform(base_model)
dataset_train = dataset.FashionInshop(opts.data,
split="train",
transform=train_transform)
dataset_query = dataset.FashionInshop(opts.data,
split="query",
transform=test_transform)
