def get_prob_dist(lst):
fdist = FreqDist(lst)
fdist_lst = fdist.most_common()
[vals, freqs] = unzip_lst(fdist_lst)
freq_total = sum(freqs)
probs = [freq / freq_total for freq in freqs]
return (vals, probs)
triple_lsts[1] = neutral_kept_lst_polite
triple_lsts[0] = triple_lsts[0] + neutral_popped_lst_polite
(neutral_kept_lst_rude, neutral_popped_lst_rude) = split_triple_lst(triple_lsts[1], bad_indices)
triple_lsts[1] = neutral_kept_lst_rude
triple_lsts[2] = triple_lsts[2] + neutral_popped_lst_rude
print([len(triple_lst) for triple_lst in triple_lsts])
if reorganize:
# Store reorganized utterances
for (filename, triple_lst) in zip(filenames[13:], triple_lsts):
dump_pickle(data_path + filename, triple_lst)
unzipped_triples = [unzip_lst(triple_lst) for triple_lst in triple_lsts]
[[polite_sources, polite_targets, _],
[neutral_sources, neutral_targets, _],
[rude_sources, rude_targets, _]] = unzipped_triples
sources_lst = [polite_sources, neutral_sources, rude_sources]
targets_lst = [polite_targets, neutral_targets, rude_targets]
labeled_sources_lst = [prepend(sources, label)
for (sources, label)
in zip(sources_lst, labels)]
# Comine the three parts to make train dataset
labeled_source_train = labeled_sources_lst[0] + labeled_sources_lst[1] + labeled_sources_lst[2]
labeled_target_train = polite_targets + neutral_targets + rude_targets
[labeled_source_train, labeled_target_train] = shuffle(labeled_source_train, labeled_target_train)
def build_classifier(inputs, seq_lengths, reuse):
max_seq_length = tf.reduce_max(seq_lengths)
keep_prob = tf.convert_to_tensor(1.0) # since we are doing inference only
# Get the mask of all valid tokens (Assuming that unk_token == pad_token == 0)
valid_mask = get_valid_mask(inputs)
# Embedding layer
with tf.variable_scope("embedding", reuse=reuse):
embedding_unk = tf.get_variable("embedding_unk",
shape=[1, embedding_size])
embedding_politeness_original = tf.get_variable(
"embedding_politeness_original",
shape=[shared_vocab_size_politeness, embedding_size])
embedding_politeness_new = tf.get_variable(
"embedding_politeness_new",
shape=[new_vocab_size_politeness, embedding_size])
embeddings = [
embedding_unk, embedding_politeness_original,
embedding_politeness_new
]
embedding = tf.concat(embeddings, axis=0)
embedded_inputs = tf.nn.embedding_lookup(embedding, inputs)
with tf.variable_scope("lstm", reuse=reuse):
cell_fw = lstm(embedding_size, hidden_size_classifier, keep_prob,
reuse)
cell_bw = lstm(embedding_size, hidden_size_classifier, keep_prob,
reuse)
(outputs, final_state) = tf.nn.bidirectional_dynamic_rnn(
cell_fw,
cell_bw,
embedded_inputs,
sequence_length=seq_lengths,
dtype=tf.float32,
swap_memory=True)
# H's shape: batch_size_per_gpu * max_seq_length * (2 * hidden_size_classifier)
H = tf.concat(outputs, axis=2)
# CNN + maxpooling layer
with tf.variable_scope("CNN_maxpooling", reuse=reuse):
H_expanded = tf.expand_dims(
H, axis=-1) # expand H to 4-D (add a chanel dim) for CNN
pooled_outputs = []
for (j, filter_size) in enumerate(filter_sizes):
with tf.variable_scope("filter_%d" %
j): # sub-scope inherits the reuse flag
# CNN layer
filter_shape = [
filter_size, 2 * hidden_size_classifier, 1, num_filters
]
W_conv = tf.get_variable(
"W_conv",
shape=filter_shape,
initializer=tf.contrib.layers.xavier_initializer_conv2d())
b_conv = tf.get_variable(
"b_conv",
shape=[num_filters],
initializer=tf.constant_initializer(0.1))
conv = tf.nn.conv2d(H_expanded,
W_conv,
strides=[1, 1, 1, 1],
padding="VALID")
output_conv = tf.nn.relu(tf.nn.bias_add(conv, b_conv))
# maxpooling layer
maxpooled_lst = [
] # maxpooled results for each example in the batch
for k in range(batch_size_per_gpu):
sequence_conv = output_conv[k, :seq_lengths[k], 0, :]
maxpooled = tf.reduce_max(sequence_conv, axis=0)
maxpooled_lst.append(maxpooled)
batch_maxpooled = tf.stack(maxpooled_lst, axis=0)
pooled_outputs.append(batch_maxpooled)
h_maxpool = tf.concat(pooled_outputs, axis=1)
h_maxpool_dropout = tf.nn.dropout(h_maxpool, keep_prob=keep_prob)
# output layer (fully connected)
with tf.variable_scope("output", reuse=reuse):
num_filters_total = num_filters * len(filter_sizes)
W_out = tf.get_variable(
"W_out",
shape=[num_filters_total, num_classes],
initializer=tf.contrib.layers.xavier_initializer())
b_out = tf.get_variable("b_out",
shape=[num_classes],
initializer=tf.constant_initializer(0.1))
logits = tf.nn.xw_plus_b(h_maxpool_dropout,
weights=W_out,
biases=b_out)
scores = tf.nn.softmax(logits, axis=-1)
politeness_scores = scores[:, 1]
optimizer = tf.train.AdamOptimizer(0.001)
if credit_assignment:
# Note: currently the tf.gather has to appear after computing gradients,
# otherwise tf.gradients returns None!
credit_weights_lst = []
for j in range(batch_size_per_gpu):
# embedding_grads = tf.convert_to_tensor(
# tf.gradients(politeness_scores[j], embedding)[0])
[embedding_grads, _] = unzip_lst(
optimizer.compute_gradients(politeness_scores[j],
var_list=embeddings))
# Display warning message if some element in embedding_grads is "None"
for grad in embedding_grads:
if grad is None:
print(
"Warning: one of the credit assignment embedding grads is None: ",
grad)
embedding_grads_concat = tf.concat(embedding_grads, axis=0)
gathered_embedding_grads = tf.gather(embedding_grads_concat,
inputs[j, :])
normed_embedding_grads = tf.norm(gathered_embedding_grads, axis=1)
credit_weights = softmax_with_mask(normed_embedding_grads,
valid_mask[j, :])
credit_weights_lst.append(credit_weights)
stacked_credit_weigths = tf.stack(credit_weights_lst, axis=0)
else:
stacked_credit_weigths = tf.zeros_like(inputs)
return (politeness_scores, stacked_credit_weigths)
if infer_only:
[source_test,
target_test] = zip_remove_duplicates_unzip([source_test, target_test])
else:
[source_train,
target_train] = zip_remove_duplicates_unzip([source_train, target_train])
print(len(source_train))
# Also eliminate empty target/source, and list of lists
no_empty = [
[source, target] for (source, target) in zip(source_train, target_train)
if (source != [] and target != [] and not isinstance(source[0], list)
and not isinstance(target[0], list))
]
[source_train, target_train] = unzip_lst(no_empty)
print(len(source_train))
if pretrain:
source_threshold = 32
else:
source_threshold = 32 * 2
zipped_lst = [(source, target)
for (source, target) in zip(source_train, target_train)
if (len(source) <= source_threshold and len(source) >= 5
and len(target) <= 32)]
# and source.count(0) == 0
# and target.count(0) == 0
print(len(zipped_lst))
def run_vhred(model, sess, mode, epoch):
training_flag = True if mode == "train" else False
norm_dialogues = norm_data_dict[mode]
adv_dialogues = adv_data_dict[mode]
generator = DataGenerator(norm_dialogues,
adv_dialogues=adv_dialogues,
feed_both_examples=feed_both_examples,
is_training=training_flag,
batch_size=batch_size,
max_dialogue_length=max_dialogue_length)
batch = generator.batch_generator()
print("Initialized data generator.")
responses = []
total_loss = 0.0
adv_total_loss = 0.0
total_num_tokens = 0.0
batch_counter = 0
if mode != "train":
source_lst = []
target_lst = []
dialogue_indices_lst = []
start_turn_indices_lst = []
if use_max_margin:
avg_margin = 0.0
while True:
next_batch = next(batch)
if next_batch is None:
break
# if it's os, we always set start_turn_indices to 0
(dialogue_indices, start_turn_indices, examples,
turn_lengths_lst) = next_batch
feed_dict_seqs = {
model.dialogue: examples,
model.turn_length: turn_lengths_lst,
model.start_turn_index: start_turn_indices,
model.start_tokens: [start_token] * batch_size
}
if mode == "train":
if use_max_margin:
fetches = [
model.batch_total_loss,
model.batch_num_tokens,
model.apply_gradients_op,
model.avg_margin_loss, # testing
model.global_step
]
else:
fetches = [
model.batch_total_loss, model.batch_num_tokens,
model.apply_gradients_op, model.global_step
]
feed_dict = {
model.keep_prob: 1 - dropout_rate,
model.is_training: training_flag
}
result = sess.run(fetches,
feed_dict={
**feed_dict_seqs,
**feed_dict
})
if use_max_margin:
avg_margin = (avg_margin * batch_counter +
result[-2]) / (batch_counter + 1)
print(
"Avg margin (this should be getting smaller (or getting larger in abs. value) over time):",
avg_margin)
if feed_both_examples:
(loss, adv_loss) = result[0]
else:
loss = result[0]
average_log_perplexity = loss / result[1]
total_loss += loss
total_num_tokens += result[1]
print("Epoch (%s) %d, Batch %d, Global step %d:" %
(mode, epoch, batch_counter, result[-1]))
print("Perplexity: %.2f" % exp(average_log_perplexity))
print("Perplexity so far:", exp(total_loss / total_num_tokens))
if feed_both_examples:
adv_average_log_perplexity = adv_loss / result[1]
adv_total_loss += adv_loss
print("Adv-perplexity: %.2f" % exp(adv_average_log_perplexity))
print("Adv-perplexity so far:",
exp(adv_total_loss / total_num_tokens))
else:
(source, target) = get_source_and_target(examples)
source_lst.extend(source)
target_lst.extend(target)
dialogue_indices_lst.extend(dialogue_indices)
start_turn_indices_lst.extend(start_turn_indices)
feed_dict = {
model.keep_prob: 1.0,
model.is_training: training_flag
}
(ids, lengths) = sess.run(
[model.batch_sample_ids_beam, model.batch_final_lengths_beam],
feed_dict={
**feed_dict_seqs,
**feed_dict
})
batch_responses = [[
index for index in response[:length]
] for (response, length) in zip(ids.tolist(), lengths.tolist())]
responses.extend(batch_responses)
print("Finished testing batch %d" % batch_counter)
batch_counter += 1
if mode == "train":
epoch_perplexity = total_loss / total_num_tokens
print("Epoch (%s) %d average perplexity: %.2f" %
(mode, epoch, exp(epoch_perplexity)))
if force_store_point == "":
store_ckpt = os.path.join(ckpt_path, f"{model_extra_str}_{epoch}")
else:
store_ckpt = force_store_point
saver_seq2seq.save(sess, store_ckpt)
print(f"Checkpoint saved for epoch {epoch}.")
else:
zipped = zip_lsts([
dialogue_indices_lst, start_turn_indices_lst, source_lst,
target_lst, responses
])
zipped.sort(key=lambda x: x[:2]
) # sort on dialogue indices & start_turn_indices
zipped_responses = zip_lsts(unzip_lst(zipped)[2:])
return zipped_responses
def run_seq2seq(sess, mode, epoch, feed_score=1.0):
"""see if we need to append end_token"""
is_training = (mode == "train")
if is_training:
(source_lst, target_lst, score_lst) = unzip_lst(LFT_examples)
else:
(source_lst, target_lst) = data_dict[mode]
score_lst = [feed_score] * len(source_lst)
# source_lst = source_lst[:batch_size * 2]
# target_lst = target_lst[:batch_size * 2]
# score_lst = score_lst[:batch_size * 2]
num_examples = len(source_lst)
assert num_examples >= batch_size
num_batches = num_examples // batch_size
keep_prob = (1 - dropout_rate) if is_training else 1.0
start_tokens = [start_token] * batch_size
total_loss = 0.0
num_tokens = 0
zipped_lst = []
for i in range(num_batches):
start = i * batch_size
end = start + batch_size
sources = source_lst[start:end]
source_lengths = list(map(len, sources))
targets = target_lst[start:end]
target_lengths = list(map(len, targets))
scores = score_lst[start:end]
feed_dict = {
model.source: pad(sources, source_lengths),
model.source_length: source_lengths,
model.target: pad(targets, target_lengths),
model.target_length: target_lengths,
model.start_tokens: start_tokens,
model.keep_prob: keep_prob,
model.is_training: is_training,
model.score: scores}
if is_training:
fetches = [model.batch_total_loss, model.batch_num_tokens, model.apply_gradients_op]
else:
fetches = [model.batch_sample_ids_beam, model.batch_final_lengths_beam]
result = sess.run(fetches, feed_dict=feed_dict)
if is_training:
total_loss += result[0]
num_tokens += result[1]
print("Epoch (%s) %d Batch %d perplexity: %.2f" %
(mode, epoch, i, exp(result[0] / result[1])))
print("Perplexity so far:", exp(total_loss / num_tokens))
else:
print("Finished testing batch %d" % i)
responses = [response[:length]
for (response, length)
in zip(result[0].tolist(), result[1].tolist())]
zipped = zip_lsts([sources, targets, responses])
zipped_lst.extend(zipped)
if is_training:
print("Epoch (%s) %d average perplexity: %.2f" %
(mode, epoch, exp(total_loss / num_tokens)))
if not get_PPL:
saver_seq2seq.save(sess, "%sseq2seq_RL%s_%d" % (ckpt_path, extra_str, epoch))
print("Checkpoint saved for epoch %d." % epoch)
return zipped_lst
def zip_remove_duplicates_unzip(lsts):
zipped = zip_lsts(lsts)
zipped_without_duplicates = remove_duplicates(zipped)
unzipped = unzip_lst(zipped_without_duplicates)
return unzipped
mode = "valid"
score_range = [1.0]
zipped = run_seq2seq(sess, mode, i + start_epoch, feed_score=score_range[0])
if infer_only and not get_PPL and (i + start_epoch - 1) % 5 == 0: # for getting perplexity of test data, use train branch
print("Inferring on test set...")
mode = "test"
responses_lst = []
source_lst = []
target_lst = []
score_range = list(np.arange(0.0, 1.1, 0.5))
for score in score_range:
zipped_responses = run_seq2seq(
sess, mode, i + start_epoch, feed_score=score)
(source_lst, target_lst, responses) = unzip_lst(zipped_responses)
responses_lst.append(responses)
num_responses = len(responses_lst[0])
zipped = zip_lsts([source_lst, target_lst] + responses_lst)
flattened = [decode2string(index2token, sent, end_token=end_token, remove_END_TOKEN=True)
for tp in zipped for sent in tp]
# now we mark sentences that are generated by our model
num_lines = len(score_range) + 2
marked_G = [("G: " + sent)
if k % num_lines == 1 else sent
for (k, sent) in enumerate(flattened)]
marked_M = [("M: " + sent)
shared_vocab_size_politeness = len(shared_vocab_politeness)
shared_vocab_size_movie = len(shared_vocab_movie)
source_train = data[5] + data[7]
target_train = data[6] + data[8]
[source_train,
target_train] = zip_remove_duplicates_unzip([source_train, target_train])
assert len(source_train) == len(target_train)
source_test = data[9]
target_test = data[10]
assert len(source_test) == len(target_test)
embedding_word2vec_politeness = data[11]
embedding_word2vec_movie = data[12]
# Load all the polite utterances
polite_lst = remove_duplicates(
unzip_lst(load_pickle("../data/polite_movie_target.pkl"))[0])
print("Loaded %d polite examples!" % len(polite_lst))
shared_vocab_size_politeness = len(shared_vocab_politeness)
shared_vocab_size_movie = len(shared_vocab_movie)
special_tokens = ["UNK_TOKEN", "START_TOKEN", "END_TOKEN"]
vocab_size = len(vocab)
# Index vocabulary
index2token = {i: token for (i, token) in enumerate(vocab)}
token2index = {token: i for (i, token) in enumerate(vocab)}
[unk_token, start_token,
end_token] = [token2index[token] for token in special_tokens]
