def _get_persona_pool(self, opt, remove_duplicate=True):
print("[loading persona pool from convai2 training data]")
# Get episodes from training dataset
datapath = make_path(opt, 'train.txt')
episodes = []
eps = []
with open(datapath) as read:
for line in read:
msg = str_to_msg(line.rstrip('\n'))
if msg:
# self.num_exs += 1
eps.append(msg)
if msg.get('episode_done', False):
episodes.append(eps)
eps = []
if len(eps) > 0:
# add last episode
eps[-1].force_set('episode_done', True)
episodes.append(eps)
# Extract personas from episodes
persona_set = OrderedSet()
for episode in episodes:
first_turn = episode[0]
text = first_turn['text']
persona, _ = _split_persona_and_context(text)
persona_set.add(persona)
# Remove duplicate
if remove_duplicate:
train_persona_fname = os.path.join(__PATH__,
'train_persona_map.pkl')
with open(train_persona_fname, 'rb') as fp:
_train_personas = pickle.load(fp)
train_personas = []
for personas in _train_personas.values():
longest_idx = 0
longest_length = -1
for idx, persona in enumerate(personas):
if len(persona) > longest_length:
longest_idx = idx
longest_length = len(persona)
selected_persona = map(lambda x: f"your persona: {x}.",
personas[longest_idx])
selected_persona = '\n'.join(selected_persona)
train_personas.append(selected_persona)
persona_set = OrderedSet()
for train_persona in train_personas:
persona_set.add(train_persona)
all_personas = []
persona_to_idx = {}
for i, persona in enumerate(persona_set):
all_personas.append(persona)
persona_to_idx[persona] = i
print(f"Total {len(all_personas)} personas in dataset")
return all_personas, persona_to_idx
def _setup_data(self, path):
logging.info(f"Loading ParlAI text data: {path}")
self.episodes = []
self.num_exs = 0
eps = []
with PathManager.open(path, newline='\n', encoding='utf-8') as read:
for line_no, line in enumerate(read, 1):
msg = str_to_msg(line.rstrip('\n'))
if msg and 'eval_labels' in msg:
raise ValueError(
f"It looks like you've written eval_labels as a key in your "
f"data file. This is not appropriate; labels will be converted "
f"for you automatically. This is happening on Line {line_no} "
f"in {path}. The line is:\n\t{line}")
if msg and 'text' not in msg:
raise ValueError(
f'ParlaiDialogTeacher requires a "text" field in every '
f'entry, but one is missing in Line {line_no} in {path}. '
f'The line is:\n\t{line}')
if msg and 'labels' not in msg:
raise ValueError(
f'ParlaiDialogTeacher requires a "labels" field in every '
f'entry, but one is missing in Line {line_no} in {path}. '
f'The line is:\n\t{line}')
if (self.opt['bad_speaker_to_eval'] != 'all'
and self.opt['bad_speaker_to_eval'] !=
msg['speaker_to_eval']):
continue
if (self.opt['bad_safety_mix'] != 'all'
and SAFETY_DICT[self.opt['bad_safety_mix']] !=
msg['labels'][0]):
continue
if self.opt['bad_num_turns'] > 0:
dialog = msg['text'].split('\n')
msg.force_set(
'text', '\n'.join(dialog[-self.opt['bad_num_turns']:]))
if msg:
self.num_exs += 1
eps.append(msg)
if msg.get('episode_done', False):
self.episodes.append(eps)
eps = []
if len(eps) > 0:
# add last episode
eps[-1].force_set('episode_done', True)
self.episodes.append(eps)
if len(self.episodes) == 1 and line_no > 100:
logging.error(
f'The data in {path} looks like one very long episode. If this '
f'is intentional, you may ignore this, but you MAY have a bug in '
f'your data.')
def _get_context_pool(self, opt):
print("[loading history pool from convai2 training data]")
datapath = make_path(opt, 'train.txt')
episodes = []
eps = []
with open(datapath) as read:
for line in read:
msg = str_to_msg(line.rstrip('\n'))
if msg:
eps.append(msg)
if msg.get('episode_done', False):
episodes.append(eps)
eps = []
if len(eps) > 0:
# add last episode
eps[-1].force_set('episode_done', True)
episodes.append(eps)
context_pool = defaultdict(list)
for ep in episodes:
context_pool[len(ep)].append([turn['labels'][0] for turn in ep])
return dict(context_pool)
def _setup_data(self, path, datatype):
# random.seed(self.opt['random_seed']) # Set this for pick same distractor persona
random.seed(46) # Set this for pick same distractor persona
# Data loading with script of ParlAIDialogTeacher
print(f"[Loading ParlAI text data: {path}]")
# Read data from ConvAI2
convai2_datapath = make_path(self.opt, f'{datatype}_both_original.txt')
convai2_episodes = self._load_convai2_data(convai2_datapath)
if self.opt['eval_type'] == 'convai2':
self.episodes = []
self.num_exs = 0
eps = []
with open(path) as read:
for line in read:
msg = str_to_msg(line.rstrip('\n'))
if msg:
self.num_exs += 1
eps.append(msg)
if msg.get('episode_done', False):
self.episodes.append(eps)
eps = []
if len(eps) > 0:
# add last episode
eps[-1].force_set('episode_done', True)
self.episodes.append(eps)
# Add label candidates and partner's persona
for episode_idx, episode in enumerate(self.episodes):
for turn_idx, turn in enumerate(episode):
convai2_turn = convai2_episodes[episode_idx][turn_idx]
convai2_text = convai2_turn[0]
label_candidates = convai2_turn[3]
turn['label_candidates'] = label_candidates
if turn_idx == 0:
my_persona, partner_persona, _ = _split_personas_and_context(
convai2_text)
turn['partner_persona'] = partner_persona
turn['my_persona'] = my_persona
else:
turn['partner_persona'] = episode[0]['partner_persona']
turn['my_persona'] = episode[0]['my_persona']
elif self.opt['eval_type'] == 'dnli':
self.episodes = []
self.num_exs = 0
for eval_set in ['attributes', 'havenot', 'likedislike']:
datapath = make_path(self.opt, f'{datatype}_{eval_set}.jsonl')
with open(datapath, 'r') as fp:
for line in fp:
msg = json.loads(line)
msg['eval_set'] = eval_set
msg['episode_done'] = True
# Make 'text'
persona_lines = [
f'your persona: {x[:-2]}.' for x in msg['persona']
]
utts = msg['prefix']
p1_token, p2_token = TorchAgent.P1_TOKEN, TorchAgent.P2_TOKEN
lines = persona_lines
# Identify the dialogue lines. It's assumed that p1 goes first.
for i, utt in enumerate(utts):
if i % 2 == 0:
lines.append(f'{p1_token} {utt}')
else:
lines.append(f'{p2_token} {utt}')
text = '\n'.join(lines)
msg['text'] = text
# Make 'label_candidates'
cands = msg['candidates']
msg['label_candidates'] = cands['label'] + cands['neg'][:10] \
+ cands['similar'][:10] + cands['rand'][:10]
# Remove unused attributes
del msg['persona']
del msg['prefix']
del msg['triple']
del msg['relevant_persona_sentence']
del msg['candidates']
self.episodes.append([msg])
self.num_exs += 1
# Get dialogue history pool
context_pool = self._get_context_pool(self.opt)
# Add distractor history
if self.opt['world_cardinality'] > 0:
for episode in self.episodes:
gt_persona, first_context = _split_persona_and_context(
episode[0]['text'], self.opt['eval_type'])
# Select distractor history
if self.opt['eval_type'] == 'convai2':
num_turn = len(episode)
else:
dialogue = first_context.split('\n')
num_turn = math.ceil(len(dialogue) / 2)
if num_turn < min(context_pool.keys()):
# orginal_num_turn = num_turn
num_turn = min(context_pool.keys())
context_indices = list(range(len(context_pool[num_turn])))
distractor_c_indices = random.sample(
context_indices, self.opt['world_cardinality'] - 1)
distractor_contexts = itemgetter(*distractor_c_indices)(
context_pool[num_turn])
# Make it to 'distractor_text'
if self.opt['eval_type'] == 'convai2':
for turn_idx, turn in enumerate(episode):
turn['distractor_text'] = turn['labels'] + [
c[turn_idx] for c in distractor_contexts
]
if turn_idx == 0:
turn['my_context'] = turn['labels']
else:
turn['my_context'] = episode[
turn_idx - 1]['my_context'] + turn['labels']
else:
# DNLI
distractor_text = [episode[0]['text']]
for c in distractor_contexts:
copied_dialogue = copy.deepcopy(dialogue)
for turn_idx, utterance in enumerate(copied_dialogue):
if turn_idx % 2 == 1:
copied_dialogue[turn_idx] = p2_token + c[
turn_idx // 2]
distractor_context = '\n'.join([gt_persona] +
copied_dialogue)
distractor_text.append(distractor_context)
episode[0]['distractor_text'] = distractor_text
def _setup_data(self, path, datatype):
random.seed(46)
# Data loading with script of ParlAIDialogTeacher
print(f"[Loading ParlAI text data: {path}]")
# Read data from ConvAI2
convai2_datapath = make_path(self.opt, f'{datatype}_both_original.txt')
convai2_episodes = self._load_convai2_data(convai2_datapath)
# Get persona pool
all_personas, persona_to_idx = self._get_persona_pool(self.opt)
sorted_personas = self._get_sorted_persona_pool(datatype)
if self.opt['eval_type'] == 'convai2':
self.episodes = []
self.num_exs = 0
eps = []
with open(path) as read:
for line in read:
msg = str_to_msg(line.rstrip('\n'))
if msg:
self.num_exs += 1
eps.append(msg)
if msg.get('episode_done', False):
self.episodes.append(eps)
eps = []
if len(eps) > 0:
# add last episode
eps[-1].force_set('episode_done', True)
self.episodes.append(eps)
# Add label candidates and partner's persona
for episode_idx, episode in enumerate(self.episodes):
for turn_idx, turn in enumerate(episode):
convai2_turn = convai2_episodes[episode_idx][turn_idx]
convai2_text = convai2_turn[0]
label_candidates = convai2_turn[3]
turn['label_candidates'] = label_candidates
if turn_idx == 0:
my_persona, partner_persona, _ = _split_personas_and_context(
convai2_text)
turn['partner_persona'] = partner_persona
turn['my_persona'] = my_persona
else:
turn['partner_persona'] = episode[0]['partner_persona']
turn['my_persona'] = episode[0]['my_persona']
elif self.opt['eval_type'] == 'dnli':
self.episodes = []
self.num_exs = 0
for eval_set in ['attributes', 'havenot', 'likedislike']:
datapath = make_path(self.opt, f'{datatype}_{eval_set}.jsonl')
with open(datapath, 'r') as fp:
for line in fp:
msg = json.loads(line)
msg['eval_set'] = eval_set
msg['episode_done'] = True
# Make 'text'
persona_lines = [
f'your persona: {x[:-2]}.' for x in msg['persona']
]
utts = msg['prefix']
p1_token, p2_token = TorchAgent.P1_TOKEN, TorchAgent.P2_TOKEN
lines = persona_lines
# Identify the dialogue lines. It's assumed that p1 goes first.
for i, utt in enumerate(utts):
if i % 2 == 0:
lines.append(f'{p1_token} {utt}')
else:
lines.append(f'{p2_token} {utt}')
text = '\n'.join(lines)
msg['text'] = text
# Make 'label_candidates'
cands = msg['candidates']
msg['label_candidates'] = cands['label'] + cands['neg'][:10] \
+ cands['similar'][:10] + cands['rand'][:10]
# Remove unused attributes
del msg['persona']
del msg['prefix']
del msg['triple']
del msg['relevant_persona_sentence']
del msg['candidates']
self.episodes.append([msg])
self.num_exs += 1
# Add distractor personas
if self.opt['world_cardinality'] > 0:
num_all_personas = len(all_personas)
persona_indices = list(range(num_all_personas))
world_cardinality = self.opt['world_cardinality']
for episode in self.episodes:
gt_persona, first_context = _split_persona_and_context(
episode[0]['text'], self.opt['eval_type'])
gt_persona_idx = persona_to_idx.get(gt_persona, -1)
# Choose random distractor personas
distractor_indices = random.sample(persona_indices,
world_cardinality - 1)
while gt_persona_idx in distractor_indices:
# Resample if gt_persona is sampled
distractor_indices = random.sample(persona_indices,
world_cardinality - 1)
distractor_personas = itemgetter(
*distractor_indices)(all_personas)
distractor_personas = list(distractor_personas)
# Make it to 'distractor_text'
for turn_idx, turn in enumerate(episode):
if turn_idx == 0:
turn['distractor_text'] = [
'\n'.join([persona, first_context])
for persona in [gt_persona] + distractor_personas
]
else:
turn['distractor_text'] = [turn['text']
] * world_cardinality