def verify(opt, printargs=None, print_parser=None):
if opt['datatype'] == 'train':
print("[ note: changing datatype from train to train:ordered ]")
opt['datatype'] = 'train:ordered'
# create repeat label agent and assign it to the specified task
agent = RepeatLabelAgent(opt)
world = create_task(opt, agent)
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
counts = {}
counts['missing_text'] = 0
counts['missing_labels'] = 0
counts['missing_label_candidates'] = 0
counts['empty_string_label_candidates'] = 0
counts['label_candidates_with_missing_label'] = 0
counts['did_not_return_message'] = 0
# Show some example dialogs.
while not world.epoch_done():
world.parley()
act = world.acts[0]
if not isinstance(act, Message):
counts['did_not_return_message'] += 1
if 'text' not in act and 'image' not in act:
warn("warning: missing text field:\n", act, opt)
counts['missing_text'] += 1
if 'labels' not in act and 'eval_labels' not in act:
warn("warning: missing labels/eval_labels field:\n", act, opt)
counts['missing_labels'] += 1
else:
if 'label_candidates' not in act:
counts['missing_label_candidates'] += 1
else:
labels = act.get('labels', act.get('eval_labels'))
is_label_cand = {}
for l in labels:
is_label_cand[l] = False
for c in act['label_candidates']:
if c == '':
warn("warning: empty string label_candidate:\n", act,
opt)
counts['empty_string_label_candidates'] += 1
if c in is_label_cand:
if is_label_cand[c] is True:
warn(
"warning: label mentioned twice in candidate_labels:\n",
act,
opt,
)
is_label_cand[c] = True
for _, has in is_label_cand.items():
if has is False:
warn("warning: label missing in candidate_labels:\n",
act, opt)
counts['label_candidates_with_missing_label'] += 1
if log_time.time() > log_every_n_secs:
text, log = report(world, counts, log_time)
if print_parser:
print(text)
try:
# print dataset size if available
print('[ loaded {} episodes with a total of {} examples ]'.format(
world.num_episodes(), world.num_examples()))
except Exception:
pass
return report(world, counts, log_time)
def verify(opt, printargs=None, print_parser=None):
if opt['datatype'] == 'train':
print("[ note: changing datatype from train to train:ordered ]")
opt['datatype'] = 'train:ordered'
# create repeat label agent and assign it to the specified task
agent = RepeatLabelAgent(opt)
world = create_task(opt, agent)
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
dictionary = DictionaryAgent(opt)
ignore_tokens = opt.get('ignore_tokens').split(',')
counts = {}
for t in {'input', 'labels', 'both'}:
counts['tokens_in_' + t] = 0
counts['utterances_in_' + t] = 0
counts['avg_utterance_length_in_' + t] = 0
counts['unique_tokens_in_' + t] = 0
counts['unique_utterances_in_' + t] = 0
# for counting the stats..
counts['token_dict_' + t] = {}
counts['utterance_dict_' + t] = {}
def tokenize(txt):
return dictionary.tokenize(txt)
def keep_token(t):
for s in ignore_tokens:
if s != '' and s in t:
return False
return True
# max number of examples to evaluate
max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf')
cnt = 0
# Show some example dialogs.
while not world.epoch_done() and cnt < max_cnt:
cnt += opt.get('batchsize', 1)
world.parley()
act = world.get_acts()[opt.get('agent')]
for itype in {'input', 'labels'}:
if itype == 'input':
if opt.get('new_line_new_utt'):
txts = act.get('text').split('\n')
else:
txts = [act.get('text')]
else:
txts = act.get('labels', act.get('eval_labels', ['']))
for txt in txts:
tokens = tokenize(txt)
retxt = []
for t in tokens:
if keep_token(t):
retxt.append(t)
counts['tokens_in_' + itype] += len(retxt)
counts['tokens_in_' + 'both'] += len(retxt)
counts['utterances_in_' + itype] += 1
counts['utterances_in_' + 'both'] += 1
counts['avg_utterance_length_in_' + itype] = (
counts['tokens_in_' + itype] / counts['utterances_in_' + itype]
)
counts['avg_utterance_length_in_' + 'both'] = (
counts['tokens_in_' + 'both'] / counts['utterances_in_' + 'both']
)
for t in retxt:
if t not in counts['token_dict_' + itype]:
counts['unique_tokens_in_' + itype] += 1
counts['token_dict_' + itype][t] = True
if t not in counts['token_dict_' + 'both']:
counts['unique_tokens_in_' + 'both'] += 1
counts['token_dict_' + 'both'][t] = True
retxt = ' '.join(retxt)
if retxt not in counts['utterance_dict_' + itype]:
counts['unique_utterances_in_' + itype] += 1
counts['utterance_dict_' + itype][retxt] = True
if retxt not in counts['utterance_dict_' + 'both']:
counts['unique_utterances_in_' + 'both'] += 1
counts['utterance_dict_' + 'both'][retxt] = True
if log_time.time() > log_every_n_secs:
text, log = report(world, counts, log_time)
if print_parser:
print(text)
try:
# print dataset size if available
print(
'[ loaded {} episodes with a total of {} examples ]'.format(
world.num_episodes(), world.num_examples()
)
)
except Exception:
pass
return report(world, counts, log_time)
def build_dict(opt, skip_if_built=False):
if isinstance(opt, ParlaiParser):
logging.error('Should be passed opt not Parser')
opt = opt.parse_args()
if not opt.get('dict_file'):
logging.error(
'Tried to build dictionary but `--dict-file` is not set. Set '
'this param so the dictionary can be saved.'
)
return
if skip_if_built and PathManager.exists(opt['dict_file']):
# Dictionary already built, skip all loading or setup
logging.debug("dictionary already built.")
return None
if opt.get('dict_class'):
# Custom dictionary class
dictionary = str2class(opt['dict_class'])(opt)
else:
# Default dictionary class
dictionary = DictionaryAgent(opt)
if PathManager.exists(opt['dict_file']) or (
hasattr(dictionary, 'is_prebuilt') and dictionary.is_prebuilt()
):
# Dictionary already built, return loaded dictionary agent
logging.debug("dictionary already built.")
return dictionary
if is_distributed():
raise ValueError('Dictionaries should be pre-built before distributed train.')
ordered_opt = copy.deepcopy(opt)
cnt = 0
# we use train set to build dictionary
ordered_opt['batchsize'] = 1
# Set this to none so that image features are not calculated when Teacher is
# instantiated while building the dict
ordered_opt['image_mode'] = 'no_image_model'
ordered_opt.log()
datatypes = ['train:ordered:stream']
if opt.get('dict_include_valid'):
datatypes.append('valid:stream')
if opt.get('dict_include_test'):
datatypes.append('test:stream')
cnt = 0
for dt in datatypes:
ordered_opt['datatype'] = dt
world_dict = create_task(ordered_opt, dictionary)
# pass examples to dictionary
log_time = TimeLogger()
total = world_dict.num_examples()
if opt['dict_maxexs'] >= 0:
total = min(total, opt['dict_maxexs'])
log_every_n_secs = opt.get('log_every_n_secs', None)
if log_every_n_secs:
pbar = tqdm.tqdm(
total=total, desc='Building dictionary', unit='ex', unit_scale=True
)
else:
pbar = None
while not world_dict.epoch_done():
cnt += 1
if cnt > opt['dict_maxexs'] and opt['dict_maxexs'] >= 0:
logging.info('Processed {} exs, moving on.'.format(opt['dict_maxexs']))
# don't wait too long...
break
world_dict.parley()
if pbar:
pbar.update(1)
if pbar:
pbar.close()
dictionary.save(opt['dict_file'], sort=True)
logging.info(
f'dictionary built with {len(dictionary)} tokens '
f'in {log_time.total_time():.1f}s'
)
return dictionary
def detect(opt, printargs=None, print_parser=None):
"""
Checks a task for offensive language.
"""
if print_parser is not None:
if print_parser is True and isinstance(opt, ParlaiParser):
print_parser = opt
elif print_parser is False:
print_parser = None
random.seed(42)
# Create model and assign it to the specified task
agent = create_agent(opt, requireModelExists=True)
world = create_task(opt, agent)
if opt['safety'] == 'string_matcher' or opt['safety'] == 'all':
offensive_string_matcher = OffensiveStringMatcher()
if opt['safety'] == 'classifier' or opt['safety'] == 'all':
offensive_classifier = OffensiveLanguageClassifier()
if print_parser:
# Show arguments after loading model
print_parser.opt = agent.opt
print_parser.print_args()
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
stats = {
'bad_words': [],
'bad_words_cnt': 0,
'string_offensive': 0,
'classifier_offensive': 0,
'total_offensive': 0,
'total': 0,
}
def report(world, stats):
report = world.report()
log = {
'word_offenses':
stats['bad_words_cnt'],
'classifier_offenses%':
100 * (stats['classifier_offensive'] / stats['total']),
'string_offenses%':
100 * (stats['string_offensive'] / stats['total']),
'total_offenses%':
100 * (stats['total_offensive'] / stats['total']),
}
text, log = log_time.log(report['exs'], world.num_examples(), log)
logging.info(text)
def classify(text, stats):
offensive = False
stats['total'] += 1
if opt['safety'] == 'string_matcher' or opt['safety'] == 'all':
bad_words = offensive_string_matcher.contains_offensive_language(
text)
if bad_words:
stats['string_offensive'] += 1
offensive = True
stats['bad_words'].append(bad_words)
if opt['safety'] == 'classifier' or opt['safety'] == 'all':
if text in offensive_classifier:
stats['classifier_offensive'] += 1
offensive = True
if offensive:
stats['total_offensive'] += 1
while not world.epoch_done():
world.parley()
stats['bad_words'] = []
for a in world.acts:
text = a.get('text', '')
classify(text, stats)
labels = a.get('labels', a.get('eval_labels', ''))
for l in labels:
classify(l, stats)
if len(stats['bad_words']) > 0 and opt['display_examples']:
logging.info(world.display())
logging.info("Offensive words detected: {}".format(', '.join(
stats['bad_words'])))
stats['bad_words_cnt'] += len(stats['bad_words'])
if log_time.time() > log_every_n_secs:
report(world, stats)
if world.epoch_done():
logging.info("epoch done")
report(world, stats)
return world.report()
def learn_arora(opt):
"""
Go through ConvAI2 data and collect word counts, thus compute the unigram
probability distribution. Use those probs to compute weighted sentence embeddings
for all utterances, thus compute first principal component.
Save all info to arora.pkl file.
"""
arora_file = os.path.join(opt['datapath'], 'controllable_dialogue',
'arora.pkl')
opt['task'] = 'fromfile:parlaiformat'
opt['log_every_n_secs'] = 2
print('Getting word counts from ConvAI2 train set...')
opt['datatype'] = 'train:ordered'
opt['fromfile_datapath'] = os.path.join(opt['datapath'],
'controllable_dialogue',
'ConvAI2_parlaiformat',
'train.txt')
# Do include inputs because ConvAI2 train set reverses every convo:
word_counter_train, total_count_train, all_utts_train = get_word_counts(
opt, count_inputs=False)
print('Getting word counts from ConvAI2 val set...')
opt['datatype'] = 'valid'
opt['fromfile_datapath'] = os.path.join(opt['datapath'],
'controllable_dialogue',
'ConvAI2_parlaiformat',
'valid.txt')
# Don't include inputs because ConvAI2 val set doesn't reverses convos:
word_counter_valid, total_count_valid, all_utts_valid = get_word_counts(
opt, count_inputs=True)
# Merge word counts
word_counter = word_counter_train
for word, count in word_counter_valid.items():
word_counter[word] += count
total_count = total_count_train + total_count_valid
# Merge all_utts
all_utts = all_utts_train + all_utts_valid
# Compute unigram prob for every word
print("Computing unigram probs for all words...")
word2prob = {w: c / total_count for w, c in word_counter.items()}
# Settings for sentence embedder
arora_a = 0.0001
glove_name = '840B'
glove_dim = 300
glove_cache = modelzoo_path(opt['datapath'], 'models:glove_vectors')
# Embed every sentence, without removing first singular value
print('Embedding all sentences...')
sent_embedder = SentenceEmbedder(
word2prob,
arora_a,
glove_name,
glove_dim,
first_sv=None,
glove_cache=glove_cache,
)
utt_embs = []
log_timer = TimeLogger()
for n, utt in enumerate(all_utts):
utt_emb = sent_embedder.embed_sent(utt.split(), rem_first_sv=False)
utt_embs.append(utt_emb)
if log_timer.time() > opt['log_every_n_secs']:
text, _log = log_timer.log(n, len(all_utts))
print(text)
# Use SVD to calculate singular vector
# https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.linalg.svd.html
print('Calculating SVD...')
utt_embs = np.stack(utt_embs, axis=0) # shape (num_embs, glove_dim)
U, s, V = np.linalg.svd(utt_embs, full_matrices=False)
first_sv = V[0, :] # first row of V. shape (glove_dim)
# Remove singular vector from all embs to get complete Arora-style sent embs
print('Removing singular vec from all sentence embeddings...')
utt_embs_adj = [
remove_first_sv(torch.Tensor(emb), torch.Tensor(first_sv)).numpy()
for emb in utt_embs
] # list of np arrays shape (glove_dim)
# Make dict mapping ConvAI2 dataset utterances to Arora sent emb
# We save this to file for convenience (e.g. if you want to inspect)
utt2emb = {utt: emb for (utt, emb) in zip(all_utts, utt_embs_adj)}
# Save unigram distribution, first singular value, hyperparameter value for a,
# info about GloVe vectors used, and full dict of utt->emb to file
print("Saving Arora embedding info to %s..." % arora_file)
with open(arora_file, "wb") as f:
pickle.dump(
{
'word2prob':
word2prob, # dict: string to float between 0 and 1
'first_sv': first_sv, # np array shape (glove_dim)
'arora_a': arora_a, # float, 0.0001
'glove_name': glove_name, # string, '840B'
'glove_dim': glove_dim, # int, 300
'utt2emb':
utt2emb, # dict: string to np array shape (glove_dim)
},
f,
)
def eval_wordstat(opt, print_parser=None):
"""
Evaluates a model.
:param opt: tells the evaluation function how to run
:param print_parser: if provided, prints the options that are set within the
model after loading the model
"""
random.seed(42)
# Create model and assign it to the specified task
agent = create_agent(opt, requireModelExists=True)
world = create_task(opt, agent)
if opt.get('external_dict'):
print('[ Using external dictionary from: {} ]'.format(
opt['external_dict']))
dict_opt = copy.deepcopy(opt)
dict_opt['dict_file'] = opt['external_dict']
dictionary = DictionaryAgent(dict_opt)
else:
print('[ Using model bundled dictionary ]')
dictionary = agent.dict
batch_size = opt['batchsize']
if print_parser:
# Show arguments after loading model
print_parser.opt = agent.opt
print_parser.print_args()
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
cnt = 0
word_statistics = {
'mean_wlength': [],
'mean_clength': [],
'freqs_cnt': Counter(),
'word_cnt': 0,
'pred_list': [],
'pure_pred_list': [],
'context_list': [],
'unique_words': set(),
}
bins = [int(i) for i in opt['freq_bins'].split(',')]
def process_prediction(prediction, word_statistics):
normalized = normalize_answer(prediction)
word_statistics['pred_list'].append(normalized)
freqs, _cnt, wlength, clength = get_word_stats(prediction,
dictionary,
bins=bins)
word_statistics['word_cnt'] += _cnt
word_statistics['mean_wlength'].append(wlength)
word_statistics['mean_clength'].append(clength)
word_statistics['freqs_cnt'] += Counter(freqs)
word_statistics['unique_words'] |= set(normalized.split(" "))
return word_statistics
while not world.epoch_done():
world.parley()
if batch_size == 1:
cnt += 1
prediction = world.acts[-1]['text']
word_statistics['context_list'].append(world.acts[0]['text'])
word_statistics['pure_pred_list'].append(prediction)
word_statistics = process_prediction(prediction, word_statistics)
else:
for w in world.worlds:
try:
if 'text' not in w.acts[-1]:
continue
prediction = w.acts[-1]['text']
word_statistics['context_list'].append(w.acts[0]['text'])
word_statistics['pure_pred_list'].append(prediction)
except IndexError:
continue
cnt += 1
word_statistics = process_prediction(prediction,
word_statistics)
if log_time.time() > log_every_n_secs:
report = world.report()
text, report = log_time.log(report['exs'], world.num_examples(),
report)
print(text)
stat_str = 'total_words: {}, '.format(word_statistics['word_cnt'])
stat_str += ', '.join([
'<{}:{} ({:.{prec}f}%)'.format(
b,
word_statistics['freqs_cnt'].get(b, 0),
(word_statistics['freqs_cnt'].get(b, 0) /
word_statistics['word_cnt']) * 100,
prec=2,
) for b in bins
])
print("Word statistics: {}, avg_word_length: {:.{prec}f}, "
"avg_char_length: {:.{prec}f}".format(
stat_str,
numpy.array(word_statistics['mean_wlength']).mean(),
numpy.array(word_statistics['mean_clength']).mean(),
prec=2,
))
if opt['num_examples'] > 0 and cnt >= opt['num_examples']:
break
if world.epoch_done():
print("EPOCH DONE")
if opt['compute_unique'] is True:
unique_list = []
cntr = Counter(word_statistics['pred_list'])
for k, v in cntr.items():
if v == 1:
unique_list.append(k)
print("Unique responses: {:.{prec}f}%".format(
len(unique_list) / len(word_statistics['pred_list']) * 100,
prec=2))
print("Total unique tokens:", len(word_statistics['unique_words']))
if opt['dump_predictions_path'] is not None:
with open(opt['dump_predictions_path'], 'w') as f:
f.writelines([
'CONTEXT: {}\nPREDICTION:{}\n\n'.format(c, p) for c, p in zip(
word_statistics['context_list'],
word_statistics['pure_pred_list'],
)
])
if opt['compute_unique'] is True:
with open(opt['dump_predictions_path'] + '_unique', 'w') as f:
f.writelines(['{}\n'.format(i) for i in unique_list])
stat_str = 'total_words: {}, '.format(word_statistics['word_cnt'])
stat_str += ', '.join([
'<{}:{} ({:.{prec}f}%)'.format(
b,
word_statistics['freqs_cnt'].get(b, 0),
(word_statistics['freqs_cnt'].get(b, 0) /
word_statistics['word_cnt']) * 100,
prec=2,
) for b in bins
])
print("Word statistics: {}, avg_word_length: {:.{prec}f}, "
"avg_char_length: {:.{prec}f}".format(
stat_str,
numpy.array(word_statistics['mean_wlength']).mean(),
numpy.array(word_statistics['mean_clength']).mean(),
prec=2,
))
report = world.report()
print(report)
return report
def self_chat(opt):
random.seed(opt['seed'])
partner = opt['partner_model_file']
partner_opt_file = opt.get('partner_opt_file')
# Create agents
agent1 = create_agent(opt, requireModelExists=True)
agent1.opt.log("Agent 1 Opt")
if partner is None:
# Self chat with same model
agent2 = agent1.clone()
else:
# Self chat with different models
if partner_opt_file:
print(f"WARNING: Loading override opts from: {partner_opt_file}")
with PathManager.open(partner_opt_file) as f:
partner_opt = json.load(f)
else:
partner_opt = {}
partner_opt['interactive_mode'] = opt.get('interactive_mode', True)
print(
f"WARNING: Setting partner interactive mode to: {partner_opt['interactive_mode']}"
)
agent2 = create_agent_from_model_file(partner, partner_opt)
agent2.opt.log("Agent 2 Opt")
# Set IDs
agent1.id = agent1.id + "_1"
agent2.id = agent2.id + "_2"
model_id = agent1.id + "_" + agent2.id
world = create_task(opt, user_agents=[agent1, agent2])
# Set up world logging
logger = WorldLogger(opt)
log_time = TimeLogger()
# Run some self chats.
for i in range(opt['num_self_chats']):
_run_self_chat_episode(opt, world, logger)
report = world.report()
text, report = log_time.log(i + 1, opt['num_self_chats'], report)
logging.info(text)
# Save chats
if opt['outfile'] is None:
outfile = '/tmp/{}_selfchat'.format(model_id)
else:
outfile = opt['outfile']
if opt['save_format'] == 'conversations' and hasattr(world, 'write'):
# use self chat specific world to write conversation
# this might be useful for logging extra contextual
# information (like personas)
world.write(logger, outfile)
else:
# use default logger write function
logger.write(outfile, world, opt['save_format'])
return logger.get_logs()
def self_chat(opt, print_parser=None):
if print_parser is not None:
if print_parser is True and isinstance(opt, ParlaiParser):
print_parser = opt
elif print_parser is False:
print_parser = None
if isinstance(opt, ParlaiParser):
print(
'[ Deprecated Warning: self_chat should be passed opt not Parser ]'
)
opt = opt.parse_args()
random.seed(opt['seed'])
# Create models
agent1 = create_agent(opt, requireModelExists=True)
agent2 = agent1.clone()
if hasattr(agent2, 'id'):
agent2.id = agent2.id + "2"
# Check for `selfchat` in the task name
if 'selfchat' not in opt['task']:
warn_once(
'You are using self chat with task {}. '.format(opt['task']) +
'If your task has an existing self chat world, then run with '
'-t {}:selfchat'.format(opt['task']))
world = create_task(opt, [agent1, agent2])
if print_parser:
# Show arguments after loading model
print_parser.opt = agent1.opt
print_parser.print_args()
# set up logging
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
logger = WorldLogger(opt)
# Run some self chats.
max_cnt = int(opt['num_examples'] * opt.get('selfchat_max_turns') /
opt.get('batchsize'))
cnt = 0
for _ in tqdm.trange(max_cnt):
cnt += opt.get('batchsize', 1)
world.parley()
logger.log(world)
if opt.get('display_examples'):
print(world.display())
if log_time.time() > log_every_n_secs:
text = log_time.log(cnt, max_cnt)
print(text)
if opt.get('display_examples'):
print('-- end of episode --')
logger.reset_world() # flush last episode
indent = opt['indent'] if opt['indent'] >= 0 else None
logger.write(opt['outfile'], opt['format'], indent=indent)
return logger.get_logs()
def dump_data(opt):
"""
Dump task data to ACUTE-Eval.
"""
# create repeat label agent and assign it to the specified task
agent = RepeatLabelAgent(opt)
world = create_task(opt, agent)
task = opt.get('task')
speaker_0_id = opt.get('speaker_0_id') or f'{task}_as_human'
speaker_1_id = opt.get('speaker_1_id') or f'{task}_as_model'
if opt['outfile'] is None:
outfile = tempfile.mkstemp(prefix='{}_{}_'.format(
opt['task'], opt['datatype']),
suffix='.txt')[1]
else:
outfile = opt['outfile']
num_episodes = (world.num_episodes() if opt['num_episodes'] == -1 else min(
opt['num_episodes'], world.num_episodes()))
log_timer = TimeLogger()
print(f'[ starting to convert, saving output to {outfile} ]')
dialogues = []
for _ in range(num_episodes):
episode = []
episode_done = False
while not episode_done:
world.parley()
acts = world.get_acts()
text = acts[0].get('text')
split_text = text.split('\n')
label = random.choice(acts[0].get('labels',
acts[0].pop('eval_labels',
None)))
if not episode and opt.get('prepended_context'):
# first turn
context = split_text[:-1]
text = split_text[-1]
context_turn = [{
'text': context,
'episode_done': False,
'id': 'context'
} for _ in range(2)]
episode.append(context_turn)
turn = [
{
'text': text,
'episode_done': False,
'id': speaker_0_id
},
{
'text': label,
'episode_done': False,
'id': speaker_1_id
},
]
episode.append(turn)
if acts[0].get('episode_done', False):
episode[-1][-1]['episode_done'] = True
episode_done = True
dialogues.append(episode)
if log_timer.time() > opt['log_every_n_secs']:
text, _log = log_timer.log(world.total_parleys,
world.num_examples())
print(text)
if world.epoch_done():
break
Conversations.save_conversations(dialogues, outfile, opt)
def verify(opt):
if opt['datatype'] == 'train':
logging.warn('changing datatype from train to train:ordered')
opt['datatype'] = 'train:ordered'
# create repeat label agent and assign it to the specified task
opt['fixed_response'] = None
agent = FixedResponseAgent(opt)
world = create_task(opt, agent)
opt.log()
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
dictionary = DictionaryAgent(opt)
ignore_tokens = opt.get('ignore_tokens').split(',')
counts = {}
for t in {'input', 'labels', 'both'}:
counts[f'{t}/tokens'] = 0
counts[f'{t}/utterances'] = 0
counts[f'{t}/avg_utterance_length'] = None
counts[f'{t}/unique_tokens'] = 0
counts[f'{t}/unique_utterances'] = 0
# for counting the stats..
counts[f'{t}/token_dict'] = {}
counts[f'{t}/utterance_dict'] = {}
def tokenize(txt):
return dictionary.tokenize(txt)
def keep_token(t):
for s in ignore_tokens:
if s != '' and s in t:
return False
return True
# max number of examples to evaluate
max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf')
cnt = 0
# Show some example dialogs.
while not world.epoch_done() and world.total_exs < max_cnt:
world.parley()
act = world.get_acts()[opt.get('agent')]
for itype in {'input', 'labels'}:
if itype == 'input':
if opt.get('new_line_new_utt'):
txts = act.get('text').split('\n')
else:
txts = [act.get('text')]
else:
txts = act.get('labels', act.get('eval_labels', ['']))
for txt in txts:
tokens = tokenize(txt)
retxt = [t for t in tokens if keep_token(t)]
counts[f'{itype}/tokens'] += len(retxt)
counts['both/tokens'] += len(retxt)
counts[f'{itype}/utterances'] += 1
counts['both/utterances'] += 1
counts[f'{itype}/avg_utterance_length'] += AverageMetric(
len(retxt), 1)
counts[f'both/avg_utterance_length'] += AverageMetric(
len(retxt), 1)
for t in retxt:
if t not in counts[f'{itype}/token_dict']:
counts[f'{itype}/unique_tokens'] += 1
counts[f'{itype}/token_dict'][t] = True
if t not in counts['both/token_dict']:
counts['both/unique_tokens'] += 1
counts['both/token_dict'][t] = True
retxt = ' '.join(retxt)
if retxt not in counts[f'{itype}/utterance_dict']:
counts[f'{itype}/unique_utterances'] += 1
counts[f'{itype}/utterance_dict'][retxt] = True
if retxt not in counts['both/utterance_dict']:
counts['both/unique_utterances'] += 1
counts['both/utterance_dict'][retxt] = True
if log_time.time() > log_every_n_secs:
report = _report(world, counts)
cnt = report.pop('exs')
text, log = log_time.log(cnt, world.num_examples(), report)
logging.info(text)
try:
# print dataset size if available
logging.info(f'loaded {world.num_episodes()} episodes with a total '
f'of {world.num_examples()} examples')
except AttributeError:
pass
retval = _report(world, counts)
retval.pop('exs')
return retval
def build_dict(opt, skip_if_built=False):
print("||||||||||||||||||||| -==================")
if isinstance(opt, ParlaiParser):
print('[ Deprecated Warning: should be passed opt not Parser ]')
opt = opt.parse_args()
if not opt.get('dict_file'):
print('Tried to build dictionary but `--dict-file` is not set. Set ' +
'this param so the dictionary can be saved.')
return
if skip_if_built and os.path.isfile(opt['dict_file']):
# Dictionary already built, skip all loading or setup
print("[ dictionary already built .]")
return None
if is_distributed():
raise ValueError(
'Dictionaries should be pre-built before distributed train.')
if opt.get('dict_class'):
# Custom dictionary class
print("\tdict_class: " + opt['dict_class'])
dictionary = str2class(opt['dict_class'])(opt)
else:
# Default dictionary class
dictionary = DictionaryAgent(opt)
print("\tdict_class: " + type(dictionary).__name__)
if os.path.isfile(opt['dict_file']):
# Dictionary already built, return loaded dictionary agent
print("[ dictionary already built .]")
return dictionary
ordered_opt = copy.deepcopy(opt)
cnt = 0
# we use train set to build dictionary
ordered_opt['numthreads'] = 1
ordered_opt['batchsize'] = 1
# Set this to none so that image features are not calculated when Teacher is
# instantiated while building the dict
# TODO: change 'none' to 'no_image_model'
ordered_opt['image_mode'] = 'none'
ordered_opt['pytorch_teacher_batch_sort'] = False
if ordered_opt['task'] == 'pytorch_teacher' or not ordered_opt['task']:
pytorch_teacher_task = ordered_opt.get('pytorch_teacher_task', '')
if pytorch_teacher_task != '':
ordered_opt['task'] = pytorch_teacher_task
datatypes = ['train:ordered:stream']
if opt.get('dict_include_valid'):
datatypes.append('valid:stream')
if opt.get('dict_include_test'):
datatypes.append('test:stream')
cnt = 0
print("|||||||||||||||||| datatypes : " + str(datatypes))
for dt in datatypes:
ordered_opt['datatype'] = dt
world_dict = create_task(ordered_opt, dictionary)
# pass examples to dictionary
print('[ running dictionary over data.. ]')
log_time = TimeLogger()
total = world_dict.num_examples()
if opt['dict_maxexs'] >= 0:
total = min(total, opt['dict_maxexs'])
log_every_n_secs = opt.get('log_every_n_secs', None)
if log_every_n_secs:
pbar = tqdm.tqdm(total=total,
desc='Building dictionary',
unit='ex',
unit_scale=True)
else:
pbar = None
while not world_dict.epoch_done():
cnt += 1
if cnt > opt['dict_maxexs'] and opt['dict_maxexs'] >= 0:
print('Processed {} exs, moving on.'.format(
opt['dict_maxexs']))
# don't wait too long...
break
world_dict.parley()
if pbar:
pbar.update(1)
if pbar:
pbar.close()
dictionary.save(opt['dict_file'], sort=True)
print('[ dictionary built with {} tokens in {}s ]'.format(
len(dictionary), round(log_time.total_time(), 2)))
return dictionary
def eval_wordstat(opt):
"""
Evaluates a model.
:param opt: tells the evaluation function how to run
"""
random.seed(42)
# Setup control information
initialize_control_information(opt)
# Create model and assign it to the specified task
agent = create_agent(opt, requireModelExists=True)
world = create_task(opt, agent)
if opt.get('external_dict'):
print('[ Using external dictionary from: {} ]'.format(
opt['external_dict']))
dict_opt = copy.deepcopy(opt)
dict_opt['dict_file'] = opt['external_dict']
dictionary = DictionaryAgent(dict_opt)
else:
print('[ Using model bundled dictionary ]')
dictionary = agent.dict
batch_size = opt['batchsize']
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
data = {} # This will be written to the output json file
data['opt'] = agent.opt # Save the opt to json
# Determine the output filename
if opt['gold_response']: # Special output file for gold response
model_dir, _ = os.path.split(opt.get('model_file'))
outfile = os.path.join(model_dir, 'goldresponse')
if opt['use_reply'] != 'label':
raise ValueError(
'You should set --use-reply label (not --use-reply model) '
'when measuring goldresponse stats')
else:
outfile = "%s.%s.%s.%s" % (
opt.get('model_file'),
opt.get('datatype'),
"use%sreply" % agent.opt['use_reply'],
"beam%i" % agent.opt['beam_size'],
)
if agent.opt['beam_size'] > 1:
outfile += ".beamminnbest%i" % agent.opt['beam_min_n_best']
if len(agent.control_settings) > 0:
outfile += ".setcontrols:" + "_".join([
"%s%s" % (c, str(agent.control_settings[c]['set_value']))
for c in sorted(agent.control_settings.keys())
])
if agent.opt['beam_reorder'] not in ['none', False]:
outfile += ".beamreorder_%s" % agent.opt['beam_reorder']
if len(agent.wd_features) > 0:
sorted_bfw = sorted(list(zip(agent.wd_features, agent.wd_wts)),
key=lambda x: x[0])
outfile += ".WDfeatures:" + "_".join(
["%s%s" % (f, str(w)) for f, w in sorted_bfw])
if opt['num_examples'] != -1:
outfile += ".numex%i" % opt['num_examples']
outfile += ".wordstats.json"
print("\nOutfile: %s\n" % outfile)
cnt = 0
word_statistics = {
'mean_wlength': [], # list of length (in words) of utterances
'mean_clength': [], # list of length (in chars) of utterances
'freqs_cnt': Counter(), # Counter for word frequencies, bucketed
'word_cnt': 0, # total number of words in all utterances
'pred_list':
[], # list of generated utterances after applying normalize_answer
'pure_pred_list': [], # list of generated utterances
'context_list':
[], # list of text inputs (persona and conversation history)
}
bins = [int(i) for i in opt['freq_bins'].split(',')]
# This dictionary records all the sentence-level controllable attributes
# For each attribute, we have a list of all the values
sent_attrs = {attr: []
for attr in ATTR2SENTSCOREFN.keys()} # str to list of floats
# histories will be a list of ConvAI2History objects
histories = []
def process_prediction(prediction, word_statistics):
word_statistics['pred_list'].append(normalize_answer(prediction))
freqs, _cnt, wlength, clength = get_word_stats(prediction,
dictionary,
bins=bins)
word_statistics['word_cnt'] += _cnt
word_statistics['mean_wlength'].append(wlength)
word_statistics['mean_clength'].append(clength)
word_statistics['freqs_cnt'] += Counter(freqs)
return word_statistics
t0 = time.time()
while not world.epoch_done():
world.parley()
# orig eval_wordstat.py handles bsz=1 but for simplicity we assume bsz>1
assert batch_size != 1
for w in world.worlds:
try:
try:
response_act = w.acts[-1]
prediction = response_act['text']
except KeyError:
continue
if opt['gold_response']:
# If we're measuring gold response, use eval_label as prediction
prediction = w.acts[0]['eval_labels'][0]
response_act = {'text': prediction}
word_statistics['context_list'].append(w.acts[0]['text'])
word_statistics['pure_pred_list'].append(prediction)
except IndexError:
continue
cnt += 1
word_statistics = process_prediction(prediction, word_statistics)
# Compute and record sentence-level attributes
history = ConvAI2History(w.acts[0]['text'])
histories.append(history)
sent_attrs = update_sent_attr_stats(sent_attrs, history,
prediction)
# Periodically log some info
if log_time.time() > log_every_n_secs:
report = world.report()
text, report = log_time.log(report['exs'], world.num_examples(),
report)
print(text)
if opt['num_examples'] > 0 and cnt >= opt['num_examples']:
break
if world.epoch_done():
print("EPOCH DONE")
print("Time to process %i examples: %f seconds" % (cnt, time.time() - t0))
# Compute percent unique
# Note this is w.r.t. normalized pred_list not original pure_pred_list
unique_list = []
cntr = Counter(word_statistics['pred_list'])
for k, v in cntr.items():
if v == 1:
unique_list.append(k)
unique_percent = len(unique_list) / len(word_statistics['pred_list']) * 100
# Print a final report
report = world.report()
if opt['gold_response']:
report['ppl'] = 0.0 # For gold responses, overwrite the perplexity
print(report)
# Put all information in data dict
data[
'unique_percent'] = unique_percent # percent of all responses that are unique
data[
'word_statistics'] = word_statistics # word stats, as in orig eval_wordstat
data['report'] = report # the final report
data['histories'] = [(hist.persona_lines, hist.partner_utts, hist.own_utts)
for hist in histories] # history for each example
data[
'sent_attrs'] = sent_attrs # all sentence attribute values for responses
# Write data to outfile
print("Writing to %s..." % outfile)
with PathManager.open(outfile, 'w') as f:
json.dump(data, f)
def _eval_single_world(opt, agent, task):
logging.info(
f'Evaluating task {task} using datatype {opt.get("datatype")}.')
# set up world logger
task_opt = opt.copy() # copy opt since we're editing the task
task_opt['task'] = task
# add task suffix in case of multi-tasking
if opt['world_logs']:
task_opt['world_logs'] = get_task_world_logs(
task,
task_opt['world_logs'],
is_multitask=len(opt['task'].split(',')) > 1)
world_logger = WorldLogger(task_opt) if task_opt['world_logs'] else None
world = create_task(task_opt, agent) # create worlds for tasks
# set up logging
log_every_n_secs = opt.get('log_every_n_secs', -1)
if log_every_n_secs <= 0:
log_every_n_secs = float('inf')
log_time = TimeLogger()
# max number of examples to evaluate
max_cnt = opt['num_examples'] if opt['num_examples'] > 0 else float('inf')
cnt = 0
total_cnt = world.num_examples()
if is_distributed():
logging.warning('Progress bar is approximate in distributed mode.')
while not world.epoch_done() and cnt < max_cnt:
cnt += opt.get('batchsize', 1)
world.parley()
if world_logger is not None:
world_logger.log(world)
if opt['display_examples']:
# display examples
print(world.display() + '\n~~')
if log_time.time() > log_every_n_secs:
report = world.report()
text, report = log_time.log(report.get('exs', 0),
min(max_cnt, total_cnt), report)
logging.info(text)
if world_logger is not None:
# dump world acts to file
world_logger.reset() # add final acts to logs
if is_distributed():
rank = get_rank()
base_outfile, extension = os.path.splitext(task_opt['world_logs'])
outfile = base_outfile + f'_{rank}' + extension
else:
outfile = task_opt['world_logs']
world_logger.write(outfile, world, file_format=opt['save_format'])
report = aggregate_unnamed_reports(all_gather_list(world.report()))
if isinstance(world.agents, list) and len(world.agents) > 1:
classifier_agent = world.agents[CLASSIFIER_AGENT]
if hasattr(classifier_agent, 'calc_auc') and classifier_agent.calc_auc:
for class_indices, curr_auc in zip(
classifier_agent.auc_class_indices, classifier_agent.aucs):
report[
f'AUC_{classifier_agent.class_list[class_indices]}'] = curr_auc
classifier_agent.reset_auc()
# for safety measures
agent.reset_auc()
world.reset()
return report
