def test_autoregressive_sample_transformerlm_quality_beam(self):
pred_model = models.TransformerLM(
d_model=64, d_ff=128, dropout=0.05, max_len=256, n_heads=2,
n_layers=2, vocab_size=13, mode='predict')
shape11 = shapes.ShapeDtype((1, 1), dtype=np.int32)
model_path = os.path.join(_TESTDATA, 'transformerlm_copy.pkl.gz')
pred_model.init_from_file(model_path, weights_only=True,
input_signature=(shape11, shape11))
inputs = np.array([[0, 3, 7, 5, 3, 2, 4, 0]], dtype=np.int32)
s = decoding.beam_search(pred_model, inputs, n_beams=3, max_length=6)
self.assertEqual(len(s), 3) # 3 beams
self.assertEqual(str(s[0][0][0]), '[3 7 5 3 2 4]')
self.assertEqual(str(s[1][0][0]), '[7 5 3 2 4 4]') # different from above
self.assertEqual(str(s[2][0][0]), '[7 5 3 2 4 3]') # different from above
def main(argv):
if len(argv) > 1:
raise absl_app.UsageError('Too many command-line arguments.')
if not FLAGS.output_dir:
raise absl_app.UsageError('--output_dir needs to be provided.')
tf.compat.v1.enable_eager_execution()
# Check that checkpoint_dir is correct: should contain model.pkl.gz file.
model_file = os.path.join(FLAGS.checkpoint_dir, 'model.pkl.gz')
_check_exists(model_file)
gin.parse_config_file(os.path.join(FLAGS.checkpoint_dir, 'config.gin'))
# Batching on our own because of possible repetitions of examples.
gin.bind_parameter('data.Batch.batch_size', 1)
if FLAGS.n_hashes is not None:
gin.bind_parameter('LSHSelfAttention.n_hashes', FLAGS.n_hashes)
gin.bind_parameter('ref2_encoder/LSHSelfAttention.n_hashes',
FLAGS.n_hashes)
vocab, model, initial_state = prepare_model(model_file, FLAGS.batch_size)
host_id, host_count = jax.host_id(), jax.host_count()
print('Running on host %d out of %d.' % (host_id, host_count))
example_count = 0
start_time = time.time()
# Creates all intermediate directories if they do not exist
tf.io.gfile.makedirs(FLAGS.output_dir)
json_to_write = os.path.join(FLAGS.output_dir, 'output%d.json' % host_id)
all_jsons = []
# In a case of a reset we have to check how much work was already done.
# We can check whether the processing of an example was finished, but
# currently we are only checking whether it was started.
done = FLAGS.starting_example
reload_count = 0
all_existing_files = tf.io.gfile.listdir(FLAGS.output_dir)
for filename in all_existing_files:
if 'processing' in filename:
# The definition of digits looks for a number after the infix "processing"
# in the file name. Example: tom_processing_532 will lead to
# digits = "processing_532" and number equal to "532".
digits = filename[filename.find('processing'):]
number = ''.join(d for d in digits if d.isdigit())
if is_number(number) and int(
number) < FLAGS.num_examples + FLAGS.starting_example:
done = max(done, int(number))
print('The done number is {}'.format(done))
if FLAGS.use_eval_set:
drop_gen = trax_data.CreateDropInputs(train=False)()
else:
drop_gen = trax_data.CreateDropInputs(train=True)()
padding_fun = trax_data.PadToLength()
# TODO(henrykm): improve managment of the counters.
# example_count_total - all numeric examples
# example_count - all numeric examples above starting_example
# reload_count - if we processed FLAGS.reload_after examples,
# then the checkpoint should be reloaded.
# idx - total number of exaples
example_count_total = 0
reload_count += 1
for idx, e in enumerate(drop_gen):
if reload_count >= FLAGS.reload_after:
vocab, model, initial_state = prepare_model(
model_file, FLAGS.batch_size)
reload_count = 0
if example_count >= FLAGS.num_examples:
print('Reached the example_count {} - breaking'.format(
example_count))
break
if not is_number(e[1]):
continue
target_answer = float(e[1])
# We count numeric starting examples
example_count_total += 1
if example_count_total <= FLAGS.starting_example:
print('Skipping example_count_total {} because it is below {}'.
format(example_count_total, FLAGS.starting_example))
continue
if example_count % 10 == 0:
elapsed_time = time.time() - start_time
start_time = time.time()
print('Starting inference on example %d, %.2fs since last log' %
(example_count, elapsed_time),
flush=True)
example_count += 1
if example_count <= done - FLAGS.starting_example + 1:
print('Skipping example_count {} because it is below {}'.format(
example_count, done - FLAGS.starting_example))
# We are increasing the example_count because the example
# was processed before
continue
if example_count % host_count != host_id:
continue
# At this point we are committed to the processing of an example with
# index example_count
processing_file = os.path.join(FLAGS.output_dir, 'processing_')
data_id = str(example_count + FLAGS.starting_example)
with tf.io.gfile.GFile(processing_file + data_id, 'w') as w:
w.write('Procesing started.')
for repetition_id, example in multiply_examples(e):
question = example[0]
question_text = question[question.find(':') + 2:]
question_text = question_text.replace('-', ' - ')
question = 'infer full calculation: ' + question_text
list_num = [
float(num.replace(',', '').rstrip('.')) for num in re.findall(
r'[-+]?[.]?[\d]+(?:,\d\d\d)*[\.]?\d*(?:[eE][-+]?\d+)?',
question)
]
for i in range(len(list_num)):
question += ' n{} = {}'.format(i, list_num[i])
# print('Question {}'.format(question))
tokenized_question = next(
padding_fun(
trax_data.tokenize([
question,
],
vocab_file=gin.query_parameter(
'trax.data.Tokenize.vocab_file'))))
state = model.state
if FLAGS.use_beam_search:
answer_beams = decoding.beam_search(
model,
tokenized_question[None, :],
n_beams=FLAGS.n_beams,
max_length=FLAGS.max_answer_len,
accelerate=False)
model.state = state
else:
answer_beams = []
# We recycle the n_beams flag to control the number
# of autoregressive samples.
for i in range(FLAGS.n_beams):
answer = decoding.autoregressive_sample(
model,
tokenized_question[None, :],
temperature=FLAGS.autoregressive_sample_temp,
max_length=FLAGS.max_answer_len,
accelerate=False)
model.state = state
answer_beams.append(answer)
correct_example_index = -1
for i in range(len(answer_beams)):
if FLAGS.use_beam_search:
answer = trax_data.detokenize(
answer_beams[i][0][0],
vocab_file=gin.query_parameter(
'trax.data.Tokenize.vocab_file'))
else:
answer = trax_data.detokenize(
answer_beams[i][0],
vocab_file=gin.query_parameter(
'trax.data.Tokenize.vocab_file'))
print('Proposed computation {}'.format(answer))
list_op = answer.split('|')
if not list_op[-1]:
list_op = list_op[:-1]
try:
result = trax_data.tf_inputs.compute_result(
list_op, list_num)
if target_answer in result:
correct_example_index = result.index(target_answer)
break
# This is a temporary hack with "broad" exceptions - the computations
# must fail sometime, because we evaluate arbitrary sequences; I am in
# the process of checking what are possible failure modes.
except Exception as e: # pylint: disable=broad-except
print(e)
try:
result = trax_data.tf_inputs.compute_result(
list_op[:-1], list_num)
if target_answer in result:
correct_example_index = result.index(target_answer)
break
except Exception as e: # pylint: disable=broad-except
print(e)
print('Infered incorrect computation.')
if correct_example_index == -1:
continue
json_record = {
'question': question_text,
'input': question,
'calculation': '|'.join(list_op[:correct_example_index + 1]),
'target_answer': target_answer
}
all_jsons.append(json.dumps(json_record) + '\n')
# Outputting the inferred data in JSONL format.
data_id = str(example_count + FLAGS.starting_example)
with tf.io.gfile.GFile(json_to_write + data_id, 'w') as w:
w.write(json.dumps(json_record) + '\n')
with tf.io.gfile.GFile(processing_file + data_id, 'w') as w:
w.write('Procesing finished.')
with tf.io.gfile.GFile(json_to_write + '_' + str(FLAGS.starting_example),
'w') as w:
for record in all_jsons:
w.write(record)
