def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
albert_config = modeling.AlbertConfig.from_json_file(FLAGS.albert_config_file)
tf.gfile.MakeDirs(FLAGS.output_dir)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.gfile.Glob(input_pattern))
tf.logging.info("*** Input Files ***")
for input_file in input_files:
tf.logging.info(" %s" % input_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
keep_checkpoint_max=FLAGS.keep_checkpoint_max,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
model_fn = model_fn_builder(
albert_config=albert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.num_train_steps,
num_warmup_steps=FLAGS.num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
optimizer=FLAGS.optimizer,
poly_power=FLAGS.poly_power,
start_warmup_step=FLAGS.start_warmup_step)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size)
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
if FLAGS.do_eval:
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
global_step = -1
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
writer = tf.gfile.GFile(output_eval_file, "w")
tf.gfile.MakeDirs(FLAGS.export_dir)
eval_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
best_perf = 0
key_name = "masked_lm_accuracy"
while global_step < FLAGS.num_train_steps:
if estimator.latest_checkpoint() is None:
tf.logging.info("No checkpoint found yet. Sleeping.")
time.sleep(1)
else:
result = estimator.evaluate(
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
checkpoint_path = estimator.latest_checkpoint()
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if result[key_name] > best_perf:
best_perf = result[key_name]
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tgt_ckpt = checkpoint_path.rsplit(
"-", 1)[0] + "-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
writer.write("saved {} to {}\n".format(src_ckpt, tgt_ckpt))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"sst-2": run_classifier.SST2Processor,
"mnli": run_classifier.MnliProcessor
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint1)
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint2)
if not tf.train.checkpoint_exists(FLAGS.init_checkpoint1):
raise TFCheckpointNotFoundError("checkpoint1 does not exist!")
if not tf.train.checkpoint_exists(FLAGS.init_checkpoint2) and \
not FLAGS.use_random:
raise TFCheckpointNotFoundError("checkpoint2 does not exist!")
bert_config1 = modeling.BertConfig.from_json_file(FLAGS.bert_config_file1)
bert_config2 = modeling.BertConfig.from_json_file(FLAGS.bert_config_file2)
if FLAGS.max_seq_length > bert_config1.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config1.max_position_embeddings))
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name, ))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
all_results = []
predict_examples = processor.get_test_examples(FLAGS.diff_input_file)
num_actual_predict_examples = len(predict_examples)
# For single sentence tasks (like SST2) eg.text_b is None
original_data = [(eg.text_a, eg.text_b) for eg in predict_examples]
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(run_classifier.PaddingInputExample())
predict_file = os.path.join(FLAGS.init_checkpoint1,
FLAGS.exp_name + ".predict.tf_record")
run_classifier.file_based_convert_examples_to_features(
predict_examples, label_list, FLAGS.max_seq_length, tokenizer,
predict_file)
for bert_config_type, output_dir in [
(bert_config1, FLAGS.init_checkpoint1),
(bert_config2, FLAGS.init_checkpoint2)
]:
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
model_fn = run_classifier.model_fn_builder(
bert_config=bert_config_type,
num_labels=len(label_list),
# This init checkpoint is eventually overriden by the estimator
init_checkpoint=FLAGS.output_dir,
learning_rate=FLAGS.learning_rate,
num_train_steps=None,
num_warmup_steps=None,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = run_classifier.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
result = list(estimator.predict(input_fn=predict_input_fn))
all_results.append(result)
all_results[0] = all_results[0][:num_actual_predict_examples]
all_results[1] = all_results[1][:num_actual_predict_examples]
assert len(all_results[0]) == len(all_results[1])
# Assuming model1's predictions are gold labels, calculate model2's accuracy
score = 0
for prob1, prob2 in zip(all_results[0], all_results[1]):
if np.argmax(prob1["probabilities"]) == np.argmax(
prob2["probabilities"]):
score += 1
tf.logging.info("Agreement score = %.6f",
float(score) / num_actual_predict_examples)
# Calculate the average value of |v1 - v2|, the distance on the simplex
# Unlike KL divergence, this is a bounded metric
# However, these results are not comparable across tasks
# with different number classes
distances = []
for prob1, prob2 in zip(all_results[0], all_results[1]):
distances.append(
np.linalg.norm(prob1["probabilities"] - prob2["probabilities"]))
tf.logging.info("Average length |p1 - p2| = %.8f", np.mean(distances))
tf.logging.info("Max length |p1 - p2| = %.8f", np.max(distances))
tf.logging.info("Min length |p1 - p2| = %.8f", np.min(distances))
tf.logging.info("Std length |p1 - p2| = %.8f", np.std(distances))
if FLAGS.diff_type == "kld1":
all_kld = []
for prob1, prob2 in zip(all_results[0], all_results[1]):
all_kld.append(
stats.entropy(prob1["probabilities"], prob2["probabilities"]))
tf.logging.info("Average kl-divergence (p1, p2) = %.8f",
np.mean(all_kld))
tf.logging.info("Max kl-divergence (p1, p2) = %.8f", np.max(all_kld))
tf.logging.info("Min kl-divergence (p1, p2) = %.8f", np.min(all_kld))
tf.logging.info("Std kl-divergence (p1, p2) = %.8f", np.std(all_kld))
elif FLAGS.diff_type == "kld2":
all_kld = []
for prob1, prob2 in zip(all_results[0], all_results[1]):
all_kld.append(
stats.entropy(prob2["probabilities"], prob1["probabilities"]))
tf.logging.info("Average kl-divergence (p2, p1) = %.8f",
np.mean(all_kld))
tf.logging.info("Max kl-divergence (p2, p1) = %.8f", np.max(all_kld))
tf.logging.info("Min kl-divergence (p2, p1) = %.8f", np.min(all_kld))
tf.logging.info("Std kl-divergence (p2, p1) = %.8f", np.std(all_kld))
if FLAGS.diff_output_file:
output = ""
# Removing padded examples
all_results[0] = all_results[0][:len(original_data)]
all_results[1] = all_results[1][:len(original_data)]
with tf.gfile.GFile(FLAGS.diff_output_file, "w") as f:
for i, (eg, prob1, prob2) in enumerate(
zip(original_data, all_results[0], all_results[1])):
if i % 1000 == 0:
tf.logging.info("Writing instance %d", i + 1)
p1_items = [p1.item() for p1 in prob1["probabilities"]]
p2_items = [p2.item() for p2 in prob2["probabilities"]]
prob1_str = "%.6f\t%.6f\t%.6f" % (p1_items[0], p1_items[1],
p1_items[2])
prob2_str = "%.6f\t%.6f\t%.6f" % (p2_items[0], p2_items[1],
p2_items[2])
output = "%s\t%s\t%s\t%s\n" % (eg[0], eg[1], prob1_str,
prob2_str)
f.write(output)
return
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"cola": run_classifier_sp.ColaProcessor,
"mnli": run_classifier_sp.MnliProcessor,
"mrpc": run_classifier_sp.MrpcProcessor,
}
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = create_tokenizer_from_hub_module(
FLAGS.albert_hub_module_handle)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size *
FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
num_labels=len(label_list),
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
albert_hub_module_handle=FLAGS.albert_hub_module_handle)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_features = run_classifier_sp.convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = run_classifier_sp.input_fn_builder(
features=train_features,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
eval_features = run_classifier_sp.convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d", len(eval_examples))
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
# Eval will be slightly WRONG on the TPU because it will truncate
# the last batch.
eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = run_classifier_sp.input_fn_builder(
features=eval_features,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
if FLAGS.use_tpu:
# Discard batch remainder if running on TPU
n = len(predict_examples)
predict_examples = predict_examples[:(
n - n % FLAGS.predict_batch_size)]
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
run_classifier_sp.file_based_convert_examples_to_features(
predict_examples, label_list, FLAGS.max_seq_length, tokenizer,
predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d", len(predict_examples))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_input_fn = run_classifier_sp.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=FLAGS.use_tpu)
result = estimator.predict(input_fn=predict_input_fn)
output_predict_file = os.path.join(FLAGS.output_dir,
"test_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as writer:
tf.logging.info("***** Predict results *****")
for prediction in result:
probabilities = prediction["probabilities"]
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
writer.write(output_line)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"xnli": XnliProcessor,
"tnews": TnewsProcessor,
"afqmc": AFQMCProcessor,
"iflytek": iFLYTEKDataProcessor,
"copa": COPAProcessor,
"cmnli": CMNLIProcessor,
"wsc": WSCProcessor,
"csl": CslProcessor,
# "cola": classifier_utils.ColaProcessor,
# "mnli": classifier_utils.MnliProcessor,
# "mismnli": classifier_utils.MisMnliProcessor,
# "mrpc": classifier_utils.MrpcProcessor,
# "rte": classifier_utils.RteProcessor,
# "sst-2": classifier_utils.Sst2Processor,
# "sts-b": classifier_utils.StsbProcessor,
# "qqp": classifier_utils.QqpProcessor,
# "qnli": classifier_utils.QnliProcessor,
# "wnli": classifier_utils.WnliProcessor,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True."
)
if not FLAGS.albert_config_file and not FLAGS.albert_hub_module_handle:
raise ValueError("At least one of `--albert_config_file` and "
"`--albert_hub_module_handle` must be set")
if FLAGS.albert_config_file:
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
if FLAGS.max_seq_length > albert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the ALBERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, albert_config.max_position_embeddings))
else:
albert_config = None # Get the config from TF-Hub.
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name](
use_spm=True if FLAGS.spm_model_file else False,
do_lower_case=FLAGS.do_lower_case)
label_list = processor.get_labels()
if FLAGS.albert_hub_module_handle:
tokenizer = tokenization.FullTokenizer.from_hub_module(
hub_module=FLAGS.albert_hub_module_handle,
spm_model_file=FLAGS.spm_model_file)
else:
tokenizer = tokenization.FullTokenizer.from_scratch(
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case,
spm_model_file=FLAGS.spm_model_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
if FLAGS.do_train:
iterations_per_loop = int(
min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
else:
iterations_per_loop = FLAGS.iterations_per_loop
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=int(FLAGS.save_checkpoints_steps),
keep_checkpoint_max=0,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
model_fn = classifier_utils.model_fn_builder(
albert_config=albert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.train_step,
num_warmup_steps=FLAGS.warmup_step,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
task_name=task_name,
hub_module=FLAGS.albert_hub_module_handle,
optimizer=FLAGS.optimizer)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
train_file = os.path.join(cached_dir, task_name + "_train.tf_record")
if not tf.gfile.Exists(train_file):
classifier_utils.file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer,
train_file, task_name)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", FLAGS.train_step)
train_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_step)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(classifier_utils.PaddingInputExample())
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
eval_file = os.path.join(cached_dir, task_name + "_eval.tf_record")
if not tf.gfile.Exists(eval_file):
classifier_utils.file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer,
eval_file, task_name)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.eval_batch_size)
best_trial_info_file = os.path.join(FLAGS.output_dir, "best_trial.txt")
def _best_trial_info():
"""Returns information about which checkpoints have been evaled so far."""
if tf.gfile.Exists(best_trial_info_file):
with tf.gfile.GFile(best_trial_info_file, "r") as best_info:
global_step, best_metric_global_step, metric_value = (
best_info.read().split(":"))
global_step = int(global_step)
best_metric_global_step = int(best_metric_global_step)
metric_value = float(metric_value)
else:
metric_value = -1
best_metric_global_step = -1
global_step = -1
tf.logging.info(
"Best trial info: Step: %s, Best Value Step: %s, "
"Best Value: %s", global_step, best_metric_global_step,
metric_value)
return global_step, best_metric_global_step, metric_value
def _remove_checkpoint(checkpoint_path):
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
if task_name == "sts-b":
key_name = "pearson"
elif task_name == "cola":
key_name = "matthew_corr"
else:
key_name = "eval_accuracy"
global_step, best_perf_global_step, best_perf = _best_trial_info()
writer = tf.gfile.GFile(output_eval_file, "w")
while global_step < FLAGS.train_step:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info(
"Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
if not steps_and_files:
tf.logging.info(
"found 0 file, global step: {}. Sleeping.".format(
global_step))
time.sleep(60)
else:
for checkpoint in sorted(steps_and_files.items()):
step, checkpoint_path = checkpoint
if global_step >= step:
if (best_perf_global_step != step
and len(_find_valid_cands(step)) > 1):
_remove_checkpoint(checkpoint_path)
continue
result = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("best = {}\n".format(best_perf))
if result[key_name] > best_perf:
best_perf = result[key_name]
best_perf_global_step = global_step
elif len(_find_valid_cands(global_step)) > 1:
_remove_checkpoint(checkpoint_path)
writer.write("=" * 50 + "\n")
writer.flush()
with tf.gfile.GFile(best_trial_info_file,
"w") as best_info:
best_info.write("{}:{}:{}".format(
global_step, best_perf_global_step, best_perf))
writer.close()
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = "model.ckpt-{}.{}".format(best_perf_global_step, ext)
tgt_ckpt = "model.ckpt-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
tf.io.gfile.rename(os.path.join(FLAGS.output_dir, src_ckpt),
os.path.join(FLAGS.output_dir, tgt_ckpt),
overwrite=True)
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(classifier_utils.PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
classifier_utils.file_based_convert_examples_to_features(
predict_examples, label_list, FLAGS.max_seq_length, tokenizer,
predict_file, task_name)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size)
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result = estimator.predict(input_fn=predict_input_fn,
checkpoint_path=checkpoint_path)
output_predict_file = os.path.join(FLAGS.output_dir,
"test_results.tsv")
output_submit_file = os.path.join(FLAGS.output_dir,
"submit_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as pred_writer,\
tf.gfile.GFile(output_submit_file, "w") as sub_writer:
sub_writer.write("index" + "\t" + "prediction\n")
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, (example, prediction)) in\
enumerate(zip(predict_examples, result)):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
pred_writer.write(output_line)
if task_name != "sts-b":
actual_label = label_list[int(prediction["predictions"])]
else:
actual_label = str(prediction["predictions"])
sub_writer.write(example.guid + "\t" + actual_label + "\n")
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
def main():
tf.logging.set_verbosity(tf.logging.INFO)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tpu.InputPipelineConfig.PER_HOST_V2
run_config = tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tpu.TPUConfig(iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
session_config = tf.ConfigProto(log_device_placement=True)
session_config.gpu_options.allow_growth = True
run_config.replace(session_config=session_config)
with open('cqa_data.pkl', 'rb') as fr:
train_dataset, dev_cid, dev_features = pkl.load(fr)
# dev_label = [feature.label_id for feature in dev_features]
model_fn = model_fn_builder(bert_config=bert_config,
num_labels=2,
init_checkpoint=FLAGS.init_checkpoint,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
dev_cid=dev_cid)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
# params={'batch_size': FLAGS.train_batch_size},
train_batch_size=FLAGS.train_batch_size,
predict_batch_size=FLAGS.eval_batch_size)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d", len(dev_features))
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = input_fn_builder(features=dev_features,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
with open("statistic.json") as fr:
statistic = json.load(fr)
print(statistic, type(statistic["sent1_length"]))
# with open("bert_train.pkl", "wb") as fw:
#
# train_keylist = train_dataset.keys()
# for name in train_keylist:
# train_features = train_dataset[name]
# train_input_fn = input_fn_builder(
# features=train_features,
# seq_length=FLAGS.max_seq_length,
# is_training=False,
# drop_remainder=eval_drop_remainder)
# train_predictions = estimator.predict(train_input_fn, yield_single_examples=True)
# compute_length(train_predictions, statistic, fw)
# # train_values = pad_examples(train_values, statistic)
#
#
# print(statistic, type(statistic["sent1_length"]))
# with open("statistic.json", "w") as fws:
# json.dump(statistic, fws)
eval_predictions = estimator.predict(eval_input_fn,
yield_single_examples=True)
# eval_values = compute_length(eval_predictions, statistic)
# eval_values = pad_examples(eval_values, statistic)
with open("bert_cqa_eval.pkl", "wb") as eval_fw, \
open("eval_cid.pkl", "wb") as cid_fw:
compute_length(eval_predictions, statistic, eval_fw)
pkl.dump(dev_cid, cid_fw)
with open("statistic.json", "w") as fw:
json.dump(statistic, fw)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {"race": race_utils.RaceProcessor}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True."
)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name](
use_spm=True if FLAGS.spm_model_file else False,
do_lower_case=FLAGS.do_lower_case,
high_only=FLAGS.high_only,
middle_only=FLAGS.middle_only)
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) #,
# spm_model_file=FLAGS.spm_model_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
if FLAGS.do_train:
iterations_per_loop = int(
min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
else:
iterations_per_loop = FLAGS.iterations_per_loop
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=int(FLAGS.save_checkpoints_steps),
keep_checkpoint_max=0,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
model_fn = race_utils.model_fn_builder(
bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=int(FLAGS.train_step),
num_warmup_steps=int(FLAGS.warmup_step),
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
max_seq_length=FLAGS.max_seq_length,
dropout_prob=FLAGS.dropout_prob)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
# if FLAGS.use_tpu:
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
# else:
# estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config)
if FLAGS.do_train:
if not tf.gfile.Exists(FLAGS.train_file):
race_utils.file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer,
FLAGS.train_file, FLAGS.max_qa_length)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", FLAGS.train_step)
train_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=FLAGS.train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size,
multiple=len(label_list))
estimator.train(input_fn=train_input_fn,
max_steps=int(FLAGS.train_step))
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(classifier_utils.PaddingInputExample())
if not tf.gfile.Exists(FLAGS.eval_file):
race_utils.file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer,
FLAGS.eval_file, FLAGS.max_qa_length)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=FLAGS.eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.eval_batch_size,
multiple=len(label_list))
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if idx != "best" and int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
key_name = "eval_accuracy"
if tf.gfile.Exists(checkpoint_path + ".index"):
result = estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
best_perf = result[key_name]
global_step = result["global_step"]
else:
global_step = -1
best_perf = -1
checkpoint_path = None
writer = tf.gfile.GFile(output_eval_file, "w")
while global_step < FLAGS.train_step:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
if cur_filename.split("-")[-1] == "best":
continue
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info(
"Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
# steps_and_files = sorted(steps_and_files, key=lambda x: x[0])
if not steps_and_files:
tf.logging.info(
"found 0 file, global step: {}. Sleeping.".format(
global_step))
time.sleep(1)
else:
for ele in sorted(steps_and_files.items()):
step, checkpoint_path = ele
if global_step >= step:
if len(_find_valid_cands(step)) > 1:
for ext in [
"meta", "data-00000-of-00001", "index"
]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
continue
result = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("best = {}\n".format(best_perf))
if result[key_name] > best_perf:
best_perf = result[key_name]
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tgt_ckpt = checkpoint_path.rsplit(
"-", 1)[0] + "-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(
src_ckpt, tgt_ckpt))
tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
writer.write("saved {} to {}\n".format(
src_ckpt, tgt_ckpt))
if len(_find_valid_cands(global_step)) > 1:
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
writer.write("=" * 50 + "\n")
writer.close()
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(classifier_utils.PaddingInputExample())
assert len(predict_examples) % FLAGS.predict_batch_size == 0
predict_steps = int(
len(predict_examples) // FLAGS.predict_batch_size)
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
race_utils.file_based_convert_examples_to_features(
predict_examples, label_list, FLAGS.max_seq_length, tokenizer,
predict_file, FLAGS.max_qa_length)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size,
multiple=len(label_list))
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result = estimator.evaluate(input_fn=predict_input_fn,
steps=predict_steps,
checkpoint_path=checkpoint_path)
output_predict_file = os.path.join(FLAGS.output_dir,
"predict_results.txt")
with tf.gfile.GFile(output_predict_file, "w") as pred_writer:
# num_written_lines = 0
tf.logging.info("***** Predict results *****")
pred_writer.write("***** Predict results *****\n")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
pred_writer.write("%s = %s\n" % (key, str(result[key])))
pred_writer.write("best = {}\n".format(best_perf))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError("At least one of `do_train`, `do_eval` must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
num_train_steps = int(
FLAGS.train_data_size / FLAGS.train_batch_size) * FLAGS.epochs
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
num_choices=FLAGS.num_choices)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
if not tf.gfile.Exists(FLAGS.train_file):
examples = model_builder.load_record(FLAGS.train_raw_data)
model_builder.file_based_convert_examples_record(
examples, 512, tokenizer, FLAGS.train_file)
train_input_fn = file_based_input_fn_builder(
input_file=FLAGS.train_file, is_training=True, drop_remainder=True)
estimator.train(input_fn=train_input_fn, steps=num_train_steps)
if FLAGS.do_eval:
# This tells the estimator to run through the entire set.
if FLAGS.eval_data_size < 0:
eval_steps = None
else:
eval_steps = int(FLAGS.eval_data_size / FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
if not tf.gfile.Exists(FLAGS.eval_file):
examples = model_builder.load_record(FLAGS.eval_raw_data)
model_builder.file_based_convert_examples_record(
examples, 512, tokenizer, FLAGS.eval_file)
# Note that we are masking inputs for eval as well as training and this will
# decrease eval performance
eval_input_fn = file_based_input_fn_builder(
input_file=FLAGS.eval_file,
is_training=False,
drop_remainder=eval_drop_remainder)
# checkpoints_iterator blocks until a new checkpoint appears.
for ckpt in contrib_training.checkpoints_iterator(estimator.model_dir):
try:
result = estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps)
tf.logging.info("********** Eval results:*******\n")
for key in sorted(result.keys()):
tf.logging.info("%s = %s" % (key, str(result[key])))
except tf.errors.NotFoundError:
tf.logging.error("Checkpoint path '%s' no longer exists.",
ckpt)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
validate_flags_or_throw(albert_config)
tf.gfile.MakeDirs(FLAGS.output_dir)
tokenizer = fine_tuning_utils.create_vocab(
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case,
spm_model_file=FLAGS.spm_model_file,
hub_module=FLAGS.albert_hub_module_handle)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
if FLAGS.do_train:
iterations_per_loop = int(
min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
else:
iterations_per_loop = FLAGS.iterations_per_loop
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
keep_checkpoint_max=0,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
train_examples = squad_utils.read_squad_examples(
input_file=FLAGS.train_file, is_training=True)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
if FLAGS.do_train:
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
# Pre-shuffle the input to avoid having to make a very large shuffle
# buffer in in the `input_fn`.
rng = random.Random(12345)
rng.shuffle(train_examples)
model_fn = squad_utils.v2_model_fn_builder(
albert_config=albert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
max_seq_length=FLAGS.max_seq_length,
start_n_top=FLAGS.start_n_top,
end_n_top=FLAGS.end_n_top,
dropout_prob=FLAGS.dropout_prob,
hub_module=FLAGS.albert_hub_module_handle)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
# We write to a temporary file to avoid storing very large constant tensors
# in memory.
if not tf.gfile.Exists(FLAGS.train_feature_file):
train_writer = squad_utils.FeatureWriter(filename=os.path.join(
FLAGS.train_feature_file),
is_training=True)
squad_utils.convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=True,
output_fn=train_writer.process_feature,
do_lower_case=FLAGS.do_lower_case)
train_writer.close()
tf.logging.info("***** Running training *****")
tf.logging.info(" Num orig examples = %d", len(train_examples))
# tf.logging.info(" Num split examples = %d", train_writer.num_features)
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
del train_examples
train_input_fn = squad_utils.input_fn_builder(
input_file=FLAGS.train_feature_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size,
is_v2=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_predict:
with tf.gfile.Open(FLAGS.predict_file) as predict_file:
prediction_json = json.load(predict_file)["data"]
eval_examples = squad_utils.read_squad_examples(
input_file=FLAGS.predict_file, is_training=False)
if (tf.gfile.Exists(FLAGS.predict_feature_file)
and tf.gfile.Exists(FLAGS.predict_feature_left_file)):
tf.logging.info("Loading eval features from {}".format(
FLAGS.predict_feature_left_file))
with tf.gfile.Open(FLAGS.predict_feature_left_file, "rb") as fin:
eval_features = pickle.load(fin)
else:
eval_writer = squad_utils.FeatureWriter(
filename=FLAGS.predict_feature_file, is_training=False)
eval_features = []
def append_feature(feature):
eval_features.append(feature)
eval_writer.process_feature(feature)
squad_utils.convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature,
do_lower_case=FLAGS.do_lower_case)
eval_writer.close()
with tf.gfile.Open(FLAGS.predict_feature_left_file, "wb") as fout:
pickle.dump(eval_features, fout)
tf.logging.info("***** Running predictions *****")
tf.logging.info(" Num orig examples = %d", len(eval_examples))
tf.logging.info(" Num split examples = %d", len(eval_features))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_input_fn = squad_utils.input_fn_builder(
input_file=FLAGS.predict_feature_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size,
is_v2=True)
def get_result(checkpoint):
"""Evaluate the checkpoint on SQuAD v2.0."""
# If running eval on the TPU, you will need to specify the number of
# steps.
reader = tf.train.NewCheckpointReader(checkpoint)
global_step = reader.get_tensor(tf.GraphKeys.GLOBAL_STEP)
all_results = []
for result in estimator.predict(predict_input_fn,
yield_single_examples=True,
checkpoint_path=checkpoint):
if len(all_results) % 1000 == 0:
tf.logging.info("Processing example: %d" %
(len(all_results)))
unique_id = int(result["unique_ids"])
start_top_log_probs = ([
float(x) for x in result["start_top_log_probs"].flat
])
start_top_index = [
int(x) for x in result["start_top_index"].flat
]
end_top_log_probs = ([
float(x) for x in result["end_top_log_probs"].flat
])
end_top_index = [int(x) for x in result["end_top_index"].flat]
cls_logits = float(result["cls_logits"].flat[0])
all_results.append(
squad_utils.RawResultV2(
unique_id=unique_id,
start_top_log_probs=start_top_log_probs,
start_top_index=start_top_index,
end_top_log_probs=end_top_log_probs,
end_top_index=end_top_index,
cls_logits=cls_logits))
output_prediction_file = os.path.join(FLAGS.output_dir,
"predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir,
"null_odds.json")
result_dict = {}
cls_dict = {}
squad_utils.accumulate_predictions_v2(
result_dict, cls_dict, eval_examples, eval_features,
all_results, FLAGS.n_best_size, FLAGS.max_answer_length,
FLAGS.start_n_top, FLAGS.end_n_top)
return squad_utils.evaluate_v2(
result_dict, cls_dict, prediction_json, eval_examples,
eval_features, all_results, FLAGS.n_best_size,
FLAGS.max_answer_length, output_prediction_file,
output_nbest_file, output_null_log_odds_file), int(global_step)
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if idx != "best" and int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
key_name = "f1"
writer = tf.gfile.GFile(output_eval_file, "w")
if tf.gfile.Exists(checkpoint_path + ".index"):
result = get_result(checkpoint_path)
best_perf = result[0][key_name]
global_step = result[1]
else:
global_step = -1
best_perf = -1
checkpoint_path = None
while global_step < num_train_steps:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
if cur_filename.split("-")[-1] == "best":
continue
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info(
"Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
if not steps_and_files:
tf.logging.info(
"found 0 file, global step: {}. Sleeping.".format(
global_step))
time.sleep(60)
else:
for ele in sorted(steps_and_files.items()):
step, checkpoint_path = ele
if global_step >= step:
if len(_find_valid_cands(step)) > 1:
for ext in [
"meta", "data-00000-of-00001", "index"
]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
continue
result, global_step = get_result(checkpoint_path)
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if result[key_name] > best_perf:
best_perf = result[key_name]
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tgt_ckpt = checkpoint_path.rsplit(
"-", 1)[0] + "-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(
src_ckpt, tgt_ckpt))
tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
writer.write("saved {} to {}\n".format(
src_ckpt, tgt_ckpt))
writer.write("best {} = {}\n".format(key_name, best_perf))
tf.logging.info(" best {} = {}\n".format(
key_name, best_perf))
if len(_find_valid_cands(global_step)) > 2:
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
writer.write("=" * 50 + "\n")
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result, global_step = get_result(checkpoint_path)
tf.logging.info("***** Final Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("best perf happened at step: {}".format(global_step))
def main(_):
// Change 1 and 2
if FLAGS.horovod:
import horovod.tensorflow as hvd
hvd.init()
tf.logging.set_verbosity(tf.logging.INFO)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
albert_config = modeling.AlbertConfig.from_json_file(FLAGS.albert_config_file)
tf.gfile.MakeDirs(FLAGS.output_dir)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.gfile.Glob(input_pattern))
// Change 4 make sure enough sharded for each node
if FLAGS.horovod and len(input_files) < hvd.size():
raise ValueError("Input Files must be sharded")
tf.logging.info("*** Input Files ***")
for input_file in input_files:
tf.logging.info(" %s" % input_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
// Change 3 for saving checkpoints
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
// Change 4 make sure it sees only single gpu and add it to run config
config = tf.compat.v1.ConfigProto()
if FLAGS.horovod:
config.gpu_options.visible_device_list = str(hvd.local_rank())
if hvd.rank() == 0:
tf.compat.v1.logging.info("***** Configuaration *****")
for key in FLAGS.__flags.keys():
tf.compat.v1.logging.info(' {}: {}'.format(key, getattr(FLAGS, key)))
tf.compat.v1.logging.info("**************************")
run_config = contrib_tpu.RunConfig(
session_config=config,
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps if not FLAGS.horovod or hvd.rank() == 0 else None,
keep_checkpoint_max=FLAGS.keep_checkpoint_max,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
model_fn = model_fn_builder(
albert_config=albert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.num_train_steps,
num_warmup_steps=FLAGS.num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
optimizer=FLAGS.optimizer,
poly_power=FLAGS.poly_power,
start_warmup_step=FLAGS.start_warmup_step,
hvd=None if not FLAGS.horovod else hvd)
// Change 6 Broadcast model weights from rank 0
training_hooks = []
if FLAGS.horovod and hvd.size() > 1:
training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size)
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True,
hvd)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps, hooks=training_hooks)
if FLAGS.do_eval and (not FLAGS.horovod or hvd.rank() == 0):
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
global_step = -1
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
writer = tf.gfile.GFile(output_eval_file, "w")
eval_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False,
hvd=None if not FLAGS.horovod else hvd)
best_perf = 0
key_name = "masked_lm_accuracy"
while global_step < FLAGS.num_train_steps:
if estimator.latest_checkpoint() is None:
tf.logging.info("No checkpoint found yet. Sleeping.")
time.sleep(1)
else:
result = estimator.evaluate(
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
checkpoint_path = estimator.latest_checkpoint()
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if result[key_name] > best_perf:
best_perf = result[key_name]
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tgt_ckpt = checkpoint_path.rsplit(
"-", 1)[0] + "-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
writer.write("saved {} to {}\n".format(src_ckpt, tgt_ckpt))
if __name__ == "__main__":
flags.mark_flag_as_required("input_file")
flags.mark_flag_as_required("albert_config_file")
flags.mark_flag_as_required("output_dir")
tf.app.run()
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"cola": ColaProcessor,
"sst-2": SST2Processor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"xnli": XnliProcessor,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True."
)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name, ))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size *
FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
file_based_convert_examples_to_features(train_examples, label_list,
FLAGS.max_seq_length,
tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(PaddingInputExample())
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
file_based_convert_examples_to_features(eval_examples, label_list,
FLAGS.max_seq_length,
tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(
FLAGS.predict_input_file)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir,
FLAGS.exp_name + ".predict.tf_record")
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length,
tokenizer, predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
result = estimator.predict(input_fn=predict_input_fn)
if FLAGS.predict_output_file is None:
predict_output_file = os.path.join(FLAGS.output_dir,
"test_results.tsv")
else:
predict_output_file = FLAGS.predict_output_file
with tf.gfile.GFile(predict_output_file, "w") as writer:
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, prediction) in enumerate(result):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
writer.write(output_line)
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
label_list = ["Yes", "No"]
if FLAGS.from_three_class_model:
label_list.append("Neutral")
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
predict_examples = get_custom(FLAGS.predict_input_file)
predict_file = os.path.join(FLAGS.init_checkpoint2,
"predict.tf_record.%s" % FLAGS.exp_name)
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length, tokenizer,
predict_file)
tf.logging.info("***** Running prediction *****")
tf.logging.info("Num examples = %d", len(predict_examples))
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False)
all_model_ans = []
for output_dir in [FLAGS.init_checkpoint1, FLAGS.init_checkpoint2]:
all_answers = []
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=output_dir,
save_checkpoints_steps=1000,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
model_fn = model_fn_builder(bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=None,
num_train_steps=None,
num_warmup_steps=None,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=32,
eval_batch_size=8,
predict_batch_size=FLAGS.predict_batch_size)
for result in estimator.predict(predict_input_fn,
yield_single_examples=True):
probs_result = result["probabilities"]
all_answers.append(bool(probs_result[0] >= probs_result[1]))
all_model_ans.append(all_answers)
first_ans, second_ans = all_model_ans[0], all_model_ans[1]
assert len(first_ans) == len(second_ans)
matching = 0
counter = 0
for a1, a2 in zip(first_ans, second_ans):
if a1 == a2:
matching += 1
counter += 1
tf.logging.info("Agreement = %.4f (%d / %d)", (float(matching) / counter),
matching, counter)
